Recovering Individual Based Model Outcomes on Spatiotemporally Coarsened Data
By Sameerah Helal, Applied Mathematics, Under supervision of Stephanie Dodson
Author’s Note: Individual Based Models (IBMs) are commonly used to study animal migrations and foraging behaviors. These flexible models are powerful in identifying the mechanisms driving animal movement; however, when fed spatially or temporally coarse environmental data, IBMs can often produce inaccurate model outcomes. Here, we investigate how model adaptations can mitigate negative consequences of poor data using an IBM of blue whales. Specifically, we find that models running on coarse data lead the simulated whale agents to clump together in their foraging behaviors and migrations paths. Algorithm adaptations, like altering the rate at which the whales update their locations, can reduce the locational clustering effect incited by spatially coarse data, and introducing available fine data to coarse data can mitigate the behavioral inaccuracies caused by temporally coarse data. These changes improve verified utilization distributions of whale positions, behavioral state plots, and associated metrics.
1 Introduction
Recognizing how species interact and forage within their environment is an important aspect for understanding animal migrations. Predicting movement and behaviors given environmental conditions is crucial for understanding how changes in environment, like climate change, will affect future migrations. Though many species migrate, we focus on the foraging behaviors of Northern Pacific blue whales.
The largest animal to have ever lived, North Pacific blue whales are well known marine mammals and seasonal migrators [1]. In winter, they can be found in breeding grounds off the coast of Mexico and Baja California; during summer and fall, they migrate to foraging grounds off the coast of California. Blue whales forage only on krill and need to consume large amounts of prey to meet their energetic needs. Their seasonal foraging is timed with coastal upwelling in the California current system, which leads to high densities of krill. Blue whales are also a threatened species, and their population growth is hindered by human intervention in the form of shipping lanes, discarded fishing gear, and changing climate [1].
Modeling is a common method used to understand animal migrations and foraging behavior. Individual Based Models, or IBMs, treat individual animals as autonomous agents whose movements and behaviors are governed by a set of probabilistic rules. An example of this is the IBM in [2]. This model has individual, autonomous, simulated blue whale agents interact with and move through a domain: a restricted geographic area throughout which the agents are allowed to move; at any time they occupy either a transit or forage state. The state is selected based on krill density and sea surface temperature (SST) [2]. This model was used to evaluate the relative importance of prey and environmental factors in driving movement distributions of whales. Previously, the IBM was only run effectively on very fine spatiotemporal resolution Regional Ocean Modeling System (ROMS) data: computer generated sea data that is assimilative and hindcast, meaning that it gives realistic ocean conditions constructed from past observations. With ROMS being computationally expensive to generate and not available in real time, ROMS data is not feasible to use for current prediction purposes.
Our long-term goal is to predict real whale locations and behaviors by simulating whale agents on current environmental conditions using real-time satellite data. However, satellite data is messy and often of poor resolution, having spatiotemporal resolutions as coarse as 12 km grid cells and 8 day time-steps compared to the ROMS data’s 3 km and 1 day resolutions. A time-step is the length of time between each day for which the satellite has collected data. This poor resolution data yields inaccurate predictions and misleading results when applied to the blue whale IBM models.
Figure 1, created using output from our own models, highlights that models run on low resolution data return misleading predictions of whale positions, forecasting them to be clustered in much smaller areas than they actually are. In Figure 2, which shows the proportion of whale agents foraging over time and gives a sense of population-level behaviors, it can be seen that the models inaccurately recreate whale behavior by causing a behavioral update lag that corrects itself in jumps. Similarly unrealistic predictions are expected for IBMs of other animal populations.
In this study, our objective is to understand how to adapt the IBM developed by Dodson et al., as well as the data, to produce realistic predictions in spite of poor input resolution [2]. To do so, we will coarsen the original ROMS data to mimic poor satellite data, then compare the results of the IBM run on the coarse data to the results of the model run on the unaltered ROMS data. From there, we will formulate model adaptations that will yield accurate results from the IBM, even when run on coarse data.
In the methods section, we detail the functions of the IBM, our data manipulation methods, and
Figure 1: Contour map comparing the 95% utilization distribution map for the Gold Standard and 12 km resolution on September 1, 2008. Points are whales from the 12 km population.
our proposed adaptations, as well as the metrics to compare outputs of the IBM. In the results, we describe the efficacy of our solutions, showing quantitatively and qualitatively how they improve the model output. We analyze these results, their advantages, shortcomings, and future implications in the discussion section.
2 Methods
2.1 The ROMS Data
Throughout the study, we utilized ROMS (Regional Ocean Modeling System) Data, with two geospatial fields: sea surface temperature (SST) and krill [3] [4]. The domain is located off the California coast (32 − 42◦N Latitude and 116 − 128◦W Longitude) for the year 2008.
The datasets are identical in shape and structure, but contain different information: SST is sea surface temperature in Celsius and krill is the relative density with values between 0 and 1. The resolution of this data, which we will henceforth refer to as the ‘gold standard’, is 3 km spatial and 1 day temporal. Imagine the environmental data to be two-dimensional maps stacked vertically, one for each time interval (Figure 3).
2.2 Description of the IBM
Our IBM, or individual based model, takes inputs of sea surface temperature (SST) and krill data as environmental information that whale agents interact with and use to inform their movements around the domain. This choice of input is motivated by the fact that real-life whales partially decide their movements based on krill availability, which is influenced by sea surface temperature. Note that all mentions of whales from this point onward refer to the simulated whale agents in the IBM. As they progress through the model, the whales can occupy either a transit state (S1), where they simply travel through the domain, or a foraging state (S2), where they “forage” for krill; the movement distribution of each state is dictated by the step length and turning angle variables. These behavioral states are inspired by the analysis of blue whale tagging data in [5]. This type of model
Figure 2: Proportion of population foraging over time for the Gold Standard and 8 day resolution.
is also referred to as a “state-switching” or “semi-Markovian” model, respectively because the whales switch to random states and because the switch depends largely on their current and previous locations. In a fully Markovian model, the state variable would not depend at all on past information, only current.
Movement updates are parametrized by step lengths and turning angles. On any position update, the turning angle determines at what angle from its current trajectory a whale should turn, and the step length determines how far in that direction to travel. For each whale these parameters are drawn from respective Gamma and Von Mises, two probability distributions with their own parameters µ and σ. Distinct distributions are defined for whales in the foraging versus transit state. The distributions of the step length and turning angle are also functions of the time intervals given as parameters in the model. They have moments µ = h · 3700, σ = h · 2150 for the transiting state, and µ = h · 1050, σ = h · 970 foraging state [5]. Here, h represents the number of hours in one time-step (the amount of time between a location update) as follows:
Figure 3: Visualization of the SST and krill data as a 3-dimensional tensor.
(a) Forage state
(b) Transit state
Figure 4: Step length probability distributions for whales in forage or transit state for rate 1, 2, and 4.
Note that, while the step length and turning angle themselves are random, the absolute direction in which the whale turns depends on its current and previous location, which form the start of the zero degree angle from which the whale will turn. Hence, the “semi-Markovian” reference. At every update, each individual reads its local SST and krill conditions, which influence the probability of foraging through a distribution defined by
where z = 3 is a normalization parameter that ensures that the probability does not exceed 1, w1 = 1 and w2 = 2 are weights, and P SST and P krill are the respective probability distributions of foraging given SST and krill.
Lower SST and higher krill densities in a whale’s surroundings are associated with an increased likelihood of it either switching to or continuing in the foraging state (Figure 5).
(a) Based on surrounding SST
(b) Based on surrounding krill density
Figure 5: Probability of a whale foraging based on its environment.
To summarize the process: the IBM is initiated on May 1st in the southern portion of the domain, and at each update, the whales’ movement aligns with a movement distance based on the distributions in Figure 4 and their current location. Their foraging state is determined from their environmental conditions. This update cycle repeats until the end of the simulation, which, in this case, is the end of the year.
2.3 Data Coarsening in Space and Time
We manipulated the ROMS data to mimic the data collection shortcomings of satellites to achieve data coarsening. Specifically, satellite readings are plagued by coarse spatial and temporal resolutions. These are in part due to a satellite’s movement, type of readings, and presence of cloud cover. Making the ROMS data ‘bad’ in the way that satellite data is flawed required coarsening the data in space and time.
Spatial coarsening of the SST and krill ROMS data was accomplished through Matlab’s meshgrid and interp3 functions, for which the input data was the original 3 km spatial resolution, and returned it in lower resolutions of 6 km, 9 km, and 12 km. These coarser resolutions were selected to match available satellite data and test the limits of our methods.
To make up for missing data over time, satellites tend to use moving means: averaging the data over an interval, shifting the interval forward, and repeating the process; thus, our temporal coarsening was accomplished through averaging the data over time. To obtain coarsened data with an n−day resolution, we averaged the data in every n time-slices. The n slices were compressed into a single slice, resulting in a total of 1/n times the number of original time-slices. We performed this temporal averaging for 3 days and 8 days to mimic time resolutions consistent with satellite data.
Figure 6: SST data from September 1, 2008, coarsened temporally and spatially. The axes of each map show the number of grid cells from the bottom left corner.
Note that minor alterations to the IBM’s parameters, not mentioned because of their triviality, were required so that the model would run given the new dimensions of the data.
2.4 Issues from Coarser Data
Once we had coarsened the data, feeding it into the IBM no longer resulted in the same whale behavior or locations as the gold standard, which used data with 3 km, 1 day spatiotemporal resolution. Unlike many of the models based on coarser data, the gold standard results in whale locations spread over the coast with continuous population foraging behavior. In model iterations parametrized by the more extreme resolutions, like 12 km in space or averaged over 8 days in time, using the coarse data resulted in perceiving the whale population to act completely differently.
When tracking movement based on data coarsened in space (Figure 1), the whales tended to move closer to each other as the simulation progressed, resulting in them clustering unrealistically at the coast instead of being spread over the domain. Adding temporal coarsening to the data only exacerbated this clumping effect.
In addition to causing the whales to cluster over time, temporal coarsening alone also altered
Figure 7: Zoomed in utilization distribution maps of spatially coarse data vs. the gold standard. Curves show the 95% contour of the UDs. The full domain is shown on the right, with the regions on the left indicated by the orange box.
whale behavior. Abrupt jumps in the percentage of whales foraging became apparent at the end of each time-slice, making the whales’ feeding behavior appear unrealistically stepwise for extremely coarse time (Figure2).
Figure 8: Percentage of population foraging over time for temporally coarse data vs. the gold standard.
These deviations from the gold standard output required that we alter the IBM or its inputs such that the IBM would continue to produce realistic results, even with poor data.
2.5 Model Adaptations: Spatial
In an effort to have the IBM running on coarse data reproduce the same movement and behavioral patterns as the gold standard IBM running on the gold standard ROMS data, we modified parts of its algorithm; in particular, we scaled the time-steps of the IBM.
To mitigate the issues that arose specifically from spatially coarse data, we adjusted the rate at which the whales’ locations and behavioral states were updated. In the gold standard model, the whales take 4 steps per day (every 6 hours), with step length selected from a Gamma distribution with parameters µ0 and σ0. In order to recover realistic whale movements, i.e. to have the whales move the same distance over the same amount of time, we scaled this distribution by a parameter h, defined in equation 1, to accommodate for the increased size of grid cells caused by coarse spatial data.
From the gold standard model’s rate of 4 time-steps, we changed our rates to 1 and 2, corresponding, respectively, to time-steps of 24 and 12 hours. This way, the whales updated locations less often, but took larger steps that were matched to the larger grid cells [5], preventing them from getting trapped in grid cells due to limited movement ability, allowing them to move freely as they did when basing their movements on the ROMS data.
2.6 Model Adaptations: Temporal
To mitigate the disruption to whale behavior caused by temporally coarse data in the IBM, we introduced temporally finer data (when available) to the coarsened data, resulting in a hybrid coarse-fine environmental dataset that we could pass to the IBM. Our intention was to develop a methodology for handling temporally coarse input data while remaining consistent with the available satellite data. Temporally finer satellite data (e.g. 1 day, or daily) is often prohibitively sparse for IBM use due to missing data from clouds or the location of the satellite, but 8-day averages are sufficiently dense. We used this structure to our advantage and augmented the coarse data with available finer resolutions.
We regard the coarse data as our primary input, and the fine data as our secondary input. First, we created gaps in the coarse data by using Matlab’s randi function to select 30% of the indices in the data matrix uniformly at random. Then, to fill in the gaps in the coarse data, we replaced the randomly selected indices of the coarse data with the fine. In short, given temporally coarse data, we backed it up, or combined it with data of similarly coarse spatial, but finer temporal (1 day) resolution. For example, we might modify the 6 km, 8 day coarse data by replacing 30% of it with the finer 6 km, 1 day data.
Note that combining data of different resolutions in this way required some interpolation of the coarser data to the larger dimensions of the fine.
Before selecting 70% as the ratio of temporally coarse data, we analyzed real GOES (Geostationary Operational Environmental Satellites) satellite data recorded from the same area of the ocean as our ROMS data. Measurements of the GOES data are gathered by the GOES Imager, a multi-channel radiometer carried aboard the satellite. This satellite data is available in 6 km spatial resolution (about 0.05 degree latitude-longitudinal resolution) and in 1, 3, 8, 14, and 30 day composites averaged in time. Satellite SST data was extracted from NOAA GOES Imager Western Hemisphere satellite and accessed via [6]. Examples of data with few holes and many holes can respectively be found in Figure 9.
(a) A day with very few holes
(b) A day with many holes
Figure 9: SST GOES satellite data on days with varying amounts of holes. Dark blue indicates missing data.
Plotting the percentage of the data that was missing (Figure 10) showed that the highest percentage of clouds at any time, for the first temporal resolution (1 day), was 80%, with an average of 40%. That is, on any given day, there is a wide range of data that might be missing. We also tested a range of coarse to fine ratios (i.e. percentages of missing data) on our own data, and the lowest percentage of fine data that returned reasonably improved results from no fine data at all was 30%. Thus, in what follows, we fix 30% as the amount of fine data to introduce, and 70% as the amount of ‘missing’ or coarse data. The improvements will be further discussed in the Results section; see table 3.
2.7 Metrics
The behaviors that we are trying to replicate in the gold standard are the foraging behaviors over time and over the whales’ habitat area. We used two established metrics to compare model outputs between the adjustments, as well as a third that we designed to be a combination of the two.
The L2 norm, or the Mean Square Error, was used to measure how close the whales’ foraging behavior resulting from any of the models was to that of the gold standard model output. We extracted the vector of the percentage of whales foraging over time for both models and, after applying a moving mean, compared them using the L2 norm of the data. This metric provided an understanding of the foraging behaviors of the whale population through time. We define
Note that XGS represents the output produced by the gold standard model. Lower L2 norms indicate a more accurate reproduction of the gold standard outcomes, and an L2 norm of zero indicates that the models have identical foraging behavior.
Figure 10: Percentage of the missing data for real, daily satellite GOES data. Dashed vertical lines indicate the beginning of each month.
Utilization distributions are probability distributions built from the data points of individual positions and are commonly used to understand and compare animal habitats. To quantify the accuracy of the utilization distribution produced by a given model, we used the volume intersection (‘VI’ in the adehabitathr R package) between the utilization distribution of the final positions of the whales from the given model versus the gold standard model. The positions were taken from September 1, 2008, a date by which the whales had had sufficient time to explore and interact with the domain. This gives a measure of how close the model output is to the gold standard in terms of whale positions. We refer to this volume as the ‘VI’. The equation for the VI integral is
for model run X compared to the gold standard model at all points (x, y) in our domain.
Because we are measuring overlap of the utilization distributions, a higher value indicates better model performance, with the maximum being 1. Due to the stochastic, meaning somewhat random, nature of the model, a VI value of 1 was not achieved even by comparing two of the same model runs from the gold standard, which returned an overlap value of around 99%; thus, for our purposes, VI values near 0.9 are considered good.
The UDs and VI values were computed using R. We formatted the final whale positions of each model, labeled by the resolution of its input data, into a dataframe, then used the kerneloverlap and kernelUD functions from the R (version 4.0.2) package adehabitatHR to return the VI overlap value [7]. Our choice of setting the method argument to ’VI’ gave us the computed volume of intersection between the gold standard and coarse data model outputs.
Finally, our own ∆ metric is the combination of the L2 and VI norms: for a model outcome X, we defined
Since all efforts to adapt the model were done in order to produce equivalent results to the gold standard when fed into the simulator, our goal was to minimize the ∆ of the model; i.e. minimize the L2 and maximize the VI values. We measured the effectiveness of a model adjustment by quantifying model improvement using the fold decrease in ∆ values before and after the adjustment was applied.
3 Results
3.1 Changing Rate Counteracts Spatially Coarsened Data
We focus first on purely spatially coarsened data, which has the same 1 day temporal resolution as the data used in the gold standard. The model run on spatially coarsened data resulted in unrealistic behaviors from the whales: resolutions lower than the gold standard of 3 km prevented the whales from exploring the entirety of the domain and caused them to move closer together as the IBM progressed. In particular, the agents ended up in clusters near the coast instead of spread over the domain. In an attempt to recreate the spread-out behavior of the gold standard IBM, we decreased the rate at which the whales updated their locations and behavioral states.
Without any temporal coarsening, adjusting the rates significantly mitigated the clustering effects of spatial coarsening on the locations of the whales. Changing the rate in the IBM caused the whales to forage in generally the same areas with coarse data as they did with the gold standard data. The utilization distributions of the whales’ positions on the date September 1, which we noted was chosen as a sufficiently advanced time in the simulation to show model- representative whale behaviors (Figure 7), became visibly more similar to that of the gold standard.
Table 1: Original and corrected VI for spatially coarsened data, with fold decrease in ∆ values.
The original VI values for the coarse data for 9 km, and 12 km were 0.705, 0.480, and 0.256 respectively. Recall that higher VI values, closer to the maximum of 1, are more desirable. After adjusting the rate, these values became 0.710, 0.654, and 0.549; all improvements, even to the 6 km, which show less dramatic improvement due to already being relatively close to the gold standard. In addition , before changing the rate, the ∆ values were about 1.9, 2.8, and 3.0; all very high and
Figure 11: Utilization distribution maps of spatially coarsened data with and without altered rates.
indicative of undesirable model results that did not align with the gold standard. After changing the rate, these values reduced to 1.2, 1.4, and 1.7; at minimum a 1.6-fold decrease each, with the 9 and 12 km resolutions seeing the greatest favorable impacts.
3.2 Introducing Finer Data Counteracts Temporally Coarsened Data
We now consider purely temporally coarsened data, keeping the spatial resolution constant at 3 km. Recall that coarsening the data temporally caused issues with population-level whale foraging behavior. For lower temporal resolutions than the gold standard’s 1 day, the whales experienced abrupt, en masse changes in foraging state, causing the percentage of whales foraging over time to look unrealistically segmented. To mitigate this effect, we introduced finer data as a replacement for 30% of the coarse data as described in the Methods section. With the addition of the finer data, the foraging percentages were visually less step-like and numerically closer to the gold standard results.
Table 2: L2 norms for temporally coarsened data before and after the addition of 30% 1 day data, with fold decrease in ∆ values.
When naively fed temporally coarse data, i.e. when no changes are made to the data or the model to counter the effects of the temporal coarseness, the IBM results had ∆ values of 1.4 and 2.0 respectively for 3 day and 8 day. These high ∆ values are largely due to jump-like behaviors in the percentage of whales foraging. After replacing 30% of the coarse data with available fine data, we found that the ∆ values decreased over 1.6-fold and 2-old, to a value near 1. Similarly, the L2 norms decreased, as desired, from 1.2 and 1.7 to 0.77 and 0.93 for the coarse temporal resolutions of 3 day and 8 day.
Figure 12: Proportion of the population foraging over time for temporally coarsened data with and without added finer data.
3.3 Combined Spatial and Temporal Fixes
Negative influences on whale foraging behaviors were amplified when the environmental data had both poor spatial and temporal resolutions: with no fixes, the spatiotemporally coarsened data had a minimum ∆ of 3 across all resolutions. Combining the fixes by substituting available fine data, then changing model rate was successful in improving the behaviors in the combined coarseness scenario. By reducing the jumps in the number of whales in each behavioral state, and increasing the overlap between the utilization distributions of the corrected and gold standard models, we were able to decrease the ∆ value to be capped at 1.6. This was at least a 2.6-fold decrease in ∆s for each of the spatiotemporally coarsened datasets, greater than for any of the adjustments applied to data that was coarse in only one dimension.
Table 3: Fold decrease in ∆ for all spatiotemporal coarsening combinations.
Figure 13: Bar chart of ∆ values for spatiotemporally coarse data before and after algorithm modifications.
4 Discussion
Our long-term goal was to run the IBM on satellite data to predict real-time locations of blue whales. However, the IBM had only been shown to produce realistic predictions on fine-scale input data. Specifically, gaps and poor resolution of SST and krill data would lead to inaccurate simulations and predictions of whale movements. To the end of mimicking this ‘bad’ data, we coarsened our data spatially and temporally. By decreasing the rate at which the whales updated their locations and introducing available fine data to the coarse data, we successfully improved the results of the IBM when running on coarse data, as measured by the VI, L2, and ∆ metrics,.
Of our two directions of coarsening, the first we dealt with was spatial. The lower spatial resolution (e.g. 12 km instead of the gold standard 3 km), or larger grid cells, caused the whales to clump together over time instead of exploring the entire domain. We anticipated that this was because the increased size of grid cells caused the whales to be trapped in areas of high foraging; the whales’ step lengths were too short to exit areas with high foraging probability. Since the step lengths are a function of the step rate, with larger average lengths corresponding to lower rates, by decreasing the rate at which the whales updated locations, the step length became better aligned with the spatial size of the grid cells. Larger step lengths prevented the whales from getting stuck in rich foraging locations and allowed agents to step out of a grid cell and continue to explore the domain. For example, changing the update from 6 to 24 hours (rate 4 to rate 1) shifted the average step length of foraging from 6.3 km to 25 km. The larger average step length then allowed individuals to exit a 12 km grid cell and continue to explore the domain.
Our second direction of coarsening was temporal; lower temporal resolutions were apparent in sudden changes in the percentage of whales foraging. The jump-like behaviors were due to the whales operating on old information: the coarser the temporal resolution, the more outdated the whales’ knowledge of their surroundings. Our model correction was to provide the agents with the most up to date information when possible. Adding finer data into the coarse data removed the unrealistic jumps in the whales’ behavior and yielded population-level foraging activity that better resembled the gold standard.
Our solution for temporal coarsening is particularly powerful because of its scale: we only required 30% of the coarse data to be randomly replaced with the fine to see great improvement. Analysis of GOES satellite data showed that the average missing data during the summer and fall, commonly due to cloud cover, is usually less than 40% and rarely exceeds 70%. Thus, utilizing 30% of 1 day temporal resolution data with 70% of temporally coarse data is a realistic quantity, and actually uses the upper bound of 70% for the amount of missing satellite data. We introduced the fine data to the coarse data uniformly at random as the natural first step, since it did not require any assumptions about the locations or shapes of the missing data. One next step would be to target localized, perhaps even moving, regions for more ‘cloud-like’ replacement.
The methods we develop here are not at all unique to modeling blue whale populations; they can be applied to any individual based model in which the agents make decisions based on environmental input data. These solutions are particularly useful for models that, like ours, have inputs that are dynamic in space and time; nearly any IBM for animal movement could be similarly adapted. By incorporating finer temporal data when possible and aligning the step lengths to be of the same order as the spatial grid resolution, we believe other individual based models can be used with coarse environmental data to make predictions that are considerably more similar to those they might make with finer, more accurate data.
The problem of predicting animal behavior based on environmental conditions is an important one, especially with continually changing impacts from humans and climate change. With these results and further exploration in our suggested direction, it is our hope that it will be possible to make accurate, real-time predictions of whale positions based on satellite data.
References
- Scales KL, Hazel EL, Jacox Mg, Edwards CA, Boustany AM, Oliver MJ, Bograd SJ. 2017. Scale of inference: on the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography [Internet]. 40:210-220. doi:10.1111/ecog.02272.
- Dodson S, Abrahms B, Bograd SJ, Fiechter J, Hazen EL. 2020. Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models. International Journal on Ecological Modelling and Systems Ecology [Internet]. 432. doi:10.1016/j.ecolmodel.2020.109225.
- Rose KA, Fiechter J, Curchitser EN, Hedstrom K, Bernal M, Creekmore S, Haynie A, Ito S, Lluch-Cota S, Megrey BA, Edwards CS, Checkley D, Koslow T, McClatchie S, Werner F, MacCall A, Agostini V. 2015. Demonstration of a fully-coupled end-to-end model for small pelagic fish using sardine and anchovy in the California Current. Progress in Oceanography [Internet]. 138(B): 348-380. doi:10.1016/j.pocean.2015.01.012.
- Fiechter J, Huckstadt LA, Rose KA, Costa DP. 2016. A fully coupled ecosystem model to predict the foraging ecology of apex predators in the California Current. Marine Ecology Progress Series [Internet]. 556:273-285. doi:10.3354/meps11849.
- Bailey H, Mate BR, Palacios DM, Irvine L, Bograd SJ, Costa DP. 2009. Behavioural estimation of blue whale movements in the Northeast Pacific from state-space model analysis of satellite tracks. Endangered Species Research [Internet]. 10:93-106. doi:10.3354/esr00239.
- 2019. SST, GOES Imager, Day and Night, Western Hemisphere. https://coastwatch. pfeg.noaa.gov/erddap/index.html.
- Calenge C. 2006. The package adehabitat for the R software: tool for the analysis of space and habitat use by animals. Ecological Modelling 197:516-519. doi:10.1016/j.ecolmodel.2006.03.017.
Making Brain Stimulation a Mainstream Treatment for Aphasia
By Eva Clubb, Cognitive Science, ’21
Author’s Note: I started research on aphasia for an upper-division writing class, and was intrigued by the potential of brain stimulation as an effective and practical treatment option for aphasia, with potential to treat other brain disorders. Finding an intersection between neuroscience, technology, and linguistics is critical to broaden speech therapy treatment. I hope readers will be excited by the possibilities that advancements in neuroscience have in healthcare.
Forms of “language” are found in many kinds of species. This suggests an innate desire for communication and a neural predisposition to do so. The human brain is specialized for spoken language, such that acute damage or acute stimulation to language processing areas can modulate a person’s fluency of speech. From having a heart-to-heart with a good friend, chatting with a local barista, or updating your doctor on your symptoms: verbal communication mediates a casual relationship between your inner world and the outer one. Wants, needs, worries, and tiny little things you just have to get off your chest can be articulated without second thought.
Now, imagine being stripped of the ability to communicate with the outer world. An individual’s increasing forgetfulness or tendency to stumble over their words might not be indicative of normal cognitive decline as a result of aging. Instead, subtle decreases in fluency might be symptoms of aphasia: a common and incurable language disorder, usually an effect of underlying brain damage. People with aphasia are perfectly intelligent and cognizant, but are limited in their ability to read, speak, and understand language.
Aphasia affects almost 2 million Americans; nearly a third of those who experienced a stroke develop aphasia because of damaged language processing areas in the brain [2]. Aphasia refers to language impairments that disrupt the ability to access ideas and thoughts through language; it does not disrupt the ideas and thoughts themselves. The onset of aphasia is associated with brain damage, developed concurrently with a disease like Dementia or following an injury like a stroke. Patients with aphasia may have trouble verbalizing their thoughts in several ways: jumbling words together, speaking gibberish, substituting one word for another, poor grammar, speaking in short sentences, trouble retrieving words or names.
Gradual yet consistent symptom improvements are typically nurtured by a speech language pathologist. To increase working vocabulary, common speech therapy techniques include conversational therapy, word finding, and naming tasks during weekly or bi-weekly sessions over the course of months or years. Although prior research in stroke patients suggest that increasing duration of sessions, sessions per week and the number of weeks of speech therapy are best for improving speech, the inconvenience and cost is a significant impediment to its implementation [3]. So how can people learn and retain more linguistic information at a faster rate? This is where neuroscientists enter the conversation. Non-invasive brain stimulation can mimic and amplify the effects of activities we regard as ‘mentally stimulating.’
Non-invasive imaging technology can pinpoint brain areas associated with speech impairments, and applying acute electrical current triggers neural changes associated with language learning. The neural changes as a result of therapy can be observed either through functional neuroimaging, like fMRI, revealing the areas of activation, or by quantifying changes in brain structure, in the number and volume of fiber bundles in processing areas [4]. Neural connections, or synapses, are the physical mechanism of learning. When synaptic connections become vulnerable due to the applied electrical currents, having higher neuroplasticity, learning occurs most rapidly. Often neuroplasticity is cited in the context of infants whose brains develop at an incredible rate. However, any experience, task, or event will modify the connections in your brain. Therefore, artificially increasing neuroplasticity through electrical brain stimulation amplifies the effects of a learning experience. It is unclear whether brain stimulation induces true ‘learning’, or the reactivation of dormant, inaccessible information [4].
In the context of aphasia, brain stimulation is paired with conventional speech therapy tasks to accelerate the rate of language improvements. Brain stimulation can be performed at little cost to patients, typically through transcranial direct current stimulation (tDCS). tDCS involves placing two electrodes on the surface of the scalp and sending a small electrical current to the brain.
Figure 1: Two electrodes placed at the surface of the scalp deliver a small electrical current. Common targets are language processing centers Wernicke’s area and Broca’s area.
The process is completely non-invasive, described by patients as a tingly feeling. tDCS treatment is in the experimental stages for other types of disorders where brain abnormalities are relatively localized and widely researched, including depression and anxiety.
Neuroscientists have identified and experimented with tDCS to major language processing areas in the brain, where damage is linked to aphasia. Within the left Inferior Frontal Gyrus, ‘Broca’s area’ is primarily responsible for speech production. Damage to Broca’s area causes trouble naming and may restrict speech to short, ungrammatical sentences. Research in stroke patients shows combining tDCS to Broca’s area with word-repetition tasks improves accuracy in speech production, while conversational therapy enhances picture, noun, and verb naming [2]. Likewise, ‘Wernicke’s area’ in the Superior Temporal Gyrus is associated with speech comprehension. Predictably, impairments to Wernicke’s area cause deficits in understanding others’ speech and writing. Researchers found the combined stimulation of this area and speech therapy improved verbal comprehension. While Broca’s and Wernicke’s area have been thoroughly investigated, several studies have used MRI to locate brain damage before experimenting with the application sites of tDCS. Other studies have applied tDCS to unconventional brain areas with mixed effects. Stimulation to the cerebellum has enhanced spelling ability, while stimulation to the primary motor cortex improved naming ability on trained words [5].
The effectiveness of tDCS is verifiable through controlled clinical trials. ‘Sham-tDCS’ acts as a placebo: it produces a tingling sensation in the scalp without affecting neural functions, leading patients to believe they were receiving true tDCS. In many instances, the groups receiving sham-tDCS showed some improvements, although not as significant as the group receiving true-tDCS. This could be attributed to effective speech therapy tasks themselves or a mild placebo effect. Regardless, the superiority of the treatment group was exacerbated over time: after 6 months the sham-tDCS group showed a significant decrease in the initial improvements made [5]. This suggests tDCS not only promotes learning but is important for long-term maintenance.
Scientists’ understanding of the short-term and long-term impacts of tDCS, the specifications of the treatment, and how exactly speech therapy modifies the brain is growing. Unanswered questions remain about the parameters and expected outlook for the treatment. What is the best combination of speech therapy tasks and stimulation sites? How many sessions of tDCS are necessary? How long should electrical current be applied for? What kind of results should be expected? How should you expect conversational ability to change?
Tentative answers to these questions establish tDCS as a feasible supplement to speech therapy. Knowledge about brain structure and linguistic function create an interdisciplinary approach to aphasia treatment. Modern treatments like tDCS are enabled by technology and mitigate the time and cost barrier of intensive speech therapy. Healthcare workers and neuroscientists, and patients might benefit from investigating how to make brain stimulation a mainstream treatment for aphasia.
References:
- Chomsky, N. “On the Biological Basis of Language Capacities.” In The Neuropsychology of Language: Essays in Honor of Eric Lenneberg, edited by R.W. Rieber, 1-24. Boston, MA: Springer. 1976
- Biou, Cassoudesalle, Cogne, Sibon, De Gabory, Dehail, Aupy, Glize. 2019. Transcranial direct current stimulation in post-stroke aphasia rehabilitation: A systematic review. Ann Phys Rehabil Med . 2019 Mar;62(2):104-121. doi: 10.1016/j.rehab.2019.01.003
- Breitenstein, Grewe, Flöel , Ziegler, Springer, Martus, Huber, Willmes, Ringelstein, Haeusler, Abel, Glindemann, Domahs, Regenbrecht, Schlenck, Thomas, Obrig, Ernst de Langen, Rocker, Wigbers, Rühmkorf, Hempen, List, Baumgaertner. 2017. Intensive speech and language therapy in patients with chronic aphasia after stroke: a randomised, open-label, blinded-endpoint, controlled trial in a health-care setting. Lancet. 389(10078):1528-1538. doi: 10.1016/S0140-6736(17)30067-3.
- Crosson B, Rodriguez AD, Copland D, Fridriksson J, Krishnamurthy LC, Meinzer M, Raymer AM, Krishnamurthy V, Leff AP. 2019. Neuroplasticity and aphasia treatments: new approaches for an old problem. J Neurol Neurosurg Psychiatry. 90(10): 1147–1155. doi:10.1136/jnnp-2018-319649.
- Meinzer M, Darkow R, Lindenberg R, Floel A. 2016. Electrical stimulation of the motor cortex enhances treatment outcome in post-stroke aphasia. Brain. 139(Pt 4):1152-63. doi: 10.1093/brain/aww002
The Impact of vasopressin and oxytocin and pair-bonding on social development in prairie voles (Microtus ochrogaster)
By Hera Choi, James Hagerty, Ananya Narasimhan, Elyza Ramirez, Rana Sherkat, Karen Bales, Logan Savidge, Academic Editors
Acknowledgement: We offer our sincerest appreciation to Dr. Karen Bales and Logan Savidge for their continued guidance and support throughout our writing process for this literature review. The edits and remarks provided on their behalf not only allowed us to polish up the paper, but also gave us many opportunities to learn more about the nature of the prairie voles we work with. We would also like to thank the editors of the Aggie Transcript for providing us excellent feedback, tools, and edits to bring us to our finished literature review.
Abstract:
Prairie voles are a monogamous rodent species that exert a variety of human-like social behaviors. Voles are often used as animal models to study certain behavioral patterns in humans. This paper attempts to review the neurobiology of prairie vole pair-bonding. Hormones such as oxytocin and vasopressin are known to have biological effects on prairie vole pair-bonding development. We hypothesize that the introduction of oxytocin and vasopressin may facilitate behaviors such as aggression since it has been revealed that pair-bonding highly impacts social behavioral displays.
Introduction:
Microtus ochrogaster, commonly known as the prairie vole, exhibits many similar behavioral patterns to humans, including biparental care, alloparenting (the presence of non-breeding male and female voles participating in pup care), pair-bonding, and social attachment. As such, prairie voles have been used widely in studies investigating various mental health disorders, namely autism spectrum disorder, depression, addiction, and schizophrenia, providing researchers with more information on human behaviors related to cognition, parenting, and interpersonal relationships [1]. This review aims to demonstrate that pair-bond formation, in conjunction with the hormones oxytocin and vasopressin, aids prairie vole social development. Although these conclusions can be made with current research, further research should can address limitations such as including more female prairie voles in these studies and comparing oxytocin uptake between both sexes.
Prairie voles live in communal groups, typically consisting of males and females with their offspring [2]. Pair-bonding between male and female prairie voles can facilitate the biparental care of their offspring as opposed to monoparental care. As a biparental species, both male and female prairie voles divide postpartum parental activities relatively equally. Both maintain and build their nest, cache food, lick, groom, and brood pups [4]. The only parental activity strictly maternal is the nursing of pups [4]. Biparental care is not the only form of parenting that vole pups can receive. Parenting styles can also vary in duration of contact and the presence of alloparental care. Extended family lines often exist, in which juveniles remain in the natal nest as alloparents [5].
Once a male and female pair form an established pair-bond, they remain socially monogamous. A standard method of measuring a pair-bond in animals in the lab is partner preference testing, or measuring the mate’s preference for their partner over a stranger of the opposite sex. Through partner preference testing, researchers have demonstrated that injecting high doses of oxytocin (OT) or vasopressin (AVP) is associated with the development of a pair-bond in both male and female prairie voles [6]. Antagonists of OT or AVP receptors interfere with pair-bond formation, further supporting that both OT and AVP are necessary for pair-bonding behaviors [5]. AVP also regulates nonresident males’ exclusion by the resident male, also known as mate-guarding, further maintaining the pair-bond between the resident male and female vole [2].
Social monogamy is a characteristic that is rarely seen in the animal kingdom. Critical hormones combined with prairie voles’ social environment make these coinciding behaviors possible.
The Role of Hormones in Affiliative Behaviors
Although hormones do not directly cause behavioral changes by influencing the three behavioral components (sensory systems, central nervous systems, and effectors), hormones can increase the possibility that appropriate responses will be expressed in response to certain stimuli [6]. Studies have aimed to reveal mechanisms in which hormones act on pair-bonding behavior. Receptor autoradiographic binding procedures, in which radioactive molecules are attached to ligands to visualize receptor distributions, showed higher vasopressin receptor (V1aR) densities in the medial preoptic area of the brain in pair-bonded male prairie voles compared to that of sexually naïve male voles [7]. It has been supported that the V1aR is necessary for both the formation and maintenance of pair-bonds in prairie voles, suggesting AVP has a significant role in pair-bonding behavior, particularly once male prairie voles reach sexual maturity [8].
However, in female prairie voles, AVP inhibition appears to have little effect on altering pair-bonding behaviors. Instead, the use of oxytocin receptor antagonist, ornithine vasotocin (OTA), results in inhibition of partner preference formation [8]. It has been demonstrated that the administration of OT with dopamine (DA) can induce partner preference without mating in female prairie voles [8,9]. Some studies have expanded on the role of OT and DA in pair-bond formation, revealing that the presence of OT and DA D2-type receptors in the nucleus accumbens (NAcc), the mediator of motivation and action, are both vital in pair-bond formation in female voles [10]. This is further supported by findings that have determined a positive correlation between affiliative behavior and oxytocin receptor density within the NAcc [11,12]. High concentrations of both OT and DA D2-type receptors within the NAcc suggest that affiliative behaviors and pair-bonding are extremely rewarding for female prairie voles.
While the effects of AVP and OT inhibition on vole pair-bonding behavior are well studied, there have also been studies that have looked at the direct impact of administration of these hormones. Through partner preference testing, researchers have demonstrated that injecting high doses of AVP or OT is associated with the development of a pair-bond in both male and female prairie voles [13]. However, it has been shown that AVP administration to juvenile male voles can later result in impediments in partner preference formation [14]. Similar to what was observed in adult female voles, treatment of OT during the neonatal stage significantly decreases the display of partner preference-related behaviors in female voles [15]. These findings demonstrate possible dual effects of a single hormone determined by the dose, age, and duration of administration.
The Role of Hormones in Social Aggression
Social recognition is an integral component of prairie vole behavior, permitting this species to distinguish one conspecific from another for protection, inbreeding avoidance, monogamous mate selection, and comfort [16]. Aggression or affiliation displays are based on the lack or presence of recognition, respectively, likely mediated by oxytocin receptors (OXTR) [16]. Prairie voles commonly show aggression towards non-familiar individuals, especially after forming a pair-bond with another vole [6]. In many species of mammals, gonadal hormones have a prominent role in mate guarding and mating-related aggression [6]. However, it has been supported in prairie voles that the removal of gonads has little effect in decreasing aggression [17,18]. Instead, AVP and OT, rather than gonadal hormones, appear to control aggressive behaviors in prairie voles. For example, injection of AVP in adult male prairie voles increases intermale aggression. Meanwhile, developmental exposures of AVP can induce post-mating-like aggressive behaviors in sexually naïve males [19]. Sexually dimorphic roles are also present in aggression behaviors, with AVP administration having less effects on aggression in female voles. AVP receptor antagonists do block female aggression, highlighting the need for AVP receptors in aggression behaviors, even in female voles [19].
Aggression in female voles and its mechanisms have not been studied as extensively as it is in males. There are indications that OT may play a significant role in female vole aggressive behaviors. Females treated with OT following weaning show increased intrasexual aggression, while males treated with the same procedures are not affected [20]. Developmental OT treatment also results in decreased social behaviors in female voles [20]. Overall, further investigation on the effects of OT on male prairie vole guarding and aggression as opposed to female prairie voles is needed to make comparative conclusions.
Vole Behavior and Hormones
This review looked in depth at prairie vole behaviors related to the hormones AVP and OT. Together, both hormones induce social behaviors in male and female prairie voles, particularly those related with affiliation and pair-bonding. It is important to note that hormonal treatment may result in very different effects based on the developmental stage of the voles, the dosage of hormones, and the surrounding environment. This may be particularly important when experimenting with voles across multiple developmental stages, but this has yet to be studied. Further research should investigate whether AVP and OT have differential effects on prairie vole development, as hormonal influences tend to change over time.
Sexual Dimorphism
In all behavioral aspects, including aggression and pair-bonding, sexual dimorphism was observed in response to specific hormone inhibitors and hormone treatments. AVP has been found to be more important for adult male prairie vole pair-bonding, whereas OT and DA are necessary for pair-bonding in adult female voles. However, the effects of AVP and OT become more complex depending on when additional injections have been administered during the prairie vole’s life. Although AVP is significant for male prairie vole pair-bonding, administration during the juvenile stage can actually impair the formation of partner preferences. This effect is also seen in neonatal females, but with OT and not AVP. The difference in hormonal physiology may be a factor in the sexually dimorphic behaviors we see in the two sexes, though more research is needed for conclusive remarks. It may also suggest that the neurobiology between males and females is different from one another, at least in the aspect of pair-bonding.
A general trend that was discovered was that there had been more research done regarding male prairie voles. Due to the fact that the two sexes of voles show dimorphic behaviors, it is important to study both sexes of voles separately to prevent generalization of prairie vole neurobiology.
Prairie voles have become valuable organisms through which we can observe many aspects of human behavior. Although prairie vole neurobiology is incredibly complex, it paves the way for more research to be done to clarify the link between hormonal activity and behavior for both prairie voles and humans alike. Further routes of research that we suggest are quantifying the relationship between AVP receptors and aggression in female voles, since current studies mostly address this relationship in males. Similarly, we can address the effect of OT on intrasexual aggression in male prairie voles to comparatively study the effects of OT between sexes. Research of these factors may also be enhanced by including trials on prairie voles of different developmental stages to study the long-term outcomes of these hormones on behavior. Overall, our hypothesis linking OT and AVP to the neurobiology of pair-bonding and subsequent behaviors is supported by the literature, but there are many gaps to fill regarding the comprehensive impact of these hormones and pair-bonding on social displays and behavior between both sexes and across developmental stages.
References:
- McGraw, L., & Young, L. 2010. The prairie vole: an emerging model organism for understanding the social brain. Trends In Neurosciences. 33(2): 103-109. Doi: =10.1016/j.tins.2009.11.006
- Carter, C. S., & Getz, L. L. 1993. Monogamy and the Prairie Vole. Scientific American. 268(6):100–106.
- Thomas, J. A., & Birney, E. C. 1979. Parental Care and Mating System of the Prairie Vole, Microtus ochrogaster. Behavioral Ecology and Sociobiology. 5(2): 171–186.
- Roberts, R., Zullo, A., & Carter, C. 1997. Sexual Differentiation in Prairie Voles: The Effects of Corticosterone and Testosterone. Physiology & Behavior. 62(6): 1379-1383. doi: 10.1016/s0031-9384(97)00365-x
- Cho, M. M., De Vries, A. C., Williams, J. R., & Carter. C. S. 1999. The Effects of Oxytocin and Vasopressin on Partner Preferences in Male and Female Prairie Voles (Microtusochrogaster). Behavioral Neuroscience. 113(5): 1071-1079. doi:10.1037//0735-7044.113.5.1071
- Nelson, R. J., & Kriegsfeld. L. J. 2018. An Introduction to Behavioral Endocrinology (5th ed). Massachusetts : Siauner.
- Gobrogge, K. L., Liu, Y., Young, L. J., & Wang, Z. 2009) Anterior Hypothalamic Vasopressin Regulates Pair-Bonding and Drug-Induced Aggression in a Monogamous Rodent. PNAS. 106(45): 19144-19149. doi:10.1073/pnas.0908620106
- Insel, T. R., & Hulihan, T. J. 1995. A Gender-Specific Mechanism for Pair Bonding: Oxytocin and Partner Preference Formation in Monogamous Voles. Behavioral Neuroscience,\. 109(4): 782-789.
- Williams, J. R., Catania, K. C., & Carter, S. 1992. Development of Partner Preferences in Female Prairie Voles (Microtus ochrogaster): The Role of Social and Sexual Experience. Hormones and Behavior. 26: 339-349.
- Liu, Y., & Wang, Z. X. (2003). Nucleus Accumbens Oxytocin and Dopamine Interact to Regulate Pair Bond Formation in Female Prairie Voles. Neuroscience, 121, 537-544. doi:10.1016/S0306-4522(03)00555-4
- Olazábal, D. E., & Young, L. J. (2006a). Oxytocin receptors in the nucleus accumbens Facilitate “spontaneous” maternal behavior in adult female prairie voles. Neuroscience, 141(2), 559–568. https://doi.org/10.1016/j.neuroscience.2006.04.017
- Olazábal, D. E., & Young, L. J. (2006b). Species and individual differences in juvenile Female alloparental care are associated with oxytocin receptor density in the striatum and the lateral septum. Hormones and Behavior, 49(5), 681–687. https://doi.org/10.1016/j.yhbeh.2005.12.010
- Ross, H. E., & Young, L. J. (2009). Oxytocin and the neural mechanisms regulating social cognition and affiliative behavior. Frontiers in Neuroendocrinology, 30(4), 534–547. https://doi.org/10.1016/j.yfrne.2009.05.004
- Simmons, T. C., Balland, J. F., Dhauna, J., Yang, S. Y., Traina, J. L., Vazquez, J., & Bales, L. (2017). Early Intranasal Vasopressin Administration Impairs Partner Preference in Adult Male Prairie Voles (Microtus ochrogaster). Frontiers in Endocrinology, 8: 145. doi:10.3389/fendo.2017.00145
- Bales, K., Westerhuyzen, J. A. V., Lewis-Reese, A. D., Grotte, N. D., Lanter, J. A., Carter, S. (2007). Oxytocin has dose-dependent developmental effects on pair-bonding and alloparental care in female prairie voles, Hormones and Behavior, 52 (2), 274-279. doi: 10.1016/j.yhbeh.2007.05.004.
- Blocker, T. D., & Ophir, A. G. 2015. Social recognition in paired but not single male prairie voles. Animal Behaviour. 108: 1–8. doi:10.1016/j.anbehav.2015.07.003
- Demas, G. E., Moffatt, C. A., Drazen, D. L., & Nelson, R. J. 1999. Castration Does not Inhibit Aggressive Behavior in Adult Male Prairie Voles (Microtus ochrogaster). Physiology & Behavior. 66(1): 59-62.
- Bowler, C. M., Cushing, B. S., & Carter, C. S. 2002. Social Factors regulate Female-Female Aggression and Affiliation in Prairie Voles. Physiology & Behavior. 76: 559-566.
- Stribley, J. M., & Carter, S. 1999. Developmental Exposure to Vasopressin Increases Aggression in Adult Prairie Voles. PNAS. 96(22): 12601-12604.
- Bales, K. L., Carter, C. S. 2003. Sex Differences and Developmental Effects of Oxytocin on Aggression and Social Behavior in Prairie Voles (Microtus ochrogaster). Hormones and Behavior. 44: 178-184. doi:10.1016/S0018-506X(03)00154-5
- Arias del Razo, R., & Bales, K. L. 2016. Exploration in a dispersal task: Effects of early experience and correlation with other behaviors in prairie voles ( Microtus ochrogaster). Behavioural Processes, 132, 66–75. doi: 10.1016/j.beproc.2016.10.002
- Carter, C. S., & Roberts, R. L. 1997. The psychobiological basis of cooperative breeding in rodents. In N. G. Solomon & J. A. French (Eds.). Cooperative breeding in mammals. Cambridge University Press. 231-236.
- DeVries, A. C., DeVries, M. B., Taymans, S., & Carter, C. S. 1995. Modulation of pair bonding in female prairie voles (Microtus ochrogaster) by corticosterone. Proceedings of the National Academy of Sciences, 92(17): 7744–7748. doi: 10.1073/pnas.92.17.7744
- Donaldson, Z. R., Spiegel, L., & Young, L. J. 2010. Central Vasopressin V1a Receptor Activation is Independently Necessary for Both Partner Preference Formation and Expression in Socially Monogamous Male Prairie Voles. Behavioral Neuroscience, 124(1): 159-163. doi:10.1037/a0018094
Physiological and Psychological Factors in Developing Comorbid Mood Disorders in Complex Regional Pain Syndrome Patients
By Clara Brewer, Neurobiology, Physiology, and Behavior ’22
Author’s Note:
In 2015, I was diagnosed with a rare pain disorder- Complex regional pain syndrome (CRPS). Not only does this disorder cause unimaginable pain, it is also virtually invisible to others, creating a discrepancy between the outside world’s perception of CRPS and the actual struggle that CRPS patients deal with, both physically and emotionally. Current trends for CRPS treatment are focused on the physical aspects of the disorder- increasing mobility and use of the affected limb. Oftentimes, this approach fails to treat the simultaneous psychological changes that can increase a patient’s risk for developing a concurrent mental health disorder. Even though I had access to a top CRPS treatment facility, I still experienced depression and anxiety which made my recovery from CRPS much harder. While writing a different paper focused on educating newly diagnosed individuals on the causes, symptoms, and available treatment options for CRPS, I realized that most of my findings addressed the physical symptoms and not the psychological changes. This discrepancy between both my own experiences and the experiences of many others who were diagnosed with CRPS and the treatment options available inspired me to try to understand this connection between CRPS and the increased diagnosis of comorbid mental health disorders.
While many students who read this paper may not go on to change the treatment for one rare disorder- it is important for anyone who wants to go into the medical field to begin reshaping their approach to medicine by reading articles like mine. I hope to shed light on the importance of reevaluating current treatment protocols for a wide range of disorders to include more mental health support for patients- a topic directed for students hoping to pursue a career in the medical field. By viewing medical diagnosis- in this case CRPS- as an interconnection between mind and body, future medical professionals will be able to holistically address disorder, instead of treating only the more obvious physical symptoms.
Complex regional pain syndrome (CRPS), a neuroinflammatory disease, ranks number one on the McGill Pain Scale, topping fibromyalgia, cancer, and amputation without anesthetics. The development of CRPS typically occurs after trauma to the arm or leg (e.g., breaking of a bone, dislocation of a joint, surgical trauma to a limb) that results in a disproportionately high sensation of pain. This painful response is chronic and characterized by constant pain with additional flare-ups that last different amounts of time from person to person. As a result, those with CRPS will often experience perpetual pain that can be made even worse with stress.
After the initial injury and subsequent chronic pain, a CRPS diagnosis follows the Budapest Diagnostic Criteria, where patients must report symptoms in three of four categories and must present symptoms in two of four categories at the time of evaluation. The categories are as follows: sensory, vasomotor (relating to blood vessels), sudomotor (relating to sweat glands), and motor/tropic (relating to muscles and bones). Symptoms include swelling of the limbs, skin discoloration, abnormal sweat response, and painful responses to non-painful or slightly painful stimuli, among others [1].
CRPS affects 200,000 people in the United States each year. Among those affected, half are also diagnosed with a psychiatric disorder [2]. More specifically, CRPS patients have a much higher prevalence of depression than the general population, with 15.6 percent of CRPS patients diagnosed with depression compared to 3.4 percent of people diagnosed with depression worldwide [2]. Since the pain in CRPS is so intense and the length of a painful flare-up varies from person to person, many patients come to develop a fear of the pain itself, altering their fear-brain circuits and creating a negative relationship between pain and their psychological state [3]. Mood disorders like depression and anxiety also have similar pathophysiology as CRPS, so the onset of CRPS can instigate the development of a comorbid mood disorder without an external trigger like grief, loss, or substance abuse. Within the physiology of CRPS, cytokine and astrocyte levels become dysregulated, mimicking the pathophysiology noted in certain psychiatric disorders and thus increasing rates of comorbid psychiatric disorders. Similarly, the fear-brain circuit is altered during the onset and prolonged management of CRPS, eventually transforming from pain-related fear to an overall manifestation of anxiety.
While the number of patients being diagnosed with comorbid mood disorders is growing, the current CRPS treatment protocol does not typically include the management of these psychiatric disorders. As more research is conducted to explore the physiological and psychological changes that occur with the onset of CRPS, mounting evidence suggests more mental health resources should be provided to alleviate CRPS-related symptoms, both physiological and psychological, and speed up the recovery timeline [4].
Dysregulation of Biomarkers in CRPS and Mood Disorders
TNF-α and Cortisol
During CRPS, depression, and anxiety, plasma levels of the pro-inflammatory cytokine tumor necrosis factor-α (TNF-α) are increased by a statistically significant degree [5]. This cell-signaling protein is integral to maintaining a healthy immune response and stimulates the release of a corticotropin-releasing factor. With CRPS, TNF-α activates and sensitizes primary afferent nociceptors, leading to the characteristic excessive pain [5]. TNF-α not only plays an important role in the development of CRPS, but also in the development and severity of depression. In fact, the more severe the reported depressive symptoms, the higher the concentrations of plasma TNF-α [6].
The increased concentration of TNF-α may be explained by its role in the regulation of the hypothalamic pituitary adrenal (HPA) axis which is responsible for neuroendocrine modulation of the body’s stress response. With an upregulation of pro-inflammatory cytokines like TNF-α in CRPS, the HPA axis increases the secretion of a corticotropin-releasing factor, adrenal-corticotropin hormone, and eventually cortisol. Prolonged elevations of cortisol lead to a shift in tryptophan usage from the tryptophan-serotonin pathway to the tryptophan-kynurenine pathway instead [7]. The shift away from the tryptophan-serotonin pathway greatly limits the production of serotonin, eventually interfering with mood stabilization, sleep cycle regulation, and neuronal communication [8]. Metabolites are then used in the tryptophan-kynurenine pathway instead, leading to the production of two neurotoxic chemicals, 3-hydroxyanthranilic acid, and quinolinic acid.
Quinolinic acid contributes to neurodegeneration seen in conditions like depression through free radical formation, mitochondrial malfunction, and energy store depletion. These changes trigger the mass destruction of neuronal cells which leads to the degeneration of brain functions such as memory and learning [9]. In fact, it has been suggested that elevated TNF-α levels is one precursor to the development of depression [5]. Therefore, TNF-α could be a potential biomarker for comorbid depression in CRPS patients.
Figure 1: TNF-a’s role in the dysregulation of tryptophan metabolism. Under normal conditions, tryptophan is transformed into serotonin in the brain and gut, producing regulatory effects on mood and the sleep cycle as well as promoting health communication between neurons. Under inflammatory conditions, like those seen in CRPS and depression, TNF-a secretion inhibits the production of serotonin through the upregulation of adrenal-corticotropin. Tryptophan is then utilized in the liver for production of neurotoxins through the Kynurenine pathway.
Catecholamines
Following the acute stage of CRPS, stimulation of the sympathetic nervous system and the resulting release of catecholamines increase the production of another important cytokine, interleukin-6 (IL-6). When the body undergoes acute stress, the sympathetic nervous system is activated and causes the release of two catecholamines, epinephrine and norepinephrine. These hormones increase blood pressure, heart rate, breathing rate, and dilate the pupils. Catecholamines are also regulators of IL-6, a cytokine that plays an important role in nociceptor sensitivity while also increasing chances of developing comorbid anxiety. Norepinephrine upregulates the translation of IL-6 by 49-fold, therefore increasing the plasma concentration of IL-6 after sympathetic stimulation [10]. In the case of CRPS, the initial trauma to the affected limb activates this fight-or-flight response and increases IL-6 levels. The chronic elevation of IL-6 not only leads to chronic inflammation, but also increases nociceptor sensitization and the transmission of signals between sensory neurons, both of which are linked to chronically elevated levels of pain [11].
Not only does IL-6 play an important role in inflammatory and pain responses and the onset of CRPS, the cytokine also modulates the expression of another cytokine, interleukin-1 (IL-1). IL-1 is critical for the onset of anxiety-type symptoms by dampening the activation of endocannabinoid receptor CB1R (GABA), which limits GABA’s anti-anxiety effects [12]. In fact, general anxiety disorder patients had statistically significant high levels of IL-6 through environmental stimulation of the sympathetic nervous system [7]. By adding the excitatory effects of CRPS on the sympathetic nervous system, there is no need for external stimulation to begin anxiety symptoms. For this reason, it has been suggested that elevated levels of IL-6 from the onset of CRPS can stimulate IL-1 and induce comorbid anxiety [12].
Figure 2: Sympathetic stimulation following acute CRPS results in an increase in catecholamines. The subsequent upregulation of interleukin-6 and interleukin-1 dampens GABA’s anti-anxiety effects and leads to increased nociceptor activation.
Astrocytes
In conjunction with the changes to cytokines in CRPS, anxiety and depression, subsequent changes to the functioning of astrocytes have been noted in all three diagnoses [13]. Astrocytes are a subclass of glial cells that hold a supportive function for neurons. Under typical conditions, astrocytes are responsible for modulating neuroendocrine functions, regulating synaptic transmission, and regulating glutamate levels in the body [14]. With CRPS, astrocytes become upregulated and activated via stimulation from excess pro-inflammatory cytokines, changing their gene expression to become A1 reactive astrocytes. A1 reactive astrocytes then go on to secrete neurotoxins and more pro-inflammatory cytokines [15]. The activation of A1 reactive astrocytes also induces higher levels of glial fibrillary acidic protein, thus increasing the number of glial glutamate transporters on astrocytes [16]. This hyperactivity and hypersensitivity of astrocytes to glutamate trigger calcium release that increases neurotransmission within nociceptors and ultimately contributes to the intense and chronic pain associated with CRPS [15].
Additionally, patients with major depressive disorder show an mean increase of 35 μmol/L of glutamate concentration in the cortex, a statistically significant changeincrease that is indicative of the severity of depressive symptoms [17]. The shift in gene expression in astrocytes and upregulation of glutamate with the onset of CRPS not only increases nociceptor activation but also neurodegeneration through the reinforcement of the tryptophan-kynurenine pathway discussed earlier. Interestingly, the increased concentration of glutamate inhibits the production of kynurenic acid and instead promotes the production of quinolinic acid [18]. As discussed, quinolinic acid is a potent neurotoxic compound that can lead to neurodegeneration associated with depression. The upregulation and activation of A1 reactive astrocytes in response to inflammation from CRPS increases extrasynaptic glutamate concentrations, causing hyperactivation of nociceptors and increased production of quinolinic acid. With this in mind, there is evidence that the pathophysiology of astrocytes during CRPS may increase the risk of comorbid depression.
CRPS Psychology: Cycle of Pain-Related Anxiety
The fear of pain, also known as harm-avoidance, complicates treating chronic pain conditions like CRPS. This is because certain treatments like physical or occupational therapy methods used in CRPS rehabilitation can quickly become unsuccessful if patients begin to avoid activities that increase symptoms or pain. These treatment techniques can include graded motor imagery, range-of-motion exercises, mirror therapy, desensitization, and electrical stimulation. Although these rehabilitation techniques may temporarily increase CRPS-related pain, therapy is an essential part of treatment and by avoiding painful activities, not only does the CRPS become increasingly worse, but the fear of pain is also cognitively reinforced.
This reinforcement is seen in the fear-learning neural pathway, a series of neurons that extend from the left amygdala to the hippocampus, cerebellum, brainstem, and other parts of the central nervous system. The chronic pain of CRPS and subsequent fear of pain continuously activates neurons that extend to this fear-learning circuit, strengthening the connection between the left amygdala, the fear control center, and the hippocampus, the learning and memory center. This repetitive activation ultimately intensifies the fear brain circuit [19]. Consequently, the cycle of pain-related anxiety begins, transitioning from a fear of pain to an avoidance of pain which then further reinforces the fear of pain [2]. This phenomenon is identified in multiple studies, suggesting a more quantitative link between CRPS and anxiety.
In one study, a group of 64 CRPS patients were evaluated to determine psychological comorbidities. Twenty-eight individuals received a psychiatric diagnosis following the onset of CRPS, with 10 of those 28 diagnosed with anxiety disorders [20]. Not only did the study reveal these diagnoses, they also found that increased anxiety was directly linked to increased pain through the fear-brain pathway [13]. In fact, another study reported that 70 percent of CRPS patients had elevated pain-related fear scores [19]. While the anticipation of pain can elicit pain response, it can also lead to an avoidance of daily responsibilities, physical inactivity, disability, poorer long-term recovery, and higher rates of anxiety and other mood disorders [20]. As a result, the altered fear-brain circuits associated with CRPS increase the likelihood of developing comorbid anxiety.
Conclusion
CRPS alters the body’s physiology and changes certain psychological processes, increasing the chances for developing a comorbid psychiatric disorder. With the dysregulation of cytokines and astrocytes, the immune system’s functioning is disturbed, which leads to abnormal levels of kynurenine and serotonin similar to that of depression [5]. As the levels of kynurenine increase, so does the concentration of extrasynaptic glutamate, upregulating processes that signal both pain and depression [9]. The fear-brain circuit is also altered as pain signals become stronger and more frequent with CRPS, catastrophizing pain and ultimately leading to elevated levels of both pain and anxiety [3]. While pain itself can be debilitating, the simultaneous occurrence of pain and comorbid psychiatric disorders seen in CRPS can lead to avoidance of daily life, thereby worsening both disorders.
Today, most patients managing CRPS disorders are reluctant to express their psychological symptoms and look for help on their own, yet neglecting the psychological side often worsens the symptoms of their diagnosis and makes recovery more difficult [19].Current research suggests that CRPS treatment should shift towards focusing on the psychological components that intensify pain in order to holistically treat CRPS. Over the past few years, more studies have explored the relationship between CRPS and psychiatric disorders, but there has been less research into treatments that would help with both disorders simultaneously. Just as CRPS is often misdiagnosed as an invisible disorder, psychological symptoms may be overlooked or undertreated when physiological responses garner priority. A new perspective of CRPS that acknowledges this association is needed to gain a more comprehensive understanding of the disorder.
References:
- Wie, C., Gupta, R., Maloney, J. et al. 2021. Interventional Modalities to Treat Complex Regional Pain Syndrome. Curr Pain Headache Rep. 25, doi:10.1007/s11916-020-00904-5
- Bean,. J., Johnson, H., Heiss-Dunlop, W., Lee, C., & Kydd, R. 2015. Do psychological factors influence recovery from complex regional pain syndrome type 1? A prospective study. Pain. 156(11), 2310–2318, doi:10.1097/j.pain.0000000000000282
- Antunovich, D.,, Horne, J., Tuck, N., Bean, D. 2021. Are Illness Perceptions Associated with Pain and Disability in Complex Regional Pain Syndrome? A Cross-Sectional Study. Pain Medicine. 22 (1): 100–111, doi:10.1093/pm/pnaa320
- Park HY, Jang YE, Oh S, Lee PB. 2020. Psychological Characteristics in Patients with Chronic Complex Regional Pain Syndrome: Comparisons with Patients with Major Depressive Disorder and Other Types of Chronic Pain. J Pain Res. 13:389-398, doi:10.2147/JPR.S230394
- Üçeyler, N., Eberle, T., Rolke, R., Birklein, F., Sommer, C. 2007. Differential expression patterns of cytokines in complex regional pain syndrome. Pain. 132(2007): 195-205. doi: 10.1016/j.pain.2007.07.031
- Zou, W., Feng, R., Yang, Y. 2018. Changes in the serum levels of inflammatory cytokines in antidepressant drug-naive patients with major depression. Plos One. 13(6): e0197267. doi: 10.1371/journal.pone.0197267
- Strong, J., Jeon, S., Jeon, S., Zhang, J., & Kim, Y. 2020. Glial Cells and Pro-inflammatory Cytokines as Shared Neurobiological Bases for Chronic Pain and Psychiatric Disorders. Overlapping Pain and Psychiatric Syndromes: Global Perspectives. doi:10.1093/med/9780190248253.003.0003
- Carhart-Harris, R. & Nutt, D. 2017. Serotonin and brain function: a tale of two receptors. J Psychopharmacol. 31(9): 1091-1120. doi: 10.1177/0269881117725915
- Pérez-De La Cruz, V., Carrillo-Mora, P., & Santamaría, A. 2012. Quinolinic Acid, an endogenous molecule combining excitotoxicity, oxidative stress and other toxic mechanisms. Int J Tryptophan Res. 5: 1–8, doi: 10.4137/IJTR.S8158
- Burger, A., Benicke, M., Deten, A., Zimmer, H. G. (2001). Catecholamines stimulate interleukin-6 synthesis in rate cardiac fibroblasts. Am J Physiol Heart Circ Physiol. 281: H14-H21. doi:10.1152/ajpheart.2001.281.1.H14
- Zhou, YQ., Liu, Z., Liu, ZH., Chen, SP., Li, M., Shahveranov, A., Ye, DW., & Tian, YK. 2016. Interleukin-6: an emerging regulator of pathological pain. J Neuroinflammation. 13: 141. doi: 10.1186/s12974-016-0607-6
- Rossi, S., Sacchetti, L., Napolitano, F., De Chiara, V., Motta, C., Studer, V., Musella, A., Barbieri, F., Bari, M., Bernardi, G., Maccarrone, M., Usiello, A., Centonze, D. 2012. Interleukin-1β cauSeS anxiety by interacting with the endocannabinoid system. J Neurosci. 32(40): 13896-13905. doi: 0.1523/JNEUROSCI.1515-12.2012
- Ji, RR., Donnelly, C.R. & Nedergaard, M. 2019. Astrocytes in chronic pain and itch. Nat Rev Neurosci. 20: 667–685, doi:10.1038/s41583-019-0218-1
- Jauregui-Huerta, F., Ruvalcaba-Delgadillo, Y., Gonzalez-Castaneda, R., Garcia-Estrada, J., Gonzalez-Perez., Luquin, S. 2010. Response of glial cells to stress and glucocorticoids. Curr Immunol Rev. 6(3): 195-204. doi: 10.2174/157339510791823790
- Li, T., Chen, X., Zhang, C., Zhang, Y., & Yao, W. 2019. An update on reactive astrocytes in chronic pain. J Neuroinflammation. 16: 140. doi: 0.1186/s12974-019-1524-2
- Wesseldijk, F., Fekks, D., Huygen, F., van de Heide-Mulder, M., Zijlstra, F. 2008. Increased plasma glutamate, glycine, and arginine levels in complex regional pain syndrome type 1. Acta Anaesthesiol Scand. 52(5): 688-94. doi: 10.1111/j.1399-6576.2008.01638.x
- Mitani, H., Shirayama, Y., Yamada, T., Maeda, K., Ashby, C., Kawahara, R. 2006. Correlation between plasma levels of glutamate, alanine and serine with severity of depression. Pro Neuro Psychoph. 30(6): 1155-1158. doi: 10.1016/j.pnpbp.2006.03.036
- Schwarcz, R. & Stone, T. 2018. The kynurenine pathway and the brain: challenges, controversies and promises. Neuropharmacology. 122(Pt B): 237-247. doi: 10.1016/j.neuropharm.2016.08.003
- Simons, L. E. 2016. Fear of pain in children and adolescents with neuropathic pain and complex regional pain syndrome. Pain. 157: 90-97, doi:10.1097/j.pain.0000000000000377
- Brinkers, M., Rumpelt, P., Lux, A., Kretzschmar, M., & Pfau, G. 2018. Psychiatric Disorders in Complex Regional Pain Syndrome (CRPS): The Role of the Consultation-Liaison Psychiatrist. Pain Res Manag. doi:10.1155/2018/289436
Investigating Anthelmintics for Vector Control
Investigating the use of anthelmintic drugs in the context of disease vectoring arthropod control
By Anna Cutshall, Animal Biology, Global Disease Biology minor ’21
Author’s Note: When considering the topic of my literature review and analysis, I wanted to select work that I could continue research on in graduate school. As I entered academia, my career and life experiences had prepared me well for the unique intersection of veterinary medicine, ecology, and epidemiology. I have been on a pre-veterinary track for many years and have worked professionally in the veterinary field for more than three years. As an Animal Biology major and Global Disease Biology minor, my coursework largely centered around the emerging threat of zoonotic and vector-borne diseases. These experiences considered, my primary research interests lie in how we may integrate veterinary medicine into One Health practices to better combat emerging disease threats. In this literature review, I investigate the viability of anthelmintic drugs against arthropod vectors of disease. The use of anthelmintics against arthropods is fairly new, and the pool of current literature is limited but promising. This review was written for those, like myself, who are interested in new approaches to the control of tropical diseases, especially through the lens One Health. I hope to leave readers with a clear picture of what is next for this field, what gaps in the data should be filled, and how we can use information gained in responsible, sustainable ways to combat both emerging and established vector-borne diseases.
Abstract
This literature review analyzes the efficacy of currently available anthelmintic drugs against key disease vectoring arthropods. When comparing effective dosages between different drugs and vector genera, we found that relatively low concentrations are effective against most vectors, but there is evidence to suggest that ivermectin resistance has been established in some species (Aedes spp). The avermectin drug class also displayed limited efficacy over time, as the drugs degrade in vertebrate species faster than the isoxazoline drug class or fipronil. We determined that the current findings related to this method of vector control are promising. However, further research must be conducted before we implement anthelmintics for mass drug administration as a part of integrated vector management.
Keywords: anthelmintics, insecticides, vector, disease vector, mosquito, sandfly, One Health, integrated vector management, mass drug administration
Introduction
Vector-borne diseases threaten the well-being of hundreds of millions of people globally. This is predicted to increase as climate change and human activity facilitate the spread of vector species to previously unoccupied locations. In a press release by the Sacramento-Yolo Mosquito & Vector Control District, it was reported that multiple invasive mosquito species, including Aedes aegypti, had been identified in northern California [1]. Recent literature suggests that these habitual expansions may be due, in part, to climate change as these species are able to adapt to broader regions that are of similar climate to their native regions [2]. The continued spread of these species leaves unprepared countries at risk for outbreaks of the diseases vectored by invading species. Moreover, most vector-borne diseases remain uncontrolled in endemic regions. The most direct way to mitigate the threat of globalizing tropical vector-borne diseases is to control the species that are vectoring them. Unfortunately, traditional insecticide-based methods of vector control have become ineffective due to the emergence of insecticide resistance. In 2012, the World Health Organization identified the status of insecticide resistance as “widespread”, as most of the globe reported resistance in at least one major malaria vector [3]. Traditional spray and topical insecticides have been compromised by such resistance. Therefore, it is essential that new methods of vector control,without acquired resistance, be discovered, evaluated, and implemented.
There are many new methods of vector control currently under evaluation. These include genetically modifying vectors to render them sterile, the use of entomopathogenic fungi and viruses, trapping, repellents, and environmental modification [4]. As we continue to evaluate each method for its efficacy, the Integrated Vector Management (IVM) method may be our best option for the elimination of many tropical diseases. Through IVM, we take careful and integrated approaches to vector control via intersectional communication between Public Health officials, Governments, Non-Governmental Organizations, and communities in which we hope to implement our strategies [5]. IVM calls for multiple vector control strategies, and increasing control efficacy via synergy between control efforts. Unfortunately, the primary tool utilized for the control of adult mosquitoes, insecticides, has lost efficacy over time. This is a result of vector insect populations developing resistance to common insecticides, such as pyrethroids and organophosphates, that are used to control adult mosquito populations. However, there is a reservoir of insecticides that have not been utilized against human disease-vectors, which therefore have minimal acquired resistance . This class is oral insecticides, or insecticides ingested by vertebrates that act when a vector is exposed via blood meal from a treated animal. The use of oral insecticides has been standard in veterinary medicine for years, in the form of flea and tick prevention. Common classes of oral insecticides include avermectins, isoxazolines, and phenylpyrazoles. These compounds have been standard in human and/or veterinary medicine as ectoparasiticides, demonstrating their safety for use in vertebrates. Avermectins, isoxazolines, and phenylpyrazoles have similar modes of action as neurotoxins, with both interrupting the function of GABA-gated chloride ion channels, resulting in insect paralysis [6, 7]. Importantly, there is still diversity within these classes as tools against vector species, as they bind to different sites on the GABA receptors [7, 8]. Investigating the efficacy of these drugs for use as insecticides, against key vectors of diseases such as malaria, zika, west nile virus, leishmaniasis, and African Trypanosomiasis, could be part of the solution to the increasingly urgent problem of insecticide resistance.
Research is currently underway, across the globe, to investigate the efficacy of ectoparasiticides against disease vectors. The question still stands, however, if the approach of oral insecticides is any more effective than the traditional insecticides available. To answer this question, we assessed the current literature regarding the testing of ectoparasiticides against disease vectors, and developed a database of studies testing the efficacy of these compounds against vector insects. This analysis aims to determine the relative efficacy of these compounds to determine if these drug classes are worth consideration for use in vector control and management.
Materials and methodology
To establish a database of the relevant literature, we first mined the scientific literature via the UC Davis Library. Using access granted to undergraduate students, the search terms utilized were input as follows: title/abstract contain “vector” AND “veterinary” AND “control” AND “arthropod” in the key word function. Papers were then selected for further analysis. These articles were input into an AI-based literature analysis tool, “Research Rabbit”, to identify additional relevant studies [9]. In addition, studies were selected from the works cited of previously selected works. Papers not testing the efficacy of oral insecticides on adult disease vectors were excluded from the study. Additionally, papers without comparable data (did not supply direct mortality or density data) were also excluded.
Each paper was analyzed to extract relevant data on the efficacy of oral insecticides against disease vectors. The data was collated into a Microsoft Excel spreadsheet. Categories selected for further evaluation included: drug type, the concentrations used, associated concentration resulting in 50% mortality (LC50 values), time to mortality, the reduction of vectors present in field study by visual count (resting density), and drug effects on vector fecundity. However, for the purposes of this study, we focused on LC50 and temporal values. Other categories were not consistent across publications.
When creating data visualizations for comparison of different drug types, R’s “ggplot2” package, “dplyr” package and “esquisse” package were used [10-13]. The categories determined to be best for visual comparison were “Temporal Data” and “LC50” data. After initial visualization was made in R, figures were exported to Adobe Illustrator to edit aesthetically, which was limited to modification of font types and caption content [14]. When creating visualizations for LC50 data, both sandfly and mosquito vectors were compared on the same figure, to compare the efficacy of not only drugs in relation to each other, but also drugs in relation to their efficacy against different disease vectors. When creating the data visualization for this comparison, the drugs “Moxidectin” and “NTBC” were excluded. Moxidectin’s LC50 value was too high to allow for reasonable comparison to other drugs, and NTBC only had a value for tsetse flies (Glossina spp), which were not represented in any other drug. Additionally, the Lutzomyia spp displayed LC50 values too high to be effectively compared to other disease vectors. When visualizing temporal data, only Anopheles spp and Phebotomus spp had enough supporting information in the literature for effective comparisons. There were 5 studies that supplied data for Anopheles spp temporal data and 2 studies that supplied data for Phlebotomus spp temporal data. These temporal data were plotted as Day of Feeding against Mortality, faceted by drug type, and grouped by dose. 2 Figures were created, one for Phlebotomus spp and another for Anopheles spp.
Results
Database Creation
From the initial search in the UC Davis Library system, 15 studies were selected. Then, based on the output from Research Rabbit, an additional 5 studies were selected. Finally, 3 additional papers were identified and integrated into the analysis from the references of the 20 studies. These 23 studies were then evaluated individually from January, 2021 through March, 2021.
We obtained multiple data categories for comparison between the selected papers. Figure 1 displays the summary of the resulting database. Due to the recent nature of this research, resources from which to draw for our database were limited. Table 1 shows a summary of the data types and the number of papers each data type was collected from. Within each paper, some investigated efficacy against multiple vector genuses while others investigated only one. Based on the data we were able to collect, we will be moving forward directly comparing temporal data as well as LC50 data.
Comparing Effectiveness of Dosages Between Drugs
We sought to compare the concentrations of drugs required to be effective against the disease vectoring arthropods studied. Figure 2 displays the LC50 values chosen for comparison as a “lollipop” plot. Within the plot, each “dot” represents a single datapoint taken from a study, and 7 studies were compiled to create the plot. In this figure, we are able to compare 2 major drug classes: avermectins and isoxazolines. The isoxazoline drug class had more available data across insect families, and it is clear that the LC50 value is variable between genuses. Sandflies have more resistance to isoxazolines (especially fluralaner) than mosquito species. Amongst the avermectins, Anopheles spp. Display the most consistent, and relatively low LC50 values. However, Aedes spp. displays higher resistance to ivermectin compared to Anopheles spp.
Comparing Temporal Data
Temporal data involving different insecticides were first to be compared. In temporal data, the “Days Post Feeding (Day of Blood Meal)” represents the number of days after the initial dosing of the vertebrate animals (for example, “Day 3” indicates mosquitos that fed on an animal 3 days after it was given the drug). We were able to create 2 figures for comparing the efficacy of drugs over time at various doses. Doses were represented as variance in color in the figures. Figure 3 displays the efficacy of oral dosing to vertebrates of eprinomectin and ivermectin against Anopheles spp. over time. Both of the drugs in this comparison were of the avermectin class, and neither displayed robust effects on mortality past the 15 day mark after single-dosing of vertebrates with the drugs. Additionally, we observe great variance in the efficacy of ivermectin, even within dosages (that is, mortality varied within dosages between different studies). Unfortunately, mosquito genuses outside of Anopheles did not provide enough data to compare efficacy over time.
Created in a similar fashion, Figure 4 displays the efficacy of oral dosing of vertebrates with fipronil and fluralaner. Here, two separate drug classes were tested for efficacy. While fluralaner (a member of the isoxazoline drug class) acts in a similar mode of action to avermectins, it maintains efficacy over time in Sandflies. Because mosquitos displayed more sensitivity to isoxazolines than sandflies, (Figure 2) one may predict similar, if not more deadly, effects when isoxazolines are tested over time for mosquitos. The drug fipronil displays varying efficacy between doses. Unfortunately, the study involving fipronil did not collect data past 21 days of administration, but it is possible some of the dosages would have remained effective from visual interpretation of the figure. Due to the limited amount of studies investigating the efficacy of oral insecticides, we were not able to compare the efficacy of all drugs over time, as other studies used different methods of efficacy measurements.
Discussion
Based on the findings of our review of currently available literature, oral insecticides certainly show promise as a method of Disease Vector management. As displayed in Figure 2, we are able to determine effective dosages for each drug as a concentration in blood. However, there was significant variance in the data between taxa and between drugs. The highest resistance was observed in Aedes spp. against ivermectin, which could be evidence of acquired resistance due to the common use of ivermectin in humans as an anthelmintic [15]. Another significant variance observed was the relative resistance of sandflies to isoxazolines, requiring approximately twice the concentration or more compared to mosquito species [16]. It is unclear if this effect carries over to other drugs, as there is no available data.
There were also studies analyzed that were not included in the visual data analyses performed. These included studies that investigated the efficacy of isoxazolines against the kissing bug, of nitisinone against the tsetse fly, a field study, and data from otherwise integrated studies measuring the effect of the drugs on fecundity of arthropods [16-22]. The investigation by Loza et al. regarding the efficacy of isoxazolines against the kissing bug showed similar temporal data results to papers investigating isoxazolines against Sandflies, which was visualized in Figure 4 [17]. The isoxazoline drug class, then, has been shown to be effective against 3 major disease vector families. Another drug class also shows promise. A study by Sterkel et al proposes the use of nitisinone (traditionally used in the treatment of hereditary tyrosinemia type 1, a genetic disorder) as an insecticide dispensed to vertebrates, and investigates its efficacy against the African Trypanosomiasis vector, the tsetse fly. This study highlights the importance of looking for alternative methods to vector control, and manipulates a characteristic of a drug originally developed to aid in human disease against disease vectors. The 2021 study found that concentrations above 0.5 micrograms per milliliter in blood impacted survival of feeding tsetse flies significantly, while also studying pharmacokinetics when ingested by mouse models [18]. Pharmacokinetic data supplied by the mouse models in this study may assist in any later calculations for human dosage. No evidence is available on the effectiveness of nitisinone on other disease vectors.
Three studies supplied data involving sublethal effects on adult arthropods, including fecundity. These studies found that there were significant effects on Anopheles spp. fecundity, regardless of vertebrate being dosed and observed across multiple doses [20-22]. There is no currently available data involving the effects of isoxazolines or phenylpyrazoles. Should they be provided, however, they show additional promise as vector management tools. When an insecticide is able to exhibit both lethal and sublethal effects, particularly regarding fecundity, insects that survive the initial exposure produce less offspring than their unexposed peers.
In order for these methods to be effective in Disease Vector management, there would need to be a considerable number of individuals in the population participating to make a significant impact on the burden of vector borne diseases [16, 19]. Mass Drug Administration (MDA) is expensive, and cost is a limiting factor in many of the areas we hope to lower disease burden in. Due to this, and issues related to the accessibility of MDA, it is important that drugs remain effective for an extended period of time. Fortunately, we found this to be the case. As evident in Figure 4, both isoxazoline drugs display extended efficacy on mortality of sandflies over 40 days after initial vertebrate dosage. Additionally, it may be that fipronil displays a similar effect in higher tested dosages, following the trajectory of the available data. Unfortunately, there is limited literature on this subject, so we are unable to say with absolute certainty that these effects would carry over to mosquito species. Figure 3 suggests that the avermectin drug class does not have the same long-term effect on arthropod mortality. For both ivermectin and eprinomectin, mortality dropped below 50% overall after just 14 days from initial dosage. For this reason, the isoxazoline and phenylpyrazole drug classes may be more effective for MDA, although their testing for safety in humans is less extensive (than ivermectin).
Additionally, there is the question of if MDA should be dispensed to humans or livestock. The field study by Poche et al. applied previous findings to the field, dosing cattle in several tribes in Africa and visually measuring the effects on the density of mosquitos found in nearby homes. They observed that the dosage of livestock with fipronil reduced the “resting density” of mosquito species known to feed on both cattle and humans, but did not significantly affect the resting density of particularly anthropophilic species [19]. This study highlights the importance of catering an MDA to the specific species you want to impact by ensuring dosage to vertebrates that it is likely to take a blood meal from.
When considering a drug for use in MDA, the safety of the drug must be copiously studied, and current findings are promising. At the dosages used in the study that were effective against adult arthropods, vertebrates suffered no severe adverse effects attributed to the dosages in all of the studies analyzed. This strongly suggests that these drugs are safe for use in IVM. Additionally, when considering MDA, taking a “One Health” approach will also be key to success. Too often, non-Governmental Organizations have gone into regions with targeted endemic diseases, and neglected to listen to native perspectives on previously used methods of disease control and basic needs. While investigating the efficacy of these drugs is important to protecting communities against vector-borne disease, giving aid to impoverished communities must first address the baseline health of individuals at risk. Only then can we hope to earn the trust of native populations, and continue to help them in sustainable ways. Continuing to thoroughly investigate the efficacy and safety of this sector of vector management before beginning any large implementation will also be essential.
Overall, it can be inferred from the amount of studies performed that there needs to be an enormous amount of research performed before we integrate oral insecticides, especially in humans, into IVM. What we do know, though, gives promise in the face of the insurmountable resistance to traditional pesticides.
References:
- First Detection of Invasive Mosquitoes in Yolo County. Sacramento-Yolo Mosquito & Vector Control https://www.fightthebite.net/news-posts/first-detection-of-invasive-mosquitoes-in-yolo-c ounty/ (2020).
- Caminade, C., McIntyre, K. M. & Jones, A. E. Impact of recent and future climate change on vector‐borne diseases. Ann N Y Acad Sci 1436, 157–173 (2019).
- World Health Organization. Global Malaria Programme & World Health Organization. Global plan for insecticide resistance management in malaria vectors. GPIRM (2012).
- Takken, W. & Knols, B. G. J. Malaria vector control: current and future strategies. Trends in Parasitology 25, 101–104 (2009).
- Beier, J. C. et al. Integrated vector management for malaria control. Malar J 7, S4 (2008).
- Wang, C. C. & Pong, S. S. Actions of avermectin B1a on GABA nerves. Prog Clin Biol Res 97, 373–395 (1982).
- Ratra, G. S. & Casida, J. E. GABA receptor subunit composition relative to insecticide potency and selectivity. Toxicology Letters 122, 215–222 (2001).
- Casida, J. E. & Durkin, K. A. Novel GABA receptor pesticide targets. Pesticide Biochemistry and Physiology 121, 22–30 (2015).
- ResearchRabbit, Version 2.0 Human Intelligence Technologies, Incorporated Available from: https://www.researchrabbit.ai/
- R Core Team (2013). R: A language and environment for statistical computing. R foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr
- Fanny Meyer and Victor Perrier (2021). esquisse: Explore and Visualize Your Data Interactively. R package version 1.0.1. https://CRAN.R-project.org/package=esquisse
- Adobe Inc. Adobe Illustrator [Internet]. 2019. Available from: https://adobe.com/products/illustrator
- Hadlett, M., Nagi, S. C., Sarkar, M., Paine, M. J. I. & Weetman, D. High concentrations of membrane-fed ivermectin are required for substantial lethal and sublethal impacts on Aedes aegypti. Parasit Vectors 14, 9 (2021).
- Miglianico, M. et al. Repurposing isoxazoline veterinary drugs for control of vector-borne human diseases. Proc Natl Acad Sci U S A 115, E6920–E6926 (2018).1.
- Loza, A. et al. Systemic insecticide treatment of the canine reservoir of Trypanosoma cruzi induces high levels of lethality in Triatoma infestans, a principal vector of Chagas disease. Parasit Vectors 10, 344 (2017).
- Sterkel, M. et al. Repurposing the orphan drug nitisinone to control the transmission of African trypanosomiasis. PLOS Biology 19, e3000796 (2021).
- Poché, R. M. et al. Preliminary efficacy investigations of oral fipronil against Anopheles arabiensis when administered to Zebu cattle (Bos indicus) under field conditions. Acta Trop 176, 126–133 (2017).
- Belay, A., Petros, B., Gebre-Michael, T. & Balkew, M. Effect of LongRangeTM eprinomectin on Anopheles arabiensis by feeding on calves treated with the drug. Malaria Journal 18, 332 (2019).
- Mekuriaw, W. et al. The effect of ivermectin® on fertility, fecundity and mortality of Anopheles arabiensis fed on treated men in Ethiopia. Malar J 18, 357 (2019).
- Pasay, C. J. et al. Treatment of pigs with endectocides as a complementary tool for combating malaria transmission by Anopheles farauti (s.s.) in Papua New Guinea. Parasites & Vectors 12, 124 (2019).
- Alout, H. et al. Evaluation of ivermectin mass drug administration for malaria transmission control across different West African environments. Malaria Journal 13, 417 (2014).
- Bongiorno, G. et al. A single oral dose of fluralaner (Bravecto®) in dogs rapidly kills 100% of blood-fed Phlebotomus perniciosus, a main visceral leishmaniasis vector, for at least 1 month after treatment. Med Vet Entomol 34, 240–243 (2020).
- Chaccour, C. J. et al. Targeting cattle for malaria elimination: marked reduction of Anopheles arabiensis survival for over six months using a slow-release ivermectin implant formulation. Parasites & Vectors 11, 287 (2018).
- Davey, R. B., Miller, J. A., George, J. E. & Klavons, J. A. Efficacy of a Single Doramectin Injection Against Adult Female Boophilus microplus (Acari: Ixodidae) in the Final Stages of Engorgement Before Detachment. Journal of Medical Entomology 44, 277–282 (2007).
- Foy, B. D. et al. Efficacy and risk of harms of repeat ivermectin mass drug administrations for control of malaria (RIMDAMAL): a cluster-randomised trial. The Lancet 393, 1517–1526 (2019).
- Fritz, M. L., Walker, E. D. & Miller, J. R. Lethal and sublethal effects of avermectin/milbemycin parasiticides on the African malaria vector, Anopheles arabiensis. J Med Entomol 49, 326–331 (2012).
- Gomez, S. A. et al. A randomized, blinded, controlled trial to assess sand fly mortality of fluralaner administered orally in dogs. Parasit Vectors 11, 627 (2018).
- Kobylinski, K. C. et al. The effect of oral anthelmintics on the survivorship and re-feeding frequency of anthropophilic mosquito disease vectors. Acta Trop 116, 119–126 (2010).
- Lozano-Fuentes, S. et al. Evaluation of a topical formulation of eprinomectin against Anopheles arabiensis when administered to Zebu cattle (Bos indicus) under field conditions. Malar J 15, 324 (2016).
- Ouédraogo, A. L. et al. Efficacy and safety of the mosquitocidal drug ivermectin to prevent malaria transmission after treatment: a double-blind, randomized, clinical trial. Clin Infect Dis 60, 357–365 (2015).
- Poché, R. M., Burruss, D., Polyakova, L., Poché, D. M. & Garlapati, R. B. Treatment of livestock with systemic insecticides for control of Anopheles arabiensis in western Kenya. Malaria Journal 14, 351 (2015).
- Poché, R. M., Garlapati, R., Singh, M. I. & Poché, D. M. Evaluation of fipronil oral dosing to cattle for control of adult and larval sand flies under controlled conditions. J Med Entomol 50, 833–837 (2013).
- Pooda, H. S. et al. Administration of ivermectin to peridomestic cattle: a promising approach to target the residual transmission of human malaria. Malaria Journal 14, 496 (2015).
- Smit, M. R. et al. Safety and mosquitocidal efficacy of high-dose ivermectin when co-administered with dihydroartemisinin-piperaquine in Kenyan adults with uncomplicated malaria (IVERMAL): a randomised, double-blind, placebo-controlled trial. Lancet Infect Dis 18, 615–626 (2018).
- Smit, M. R. et al. Pharmacokinetics-Pharmacodynamics of High-Dose Ivermectin with Dihydroartemisinin-Piperaquine on Mosquitocidal Activity and QT-Prolongation (IVERMAL). Clin Pharmacol Ther 105, 388–401 (2019).
Treatments for Eye Strain From Screen Exposure
By Anisha Narsam, Neurobiology, Physiology and Behavior ‘23
Author’s Note: I hope to raise awareness about treatments for eye strain from screen exposure because of the current pandemic and the increase in online interactions. This article is meant for students and individuals who work on devices with screens, such as computers or tablets, and want to treat their eye strain. I chose this topic because I have noticed an increase in my eye strain since the pandemic began and I wanted to research how to alleviate this condition for myself and for my peers. Through this article, I hope readers can understand the effectiveness of a range of treatments for eye strain from screen exposure with my analysis of seven peer-reviewed journal articles.
Abstract
Excessive screen time, due to remote learning, is dramatically increasing the incidence of eye strain. Since this condition can elevate tiredness and reduce an individual’s ability to concentrate, promising treatments must be considered to avoid further ocular harm. Previous research on eye strain shows that when people spend a lot of time on their computer, they can benefit from taking frequent breaks away from their screens. However, with increased reliance on technology for everyday tasks, these techniques are not enough. As a result, treatments must be developed and implemented to counter the degenerative effects of long-term eye strain, which may eventually lead to headaches and farsightedness. This paper analyzes seven peer-reviewed journal articles centered around the effectiveness of each treatment on eye strain. Supplemental medications and ergonomic techniques show promising results for combatting dry eye and ocular pain, specific symptoms of eye strain. Studies evaluating the effectiveness of blue light glasses demonstrate conflicting results. Further research and implementation of these methods can decrease eye strain symptoms while improving concentration.
Introduction
Eye strain is one of the most common ocular conditions faced by adults and students around the world [1]. As of 2017, around 64 percent to 90 percent of computer users reported eye strain symptoms [2]. Physiologically, these symptoms result from repetitive rapid eye movements between the keyboard, screen, and other documents [2]. With more interactions taking place virtually because of the COVID-19 pandemic, addressing this condition is crucial for decreasing headaches and improving focus when completing tasks on screens. Many studies have only focused on treatment through minor modifications in a person’s behavioral tendencies, such as by following the 20/20/20 rule. This rule asks individuals to look 20 feet away from their screens for 20 seconds every 20 minutes [3]. Since taking frequent screen breaks may not be helpful for severe eye strain, the purpose of this literature review is to evaluate the effectiveness of medications [1, 4, 5], blue light-blocking glasses [6,7], and ergonomic techniques [2, 3] in combating this condition. By evaluating these treatments, we can determine reliable methods for alleviating eye fatigue.
Medication
In terms of medication, omega-3 fatty acids (O3Fas) can reduce eye strain [1]. Specifically, O3Fas treat patients with dry eye, which is a symptom of eye strain. When people stare at one location for extended periods of time, they often do not blink as much, which results in dry eye because of decreased eye lubrication [2]. On a cellular level, O3Fas has been shown to increase the density of goblet cells, which lubricate the eye. The patients chosen for the study used a computer for more than three hours each day [1]. While 120 patients received the O3Fas treatment, the remaining 236 patients received an olive oil placebo daily and their symptoms were evaluated. Each week, patients assigned a score between zero and three for symptoms such as blurry vision, dry eyes, and red eyes [1]. At the baseline, 60 percent of patients had moderate dry eye symptoms in both the O3Fas and the placebo groups [1]. By the end of the experiment, 70 percent of the O3Fas group and 15 percent of the placebo group were symptom free. In addition, Schirmer’s test was performed, which involves gently placing a filter paper onto the participants’ eyes and analyzing the cells found on this paper for tear production [1, 4]. A statistically significant improvement in dry eye symptoms was found in the O3Fas group compared to the placebo group, demonstrating the effectiveness of O3Fas in combatting dry eye resulting from computer vision syndrome [1]. They can also improve epithelial cellular morphology in the eyes while decreasing tear evaporation rates, which ends up reducing eye strain symptoms. While this study analyzed the effects of O3Fas, it did not test for how the dosage of O3Fas affects an individual’s symptoms.
Besides O3Fas, researchers examined how the dosage of a particular botanical formula can decrease eye strain, while using a machine learning-based model to predict an accurate dosage for each patient [4]. Botanical formulas are natural, plant-based ingredients and oils that are combined in order to treat or supplement a condition. This specific botanical formula is made of carotenoids, naturally derived pigments in plants that support eye health, as well as blackcurrant, chrysanthemum, zeaxanthin, and goji berry. These formulas are antioxidants and are known for their ability to absorb the blue light that typically radiates from visual display units. Researchers split the participants, who are each exposed to screens daily, into four groups. The three experimental groups ingested six, ten, or fourteen milligrams of the chewable tablet, while the fourth group received a placebo [4]. Similar to the O3Fas study, this study also asked patients to take these tablets once daily for 90 days, while self-reporting how often they felt symptoms such as eye soreness and dry eye [1, 4]. In addition, both studies used Schirmer’s test to evaluate dry eye symptoms, which found that both the 10-milligram and 14-milligram groups had increased tear production [1, 4]. Researchers input the symptoms and dosages of 56 of the participants into their machine learning model, XGBoost. The study found that the optimal dosage is 14 milligrams for 39 individuals, 10 milligrams for 17 individuals, and zero milligrams for two participants [4]. Researchers determined that the botanical formula significantly improves eye strain, while outlining the potential for machine learning to determine optimal dosages [4].
Bilberry extract (BE), another naturally-derived medication, has proven to be quite effective in reducing eye strain caused by acute video display terminal (VDT) loads, or devices with a display screen [5]. BE is loaded with anthocyanins, which are known for their ability to decrease further visual disturbance and eye strain. The participants use VDTs daily and have eye strain symptoms. Over an eight-week period, the experimental group ingested 480 milligrams of BE per day, in contrast to the placebo group [5]. The researchers evaluated their symptoms using the Critical Flicker Fusion device (CFF), which analyzes the frequency of a human eye identifying a blinking light as continuous [5, 6]. Lower CFF values correlate to less eye strain symptoms. This contrasts to the O3Fas and botanical formula studies that analyze the dryness of a patient’s eyes through Schirmer’s test [1, 4, 5]. Moreover, all three studies focused on patients with existing eye strain [1, 4, 5]. A self-reported questionnaire asked participants to rate the intensity of their symptoms on a scale from one to ten every week. Based on the CFF tests, researchers found a statistically significant lower CFF value, or less eye strain, for the BE group compared to the baseline [5].This CFF reduction was not observed in the placebo group. Participants in the BE group felt less ocular pain compared to the control group.6 The researchers argued that BE supplements can reduce eye strain caused by VDT loads, while further research can eventually analyze how BE works [5].
(a) Blackcurrant, (b) chrysanthemum, and (c) goji berry compose a botanical formula that can significantly improve eye strain [4].
Blue Light-Blocking Glasses
Medications are important treatments to consider, but there are many behavioral changes that may decrease eye strain symptoms too. One possible behavioral change is wearing blue light-blocking glasses in treating eye strain. Blue light is short-wavelength electromagnetic radiation, which ranges from around 400 to 500 nm in length, and carries one of the highest amounts of energy [7]. There have been many hypotheses previously considered, which shows how blue light could potentially cause retinal damage, especially towards the aging eye. In fact, from studies in animals, increasing amounts of blue light exposure can increase the amount of cell apoptosis in the eyes [7]. It is important to note that blue light is emitted by the sun onto Earth’s surface, but it is the excessive exposure to blue light from screens that can have negative effects [6]. Based on these previous experiments, researchers aim to understand whether or not blocking blue light is effective in preventing eye strain and retinal damage by testing the effects of the blue light-blocking glasses.
Lin et al. assigned 36 participants into groups with the clear lens placebo, low-blocking glasses, and high-blocking glasses [6]. Afterwards, participants performed a 2-hour task on identical computers in similar controlled environments. Researchers used the CFF device and a participant-reported survey to evaluate symptoms of eye strain and fatigue [6]. The CFF depicted significantly less eye fatigue in the high-blocking group compared to the low-blocking and placebo groups. Participant surveys suggested that the high-blocking group reported feeling less pain and itchiness in their eyes after the computer task. However, there was no statistically significant correlation between blue light-blocking glasses and eye strain specifically, based on the participants’ self-reported scores for the intensity of their eye strain symptoms [6]. Therefore, researchers determined that blocking large amounts of blue light may reduce eye strain from screen exposure, but more research needs to be done to determine if this effect is substantial. Awareness of these results can encourage the usage of blue light-blocking glasses to decrease eye pain and itchiness, while further studies can also evaluate its effects on eye strain and the specific amount of blue light blocked by different glasses [6].
Similar to Lin et al., Leung et al. also presented inconclusive results. Leung et al. compared the symptoms of patients wearing blue light-filtering glasses, brown tinted glasses, and a placebo, and found that while blue light filters decrease eye sensitivity, most participants could not detect these changes [7]. A group of 80 computer users wore the lenses for two hours each day for around one month. The participants switched between the lenses during this time period. Researchers performed contrast sensitivity tests on the participants to see how accurately they can read a chart. Researchers use these tests to evaluate how accurately participants could read a chart in which the contrast of each black letter fades into the white background in small increments [7]. This test found no significantly different contrast sensitivity results between the experimental and placebo groups. Based on weekly questionnaires, more than 45 percent of patients reported no changes in their eyesight or eye strain symptoms, while the majority reported no differences between the blue light-blocking glasses and the control lenses [7]. Researchers concluded that analyzing the effects of blue light-blocking glasses is difficult. Spectral transmittance, which evaluates the amount of blue light blocked, showed that the glasses reflected around 10.3 percent to 23.6 percent of harmful blue light [7]. Based on this discovery, researchers found that it is still important to wear these glasses to block the harmful radiation present between 400 and 500 nm in the blue light range, even when participants found no noticeable benefits. The differences between these two studies suggest how these various approaches could lead to similarly inconclusive results.
To compare the two studies, Lin et al.’s research included only 36 participants in a two-hour-long study in identical environments, while Leung et al.’s study had 80 participants and occurred over two months with no supervision [6, 7]. While Leung et al.’s study could analyze long-term effects of the glasses, Lin et al.’s experiment could only analyze the short-term benefits but in a controlled environment. Additionally, Lin et al.’s study only allowed each participant to try on one of the glasses, while Leung et al.’s experiment allowed participants to wear each one of the glasses [6, 7]. As a result, Leung et al.’s study eliminated differences in personal opinion between participants by only evaluating how one group responded to each of the variables. The outcomes of both studies showed how blue light-blocking glasses could relieve eye strain, although more research still needs to be done on this topic.
Ergonomics
While there is conflicting evidence for wearing blue light glasses as a behavioral modification technique, the ergonomic approach shows more promise. To determine the benefits of ergonomics on computer vision syndrome (CVS), Mowatt et al. evaluated the prevalence of eye strain in students at The University of the West Indies (UWI) [2]. Specifically, researchers analyzed how the angle of a computer screen affects eye strain. In this cross-sectional study, 409 students answered a questionnaire related to how often they use a computer, the severity of their eye strain symptoms, and the angle of their screens in relation to their eyes [2]. The results depicted how severe eye strain occurred in 63 percent of the students who look down at their device compared to 21 percent of the participants who keep their device at eye-level [2]. However, the data did not present a relationship between the prevalence of eye strain and the length of time spent on a computer. These results support the use of ergonomic practices, such as keeping a screen at eye-level, to reduce eye fatigue. Increased awareness of such behavioral modification techniques, especially by universities, can prevent eye strain in students [2]. A similar study also uses a survey to analyze practices among individuals who work on computers daily.
Using surveys, researchers analyzed how ergonomics and symptoms of eye strain can be correlated. Office workers answered a questionnaire about eye strain symptoms and workplace conditions [3]. Researchers found that a higher angle of gaze towards a monitor is associated with more CVS prevalence [3]. In addition, looking upwards at a screen should be avoided as it results in muscular strain on the trapezius and neck muscles. This contrasts with the study at UWI, which determined that patients who looked down at their screens, at relatively large angles from eye level, tended to have more strained eyes [2,3]. Based on the results of both studies, placing the screen between eye level and at a small angle of 10 degrees downwards may be the best resolution. Moreover, using a monitor with a filter and adjusting the brightness of an individual’s screen to match that of the environment is correlated with less CVS [3]. Although these results may seem to be solutions for CVS, they are based on surveys rather than controlled studies [2, 3]. Therefore, there is no definite causation between a certain ergonomic practice and eye strain.
Conclusion
When looking at all the possible treatments for eye strain from screen exposure, there are many different medications [1, 4, 5], types of blue light-blocking glasses [6, 7], and ergonomic techniques [2, 3] that can reduce symptoms. O3Fas and the presented botanical formula both show reduction in eye strain symptoms when evaluated with Schirmer’s test for dry eye [1, 4]. The BE study also showed promising results in reducing symptoms of eye fatigue through the CFF test, which focuses more on the temporal processing ability of the eyes [5]. Though blue light-blocking glasses show positive results on the CFF tests and through measured spectral transmittance data, there are mixed results as to whether or not participants detect any changes in eye strain when wearing these glasses [6, 7]. Further testing can be done to evaluate the effects of blue light glasses, such as by examining a larger population or through a longitudinal study. Ergonomic techniques are correlated with less eye strain, according to recent surveys [2, 3]. Clinical trials in controlled environments can show more direct implications of ergonomic practices on eye strain from screen exposure. These treatments combined have the potential to reduce eye strain symptoms, leading to fewer headaches and improved concentration.
References:
- Bhargava R, Kumar P, Phogat H, Kaur A, Kumar M. 2015. Oral Omega-3 Fatty Acids Treatment in Computer Vision Syndrome Related Dry Eye. Cont Lens Anterior Eye [Internet]. 38(3):206-210. doi:10.1016/j.clae.2015.01.007
- Mowatt L, Gordon C, Santosh ABR, Jones T. 2018. Computer Vision Syndrome and Ergonomic Practices Among Undergraduate University Students. Int J Clin Pract [Internet]. 72(1):10.1111/ijcp.13035. doi:10.1111/ijcp.13035
- Ranasinghe P, Wathurapatha WS, Perera YS, et al. 2016. Computer vision syndrome among computer office workers in a developing country: an evaluation of prevalence and risk factors. BMC Res Notes [Internet]. 9:150. doi:10.1186/s13104-016-1962-1
- Kan J, Li A, Zou H, Chen L, Du J. 2020. A Machine Learning Based Dose Prediction of Lutein Supplements for Individuals With Eye Fatigue. Front Nutr [Internet]. 7:577923. doi:10.3389/fnut.2020.577923
- Ozawa Y, Kawashima M, Inoue S, et al. 2015. Bilberry Extract Supplementation for Preventing Eye Fatigue in Video Display Terminal Workers. J Nutr Health Aging [Internet]. 19(5):548-554. doi:10.1007/s12603-014-0573-6
- Lin JB, Gerratt BW, Bassi CJ, Apte RS. 2017. Short-Wavelength Light-Blocking Eyeglasses Attenuate Symptoms of Eye Fatigue. Invest Ophthalmol Vis Sci [Internet]. 58(1):442-447. doi:10.1167/iovs.16-20663
- Leung, T. W., Li, R. W., & Kee, C. S. 2017. Blue-Light Filtering Spectacle Lenses: Optical and Clinical Performances. PloS one [Internet]. 12(1): e0169114. doi:10.1371/journal.pone.0169114
Review: The role of gut microbiota on Autism Spectrum Disorder (ASD) and clinical implications
By Nikita Jignesh Patel, Neurobiology, Physiology, & Behavior ’22
Author’s Note: Ever since I took BIS2C at UC Davis, I was intrigued as to how our gut microbiome plays such a huge role in our homeostasis beyond just digestion – in particular, the correlation between decreased microbiome diversity and allergies we learned about in the lab fascinated me. I recently stumbled upon the term “gut-brain axis” and was in awe as to how this connection between our gut microbes and our brain even exists, and learned that gut microbiome diversity is implicated in a plethora of mental disorders, from depression and anxiety, to autism. I decided to write this review to share my learning of how the gut microbiome can change the brain and potentially contribute to Autism Spectrum Disorder (ASD), because I feel as if this is not a widely known correlation – even as a physiology major, I never learned about the gut-brain axis in my courses. Moreover, the cause of autism is still widely undefined and the gut microbiome may provide a possible explanation for ASD onset in some individuals. I believe a wide range of students will find this upcoming research interesting, but my intended audience is those who research autism or work with autistic individuals, as it may provide an explanation for ASD and seems to be a likely target for clinical therapy for autism in the future. Above all, I want my readers to take away a better understanding of the gut-brain axis and how its imbalance can be implicated in brain disorders like autism.
Introduction
Autism Spectrum Disorder (ASD) is a lifelong neurodevelopmental disorder characterized by a range of symptoms including difficulty with communication, social interaction, and restricted and repetitive behaviors that present differently in every individual [1]. Although 1 in 54 children are estimated to be on the autism spectrum according to the CDC [2], the etiology of the condition remains poorly understood. Factors including genetics and certain maternal environmental conditions have been identified as potential contributors to the development of ASD in children, but the exact cause is still unknown [3].
A common comorbidity experienced by ASD individuals is gastrointestinal (GI) problems—including abdominal pain, constipation, and diarrhea —as such, autism research is pivoting towards studying the gut microbiome. [1]. Specifically, a link between the composition of the gut microbiome and brain development has been established in recent years — termed the “gut-brain axis”— and it appears to be the future of autism research. This literature review aims to identify the role of the human gut microbiome on the development of autism-like behavior and investigate whether therapies targeting the gut microbiome can be effective clinical treatments for Autism Spectrum Disorder (ASD). The article will first define the differences observed in gut microbiota between autistic and neurotypical individuals, then discuss how these differences in composition may affect brain development, and finally propose clinical implications targeting gut microbiota that appear promising in the treatment and diagnosis of ASD-related behaviors.
Gut Microbiome of ASD Patients Differ From Neurotypical Individuals
The gut microbiomes of Autism Spectrum Disorder (ASD) patients have defining characteristics that significantly differ from those of neurotypical individuals. The human gut microbiome consists of a diverse array of predominantly bacteria but also archaea, eukarya and viruses that possess unique microbial enzymes to aid humans in digestion and also a variety of other physiological functions [4]. The three phyla Firmicutes, Bacteroidetes, and Actinobacteria [5] encompass the majority of bacteria present in the gastrointestinal (GI) tract that aid in these functions. However, an imbalance between the ratio of Firmicutes to Bacteroidetes bacteria is found in autistic individuals when compared to the microbiome composition of neurotypical subjects; in particular, patients with autism tend to have an overexpression of Firmicutes in their gut [6,7]. Other studies have demonstrated an excess of the Clostridium genus in the ASD microbiome [8] as well as an overexpression of the genus Bacilli in the mouths and gut of autistic individuals [7]. While the cause behind this imbalance is unknown, these findings signify a consistent pattern of microbial imbalance in the autistic gut microbiome. In fact, a Random Forest prediction model, a computer algorithm that can classify large sets of data into subgroup “trees” based on data similarity, was able to distinguish ASD children from neurotypical children with a high degree of certainty from just microbiome sequencing data [7], demonstrating the predictability of this dysbiosis by artificial intelligence.
Figure 1: This illustrates the difference between species richness and species abundance. Species richness, a measure of alpha diversity, informs on how many species are present in a sample. Species abundance describes how many organisms of each species are present.
Along with microbial imbalance—termed dysbiosis—autistic children also tend to have a decreased alpha diversity [7,9], which measures mean species diversity, as well as significantly lower gut species richness [7], the number of species present, when compared to age and sex-matched neurotypical children. One study found that for neurotypical children, alpha diversity, species richness, and species abundance all increased between the age groups 2-3 to 7-11; yet for ASD children, no significant development in microbial composition was observed with increase in age [7]. Since autism has been found to slow brain development as children age [9], this reduced development of the microbiome mirrors the altered brain development characteristic of ASD pathophysiology, proposing an association between decreased microbial diversity and autism.
Due to this observed correlation between dysbiosis and ASD, whether gut dysbiosis is truly causal for autism has come into question. In a preliminary study, Sharon et al transplanted fecal microbiota from autistic donors into otherwise germ-free mice (mice with a sterile gut) and observed their offspring’s behavior compared to offspring of mice inoculated with microbiota from neurotypical donors. Notably, mice with the ASD microbiome— characterized by decreased alpha and beta diversity and decreased Bacteroidetes — exhibited behaviors paralleling those of autism, including repetitive behaviors, decreased locomotion and decreased communication [9]. This demonstrated that gut dysbiosis can in fact induce the behavioral deficits observed in ASD. This is significant evidence toward the theory that gut dysbiosis indeed contributes to ASD – an important finding that changes our current understanding of the etiology of autism.
Figure 2: Above is a visual depiction of the study conducted by Sharon & colleagues, where germ-free mice were inoculated with gut microbiota from either autistic or neurotypical donors. Offspring of the mice transplanted with ASD microbiome were shown to exhibit autism-like behavior.
How Microbiota Imbalance Affects Brain Function: The Gut-Brain Axis
Since the microbial dysbiosis found to be common in ASD patients contributes to behavioral deficits, several different mechanisms have been proposed for how the altered microbial environment in ASD patients can affect brain development.
Intestinal permeability
The microbes that line the GI tract provide structural and protective benefits to our intestines, including stimulating epithelial cell regeneration and mucus production by the intestinal walls. When microbial diversity is decreased, the integrity of the intestinal walls may be compromised which can lead to increased intestinal permeability [8]. This may allow for lipopolysaccharide (LPS), a pro-inflammatory endotoxin that is found in gram-negative bacterial cell walls, to escape out of the GI tract and into the bloodstream. Serum levels of LPS are in fact found to be significantly higher in autistic individuals [12]. LPS causes inflammation in the central nervous system (CNS) and is found to impair cognition and motivation in the mouse model. Specifically, implications for impaired continuous attention and curiosity behaviors, along with modulation of other areas of the brain like the central amygdala have been associated with circulating LPS [11]. Therefore, altered intestinal permeability is a possible mechanism by which dysbiosis modulates brain inflammation, a hallmark of autism that is thought to contribute to its behavioral symptoms.
Microbial metabolites
As gut microbes carry out cellular functions inside their human hosts, they also secrete compounds as by-products of metabolism. Two such metabolites are 5AV and taurine, which are secreted by gut Bacteroides xylanisolvens and other bacteria. 5AV and taurine levels are found to be significantly lower in autistic individuals [13,14] as well as mice transplanted with ASD microbiome [9], likely due to dysbiosis. Both 5AV and taurine are gamma-aminobutyric (GABA) receptor antagonists, meaning that lower levels of these circulating microbial metabolites can alter the inhibitory signaling of GABA in the nervous system [9]. GABA regulates various developmental processes in the brain, including cell differentiation and synapse formation, so dysfunction in GABA signalling is thought to account for ASD symptoms [15]. Oral administration of taurine and 5AV in a mouse model of ASD with an altered microbiome is shown to reduce repetitive behavior and increase social behavior, suggesting that the deficiencies in these metabolites may contribute to the behavioral manifestations of autism [9]. There are other microbial metabolite imbalances in autistic children, including dopaquinone, pyroglutamic acid, and other molecules involved in neurotransmitter production. These imbalances affect brain signaling pathways, and therefore could contribute to the behavioral deficits often present in autistic children. Further, these metabolite imbalances correlate with the levels of certain gut bacteria, further emphasizing the link between the gut microbiome and neurological disorders such as ASD.
Clinical Implications for ASD Diagnosis and Treatment
Today, symptoms of autism are alleviated with behavioral and educational therapy, and no pharmaceutical treatment exists [1]. With the knowledge that the gut microbiome significantly differs in autistic individuals and that these differences are shown to interfere with the nervous system, preliminary research has been done on potential diagnostics and pharmaceutical therapeutics for ASD that target dysbiosis in the gut.
Diagnostics
To date, there is no objective laboratory test to detect Autism Spectrum Disorder (ASD) in children, so autism is primarily diagnosed through a doctor’s evaluation of a patient’s behavior and developmental history. However, the ability of a computer program to distinguish the autistic microbiome from the neurotypical microbiome holds potential for use in ASD clinical risk assessments through analysis of the gut microbiome, and subsequent gut health monitoring interventions for those detected to have ASD-like dysbiosis [7]. The strong association between the presence of certain bacterial species in the mouth and bacteria in the gut — in particular the significant positive correlation between saliva Chloroflexi and gut Firmicutes—may suggest possible oral biomarkers to predict gut microbial diversity [6]. In addition, the overexpression of certain bacteria in the gut have been identified to be associated with certain symptoms like allergies and abdominal pain, opening an avenue to improve the diagnosis process of ASD through the inclusion of a more objective, laboratory-based test [6].
Microbiota Transfer Therapy
Microbiota Transfer Therapy (MTT) is an emerging therapy that aims to replace the gut microbiome of ASD individuals with a more diverse, healthy gut microbiome. One form of MTT consists of a two-week oral vancomycin antibiotic treatment, followed by a bowel cleanse using MoviPrep, and then finally an extended fecal microbiota transplant for 7-8 weeks, administered orally or rectally. In a clinical trial involving autistic children, MTT significantly increased gut bacterial diversity 8 weeks after treatment stopped, along with improving GI symptoms (including abdominal pain, indigestion, diarrhea and constipation) measured through the Gastrointestinal Symptom Rating Scale (GSRS). Significant improvements in behavioral ASD symptoms were found post treatment as well, measured through increases from baseline scores on a variety of exams that evaluate social skills, irritability, hyperactivity and communication, among other behaviors [16]. These improvements in microbial diversity and subsequently ASD-related behavior were all found to have been maintained at follow-up study two years later, indicating that MTT is a safe and efficient therapy that has potential to improve ASD outcomes in the long-term [17]. However, further studies on the efficacy of MTT are necessary to establish this connection, as the above study sample was small and most symptoms and improvements were self-reported.
Probiotics
Because imbalances in the microbiome are correlated with autism, direct administration of bacterial cultures using probiotics seems to be a potential approach to treat ASD behavioral symptoms. Probiotic treatment that included a combination of Streptococcus, Lactobacillus, and Bifildobacterium was found to be effective in improving core behavioral symptoms of ASD, specifically adaptive functioning, developmental pathways, and multisensory processing in autistic children with GI symptoms [18]. Probiotics have been shown to improve symptoms of other mood disorders like anxiety and depression, both of which are associated with dysbiosis and the gut-brain axis [8], warranting further research on probiotics as a treatment for ASD. Therapies that target microbial metabolite imbalances in ASD individuals, like 5AV and taurine, may also open an avenue for future autism research [9].
Conclusion
The gut microbiome contributes to the maintenance of much of human physiology, with involvement in not only the digestive system but also the immune system and the brain. Dysbiosis of the gut microbiome has been found to be prevalent in children and adults with Autism Spectrum Disorder (ASD), and this dysbiosis may be linked to the behavioral symptoms observed. Treatments that target the gut microbiome, therefore, serve to be useful in improving behavioral deficits associated with ASD and should be a consideration for future research with more rigorous experimental design.
References:
- Mayo Clinic. Autism Spectrum Disorder. Accessed July 30, 2021. Available from: https://www.mayoclinic.org/diseases-conditions/autism-spectrum-disorder/symptoms-causes/syc-20352928.
- Centers for Disease Control and Prevention. Data & Statistics on Autism Spectrum Disorder. Accessed July 30, 2021. Available from: https://www.cdc.gov/ncbddd/autism/data.html.
- Fattorusso A, Genova L, Dell’Isola G, Mencaroni E, Esposito S. 2019. Autism Spectrum Disorders and the gut microbiota. Nutrients.11(2):521.
- Kho Z, Lal S. 2018.The human gut microbiome—A potential controller of wellness and disease. Frontiers in Microbiology. 9:1835.
- Thursby E, Juge N. 2017. Introduction to the human gut microbiota. Biochemical Journal. 474(11): 1823-1836.
- Kong X, Liu J, Cetinbas M, Sadreyev R, Koh M, Huang H, Adeseye A, He P, Zhu J, Russell H, Hobbie C, Liu K, Onderdonk A. 2019. New and preliminary evidence on altered oral and gut microbiota in individuals with Autism Spectrum Disorder (ASD): Implications for ASD diagnosis and subtyping based on microbial biomarkers. Nutrients. 11(9): 2128
- Dan Z, Mao X, Liu Q, Guo M, Zhuang Y, Liu Z, Chen K, Chen J, Xu R, Tang J, Qin L, Gu B, Liu K, Su C, Zhang F, Xia Y, Hu Z, Liu X. 2020. Altered gut microbial profile is associated with abnormal metabolism activity of Autism Spectrum Disorder. Gut Microbes. 11(5): 1246-1267
- Mangiola F, Ianiro G, Franceschi F, Fagiuoli S, Gasbarrini G, Gasbarrini, A. 2016. Gut microbiota in autism and mood disorders. World Journal of Gastroenterology. 22(1): 361-368.
- Sharon G, Cruz N, Kang D, Gandal M, Wang B, Kim Y, Zink E, Casey C, Taylor B, Lane C, Bramer L, Isern N, Hoyt D, Noecker C, Sweredoski M, Moradian A, Borenstein E, Jansson J, Knight R, Metz T, Lois C, Geschwind D, Krajmalnik-Brown R, Mazmanian S. 2019. Human gut microbiota from Autism Spectrum Disorder promote behavioral symptoms in mice. Cell. 177(6): 1600-1618
- Hua X, Thompson P, Leow A, Madsen S, Caplan R, Alger J, O’Neill J, Joshi K, Smalley S, Toga A, Levitt J. 2013. Brain growth rate abnormalities visualized in adolescents with autism. Human Brain Mapping. 34(2):425-36.
- Haba R, Shintani N, Onaka Y, Wang H, Takenaga R, Hayata A, Baba A, Hashimoto H. 2012. Lipopolysaccharide affects exploratory behaviors toward novel objects by impairing cognition and/or motivation in mice: Possible role of activation of the central amygdala. Behavioral Brain Research. 228(2):423-31.
- Emenuele E, Orsi P, Boso M, Broglia D, Brondino N, Barale F, Ucelli di Nemi S, Politi P. 2010. Low-grade endotoxemia in patients with severe autism. Neuroscience Letters. 471(3):162-5
- Ming X, Stein T, Barnes V, Rhodes N, Guo L. 2012. Metabolic perturbance in autism spectrum disorders: a metabolomics study. Journal of Proteome Research. 11(12): 5856-62
- Park E, Cohen I, Gonzalez M, Castellano M, Flory M, Jenkins E, Brown W, Schuller-Levis G. 2017. Is taurine a biomarker in Autistic Spectrum Disorder. Advances in Experimental Medicine and Biology. 975
- Pizzarelli R, Cherubini E. 2011. Alterations of GABAergic signaling in Autism Spectrum Disorders. Neural Plasticity. 2011:297153
- Kang D, Adams J, Gregory A, Borody T, Chittick L, Fasano A, Khoruts A, Geis E, Maldonado J, McDonough-Means S, Pollard E, Roux S, Sadowsky M, Lipson K, Sullivan M, Caporaso J, Brown R. 2017. Microbiota Transfer Therapy alters gut ecosystem and improves gastrointestinal and autism symptoms: an open-label study. Microbiome. 5(1):10
- Kang D, Adams J, Coleman D, Pollard E, Maldonado J, McDonough-Means S, Caporaso J, Krajmalnik-Brown, R. 2019. Long-Term benefit of Microbiota Transfer Therapy on autism symptoms and gut microbiota. Scientific Reports. 9(1):5821
- Santocchi E, Guiducci L, Prosperi M, Calderoni S, Gaggini M, Apicella F, Tancredi R, Billeci L, Mastromarino P, Grossi E, Gastaldelli A, Morales M, Muratori F. 2020. Effects of probiotic supplementation on gastrointestinal, sensory and core symptoms in Autism Spectrum Disorders: A randomized controlled trial. Frontiers in Psychiatry. 11:550593
How Poop is Fighting COVID-19
By Laura Gardner, Biochemistry and Molecular Biology ‘22
Author’s Note: With so much information in the media and online about COVID-19, I find many people get lost in, and fall victim to, false information. I want to reassure the Davis community with factual information on how Davis is fighting COVID-19. With UC Davis’ strong scientific community, I was curious what tools were being used to mitigate the spread of COVID-19. In January 2021, I attended a virtual COVID-19 symposium called Questions about Tests and Vaccines led by Walter S Leal, distinguished Professor of the Department of Molecular and Cellular Biology at University of California-Davis (UC Davis). In this symposium, I learned about Dr. Heather Bischel’s work testing the sewer system. This testing is another source for early detection of COVID-19. In combination with biweekly testing, I have no doubt that UC Davis is being proactive in their precautions throughout the pandemic, which made me personally feel more safe. I hope that this article will shed light on wastewater epidemiology as a tool that can be implemented elsewhere.
Dr. Heather Bischel is an assistant professor in the Department of Civil and Environmental Engineering at the University of California, Davis. Bischel has teamed up with the city of Davis through the Healthy Davis Together initiative to use wastewater epidemiology, a technique for measuring chemicals in wastewater, to monitor the presence of SARS-CoV-2, the virus that causes COVID-19 [6]. When a person defecates, their waste travels through the pipes and is collected in the sewer system. In both pre-symptomatic and asymptomatic individuals, their feces will carry the genetic material that indicates the virus is present. This is because SARS-CoV-2 uses angiotensin-converting enzyme 2, also known as ACE2, as a cellular receptor, which is abundantly expressed in the small intestine allowing viral replication in the gastrointestinal tract [1]. This serves as an early indicator of a possible COVID-19 outbreak and leads to quick treatment and isolation, which are important to stop the spread of the disease.
Samples are taken periodically from manholes around campus using a mechanical device called an autosampler. These autosamplers are lowered into manholes to collect wastewater flow samples every 15 minutes for 24 hours. Next, the samples are taken to the lab where they are able to extract genetic material and use Polymerase Chain Reaction (PCR) to detect the virus. Chemical markers that attach to the specific genetic sequence of the virus are added to the sample, which reacts to the COVID-19 virus by fluorescing visible light. This light is the signal that indicates positive test results.
The samples are collected throughout campus, with a focus on residential halls. An infected person will excrete the virus through their bowel movements before showing symptoms. The samples are so sensitive that if even just one person among thousands is sick, they are still able to detect the presence of COVID-19 genetic material. When a PCR test provides a positive signal, the program works closely with the UC Davis campus to identify if there has been someone who has reported a positive COVID-19 test. If no one from the building is known to be positive, they send out a communication email asking all the students of the building to get tested as soon as possible. That way the infected person can be identified and isolated as soon as possible, eliminating exposure from unidentified cases [4].
In collaboration with the UC Davis campus as well as the city of Davis, Dr. Bische has implemented wastewater epidemiology throughout the community. Since summer 2020, Dr. Bische’s team of researchers have collected data which is available online through the Healthy Davis Together initiative [4].
In addition to being an early indicator, this data has also been used to determine trends, which can indicate if existing efforts to combat the virus are working or not [2]. Existing efforts include vaccinations, mask wearing, washing hands, maintaining proper social distancing, and staying home when one feels ill. UC Davis has implemented protocols including biweekly testing and a daily symptom survey that must be completed and approved in order to be on campus.
Wastewater epidemiology has been implemented all over the world, at more than 233 Universities and in 50 different countries, according to monitoring efforts from UC Merced [3]. This testing has been used in the past to detect polio, but has never before been implemented on the scale of a global pandemic. Lacking infrastructure, such as ineffective waste disposal systems, open defecation, and poor sanitation pose global challenges, especially in developing countries [2]. Without tools for early detection, these communities are in danger of having an exponential rise in cases.
“Our work enables data-driven decision-making using wastewater infrastructure at city, neighborhood, and building scales,” Dr. Bische stated proudly in her latest blog post [2]. These decisions are crucial in confining COVID-19 as we continue to push through the pandemic.
Summary of how wastewater epidemiology is used to fight COVID-19
References:
- Aguiar-Oliveira, Maria de Lourdes et al. “Wastewater-Based Epidemiology (WBE) and Viral Detection in Polluted Surface Water: A Valuable Tool for COVID-19 Surveillance-A Brief Review.” International journal of environmental research and public health vol. 17,24 9251. 10 Dec. 2020, doi:10.3390/ijerph17249251
- Bischel, Heather. Catching up with our public-facing COVID-19 wastewater research. Accessed August 15, 2021.Available from H.Bischel.faculty.ucdavis
- Deepshikha Pandey, Shelly Verma, Priyanka Verma,et al. SARS-CoV-2 in wastewater: Challenges for developing countries, International Journal of Hygiene and Environmental Health,Volume 231,2021,113634, ISSN 1438-4639, https://doi.org/10.1016/j.ijheh.2020.113634.
- Healthy Davis Together. Accessed February 2, 2021. Available from Healthy Davis Together – Working to prevent COVID-19 in Davis
- UCMerced Researchers. Covid Poops Summary of Global SARS-CoV-2 Wastewater Monitoring Efforts. Accessed February 2, 2021. Available from COVIDPoops19 (arcgis.com)
- Walter S Leal. January 13, 2021. COVID symposium Questions about Tests and Vaccines. Live stream online on zoom.
Investigating Anthelmintics for Vector Control
By Anna Cutshall, Animal Biology, ’21
Author’s Note: When considering the topic of my literature review and analysis, I wanted to select work that I could continue research on in graduate school. As I entered academia, my career and life experiences had prepared me well for the unique intersection of veterinary medicine, ecology, and epidemiology. I have been on a pre-veterinary track for many years and have worked professionally in the veterinary field for more than three years. As an Animal Biology major and Global Disease Biology minor, my coursework largely centered around the emerging threat of zoonotic and vector-borne diseases. These experiences considered, my primary research interests lie in how we may integrate veterinary medicine into One Health practices to better combat emerging disease threats. In this literature review, I investigate the viability of anthelmintic drugs against arthropod vectors of disease. The use of anthelmintics against arthropods is fairly new, and the pool of current literature is limited but promising. This review was written for those, like myself, who are interested in new approaches to the control of tropical diseases, especially through the lens One Health. I hope to leave readers with a clear picture of what is next for this field, what gaps in the data should be filled, and how we can use information gained in responsible, sustainable ways to combat both emerging and established vector-borne diseases.
Abstract
This literature review analyzes the efficacy of currently available anthelmintic drugs against key disease vectoring arthropods. When comparing effective dosages between different drugs and vector genera, we found that relatively low concentrations are effective against most vectors, but there is evidence to suggest that ivermectin resistance has been established in some species (Aedes spp). The avermectin drug class also displayed limited efficacy over time, as the drugs degrade in vertebrate species faster than the isoxazoline drug class or fipronil. We determined that the current findings related to this method of vector control are promising. However, further research must be conducted before we implement anthelmintics for mass drug administration as a part of integrated vector management.
Keywords: anthelmintics, insecticides, vector, disease vector, mosquito, sandfly, One Health, integrated vector management, mass drug administration
Introduction
Vector-borne diseases threaten the well-being of hundreds of millions of people globally. This is predicted to increase as climate change and human activity facilitate the spread of vector species to previously unoccupied locations. In a press release by the Sacramento-Yolo Mosquito & Vector Control District, it was reported that multiple invasive mosquito species, including Aedes aegypti, had been identified in northern California [1]. Recent literature suggests that these habitual expansions may be due, in part, to climate change as these species are able to adapt to broader regions that are of similar climate to their native regions [2]. The continued spread of these species leaves unprepared countries at risk for outbreaks of the diseases vectored by invading species. Moreover, most vector-borne diseases remain uncontrolled in endemic regions. The most direct way to mitigate the threat of globalizing tropical vector-borne diseases is to control the species that are vectoring them. Unfortunately, traditional insecticide-based methods of vector control have become ineffective due to the emergence of insecticide resistance. In 2012, the World Health Organization identified the status of insecticide resistance as “widespread”, as most of the globe reported resistance in at least one major malaria vector [3]. Traditional spray and topical insecticides have been compromised by such resistance. Therefore, it is essential that new methods of vector control,without acquired resistance, be discovered, evaluated, and implemented.
There are many new methods of vector control currently under evaluation. These include genetically modifying vectors to render them sterile, the use of entomopathogenic fungi and viruses, trapping, repellents, and environmental modification [4]. As we continue to evaluate each method for its efficacy, the Integrated Vector Management (IVM) method may be our best option for the elimination of many tropical diseases. Through IVM, we take careful and integrated approaches to vector control via intersectional communication between Public Health officials, Governments, Non-Governmental Organizations, and communities in which we hope to implement our strategies [5]. IVM calls for multiple vector control strategies, and increasing control efficacy via synergy between control efforts. Unfortunately, the primary tool utilized for the control of adult mosquitoes, insecticides, has lost efficacy over time. This is a result of vector insect populations developing resistance to common insecticides, such as pyrethroids and organophosphates, that are used to control adult mosquito populations. However, there is a reservoir of insecticides that have not been utilized against human disease-vectors, which therefore have minimal acquired resistance . This class is oral insecticides, or insecticides ingested by vertebrates that act when a vector is exposed via blood meal from a treated animal. The use of oral insecticides has been standard in veterinary medicine for years, in the form of flea and tick prevention. Common classes of oral insecticides include avermectins, isoxazolines, and phenylpyrazoles. These compounds have been standard in human and/or veterinary medicine as ectoparasiticides, demonstrating their safety for use in vertebrates. Avermectins, isoxazolines, and phenylpyrazoles have similar modes of action as neurotoxins, with both interrupting the function of GABA-gated chloride ion channels, resulting in insect paralysis [6, 7]. Importantly, there is still diversity within these classes as tools against vector species, as they bind to different sites on the GABA receptors [7, 8]. Investigating the efficacy of these drugs for use as insecticides, against key vectors of diseases such as malaria, zika, west nile virus, leishmaniasis, and African Trypanosomiasis, could be part of the solution to the increasingly urgent problem of insecticide resistance.
Research is currently underway, across the globe, to investigate the efficacy of ectoparasiticides against disease vectors. The question still stands, however, if the approach of oral insecticides is any more effective than the traditional insecticides available. To answer this question, we assessed the current literature regarding the testing of ectoparasiticides against disease vectors, and developed a database of studies testing the efficacy of these compounds against vector insects. This analysis aims to determine the relative efficacy of these compounds to determine if these drug classes are worth consideration for use in vector control and management.
Materials and methodology
To establish a database of the relevant literature, we first mined the scientific literature via the UC Davis Library. Using access granted to undergraduate students, the search terms utilized were input as follows: title/abstract contain “vector” AND “veterinary” AND “control”
AND “arthropod” in the key word function. Papers were then selected for further analysis. These articles were input into an AI-based literature analysis tool, “Research Rabbit”, to identify additional relevant studies [9]. In addition, studies were selected from the works cited of previously selected works. Papers not testing the efficacy of oral insecticides on adult disease vectors were excluded from the study. Additionally, papers without comparable data (did not supply direct mortality or density data) were also excluded.
Each paper was analyzed to extract relevant data on the efficacy of oral insecticides against disease vectors. The data was collated into a Microsoft Excel spreadsheet. Categories selected for further evaluation included: drug type, the concentrations used, associated concentration resulting in 50% mortality (LC50 values), time to mortality, the reduction of vectors present in field study by visual count (resting density), and drug effects on vector fecundity. However, for the purposes of this study, we focused on LC50 and temporal values.
Other categories were not consistent across publications.
When creating data visualizations for comparison of different drug types, R’s “ggplot2” package, “dplyr” package and “esquisse” package were used [10-13]. The categories determined to be best for visual comparison were “Temporal Data” and “LC50” data. After initial visualization was made in R, figures were exported to Adobe Illustrator to edit aesthetically, which was limited to modification of font types and caption content [14]. When creating visualizations for LC50 data, both sandfly and mosquito vectors were compared on the same figure, to compare the efficacy of not only drugs in relation to each other, but also drugs in relation to their efficacy against different disease vectors. When creating the data visualization for this comparison, the drugs “Moxidectin” and “NTBC” were excluded. Moxidectin’s LC50 value was too high to allow for reasonable comparison to other drugs, and NTBC only had a value for tsetse flies (Glossina spp), which were not represented in any other drug. Additionally, the Lutzomyia spp displayed LC50 values too high to be effectively compared to other disease vectors. When visualizing temporal data, only Anopheles spp and Phebotomus spp had enough supporting information in the literature for effective comparisons. There were 5 studies that supplied data for Anopheles spp temporal data and 2 studies that supplied data for Phlebotomus spp temporal data. These temporal data were plotted as Day of Feeding against Mortality, faceted by drug type, and grouped by dose. 2 Figures were created, one for Phlebotomus spp and another for Anopheles spp.
Results
Database Creation
From the initial search in the UC Davis Library system, 15 studies were selected. Then, based on the output from Research Rabbit, an additional 5 studies were selected. Finally, 3 additional papers were identified and integrated into the analysis from the references of the 20 studies. These 23 studies were then evaluated individually from January, 2021 through March, 2021.
We obtained multiple data categories for comparison between the selected papers. Figure 1 displays the summary of the resulting database. Due to the recent nature of this research, resources from which to draw for our database were limited. Table 1 shows a summary of the data types and the number of papers each data type was collected from. Within each paper, some investigated efficacy against multiple vector genuses while others investigated only one. Based on the data we were able to collect, we will be moving forward directly comparing temporal data as well as LC50 data.
Comparing Effectiveness of Dosages Between Drugs
We sought to compare the concentrations of drugs required to be effective against the disease vectoring arthropods studied. Figure 2 displays the LC50 values chosen for comparison as a “lollipop” plot. Within the plot, each “dot” represents a single datapoint taken from a study, and 7 studies were compiled to create the plot. In this figure, we are able to compare 2 major drug classes: avermectins and isoxazolines. The isoxazoline drug class had more available data across insect families, and it is clear that the LC50 value is variable between genuses. Sandflies have more resistance to isoxazolines (especially fluralaner) than mosquito species. Amongst the avermectins, Anopheles spp. Display the most consistent, and relatively low LC50 values. However, Aedes spp. displays higher resistance to ivermectin compared to Anopheles spp.
Comparing Temporal Data
Temporal data involving different insecticides were first to be compared. In temporal data, the “Days Post Feeding (Day of Blood Meal)” represents the number of days after the initial dosing of the vertebrate animals (for example, “Day 3” indicates mosquitos that fed on an animal 3 days after it was given the drug). We were able to create 2 figures for comparing the efficacy of drugs over time at various doses. Doses were represented as variance in color in the figures. Figure 3 displays the efficacy of oral dosing to vertebrates of eprinomectin and ivermectin against Anopheles spp. over time. Both of the drugs in this comparison were of the avermectin class, and neither displayed robust effects on mortality past the 15 day mark after single-dosing of vertebrates with the drugs. Additionally, we observe great variance in the efficacy of ivermectin, even within dosages (that is, mortality varied within dosages between different studies). Unfortunately, mosquito genuses outside of Anopheles did not provide enough data to compare efficacy over time.
Created in a similar fashion, Figure 4 displays the efficacy of oral dosing of vertebrates with fipronil and fluralaner. Here, two separate drug classes were tested for efficacy. While fluralaner (a member of the isoxazoline drug class) acts in a similar mode of action to avermectins, it maintains efficacy over time in Sandflies. Because mosquitos displayed more sensitivity to isoxazolines than sandflies, (Figure 2) one may predict similar, if not more deadly, effects when isoxazolines are tested over time for mosquitos. The drug fipronil displays varying efficacy between doses. Unfortunately, the study involving fipronil did not collect data past 21 days of administration, but it is possible some of the dosages would have remained effective from visual interpretation of the figure. Due to the limited amount of studies investigating the efficacy of oral insecticides, we were not able to compare the efficacy of all drugs over time, as other studies used different methods of efficacy measurements.
Discussion
Based on the findings of our review of currently available literature, oral insecticides certainly show promise as a method of Disease Vector management. As displayed in Figure 2, we are able to determine effective dosages for each drug as a concentration in blood. However, there was significant variance in the data between taxa and between drugs. The highest resistance was observed in Aedes spp. against ivermectin, which could be evidence of acquired resistance due to the common use of ivermectin in humans as an anthelmintic [15]. Another significant variance observed was the relative resistance of sandflies to isoxazolines, requiring approximately twice the concentration or more compared to mosquito species [16]. It is unclear if this effect carries over to other drugs, as there is no available data.
There were also studies analyzed that were not included in the visual data analyses performed. These included studies that investigated the efficacy of isoxazolines against the kissing bug, of nitisinone against the tsetse fly, a field study, and data from otherwise integrated studies measuring the effect of the drugs on fecundity of arthropods [16-22]. The investigation by Loza et al. regarding the efficacy of isoxazolines against the kissing bug showed similar temporal data results to papers investigating isoxazolines against Sandflies, which was visualized in Figure 4 [17]. The isoxazoline drug class, then, has been shown to be effective against 3 major disease vector families. Another drug class also shows promise. A study by Sterkel et al proposes the use of nitisinone (traditionally used in the treatment of hereditary tyrosinemia type 1, a genetic disorder) as an insecticide dispensed to vertebrates, and investigates its efficacy against the African Trypanosomiasis vector, the tsetse fly. This study highlights the importance of looking for alternative methods to vector control, and manipulates a characteristic of a drug originally developed to aid in human disease against disease vectors. The 2021 study found that concentrations above 0.5 micrograms per milliliter in blood impacted survival of feeding tsetse flies significantly, while also studying pharmacokinetics when ingested by mouse models [18]. Pharmacokinetic data supplied by the mouse models in this study may assist in any later calculations for human dosage. No evidence is available on the effectiveness of nitisinone on other disease vectors.
Three studies supplied data involving sublethal effects on adult arthropods, including fecundity. These studies found that there were significant effects on Anopheles spp. fecundity, regardless of vertebrate being dosed and observed across multiple doses [20-22]. There is no currently available data involving the effects of isoxazolines or phenylpyrazoles. Should they be provided, however, they show additional promise as vector management tools. When an insecticide is able to exhibit both lethal and sublethal effects, particularly regarding fecundity, insects that survive the initial exposure produce less offspring than their unexposed peers.
In order for these methods to be effective in Disease Vector management, there would need to be a considerable number of individuals in the population participating to make a significant impact on the burden of vector borne diseases [16, 19]. Mass Drug Administration (MDA) is expensive, and cost is a limiting factor in many of the areas we hope to lower disease burden in. Due to this, and issues related to the accessibility of MDA, it is important that drugs remain effective for an extended period of time. Fortunately, we found this to be the case. As evident in Figure 4, both isoxazoline drugs display extended efficacy on mortality of sandflies over 40 days after initial vertebrate dosage. Additionally, it may be that fipronil displays a similar effect in higher tested dosages, following the trajectory of the available data. Unfortunately, there is limited literature on this subject, so we are unable to say with absolute certainty that these effects would carry over to mosquito species. Figure 3 suggests that the avermectin drug class does not have the same long-term effect on arthropod mortality. For both ivermectin and eprinomectin, mortality dropped below 50% overall after just 14 days from initial dosage. For this reason, the isoxazoline and phenylpyrazole drug classes may be more effective for MDA, although their testing for safety in humans is less extensive (than ivermectin).
Additionally, there is the question of if MDA should be dispensed to humans or livestock. The field study by Poche et al. applied previous findings to the field, dosing cattle in several tribes in Africa and visually measuring the effects on the density of mosquitos found in nearby homes. They observed that the dosage of livestock with fipronil reduced the “resting density” of mosquito species known to feed on both cattle and humans, but did not significantly affect the resting density of particularly anthropophilic species [19]. This study highlights the importance of catering an MDA to the specific species you want to impact by ensuring dosage to vertebrates that it is likely to take a blood meal from.
When considering a drug for use in MDA, the safety of the drug must be copiously studied, and current findings are promising. At the dosages used in the study that were effective against adult arthropods, vertebrates suffered no severe adverse effects attributed to the dosages in all of the studies analyzed. This strongly suggests that these drugs are safe for use in IVM. Additionally, when considering MDA, taking a “One Health” approach will also be key to success. Too often, non-Governmental Organizations have gone into regions with targeted endemic diseases, and neglected to listen to native perspectives on previously used methods of disease control and basic needs. While investigating the efficacy of these drugs is important to protecting communities against vector-borne disease, giving aid to impoverished communities must first address the baseline health of individuals at risk. Only then can we hope to earn the trust of native populations, and continue to help them in sustainable ways. Continuing to thoroughly investigate the efficacy and safety of this sector of vector management before beginning any large implementation will also be essential.
Overall, it can be inferred from the amount of studies performed that there needs to be an enormous amount of research performed before we integrate oral insecticides, especially in humans, into IVM. What we do know, though, gives promise in the face of the insurmountable resistance to traditional pesticides.
References
- First Detection of Invasive Mosquitoes in Yolo County. Sacramento-Yolo Mosquito & Vector Control https://www.fightthebite.net/news-posts/first-detection-of-invasive-mosquitoes-in-yolo-c ounty/ (2020).
- Caminade, C., McIntyre, K. M. & Jones, A. E. Impact of recent and future climate change on vector‐borne diseases. Ann N Y Acad Sci 1436, 157–173 (2019).
- World Health Organization. Global Malaria Programme & World Health Organization. Global plan for insecticide resistance management in malaria vectors. GPIRM (2012).
- Takken, W. & Knols, B. G. J. Malaria vector control: current and future strategies. Trends in Parasitology 25, 101–104 (2009).
- Beier, J. C. et al. Integrated vector management for malaria control. Malar J 7, S4 (2008).
- Wang, C. C. & Pong, S. S. Actions of avermectin B1a on GABA nerves. Prog Clin Biol Res 97, 373–395 (1982).
- Ratra, G. S. & Casida, J. E. GABA receptor subunit composition relative to insecticide potency and selectivity. Toxicology Letters 122, 215–222 (2001).
- Casida, J. E. & Durkin, K. A. Novel GABA receptor pesticide targets. Pesticide Biochemistry and Physiology 121, 22–30 (2015).
- ResearchRabbit, Version 2.0 Human Intelligence Technologies, Incorporated Available from: https://www.researchrabbit.ai/
- R Core Team (2013). R: A language and environment for statistical computing. R foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/
- H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.
- Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2021). dplyr: A Grammar of Data Manipulation. R package version 1.0.5. https://CRAN.R-project.org/package=dplyr
- Fanny Meyer and Victor Perrier (2021). esquisse: Explore and Visualize Your Data Interactively. R package version 1.0.1. https://CRAN.R-project.org/package=esquisse
- Adobe Inc. Adobe Illustrator [Internet]. 2019. Available from: https://adobe.com/products/illustrator
- Hadlett, M., Nagi, S. C., Sarkar, M., Paine, M. J. I. & Weetman, D. High concentrations of membrane-fed ivermectin are required for substantial lethal and sublethal impacts on Aedes aegypti. Parasit Vectors 14, 9 (2021).
- Miglianico, M. et al. Repurposing isoxazoline veterinary drugs for control of vector-borne human diseases. Proc Natl Acad Sci U S A 115, E6920–E6926 (2018).1.
- Loza, A. et al. Systemic insecticide treatment of the canine reservoir of Trypanosoma cruzi induces high levels of lethality in Triatoma infestans, a principal vector of Chagas disease. Parasit Vectors 10, 344 (2017).
- Sterkel, M. et al. Repurposing the orphan drug nitisinone to control the transmission of African trypanosomiasis. PLOS Biology 19, e3000796 (2021).
- Poché, R. M. et al. Preliminary efficacy investigations of oral fipronil against Anopheles arabiensis when administered to Zebu cattle (Bos indicus) under field conditions. Acta Trop 176, 126–133 (2017).
- Belay, A., Petros, B., Gebre-Michael, T. & Balkew, M. Effect of LongRangeTM eprinomectin on Anopheles arabiensis by feeding on calves treated with the drug. Malaria Journal 18, 332 (2019).
- Mekuriaw, W. et al. The effect of ivermectin® on fertility, fecundity and mortality of Anopheles arabiensis fed on treated men in Ethiopia. Malar J 18, 357 (2019).
- Pasay, C. J. et al. Treatment of pigs with endectocides as a complementary tool for combating malaria transmission by Anopheles farauti (s.s.) in Papua New Guinea. Parasites & Vectors 12, 124 (2019).
- Alout, H. et al. Evaluation of ivermectin mass drug administration for malaria transmission control across different West African environments. Malaria Journal 13, 417 (2014).
- Bongiorno, G. et al. A single oral dose of fluralaner (Bravecto®) in dogs rapidly kills 100% of blood-fed Phlebotomus perniciosus, a main visceral leishmaniasis vector, for at least 1 month after treatment. Med Vet Entomol 34, 240–243 (2020).
- Chaccour, C. J. et al. Targeting cattle for malaria elimination: marked reduction of Anopheles arabiensis survival for over six months using a slow-release ivermectin implant formulation. Parasites & Vectors 11, 287 (2018).
- Davey, R. B., Miller, J. A., George, J. E. & Klavons, J. A. Efficacy of a Single Doramectin Injection Against Adult Female Boophilus microplus (Acari: Ixodidae) in the Final Stages of Engorgement Before Detachment. Journal of Medical Entomology 44, 277–282 (2007).
- Foy, B. D. et al. Efficacy and risk of harms of repeat ivermectin mass drug administrations for control of malaria (RIMDAMAL): a cluster-randomised trial. The Lancet 393, 1517–1526 (2019).
- Fritz, M. L., Walker, E. D. & Miller, J. R. Lethal and sublethal effects of avermectin/milbemycin parasiticides on the African malaria vector, Anopheles arabiensis. J Med Entomol 49, 326–331 (2012).
- Gomez, S. A. et al. A randomized, blinded, controlled trial to assess sand fly mortality of fluralaner administered orally in dogs. Parasit Vectors 11, 627 (2018).
- Kobylinski, K. C. et al. The effect of oral anthelmintics on the survivorship and re-feeding frequency of anthropophilic mosquito disease vectors. Acta Trop 116, 119–126 (2010).
- Lozano-Fuentes, S. et al. Evaluation of a topical formulation of eprinomectin against Anopheles arabiensis when administered to Zebu cattle (Bos indicus) under field conditions. Malar J 15, 324 (2016).
- Ouédraogo, A. L. et al. Efficacy and safety of the mosquitocidal drug ivermectin to prevent malaria transmission after treatment: a double-blind, randomized, clinical trial. Clin Infect Dis 60, 357–365 (2015).
- Poché, R. M., Burruss, D., Polyakova, L., Poché, D. M. & Garlapati, R. B. Treatment of livestock with systemic insecticides for control of Anopheles arabiensis in western Kenya. Malaria Journal 14, 351 (2015).
- Poché, R. M., Garlapati, R., Singh, M. I. & Poché, D. M. Evaluation of fipronil oral dosing to cattle for control of adult and larval sand flies under controlled conditions. J Med Entomol 50, 833–837 (2013).
- Pooda, H. S. et al. Administration of ivermectin to peridomestic cattle: a promising approach to target the residual transmission of human malaria. Malaria Journal 14, 496 (2015).
- Smit, M. R. et al. Safety and mosquitocidal efficacy of high-dose ivermectin when co-administered with dihydroartemisinin-piperaquine in Kenyan adults with uncomplicated malaria (IVERMAL): a randomised, double-blind, placebo-controlled trial. Lancet Infect Dis 18, 615–626 (2018).
- Smit, M. R. et al. Pharmacokinetics-Pharmacodynamics of High-Dose Ivermectin with Dihydroartemisinin-Piperaquine on Mosquitocidal Activity and QT-Prolongation (IVERMAL). Clin Pharmacol Ther 105, 388–401 (2019).
First steps in the development of small-scale 3D printed hydrogel bioreactors for protein production in space travel
By Maya Mysore, Laura Ballou, Anna Rita Moukarzel, Alex Cherry, David Duronslet, Lisette Werba, Nathan Tran, Hannah Mosheim, Stephen Curry, Simon Coelho
Advisors: Kantharakorn Macharoen, Matthew McNulty, Andrew Yao, and Dr. McDonald, Dr. Nandi, and Dr. Facciotti
Author’s Note: My name is Maya Mysore, and I am a team lead on the BioInnovation Group’s Plant Bioprinter project. The BioInnovation Group is a student organization that creates research and leadership opportunities for undergraduates. The Bioprinter project is one of these opportunities.
I joined the BioInnovation group (BIG) in the winter quarter of 2019, as a freshman looking for ways to get involved on campus. I knew I liked research; I had been working in another lab. However, I was looking to explore different aspects of research. I heard about BIG through some friends in my major and went to an information session. There, I tried to join the more tech-based microfluidics project; however, my previous lab experience with cell culture convinced the lead for the Bioprinter project to get me involved in their work. I spent the next couple quarters investigating how to trap viruses in hydrogel. In Fall 2019, I was offered the role of lead. I was shocked, surprised, and a little out of my depth– after all, I had practically joined the project by accident! But I took on the role, excited about the leadership opportunity and the freedom. Now over a year into being project lead, I am planning to transition into the organization’s leadership. However, as a swan song to my time in charge, I wanted to compile all the hard work those involved with the project have accomplished. This paper is a celebration of the work of tens of student researchers over a period of several years. Hopefully, this paper will be the first of many for the Bioprinter project and the BioInnovation Group.
Abstract
As human space exploration expands to include potential settlement on the Moon and Mars, the ability to build shelter, manufacture food, produce medicine, and create other necessities in space will become increasingly important. Currently, the high cost and size constraints of sending payloads into space challenges us to think beyond the traditional manufacturing and agricultural tool-kit. Engineers have proposed that additive manufacturing, particularly 3D printing, is a solution to lower the payload costs and to enable the manufacturing of a variety of products in situ. This study focuses on 3D printing engineered biological cells for the production of biologics (e.g. pharmaceuticals that are living or derived from a biological source). We describe in-progress work to design, build, and test a small and affordable 3D bioprinter capable of printing 3D structured hydrogels that can carry living cells. We provide a general overview of the project, our progress in converting a low-cost and compact 3D printer from printing plastics to printing hydrogels, and preliminary work testing the compatibility of bioink formulations with genetically engineered rice cells that produce and secrete the enzyme butyrylcholinesterase.
Background
As humans continue to explore space and potentially settle in distant locations such as the Moon, Mars, and beyond, it will become increasingly necessary to build shelter, create food, and develop medicine while in space. However, the major costs (roughly $20,000/kg) and size constraints of sending payloads into space create challenges for such long-duration space travel beyond low Earth orbit [1-4]. Challenges include the manufacturing of food, shelter, and even medicine. 3D printing has been proposed as a cost-effective method for addressing some of these challenges, as it might allow the opportunity to ship only the printer to remote sites and to source the majority of the printing materials from the settlement location [5].
Biological systems may also play a large role in this approach. Microorganisms have been envisioned to help construct habitats through biocementation, a process that uses microorganisms to solidify inorganic matter into 3D structures [6-8]. Plants and microbes together are proposed as possible tools for the creation of sustainable ecosystems that recycle and detoxify waste and produce food [9-11]. A purported advantage of biological systems is that they can self-replicate, as each organism carries the full set of genetic instructions to create copies of itself. This means that biological systems could be delivered as light-weight “seeds”, i.e. self-replicating units that can be shipped in small and light quantities and grown to larger quantities upon permanent settlement at remote bases.
We and others envision that the 3D printing of engineered living systems (e.g bioprinting) may prove useful for the manufacturing of biologicals; this includes pharmaceuticals of or derived from a biological source [12]. In this context, the engineered living system serves as an on-demand expandable factory for the production of the biological while the 3D printer serves to produce custom-made culturing and purification hardware that can be produced in the geometries required for specific cells and production sizes. We were interested in exploring this concept and better understanding the challenges associated with the proposed process of drug production through bioprinting. In order to do this, we needed a bioprinter. Depending on their feature sets, commercial bioprinters can cost anywhere between $10,000 and $200,000, which was well outside our budget. Therefore, as a first step, we sought to design, build, and test a low-cost and compact bioprinter that we could later customize and use to explore novel design ideas.
FExisting modalities of bioprinting were considered and four main existing modalities of 3D bioprinting were considered: inkjet, pressure-assisted, laser-assisted, and stereolithography. For a detailed review on this subject, see Li et. al [13]. The major factors that were considered in the selection of a printer were types of usable bioinks, potential for good cell viability, cost, and complexity of the system (e.g. ease with which it can be modified). Inkjet-based bioprinting uses computer controls to drop small drops of bioink onto a surface. This type of printing maintains high short-tem cell viability and is widely available at low cost. However, it is limited in printing materials and creates high thermal and mechanical stress on cells which risks damage to cells and may affect long-term viability. Pressure-assisted bioprinters extrude bioink continuously onto a surface. While the extrusion process is slower and can lower cell viabilities immediately after printing (ranging from 40-80%, compared to 90% for inkjet printing), it allows use of a greater variety of materials and incorporates cells directly into the bioink. Laser-assisted bioprinters use a laser to irradiate a bioink such that the droplets adhere to the desired surface. This method of bioprinting is very precise and results in the highest cell viability; however, it is the most expensive, time-consuming, and has the highest risk of metal contamination. Finally, stereolithography printing uses illumination of a light-sensitive polymer to solidify 3D shapes. This method is fast, cost-effective, and has high final cell viabilities, but it is primarily limited by the need for a light-sensitive bioink, many of which are not biocompatible.
We chose to build a pressure-assisted bioprinter primarily due to practical factors: (a) the availability of low cost and compact fused deposition modeling (FDM) printers that could be used as chassis, theoretically enabling a “simple” swap of printing nozzles and pumps while taking advantage of the existing build platforms and 3D control systems; (b) the easy access to safe and low cost of compatible bioinks, and (c) the ability to incorporate cells directly into the bioink for prototyping.
This paper describes the progress of our project in developing a functional bioprinter. In addition, we describe the chemical assays used to evaluate engineered rice cell viability within hydrogels and these cells’ cell’s ability in gels to produce the pharmacologically-relevant enzyme Butrylcholinesterase (BChE), which is a complex human serine hydrolase enzyme that provides protection against organophosphorus poisoning from toxic agents such as sarin.
Figure 1. This diagram demonstrates the model methodology for seeding the cells into the hydrogel, printing out the cell-gel complex, and extracting the protein of interest from this system.
Methods
Printer selection, modification and testing
Selection of chassis
We sought to find a low-cost and compact FDM printer system that could be reasonably modified to extrude bioink rather than plastic filament. We ultimately selected the Monoprice MP Select Mini 3D Printer V2 because of its high availability, low cost ($250), and relative ease of modification. An accurate open source 3D computer-aided design (CAD) model (https://www.thingiverse.com/thing:2681912) of this printer was already available, making it easier to design new features for this specific unit.
Construction of an bioink extruder
To start converting the 3D plastic printer into a bioprinter, the printer’s original extrusion mechanism was replaced with a standard syringe/syringe-pump mechanism typical of bioprinters [14].
Incorporating the syringe-based bioink extruder required the design and construction of the entire extrusion system. An interchangeable mount was designed to hold the 10 mL syringe on the printer access, as seen in Figures 2b and 2c. In Figure 2b, the interchangeable mount design is shown with a trapezoidal connection piece, allowing the mount to swap between holding the 3D printer plastic extruder and the bioprinter syringe extruder system.
Figure 2. a) Inside of the 3D printer after all electrical components and panels were removed b) 3D printed interchangeable mount used to exchange the plastic extruder and the syringe extruder. c) The hydraulic extrusion system as connected to the bioprinter d) The hydraulic extrusion tubing system
The 10 mL syringe was connected to a hydraulic pumping system through a plastic tube. The hydraulic system is controlled using a Nema 17 Bipolar 40 mm Stepper Motor connected to an 8 mm threaded rod, forming a linear actuation mechanism. Connected to the rod is a 60 mL syringe plunger which is pushed through a 60 mL syringe. A liquid is placed in the 60 mL syringe and the bioink is placed in the 10 mL syringe also with a plunger sitting on top of the syringe. When the motor turns on, this liquid is pushed from the 60 mL syringe through the tubing and into the 10 mL syringe. This system pushes the plunger through the 10 mL syringe and extrudes the bioink onto the printing surface.
A T fitting made from 6 mm brass tubes was attached to the middle of the tubing system in order to remove air bubbles from the tube, as shown in Figure 2d.
Integration of hydraulic motors with chassis
To power the motor for the syringe extruder, the electrical components needed to be rebuilt. With this in mind, an Arduino Mega 2560 was connected with the HiLetGo RAMPS 1.4 control panel and the A4988 stepper motor driver boards using the wiring setup diagrammed in Figure 3.
Figure 3. This diagram shows the wiring for the 3D printer using the Arduino.
The Z-axis switch was then repositioned and mounted to the printer chassis directly under the print head, as seen in Figure 2c.
Firmware
For the firmware, Marlin was selected because it is open sourced and easily modified with the Arduino IDE. After the firmware and electronics were set up, a G code file was needed to determine the print pattern. Cura was used to develop the file due to its compatibility with the Monoprice 3D printer. The Cura profile used with the bioprinter tests followed a cylindrical shape with a square-shaped infill grid. With this information established, the Cura profile was exported as G Code. In the printer design, an SD card is required to flash the firmware and upload the G code to the bioprinter. With the firmware and G code loaded onto the SD card, the bioprinter could be set up to run test prints with the bioink. The final cost spent to make the bioprinter came out to $375. Further information on the process of building the bioprinter can be found at https://www.instructables.com/Low-Cost-Bioprinter/.
Hydrogels
Hydrogels are porous water-based polymers that have many valuable uses, especially in fields such as drug delivery and tissue engineering. Here, we use hydrogels for their ability to selectively trap materials on a size basis, as this is what allows us to trap cells and release the protein of interest. Our hydrogel protocol was adapted from Seidel et al., 2017. Briefly, the hydrogel mixture contained agarose (0.2275% w/v), alginate (2.52% w/v), methyl cellulose (3% w/v), and sucrose (3% w/v). Agarose, alginate, and sucrose were mixed into deionized water at room temperature until dissolved. This mixture and the methyl cellulose powder were then autoclaved in separate containers for 20 minutes at 121 C. Upon completion of the autoclave cycle, methyl cellulose was mixed into the gel. The mixture was then left for 12 to 24 hours in a 2-8 C fridge to allow swelling to occur [15]. After this, the gel was ready to be seeded.
Seeding and Crosslinking the Gels
Transgenic rice cells were supplied by the McDonald-Nandi lab. The cells were genetically modified with the addition of a human BChE gene optimized for rice cell compatibility and cloned into the RAmy3D expression system for transformation into A. tumefaciens to allow insertion into rice cells [16]. This allowed the engineered cells to produce the pharmacologically-relevant BChE protein. The provided cell suspensions were mixed thoroughly via pipetting to obtain even distribution of cells. This suspension was then added directly to the hydrogel in a 50% volume split of cell suspension and gel and gently mixed to distribute cells evenly. To crosslink the gels and create solid structures for later use, a 0.1 M calcium chloride solution was prepared. The hydrogel was loaded into a syringe and deposited into weight boats containing enough CaCl2 solution to half-cover the extruded hydrogel. The hydrogel would then cure in the solution for at least 5 minutes or until the shape solidified. Upon completion of curing, the hydrogel could be removed and used for experiments.
Tetrazolium Chloride Viability Assay on Hydrogels
The TTC (2,3,5-triphenyltetrazolium chloride) assay is a method for testing cell viability. TTC is turned red from a colorless solution in the presence of metabolizing cells, allowing for quantification of cell viability. When used with defined standards and run on a spectrometer, it can be used to monitor cell survival over time.
Preparation of the TTC solution involved mixing 0.4% w/v TTC in 0.05 M sodium phosphate buffer, pH 7.5. Once the TTC solution was prepared, the TTC assay was performed.
5-6 mL of 0.05 M sodium phosphate buffer was added to a 15 mL Falcon tube with cured gel to submerge the cured gel entirely. The gel remained in the solution for 15 minutes. Then the Ellman buffer was removed from the tube and 500 μL of TTC were added to the tube with gel while mixing slightly. This tube was stored in a dark area for 24 hours.
If the gel was not cured, roughly 5 mL of gelled cells were first centrifuged in a 15 mL conical tube at 4500 g for 20 minutes. The supernatant was removed and 1 mL of Ellman buffer was added and mixed. The sample was centrifuged again at 4500 g for 15 minutes, the supernatant was removed, and 500 μL of TTC solution were added to the gel-cell mix. This sample was stored for 24 hours in a dark area.
After the 24 hours period ended, the sample-TTC mix was centrifuged at 4500 g for 15min. The supernatant was removed and the gel-cell mix was washed with 1 mL deionized water. The mixture was re-centrifuged at 4500 g for 10 minutes. The supernatant was removed again and 1 mL of 95% ethanol was added to the gel-cell mix. The sample was transferred to a microcentrifuge tube and placed in a 60C water bath for approximately 10 minutes. The sample is then centrifuged at 21.1 g for 15 minutes to recover the final supernatant. The supernatant was then run on a colorimeter or Tecan and the absorbance value was read at 485 nm. Beer’s law was then used to determine concentration from this value.
Seeded Cell-Ellman BChE Concentration Assay
The Ellman assay was used to measure BChE concentration for a sample at a given time point. This assay uses the kinetics of a color changing reaction to quantify the amount of BChE in solution. When in the presence of specific substrates, BChE turns a colorless solution yellow; the peak rate of this reaction can be determined and used to calculate BChE mass in a sample.
After cells were seeded into a hydrogel complex with a disc shape approximately 7 cm in diameter and 1 cm thick, the complex was suspended in 40 mL sucrose-free nutrient broth (NB-S).
The flask was then covered with a cloth filter and placed in the shaking incubator (37C, 5% CO2, 80 rpm). 50 μL media samples were collected from the flask daily over 14 days and the Ellman assay was run directly following collection of each of these samples.
The Ellman assay protocol was based on the Cerasoli lab protocol, which was adapted from Ellman et al., 1961 [17]. To perform the Ellman assay, a 20 mm stock solution of 5, 5’ – dithiobis-(2–nitrobenzoic acid) (DTNB) was prepared. A 75mM stock solution of S-Butyrylthiocholine (BTCh) iodide was also prepared.
Immediately prior to performing the Ellman assay, the Ellman substrate was prepared. 60 μL of DTNB and 30 μL of BTCh were added to the phosphate buffer in the falcon tube. The tube was temporarily stored in ice with light protection.
Then the Ellman assay was performed. In a 96-well plate, 50 μL of sample containing BChE was were diluted into 0.1 M phosphate buffer, pH 7.4, to ensure the generated? outputted slope readings (mOD/min) would fall in the range of 200-1000 when read for 3-5 minutes at 25 C. This dilution was done by estimating the approximate BChE concentration and estimating the mOD/min based on the expected value. 150 μL of Ellman substrate was added to each sample containing well. The optical density of the sample was immediately read at a wavelength of 405 nm for a total of 300 s (5 min) after the measurement was started.
After collecting data from the assay, Beer’s law was used to determine the concentration of product formed. From that value, we could estimate the mass of functional BChE in the total volume of the sample collected [18].
Results
TTC-Gel compatibility
To measure in-gel cell viability, we evaluated the use of the tetrazolium chloride (TTC) assay. This assay measures metabolic activity in live cells by reducing tetrazolium chloride to red formazan through the process of cell metabolism. Effectively, it provides an indication of how well the cells survive over time. Our team modified the assay for use in gels by including extra Ellman buffer and centrifugation steps to provide more opportunity for cells in the gel to be washed.
Figure 4. This figure shows the results of the TTC assay run on the transgenic rice cells in suspension. The leftmost tube is a positive control showing the TTC assay done on cell aggregates in suspension (i.e. without gel) that have been centrifuged into a pellet after the assay was performed. The middle and rightmost tubes are cells suspended in a hydrogel; the TTC assay was performed on this combination of cells in gels. In each tube, the cells have been stained red from the assay, indicating the presence of metabolic activity. These samples can go on to be washed and suspended in ethanol to obtain a viability data value.
To qualitatively assess how different factors like cell distribution and crosslinking might influence the results of the TTC assay, we performed additional variations of the assay. We first visually examined whether cell homogeneity was impacted by the gel. Then, we performed the TTC assay on E. coli cells alone as a positive control. After that, we tested the effects of non-crosslinked and crosslinked gel to ensure neither condition would prevent the use of the assay. E. coli was used for these tests due to our group’s ability to access it more regularly and grow it more easily than the genetically modified rice cells from Dr. McDonald’s lab. All of these tests together allowed us to determine that cell survival could indeed be measured within the gel, allowing us to monitor culture health over time. This will be critical in future use of the model, allowing us to determine ways to improve cell health and protein output by providing a metric for us to test against.
Homogeneous mixing of biological sample
To determine later TTC accuracy, the first key issue to address was homogeneity of cells in a hydrogel. This would determine whether sectioned off samples of cell-gel complexes would be representative of a whole sample. To ensure that the gel mixing protocol yielded a homogeneous suspension of the cells, we first tested our procedure by mixing E. coli expressing a transgenic green fluorescent protein (GFP) and imaged the suspension under UV light. We expect E. coli to distribute homogeneously in the gel similarly to the transgenic rice cells. This mixture was observed (Figure 5a) and confirmed by visual inspection of a homogeneous mix.
Test of TTC assay with bacterial suspension
To ensure that the TTC assay in later tests would be effective with E. coli, we first tested the TTC assay on an E. coli suspension as a positive control for later tests. We ran the modified TTC assay protocol described in Methods, and observed a color change in the solution. The resultant red solution (Figure 5b) matches the literature expectations for the output of this assay on living cells and indicates the assay is effective for E. coli.
Test of TTC assay with bacteria seeded in hydrogel
After confirming the TTC assay was effective with E. coli, it became important to determine how the presence of gel would affect the assay. We suspended the E. coli cells in the hydrogel and ran the modified TTC assay. The results seen in Figure 4c show the suspension turning red, which visually indicates the presence of cell metabolic activity and the effectiveness of the TTC assay.
Test of TTC assay with bacteria seeded in a crosslinked hydrogel
Upon determining the gel did not qualitatively affect the output of the TTC assay, it became necessary to determine whether crosslinking the gel had any effect on the effectiveness of the TTC assay. We reran the same experiment as the non-crosslinking hydrogel experiment, with the only change being the crosslinking process and the different first wash step. We found that the result of the TTC assay appears to be unaffected by the presence of the crosslinked out layer, as the solution turns red in the same way it does for the positive control and the non-crosslinked gel (Figure 5d).
These experiments allowed us to qualitatively determine whether the TTC assay could be an effective measure of cell viability. They also demonstrated that the introduction of a crosslinked hydrogel will not have visible impacts on measuring cell viability.
Figure 5. Qualitative TTC assays were run on E. coli with the pMax plasmid to test homogeneity within the gel and the effectiveness of the TTC assay in different hydrogel conditions. 5a shows the bacteria mixed homogeneously within the hydrogel, which is visible in the fluorescence that is present homogeneously through the sample. 5b shows the ethanol suspension output for a TTC assay run on a pMax E. coli culture, providing a control for later experiments and showing that the TTC assay is effective for E. coli. The left image is the control and the right image is the test condition. The control is run in the same conditions as the test, except the cells are placed in a 60C water bath for ten minutes prior to adding TTC in order to kill them. 5c shows the output prior to ethanol suspension for a TTC assay on E. coli pMax cells that were suspended in an uncured hydrogel. The left tube is the control and the right tube is the experimental condition. The red color visible in the right tube shows that the presence of the hydrogel does not prevent use of the TTC assay. 5d shows the ethanol suspension output for a TTC assay run on E. coli pMax cells that had been suspended in a cured hydrogel. The left image is the control and the right image is the experimental condition. The red color of the suspension indicates the TTC assay remained effective even with the addition of the crosslinked outer layer of the gel. Throughout this figure, variation in intensity of the redness of the samples is related to variations in time spent in suspension of the TTC solution, with redder samples correlating to longer time.
Initial Attempts at Measuring BChE Production
Our second major goal was to determine whether BChE could be collected from our model system (as seen in figure 1). This would allow us to determine if our model system was an effective way to collect our protein of interest for future space travel applications, as well as confirm that our test for BChE quantity would be effective in this system. To test this, our team ran the seeded cell-Ellman assay as described in methods to assess the amount of BChE that was escaping into the media. We first prepared a hydrogel, mixed the transgenic rice cells in, and cured it into a disc shape roughly 7 cm in diameter and 1 cm in height. We then suspended this cured cell-gel complex in NB-S media to stimulate BChE production, and we kept this mix in a spinning incubator to ensure aeration and adequate diffusion of materials in and out of the gel. Media samples were collected over the course of 14 days and were run with the Ellman assay for BChE detection on a spectrometer. The Ellman assay uses the enzyme kinetics of a color-changing reaction between BChE and a substrate to quantify the amount of BChE present in a sample at a given time point.
It is important to note that this test was intended as a trial run of the system in order to ensure that the assay works and that useful data is being collected. In addition, we sought to assess if BChE could escape from the gel at all. Therefore, no negative control was run and only one run of data was collected (shown in the figure below.) As a result, we cannot conclusively state anything about the data. However, the data does show a trend worth noting for future experimentation. The This is that active BChE concentration in the media increased for the first roughly 100 hours, after which the values dropped off. At the time point marked in figure 5, 96 hours, we see the maximal BChE present. If the unusually low value seen at the roughly 120 hour time point is considered erroneous (which we suspect), the data suggests increased production of BChE over the first 4 days of culture followed by a slow decay thereafter with production ending at around day 8. This provides an early quantitative estimate of the time-dependence of BChE production in this model system. This experiment is a first attempt and will be repeated with various parameter variations in the future.
Figure 6. This plot shows the approximate active concentration of BChE released into the media for various time points over 14 days. Each sample was a 50 μL amount of media pulled from the small scale system model. This figure shows a burst in production of active BChE until the 96 hour time point (denoted with a dashed red line), after which the values drop off. The data point at t=120 hours is most likely an outlier resulting from this data being for one set of samples from one test condition.
Preliminary Bioprinter Testing
The process of building and testing the bioprinter was done in parallel with the TTC and Ellman assay testing. Detailed bioprinter testing has not been performed; however, initial testing of the printer showed its ability to print hydrogel into pre-programmed patterns. The grid pattern seen in Figure 7 was printed into a petri dish containing CaCl2 curing solution. The print shows excess hydrogel accumulation near the edges where the printhead briefly paused and reversed direction. In the center of the print, the lines in the grid averaged 1.25mm +/- 0.4 in width. Further testing and refinement is currently in process.
Figure 7. This shows a test print from our modified 3D printer using the hydrogel described in the methods section and cured in standard CaCl2 curing solution. This structure is described as a lattice shape and will be the primary pattern for future prints.
Discussion
In these experiments we determined that the TTC assay was effective in hydrogels, the Ellman assay showed the ability of protein to be detected from solution, and the bioprinter was able to create the desired lattice shape for later use.
Printer Performance
Our experiments to date have demonstrated our ability to convert a low-cost and compact FDM printer into a preliminarily functional bioprinter. The conversion of the original chassis required the modification of the printhead support, the development of a syringe-based hydraulic pump, and the modification of electronic and software control systems. Preliminary prints indicate that the printer can successfully deposit a programmed pattern with feature sizes in the range of 1.5mm. Existing conventional commercial bioprinters can achieve resolutions of 100-200µm, (some even claim filament diameters as low as 3µm), suggesting that we have room to improve the resolution of our system [19]. In addition to improving the resolution of the prints, we want to explore alternate methods for delivering the CaCl2 curing solution during alginate filament deposition to minimize user interaction and allow complete processing inside a biosafety cabinet; this should allow us to increase sterility during printing and print quality.
Cell Viability
Since it is known that pressure-assisted printing may negatively impact cell viability during printing, a key concern was the resulting cell viability of the system. As a result, our general goal for this phase of the project was to test whether a pressure-assisted bioprinter system could maintain cell viability after extrusion. We adapted the TTC assay for this purpose and tested our protocol to determine the effect of bioink and extrusion on cell viability under conditions mimicking those experienced during bioprinting.
Generally, the TTC assay demonstrated the ability of the assay to cellular viability in the crosslinked hydrogel, despite the unknown nature of how crosslinking affects pore size. Despite this success, the TTC assay remains largely qualitative as it is challenging to get quantitative measurements of cell viability when cells are embedded in a gel. This is further complicated by factors like the heterogeneous distribution of cells (or cellular aggregates) in the gel (see figure 4, rightmost sample). If homogeneity is not maintained, we need to design assays that take into account heterogeneous distribution of cells in the gel. In future experiments, we seek to determine whether samples from a large complex of cells in a hydrogel will provide a representative sample.
In later experiments, additional key variables that may potentially affect viability will be tested. These variables include media composition, culture duration, environmental conditions such as temperature, gel architecture, and the additional variables associated with the printer extrusion process (e.g. pressure, needle pore diameter, etc.). Determining how these specific factors affect viability will allow us to modify the printer design to minimize the drop in cell viability upon extrusion.
Protein production
Having confirmed the effectiveness of the TTC assay in the hydrogel, we moved forward to analyzing BChE production and its diffusion into the media. The assay we adopted allowed us to develop a standard method for data collection that can be used to analyze how various factors impact the cells’ ability to produce BChE. Figure 6, for example, shows that we can measure BChE production and diffusion out of the gel, and that under our preliminary experimental conditions, production peaks at 96 hours and then falls over the next 150 hours. While encouraging, this experiment needs to be repeated with many more samples and replicates to obtain a more reliable assessment of measurement error associated with the assay. Despite needing to replicate the experiment, we are confident that this preliminary experiment answered the core question of whether such a large protein – 85 kDa monomers and 4 units in quaternary form, with a total size of 574 monomers [20] – can effectively diffuse out of the hydrogel and avoid denaturation long enough to be collected and purified.
In addition to replication, future experiments should be explored to further improve protein escape from the hydrogel. These tests could increase the mixing speed to use centrifugal force to free proteins from the gel, increase pore size to create more physical space for protein escape, or print the 3-dimensional lattice structure to increase surface area and allow greater escape. Other relevant variables whose impact on BChE production should be tested include media composition and media changing schedules, culture duration, environmental conditions, gel architecture, and growth temperature. In our initial experiments, plant cells were grown in a shaking incubator at 37C to mimic the environment of protein production in mammalian hosts. However, this growth condition may have stressed the plant cells for which growth at 27C is more typical [16, 21]. This may explain the trend shown in figure 6, where die-off occurs after 96 hours.
Finally, in our current studies, the presence of sucrose in gel formulation (which inhibits BChE production) may have adversely impacted the amount of protein produced. While we expected that overlaying a relatively large volume of sucrose-free media would effectively dilute the sucrose to low levels, the presence of sucrose in the initial formulation could have nevertheless impacted the cells’ initial states and therefore protein production. A followup experiment that more stringently controls for the presence of sucrose in the gel than in the studies described above seems warranted.
Conclusion
In this work, we successfully modified an off-the-shelf pressure-assisted 3D printer into a working bioprinter. In addition, we established that BChE producing rice cells are biocompatible with the different bioink gel formulations and that our assays for testing cell viability and protein production are effective when analyzing the cells within the gel. Having shown that we can print gel, assess cell survival, produce BChE, and quantify its abundance, we next seek to optimize both printer function and the measurement assays for cell viability and protein concentration in ways that provide more quantitative data and more refined control over printed structures. Eventually, we expect that such advances will allow us to optimize protein production itself and ultimately develop a bioprinter suitable for protein production during space travel or in other remote locations.
Acknowledgements
Thank you to the Molecular Prototyping and BioInnovation Lab for the lab space, the BioInnovation Group for the administrative, scientific, safety, and monetary support, the McDonald-Nandi lab for materials and mentorship, and all past, present and future members of the Bioprinter team for contributing to these experiments.
References:
- Joshi SC, Sheikh AA. 2015. 3D printing in aerospace and its long-term sustainability. Virtual Phy Prototy [Internet]. 10(4):175–185. doi.org/10.1080/17452759.2015.1111519
- Hodkinson PD, Anderton RA., Posselt BN, Fong KJ. 2017. An overview of space medicine. Br J Anaesth [Internet]. 119(suppl_1):i143–i153. doi.org/10.1093/bja/aex336
- Aglietti, GS. 2020. Current Challenges and Opportunities for Space Technologies. Front Space Tech [Internet]. 1:1. doi.org/10.3389/frspt.2020.00001
- Coopersmith J. 2011. The cost of reaching orbit: Ground-based launch systems. Space Policy [Internet]. 27(2):77–80. doi.org/10.1016/j.spacepol.2011.03.001
- Ghidini T. 2018. Regenerative medicine and 3D bioprinting for human space exploration and planet colonisation. J Thorac Dis [Internet]. 10(Suppl 20):S2363–S2375. doi.org/10.21037/jtd.2018.03.19
- Gleaton J, Lai Z, Xiao R, Chen Q, Zheng Y. 2019. Microalga-induced biocementation of martian regolith simulant: Effects of biogrouting methods and calcium sources. Constr Build Mater [Internet]. 229:116885. doi.org/10.1016/j.conbuildmat.2019.116885
- Pilehvar S, Arnho M, Pamies R, Valentini L, Kjøniksen AL. 2020. Utilization of urea as an accessible superplasticizer on the moon for lunar geopolymer mixtures. J Clean Prod [Internet]. 247:119177. doi.org/10.1016/j.jclepro.2019.119177
- Kumar A, Dikshit R, Gupta N, Jain A, Dey A, Nandi A., … Rajendra A. 2020. Bacterial Growth Induced Biocementation Technology, ‘Space-Brick’ – A Proposal for Experiment at Microgravity and Planetary Environments. BioRxiv [Internet]. 2020.01.22.914853. doi.org/10.1101/2020.01.22.914853
- Lopez JV, Peixoto RS, Rosado AS. 2019. Inevitable future: space colonization beyond Earth with microbes first. FEMS Microbiol Ecology [Internet]. 95(10). doi.org/10.1093/femsec/fiz127
- Bornemann G, Waßer K, Tonat T, Moeller R, Bohmeier M, Hauslage J. 2015. Natural microbial populations in a water-based biowaste management system for space life support. Life Sci Space Res [Internet]. 7:39–52. doi.org/10.1016/j.lssr.2015.09.002
- Bokulich NA, Lewis ZT., Boundy-Mills K, Mills DA. 2016. A new perspective on microbial landscapes within food production. Curr Opin Biotechnol [Internet]. 37:182–189. doi.org/10.1016/j.copbio.2015.12.008
- Varma A, Gemeda HB, McNulty MJ, McDonald KA, Nandi S, Knipe JM. 2021. Bioprinting transgenic plant cells for production of a recombinant biodefense agent. BioRxiv [Internet]. 2021.02.01.429263. doi.org/10.1101/2021.02.01.429263
- Li J, Chen M, Fan X, Zhou H. 2016. Recent advances in bioprinting techniques: approaches, applications and future prospects. J Transl Med [Internet]. 14:271. doi.org/10.1186/s12967-016-1028-0
- Pusch K, Hinton TJ, Feinberg AW. 2018. Large volume syringe pump extruder for desktop 3D printers. HardwareX [Internet]. 3:49–61. doi.org/10.1016/j.ohx.2018.02.001
- Seidel J, Ahlfeld T, Adolph M, Kümmritz S, Steingroewer J Krujatz F, … Lode A. 2017. Green bioprinting: extrusion-based fabrication of plant cell-laden biopolymer hydrogel scaffolds. Biofabrication [Internet]. 9(4):045011. doi.org/10.1088/1758-5090/aa8854
- Corbin JM, Hashimoto BI, Karuppanan K, Kyser ZR, Wu L, Roberts BA, … Nandi S. 2016. Semicontinuous Bioreactor Production of Recombinant Butyrylcholinesterase in Transgenic Rice Cell Suspension Cultures. Front Plant Sci [Internet]. 7:412. doi.org/10.3389/fpls.2016.00412
- Ellman GL, Courtney KD, Andres VJ, Feather-Stone RM. 1961. A new and rapid colorimetric determination of acetylcholinesterase activity. Biochem Pharmacol [Internet]. 7:88–95. doi.org/10.1016/0006-2952(61)90145-9
- Alkanaimsh S, Karuppanan K, Guerrero A, Tu AM, Hashimoto B, Hwang MS, … McDonald KA. 2016. Transient Expression of Tetrameric Recombinant Human Butyrylcholinesterase in Nicotiana benthamiana. Front Plant Sci [Internet]. 7:743. doi.org/10.3389/fpls.2016.00743
- Gu Z, Fu K, Lin H, He Y. 2020. Development of 3D bioprinting: From printing methods to biomedical applications. Asian J Pharm Sci [Internet]. 15(5):529–557. doi.org/10.1016/j.ajps.2019.11.003
- Ngamelue MN, Homma K, Lockridge O, Asojo OA. 2007. Crystallization and X-ray structure of full-length recombinant human butyrylcholinesterase. Acta Crystallogr [Internet]. 63(9):723-727. doi.org/10.1107/S1744309107037335
- Macharoen K, McDonald KA, Nandi S. 2020. Simplified bioreactor processes for recombinant butyrylcholinesterase production in transgenic rice cell suspension cultures. Biochem Eng J [Internet]. 163:107751. doi.org/10.1016/j.bej.2020.107751