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Feeding 8 Billion People: Engineering Crops for Climate Resiliency
By Shaina Eagle, Global Disease Biology ’24
Feeding the world’s 8 billion– and growing– people [2] is an Augean task that requires cooperation between farmers, scientists, government agencies, and industry stakeholders across the globe. Agriculture and climate are deeply intertwined and climate conditions play a critical role in determining agricultural productivity and have a significant impact on global food security. The climate crisis poses immense challenges to food security and the farmers whose livelihoods depend on crop production. As the consequences of the climate crisis increase and intensify, developing resilient agricultural systems is essential to ensuring that our food and those who grow it can adapt without further depleting carbon and water resources.
Climate-smart agriculture identifies technologies that can best respond to the impacts of climate change, such as increasing temperatures and heat waves, changing rainfall patterns, severe storms, drought, and wildfires that adversely affect crop yield and quality [1]. Agronomists, plant biologists, and farmers are working to develop crops that will increase sustainable production and better withstand a changing climate via various genetic techniques.
Clonal Seeds
A team including a UC Davis Assistant Professor of Plant Sciences, Imtiyaz Khanday, genetically engineered rice seeds that reproduce clonally, or without sexual reproduction, in order to maintain the desirable traits found in the F1 generation (Vernet et al. 2022). They developed a breeding technique that allows for high-frequency production– or the ability to produce a large quantity in a short amount of time in a cost-effective manner– of hybrid rice using synthetic apomixis. Apomixis, a type of asexual reproduction in plants, allows for the production of seeds without fertilization, which can be useful in hybrid breeding programs. The study used CRISPR/Cas9 gene editing to introduce mutations in the genes responsible for sexual reproduction in rice. These seeds were planted and produced the F1 generation of plants, which were genetically stable and had high yield potential. Subsequent generations were clonally propagated from the F1 plants. In agriculture, high-frequency production has the ability to produce a large number of crops or seeds using advanced breeding techniques. High-frequency production is important for meeting the increasing demand for food and other agricultural products, as well as for improving the efficiency and profitability of farming operations.
The study suggests that this technique could be a valuable tool for plant breeders to produce high-quality hybrid rice seeds with more efficient and cost-effective methods. Clonal propagation can help maintain desirable traits as the climate crisis threatens agriculture, such as disease resistance, yield potential, or drought tolerance that might otherwise be lost through sexual reproduction. It is a faster alternative to sexual reproduction methods such as cross-breeding, which can take several generations and require extensive testing to identify desirable traits.
De Novo Domestication
De novo is a Latin term that means “from the beginning” or “anew”. In the context of genetics and plant breeding, de novo refers to the creation of something new or the starting point for the development of a new organism or trait. De novo domestication, for example, refers to the process of identifying and selecting wild plants with desirable traits and developing them into new crops that are better adapted to agricultural use. This approach differs from traditional domestication, which involves selecting and breeding plants that have already been used by humans for thousands of years. Eckhardt et al. highlight the potential benefits of de novo domestication, including the creation of new crops that are better adapted to changing environmental conditions, and the conservation of genetic diversity by using previously unexploited wild species.
A study by Lemmon et al. (2021) aimed to create a domesticated tomato variety with desirable traits by introducing mutations into genes related to fruit size and shape via CRISPR-Cas9. While there are many tomato cultivars available, they often have limitations in terms of yield, quality, or other traits that are important for consumers and growers. Therefore, there is a need to develop new tomato varieties with improved characteristics, and the de novo domestication of a wild tomato variety using genome editing offers a potential solution to this challenge. The domesticated variety has several desirable traits, including larger fruit size, smoother fruit shape, reduced seed count, and prolonged fruit shelf life. Additionally, the domesticated tomato plants have increased branching and produced more fruit per plant compared to the wild-type tomato plants.
Kaul et al. (2022) conducted a de novo genome assembly of rice bean (Vigna umbellata), a nutritionally rich crop with potential for future domestication. The study revealed novel insights into the crop’s flowering potential, habit, and palatability, all of which are important traits for efficient domestication. Flowering potential refers to the crop’s ability to produce flowers, which is important for seed production and crop yield. Understanding the genetic basis of flowering potential can help breeders select plants that flower earlier or later, depending on their needs. Habit refers to the overall growth pattern of the plant, such as its height, branching, and leaf morphology. Understanding the genetic basis of habit can help breeders select for plants that are more suitable for specific growing conditions or cultivation methods. Palatability refers to the taste and nutritional value of the crop, which are important factors for its acceptance as a food source. Identifying genes involved in carbohydrate metabolism and stress response can help breeders develop crops with better nutritional value and resistance to environmental stressors. Overall, these traits are desirable because they can contribute to the development of a more productive, nutritious, and resilient crop. The researchers also identified genes involved in key pathways such as carbohydrate metabolism, plant growth and development, and stress response. Climate change is expected to have a significant impact on crop yields, water availability, and soil fertility. One NASA study found that maize yields may decrease by 24% by 2030 [3]. Understanding the genetic basis of stress response and carbohydrate metabolism can help breeders develop crops that are more resilient to environmental stressors, such as drought, heat, and pests. Furthermore, identifying genes involved in plant growth and development allows breeders to introduce desirable traits, such as earlier flowering or increased yield. This is important for domestication because it can help accelerate the process of crop improvement and make it easier to develop new varieties with desirable traits. Overall, the genes identified in the study provide a foundation for developing crops that are better adapted to changing environmental conditions and more suitable for cultivation, which is crucial for ensuring food security in the face of climate change.
Genetically enhancing common crops
Molero et al. (2023) identified exotic alleles (germplasm unadapted to the target environment) associated with heat tolerance in wheat through genomic analysis and conducted breeding experiments to develop new wheat with improved heat tolerance. The exotic alleles were obtained from wheat lines that originated from diverse regions around the world, including Africa, Asia, and South America. The identified alleles increased heat tolerance in wheat under field conditions, and the effect was consistent across multiple environments. The authors obtained these lines from the International Maize and Wheat Improvement Center (CIMMYT) and used genomic analysis to identify the specific exotic alleles associated with heat tolerance. These alleles were then incorporated into breeding programs to develop new wheat varieties with improved heat tolerance.
The authors used genomic analysis to identify these alleles, which had diverse functions, including regulating heat shock proteins, osmotic stress response, and photosynthesis. The study provides evidence that the use of multiple exotic alleles could lead to the development of wheat varieties with improved heat tolerance under field conditions. The authors crossed the heat-tolerant lines carrying the exotic alleles with local commercial varieties to develop new breeding populations. They then evaluated the heat tolerance of these populations under field conditions to identify the lines with improved heat tolerance. The selected lines were further evaluated in multiple environments to confirm their performance and stability. Heat tolerance was measured by exposing the plants to high temperatures under field conditions and evaluating their performance. Specifically, they conducted experiments in three different environments, including a dry and hot irrigated environment, a semi-arid rainfed environment, and a temperate irrigated environment, all of which are known to impose high-temperature stress on wheat. The authors evaluated multiple traits related to heat tolerance, including yield, plant height, spike length, and the number of spikes per plant.
They also measured physiological traits such as chlorophyll fluorescence, canopy temperature, and photosynthetic activity. By evaluating these traits, they were able to identify the wheat lines with improved heat tolerance. By combining both phenotypic and genomic analyses, they were able to identify the wheat lines and alleles with the greatest potential for improving heat tolerance in wheat under field conditions. This demonstrates the potential for the use of exotic alleles in plant breeding to improve crop performance and address the challenges of climate change.
Porch et al. (2020) report the release of a new tepary bean germplasm (seeds or plant parts that can be passed onto the next generation and are helpful in breeding efforts) called TARS-Tep 23, which exhibits broad abiotic stress tolerance, as well as resistance to rust and common bacterial blight. Tepary bean (Phaseolus acutifolius) is a drought-tolerant legume crop that is native to the southwestern United States and northern Mexico. Tepary beans are generally grown in arid and semi-arid regions of North America, including the Sonoran Desert, Chihuahuan Desert, and the Great Basin. They are also grown in parts of Central and South America. According to FAO statistics, the total world production of tepary beans in 2019 was around 4,000 metric tons. Rust and common bacterial blight are two diseases that can affect the growth and productivity of tepary beans. Rust is a fungal disease that causes orange or brown spots on the leaves and stems of plants, leading to reduced photosynthesis and yield loss. Common bacterial blight is a bacterial disease that can cause wilting, necrosis, and reduced yield in affected plants.
The researchers conducted field trials and laboratory experiments to evaluate the performance and traits of TARS-Tep 23 under different conditions. Laboratory experiments involved inoculating TARS-Tep 23 with rust and common bacterial blight pathogens, then comparing the performance and traits with other tepary beans under these conditions. Field trials were carried out under conditions such as normal rainfall, drought, and heat stress. The results showed that TARS-Tep 23 had higher yields and better growth under drought and heat stress compared to other tepary bean varieties. It also showed high resistance to rust and common bacterial blight. The release of TARS-Tep 23 provides a valuable resource for breeding programs and can contribute to enhancing the productivity and sustainability of tepary bean cultivation. Developing climate-resistant germplasm is a critical resource for crop improvement and biodiversity cultivation, and it is used by plant breeders and researchers to develop new varieties with desirable traits such as disease resistance, stress tolerance, and improved yield.
Conclusion
The urgent need to address the challenge of climate change and its impact on global food security cannot be overemphasized. The world is already experiencing food shortages due to the adverse effects of climate change, and this problem is likely to worsen in the future unless appropriate measures are taken. Significant strides are being made in the research and development of new agricultural and genetic technologies that can engineer crops for climate resiliency. These technologies offer hope for a more sustainable future by enhancing food production, increasing resilience to extreme weather conditions, and mitigating the impact of climate change. However, it is essential to recognize that research and development efforts should not only focus on genetic engineering but should also involve all levels of the food production process, including better management practices, more efficient use of resources, and improved supply chain management. Only by taking a comprehensive approach can we hope to achieve a sustainable and resilient food system that can withstand the challenges of climate change.
References
[1] Eckardt, Nancy A, Elizabeth A Ainsworth, Rajeev N Bahuguna, Martin R Broadley, Wolfgang Busch, Nicholas C Carpita, Gabriel Castrillo, et al. “Climate Change Challenges, Plant Science Solutions.” The Plant Cell 35, no. 1 (January 2, 2023): 24–66. https://doi.org/10.1093/plcell/koac303.
[2] Frayer, Lauren. “Earth Welcomes Its 8 Billionth Baby. Is That Good or Bad News… or a Bit of Both?” NPR, November 15, 2022, sec. Goats and Soda. https://www.npr.org/sections/goatsandsoda/2022/11/15/1136745637/earth-welcomes-its-8-billionth-baby-is-that-good-or-bad-news-or-a-bit-of-both.
[3] Gray, Ellen. NASA’s Earth Science News. “Global Climate Change Impact on Crops Expected Within 10 Years, NASA Study Finds.” Climate Change: Vital Signs of the Planet. Accessed May 30, 2023. https://climate.nasa.gov/news/3124/global-climate-change-impact-on-crops-expected-within-10-years-nasa-study-finds.
[4] Jägermeyr, Jonas, Christoph Müller, Alex C. Ruane, Joshua Elliott, Juraj Balkovic, Oscar Castillo, Babacar Faye, et al. “Climate Impacts on Global Agriculture Emerge Earlier in New Generation of Climate and Crop Models.” Nature Food 2, no. 11 (November 1, 2021): 873–85. https://doi.org/10.1038/s43016-021-00400-y.
[5] Jia, Huicong, Fang Chen, Chuanrong Zhang, Jinwei Dong, Enyu Du, and Lei Wang. “High Emissions Could Increase the Future Risk of Maize Drought in China by 60–70 %.” Science of The Total Environment 852 (December 2022): 158474. https://doi.org/10.1016/j.scitotenv.2022.158474.
[6] Liu, Weihang, Tao Ye, Jonas Jägermeyr, Christoph Müller, Shuo Chen, Xiaoyan Liu, and Peijun Shi. “Future Climate Change Significantly Alters Interannual Wheat Yield Variability over Half of Harvested Areas.” Environmental Research Letters 16, no. 9 (September 1, 2021): 094045. https://doi.org/10.1088/1748-9326/ac1fbb.
[7] McMillen, Michael S., Anthony A. Mahama, Julia Sibiya, Thomas Lübberstedt, and Walter P. Suza. “Improving Drought Tolerance in Maize: Tools and Techniques.” Frontiers in Genetics 13 (October 28, 2022): 1001001. https://doi.org/10.3389/fgene.2022.1001001.
[8] Molero, Gemma, Benedict Coombes, Ryan Joynson, Francisco Pinto, Francisco J. Piñera-Chávez, Carolina Rivera-Amado, Anthony Hall, and Matthew P. Reynolds. “Exotic Alleles Contribute to Heat Tolerance in Wheat under Field Conditions.” Communications Biology 6, no. 1 (January 9, 2023): 21. https://doi.org/10.1038/s42003-022-04325-5.
[9] Ozias-Akins, Peggy, and Joann A. Conner. “Clonal Reproduction through Seeds in Sight for Crops.” Trends in Genetics 36, no. 3 (March 2020): 215–26. https://doi.org/10.1016/j.tig.2019.12.006.
[10] Raphael Tiziani, Begoña Miras-Moreno, Antonino Malacrinò, Rosa Vescio, Luigi Lucini, Tanja Mimmo, Stefano Cesco, Agostino Sorgonà. “Drought, heat, and their combination impact the root exudation patterns and rhizosphere microbiome in maize roots.” Environmental and Experimental Botany, Volume 203, 105071. 2022. https://doi.org/10.1016/j.envexpbot.2022.105071.
[11] Underwood, Charles J., and Raphael Mercier. “Engineering Apomixis: Clonal Seeds Approaching the Fields.” Annual Review of Plant Biology 73, no. 1 (May 20, 2022): 201–25. https://doi.org/10.1146/annurev-arplant-102720-013958.
[12] Vernet, Aurore, Donaldo Meynard, Qichao Lian, Delphine Mieulet, Olivier Gibert, Matilda Bissah, Ronan Rivallan, et al. “High-Frequency Synthetic Apomixis in Hybrid Rice.” Nature Communications 13, no. 1 (December 27, 2022): 7963. https://doi.org/10.1038/s41467-022-35679-3.
[13] Yu, Chengzheng, Ruiqing Miao, and Madhu Khanna. “Maladaptation of U.S. Corn and Soybeans to a Changing Climate.” Scientific Reports 11, no. 1 (June 11, 2021): 12351. https://doi.org/10.1038/s41598-021-91192-5.
Genetic algorithms: An overview of how biological systems can be represented with optimization functions
By Aditi Goyal, Genetics & Genomics, Statistics ‘22
Author’s Note: As the field of computational biology grows, machine learning continues to have larger impacts in research, genomics research in particular. Genetic algorithms are an incredible example of how computer science and biology work hand in hand and can provide us with information that would otherwise take decades to obtain. I was inspired to write a review overviewing genetic algorithms and their impact on biology research after reading a news article about them. This paper is not intended to serve as a tutorial of any kind when it comes to writing a genetic algorithm. Rather, it serves to introduce this concept to someone with little to no background in computer science or bioinformatics.
Introduction
In 2008, Antoine Danchin wrote that “there is more than a crude metaphor behind the analogy between cells and computers.” [1] He also stated that the “genetic program is more than a metaphor and that cells, bacteria, in particular, are Turing machines.” [1] This is the fundamental theory that has been the basis of systems biology and has inspired the development of genetic algorithms. Genetic algorithms (GAs) provide a method to model evolution. They are based on Darwin’s theory of evolution, and computationally create the conditions of natural selection. Using genetic algorithms, one can track the progression of a certain gene or chromosome throughout multiple generations. In this paper, we discuss the components of a genetic algorithm, and how they can be modified and applied for various biological studies.
Background
GA’s are an example of a metaheuristic algorithm that is designed to find solutions to NP-hard problems [2, 3]. NP problems, aka Non-deterministic Polynomial-time problems, describe optimization problems that take a polynomial amount of time to solve via a brute force method. This is best understood through an example, the most classic one being the Traveling Salesman Problem [4]. If a salesman has to travel to five different locations, how should he pick the order of his destinations, in order to minimize the distance he travels? The solution to this problem is to calculate the total distance for each combination and pick the shortest route. At five destinations alone, there are 120 possible routes to consider. Naturally, as the number of ‘destinations’ increases, the number of possible routes will increase, as will the time it takes to calculate all options. In more complicated scenarios, such as an evolution prediction system, this problem becomes exponentially more difficult to solve, and therefore requires optimization.
GA’s are “problem independent” optimization algorithms [2, 3]. This means that the parameterization of the algorithm does not depend on any certain situation, and can be modified by the user depending on the situation. This class of optimization algorithms is often referred to as a metaheuristic algorithm. The key idea is that these types of optimization functions trade accuracy for efficiency. Essentially, they aim to approximate a solution using the least amount of time and computing power, as opposed to providing a high degree of accuracy that may take significantly more resources.
Components of a Genetic Algorithm
There are only two components essential to creating a GA: a population, and a fitness function [I*]. These two factors are sufficient to create the skeleton of a GA. Withal, most GA’s are modeled after Darwin’s theory of evolution [j*]. They use the components of fitness, inheritance, and natural variation through recombination and mutation to model how a genetic lineage will change and adapt over time [j*]. Therefore, these components must also be incorporated into the GA in order to more accurately mimic natural selection.
Population
A population is defined using the first generation, or F1. This can be a set of genes, chromosomes, or other units of interest [7]. This generation can be represented in several ways, but the most common technique is to use a bit array where different permutations of 0’s and 1’s represent different genes [7].
Selection & Fitness Functions
Now that a population has been initialized, it needs to undergo selection. During selection, the algorithm needs to select which individuals from the population will be continuing onto the next generation. This is done through the fitness function [3]. The fitness function aims to parameterize the survival of a certain individual within the population and provide a fitness score. This accounts for the fitness of each genetic trait and then computes the probability that the trait in question will continue onwards. The fitness score can be represented in different ways. A common method is using a binary system. For example, consider a chromosome being defined as a set of bits (see Figure 1). A neutral, or wild-type allele can be represented with a zero. A beneficial allele or one that confers some sort of advantage over the wild-type is represented using a 1. The fitness function would then be defined to calculate the fitness of each chromosome. In this example, the fitness is equivalent to the sum of the binary scores.
Chromosomes with a higher fitness score represent chromosomes that have more beneficial traits as compared to chromosomes with lower fitness scores. Therefore, chromosomes that maximize the fitness score will be preferred.
Inheritance & Genetic Variation
The fittest individuals are then propagated onwards to the “breeding” phase, while only a small proportion of the fewer fit individuals are carried forward. This is the step that mimics “natural selection”, as we are selecting for the more fit individuals, and only a small proportion of the fewer fit individuals are surviving due to chance.
Now that the survivors have been identified, we can use GA operators to create the next generation. GA operators are how genetic variation is modeled [7]. As such, the two most common operators in any GA are mutation rates and recombination patterns. The F2 generation is created by pairing two individuals from F1 at random and using our operators to create a unique F2.
Mutations are commonly represented using bit changes [3]. Because our original population was defined in binary, our mutation probability function represents the probability of a bit switch, i.e. the probability that a 0 would switch to a 1, or vice versa. These probabilities are usually quite low and have a minor impact on the genetic variation.
Recombination, or crossovers, is where the majority of new genetic variations arise. These are modeled by choosing a point of recombination, and essentially swapping bit strings at that point. A simple GA uses a single point crossover, where only one crossover occurs per chromosome. However, a GA can easily be adapted to have multiple crossover points [8, 9].
On average, via the mutation and crossover operators, the fitness level of F2 should be higher than F1. By carrying some of the fewer fit individuals, we allow for a larger gene pool and therefore allow for more possibilities for genetic combinations, but the gene pool should be predominated by favorable genes [3].
Termination
This three-step pattern of selection, variation, and propagation is repeated until a certain threshold is reached. This threshold can be a variety of factors, ranging anywhere from a preset number of generations to a certain average fitness level. Typically, termination occurs when population convergence occurs, meaning that the offspring generation is not significantly better than the generation before it [10].
Modifications to GA’s
As one can see, this is a rather simplistic approach to evolution. There are several biological factors that remain unaddressed in a three-step process. Consequently, there are many ways to expand a GA to more closely resemble the complexity of natural evolution. The following section shall briefly overview a few of the different techniques used in tandem with a GA to add further resolution to this prediction process.
Speciation
A GA can be combined with a speciation heuristic that discourages crossover pairs between two individuals that are very similar, allowing for more diverse offspring generations [11, 12]. Without this speciation parameter, early convergence is a likely possibility [12]. Early convergence describes the event that the ideal individual, i.e. the individual with the optimized fitness score, is reached in too few generations.
Elitism
Elitism is a commonly used approach to ensure that the fitness level will never decrease from one generation to the next [13]. Elitism describes the decision to carry on the highest-rated individuals from one generation to the next with no change [13, 14]. Elitism also ensures that genetic information is not lost. Since each offspring must be ‘equal or better’ than the generation before it, it is guaranteed that the parental genotypes will carry through generations, changing at a much slower rate than a pure GA would model [15].
Adaptive Genetic Algorithms
Adaptive Genetic Algorithms (AGA’s) are a burgeoning subfield of GA development. An AGA will continuously modify the mutation and crossover operators in order to maintain population diversity, while also keeping the convergence rate consistent [16]. This is computationally expensive but often produces more realistic results, especially when calculating the time it would take to reach the optimal fitness. The Mahmoodabadi et al team compared AGA’s to 3 other optimization functions and found that “AGA offers the highest accuracy and the best performance on most unimodal and multimodal test functions” [17].
Interactive Genetic Algorithms
As previously stated, the fitness function is critical to creating a GA. However, there arise several instances where a fitness function cannot be accurately defined. This is particularly true for species that have elaborate mating rituals, as that is a form of selection that would be computationally expensive to recreate. In these situations, one can use an interactive genetic algorithm (IGA). IGA’s operate in a similar fashion to GA’s, but they require user input at the fitness calculation point.
While this method does provide some way of modeling a population without having a predefined fitness function, it has glaring drawbacks. Primarily, this process is not feasible for large populations, as it puts the burden of calculating the fitness on the user, and it also leaves room for subjective bias from the user. However, this subjective component been viewed as an advantage in several fields, particularly the fashion industry [18]. Designers have been investigating IGA’s as a method to generate new styles, as the algorithm depends on user validation of what is considered to be a good design versus a bad one [18].
Applications
Genetic algorithms have a far-reaching effect on computational efforts in every field, especially in biology. As the name suggests, genetic algorithms have a huge impact on evolutionary biology, as they can assist with phylogeny construction for unrooted trees [19]. Oftentimes, evolutionary data sets are incomplete. This can result in billions of potential unrooted phylogenetic trees. As the Hill et al team describes, “for only 13 taxa, there are more than 13 billion possible unrooted phylogenetic trees,” [19].
Testing each of these combinations and determining the best fit is yet another example of an optimization problem– one which a GA can easily crack. Hill et al applied a GA to a set of amino acid sequences and built a phylogenetic tree comparing protein similarities [19]. They found that a program called Phanto, “infers the phylogeny of 92 taxa with 533 amino acids, including gaps in a little over half an hour using a regular desktop PC” [19].
Similarly, the Wong et al team tackled the infamous protein folding prediction problem using GA’s [20]. They used the HP Lattice model to simplify a protein structure and used the iterative nature of a GA to find a configuration that minimized the energy required to fold a protein into that shape. The HP Lattice model stands for Hydrophobic Polar Lattice and seeks to model the hydrophobicity interactions that occur between different amino acid residues in the secondary structure of a protein [20]. They found that a GA performed better than some of the newer protein folding predictive programs available today [20].
GA’s are an incredible tool for cancer research as well. The Mitra et al team used a GA to study bladder cancer [21]. They conducted quantitative PCR on tissue samples from 65 patients and identified 70 genes of interest. Of these 70 genes, three genes in particular, were identified in a novel pathway. They discovered that ICAM1 was up-regulated relative to MAP2K6, while MAP2K6 was up-regulated relative to KDR. This pathway was considered to be novel because individually, all three genes displayed no signs of significant changes in regulation. By applying a GA, the Mitra team was able to identify this pattern between all three genes. Uncoincidentally, “ICAM1 and MAP2K6 are both in the ICAM1 pathway, which has been reported as being associated with cancer progression, while KDR has been reported as being associated with the vascularization supporting tumors” [21, 22, 23].
Another groundbreaking discovery was made by applying GA’s to p53 research. P53 is an essential tumor suppressor [24]. Most cancerous tumors can be attributed, in part, to a mutation in the p53 gene, making it an excellent candidate for oncology research. The Millet et al team investigated a possible p53 gene signature for breast cancer, hoping to find an accurate prediction system for the severity of breast cancer [25]. They analyzed 251 transcriptomes from patient data and found a 32 gene signature that could serve as a predictor for breast cancer severity [23, 25]. They also found that “the p53 signature could significantly distinguish patients having more or less benefit from specific systemic adjuvant therapies and locoregional radiotherapy,” [25].
GA’s have also had a huge impact on immunology, vaccine development in particular. Licheng Jiao and Lei Wang developed a new type of GA called the Immunity Genetic Algorithm [26]. This system mimics a typical GA but adds a two-step ‘immunological’ parameter (Figure 3). Much like a GA, the fitness function is applied to a population, which then triggers mutation and crossover. However, after these steps, the program mimics ‘vaccination’ and ‘immune selection. These two steps are referred to as the “Immune Operator” [26]. They are designed to raise a genetic advantage in individuals who respond well to the vaccine and confer a disadvantage to those with a ‘weaker’ immune response. In essence, the vaccination step acts as a secondary mutation, as it is acting as an external change factor in each individual’s fitness. Similarly, the ‘immune selection’ step acts as a secondary fitness function, as it measures the immune response post-vaccine. If evolution is continuing as it should, each generation should have an improved immune response to the vaccine until convergence is reached.
Conclusion
GA’s have a broad reach in all fields of research, from fashion to immunology. Their success is due to three critical components underlying their programming: they are simple to write, easy to customize, and efficient to run. This flexibility and independence are what will allow programs like GA’s to become commonplace in research, across all disciplines. In particular, as biology research continues to merge with computer science and advanced modeling techniques, applications like GA’s have the potential to solve problems and raise questions about our world that we may have never imagined before.
References:
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Pharmacogenomics in Personalized Medicine: How Medicine Can Be Tailored To Your Genes
By: Anushka Gupta, Genetics and Genomics, ‘20
Author’s Note: Modern medicine relies on technologies that have barely changed over the past 50 years, despite all of the research that has been conducted on new drugs and therapies. Although medications save millions of lives every year, any one of these might not work for one person even if it works for someone else. With this paper, I hope to shed light on this new rising field and the lasting effects it can have on the human population.
Future of Modern Medicine
Take the following scenario: You’re experiencing a persistent cough, a loss of appetite, and unexplained weight loss to only then find an egg-like swelling under your arm. Today, a doctor would determine your diagnosis by taking a biopsy of your arm and analyzing the cells using the microscope, a 400-year-old technology. You have non-Hodgkins lymphoma. Today’s treatment plan for this condition is a generic one-size-fits-all chemotherapy with some combination of alkylating agents, anti-metabolites, and corticosteroids (just to name a few) that would be injected intravenously to target fast-dividing cells that can harm both cancer cells and healthy cells [1]. This approach may be effective, but if it doesn’t work, your doctor tells you not to despair – there are some other possible drug combinations that might be able to save you.
Flash forward to the future. Your doctor will now instead scan your arm with a DNA array, a computer chip-like device that can register the activity patterns of thousands of different genes in your cells. It will then tell you that your case of lymphoma is actually one of six distinguishable types of T-cell cancer, each of which is known to respond best to different drugs. Your doctor will then use a SNP chip to flag medicines that won’t work in your case since your liver enzymes break them down too fast.
Tailoring Treatment to the Individual
The latter case is one that we all wish to encounter if we were in this scenario. Luckily, this may be the case one day with the implementation of pharmacogenomics in personalized medicine. This new field takes advantage of the fact that new medications typically require extensive trials and testing to ensure its safety, thus holding the potential as a new solution to bypass the traditional testing process of pharmaceuticals.
Even though only the average response is reported, if the drug is shown to have adverse side effects to any fraction of the population, the drug is immediately rejected. “Many drugs fail in clinical trials because they turn out to be toxic to just 1% or 2% of the population,” says Mark Levin, CEO of Millennium Pharmaceuticals [2]. With genotyping, drug companies will be able to identify specific gene variants underlying severe side effects, allowing the occasional toxic reports to be accepted, as gene tests will determine who should and shouldn’t get them. Such pharmacogenomic advances will more than double the FDA approval rate of drugs that can reach the clinic. In the past, fast-tracking was only reserved for medications that were to treat untreatable illnesses. However, pharmacogenomics allows for medications to undergo an expedited process, regardless of the severity of the disease. There would be fewer guidelines to follow because the entire population would not need to produce a desirable outcome. As long as the cause of the adverse reaction can be attributed to a specific genetic variant, the drug will be approved by the FDA [3].
Certain treatments already exist using this current model, such as for those who are afflicted with a certain genetic variant of cystic fibrosis. Additionally, this will contribute to reducing the number of yearly cases of adverse drug reactions. As with any field, pharmacogenomics is still a rising field and is not without its challenges, but new research is still being conducted to test its viability.
With pharmacogenomic informed personalized medicine, individualized treatment can be designed according to one’s genomic profile to predict the clinical outcome of different treatments in different patients [4]. Normally, drugs would be tested on a large population, where the average response would be reported. While this method of medicine relies on the law of averages, personalized medicine, on the other hand, recognizes that no two patients are alike [5].
Genetic Variants
By doubling the approval rate, there will be a larger variety of drugs available to patients with unique circumstances where the generic treatment fails. In pharmacogenomics, genomic information is used to study individual responses to drugs. Experiments can be designed to determine the correlation between particular gene variants with exact drug responses. Specifically, modern approaches, including multigene analysis or whole-genome single nucleotide polymorphism (SNP) profiles, will assist in clinical trials for drug discovery and development [5]. SNPs are especially useful as they are genetically unique to each individual and are responsible for many variable characteristics, such as appearance and personality. A strong grasp of SNPs is fundamental to understand why an individual may have a specific reaction to a drug. Furthermore, SNPs can also be applied so that these genetic markers can be mapped to certain drug responses.
Research regarding specific genetic variants and their association with a varying drug response will be fundamental in prescribing a drug to a patient. The design and implementation of personalized medical therapy will not only improve the outcome of treatments but also reduce the risk of toxicity and other adverse effects. A better understanding of individual variations and their effect on drug response, metabolism excretion, and toxicity has the potential to replace the trial-and-error approach of treatment. Evidence of the clinical utility of pharmacogenetic testing is only available for a few medications, and the Food and Drug Administration (FDA) labels only require pharmacogenetics testing for a small number of drugs [6].
Cystic Fibrosis: Case Study
While this concept may seem far-fetched, a few select treatments have been approved by the FDA for certain populations, as this field of study promotes the development of targeted therapies. For example, the drug Ivacaftor was approved for patients with cystic fibrosis (CF), a genetic disease that causes persistent lung infections and limits the ability to breathe. Those diagnosed with CF have a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene, rendering the resulting CFTR protein defective. This protein is responsible for moving chloride to the cell surface, attracting water that will then generate mucus. However, those with the mutation have thick and sticky mucus, leaving the patient susceptible to germs and other infections as the bacteria that would normally be cleared [7]. Ivacaftor is only approved for CF patients who bear the specific G551D genetic variant, a specific mutation in the CFTR gene. This drug can then target the CFTR protein, increase its activity, and consequently improve lung function [8]. It’s important to note that the G551D is only just one genetic variant out of 1,700 currently known mutations that can cause CF.
Adverse Drug Reactions
Pharmacogenomics also addresses the unknown adverse effects of drugs, especially for medications that are taken too often or too long. These adverse drug reactions (ADRs) are estimated to cost $136 billion annually. Additionally, within the United States itself, serious side effects from pharmaceutical drugs occur in 2 million people each year and may cause as many as 100,000 deaths, making it the fourth most common cause of death according to the FDA [9].
The mysterious and unpredictable side effects of various drugs have been chalked up to individual variation encoded in the genome and not drug dosage. Genetics also determines hypersensitivity reactions in patients who may be allergic to certain drugs. In these cases, the body will initiate a rapid and aggressive immune response that can hinder breathing and may even lead to a cardiovascular collapse [5]. This is just one of the countless cases where unknown patient hypersensitivity to drugs can lead to extreme outcomes. However, some new research in pharmacogenomics has shown that 80% of the variability in drugs can be reduced. The implications of this new research could mean that a significant amount of these ADRs could be significantly decreased inpatient management, leading to better outcomes [11].
Challenges
Pharmacogenomic informed medicine may suggest the ultimate demise of the traditional model of drug development, but the concept of targeted therapy is still in its early stages. One reason that this may be the case is due to the fact that most pharmacogenetic traits involve more than one gene, making it even more difficult to understand or even predict the different variations of a complex phenotype like a drug response. Through genome-wide approaches, there is evidence of drugs having multiple targets and numerous off-target results [4].
Even though this is a promising field, there are challenges that must be overcome. There is a large gap between integrating the primary care workforce with genomic information for various diseases and conditions as many healthcare workers are not prepared to integrate genomics into their daily practice. Medical school curriculums would need to be updated in order to implement information and knowledge regarding pharmacogenomics incorporated personalized medicine. This would also create a barrier in presenting this new research to broader audiences including medical personnel due to the complexity of the field and its inherently interdisciplinary nature [12].
Conclusion
The field has made important strides over the past decade, but clinical trials are still needed to not only identify the various links between genes and treatment outcome, but also to clarify the meaning of these associations and translate them into prescribing guidelines [4]. Despite its potential, there are not many examples where pharmacogenomics impacts clinical utility, especially since many genetic variants have not been studied yet. Nonetheless, progress in the field gives us a glimpse of a time where pharmacogenomics and personalized medicine will be a part of regular patient care.
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CRISPR Conundrum: Pursuing Consensus on Human Germline Editing
By Daniel Erenstein, Neurobiology, Physiology, and Behavior, ‘21
Author’s Note: In November 2018, a scientist in China became the first person to claim that they had edited the genes of human embryos carried to term. Two twins, named with the pseudonyms Lulu and Nana, were born from these very controversial experiments. This news rapidly propelled the debate on human germline genome editing into the mainstream. My interest in this issue was inspired by my involvement with the Innovative Genomics Institute, located in Berkeley, CA. While attending Berkeley City College during the spring and fall semesters of 2019, I participated in the institute’s CRISPR journal club for undergraduates. Each week, we discussed the latest research from the field of CRISPR gene editing. I also took part in a conference, attended by leading geneticists, bioethicists, philosophers, professors of law and policy, science journalists, and other stakeholders, examining where the consensus, if any, lies on human germline genome editing. Discussions from this conference serve as a foundation for this submission to The Aggie Transcript.
New details have emerged in the ongoing controversy that kicked off in November of 2018 when a Chinese biophysicist claimed that, during in vitro fertilization, he had genetically edited two embryos that were later implanted into their mother. Twins, anonymously named Lulu and Nana, are believed to have been born as a result of these experiments. This announcement from He Jiankui in a presentation at the Second International Summit on Human Germline Editing in Hong Kong was largely met with swift condemnation from scientists and bioethicists [1, 2].
Late last year, excerpts of the unpublished research report were made public for the first time since He’s announcement, shedding light on his approach to edit resistance to human immunodeficiency virus, or HIV, into human genomes using CRISPR-Cas9 [3]. CRISPR, short for clustered regularly interspaced short palindromic repeats, are specific patterns in bacterial DNA. Normally, a bacterium that has survived an attack by a bacteriophage—a virus that infects bacteria and depends on them in order to reproduce—will catalog bacteriophage DNA by incorporating these viral sequences into their own DNA library. This genetic archive of viral DNA, stored between the palindromic repeats of CRISPR, can be revisited as a reference when the bacterium faces future attacks, aiding in its immune response [4].
To respond effectively, bacteria will transcribe a complementary CRISPR RNA molecule from the existing CRISPR sequence. Using crRNA—short for CRISPR RNA—as a guide, CRISPR-associated proteins play the part of a search engine, scanning the cell for any entering viral DNA that matches the crRNA sequence [5]. There are many subtypes of CRISPR-associated proteins [6], but Cas9 is one such type that acts as an enzyme by catalyzing double-stranded breaks in sequences complementary to the guide [7]. This immune system effectively defends against the DNA of invading bacteriophages, protecting the bacterium from succumbing to the virus [5].
A cell’s built-in mechanisms typically repair any double-stranded breaks in DNA via one of two processes: nonhomologous end-joining (NHEJ) or homology-directed repair (HDR) [8]. During NHEJ, base pairs might be unintentionally inserted or deleted, causing frameshift mutations called indels in the repaired DNA sequence. These mutations significantly affect the structure and function of any protein encoded by the sequence and can result in a completely nonfunctional gene product. NHEJ is frequently relied upon by gene editing researchers to “knock out” or inactivate certain genes. HDR is less efficient, but the process is often exploited by scientists to “knock in” genes or substitute DNA [9].
CRISPR is programmable, meaning that certain DNA sequences can be easily added to these sites, precisely altering the cell’s genetic code at specific locations. Jiankui He was not the first to use CRISPR to edit the genes of human embryos, but no one was known to have ever performed these experiments on viable embryos intended for a pregnancy. He and two of his colleagues have since been fined and sentenced to prison for falsifying ethical review documents and misinforming doctors, the state-run Chinese news agency Xinhua reported in December 2019 [10]. But He’s experiments supposedly yielded another birth during the second half of 2019 [11], confirmed by China in January [12], and Russian scientist Denis Rebrikov has since expressed strong interest in moving forward with human germline genome editing to explore a potential cure for deafness [13].
Despite what seems like overwhelming opposition to human germline genome editing, He’s work has even generated interest from self-described biohackers like Josiah Zayner, CEO of The ODIN, a company which produces do-it-yourself genetic engineering kits for use at home and in the classroom.
“As long as the children He Jiankui engineered haven’t been harmed by the experiment, he is just a scientist who forged some documents to convince medical doctors to implant gene-edited embryos,” said Zayner in a STAT opinion reacting to news of He’s sentence [14]. “The 4-minute mile of human genetic engineering has been broken. It will happen again.”
Concerns abound, though, about the use of this technology to cure human diseases. And against the chilling backdrop of a global COVID-19 pandemic, fears run especially high about bad actors using CRISPR gene editing with malicious intent.
“A scientist or biohacker with basic lab know-how could conceivably buy DNA sequences and, using CRISPR, edit them to make an even more panic-inducing bacteria or virus,” said Neal Bear, a television producer and global health lecturer at Harvard Medical School, in a recent STAT opinion [15]. “What’s to stop a rogue scientist from using CRISPR to conjure up an even deadlier version of Ebola or a more transmissible SARS?”
Into the unknown: understanding off-target effects
In his initial presentation, He said that he had targeted the C-C chemokine receptor type 5 (CCR5) gene, which codes for a receptor on white blood cells recognized by HIV during infection. His presentation suggested that gene editing introduced a known mutation named CCR5Δ32 that changes the receptor enough to at least partially inhibit recognition by HIV. The babies’ father was a carrier of HIV, so this editing was performed to supposedly protect the twins from future HIV infection [16].
He’s edits to the CCR5 gene—and human germline genome editing, in general—worry geneticists because the off-target effects of introducing artificial changes into the human gene pool are largely unknown. In a video posted on his lab’s YouTube channel [17], He claimed that follow-up sequencing of the twins’ genomes confirmed that “no gene was changed except the one to prevent HIV infection.”
Excerpts from the unpublished study indicate otherwise, according to an expert asked to comment on He’s research in MIT Technology Review, because any cells taken from the twins to run these sequencing tests were no longer part of the developing embryos [3].
“It is technically impossible to determine whether an edited embryo ‘did not show any off-target mutations’ without destroying that embryo by inspecting every one of its cells,” said Fyodor Urnov, professor of molecular and cell biology at UC Berkeley and gene-editing specialist [3]. “This is a key problem for the entirety of the embryo-editing field, one that the authors sweep under the rug here.”
Urnov’s comments raise concerns about “mosaicism” in the cells of Lulu and Nana—and any other future babies brought to term after germline genome editing during embryonic stages of development. In his experiments, He used preimplantation genetic diagnosis to verify gene editing. Even if the cells tested through this technique showed the intended mutation, though, there is a significant risk that the remaining cells in the embryo were left unedited or that unknown mutations with unforeseeable consequences were introduced [16].
While the CCR5Δ32 mutation has, indeed, been found to be associated with HIV resistance [18, 19], even individuals with both copies of CCR5Δ32 can still be infected with certain strains of HIV [20]. In addition, the CCR5Δ32 mutation is found almost exclusively in certain European populations and in very low frequencies elsewhere, including China [21, 22], amplifying the uncertain risk of introducing this particular mutation into Chinese individuals and the broader Chinese gene pool [16].
Perhaps most shocking to the scientific community is the revelation that He’s experiment did not actually edit the CCR5 gene as intended. In He’s November 2018 presentation, he discussed the rates of mutation via non-homologous end-joining but made no mention of the other repair mechanism, homology-directed repair, which would be used to “knock in” the intended mutation. This “[suggests] that He had no intention of generating the CCR5Δ32 allele,” wrote Haoyi Wang and Hui Yang in a PLoS Biology paper on He’s experiments [16].
Gauging the necessity of germline genome editing
The potential of CRISPR to revolutionize how we treat diseases like cystic fibrosis, sickle cell disease, and muscular dystrophy is frequently discussed in the news; just recently, clinical trials involving a gene-editing treatment for Leber congenital amaurosis, a rare genetic eye disorder, stirred enthusiasm, becoming the first treatment to directly edit DNA while it’s still in the body [23]. While this treatment edits somatic cells—cells that are not passed onto future generations during reproduction—there is increasing demand for the use of germline genome editing as well, even despite the reservations of scientists and bioethicists.
This begs the question: how will society decide what types of genetic modifications are needed? In the case of He’s experiments, most agree that germline genome editing was an unnecessary strategy to protect against HIV. Assisted reproductive technology (ART), a technique that features washing the father’s sperm of excess seminal fluids before in vitro fertilization (IVF), was used in He’s experiments [3] and has already been established as an effective defense against HIV transmission [24]. Appropriately handling gametes—another word for sperm and egg cells—during IVF is an additional method used to protect the embryo from viral transmission, according to Jeanne O’Brien, a reproductive endocrinologist at the Shady Grove Fertility Center [3].
“As for considering future immunity to HIV infection, simply avoiding potential risk of HIV exposure suffices for most people,” wrote Wang and Yang in their PLoS Biology paper [16]. “Therefore, editing early embryos does not provide benefits for the babies, while posing potentially serious risks on multiple fronts.”
One such unintended risk of He’s experiments might be increased susceptibility to West Nile virus, an infection thought to be prevented by unmutated copies of the CCR5 receptor [11].
In a paper that examines the societal and ethical impacts of human germline genome editing, published last year in The CRISPR Journal [25], authors Jodi Halpern, Sharon O’Hara, Kevin Doxzen, Lea Witkowsky, and Aleksa Owen add that “this mutation may increase vulnerability to other infections such as influenza, creating an undue burden on these offspring, [so] we would opt instead for safer ways to prevent HIV infection.”
The authors go on to propose the implementation of a Human Rights Impact Assessment. This assessment would evaluate germline editing treatments or policies using questions that weigh the benefits of an intervention against its possible risks or its potential to generate discrimination. The ultimate goal of such an assessment would be to “establish robust regulatory frameworks necessary for the global protection of human rights” [25].
Most acknowledge that there are several questions to answer before human germline genome editing should proceed: Should we do it? Which applications of the technology are ethical? How can we govern human germline genome editing? Who has the privilege of making these decisions?
Evaluating consensus on germline genome editing
In late October of last year, scientists, bioethicists, policymakers, patient advocates, and religious leaders gathered with members of the public in Berkeley for a discussion centered around some of these unanswered questions. One of the pioneers of CRISPR gene editing technologies, Jennifer Doudna, is a professor of biochemistry and molecular biology at UC Berkeley, and the Innovative Genomics Institute, which houses Doudna’s lab, organized this CRISPR Consensus? conference in collaboration with the Initiative on Science, Technology, and Human Identity at Arizona State University and the Keystone Policy Center.
The goal of the conference was to generate conversation about where the consensus, if any, lies on human germline genome editing. One of the conference organizers, J. Benjamin Hurlbut, emphasized the role that bioethics—the study of ethical, social, and legal issues caused by biomedical technologies—should play in considerations of germline genome editing.
He’s “aim was apparently to race ahead of his scientific competitors but also to reshape and speed up, as he put it, the ethical debate. But speed is surely not what we need in this case,” said Hurlbut, associate professor of biology and society at Arizona State University, at the conference [26].
Central to the debate surrounding consensus is the issue of stakeholders in decision-making about germline genome editing. Experts seem to be divided in their definitions of a stakeholder, with varying opinions about the communities that should be included in governance. They do agree, however, that these discussions are paramount to ensure beneficence and justice, tenets of bioethical thought, for those involved.
An underlying reason for these concerns is that, should human germline genome editing become widely available in the future, the cost of these therapies might restrict access to certain privileged populations.
“I don’t think it’s far-fetched to say that there’s institutionalized racism that goes on around access to this technology, the democratization and self-governance of it,” said Keolu Fox, a UC San Diego scholar who studies the anthropology of natural selection from a genomics perspective. Fox focused his discussion on indigenous populations when addressing the issue of autonomy in governance of germline genome editing [26].
“If we don’t put indigenous people or vulnerable populations in the driver’s seat so that they can really think about the potential applications of this type of technology, self-governance, and how to create intellectual property that has a circular economy that goes back to their community,” Fox said, “that is continued colonialism in 2020.”
Indeed, marginalized communities have experienced the evil that genetics can be used to justify, and millions of lives have been lost throughout human history to ideologies emphasizing genetic purity like eugenics and Nazism.
“We know that history with genetics is wrought with a lot of wrongdoings and also good intentions that can go wrong, and so there’s a community distrust [of germline editing],” said Billie Liangolou, a UC San Francisco (UCSF) Benioff Children’s Hospital genetic counselor, during a panel on stakeholders that included Fox. Liangolou works with expecting mothers, guiding them through the challenges associated with difficult genetic diagnoses during pregnancy [26].
Others agree that the communities affected most by human germline genome editing should be at the forefront of decision-making about this emerging technology. Sharon Begley, a senior science writer at STAT News, told the conference audience that a mother with a genetic disease once asked her if she could “just change my little drop of the human gene pool so that my children don’t have this terrible thing that I have” [26].
This question, frequently echoed throughout society by other prospective parents, reflects the present-day interest in human germline genome editing technologies, interest that will likely continue to grow as further research on human embryos continues.
In an opinion published by STAT News, Ethan Weiss, a cardiologist and associate professor of medicine at UCSF, acknowledges the concerns of parents faced with these decisions [27]. His daughter, Ruthie, has oculocutaneous albinism, a rare genetic disorder characterized by mutations in the OCA2 gene, which is involved in producing melanin. Necessary for normally functioning vision, melanin is a pigment found in the eyes [28].
Weiss and his partner “believe that had we learned our unborn child had oculocutaneous albinism, Ruthie would not be here today. She would have been filtered out as an embryo or terminated,” he said.
But, in the end, Weiss offers up a cautionary message to readers, encouraging people to “think hard” about the potential effects of human germline genome editing.
“We know that Ruthie’s presence in this world makes it a better, kinder, more considerate, more patient, and more humane place,” Weiss said. “It is not hard, then, to see that these new technologies bring risk that the world will be less kind, less compassionate, and less patient when there are fewer children like Ruthie. And the kids who inevitably end up with oculocutaneous albinism or other rare diseases will be even less ‘normal’ than they are today.”
Weiss’ warning is underscored by disability rights scholars who say that treating genetic disorders with CRISPR or other germline editing technologies could lead to heightened focus on those who continue to live with these disabilities. In an interview with Katie Hasson of the Center for Genetics and Society, located in Berkeley, Jackie Leach Scully commented on the stigmatization that disabled people might face in a world where germline editing is regularly practiced [29].
“Since only a minority of disability is genetic, even if genome editing eventually becomes a safe and routine technology it won’t eradicate disability,” said Scully, professor of bioethics at the University of New South Wales in Australia. “The concern then would be about the social effects of [heritable genome editing] for people with non-genetic disabilities, and the context that such changes would create for them.”
Others worry about how to define the boundary between the prevention of genetic diseases and the enhancement of desirable traits—and what this means for the decisions a germline editing governing body would have to make about people’s value in society. Emily Beitiks, associate director of the Paul K. Longmore Institute on Disability at San Francisco State University, is among the community of experts who have raised such concerns [30].
“Knowing that these choices are being made in a deeply ableist culture,” said Beitiks in an article posted on the Center for Genetics and Society’s blog [30], “illustrates how hard it would be to draw lines about what genetic diseases ‘we’ agree to engineer out of the gene pool and which are allowed to stay.”
Religious leaders have also weighed in on the ethics of human germline genome editing. Father Joseph Tham, who has previously published work on what he calls “the secularization of bioethics,” presented his views on the role of religion in this debate about bioethics at the conference [26].
“Many people in the world belong to some kind of religious tradition, and I think it would be a shame if religion is not a part of this conversation,” said Tham, professor at Regina Apostolorum Pontifical University’s School of Bioethics.
Tham explained that the church already disapproves of IVF techniques, let alone human germline editing, “because in some way it deforms the whole sense of the human sexual act.”
Islamic perspectives on germline editing differ. In a paper published last year, Mohammed Ghaly, one of the conference panelists, discussed how the Islamic religious tradition informs perspectives on human genome editing in the Muslim world [31].
“The mainstream position among Muslim scholars is that before embryos are implanted in the uterus, they do not have the moral status of a human being,” said Ghaly, professor of Islam and biomedical ethics at Hamad Bin Khalifa University. “That is why the scholars find it unproblematic to use them for conducting research with the aim of producing beneficial knowledge.”
Where Muslim religious scholars draw the line, Ghaly says, is at the applications of human germline genome editing, not research about it. Issues regarding the safety and effectiveness of germline editing make its current use in viable human embryos largely untenable, according to the majority of religious scholars [31].
The unfolding, back-and-forth debate about who and how to design policies guiding human germline genome editing continues to rage on, but there is little doubt about consensus on one point. For a technology with effects as far-reaching as this one, time is of the essence.
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Applications of Machine Learning in Precision Medicine
By Aditi Goyal, Statistics, Genetics and Genomics, ‘22
Author’s Note: I wrote about this topic after being introduced to the idea through a speaker series. I think the applications of modern day computer science, genetics and statistics creates a fascinating crossroads between these academic fields, and the applications are simply astounding.
Next Generation Sequencing (NGS) has revolutionized the field of clinical genomics and diagnostic genetic tests. Now that sequencing technologies can be easily accessed and results can be obtained relatively quickly, several scientists and companies are relying on this technology to learn more about genetic variation. There is just one problem: magnitude. NGS and other genome sequencing methods generate data sets in the size of billions. As a result, simple pairwise comparisons of genetic data that have served scientists well in the past, cannot be applied in a meaningful manner to these data sets [1]. Consequently, in efforts to make sense of these data sets, artificial intelligence (AI), also known as deep learning or machine learning, has introduced itself to the biological sciences. Using AI, and its adaptive nature, scientists can design algorithms aimed to identify meaningful patterns within genomes and to highlight key variations. Ideally, with a large enough learning data set, and with a powerful enough computer, AI will be able to pick out significant genetic variations like markers for different types of cancer, multi-gene mutations that contribute to complex diseases like diabetes, and essentially provide geneticists with the information they need to eradicate these diseases, before they manifest in the patient.
The formal definition for AI is simply “the capability of a machine to imitate intelligent human behavior” [2]. But what exactly does that imply? The key feature of AI is simply that it is able to make decisions, much like a human would, based on previous knowledge and the results from past decisions. AI algorithms are designed to take in information, generate patterns from that information, and apply it to new data, about which we know very little about. Using its adaptive strategies, AI is able to “learn as it goes,” by fine-tuning its decision-making process with every new piece of data provided to it, eventually making it the ultimate decision-making tool. While this may sound highly futuristic, AI has been used for several years in applications throughout our daily lives from the self-driving cars being tested in the Silicon Valley, to the voice recognition program available on every smartphone today. Most chess fans will remember the iconic “Deep Blue vs Kasparov” match, where Carnegie Mellon students developed an IBM supercomputer using a basic AI algorithm designed to compete against the reigning chess champion of the world [3]. Back then, in 1997, this algorithm was revolutionary, as it was one of the major signs that AI was on par with human intelligence. [4]. Obviously, there is no question that AI has immense potential to be applied in the field of genomics.
Before we can begin to understand what AI can do, it is important to understand how AI works. Generally speaking, there are two ways AI algorithms are developed: supervised and unsupervised learning. The key difference between the two groups is that in supervised learning, the data sets we provide to AI to “learn” are data sets that we have already analyzed and understand. In other words, we already know what the output will be, before providing it to AI [5]. The goal, therefore, is for the AI algorithm to generate an output as close to our prior knowledge as possible. Eventually, by using larger and more complex data sets, the algorithm will have modified itself enough to the point where it does the job of the data scientist, but is capable of doing so on a much larger scale. Unsupervised learning, on the other hand, does not have a set output predefined. So, in a sense, the user is learning along with the algorithm. This technique is useful when we want to find patterns or define clusters within our data set without predefining what those patterns or clusters will be. For the purposes of genomic studies, scientists use unsupervised learning patterns to analyze their data sets. This is beneficial over supervised learning, since the gigantic data sets produced by omics studies are difficult to fully understand.
Some of the clearest applications of AI in biology are in cancer biology, especially for diagnosing cancer [6]. “AI has outperformed expert pathologists and dermatologists in diagnosing metastatic breast cancer, melanoma, and several eye diseases. AI also contributes to innovations in liquid biopsies and pharmacogenomics, which will revolutionize cancer screening and monitoring, and improve the prediction of adverse events and patient outcomes” [7]. By providing a data set of genomic or transcriptomic information, we can develop an AI program that is designed to identify key variations within the data. The problem lies, primarily, in providing the initial data set.
In the 21st century, an era of data hacks and privacy breaches, the general public is not keen to release their private information, especially when this information contains everything about their medical history. Because of this, “Research has suffered for lack of data scale, scope, and depth, including insufficient ethnic and gender diversity, datasets that lack environment and lifestyle data, and snapshots-in-time versus longitudinal data. Artificial intelligence is starved for data that reflects population diversity and real-world information” [8]. The ultimate goal of using AI is to identify markers and genetic patterns that can be used to treat or diagnose a genetic disease. However, until we have data that accurately represents the patient, this cannot be achieved. A study in 2016 showed that 80% of participants of Genome Wide Association Study (GWAS) were of European descent [9]. At first glance, the impacts of this may not be so clear. But when a disease such as sickle cell anemia is considered, the disparity becomes more relevant. Sickle cell anemia is a condition where red blood cells are not disk-shaped, as they are in most individuals, but rather in the shape of a sickle, which reduces their surface area, which in turn reduces their ability to carry oxygen around the body. This is a condition that disproportionately affects people of African descent, so it is not reasonable to expect to be able to find a genetic marker or cure for this disease when the data set does not accurately reflect this population.
Another key issue is privacy laws. While it is important to note that any genomic data released to a federal agency such as the NIH for research purposes will be de-identified, meaning that the patient will be made anonymous, studies have shown that people can be re-identified using their genomic data, the remaining identifiers still attached to their genome, and the availability of genealogical data and public records [10]. Additionally, once your data is obtained, policies like the Genetic Information Nondiscrimination Act do protect you in some ways, but these pieces of legislation are not all-encompassing, and still leave the window open for some forms of genetic discrimination, such as school admissions. The agencies conducting research have the infrastructure to store and protect patient data, but in the era of data leaks and security breaches, there are some serious concerns that need to be addressed.
Ultimately, AI in genomics could transform the world within a matter of days, allowing Modern biology, defined by the innovation of NGS technologies, has redefined what is possible. Every day, scientists all around the world generate data sets larger than ever before, making a system to understand them all the more necessary. AI could be the solution, but before any scientific revolution happens, it is vital that the legislation protecting citizens and their private medical information be updated to reflect the technology of the times. Our next challenge as a society in the 21st century is not developing the cure for cancer or discovering new secrets about the history of human evolution, but rather it is developing a system that will support and ensure the protection of all people involved in this groundbreaking journey for the decades to come.
References
- https://www.nature.com/articles/s41576-019-0122-6
- https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/
- https://en.chessbase.com/post/kasparov-on-the-future-of-artificial-intelligence
- http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.278.5274&rep=rep1&type=pdf#page=41
- https://www.nature.com/articles/s41746-019-0191-0
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6373233/
- https://www.genengnews.com/insights/looking-ahead-to-2030/
- https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5089703/
- https://www.genome.gov/about-genomics/policy-issues/Privacy
Stem Cells: Miracle Cure or Hoax? A Review of Present Application and Potential Uses of Stem Cells
By Vita Quintanilla, Genetics 23’
Author’s Note: My purpose in writing this piece is to educate the current safe applications of stem cell as misuse and damage due to the same is so prevalent in the US and abroad. While not detracting from the great advances being made in the field currently this piece is to take stock of the reality of this treatment.
Large segments of the American and world population living with medical conditions that cause significant loss of mobility and quality of life are searching for hope in Stem Cell therapy. The unfortunate reality is that many of these “therapies” are not only ineffective but potentially harmful and the clinics that distribute them are not always properly certified. While stem cell therapies are promising, run away hope for a miracle cure coupled with unethical advertising and untested procedures have caused patients in the United States and beyond to be harmed by a potentially life saving tool. Here we will examine the current state of stem cell investigation, treatment, US Regulation, prospects in the future of medicine, and information for consumers to consider in deciding to receive a stem cell treatment.
Stem cells are undifferentiated cells that are at the start of all cell lines. Embryonic stem cells come from the blastocyst, a small clump of cells that forms several days after conception, and are pluripotent, meaning that they can give rise to any cell type (except specific embryonic tissues not present out of utero). [1] While these are the most often referred to type of stem cells there are also multipotent stem cells that can only give rise to a specific kind of tissue and are present into adulthood. Somatic cells, or differentiated cells, can be reverted to a pluripotent state. Induced pluripotent stem cells (IPS) are a growing area of interest in the field as they carry with them the possibility of culturing tissues for transplant using the existing cells of a patient thus eliminating the possibility of rejection.[2]
IPS exemplify an unfortunate reality in the whole of stem cell research, that at present widespread stem cell therapies are not ready for the general public. While these cells have great potential, a major hurdle is the cost in both time and labor required to culture them in a safe and sterile environment. A single vial of research grade cells that will produce fewer than thirty colonies in five days under ideal circumstances can cost over 1,000 dollars. This does not include the cost of facilities, culture equipment, and labor making these therapies cost prohibitive as the resulting therapy can run as far as 10,000 dollars per treatment. [3&4] Furthermore, colonies of cells are far from fully developed tissues that could potentially be implanted. A patient in critical condition in need of a transplant likely cannot wait for the cells to grow into tissue in culture, even if they can afford it.
Difficulties in access however are not the greatest barrier to stem cell therapy, but rather the lack of widespread testing and approval for the treatment of the diverse conditions for which they are sometimes advertised. While these cells are promising for usage in widespread areas of medicine, at present they do not live up to the claims that many unscrupulous clinics make for them. US Stem Cell Clinic, with a sleek website, and moving testimonials, advertises the use of stem cells as a magical cure that make the old feel young again using stem cells to treat a host of orthopedic maladies. These claims are highly suspicious as the FDA website says, as of January 2019, that only stem cell therapies for blood disorders are approved. [5]
These cells have been proclaimed cure-alls and medical miracles by the mass media but the reality is that the research into the application of stem cells for diverse ailments in humans is not conclusive at the present moment. [5] The FDA only approves stem cell treatments for blood disorders using stem cells from umbilical cord blood or bone marrow, but many clinics are offering stem cell treatments for everything from vision problems to COPD. The FDA recently filed two complaints against US Stem Cell Clinic LLC in Florida and California Stem Cell Treatment Inc. for marketing stem cell products that do not have the proper approval and for having unsafe manufacturing conditions that compromised sterility and patient safety. Patients filed lawsuits against California based stem cell supplier Liveyon who sold umbilical cord stem cells contaminated with E. Coli that resulted in sepsis and several patient hospitalizations after the stem cells were used for unapproved treatments. [6] In a recent lawsuit Florida based US Stem Cell was ordered to cease and desist, destroy all stem cells in their possession and pay for twice annual facilities inspections after taking cells from fat and injecting them into the eyes of patients causing five women to be blinded. In a 2018 statement FDA Commissioner Scott Gottlieb, M.D. said “We support sound, scientific research and regulation of cell-based regenerative medicine, and the FDA has advanced a comprehensive policy framework to promote the approval of regenerative medicine products. But at the same time, the FDA will continue to take enforcement actions against clinics that abuse the trust of patients and endanger their health” [7] The FDA, has in the past been accused of slowing down progress with novel treatments, but in the case of stem cells it is apparent that their actions hold patient safety as first priority, protecting the public from doctors and companies that value monetization over public health.
Patients in the United States have been harmed by these clinics including adverse injection site reactions, migration of cells to the improper location, the failure of cells to work in the desired way, and even the growth of tumors. Clinics that operate these studies may even be operating criminally as the FDA has pressed charges against these clinics in the past in the form of permanent injunction, an order to cease and desist permanently. [7]
Patients are often motivated to take these risky treatments because there is no other hope for a cure, however, unapproved treatments can make the condition worse or even lead to death. The dangers of receiving unapproved therapies is illustrated in the case of a 38-year-old man, who developed a spinal tumor after a stem cell treatment in preformed in Portugal where doctors injected cells taken from his nose into his spine. The treatment was attempting to cure paralysis in his legs and arms. It had no effect on his paralysis, but twelve years later the tumor that formed further limited his mobility and quality of life as his bladder control and motor function in arms steadily declined. Complications have been even more dire as a thirteen-year-old male in Israel who was treated at a clinic in Moscow for Ataxia telangiectasia, which affects the nervous system, died of a tumor that arose from donor cells. These are not isolated instances of unsuccessful treatment in patients that were already ill, the stem cells themselves were directly the cause of degeneration in the patients, and more than 19 deaths confirmed by the National Institute of Health as of 2018. [8&9]
Predatory clinics that perform these unapproved procedures can be especially hard to identify. Many have sleek well-designed websites with official looking personnel and lofty claims of unrealistic success rates and propositions for stem cells as cures for many diverse and at times totally unrelated disorders. Many clinics are located in Florida and Southern California however there are hundreds of clinics across the United States. [10]*** Patients should be advised to do some research into these claims and check to see if the clinic in question as well as the treatment has FDA approval. A good strategy for determining the legitimacy of a clinic is to do research on the main doctors performing the procedure. If a clinic is claiming to be able to cure numerous unrelated and debilitating disorders, the doctors performing these procedures should be of high esteem in the community and have visible external measures to the importance of their work or the prestige of their practice. If this is not the case the patient should proceed with great caution.
The issue of deceptive stem cell clinics is not a mere issue of public health but an example of a greater problem, a break between scientific community and the public perpetuated by a few unscrupulous characters for the sake of profit. Stem cells have the potential to be life saving tools and usher in a whole new chapter of regenerative medicine, but if the reputation of this technology continues to be tarnished by clinics that do not abide by the laws and conventions put in place to keep consumers safe, this technology may never get an opportunity to reach its full potential.While stem cells have great potential for diverse treatments at some point in the future, at present their efficacy and safety for regenerative medicine has not been firmly established in the context of current technology. Not all stem cell treatments are to be feared, stem cell treatments for some blood disorders have been shown to be effective and safe. At some point in the future when culture and delivery techniques improve stem cells could revolutionize transplant and regenerative medicine. At present the best course of action for consumers in regard to these therapies is to partake only in treatments or clinical trials operating with the approval of the FDA, and keep up with developments in the field by reading peer reviewed papers published in reputable journals. Exercise great caution but do not lose hope for the future. Stay current with research and, considering the risks and benefits, consumers may choose to enroll in FDA supervised clinical trials that adhere to the three phase clinical trial process, but always be sure to exclusively receive treatment from FDA regulated and approved clinicians.
Sources
- Yu, Junying, and James Thomson. “Embryonic Stem Cells.”National Institutes of Health, U.S. Department of Health and Human Services, 2016, stemcells.nih.gov/info/Regenerative_Medicine/2006Chapter1.htm.
- “Home.” A Closer Look at Stem Cells, www.closerlookatstemcells.org/learn-about-stem-cells/types-of-stem-cells/.
- McCormack, Kevin. “Patients Beware: Warnings about Shady Clinics and Suspect Treatments.” The Stem Cellar, CRIM, 19 Jan. 2016, blog.cirm.ca.gov/2016/01/19/patients-beware-warnings-about-shady-clinics-and-suspect- treatments/.
- https://www.atcc.org/search?title=Human%20IPS%20(Pluripotent)#q=%40productline%3DL035&sort=relevancy&f:contentTypeFacetATCC=[Products]
- Office of the Commissioner. “Consumer Updates – FDA Warns About Stem Cell Therapies.” U S Food and DrugAdministration Home Page, Center for Drug Evaluation and Research, 16 Nov. 2016, www.fda.gov/ForConsumers/ConsumerUpdates/ucm286155.htm.
- William Wan, Laurie McGinley. “’Miraculous’ Stem Cell Therapy Has Sickened People in Five States.” The Washington Post, WP Company, 27 Feb. 2019, www.washingtonpost.com/national/health-science/miraculous-stem-cell-therapy-has-sickened-people-in-five-states/2019/02/26/c04b23a4-3539-11e9-854a-7a14d7fec96a_story.html.
- Commissioner, Office of the. “FDA Seeks Permanent Injunctions against Two Stem Cell Clinics.” U.S. Food and Drug Administration, FDA, 9 May 2018, www.fda.gov/news-events/press-announcements/fda-seeks-permanent-injunctions-against-two-stem-cell-clinics.
- Bauer, Gerhard, et al. “Concise Review: A Comprehensive Analysis of Reported Adverse Events in Patients Receiving Unproven Stem Cell-Based Interventions.” Stem Cells Translational Medicine, John Wiley & Sons, Inc., Sept. 2018, www.ncbi.nlm.nih.gov/pmc/articles/PMC6127222/#!po=19.4444.
- Flaherty, Brittany, et al. “Case Highlights the Risks of Experimental Stem Cell Therapy.” STAT, Staten News, 11 July 2019, www.statnews.com/2019/07/11/canada-case-long-term-risks-experimental-stem-cell-therapy/.
- https://usstemcellclinic.com/ [10]
- Commissioner, Office of the. “Step 3: Clinical Research.” U.S. Food and Drug Administration, FDA , 4 Jan. 2018, www.fda.gov/patients/drug-development-process/step-3-clinical-research.
- Hiltznik, Micheal. “Column: Judge Throws the Book at a Clinic Offering Unproven Stem Cell ‘Treatments’.” Los Angeles Times, Los Angeles Times, 26 June 2019, www.latimes.com/business/hiltzik/la-fi-hiltzik-stem-cell-injunction-20190626-story.html.
You might have to use more than a microscope, there’s more to genetics than what meets the eye: An interview with Dr. Gerald Quon
By Tannavee Kumar, Genetics & Genomics 20’
Author’s Note: As an undergraduate studying genetics and genomics and computer science, I wanted to interview a former professor to find out the steps he took in order to do computational research in the biological sciences. I was interested in finding out more about the growing field of computational biology and wanted to help shed light on a field to students that may be similarly interested.
Background
You received a Bachelors in Math, then a masters in Biochemistry, then a PhD in Computer Science; did you always know that you wanted to do research in biology? If so, what made you want to start off with a technical education rather than something traditional like biology or chemistry? If not, how did you come to discover applications of computer science in Biology even 15 years ago?
No, I did not start off wanting to do anything related to biology. I started my undergrad thinking I would make computer games. How I kind of got into this was a week before my undergrad, I got an email from my university asking if I wanted to be a part of the first cohort of a bioinformatics program. I initially declined.
As I was looking for my third internship in my co-op program, I had a friend who found a job for a professor in Toronto, and my friend asked if I wanted to work on this cool project about predicting how proteins fold into these 3D structures. I told him I don’t know anything about protein structures, but sure! It was a lot of fun.
Is that what inspired you to pursue Biochemistry for further education?
I think that first internship was very pivotal, because it really nurtured my interest in protein structures. When I finished my undergrad, I was kind of bored of computer science, which is why I thought I would do a PhD studying protein structures.
How do you see life sciences research evolving and progressing in the coming years, given the inclusion of this new field?
In my opinion, we will see more and more blurring of boundaries. In 25 years, there is going to be more undergraduate programs less defined by walls like “life sciences,” “chemistry,” and better recognition that everybody borrows knowledge and skills from many fields. It will be very difficult to do a life sciences degree without learning anything about math or statistics. Similarly, more people in traditional quantitative disciplines will want to take those classes in the life sciences. Essentially there will be fewer walls.
How would undergraduates studying quantitative subjects, like mathematics, statistics, or computer science be made aware of the growing demand for such skills in the Life Sciences?
The classic way to become introduced to such areas would be through coursework, internship with a company, or research with a professor. The last two ways are not very optimal. At the end of the day, in a standard undergraduate program, you have summers, and if you are ambitious you can try to do research during the school year. However, you can only do so many different kinds of internships before you graduate. If you did one every summer of college, and even two before hand, even that would not be sufficient for getting a nice, representative sample for all the things you can work on.
That is where universities need to do a better job of creating opportunities for students to engage with people from industry and research so that they don’t need 4-5 months to figure many essential things out.
Research and Beyond
Can you briefly describe some of the research that your lab does?
We are currently working towards a few different directions. A large project at the moment is studying the genetics of mental disorders and neurodegeneration, for example we look at the genetics of Alzheimer’s disease, schizophrenia, autism, etc. Our main goal is to mechanistically understand how genetic variants associated with these mental conditions modify disease risk. Much of those mechanistic studies currently look at events that happen at the molecular level. This is great and very useful; however, since the majority of the research is geared at the molecular level we don’t have a good understanding of what variants do functionally at the level of the cell. How does it affect the functional properties of the cell, such as neuron electrophysiology? Or, how is the organization of the tissue affected?
Other areas we work on are on building better models to understand how cells are spatially organized in the brain, as well as building models that quantitatively describe cell population behavior. We know that cells behave differently when put into different contexts. It’s of interest to build a model to predict what happens when you put together different kinds of cells in different combinations, orientations, or conditions.
Lastly, a third project being worked on is on the therapeutic end. We are essentially trying to identify the druggable region of the genome. There are a lot of computational problems in trying to determine what is druggable.
How do you think the integration of the computational sciences has shed light on how biological processes are interconnected, and what do they make clear that a molecular approach may not be able to?
In human genetics, computational models play a huge role in hypothesis generation. They do a good job leveraging big data, such as genomics, to prioritize which variants should be tested using molecular approaches, for example, when molecular approaches are costly or too slow to systematically test many variants across a genome. The role of computation is parsing through the many possibilities that you can’t explore molecularly.
For example, a study we worked on four years ago was to try and find a causal variant for obesity. Most human genetic studies only point to a region of the genome where causal variants might hide, but don’t tell you exactly which one is the true causal one. When these regions are big, like hundreds of kilobases long, you need computational tools to identify the precise causal one to test experimentally. In that study, computational tools played the pivotal role of identifying the causal variant that was ultimately tested and shown to drive large changes in obesity risk.
How does computational research like your own lead to the progression of curated care in the health industry?
At a superficial level, in some ways it accelerates some of the biomedical discoveries that are being done today. The obesity study is one example. If you didn’t have the computational resources, you would spend years and years trying to find the right variant. However, we found it relatively quickly with computation.
For healthcare specifically, fields such as machine learning are revolutionizing care today. People from statistics, computer science, and math are working directly with clinicians and hospitals to develop highly accurate ‘digital pathology’ software, they help predict when patients will need to come into the hospital or whether they are high risk for a disease.
Oftentimes, conditions and diseases are misdiagnosed, which leads to inappropriate treatments. How would research in this area begin to remedy this common problem in the healthcare industry?
Most diseases are heterogeneous, which means that a group of people who are diagnosed with the same condition might actually have different underlying conditions, and need different treatments. Many computational approaches based on molecular and clinical data are being developed to identify more homogeneous groups of patients, to help achieve precision medicine. This allows for the most accurate prescription of medication and treatments. This is because these homogenous groups help identify the underlying disease phenotype which means access to better directed medication.
During your time as a PhD student, you also “explored the application of models built from deconvolving gene expression profiles, for personalized medicine.” Can you go more in depth to how these models were built and how it can advance our ability to provide a more accurate prognosis to patients?
During my PhD, we were trying to predict the prognosis of early stage lung cancer patients. If you are diagnosed with early stage lung cancer say stage 1B, clinicians have to make important decisions, such as how much e.g. chemotherapy to give you. If they give you too much, it will get rid of the primary tumor, but you will increase your risk of recurrence. But if you don’t give enough, you don’t get rid of the primary tumor.
Back then, fourteen year ago or so, genome expression profiling was just becoming popular. People were thinking maybe we can predict whether these stage 1B patients were going to be at high risk of recurrence or not. Our motivation for that problem was essentially to build a computational model to predict based on molecular signatures if they should be given extra therapy or not. That in itself is a hard problem. Additionally, before single cell sequencing was available, it was hard to take a sample of a tumor and only sequence the tumor cells. Often times you would have contamination of normal cells that would mess up the signatures you would get. We had to develop a computational method to extract out only the signatures due to tumor cells, and show that once you do that it is much easier to predict prognosis.
Where do you think research will be in the next 10-20 years?
We are going to see a lot more connections across more previously isolated fields. For example, with respect to human psychiatric genetics, a lot of focus right now is at the molecular impact of genetic variants, but in the near future I’d expect there to be much closer integration with clinicians to also study the impact on behavior, and with the experimental biologists to study the impact on brain development and organization.
***Special thanks to Dr. Gerald Quon for this interview
Reading into the Future: Development of Long-read DNA Sequencing
By Aditi Goyal, Genetics and Genomics, ‘22
At this moment, the next revolution in the field of biology is currently underway: third-generation sequencing, or Long-Read sequencing. Instead of relying on cluster-based short read technology (1), third-generation sequencing builds a DNA sequence on a nucleotide basis, therefore eliminating the extensive process of read alignment.
Until now, scientists across the world have been heavily relying on Next Generation Sequencing (NGS) for getting DNA sequences. This technology creates clusters of short DNA sequences, which range anywhere from 50 to 150 base pairs in length, by using fluorescent nucleotides (2). It is often referred to as sequencing by synthesis because a DNA sequence is created by tracking which nucleotides are being used to build the parallel strand. NGS has served the scientific community well, providing extremely high coverage and high accuracy reads, as well as slashing the cost and time to sequence an entire genome (2). However, the drawbacks are just as serious. While NGS is a fantastic candidate for bacterial or archaeal genomes, it fails to capture the complexity of eukaryotic genomes. About half of the human genome is comprised of repeated sequences (2). Currently, the function of these repeated regions remains unclear, partially due to the fact that it is not possible to get an accurate DNA sequence of these areas using short-read sequences. With a maximum read size of 150 base pairs for NGS, there are too many potential matches for a read that small for scientists to accurately assign that read to a region in the genome. Another major problem is the quality of each read. While the technology itself is very accurate, there are several sources of error that quickly cause the quality of each read to deteriorate, such as biases during the PCR of mixtures, polymerase errors, base misincorporation, cluster amplification errors, sequencing cycle errors, and incorrect image analysis (3). All these errors result in about 1% of bases being read incorrectly, which, when applied to a 3 billion base pair genome, can be incredibly damaging.
This is why long-read sequencing is such a breakthrough. By analyzing a DNA sequence from nucleotide to nucleotide, scientists can build considerably longer reads with a much higher confidence level as compared to NGS. Ideally, with this technology, scientists will be able to produce de novo whole genome sequences for patients with genetic disorders, allowing them to understand the root of their disease at an unmatched resolution. This could pave the way to accurately diagnosing and curing complex genetic diseases. In the last few years alone, several papers have been published on the impacts of long-read sequencing investigating diseases such as Parkinson’s disease, fragile X syndrome, Alzheimers, and ALS (11). Other applications include improving our understanding of human genetic diversity. Recent studies show that the reference human genomes available today do not accurately represent humanity at a global level, but rather significantly overrepresent people of european descent (12). With the rise of long-read sequencing, it will be easier and cheaper to fully sequence a human genome, allowing us to expand the resources available and accurately reflect the human population.
There are currently several companies researching long-read sequencing, however, the most promising company appears to be Pacific Biosciences (Pac Bio) due to their development of single molecule real time sequencing (SMRT) (4, 5).
There are 2 key inventions that allow for the success of SMRT. The first is the fluorescent tagging.
Like with NGS, each nucleotide is modified to fluoresce a certain color, indicating which nucleotide it is, however with SMRT, the fluorescence is linked to the terminal phosphate of a nucleotide, instead of the base itself (8). Also similar to the NGS, the complementary strand continues to build. Now, when the DNA polymerase cleaves off the terminal phosphate, it releases the fluorescent group, which allows us to track which nucleotide was incorporated based on the color of the fluorescent.
The second innovation is the zero-mode waveguide (ZMW). The ZMW is a small nano chamber that contains the DNA sample during the sequencing process. It passes refracted light through so that the fluorescence of the nucleotides can be seen. This technology essentially acts as a microscope, allowing us to gain a powerful resolution of the DNA structure. Each ZMW can recognize over 10 base pairs per second with extreme accuracy. Additionally, given the ability for these ZMWs to be run in parallel, thousands of chambers can be sequenced at the same time, allowing for a fast cycle and long reads.
The advantages of SMRT are clear: it allows for long reads to be built. This means that scientists will have the ability to understand the overall complexity of large eukaryotic genomes. Another advantage is the speed and portability of the technology. Once it is completely developed, SMRT will be able to sequence an entire human genome in under 3 minutes for less than $100 in a device the size of a flash drive, a stark difference from today’s estimate (9).
Like any novel technology, there are some challenges that must be overcome before SMRT can be used commercially. The most pressing is concerns over accuracy. Individual reads can contain 11-14% errors on average, dragging the quality score of the read down. However, developers have noticed that these errors occur at random across the genome. By using a 10x coverage method, 9 out of 10 times, SMRT will provide the correct sequence for that point, which allows the accuracy to rise to approximately 99.99%.
Overall, SMRT is a revolutionary development that will soon change the way we understand biology. It will allow us to gain a holistic understanding of complex eukaryotic genome and will provide a higher resolution of the genome that we can use for further analysis.
References
- “Illumina Sequencing Technology.” Illumina, October 11, 2010. https://www.illumina.com/documents/products/techspotlights/techspotlight_sequencing.pdf.
- Treangen, Todd J, and Steven L Salzberg. “Repetitive DNA and next-Generation Sequencing: Computational Challenges and Solutions.” Nature reviews. Genetics. U.S. National Library of Medicine, November 29, 2011. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3324860/.
- Fox, Edward J, Kate S Reid-Bayliss, Mary J Emond, and Lawrence A Loeb. “Accuracy of Next Generation Sequencing Platforms.” Next generation, sequencing & applications. U.S. National Library of Medicine, 2014. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4331009/.
- Check Hayden, Erika. “Genome Sequencing: the Third Generation.” Nature News. Nature Publishing Group, February 6, 2009. https://www.nature.com/news/2009/090206/full/news.2009.86.html.
- Check Hayden, Erika. “Genome Sequencing: the Third Generation.” Nature News. Nature Publishing Group, February 6, 2009. https://www.nature.com/news/2009/090206/full/news.2009.86.html.
- Eid, John, Adrian Fehr, Jeremy Gray, Khai Luong, John Lyle, Geoff Otto, Paul Peluso, et al. “Real-Time DNA Sequencing from Single Polymerase Molecules.” Science. American Association for the Advancement of Science, January 2, 2009. https://science.sciencemag.org/content/323/5910/133.
- “Video: Introduction to SMRT Sequencing.” PacBio. Accessed November 7, 2019. https://www.pacb.com/videos/video-introduction-to-smrt-sequencing/.
- “Single Molecule Real Time Sequencing – Pacific Biosciences.” YouTube. YouTube. Accessed November 7, 2019. https://www.youtube.com/watch?v=v8p4ph2MAvI.
- Schadt, Eric E., Steve, Andrew, and Turner. “Window into Third-Generation Sequencing.” OUP Academic. Oxford University Press, September 21, 2010. https://academic.oup.com/hmg/article/19/R2/R227/641295.
- Roberts1, Richard J, Mauricio, and Michael C Schatz3. “The Advantages of SMRT Sequencing.” Genome Biology. BioMed Central, July 3, 2013. https://genomebiology.biomedcentral.com/articles/10.1186/gb-2013-14-7-405.
- Martin O Pollard, Deepti Gurdasani, Alexander J Mentzer, Tarryn Porter, Manjinder S Sandhu, Long reads: their purpose and place, Human Molecular Genetics, Volume 27, Issue R2, 01 August 2018, Pages R234–R241, https://doi.org/10.1093/hmg/ddy177
CRISPR/HDR Platform Allows for the Production of Monoclonal Antibodies with the Constant Region of Choice
By Sharon Yang, Cell Biology, ‘20
Author’s Note: I first came across an article talking about this new innovation on Science X. Having worked with hybridomas and antibodies through various internships, I was deeply intrigued by this discovery and secured an original paper to learn more about its potential applications. Because of the revolutionizing usage of antibodies in the medical field, it is vital to understand how this finding will facilitate antibody-based therapies in clinical research.
Introduction
Since the discovery of antibodies and their applications in therapeutics, many diseases once deemed incurable now have a treatment, if not a cure. Antibodies are proteins that recognize and bind to specific antigens (proteins that are considered “foreign” to the body). The immune system recognizes this antibody-antigen complex and removes the foreign substance from the body. Monoclonal antibodies (mAbs) are specific for one type of antigen and are produced using hybridomas, immortal cell lines that secrete only one type of antibody. The specificity of a mAb is determined by its antigen binding variable region. Though the variable region is of critical importance, the constant region (also known as the Fc region) is also essential to the therapeutic efficacy of mAbs. The Fc region has many different variants, called isotypes. Each isotype has its own unique function in making the immune system respond in different ways. After an antibody binds to an antigen by its variable region, the Fc region of the antibody elicits a response from the immune system, which serves as the basis for antibody-based therapeutics.
A recent study conducted in the summer of 2019 by Schoot and colleagues demonstrates how the use of genetic engineering on hybridomas can modify the Fc region of mAbs to that of a different species, isotype, or format. This new versatile platform grants ease of production of monoclonal antibodies that have different constant regions but retain the same variable regions.
The research team utilized a one-step clustered regularly interspaced short palindromic repeat (CRISPR)/homology-directed repair (HDR) technique to create a recombinant hybridoma that secretes a mAb in the Fc format of choice — a highly attractive alternative to the conventional recombinant production methods that were often time-consuming, challenging, and expensive.
As the team emphasizes, “[CRISPR/HDR] is a simple alternative approach requiring a single electroporation step to obtain an unlimited source of target antibody in the isotype format of choice” (1). Through using CRISPR/HDR, the team was able to seamlessly generate monovalent Fab’ fragments and a panel of different isotypes for the same monoclonal antibody.
CRISPR/Cas9 and Homology-Directed Repair
In their genetic engineering method, the researchers took advantage of an ancient bacterial immunity mechanism: the CRISPR/Cas9 system. When a bacteria is invaded by a virus, the bacteria stores snippets of viral DNA and creates segments of DNA called CRISPR arrays. When a virus with the same DNA segment attacks again, the bacteria creates RNA from the CRISPR arrays to target the virus; the RNA is called the guide RNA. The nuclease protein Cas9 is used to cut the DNA apart at a very specific site determined by the guide RNA, disabling the virus. CRISPR/Cas9 works in a similar fashion in the lab. Scientists create a guide RNA that binds to Cas9, which then targets a specific site on the DNA to be cut (2).
When CRISPR/Cas9 cuts DNA, it induces a double-strand break (DSB). Homology-directed repair (HDR) occurs when the intact donor strand contains high sequence homology to the damaged DNA strand. Through HDR, scientists can integrate a sequence or gene of their liking into the genome, which Schoot and colleagues perform in their study (3).
The Generation of Fab’ Fragments
The fragment antigen-binding (Fab’) is a region on the antibody that binds to the antigen. It consists of a single heavy chain and light chain. To create a Fab’ fragment-secreting hybridoma using CRISPR/HDR, the team selected NLDC-145, a hybridoma clone that secretes mAbs of rat IgG2a (rIgG2a) isotype. The antigen of rIgG2a is DEC205, an endocytic receptor found on immune cells. The team electroporated NLDC-145 cells with Cas9 and an appropriate guide RNA to induce double-strand breaks at the hinge region; to repair the double-strand break, they designed an HDR Fab’ donor construct for homology-directed repair. The HDR Fab’ donor construct also inserts specific tags onto the protein, allowing for easy purification of the Fab’ fragment.
To test secretion of the Fab’ fragment, they stained JAWSII, a DEC205-expressing cell line, with the supernatants of NLDC-145 clones that had undergone CRISPR/HDR. Flow cytometry assays showed that a large portion of Fab’-secreting hybridomas were successfully created. Further assays showed that the secreted Fab’ fragments retained their binding capabilities. It is worth noting that the researchers also used the same strategy to convert other hybridoma lines to become recombinant, Fab’-producing lines, with similar success; this demonstrates that this engineering technique is flexible and not just limited to one cell line (1).
The Generation of Isotype Panels
In a similar manner to creating monoclonal Fab’-generating hybridomas, the team also used the one-step CRISPR/HDR technique to create hybridomas capable of producing a wide array of isotype variants for the same mAb. This time, the cell line subject was hybridoma MIH5, which secretes monoclonal rIgG2a that targets mouse PD-L1, an immune checkpoint protein. The goal was to make clones of MIH5 to each produce one isotype of the chimeric (having both rat and mouse-related parts) monoclonal antibodies: mIgG1, mIgG2a, mIgG2b, mIgG3, mIgA, and a mutant form of mIgG2a (mIgG2asilent).
MIH5 cells were cotransfected (introduced with DNA) with a Cas9 vector containing the appropriate guide RNA and a construct from a panel of isotype HDR donor constructs (each isotype had its own unique HDR donor construct). Following knock-in integration, flow cytometry analysis showed that the engineered chimeric mAbs were successfully secreted. Thus, the creation of recombinant hybridomas for a panel of isotypes was successfully engineered (1). This invention allows for the creation of monoclonal antibodies with different Fc regions, providing researchers an easy way to “customize” their antibodies to elicit a specific response from the immune system. Researchers may choose which isotype variant they want on their antibody, which is fully dependent on their target (antigen) of interest and how the immune system behaves towards it. This has vast potential in antibody-based therapeutics, in that this system can be used for the optimization of potential drugs to become more potent and dynamic.
Biochemical Applications
To test the functional capability of isotype-switched mAbs, Schoot and colleagues tested the antibodies’ capability to induce an important immune mechanism: antibody-dependent cellular cytotoxicity (ADCC). In order to test ADCC in vitro, mouse colon adenocarcinoma cells were labeled with chromium-51, and then taken in by MIH5 Fc isotype variants. After adding whole blood, they measured chromium-51 release. On the other hand, B cell depletion by MIH5 Fc variants was used to measure ADCC in in vivo experiments. Analyses of these studies show that chimeric mAbs created by CRISPR/HDR hybridomas have the same biochemical and immune effector characteristics as their recombinant and naturally occurring counterparts (1). Something to highlight is that instead of treading through the laborious process of producing recombinant antibodies in the conventional way (often consisting of multiple rounds of optimized sequencing, cloning, transfection), this one-step mechanism grants smooth and rapid generation of recombinant antibodies that perform their expected functions (1).
Conclusion
The ability to create monoclonal antibodies with the freedom to choose what goes on their constant regions possess many applications in the vast field of medicine and engineering. Being able to construct a very specific monoclonal antibody (the engineering element) that stimulates the immune system in a certain, beneficial way (the medical component) intertwines the two fields together to propel us closer towards treating diseases more efficiently and effectively. This system also represents an optimized version of recombinant engineering, which saves valuable time and funds that can be used towards conducting further studies. A simple, yet powerful and flexible approach, this versatile CRISPR/HDR platform aims to facilitate antibody engineering and research for the scientific community, and is accelerating the rate at which new clinical trials can be performed.
References
- Schoot, J. M. V. D. et al. Functional diversification of hybridoma produced antibodies by CRISPR/HDR genomic engineering. Science Advances 5, (2019).
- Ran, F Ann et al. “Genome engineering using the CRISPR-Cas9 system.” Nature protocols vol. 8,11 (2013): 2281-2308. doi:10.1038/nprot.2013.143
- Cortez, Chari. “CRISPR 101: Homology Directed Repair.” Addgene Blog, Addgene, 12 Mar. 2015, blog.addgene.org/crispr-101-homology-directed-repair.
The Effect of Trastuzumab on HER2-Signaling in Breast Cancers to Induce Cardiotoxicity
By Karissa Cruz, B.S. Biochemistry and Molecular Biology, Spring ‘19
Author’s Note: I wrote this piece as part of my UWP 104F assignments and ended up becoming really interested in what I wrote about. I specifically chose this topic because I think breast cancer is a smart, complex disease, and the treatment can change day-to-day. I wanted to shed light on a widely accepted breast cancer treatment that is now under review after discovering that it can cause cardiac dysfunction.