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Interview: John Davis
By Isabella Krzesniak.
INTRODUCTION
John Davis is a 5th year Ph.D. candidate in the Integrative Genetics and Genomics graduate group at UC Davis. He works in the Maloof Lab and uses bioinformatics to analyze genetic variation among native California wildflowers in the Streptanthus clade in different environments and uses data to create gene models.
The project he is working on has two main goals. First, he aims to create genomic resources for Streptanthus clade species through reference genomes and transcriptomes, which can be used to analyze differential gene expression in different individuals. Second, he aims to examine the germination niche of Streptanthus clade species, the conditions required for them to germinate and the gene networks expressed during this life stage.
These models have many applications concerning adaptation in the wake of climate change; for instance, they can help ecologists make informed decisions such as whether a crop will function well in a given region as the climate warms. Davis’ work is part of a collaborative study between the Maloof, Gremer, Strauss, and Schmitt labs in the Department of Plant Biology.
What does your research consist of and what are its potential applications?
We’re looking at how plant populations persist in different environments. So even though it’s wildflowers that are closely related, you can also look at how they differ in terms of survival in different environments. If you have an environment that’s great for one crop, but it’s either getting wetter or hotter, the crop might not survive very well. But if you know which genes it has or how it functions, you can move it to a different location or potentially just bring in a different crop that will function well in that region. From an ecological standpoint, it’s a matter of which species will survive and which ones will die off. Underlying all of it are what genes the plant has.
What work are you doing with the project in particular?
The main thing I’m doing right now is building genomic references. We’re trying to do gene expression studies, but if we don’t know what the genes are, we can’t compare the differences in gene expression. So, one of the things I’m doing is building these reference genomes and transcriptomes to determine which genes are in the species. And then from there, I hope to build gene models, construct coexpression networks, and predict germination based on gene expression profiles. To analyze the data, I use Python, Linux, Excel, and R. Another thing I’m doing is building transcriptomes which are collections of just the genes that are expressed. Then, ideally, my goal would be to develop gene networks that would basically tell us which species have these genes that are needed to survive in these environments and which ones don’t.
Why are you studying Streptanthus in particular and what exactly are you doing as part of the study?
The Streptanthus clade has a well-documented phylogeny of closely-related species. Adding genomic resources will improve our ability to perform genetic analyses.
After seed collection, what steps do you take to analyze your data?
We took our seeds and sent them to a collaborating company where they extracted the RNA and then prepared RNA-seq libraries (where they extracted the RNA and then prepared the data), which were then sent to the UC Davis Genome Center where they were sequenced. and then the Genome Center sends us back the sequence reads. We have those reads, we use those to assemble transcripts and to also do gene expression analysis, where we start relating and making models to compare gene expression to different climate variables like precipitation, temperature, and elevation. They’re all correlated.
What kind of models do you employ for data analysis?
It’s just basic linear models and other types of models. You have your variable, which in our case would be germination proportion, and that is a function of gene expression. Gene expression is affected by temperature, genotype, and precipitation, so it’s just models on models.
Has the project been successful?
We did what we set out to accomplish with the funding. Right now, the final bit of sequencing data is coming in and then we’re actually starting to dive into it and produce actual results.
What are the difficulties of working with plants?
I love genetics and genomics stuff and I just fell into working with plants. Plants are the hardest compared to bacteria and humans. Plant genomes are ridiculous and weird things happen all the time. Humans are diploid–we’re boring. I finished working on a project with Brassica napus. It’s an allotetraploid (having four sets of chromosomes derived from different species), which is a hybrid of two different plants, Brassica rapa and Brassica oleracea, so it has two separate diploid genomes in itself. You have the two genomes that are crossing over with each other through homeologous exchange. So when you’re going try to assemble that genome, you don’t know if it came from the Rapa genome or the Oleracea genome. I think strawberries are up to eight copies of each chromosome, so it makes it a lot harder when you’re trying to find alleles. When you’re doing an experiment where you’re trying to knock out alleles of a genome, you have to knock out every copy in each chromosome. Whereas in humans, you only have to knock out two of them to make it homozygous. But in a strawberry, you have to knock out all six of those mutations. Plants just seem like the hardest of the group. And then you have pine trees where genomes have 22.5 gigabases (20 billion base pairs) and humans only have 3.2 gigabases.
How has extreme weather (wildfires, flooding) over the past years affected the study?
So one of the struggles of our project is that we’re looking at how the climate affects germination, but at the beginning of our project, there were droughts like crazy and wildfires, and that affects the genetics of the population and what survives and what doesn’t.
You’re trying to do all these environmental studies that look at the long-term effects compared to now, but when you’re a grad student on a grant, the grant only lasts four to five years. But, how do you take four to five years of data and project it out decades ahead without having data from decades prior? It just gets difficult when you only have four seasons that you can collect data from, and two of those are on fire and one of them is flooding. None of this seems like normal conditions historically. So it can make it a little bit tough to tease out what’s long-term variation in genetics in response to what’s happening in the environment right now.
What makes ecological, as opposed to transgenic research, difficult?
With our studies, we don’t knock out any genes or use any transgenics. Ours is all ecology. That’s the difficulty of our project. With Arabidopsis (a model organism in plant biology), the genes are pretty much homozygous and it’s a lot easier. In our case, all the seeds are collected in the wild, so they’re going to be heterozygous. We can try to make more of the seed by breeding the greenhouse to expand our seed stock, but we can only do so much since it takes up space to make more seed. The field is always going to be changing too. When you collect seeds from one year, the genetics could be completely different from the genetics of the next year.
Why don’t you use transgenics in your studies?
You don’t want to dive into transgenics (organisms whose genes have been altered) because there’s so much pushback against it. These are all natural California species and you don’t want to put something in the environment that can outcompete the natural population.
We’re trying not to affect the study environment that we’re looking at. When we do seed collections, we don’t take from at-risk populations of the certain species, and when we collect seeds, we only take a percentage of the seeds from each plant. We don’t want to affect the growth for the next season, so ultimately, we’re trying to do the minimum amount of disruption to the environment that we’re studying. We potentially hope to use our results for rehabilitation efforts. We’ll be able to tell which ones need more help to survive and which ones are fine.
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
A Conversation with Dr. Kate Scow: “I just totally lost my heart to soil”
By Sara Ludwick, Environmental Science and Management, 2019
Author’s note: I read about Dr. Scow’s research while looking for a faculty member to interview for a class assignment. She is a professor of Soil Science and Microbial Ecology at UC Davis, and her research emphasizes microorganisms’ roles in providing ecosystems services. Dr. Scow was featured in an article on a UC Davis website about carbon sequestration as a tactic to address climate change, and from there I discovered Russell Ranch, where she serves as Director (1). I immediately became interested in the variety of experiments conducted on the experimental farm, and began to learn more about Dr. Scow’s work. Her work is extensive; in addition to directing Russell Ranch, she actively works with Ugandan smallholder farms on irrigation. I sat down with Dr. Scow to discuss her passion for soil, what her research has taught her, how her relationship with soil has evolved, and what other people can learn from the powerful ecosystem that lives underground. (more…)
Aggie Transcript Interview—Dr. Walter Leal
By Bukre Coskun, Cell Biology, ‘18
Author’s Note:
“As a student in Professor Walter Leal’s biochemistry class, I was inspired by his dedication to motivating students and obvious enthusiasm for his field of research. Professor Walter Leal has achieved international recognition for his research on the molecular basis of insect communication and insect olfaction. Leal, a professor in the UC Davis Department of Molecular and Cellular Biology and former chair of the UC Davis Department of Entomology, has made significant strides towards understanding how chemicals deter mosquitos. He has identified key mosquito receptors that can guide the development of better mosquito repellents to prevent the spread of deadly diseases. He is a past president of the International Society of Chemical Ecology, an elected fellow of the American Association for the Advancement of Science (AAAS), and the first non-Japanese scientist to earn tenure in the Japan Ministry of Agriculture. I had a conversation with Professor Leal about his path to research, his philosophy on teaching, and the significance of his work with insects.”
Aggie Transcript Interview—Dr. Daniel Starr
By Lauren Uchiyama, Biochemistry and Molecular Biology, ’17
Author’s Note:
“I chose to write this piece because I felt Dr. Dan Starr is unique in that he is equally passionate about teaching and research. As an undergraduate in his BIS 104 cell biology class, I feel he highlights research well by teaching us from an experimental and historical perspective, which makes learning even more fun and interesting. His reputation as a difficult, yet acclaimed educator has made him one of the most prominent biology professors at UC Davis. I hope you enjoy getting to know him as much as I did!”
UC Davis Hosts DataRescue Event To Archive Climate Research
By N. J. Griffen, English, ‘17
Author’s Note:
“I chose to write about this topic as a response to one of the many uncertainties that exists under our newly elected president, Donald Trump. More specifically, this article is meant to encompass the nationwide effort by scientists, professors, researchers and archivists to safeguard, backup and protect work conducted in the realm of climate science. This topic, I believe, should be integrally important to most residents of this planet; due to the fact that we have no choice but to live the entirety of our lives here on earth. Therefore, my interview of the archivists at UC Davis seeks to uncover the motives and connotations that the DataRescue Davis event assumes.”