Insights from the Department of Energy Joint Genomics Institute (JGI) 2019 Meeting
This piece was written with other staff writers at the Berkeley Scientific Journal (BSJ). I interviewed Dr. Arturo Casadevall and Dr. Dan Jacobson. My sections are pasted below.
The Department of Energy Joint Genomics Institute (JGI) aims to solve problems in renewable energy, ecosystem nutrient cycling, and decontamination using genomics techniques (1). BSJ covered the 2019 JGI User Meeting where we had the opportunity to speak with four distinguished scientists working with genomics to tackle issues as wide-ranging as the fate of carbon in soil systems to the discovery of higher-order combinatorial interactions in cells. BSJ spoke with... immunologist Arturo Casadevall from Johns Hopkins, and computational biologist Dan Jacobson from Oak Ridge National Laboratory. Our conversations highlighted some of the widely varied genomics research taking place today.
On April 5th, the focus of the conference shifted to computational biology. Dr. Arturo Casadevall, a Professor of Immunology and Microbiology at Johns Hopkins University, discussed how developments in host-microbe modeling can change the way scientists tackle broad-scale problems. “If you go into a hospital today, the microbes you recover from infected individuals look very different from those that would have been causing disease in the 1900s,” Casadevall said. This is because the virulence of a pathogen changes as it mutates and interacts with its human host. But, Casadevall explained, most current microbial models fail to incorporate these adaptations. Without considering the host and other aspects of the microbe’s environment, “microbe-centric theories cannot cope with these changes.”
In his talk, Casadevall introduced his formula for calculating “pathogenic potential.” This formula accounts for the different aspects of host-microbe interactions that are neglected in models where all microbes are assumed pathogens. Casadevall wants to tackle the nuances of immunology with a quantitative approach. “If I lived in the ancient world, I’d be called part of the cult of Pythagoras,” he said. He believes that “the mathematics of the system will tell you the degree to which anything is predictable,” and his current collaboration with biomathematician Aviv Bergman at Yeshiva University turns to dynamical systems as a way to predict trends within biological systems. As Casadevall explained, dynamical systems use a function to describe the time dependence of a point in space. Rather than hold the microbe constant or the host constant, as in most contemporary experimental designs, dynamical systems allow Casadevall and Bergman to look at the interactions of both organisms over time. By analyzing patterns from many simulations, they have concluded that virulence is an emergent property—a characteristic exhibited of a complex system like the host-microbe relationship, rather than an individual microbe. Casadevall explained that microbes “hedge their bets” by randomly varying their behavior in order to better infect their hosts. These small variations can perturb a biological system with dramatic consequences.
Currently, Casadevall studies immuno-compromised individuals who suffer from human immunodeficiency virus (HIV). HIV patients lack adaptive immunity, and are thus more susceptible to fungal infections. But Casadevall explained that immune-compromised individuals are not restricted to patients of disease. “Anybody who ages becomes immuno-compromised,” Casadevall explained, “and medical progress is often associated with immuno-suppressed states.” For older individuals or those seeking medical treatments, studying fungal infection in compromised immune systems can reveal useful insights into human health. In fact, Casadevall theorizes that fungi are responsible for the emergence of the human species, and mammalian life in general. He cites studies that demonstrate how endothermy, the ability to regulate body temperature, protects mammals and birds against many fungal infections.4 Specifically in humans, most fungal diseases are considered “opportunistic”—associated with mutations in the host genome or bacterial infections.
“Instead of asking, ‘What killed the dinosaurs?’ ask, ‘What kept down the reptiles, such that we did not have a second reptilian age?’” Casadevall explained how fossil evidence indicates abundant fungal growth during the time of dinosaur extinction, possibly due to the cool and moist conditions that volcanic activity and resulting ashen skies promoted. Most dinosaurs were probably killed by the cataclysm but the surviving reptiles were ectotherms—their body temperatures were dependent on the temperature of their environments—so fungal diseases from the outside environment may have been able to adapt easily to the dinosaurs’ immune systems, potentially killing them. As a result, endotherms with internal body temperatures that exceed the thermal tolerance of fungi might have emerged, leading ultimately to the age of mammals and the evolution of humans. Casadevall illustrated this theory with paintings. “I urge you to draw your science,” he said. “It doesn’t take that long and it doesn’t have to be that good, but I’m really happy with my meteorite.”
In addition to our past, Casadevall also illustrated what the future condition of the human race may look like in the context of climate change. “As the planet gets warmer, some of these fungi will adapt to higher temperatures.” He points out that the average fungal thermal tolerance over the past 30 years has already risen. One such example is Candida auris, a fungus that has independently emerged around the globe in the past five years and is killing many immuno-compromised individuals (5).
Though he remains passionate about his ideas, Casadevall has been largely limited to publishing his theories in mini-reviews, “because thought is very difficult to get published.”
“I often wonder, if Darwin had written On the Origin of Species today, where would he have published?” Casadevall asked. Today, he promotes scientific thought as the Editor-in-Chief of mBio, an open access journal sponsored by The American Society for Microbiology that publishes many different forms of science communication including mini-reviews, opinions and hypotheses, commentaries, and perspectives. Casadevall is optimistic about the potential of his research. “I went into science because I like to explore ideas,” he said.
Another JGI speaker with a focus in computational modeling was Dr. Dan Jacobson, a computational systems biologist at Oak Ridge National Laboratory. Jacobson is a world record holder addressing the world’s biggest problems with big data.
Jacobson’s team developed the software for the supercomputer Summit, breaking the exascale barrier, which had previously limited computations to a billion floating point operations per second. The use of tensor cores on graphical processing units (GPUs) allowed for a massive boost in computation performance which enabled the discovery of higher-order combinatorial interactions in cells. Modeling these higher-order interactions is leading to a better mechanistic understanding of biological systems. A challenge associated with these tensor cores is the use of mixed precision (16-bit) numbers. However, tensor cores boosted the speed of calculations, allowing scientists to process much larger datasets. “For this application, by going to 16-bit numbers from 64-bit, we actually lost no accuracy whatsoever,” Jacobson explained. “There was just a tremendous speed boost.” Jacobson would like to increase speed even more by going down to 8- and even 1-bit numbers. He revealed that currently 11% of computing speed is attributed not to calculating, but to communication overhead associated with moving data onto and off of the GPUs. The remaining limitations of computation speed for 16-bit numbers, therefore, may lie not within the calculations themselves, but in the computer’s ability to communicate them and relay their information.
Jacobson is using Summit to bring supercomputing to biology. With explainable artificial intelligence (AI), he’d like to create more comprehensive epistatic calculations. Epistasis is the phenomena by which multiple mutations interact to affect a phenotype even each individual mutation has little to no effect by itself. AI looks at many iterations of data and classifies them based on patterns the computer observes. “Most AI methods.... don’t tell you the pattern that they’re using,” Jacobson said, “What we want for biology is that pattern that the algorithm is using for classification.” Explainable AI identifies this pattern that traditional AI algorithms do not normally reveal on their own. Summit can also help geneticists fill in holes in their data by recapturing rare genetic variants. Statistically, these are convenient to ignore. However, “they often have some of the largest effects on phenotypes,” according to Jacobson. Supercomputing can also help construct pan-genomes for whole populations, which will more accurately capture what an entire genome of a species looks like.
In addition to improving how scientists interpret data in the lab, Jacobson’s work can also benefit agricultural scientists and farmers in the field. “If you design a new plant genotype that’s going to make lots of biofuels or food, but then put it someplace where it dies, you have failed,” he explains. Jacobson took data from genomes of many different plants and revealed that different climate types correlated with specific genetic adaptations. He is discovering useful genome-wide associations that plant biologists can use to engineer crops for abiotic stresses like drought, and carbon sequestration for bioenergy, both challenges associated with climate change (6). “As [biologists] design genotypes that we want to deploy, we want to know whether they’ll be successful.” Jacobson uses clustering algorithms for different regions of the world to test how successfully different plant genotypes would survive projected climate conditions around the globe.
Jacobson believes these genomic computing techniques have the potential to change the way we look at macroscale biological patterns. “If we look at a big scary problem, we should stop saying, ‘That’s astronomical,’” he said, “We should say, ‘That’s huge. That’s biological.’”
References:
5. Casadevall, A. (2012). Fungi and the Rise of Mammals. PLoS Pathogens,8(8). doi:10.1371/journal.ppat.1002808.
6. Richtel, M., & Jacobs, A. (2019, April 06). A Mysterious Infection, Spanning the Globe in a Climate of Secrecy. Retrieved from https://www.nytimes.com/2019/04/06/health/drug-resistant-candida-auris.html
7. Jacobson, D. (2019, April). Exascale Biology: From Genome to Climate With a Few Stops Along the Way. Presented at 2019 JGI User Meeting, San Francisco, CA.
I wrote on the staff of the Berkeley Scientific Journal (BSJ) for my 2019 Spring semester at UC Berkeley. The original piece is no longer accessible via the BSJ website. However, you can find it posted here.
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