Our laboratory has three main projects in progress.
1. Population Dynamics and Evolution of Infectious Diseases
Fifty to one hundred million people, or ~5% of the world’s population died when the last major influenza pandemic swept the globe in 1918. Since then the world’s population has increased by 4.5 billion and its connectivity prompts the term “global village”. If, in today’s world, direct contact transmitted HIV can cause ~60 million infections and ~30 million deaths, then a highly virulent airborne virus would be catastrophic. Unfortunately it is not a case of if, but when. Yet our understanding of emerging infectious diseases has only increased superficially since 1918. We still have no answers to basic questions. Why and how do viruses switch hosts? Why do some viruses, such as HIV, spread pandemically through populations whereas others, such as influenza A virus H5N1, appear briefly before petering out? How do viruses evolve in mixed host populations? These questions can be addressed using theory from evolutionary ecology.
To explore emergence from an evolutionary ecological perspective, we study the dynamics of bacteriophage phi6 infection of a native host Pseudomonas phaseolicola and a novel host P. pseudoalcaligenes. The long term goal is to understand the population dynamics of viral adaptation to new host types.
We are currently expanding our research on dsRNA viruses into rotaviruses. Like phi6, rotaviruses have a segmented genome giving them the potential to undergo reassortment. A broad cell tropism/host range, make them an especially significant zoonotic pathogen, particularly in children and agricultural environments. We are interested in understanding the fundamental barriers and probabilities of reassortment in rotaviruses. Understanding rotavirus ecology and evolution is only becoming more important given the rapidly changing climate and advancement in agriculture.
We’ve recently branched out to include influenza virus A in our studies. We aim to ascertain the genetic and phenotypic changes incurred during host switches. We hope to connect the topology of the adaptive landscape with models of virus emergence to estimate probability of epidemic spread.
2. Control of Cellular Event Timing
The inherent probabilistic nature of biochemical reactions and low copy numbers of molecules involved, results in significant random fluctuations (noise) in mRNA/protein levels inside individual cells. Such stochastic expression is an unavoidable aspect of life at the single-cell level, and creates considerable variation in gene product levels across isogenic cells exposed to the same environment. Increasing evidence shows that stochastic expression affects biological functions ranging from driving genetically identical cells to different fates to corrupting information processing by cells. While the origins of stochastic expression have been extensively studied across organisms, how noisy expression of key regulatory proteins impacts timing of intracellular events is not well understood. Characterization of control strategies that buffer stochasticity in event timing is critically needed to understand the reliable functioning of diverse cellular processes that rely on precise temporal triggering of events.
We use the highly malleable bacteriophage λ as a model system for studying event timing in individual cells. Here, an easily observable event (cell lysis) is the result of expression and accumulation of a single protein (holin) in the E. coli cell membrane up to a threshold level. Preliminary data reveals precision in timing: lysis occurs on average at 65 min with a coefficient of variation of less than 5%. Intriguingly, mutations in the holin coding sequence can increase lysis time variation while keeping the mean fixed, illustrating independent tuning of noise in λ’s lysis system. We mathematically model timing of intracellular events as a first-passage time problem, where an event is triggered when a stochastic process (holin level) hits a threshold for the first time. Theoretical predictions are integrated with single-cell lysis time measurements in wild-type λ and strains containing mutations in regulatory regions controlling holin expression. Our preliminary results are promising and indicate that λ uses various mechanisms for buffering noise in order to schedule lysis at an optimal time.
3. Impact of Urbanization on Microbial Soil Communities
Soil microbial communities provide key ecosystems services to humans including bioremediation of pollutants, providing nutrients for food and fiber production, and recycling organic wastes. These ecosystem services are provided by microbial communities that hold staggering levels of species diversity when compared to macro organisms. Despite recent efforts, how soil microbial diversity varies across space and time, and the factors that control this variation, remains largely unexplored.
A particularly relevant dimension of this question is how humans, through the urban environment, impact the composition and function of soil microbial communities. Urbanization, the most extreme anthropogenic land-cover transformation, has recently become an important theme in ecological and sustainability research. Recent studies suggest that a) urban managed systems harbor large amounts of soil microbial biodiversity, with as many distinct soil microbial phylotypes and types of soil communities as we found in biomes across the globe, and that b) functionally, soil microbial communities in urban environments differ from communities in more natural areas, associated with the increase in C and N supply. However studies on changes in soil microbial communities along gradients of urbanization at a large scale have not been carried out. In this sense, Long Island, NY represents a perfect study area to address this issue.
In this project, we will quantify microbial and viral diversity and relative frequencies across an urbanization gradient spanning the length of New York City and Long Island, NY. The specific questions we will address include: 1) How are microbial communities organized along this urbanization gradient? 2) What are the main drivers of microbial community composition?, 3) How do archaeal and bacterial diversity correlate with that of their viruses and 4) What is the effect of urbanization on these patterns?