Our laboratory has five main projects in progress.
1. Monitoring SARS-CoV-2 Genetic Signal in NYC Wastewater to Track SARS-CoV-2 Evolution
In collaboration with Dr. Marc Johnson of the University of Missouri, Dr. Davida Smyth of Texas A&M University, Dr. Monica Trujillo of Queensborough Community College, and the NYC Departments of Environmental Protection and Health and Mental Hygiene, we are monitoring the SARS-CoV-2 genetic signal in NYC wastewater. A major thrust of this effort is to track Variants of Concern, such as Omicron. “With a newly emergent virus, like SARS-CoV-2, studies to characterize genetic diversity, including those that assess the potential for the virus to evolve and escape host immunity, are pivotal for understanding disease progression and transmission dynamics and may have implications for countermeasure development” (NIH NIAID Strategic Plan).
Sequences from clinical cases are a fraction of what we can detect in wastewater. Moreover, most sequences are from patients presenting severe symptoms, but there are many people who are asymptomatic and their viruses may go unsequenced. Deep sequencing the receptor binding domain of SARS-CoV-2 RNA obtained from sewage can provide an unbiased snapshot of ALL SARS-CoV-2 viruses currently circulating in a population. Using this approach, we have been able to detect the earliest known evidence of Omicron in the US and also cryptic SARS-CoV-2 variants whose origin is currently unknown. These variant sequences do not appear in any clinical sequences available in GISAID.
2. Modeling SARS-CoV-2 Spread in the Built Environment
In collaboration with Dr. Davida Smyth of Texas A&M University, Dr. Fabrizio Spagnolo of Long Island University, Dr. Antun Skanata of Syracuse University, we are modeling the spread of SARS-CoV-2 in the built environment such as classrooms and offices.
We are attempting to apply approaches developed in microbial ecology to understand how a viral pathogen such as SARS-CoV-2 can survive and be transmitted in academic environments. Our approach is a hybrid and uses both experimental and computational methods. First, we are repurposing a computational approach first used to study bacterial pathogens in hospitals to understand how academic environments facilitate viral transmission amongst students, faculty, and staff. In order to better inform and apply the model, we are testing viral survival and transmission in a variety of contexts that are common in these environments, such as airborne or droplet spread, or even aerosols. Preliminary results show that aerosolized virus can travel up to 6 meters across a room, far greater than the 2 meters recommended for “social distancing”. Additionally we note a strong effect of humidity on aerosol transmission. Above a threshold 40% humidity, transmission drops precipitously.
We can then apply the model to test and optimize mitigation strategies. For instance, does an increase in the frequency of classroom cleaning significantly lower the survival time of viruses in the room? Can we achieve similar or better results by treating or changing room ventilation? Computationally, we hope to investigate whether short-term mitigation, such as cleaning, remains equally viable over the longer term as well.
3. 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 the 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. Recent publications include “Optimum threshold minimizes noise in timing of intracellular events” and “First-passage time approach to controlling noise in timing of intra-cellular events“
4. 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.