Revealing the Causes and Consequences of Microbial Non-genetic Variability in Energy-relevant Processes

Christopher Marx, University of Idaho (Principial Investigator)

Laurel Lynch, University of Idaho (Co-Investigator)

Benjamin J. Ridenhour, University of Idaho (Co-Investigator)

Klas I. Udekwu, University of Idaho (Co-Investigator)

Andreas E. Vasdekis, University of Idaho (Co-Investigator)

Leonora S. Bittleston, Boise State University (Co-Investigator)

Marie-Anne de Graaff, Boise State University (Co-Investigator)

Carey D. Nadell, Dartmouth College (Co-Investigator)

Daniel Schultz, Geisel School of Medicine at Dartmouth (Co-Investigator)

Microbes support national energy priorities by performing biological processes that enable the development of novel energy sources and facilitate the capture of CO₂ in soil. Research aimed at understanding the role of microbes in these processes has primarily focused on microbial community composition. However, breakthroughs in medical microbiology have demonstrated that phenotypic variability among genetically identical cells profoundly affects important traits such as virulence, immune evasion, and evolvability. Individual cells can be driven into alternative phenotypic states when exposed to stress, and the ‘memory’ resulting from such stress—which is generally ignored—can impact processes for generations. Recent work in multiple labs, including ours, suggests non-genetic variability is widespread and occurs at high rates in stressful environments. Thus, it is logical to hypothesize that variability due to cellular memory may also have significant, unexplored impacts on energy-relevant processes. Our goal is to develop a generalizable experimental and theoretical framework that is ubiquitously applicable to microbial systems for the identification, elucidation, and ultimately exploitation of single-cell variation in energy-relevant processes. We aim for these heuristics to encompass such processes as the bioconversion of plant biomass, enhancement of soil productivity through enrichment of nutrients, and improvement of bioenergy crop yield and resilience.

We have assembled a highly interdisciplinary team of scientists, including microbiologists, ecosystem scientists, data scientists, and theoreticians. Our collective expertise and many existing collaborations position us to deliver tangible outcomes within two years and set the stage for future goals by addressing three aims. We will: (1) Discover which environmentally relevant stressors elicit non-genetic variability; (2) Uncover the underlying molecular basis of non-genetic variability, and (3) Use experiment-based modeling approaches to quantitatively link single-cell variation to process-level outcomes. We will focus efforts on two model bacteria that are each commercially utilized in bioindustry and agricultural productivity. By leveraging high-throughput approaches, such as uncovering gene expression for many single cells at once, we will accelerate discovery for both model systems. This will allow us to efficiently translate findings into broadly applicable, predictive modeling frameworks amenable for systems’ refinement and rapid scaleup to achieve DOE relevant goals.

Our Implementation proposal is designed to foster research capacity, workforce development, and collaboration across three participating institutions in Idaho and New Hampshire. Our team includes PIs from different career stages, ranging from assistant professors to full professors and administrators, creating a unique opportunity for inter-institutional mentoring relationships among PIs. In addition, we will increase the synergy between our labs by ensuring project trainees gain hands-on training through cross-institutional mentorships and lab exchanges. These investments will produce unprecedented insights into non-genetic variability and cellular memory that are needed to (1) overcome methodological and conceptual barriers in single-cell biology and (2) optimize energy-relevant processes.