MISSION
Microbial communities drive essential chemistry across ecosystems and hosts, from soils to oceans to the human body. But microbes rarely work alone; they divide metabolic labor, with different organisms specializing in different steps of biochemical pathways. Why does this division of labor happen, and what rules govern it?
The Gowda Lab studies these questions using bacterial denitrification, a form of anaerobic respiration found throughout nature and the human body. Denitrification sustains pathogens in chronic infections, fuels nitric oxide signaling via oral bacteria, and shapes the gut microbiome during inflammation. Curiously, most denitrifying bacteria in nature perform only part of the pathway, even though using the complete pathway can yield more energy. We investigate why by asking a) how do cells allocate scarce internal resources (e.g., carbon, electrons, proteins) among competing demands, and b) how do these allocation decisions shape which metabolic strategies succeed? By uncovering the cellular logic behind metabolic tradeoffs, we aim to ultimately build a mechanistic understanding for how microbial communities assemble and function. We are an interdisciplinary team of researchers in the Department of Microbiology and Biophysics Graduate Program at the Ohio State University.
NEWS
- March 2026: We welcome Arthur Franklin who will join the lab as a Microbiology PhD student. Arthur will work on microbial interactions in biofilms.
- Jan 2026: We are super excited to welcome Andrew Schwieters to the lab as a postdoctoral researcher. Andrew will develop new synthetic biology tools for studying bacterial denitrification.
- Aug 2025: Big congrats to Bryce Guidry for successfully completing his PhD candidacy examinations in the Biophysics Graduate Program.
- May 2025: We welcome summer REU student Emily Yoon to the lab. Emily is a rising junior at Williams College, and will be working on enrichhing denitrifying biofilms.
- February 2025: We are thrilled to have Dominic Cipiti joining the lab as a Microbiology PhD student. He will take a resource-allocation perspective to the study of denitrification physiology.
- January 2025: We welcome Bryce Guidry and Anja Steinert to the lab. Bryce joins as a PhD student through the Biophysics Graduate Program and will be working on single-cell approaches for studying bacteria. Anja will be taking over as lab manager.
- July 2024: Our paper on how interactions shape denitrifying communities in the soil microbiome is now out in Nature Microbiology.
PEOPLE
Karna Gowda, PhD [CV]
Principal InvestigatorDepartment of Microbiology
Biophysics Graduate Program
The Ohio State University
Dominic Cipiti
PhD Student, MicrobiologyBS in Biology, Baldwin Wallace University
Arthur Franklin
PhD Student, MicrobiologyBS in Biology, The Ohio State University
Ava Galek
Undergraduate Research Assistant
Bryce Guidry
PhD Candidate, BiophysicsBS in Biophysics, Emory University
Anja Howard
Lab ManagerBS in Biology, The Ohio State University
Andrew Schwieters
Postdoctoral ResearcherBS/BA in Biology and Chemistry, University of Minnesota, Morris
PhD in Microbiology, The Ohio State University
Aayushi Shah
High School Intern
Kuromatsu
DogAlumni
- Emily Yoon (Williams College), SROP Researcher (2025)
- Molly Easton, Undergraduate Lab Assistant (2024-2025)
- Aouss Azzouz (Earlham College→UPenn Medical School), SROP Researcher (2024)
PUBLICATIONS
Recent highlights
An old truism in microbial ecology is that "everything is everywhere but the environment selects". But we know that interactions between organisms in microbial communities are widespread—shouldn't that matter as well? In this paper, we show that community assembly is shaped by microbial physiology and interactions, not just environmental factors. Using a statistical approach to analyze global topsoil microbiome data, we uncovered a novel link between denitrification reductase gene abundances and soil pH. Lab experiments and isolate characterizations showed that low pH promotes toxin-antitoxin codependency between strains, explaining statistical patterns observed in soils around the planet.
News & Views by Daniel Amor
Imagine opening a freezer box full of strains and asking which combination of them would be best at performing a given function (e.g., producing or degrading a compound of interest). Screening all combinations of 10 strains requires at least a thousand experiments, and all combinations of 20 strains requires a million. Here, we show that searching the space of community configurations may in fact be much easier than brute force. We do this by applying statistical learning to the concept of community-function landscapes, showing in datasets and models that these landscapes are often smooth and thus possible to explore with only a handful of experiments.
Dispatch by Avi Flamholz and Dianne Newman
In natural microbial communities, measuring gene content is one of the easiest things to do, while measuring metabolic process rates is one of the hardest. But what if we could predict the latter from the former? In this paper, we establish a proof of concept that this is possible using bacterial denitrification as a model system. Through experimental measurements of synthetic communities composed of diverse isolates, we use statistical learning to map genotypes to phenotypes, and consumer-resource modeling to map phenotypes to community metabolic dynamics.
2025
Urvoy, M., Howard-Varona, C., Owusu-Ansah, C., Stai, A. J., Bouranis, J. A., Burris, M., Solonenko, N., Hettich, R. L., Holmfeldt, K., Tfaily, M., Gowda, K., Sullivan & M. B. (2025). Phage resistance mutations in a marine bacterium impact biogeochemically relevant cellular processes. Nature Microbiology, 11, 195–210. doi: 10.1038/s41564-025-02202-5
2024
Crocker, K., Lee, K. K., Chakraverti-Wuerthwein, M., Li, Z., Tikhonov, M., Mani, M., Gowda, K., & Kuehn, S. (2024). Environmentally dependent interactions shape patterns in gene content across natural microbiomes. Nature Microbiology, 9(8), 2022–2037. doi: 10.1038/s41564-024-01752-4
2023
Skwara, A., Gowda, K., Yousef, M., Diaz-Colunga, J., Raman, A. S., Sanchez, A., Tikhonov, M., & Kuehn, S. (2023). Statistically learning the functional landscape of microbial communities. Nature Ecology & Evolution, 7(11), 1823–1833. doi: 10.1038/s41559-023-02197-4
Diaz-Colunga, J., Skwara, A., Gowda, K., Diaz-Uriarte, R., Tikhonov, M., Bajic, D., & Sanchez, A. (2023). Global epistasis on fitness landscapes. Philosophical Transactions of the Royal Society B, 378(1877), 1–20. doi: 10.1098/rstb.2022.0053
2022
Gowda, K., & Kuehn, S. (2022). Microbial biofilms: An ecological tale of Jekyll and Hyde. Current Biology, 32(24), R1349–R1351. doi: 10.1016/j.cub.2022.10.068
Gowda, K., Ping, D., Mani, M., & Kuehn, S. (2022). Genomic structure predicts metabolite dynamics in microbial communities. Cell, 185(3), 530-546.e25. doi: 10.1016/j.cell.2021.12.036
Gopalakrishnappa, C., Gowda, K., Prabhakara, K. H., & Kuehn, S. (2022). An ensemble approach to the structure-function problem in microbial communities. iScience, 25(2), 103761. doi: 10.1016/j.isci.2022.103761
2020
Fraebel, D. T., Gowda, K., Mani, M., & Kuehn, S. (2020). Evolution of Generalists by Phenotypic Plasticity. iScience, 23(11), 1–22. doi: 10.1016/j.isci.2020.101678
PhD
Gandhi, P., Werner, L., Iams, S., Gowda, K., & Silber, M. (2018). A topographic mechanism for arcing of dryland vegetation bands. Journal of the Royal Society Interface, 15(147). doi: 10.1098/rsif.2018.0508
Gowda, K., Iams, S., & Silber, M. (2018). Signatures of human impact on self-organized vegetation in the Horn of Africa. Scientific Reports, 8(1), 1–8. doi: 10.1038/s41598-018-22075-5
Gowda, K., Chen, Y., Iams, S., & Silber, M. (2016). Assessing the robustness of spatial pattern sequences in a dryland vegetation model. Proceedings of the Royal Society A, 472(2187). doi: 10.1098/rspa.2015.0893
Gowda, K., & Kuehn, C. (2015). Early-warning signs for pattern-formation in stochastic partial differential equations. Communications in Nonlinear Science and Numerical Simulation, 22(1–3), 55–69. doi: 10.1016/j.cnsns.2014.09.019
Gowda, K., Riecke, H., & Silber, M. (2014). Transitions between patterned states in vegetation models for semiarid ecosystems. Physical Review E, 89(2), 1–8. doi: 10.1103/PhysRevE.89.022701