Michael S. Robeson II, Ph.D.

Dr. Robeson uses computational approaches to study how microbes affect our health. Nearly every environment on our body contains microbes, collectively called the human microbiome. The human microbiome can impact a person’s overall health and can influence the outcome of chemotherapy and immunotherapy treatments. The largest collection of diverse microbes resides within the gut, and they can affect brain function and behavior, this is referred to as the gut-microbiome-brain-axis.

Dr. Robeson can help researchers better investigate complex biological systems using scientific computing to create open-source tools for research. He currently serves on the Editorial Board at npj Biofilms and Microbiomes. Before joining UAMS, he was a microbiomics and bioinformatics consultant for several laboratories.

Research Interests

Microbial community dynamics, disease resistance, human health, and agricultural productivity are all highly affected by, or dependent upon, microbes. Resident microbiota are known to influence host-targeted chemotherapy and immunotherapy treatments, and have been shown to substantially affect clinical outcomes, e.g. mediating therapeutic efficacy. Similarly, the presence of cancer alone, or even anticancer treatments, can negatively affect the host’s healthy microbial profile leading to dysbiosis (imbalance of microbial homeostatis). These dysbiotic microbial communities can exacerbate cancer pathogenesis and progression, further altering therapeutic outcomes. Additionally, dysbiotic microbial communities have also been implicated in a variety neurological, cognitive, and behavioral deficit of several diseases and anticancer therapeutics. Much of which are thought to be mediated through the “gut-microbiome-brain-axis.

Selected Publications

• NA Bokulich, MS Robeson (2024) “Bioinformatics Challenges for Profiling the Microbiome in Cancer: Pitfalls and Opportunities.” Trends in Microbiology. https://doi.org/10.1016/j.tim.2024.08.011.
• Piccolo BD, Graham JL, Tabor-Simecka L, Randolph CE, Moody B, Robeson MS, Kang P, Fox R, Lan R, Pack L, Woford N, Yeruva L, LeRoith T, Stanhope KL, Havel PJ (2024) Colonic Epithelial Hypoxia Remains Constant during the Progression of Diabetes in Male UC Davis Type 2 Diabetes Mellitus Rats. BMJ Open Diabetes Research & Care. 12 (2). https://doi.org/10.1136/bmjdrc-2023-003813
• Corley C, McElroy T, Sridharan B, Trujillo M, Simmons P, Kandel S, Sykes DJ, Robeson MS, Allen AR (2023) Physiological and Cognitive Changes after Treatments of Cyclophosphamide, Methotrexate, and Fluorouracil: Implications of the Gut Microbiome and Depressive-Like Behavior. Frontiers in Neuroscience. Volume 17. https://doi.org/10.3389/fnins.2023.1212791
• Thurman TJ, Washam CL, Alkam D, Bird JT, Gies A, Dhusia K, Robeson MS, Byrum SD (2023) proteoDA: a package for quantitative proteomics. Journal of Open Source Software. 8(85): 5184. https://doi.org/10.3389/fnins.2023.1212791
• MS Robeson, DR O’Rourke, BD Kaehler, M Ziemski, MR Dillon, JT Foster,  NA Bokulich (2021) “RESCRIPt: Reproducible Sequence Taxonomy Reference Database Management.” PLoS Computational Biology 17 (11): e1009581. https://doi.org/10.1371/journal.pcbi.1009581
• S Graw, K Chappell, CL Washam, Allen Gies, Jordan Bird, MS Robeson*, SD Byrum* (2021) “Multi-Omics Data Integration Considerations and Study Design for Biological Systems and Disease.” Molecular Omics 17 (2): 170–85. https://doi.org/10.1039/d0mo00041h
• KZ Abram, Z Udaondo, C Bleker, V Wanchai, TM Wassenaar, MS Robeson, DW Ussery (2021) “Mash-Based Analyses of Escherichia Coli Genomes Reveal 14 Distinct Phylogroups.” Communications Biology 4 (1): 117. https://doi.org/10.1038/s42003-020-01626-5
• E Bolyen*, JR Rideout*, M Dillon*, N Bokulich*, C Abnet, G Al-Ghalith, H Alexander, E Alm, M Arumugam, F Asnicar, Y Bai, J Bisanz, K Bittinger, A Brejnrod, C Brislawn, CT Brown, B Callahan, A Caraballo-Rodríguez, J Chase, E Cope, R Da Silva, C Diener, P Dorrestein, G Douglas, D Durall, C Duvallet, C Edwardson, M Ernst, M Estaki, J Fouquier, J Gauglitz, S Gibbons, D Gibson, A Gonzalez, K Gorlick, J Guo, B Hillmann, S Holmes, H Holste, C Huttenhower, G Huttley, S Janssen, A Jarmusch, L Jiang, B Kaehler, KB Kang, C Keefe, P Keim, S Kelley, D Knights, I Koester, T Kosciolek, J Kreps, M Langille, J Lee, R Ley, YX Liu, E Loftfield, C Lozupone, M Maher, C Marotz, B Martin, D McDonald, L McIver, A Melnik, J Metcalf, S Morgan, J Morton, AT Naimey, J Navas-Molina, L Nothias, S Orchanian, T Pearson, S Peoples, D Petras, M Preuss, E Pruesse, LB Rasmussen, A Rivers, MS Robeson, P Rosenthal, N Segata, M Shaffer, A Shiffer, R Sinha, SJ Song, J Spear, A Swafford, L Thompson, P Torres, P Trinh, A Tripathi, P Turnbaugh, S Ul-Hasan, J van der Hooft, F Vargas, Y Vázquez-Baeza, E Vogtmann, M von Hippel, W Walters, Y Wan, M Wang, J Warren, K Weber, C Williamson, A Willis, ZZ Xu, J Zaneveld, Y Zhang, Q Zhu, R Knight, JG Caporaso (2019) “Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2”. Nature Biotechnology. 37 (8): 852–57. https://doi.org/10.1038/s41587-019-0209-9

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