A native Texan, Dr. Proctor is an Assistant Professor in the Department of Microbiology and Molecular Genetics at UTHealth Houston’s McGovern Medical School. Dr. Proctor joined the department in 2024 after completing her postdoctoral training in microbial genomics with Dr. Julie Segre at the National Institute of Health, National Human Genome Research Institute in Bethesda, MD, where her work focused on describing the interactions between the emerging fungal pathogen Candida auris and the skin microbiome, as well as the genomic epidemiology of this pathogen and the ESKAPE pathogens in nursing homes in the U.S.
Dr. Proctor received her Ph.D. in 2016 in the Department of Microbiology and Immunology at Stanford University, where she developed expertise in the microbiome, numerical ecology, and reproducible research under the mentorship of Dr. David Relman and Dr. Susan Holmes. Her doctoral work focused on describing patterns of spatial and temporal variation (i.e., biogeography) in the bacterial component of the human oral microbiome. She obtained an M.S. in Biological Sciences at Smith College in Northampton, MA, where she was a recipient of the United States Environmental Protection Agency STAR fellowship, and a B.A. at Hampshire College in Amherst, MA.
Dr. Proctor is a mentor to the early career editorial board at mBio and is a reviewing editor at Microbiology Resource Announcements.
Our Mission
The Candida & Microbiome Dynamics Research Lab in the Department of Microbiology and Molecular Genetics studies the global impact, pathogenesis, and evolution of host-associated Candida species within the microbiome. Candida spp. are widely recognized as an important and growing threat to human health, particularly in underserved populations. We aim to define the mechanisms by which Candida spreads, establishes infections, and resists eradication, addressing a fundamental need in public health.
The lab employs a range of techniques, including comparative genomics, in vitro growth assays, and model systems, to explore the mycobiome and the evolution of antifungal resistance. We are committed to training the next generation of scientists who will excel in diverse careers within public health, academia, industry, and beyond. Our lab places a strong emphasis on rigorous and reproducible scientific research, while also promoting a healthy balance between work and personal life and fostering an inclusive and supportive atmosphere.
Candida spp. are a global and emerging threat
Six Candida species top the World Health Organization’s list of critical and high-priority fungal threats, with infections caused by non-albicans Candida species rising globally. Candida asymptomatically colonizes most healthy individuals as a member of the microbiome but can cause deadly infections in susceptible hosts. In fact, Candida spp. are the fourth leading cause of bloodstream infection in the United States. Additionally, the antifungals used in clinical practice are also employed in agriculture, posing unique One Health challenges, and possibly contributing to intractable fungal infections.
Using state-of-the-art methods in microbial genomics, Dr. Proctor’s research program centers on studying the ecology and evolution of Candida in diverse contexts, including humans, the environment, and agriculture. By coupling clinical and field-based studies with in vitro experimental evolution, her group strives to develop novel, ecologically informed diagnostics, therapeutics, and surveillance strategies to combat Candida infections worldwide. Our research is organized around two central themes.
Theme 1: Impact of host physiology and the microbiome and Candida colonization
One major focus of our laboratory is understanding how low salivary flow favors Candida colonization and outgrowth, building on our previous work. Patients with low salivary flow are at an increased risk of oral infection with Candida. We aim to test the hypothesis that low salivary flow selects for acidogenic and aciduric bacterial species, opening a niche for Candida colonization. Leveraging a retrospective collection of thousands of oral microbiome specimens and matched oral health records, this study investigates interkingdom interactions between fungi and bacteria before, during, and after experimental induction of low salivary flow in humans.
Through genomic analysis of approximately 10,000 human oral samples, we found that bacterial communities in the mouth vary along an ecological gradient from the front to the back of the mouth in healthy individuals with normal salivary flow. Bacteria in different oral habitats—such as supragingival surfaces, keratinized gingiva, buccal mucosa, and alveolar mucosa—show significant variation in healthy humans. In contrast, this gradient is attenuated in patients with low salivary flow, suggesting that saliva plays a crucial role in shaping species diversity across teeth. This finding is clinically significant, as it suggests bacterial biomarkers could be developed to diagnose low salivary flow, a condition often identified nine years after the first tooth is lost to disease.
Our findings have established a novel framework for understanding the ecology of the human oral microbiome. This framework prompts several key questions the group seeks to address, such as: How does the spatial organization of the bacterial component of the microbiome constrain or facilitate Candida colonization? What host factors (e.g., flow rate, salivary quality, etc.) facilitate or inhibit Candida colonization? How do microbe-microbe, including inter-kingdom interactions, influence Candida colonization?
Theme 2: Evolution of antifungal resistance
A second major focus of our laboratory is on the ecology and evolution of antifungal resistance in Candida species, particularly non-albicans Candida. Recurrent exposure to antibiotics and antifungals is common in individuals with risk factors for Candida infections (e.g., elderly, patients with diabetes, patients in the NICU) with some data suggesting prior exposure to antibiotics modifies antifungal efficacy and the evolution of fluconazole resistance. Our group replicates common clinical scenarios associated with Candida infection in vitro. Integrating culturomics with shotgun metagenomics and whole genome sequencing, my group aims to identify bacterial species from humans, the environment, and in agricultural settings that exhibit non-random interactions with Candida. Context-specific synthetic communities comprised of these species will be subjected to experimental evolution in the presence of alternating pulses of subinhibitory doses of antibiotics and antifungals. Shotgun metagenomic sequencing will be used to reconstruct the compositional and evolutionary trajectories of Candida within the microbial consortia.
By integrating clinical and field-based studies with in vitro experimental evolution, we aim to address critical questions such as: How do antifungal-resistant strains of Candida emerge and spread in and across different environments? What are the evolutionary trajectories of these resistant strains? How do these resistance elements impact the fitness and associated genomic epidemiology of Candida species within medical and agricultural settings? The long-term goals of this research are to develop strategies to mitigate the spread of resistance and inform policy on the use of antifungals in agriculture and healthcare.
Proctor DM, Drummond RA, Lionakis MS, Segre JA. One population, multiple lifestyles: Commensalism and pathogenesis in the human mycobiome. Cell Host Microbe. 2023 Apr 12;31(4):539-553. doi: 10.1016/j.chom.2023.02.010. PMID: 37054674.
Proctor DM, Dada N, Serquiña A, Willett JLE. Problems with Peer Review Shine a Light on Gaps in Scientific Training. mBio. 2023 Jun 27;14(3): e0318322. doi: 10.1128/mbio.03183-22. Epub 2023 Apr 13. PMID: 37052475; PMCID: PMC10294629.
Proctor DM. Nothing about Us without Us: The Roles of Diverse Stakeholders in Scientific Publishing. mBio. 2023 Apr 25;14(2): e0268522. doi: 10.1128/mbio.02685-22. Epub 2023 Mar 7. PMID: 36880759; PMCID: PMC10127632.
Liang H, Jo JH, Zhang Z, MacGibeny MA, Han J, Proctor DM, Taylor ME, Che Y, Juneau P, Apolo AB, McCulloch JA, Davar D, Zarour HM, Dzutsev AK, Brownell I, Trinchieri G, Gulley JL, Kong HH. Predicting cancer immunotherapy response from gut microbiomes using machine learning models. Oncotarget. 2022 Jul 19; 13:876-889. doi: 10.18632/oncotarget.28252. PMID: 35875611; PMCID: PMC9295706.
Sim CK, Kashaf SS, Stacy A, Proctor DM, Almeida A, Bouladoux N, Chen M; NISC Comparative Sequencing Program, Finn RD, Belkaid Y, Conlan S, Segre JA. A mouse model of occult intestinal colonization demonstrating antibiotic-induced outgrowth of carbapenem-resistant Enterobacteriaceae. Microbiome. 2022 Mar 10;10(1):43. doi: 10.1186/s40168-021-01207-6. PMID: 35272717.
Saheb Kashaf S, Proctor DM, Deming C, Saary P, Hölzer M, NISC Comparative Sequencing Program, Taylor ME, Kong HH, Segre JA, Almeida A, Finn RD. Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. Nat Microbiol. 2022 Jan;7(1):169-179. doi: 10.1038/s41564-021-01011-w. Epub 2021 Dec 24. PMID: 34952941; PMCID: PMC8732310.
Proctor DM, Dangana T, Sexton DJ, Fukuda C, Yelin RD, Stanley M, Bell PB, Baskaran S, Deming C, Chen Q, Conlan S, Park M; NISC Comparative Sequencing Program, Welsh RM, Vallabhaneni S, Chiller T, Forsberg K, Black SR, Pacilli M, Kong HH, Lin MY, Schoeny ME, Litvintseva AP, Segre JA, Hayden MK. Integrated genomic, epidemiologic investigation of Candida auris skin colonization in a skilled nursing facility. Nat Med. 2021 Aug;27(8):1401-1409. doi: 10.1038/s41591-021-01383-w. Epub 2021 Jun 21. PMID: 34155414.
Lisco A, Hsu AP*, Dimitrova D*, Proctor DM*, Mace EM, Ye P, Anderson MV, Hicks SN, Grivas C, Hammoud DA, Manion M, Starrett GJ, Farrel A, Dobbs K, Brownell I, Buck C, Notarangelo LD, Orange JS, Leonard WJ, Orestes MI, Peters AT, Kanakry JA, Segre JA, Kong HH, Sereti I. Treatment of Relapsing HPV Diseases by Restored Function of Natural Killer Cells. N Engl J Med. 2021 Sep 2;385(10):921-929. doi: 10.1056/NEJMoa2102715. PMID: 34469647; PMCID: PMC8590529.
Huang X, Welsh RM, Deming C, Proctor DM, Thomas PJ; NISC Comparative Sequencing Program, Gussin GM, Huang SS, Kong HH, Bentz ML, Vallabhaneni S, Chiller T, Jackson BR, Forsberg K, Conlan S, Litvintseva AP, Segre JA. Skin Metagenomic Sequence Analysis of Early Candida auris Outbreaks in U.S. Nursing Homes. mSphere. 2021 Aug 25;6(4): e0028721. doi: 10.1128/mSphere.00287-21. Epub 2021 Aug 4. PMID: 34346704; PMCID: PMC8386442.
Proctor DM*, Shelef KM*, Gonzalez A, Davis CL, Dethlefsen L, Burns AR, Loomer PM, Armitage GC, Ryder MI, Millman ME, Knight R, Holmes SP, Relman DA. Microbial biogeography and ecology of the mouth and implications for periodontal diseases. Periodontol 2000. 2020 Feb;82(1):26-41. doi: 10.1111/prd.12268. PMID: 31850642; PMCID: PMC6924627.
Davis NM*, Proctor DM*, Holmes SP, Relman DA, Callahan BJ. Simple statistical identification and removal of contaminant sequences in marker-gene and metagenomics data. Microbiome. 2018 Dec 17;6(1):226. doi: 10.1186/s40168-018-0605-2. PMID: 30558668; PMCID: PMC6298009.
Jeganatha P, Callahan BJ, Proctor DM, Relman DA, Holmes SP, The Block Bootstrap Method for Longitudinal Microbiome Data. 2018. arXiv:1809.01832v2.
Goltsman DSA, Sun CL, Proctor DM, DiGiulio DB, Robaczewska A, Thomas BC, Shaw GM, Stevenson DK, Holmes SP, Banfield JF, Relman DA. Metagenomic analysis with strain-level resolution reveals fine-scale variation in the human pregnancy microbiome. Genome Res. 2018 Oct;28(10):1467-1480. doi: 10.1101/gr.236000.118. Epub 2018 Sep 19. PMID: 30232199; PMCID: PMC6169887.
Proctor DM, Fukuyama JA, Loomer PM, Armitage GC, Lee SA, Davis NM, Ryder MI, Holmes SP, Relman DA. A spatial gradient of bacterial diversity in the human oral cavity shaped by salivary flow. Nat Commun. 2018 Feb 14;9(1):681. doi: 10.1038/s41467-018-02900-1. PMID: 29445174; PMCID: PMC5813034.
Proctor DM, Relman DA. The Landscape Ecology and Microbiota of the Human Nose, Mouth, and Throat. Cell Host Microbe. 2017 Apr 12;21(4):421-432. doi: 10.1016/j.chom.2017.03.011. PMID: 28407480; PMCID: PMC5538306.
Callahan B, Proctor D, Relman D, Fukuyama J, Holmes S. Reproducible Research Workflow in R for the Analysis of Personalized Human Microbiome Data. Pac Symp Biocomput. 2016; 21:183-94. PMID: 26776185; PMCID: PMC4873301.