PreMiEr Publications

Fernander MC, McClure KR, Sanders BT, Solomon JE, Graves JL Jr. and Thomas MD (2026) Adaptive transcriptional remodeling of Streptococcus mutans under simulated microgravity and silver stress reveals evolutionary innovation in artificial environments. npj Complex 3, 6 (2026). https://doi.org/10.1038/s44260-025-00068-6

This study examines how the human oral bacterium Streptococcus mutans adapts to long-term exposure to simulated microgravity and combined microgravity–silver stress, conditions that model key selective pressures in enclosed spaceflight habitats. Using experimentally evolved populations maintained for 100 days, the authors integrate transcriptomic, genomic, and phenotypic data to characterize adaptive responses across multiple biological scales.

Populations evolved under simulated microgravity alone exhibited diverse and unpredictable transcriptional and physiological outcomes, including altered carbohydrate metabolism, variable reactive oxygen species (ROS) responses, and retention of alternative sugar utilization pathways. In contrast, populations evolved under combined microgravity and silver stress showed more convergent adaptations, marked by coordinated activation of redox and metal-handling pathways, elevated basal oxidative stress, and consistent gains in nitrate-reduction capacity. These findings demonstrate that no single data type is sufficient to capture the structure of adaptive responses in extreme environments. Instead, integrated multi-level datasets are essential for resolving how physical and chemical stress regimes constrain or expand evolutionary trajectories, with implications for spaceflight health, microbial control strategies, and the design of closed built environments.

Fry Brumit D, Sorgen AA and Fodor A (2026) Beta diversity meta-analysis shows transformations have broadly similar performance in machine learning applications regardless of compositional or phylogenetic awareness. https://doi.org/10.64898/2026.01.20.699043

This meta-analysis evaluates whether increasingly complex beta diversity transformations—designed to account for phylogeny, compositionality, and sparsity—meaningfully improve performance in machine learning applications compared to simpler, established methods. Using five publicly available microbiome datasets and 107 features, the authors assess classification performance with random forest models across a range of beta diversity approaches.

The results show no consistent improvement in classification accuracy associated with phylogenetically or compositionally aware transformations. While limiting analyses to only a small number of eigenvalue decomposition (EVD) axes slightly reduced performance, expanding to higher-dimensional representations (approximately 10–20 axes) improved results across methods. Importantly, elevated PERMANOVA pseudo-F scores observed for some advanced transformations did not translate into improved machine learning resolution, suggesting that pseudo-F scores are an unreliable proxy for algorithmic performance. Overall, the study concludes that method choice is unlikely to substantially affect downstream biological inference, allowing analysts to balance simplicity and correction strategies based on dataset-specific needs rather than expected gains in predictive power.

Barry N, Landreville KD, Blackwood D, Yard JK, Noble R and Kuzma J (2026) Experiences with household mold and perceptions of microbiome engineering to mitigate mold. Frontiers in Public Health, 14. https://doi.org/10.3389/fpubh.2026.1725172

This qualitative study examines how residents in eastern North Carolina experience, interpret, and manage household mold, and how these lived experiences shape perceptions of emerging microbiome-engineering technologies for mold mitigation. Drawing on 22 in-depth interviews and guided by the Health Belief Model (HBM), the research explores residents’ conceptualizations of mold, perceived health and structural risks, prevention strategies, and trust in novel remediation approaches.

Participants described mold as a multifaceted presence—understood biologically as a fungus, environmentally as a dampness-driven growth, and experientially through sensory cues such as smell and sight. Mold was widely associated with respiratory illness, broader health concerns, property damage, and financial strain, particularly following flooding and severe storms. While residents employed layered mitigation strategies including ventilation, dehumidification, cleaning, and professional remediation, cost, uncertainty, and trust remained persistent barriers. Views on microbiome-engineered solutions reflected cautious and conditional acceptance: participants acknowledged potential benefits but raised concerns about unintended consequences, lack of visibility, and loss of control. Acceptance was contingent on rigorous testing, transparent regulation, and demonstrated safety and effectiveness. Overall, the findings frame mold as a socio-environmental challenge embedded in health, housing, and equity considerations, highlighting the need for socially responsive policies and technologies in the built environment.

Talma K, Bossa N, Hankinson E, Gao L, El Kharraf A and Wiesner M (2026) Minimal influence of material surface properties on initial bacterial attachment to built environment surfaces. https://doi.org/10.64898/2026.01.28.702373

This study evaluates the role of material surface properties in governing early-stage bacterial attachment in the built environment, a key step in biofilm formation linked to pathogen persistence in settings such as hospitals. Using seven materials with varying roughness, wettability, surface chemistry, and charge, the authors investigated initial attachment behaviors of Escherichia coli, Pseudomonas aeruginosa, Bacillus subtilis, and Staphylococcus aureus.

Across both column-based and batch experiments, the results show that surface material properties had minimal influence on initial bacterial attachment, with similar attachment levels observed across all tested materials. Instead, bacterial traits—particularly cell envelope morphology—played a stronger role, with gram-negative species exhibiting greater attachment than gram-positive species. Attachment efficiency (α) emerged as a sensitive predictor of attachment behavior, outperforming traditional batch assays. These findings suggest that engineering surface materials alone is unlikely to meaningfully limit microbial colonization and that strategies promoting stable, non-pathogenic communities may be more effective for controlling biofilms in the built environment.

Cummings CL, Landreville KD and Kuzma J (2026) Public perceptions and support for microbiome engineering to combat mold growth in disaster relief efforts. Environment Systems and Decisions, 46, Article 3. https://doi.org/10.1007/s10669-025-10062-x

This study presents the first empirical analysis of public attitudes toward microbiome engineering as a strategy for controlling mold growth in disaster relief shelters. Mold contamination is a persistent challenge in emergency housing due to moisture, limited ventilation, and dense occupancy, and existing mitigation strategies—such as chemical fungicides or mechanical drying—often face logistical, resource, and health-related constraints. As microbiome engineering emerges as a potential alternative, understanding public acceptance is essential for responsible development and deployment.

Using a nationally representative survey of 1,000 U.S. adults, the authors examine predictors of support across three dimensions: willingness to adopt introduced microbiomes (IM) in disaster shelters, support for rigorous testing and evaluation, and support for survivor education. Hierarchical regression models explained substantial variance in each outcome, with adoption support driven primarily by perceived efficacy, trust, and affective responses, while testing and education support reflected demographic differences and risk appraisals. Across all models, interest in learning more about microbiome engineering consistently predicted higher support, whereas prior information-seeking was often associated with greater caution. The findings underscore the importance of integrating public perspectives early in the development of microbiome-based interventions, particularly in post-disaster contexts where vulnerability and trust are critical considerations.

Saber LB, Rojas M, Anderson DM, Anderson DJ, Claus H, Cronk R, Linden KG, Lott MEJ, Radonovich LJ Jr., Warren BG, Williamson RD, Vincent RL, Gutiérrez-Cortez S, Calderón Toledo C and Brown J (2026) The effects of Far-UVC irradiation on the presence and concentration of ESKAPEE pathogens on hospital surfaces: study protocol for a multi-site, double-blinded randomized controlled trial in La Paz, Bolivia. https://doi.org/10.64898/2026.02.04.26345557

This study protocol describes a multi-site, double-blinded randomized controlled trial designed to evaluate the effectiveness of Far-UVC irradiation in reducing hospital-associated ESKAPEE pathogens on surfaces and in air. Far-UVC is a promising disinfection technology that inactivates microorganisms while having limited penetration into human skin and eyes, potentially enabling continuous use in occupied spaces.

The trial will be conducted in two hospitals in La Paz, Bolivia, using a clustered design in which hospital sinks are assigned to either active Far-UVC lamps or visually identical sham lamps. Environmental sampling will include surface swabs, air samples, and chemical monitoring, analyzed through culture-based methods and sequencing. By rigorously evaluating both microbiological and environmental outcomes, this study aims to generate evidence on the real-world efficacy and safety of Far-UVC as a scalable infection-control intervention in low- and middle-income healthcare settings.

Kim, H., Siddiqui, N.Y., Karstens, L. et al. A negative binomial latent factor model for paired microbiome sequencing data. BMC Bioinformatics (2026). https://doi.org/10.1186/s12859-025-06362-3

This study introduces a Bayesian negative binomial latent factor model designed for the joint analysis of microbiome sequencing data collected from paired body sites. By incorporating shared latent factors, the model captures cross-site dependencies that are often ignored in standard analyses, improving inference when microbial communities across sites are biologically linked.

Using simulations and a case study of the female urogenital microbiome, the authors demonstrate that accounting for cross-site associations enhances detection of covariate effects, predictive performance, and overall efficiency. The framework also supports subject clustering based on association strength, offering new insights into heterogeneity in microbiome interactions across body sites.

Waheed, H., Zhang, Y., Nguyen, L., Joyce, A. S., Ferguson, L., & Delgado Vela J. (2026). Autoinducer-2 and acyl homoserine lactones have contrasting effects on ammonia and nitrite-oxidizing sludge. https://doi.org/10.64898/2026.01.08.698431

This study examines how manipulating quorum sensing signals shapes nitrification in activated sludge systems. The authors show that acyl homoserine lactones (AHLs) strongly enhance ammonia oxidation, while autoinducer-2 (AI-2) suppresses ammonia oxidation but markedly stimulates nitrite oxidation, highlighting opposing regulatory roles across nitrifying guilds.

Together, the findings demonstrate that balanced control of microbial signaling molecules can be used as a biologically tunable strategy to synchronize ammonia and nitrite oxidation, improving nitrogen removal efficiency in wastewater treatment systems.

Cummings CL, Landreville KD and Kuzma J (2025) Natural vs. genetically engineered microbiomes: understanding public attitudes for indoor applications and pathways for future engagement. Front. Genet. 16:1560601. doi: 10.3389/fgene.2025.1560601

This study examines public preferences for natural microbiomes and support for genetically engineered (GE) microbiomes in the built environment, focusing on the demographic, sociographic, and attitudinal factors that influence these preferences. Using data from a nationally representative survey of 1,000 U.S. adults, we employed hierarchical regression analyses to assess the relative contribution of these variables. While demographic and sociographic factors explained limited variance, topic-specific attitudes, including positive perceptions of microbiome engineering’s potential to improve quality of life, were the most significant predictors of support. Conversely, age, distrust in science, and perceived knowledge negatively influenced support for GE microbiomes, reflecting skepticism among some audiences. The findings highlight the potential of the Responsible Research and Innovation (RRI) framework to align the development of microbiome engineering with societal values and to address diverse public perspectives. This research provides actionable insights for policymakers, researchers, and communicators seeking to navigate the complexities of public engagement with emerging biotechnologies.

Kim Y, Ihrie V, Shi P, Ihrie MW, JT, Meares A, Granek J, Gunsch C, Ingram J. 2025. Glucagon-like peptide 1 receptor (Glp1r) deficiency does not appreciably alter airway inflammation or gut-lung microbiome axis in a mouse model of obese allergic airways disease and bariatric surgery. J Asthma Allergy 18:285-305. https://doi.org/10.2147/JAA.S478329

Purpose: High body mass index (≥ 30 kg/m²) is associated with asthma severity, and nearly 40% of asthma patients exhibit obesity. Furthermore, over 40% of patients with obesity and asthma that receive bariatric surgery no longer require asthma medication. Increased levels of glucagon-like peptide 1 (GLP-1) occur after bariatric surgery, and recent studies suggest that GLP-1 receptor (GLP-1R) signaling may regulate the gut microbiome and have anti-inflammatory properties in the lung. Thus, we hypothesized that increased GLP-1R signaling following metabolic surgery in obese and allergen-challenged mice leads to gut/lung microbiome alterations, which together contribute to improved features of allergic airways disease.
Methods: Male and female Glp1r-deficient (Glp1r^−/−) and replete (Glp1r^+/+) mice were administered high fat diet (HFD) to induce obesity with simultaneous intranasal challenge with house dust mite (HDM) allergen to model allergic airway disease with appropriate controls. Mice on HFD received either no surgery, sham surgery, or vertical sleeve gastrectomy (VSG) on week 10 and were sacrificed on week 13. Data were collected with regard to fecal and lung tissue microbiome, lung histology, metabolic markers, and respiratory inflammation.
Results: HFD led to metabolic imbalance characterized by lower GLP-1 and higher leptin levels, increased glucose intolerance, and alterations in gut microbiome composition. Prevalence of bacteria associated with short chain fatty acid (SCFA) production, namely Bifidobacterium, Lachnospiraceae UCG-001, and Parasutterella, was reduced in mice fed HFD and positively associated with serum GLP-1 levels. Intranasal HDM exposure induced airway inflammation. While Glp1r^−/− genotype affected fecal microbiome beta diversity metrics, its effect was limited.
Conclusion: Herein, GLP-1R deficiency had surprisingly little effect on host gut and lung microbiomes and health, despite recent studies suggesting that GLP-1 receptor agonists are protective against lung inflammation.

Plain Language Summary: Asthma is a chronic lung disease that affects over 20 million Americans. In addition, obesity is a major worldwide healthcare problem whose impact is increasing each year. Asthma patients with obesity have worse symptoms, use more medications and have reduced asthma control than lean patients. Also, both asthma and obesity are linked to changes in the normal bacteria that live in the gut and the airways. Weight loss, either through bariatric surgery or through diet and exercise, improves asthma symptoms, through unknown mechanisms. One way that bariatric surgery may impact asthma is through the increase in beneficial gut hormones (GLP-1) and restoration of altered bacteria in the gut and airways. Our study shows that overall, obese mice experience changes in hormones involved in weight gain and glucose metabolism as well as gut bacteria, particularly those that may produce important factors that could improve health. We did not find any links between changes in the bacteria in the gut or lung and features of airway disease or effects from bariatric surgery in the mice. Mice that lacked GLP-1 activity did not differ from normal mice for weight, glucose tolerance, or prevalence of bacteria in the lung. Future studies will explore the timeline of these bacterial changes and the role of the gut and lung microbiomes in human obesity-associated asthma.

Dai Q, Gunsch CK and Granek JA (2025) Minor library preparation modification significantly reduces barcode crosstalk in ONT multiplexed sequencing. https://doi.org/10.1101/2025.11.19.689316

This study addresses barcode crosstalk, a persistent source of error in multiplexed high-throughput sequencing that can lead to read misassignment and false signals of cross-contamination. Focusing on Oxford Nanopore Technologies (ONT) sequencing workflows, the authors systematically identify and quantify the extent of barcode misassignment introduced during standard library preparation.

They demonstrate that a simple procedural change—post-ligation pooling—reduces barcode crosstalk by more than an order of magnitude without introducing significant additional complexity or cost. This minor modification substantially improves sequencing accuracy and data integrity, particularly in studies where low-abundance signals are critical. The findings provide a practical, immediately deployable solution for improving ONT multiplexed sequencing quality and highlight the importance of library preparation choices in downstream data interpretation.

You, L., David, L., Holmes, Z. A., Shyti, I., Hoffman, A. L., Duncker, K. E., Ma, H. R., Zhou, Z., Lee, D., Maddamsetti, R., Kim, K., Şimşek, E., Hamrick, G. S., Son, H., Villalobos, C. A., Lu, J., Ha, Y., Shende, A. R., Yao, Z., Liu, S., Shapiro, D. M., & Kholina, K. (2025). A foundation model for microbial growth dynamics. https://doi.org/10.64898/2025.12.01.691707

This work presents a large-scale foundation model designed to learn generalizable representations of microbial growth dynamics across diverse species, environmental conditions, and community contexts. The model is trained using self-supervised learning on approximately 370,000 experimental and simulated microbial growth curves, enabling it to capture essential dynamical features in compact, low-dimensional latent embeddings.

These learned representations allow accurate reconstruction of raw growth data and significantly enhance performance across a range of downstream tasks. The authors demonstrate few-shot learning for antibiotic classification and concentration prediction, improved forecasting of both simulated and experimental microbial communities, and inference of total population abundance from relative-abundance measurements. By extracting transferable structure from heterogeneous growth datasets, this model provides a unifying framework for analyzing and predicting microbial population behavior in settings where task-specific data are limited, with implications for antibiotic testing, microbiome engineering, and systems biology.

This article is a preprint and has not yet been certified by peer review.

Rabasco JT, Kisthardt SC, Theriot CM and Callahan BJ (2025) Assessing contamination in DNA extraction kits commonly used for microbiome research. https://doi.org/10.1101/2025.09.23.677068

This study revisits and updates the problem of background DNA contamination originating from DNA extraction kits—often referred to as the “kitome”—in the context of modern microbiome research. Although best practices for contamination control have been widely adopted, contaminant DNA continues to obscure true microbial signals, particularly in low-biomass samples where spurious sequences can dominate results.

The authors systematically characterize contamination profiles across DNA extraction kits commonly used in microbiome studies over the past several years, building on foundational work that first identified kit-derived contaminants. By providing an updated assessment aligned with contemporary sequencing workflows, this work offers actionable guidance for kit selection and experimental design. The findings are especially relevant for researchers working in low-biomass environments, where contamination remains a critical barrier to accurate biological interpretation.

Zhou Z, Weiss A, Yao Z, Chen X, Lok K, Son H and You L (2025) Dynamical memory underlies prolonged plasmid persistence after transient antibiotic treatment. https://doi.org/10.1101/2025.10.31.685803

This study uncovers a previously unrecognized mechanism that explains why antimicrobial resistance plasmids can persist long after antibiotic exposure, even when they impose substantial fitness costs and exhibit poor horizontal transfer. Rather than relying solely on low plasmid burden or rapid transmission, the authors identify a form of ecological “dynamical memory” that emerges from the decoupling of fast growth competition and slow plasmid segregation loss.

Through a combination of theoretical modeling, simulations, and quantitative experiments across clonal populations and multi-species communities, the study shows that a transient antibiotic pulse can eliminate plasmid-free cells and generate a ghost state that dramatically slows plasmid decay. This effect can extend plasmid persistence from days to months, effectively encoding past antibiotic exposure into population dynamics. The findings reveal a generalizable mechanism for prolonged plasmid survival and underscore the need for proactive antimicrobial stewardship strategies that account for long-lived ecological memory in microbial communities.

Duncker KE, Shende AR, Shyti I, Ruan A, D’Cunha R, Ma HR, Venugopal Lavanya H, Liu S, Gottel N, Anderson DJ, Gunsch CK and You L (2025) Engineering microbial consortia for distributed signal processing. https://doi.org/10.1101/2025.04.23.650302

This study introduces a generalizable strategy for multiplexed biological signal detection using engineered microbial consortia rather than single, highly complex genetic circuits. Instead of enforcing strict sensor orthogonality within individual cells, the authors distribute sensing functions across distinct microbial populations, reducing crosstalk and simplifying experimental optimization. Individual populations can be swapped or reconfigured without requiring genetic redesign, improving scalability and adaptability.

The authors pair this modular biological design with a computational pipeline that integrates mechanistic modeling and machine learning to decode the unique temporal responses of microbial communities and infer multiple input signal concentrations. The platform is validated across diverse contexts, including detection of highly crosstalk-prone signals, antibiotic sensing using natural microbial communities, and chemical quantification in hospital sink wastewater. This work demonstrates how combining microbial engineering with computational inference enables robust, flexible biosensing systems with applications in environmental monitoring, healthcare, and industrial settings.

Warren BG, Graves AM, Fils-Aime G, Barrett A, Mamikunian I, Gunsch C, Smith BA and Anderson DJ (2026) Efficacy of a foamed disinfectant in reducing pathogen contamination in renovated inpatient in-room sinks: a randomized controlled trial. Infection Control & Hospital Epidemiology, 47(1):13–19. doi: 10.1017/ice.2025.10318

This randomized controlled trial evaluates the effectiveness of a hydrogen peroxide–peracetic acid foamed disinfectant in reducing epidemiologically important pathogens (EIPs) in hospital sink environments. Conducted in a newly renovated inpatient unit, the study followed 30 in-room sinks over 35 weeks, comparing intervention sinks treated with foamed disinfectant three times weekly to control sinks receiving standard daily surface cleaning.

Although nearly all sinks experienced at least one sink conversion event during the study period, intervention sinks showed dramatically reduced pathogen recovery across drain components, including P-traps and tail pipes. Only 9% of samples from intervention sinks tested positive for EIPs compared to 47% in control sinks, and time to initial contamination was significantly delayed. The findings demonstrate that foam-based disinfection protocols can substantially reduce and slow the establishment of environmental reservoirs for multidrug-resistant organisms. This work provides strong evidence for scalable, practical interventions to mitigate pathogen persistence in healthcare built environments.

Li Q, Al’ Abri I, Crook N and Vento JM (2025) Investigating the restriction-modification barrier to DNA delivery in human gut probiotic bacteria for streamlined genetic tool development. Advanced Drug Delivery Reviews. https://doi.org/10.1016/j.addr.2025.115723

This review examines a fundamental bottleneck limiting the development of next-generation engineered probiotics: the difficulty of delivering foreign DNA into many human gut bacteria. While probiotic species hold significant promise as living therapeutics, their genetic intractability has restricted mechanistic study and engineering efforts to a small subset of model organisms. A major contributor to this challenge is the widespread presence of restriction–modification (R–M) systems, which act as bacterial defense mechanisms that degrade incoming DNA.

The authors synthesize current knowledge on DNA delivery strategies and systematically analyze available genomic data to characterize the abundance, diversity, and complexity of R–M systems across human gut bacterial species with high probiotic potential. Their analysis reveals substantial variability in R–M system composition, even among closely related strains, and identifies species with particularly high barriers to DNA delivery. The review also surveys established and emerging methods for bypassing R–M defenses. Together, this work provides a generalizable framework to guide genetic tool development across a broader range of gut bacteria, supporting the expansion of engineered probiotics as viable living therapeutics.

Ezeanowai FC, Ewunkem AJ, Morikwe UC, Kiki LC, McGee LW, Graves JL Jr. and Jeffers-Francis LK (2025) Phage resistance modulates Escherichia coli B response to metal-based antimicrobials. Antibiotics, 14(9):942. https://doi.org/10.3390/antibiotics14090942

This experimental evolution study investigates how prior exposure to bacteriophages shapes bacterial adaptation to metal-based antimicrobial stress. Using Escherichia coli B, the authors examined evolutionary outcomes under phage-only, iron-only, and sequential phage–iron exposure over 35 days, allowing them to assess how historical contingency influences resistance trajectories.

Phage resistance evolved rapidly and persistently, while iron-selected populations developed tolerance to high iron concentrations at the cost of reduced resistance to other metals and antibiotics. Notably, prior phage exposure altered these trade-offs: populations exposed sequentially to phage and iron retained resistance to both stressors but displayed distinct antibiotic sensitivity profiles. Whole-genome sequencing revealed stressor-specific genetic adaptations, including deletions in phage receptor genes and mutations affecting regulatory and membrane-associated pathways under iron selection. The findings highlight how the sequence of selective pressures drives fitness trade-offs and resistance outcomes, with implications for the design of evolution-informed antimicrobial and combination therapy strategies.

Chung HA, Fralish Z, Tu T and Reker D (2025) Profiling biological effects of microbiome metabolites via machine learning. https://doi.org/10.26434/chemrxiv-2025-15mw9

This study introduces a machine learning platform designed to systematically predict the biological activities of human microbiome-derived metabolites, addressing a major bottleneck in microbiome research. Traditional experimental approaches for characterizing metabolite function are often low-throughput, costly, and untargeted, leaving many microbiome-associated compounds functionally unannotated.

By training predictive models on publicly available drug development datasets, the authors rapidly infer chemical and biological properties across a broad set of microbiome metabolites. Prospective experimental validation confirmed strong predictive performance and revealed previously unknown biological effects, including the stimulation of interleukin-8 secretion by spermine and spermidine—metabolites previously regarded as anti-inflammatory. This work demonstrates the power of machine learning to accelerate functional annotation of microbiome metabolites and opens new avenues for biomarker discovery and therapeutic development.

Şimşek E, Villalobos CA, Sahu K, Zhou Z, Luo N, Lee D, Ma HR, Anderson DJ, Lee CT and You L (2025) Spatial proximity dictates bacterial competition and expansion in microbial communities. Nature Communications, 16:10885. https://doi.org/10.5281/zenodo.17343543

This study reveals how spatial structure fundamentally reshapes microbial interactions, uncovering a facilitation mechanism that is invisible in well-mixed environments. While most studies of microbial competition focus on resource availability and growth rates, the authors demonstrate that spatial proximity can enable one species to facilitate the range expansion of another—even when the facilitating species is later suppressed.

Using both mathematical modeling and experiments, the authors show that in antibiotic-treated environments, an immotile, drug-degrading species enables the spatial expansion of a motile, drug-tolerant species by locally detoxifying the environment. Focusing on Klebsiella pneumoniae and Pseudomonas aeruginosa, they demonstrate that this facilitation operates at millimeter scales and results in the facilitating species becoming a hidden initiator of community expansion. Similar dynamics were observed in communities containing environmental isolates from hospital sinks, including Bacillus species. These findings highlight the necessity of spatially explicit experiments for understanding microbial ecology and have important implications for surface-associated communities, biofilms, and polymicrobial infections.

Bhatia S, Maswanganye TN, Jeje O, Winston D, Lamssali M, Deng D, Blakley I, Fodor AA and Jeffers-Francis L (2025) Wastewater Speaks: Evaluating SARS-CoV-2 surveillance, sampling methods, and seasonal infection trends on a university campus. Microorganisms, 13(4):924. https://doi.org/10.3390/microorganisms13040924

This study evaluates the effectiveness of wastewater-based epidemiology (WBE) for monitoring SARS-CoV-2 prevalence on a university campus over a three-year period (2021–2023). Wastewater samples collected from 11 dormitory-linked manholes were analyzed using RT-qPCR and compared with weekly clinical case data from the campus health center.

The authors found strong correlations between grab and composite sampling methods, supporting the robustness of both approaches for viral detection. Seasonal trends revealed higher viral RNA concentrations during spring semesters and notable spikes following student returns in January and August. However, normalization using Pepper Mild Mottle Virus (PMMoV) did not improve correlations with clinical case data, and WBE was not consistently reliable as a complement to clinical testing in this campus setting. Limitations included incomplete coverage of off-campus students and changes in clinical reporting practices. Despite these challenges, the study provides valuable insights into the practical constraints and continued potential of wastewater surveillance for infectious disease monitoring in semi-contained populations.

Liu Y, Babusci E, Gunsch CK and Chen B (2025) Scensory: Automated real-time fungal identification and spatial mapping. https://doi.org/10.48550/arXiv.2509.19318

This paper introduces Scensory, a robot-enabled olfactory sensing system for real-time identification and localization of indoor fungal contamination. Existing fungal detection methods are often slow, expensive, and lack spatial resolution, limiting their usefulness for large-scale or real-time monitoring in buildings.

Scensory combines low-cost volatile organic compound (VOC) sensor arrays with deep learning models trained on robot-automated data collection. By leveraging temporal VOC dynamics, the system simultaneously decodes fungal species identity and spatial origin. The authors demonstrate both passive multi-array monitoring and mobile single-array source tracking configurations. Across five fungal species, Scensory achieves up to 89.85% accuracy in species detection and 87.31% accuracy in localization using only 3–7 seconds of sensor input per prediction. Beyond detection, the system enables computational interpretation of biochemical signatures without additional laboratory experiments, establishing a scalable framework for autonomous, spatially aware environmental sensing.

Cummings CL, Landreville KD, Kuzma J (2025) Public perceptions and support for introduced microbes to combat hospital-acquired infections and antimicrobial resistance. PLoS One 20(10): e0332578. https://doi.org/10.1371/journal.pone.0332578

Using a nationally representative U.S. survey, this study examines public support for introducing beneficial microbes in hospital environments to reduce infections and antimicrobial resistance. Results show that support is driven more by trust, perceived efficacy, and emotional responses than by demographics, highlighting the importance of transparent communication and education for responsible microbiome innovation.

Sun G and Zhou Y. 2024. Predicting microbiome growth dynamics under environmental perturbations. Appl. Microbiol. 4:948-958. https://doi.org/10.3390/applmicrobiol4020064

MicroGrowthPredictor is a model that leverages Long Short-Term Memory (LSTM) networks to predict dynamic changes in microbiome growth in response to varying environmental perturbations. In this article, we present the innovative capabilities of MicroGrowthPredictor, which include the integration of LSTM modeling with a novel confidence interval estimation technique. The LSTM network captures the complex temporal dynamics of microbiome systems, while the novel confidence intervals provide a robust measure of prediction uncertainty. We include two examples—one illustrating the human gut microbiota composition and diversity due to recurrent antibiotic treatment and the other demonstrating the application of MicroGrowthPredictor on an artificial gut dataset. The results demonstrate the enhanced accuracy and reliability of the LSTM-based predictions facilitated by MicroGrowthPredictor. The inclusion of specific metrics, such as the mean square error, validates the model’s predictive performance. Our model holds immense potential for applications in environmental sciences, healthcare, and biotechnology, fostering advancements in microbiome research and analysis. Moreover, it is noteworthy that MicroGrowthPredictor is applicable to real data with small sample sizes and temporal observations under environmental perturbations, thus ensuring its practical utility across various domains.

Background: Understanding the cause vs consequence relationship of gut inflammation and microbial dysbiosis in inflammatory bowel diseases (IBD) requires a reproducible mouse model of human-microbiota-driven experimental colitis.

Results: Our study demonstrated that human fecal microbiota transplant (FMT) transfer efficiency is an underappreciated source of experimental variability in human microbiota-associated (HMA) mice. Pooled human IBD patient fecal microbiota engrafted germ-free (GF) mice with low amplicon sequence variant (ASV)-level transfer efficiency, resulting in high recipient-to-recipient variation of microbiota composition and colitis severity in HMA Il-10^-/- mice. In contrast, mouse-to-mouse transfer of mouse-adapted human IBD patient microbiota transferred with high efficiency and low compositional variability resulting in highly consistent and reproducible colitis phenotypes in recipient Il-10^-/- mice. Engraftment of human-to-mouse FMT stochastically varied with individual transplantation events more than mouse-adapted FMT. Human-to-mouse FMT caused a population bottleneck with reassembly of microbiota composition that was host inflammatory environment specific. Mouse-adaptation in the inflamed Il-10^-/- host reassembled a more aggressive microbiota that induced more severe colitis in serial transplant to Il-10^-/- mice than the distinct microbiota reassembled in non-inflamed WT hosts.

Conclusions: Our findings support a model of IBD pathogenesis in which host inflammation promotes aggressive resident bacteria, which further drives a feed-forward process of dysbiosis exacerbated by gut inflammation. This model implies that effective management of IBD requires treating both the dysregulated host immune response and aggressive inflammation-driven microbiota. We propose that our mouse-adapted human microbiota model is an optimized, reproducible, and rigorous system to study human microbiome-driven disease phenotypes, which may be generalized to mouse models of other human microbiota-modulated diseases, including metabolic syndrome/obesity, diabetes, autoimmune diseases, and cancer.

Cummings C, Landreville KD and Kuzma J. 2024. Taking the temperature of the United States public regarding microbiome engineering. Front. Public Health 12:1477377. doi: 10.3389/fpubh.2024.1477377

This paper presents the first representative survey of U.S. adults’ opinions on microbiome engineering within the built environment, revealing public awareness, perceived benefits and risks, and attitudes toward genetically engineered microbiomes. Using data from a cross-sectional survey of 1,000 nationally representative U.S. residents over 18 years of age, we examined demographic and cultural factors influencing public sentiment. Results indicate that younger generations report higher knowledge levels, optimism, and perceived benefits of microbiome engineering, while older generations exhibit more caution and concern about risks. Political affiliation, education level, and trust in science also shape public attitudes, with Democrats, college-educated individuals, and those with higher trust in science more likely to view microbiome engineering positively. Notably, nearly half of respondents across demographic groups remain uncertain about the technology’s benefits and risks, and a majority of participants support government oversight to ensure ethical and responsible development. These insights provide a foundation for policymakers and researchers to foster informed public engagement and guide responsible innovation in microbiome engineering for built environments.

PreMiEr-Associated Project

Moon JF, Kunkleman S, Taylor W, Harris A, Gibas CJ, Schlueter JA. 2024. A gold standard dataset and evaluation of methods for lineage abundance estimation from wastewater. Science of The Total Environment:174515. doi.org/10.1016/j.scitotenv.2024.174515

During the SARS-CoV-2 pandemic, genome-based wastewater surveillance sequencing has been a powerful tool for public health to monitor circulating and emerging viral variants. As a medium, wastewater is very complex because of its mixed matrix nature, which makes the deconvolution of wastewater samples more difficult. Here we introduce a gold standard dataset constructed from synthetic viral control mixtures of known composition, spiked into a wastewater RNA matrix and sequenced on the Oxford Nanopore Technologies platform. We compare the performance of eight of the most commonly used deconvolution tools in identifying SARS-CoV-2 variants present in these mixtures. The software evaluated was primarily chosen for its relevance to the CDC wastewater surveillance reporting protocol, which until recently employed a pipeline that incorporates results from four deconvolution methods: Freyja, kallisto, Kraken 2/Bracken, and LCS. We also tested Lollipop, a deconvolution method used by the Swiss SARS-CoV-2 Sequencing Consortium, and three additional methods not used in the C-WAP pipeline: lineagespot, Alcov, and VaQuERo. We found that the commonly used software Freyja outperformed the other CDC pipeline tools in correct identification of lineages present in the control mixtures, and that the VaQuERo method was similarly accurate, with minor differences in the ability of the two methods to avoid false negatives and suppress false positives. Our results also provide insight into the effect of the tiling primer scheme and wastewater RNA extract matrix on viral sequencing and data deconvolution outcomes.

McCumber AW, Kim YJ, Granek J, Tighe RM, Gunsch CK. 2024. Soil exposure modulates the immune response to an influenza challenge in a mouse model. Science of The Total Environment 922:170865. DOI: 10.1016/j.scitotenv.2024.170865

There is increasing evidence that early life microbial exposure aids in immune system maturation, more recently known as the “old friends” hypothesis. To test this hypothesis, 4-week-old mice were exposed to soils of increasing microbial diversity for four weeks followed by an intranasal challenge with either live or heat inactivated influenza A virus and monitored for 7 additional days. Perturbations of the gut and lung microbiomes were explored through 16S rRNA amplicon sequencing. RNA-sequencing was used to examine the host response in the lung tissue through differential gene expression. We determined that compared to the gut microbiome, the lung microbiome is more susceptible to changes in beta diversity following soil exposure with Lachnospiraceae ASVs accounting for most of the differences between groups. While several immune system genes were found to be significantly differentially expressed in lung tissue due to soil exposures, there were no differences in viral load or weight loss. This study shows that exposure to diverse microbial communities through soil exposure alters the gut and lung microbiomes resulting in differential expression of specific immune system related genes within the lung following an influenza challenge.

Hardwick, A., Cummings, C., Graves, J. et al. Can societal and ethical implications of precision microbiome engineering be applied to the built environment? A systematic review of the literature. Environ Syst Decis (2024). https://doi.org/10.1007/s10669-024-09965-y

The goal of engineering the microbiome of the built environment is to create places and spaces that are better for human health. Like other emerging technologies, engineering the microbiome of the built environment may bring considerable benefits but there has been a lack of exploration on its societal implication and how to engineer in an ethical way. To date, this topic area has also not been pulled together into a singular study for any systematic review or analysis. This study fills this gap by providing the first a systematic review of societal and ethical implications of engineering microbiomes and the application of this knowledge to engineering the microbiome of the built environment. To organize and guide our analysis, we invoked four major ethical principles (individual good/non-maleficence, collective good/beneficence, autonomy, and justice) as a framework for characterizing and categorizing 15 distinct themes that emerged from the literature. We argue that these different themes can be used to explain and predict the social and ethical implications of engineering the microbiome of the built environment that if addressed adequately can help to improve public health as this field further develops at global scales.

PreMiEr-Associated Project

Prince J, Jones AD, 3rd. 2023. Heterogenous biofilm mass-transport model replicates periphery sequestration of antibiotics in Pseudomonas aeruginosa PAO1 microcolonies. Proc Natl Acad Sci U S A 120:e2312995120.

https://doi.org/10.1073/pnas.2312995120

A model for antibiotic accumulation in bacterial biofilm microcolonies utilizing heterogenous porosity and attachment site profiles replicated the periphery sequestration reported in prior experimental studies on Pseudomonas aeruginosa PAO1 biofilm cell clusters. These P. aeruginosa cell clusters are in vitro models of the chronic P. aeruginosa infections in cystic fibrosis patients which display recalcitrance to antibiotic treatments, leading to exacerbated morbidity and mortality. This resistance has been partially attributed to periphery sequestration, where antibiotics fail to penetrate biofilm cell clusters. The physical phenomena driving this periphery sequestration have not been definitively established. This paper introduces mathematical models to account for two proposed physical phenomena driving periphery sequestration: biofilm matrix attachment and volume-exclusion due to variable biofilm porosity. An antibiotic accumulation model which incorporated these phenomena better fit observed periphery sequestration data compared to previous models.

PreMiEr-Associated Project

Childs SK, Jones AAD. 2023. A microtiter peg lid with ziggurat geometry for medium-throughput antibiotic testing and in situ imaging of biofilms. Biofilm 6:100167.

doi.org/10.1016/j.bioflm.2023.100167

Bacteria biofilm responses to disinfectants and antibiotics are quantified and observed using multiple methods, though microscopy, particularly confocal laser scanning microscopy (CLSM) is preferred due to speed, a reduction in user error, and in situ analysis. CLSM can resolve biological and spatial heterogeneity of biofilms in 3D with limited throughput. The microplate peg-lid-based assay, described in ASTM E2799-22, is a medium-throughput method for testing biofilms but does not permit in situ imaging. Breaking off the peg, as recommended by the manufacturer, risks sample damage, and is limited to easily accessible pegs. Here we report modifications to the peg optimized for in situ visualization and visualization of all pegs. We report similar antibiotic challenge recovery via colony formation following the ASTM E2799-22 protocol and in situ imaging. We report novel quantifiable effects of antibiotics on biofilm morphologies, specifically biofilm streamers. The new design bridges the MBEC® assays design that selects for biofilm phenotypes with in situ imaging needs.

LaMontagne CD, Christenson EC, Rogers AT, Jacob ME, Stewart JR. 2023. Relating antimicrobial resistance and virulence in surface-water E. coli. Microorganisms 11:2647.

https://www.mdpi.com/2076-2607/11/11/2647

The role of the environment in the emergence and spread of antimicrobial resistance (AMR) is being increasingly recognized, raising questions about the public health risks associated with environmental AMR. Yet, little is known about pathogenicity among resistant bacteria in environmental systems. Existing studies on the association between AMR and virulence are contradictory, as fitness costs and genetic co-occurrence can be opposing influences. Using Escherichia coli isolated from surface waters in eastern North Carolina, we compared virulence gene prevalence between isolates resistant and susceptible to antibiotics. We also compared the prevalence of isolates from sub-watersheds with or without commercial hog operations (CHOs). Isolates that had previously been evaluated for phenotypic AMR were paired by matching isolates resistant to any tested antibiotic with fully susceptible isolates from the same sample date and site, forming 87 pairs. These 174 isolates were evaluated by conventional PCR for seven virulence genes (bfp, fimH, cnf-1, STa (estA), EAST-1 (astA), eae, and hlyA). One gene, fimH, was found in 93.1% of isolates. Excluding fimH, at least one virulence gene was detected in 24.7% of isolates. Significant negative associations were found between resistance to at least one antibiotic and presence of at least one virulence gene, tetracycline resistance and presence of a virulence gene, resistance and STa presence, and tetracycline resistance and STa presence. No significant associations were found between CHO presence and virulence, though some sub-significant associations merit further study. This work builds our understanding of factors controlling AMR dissemination through the environment and potential health risks.

Mullowney, M.W., Duncan, K.R., Elsayed, S.S. et al. (…Reker D.). Artificial intelligence for natural product drug discovery. Nat Rev Drug Discov 22, 895–916 (2023).

https://doi.org/10.103/s41573-023-00774-7

Developments in computational omics technologies have provided new means to access the hidden diversity of natural products, unearthing new potential for drug discovery. In parallel, artificial intelligence approaches such as machine learning have led to exciting developments in the computational drug design field, facilitating biological activity prediction and de novo drug design for molecular targets of interest. Here, we describe current and future synergies between these developments to effectively identify drug candidates from the plethora of molecules produced by nature. We also discuss how to address key challenges in realizing the potential of these synergies, such as the need for high-quality datasets to train deep learning algorithms and appropriate strategies for algorithm validation.

Kelly, G.T., Granek, J., Gunsch C.K., Graves, J.L., Singleton, D. Advances in graduate training in Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM). Presented at 2023 ASEE Annual Conference and Exposition, Baltimore, Maryland. (2023).

https://peer.asee.org/42588

Innovations by engineers and physical scientists working at the frontiers of microbiome engineering and discovery requires in-depth understanding of microbiome systems with parallel skills to apply bioinformatics and biostatistics. Despite the importance of integrating bioinformatics and biology into graduate student training in fields outside traditional biological sciences, academic institutions remain challenged with including these disciplines across departmental boundaries. Furthermore, it is critical for students in engineering, bioinformatics, and biostatistics to understand fundamentals behind the biological systems they model, and for biology students to gain competencies to apply bioinformatics and biostatistics in quantitative biology arenas. To address these needs, the Integrative Bioinformatics for Investigating and Engineering Microbiomes (IBIEM) graduate training partnership between Duke University and North Carolina Agricultural and Technical State University was developed and funded by the National Science Foundation Research Traineeship (NRT) program. IBIEM’s goals include training interdisciplinary groups of students to: (a) transform conceptualization and develop skills for application of quantitative biology in microbiome areas; b) perform cutting edge research requiring interdisciplinary team skills; and to (b) communicate their research across disciplinary barriers and to diverse audiences. The pedagogical framework adapted to foster trainee engagement is learner-centered teaching which emphasizes the importance of self-directed learning with parallel ongoing assessment to optimize student outcomes. Since IBIEM trainee goals as well as entry-level knowledge and skills across disciplines varied greatly, program implementation was found to be challenging and required rigorous evaluation and refinements for effective training across disciplines and skill levels. A comprehensive program evaluation over five years found that the strongest learning and skills outcomes were linked to several “best practices”. Early provision of depth in fundamentals in R studio and Git Hub was found to be critical to “jump start” students without coding backgrounds. Addition of an overview of microbiome experimental design and analysis added important context as to how and where in the research process informatics fits into design progression and was highly motivating to students. Course modality was found to impact trainee outcomes with in-person classes that included hands-on practice and feedback showing greater improvements in training outcomes over hybrid, flipped and virtual course modalities. Furthermore, introduction of low, medium, and high level “challenges” along with in-person tutoring was found to be impactful in building a common foundation to span expertise levels and for engaging students across entry and advanced levels. Training impacts peaked during year four with cumulative implementation of revised strategies. Innovative training revisions and inclusion of critical elements was strongly linked to program satisfaction and ratings of advances in technical, professional and career skills as well as post-training carry over into trainees’ own research and leadership in their labs and careers. Furthermore, this training collaboration and partnership provided the foundation and training model for a newly funded NSF Engineering Research Center for Precision Microbiome Engineering (PreMiEr) for work in the critical area of engineering the microbiome in built environments.

PreMiEr-Associated Project

Jeje O, Ewunkem AJ, Jeffers-Francis LK, Graves JL. 2023. Serving two masters: Effect of Escherichia coli dual resistance on antibiotic susceptibility. Antibiotics 12:603.

https://www.mdpi.com/2079-6382/12/3/603

The prevalence of multidrug-resistant bacteria and their increased pathogenicity has led to a growing interest in metallic antimicrobial materials and bacteriophages as potential alternatives to conventional antibiotics. This study examines how resistance to excess iron (III) influences the evolution of bacteriophage resistance in the bacterium Escherichia coli. We utilized experimental evolution in E. coli to test the effect of the evolution of phage T7 resistance on populations resistant to excess iron (III) and populations without excess iron resistance. Phage resistance evolved rapidly in both groups. Dual-resistant (iron (III)/phage) populations were compared to their controls (excess iron (III)-resistant, phage-resistant, no resistance to either) for their performance against each stressor, excess iron (III) and phage; and correlated resistances to excess iron (II), gallium (III), silver (I) and conventional antibiotics. Excess iron (III)/phage-resistant populations demonstrated superior 24 h growth compared to all other populations when exposed to increasing concentrations of iron (II, III), gallium (III), ampicillin, and tetracycline. No differences in 24 h growth were shown between excess iron (III)/phage-resistant and excess iron (III)-resistant populations in chloramphenicol, sulfonamide, and silver (I). The genomic analysis identified selective sweeps in the iron (III) resistant (rpoB, rpoC, yegB, yeaG), phage-resistant (clpX →/→ lon, uvaB, yeaG, fliR, gatT, ypjF, waaC, rpoC, pgi, and yjbH) and iron (III)/phage resistant populations (rcsA, hldE, rpoB, and waaC). E. coli selected for resistance to both excess iron (III) and T7 phage showed some evidence of a synergistic effect on various components of fitness. Dual selection resulted in correlated resistances to ionic metals {iron (II), gallium (III), and silver (I)} and several conventional antibiotics. There is a likelihood that this sort of combination antimicrobial treatment may result in bacterial variants with multiple resistances.

Ji, Z., Ma, L. Controlling taxa abundance improves metatranscriptomics differential analysis. BMC Microbiol 23, 60 (2023).

https://doi.org/10.1186/s12866-023-02799-9

Song, K.; Zhou, Y.-H. Leveraging Scheme for Cross-Study Microbiome Machine Learning Prediction and Feature Evaluations. Bioengineering 2023, 10, 231.

https://doi.org/10.3390/bioengineering10020231

The microbiota has proved to be one of the critical factors for many diseases, and researchers have been using microbiome data for disease prediction. However, models trained on one independent microbiome study may not be easily applicable to other independent studies due to the high level of variability in microbiome data. In this study, we developed a method for improving the generalizability and interpretability of machine learning models for predicting three different diseases (colorectal cancer, Crohn’s disease, and immunotherapy response) using nine independent microbiome datasets. Our method involves combining a smaller dataset with a larger dataset, and we found that using at least 25% of the target samples in the source data resulted in improved model performance. We determined random forest as our top model and employed feature selection to identify common and important taxa for disease prediction across the different studies. Our results suggest that this leveraging scheme is a promising approach for improving the accuracy and interpretability of machine learning models for predicting diseases based on microbiome data.

Song, K., Zhou, YH. C3NA: correlation and consensus-based cross-taxonomy network analysis for compositional microbial data. BMC Bioinformatics 23, 468 (2022).

https://doi.org/10.1186/s12859-022-05027-9