SEARCH · Search bioRxiv

Results for “Microbiology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 631 records · Page 35Linked to original sources

bioRxiv2019

Genomic characterization and computational phenotyping of nitrogen-fixing bacteria isolated from Colombian sugarcane fields

Previous studies have shown that the sugarcane microbiome harbors diverse plant growth promoting (PGP) microorganisms, including nitrogen-fixing bacteria, and the objective of this study was to design a genome-enabled approach to prioritize sugarcane associated nitrogen-fixing bacteria according to their potential as biofertilizers. Using a systematic high throughput approach, 22 pure cultures of nitrogen-fixing bacteria were isolated and tested for diazotrophic potential by PCR amplification of nitrogenase (nifH) genes, common molecular markers for nitrogen fixation capacity. Genome sequencing confirmed the presence of intact nitrogenase nifH genes and operons in the genomes of 18 of the isolates. Isolate genomes also encoded operons for phosphate solubilization, siderophore production operons, and other PGP phenotypes. Klebsiella pneumoniae strains comprised 14 of the 22 nitrogen-fixing isolates, and four others were members of closely related genera to Klebsiella. A computational phenotyping approach was developed to rapidly screen for strains that have high potential for nitrogen fixation and other PGP phenotypes while showing low risk for virulence and antibiotic resistance. The majority of sugarcane isolates were below a genotypic and phenotypic threshold, showing uniformly low predicted virulence and antibiotic resistance compared to clinical isolates. Six prioritized strains were experimentally evaluated for PGP phenotypes: nitrogen fixation, phosphate solubilization, and the production of siderophores, gibberellic acid and indole acetic acid. Results from the biochemical assays were consistent with the computational phenotype predictions for these isolates. Our results indicate that computational phenotyping is a promising tool for the assessment of benefits and risks associated with bacteria commonly detected in agricultural ecosystems.\n\nIMPORTANCEA genome-enabled approach was developed for the prioritization of native bacterial isolates with the potential to serve as biofertilizers for sugarcane fields in Colombias Cauca Valley. The approach is based on computational phenotyping, which entails predictions related to traits of interest based on bioinformatic analysis of whole genome sequences. Bioinformatic predictions of the presence of plant growth promoting traits were validated with experimental assays and more extensive genome comparisons, thereby demonstrating the utility of computational phenotyping for assessing the benefits and risks posed by bacterial isolates that can be used as biofertilizers. The quantitative approach to computational phenotyping developed here for the discovery of biofertilizers has the potential for use with a broad range of applications in environmental and industrial microbiology, food safety, water quality, and antibiotic resistance studies.

microbiology↗

bioRxiv2019

Sticky Situations: Bacterial Attachment Deciphered by Interferometry of Silicon Microstructures

The peculiarities of surface-bound bacterial cells are often overshadowed by the study of planktonic cells in clinical microbiology. Thus, we employ phase-shift reflectometric interference spectroscopic measurements to observe the interactions between bacterial cells and abiotic, microstructured material surfaces in a label-free, real-time manner. Both material characteristics (i.e., substrate surface charge and wettability) and characteristics of the bacterial cells (i.e., motility, cell charge, biofilm formation, and physiology) drive bacteria to adhere to a particular surface. We conclude that the attachment of bacterial cells to a surface is determined by the culmination of numerous factors. When specific characteristics of the bacteria are met with factors of the surface, enhanced cell attachment and biofilm formation occur. Such knowledge can be exploited to predict antibiotic efficacy, biofilm development, enhance biosensor development, as well as prevent biofouling.

microbiology↗

bioRxiv2019

Understanding and predicting ciprofloxacin minimum inhibitory concentration in Escherichia coli with machine learning

2.A possible way to tackle the crisis of antimicrobial resistance development is a strict policy when prescribing antibiotics. Thus, it is important that prescriptions are based on antimicrobial susceptibility data to ensure effective treatment outcomes. The increasing availability of next-generation sequencing (NGS), bacterial whole genome sequencing (WGS) can facilitate a more reliable and faster alternative to traditional phenotyping for the detection and surveillance of AMR. This work proposes a machine learning approach that can predict the minimum inhibitory concentration (MIC) for a given antibiotic, here ciprofloxacin, on the basis of both genome-wide mutation profiles and profiles of acquired antimicrobial resistance genes (ARG). We analysed 704 Escherichia coli genomes combined with their respective MIC measurements for ciprofloxacin originating from different countries. The four most important predictors found by the model, mutations in gyrA residues Ser83 and Asp87, a mutation in parC residue Ser80 and presence of any qnrS gene, have been experimentally validated before. Using only these four predictors in a linear regression model, 65% and 92% of the test samples MIC were correctly predicted within a two- and a four-fold dilution range, respectively. The presented work goes further than the typical predictions that use machine learning as a black box model concept. The recent progress in WGS technology in combination with machine learning analysis approaches indicates that in the near future WGS of bacteria might become cheaper and faster than a MIC measurement. 3. Impact statementWhole genome sequencing has become the standard approach to study molecular epidemiology of bacteria. However, the application of WGS in the clinical microbiology laboratory as part of individual patient diagnostics still requires significant steps forward, in particular with respect to prediction of antibiotic susceptibility based on DNA sequence. Whilst the majority of studies of prediction of susceptibility have used a binary outcome (susceptible/resistant), a quantitative prediction of susceptibility, such as the MIC, will allow for earlier detection of trends in increasing resistance as well as the flexibility to follow potential adjustments in definitions of susceptible (wild type) and resistant (non-wild type) categories (breakpoints/ epidemiological cut-off values). 4. Data summaryIn this study, 704 E. coli genomes combined with MIC measurement for ciprofloxacin were analysed (24). Paired-end sequencing was performed on all isolates and the results were stored in FASTQ format. The isolates originated from five countries, Denmark, Italy, USA, UK, and Vietnam. The MIC distribution for these isolates is depicted in Table 1. Out of 704, 266 E. coli genomes had no country metadata available and were used as an independent test set. All data were deposited in the AMR Data Hub (24) which consists of raw sequencing data, ciprofloxacin minimum inhibitory concentrations, and additional metadata such as the origin of the samples. O_TBL View this table: org.highwire.dtl.DTLVardef@5a31d2org.highwire.dtl.DTLVardef@17cfa2eorg.highwire.dtl.DTLVardef@ce3ccorg.highwire.dtl.DTLVardef@3f6721org.highwire.dtl.DTLVardef@8bb604_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTABLE 1C_FLOATNO O_TABLECAPTIONThe collected and used data in the analysis grouped by country and MIC values. C_TABLECAPTION C_TBL Publicly available sequencing data was used from projects PRJEB21131, PRJNA266657, PRJNA292901, PRJNA292904, PRJNA292902, PRJDB7087, PRJEB21880, PRJEB21997, PRJEB14086 and PRJEB16326. Download and analysis scripts are available at https://github.com/patbaa/AMR_ciprofloxacin. iTOL phylogenetic tree is available at https://itol.embl.de/tree/14511722611491391569485969. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

microbiology↗

bioRxiv2019

AMR - An R Package for Working with Antimicrobial Resistance Data

Antimicrobial resistance is an increasing threat to global health. Evidence for this trend is generated in microbiological laboratories through testing microorganisms for resistance against antimicrobial agents. International standards and guidelines are in place for this process as well as for reporting data on (inter-)national levels. However, there is a gap in the availability of standardized and reproducible tools for working with laboratory data to produce the required reports. It is known that extensive efforts in data cleaning and validation are required when working with data from laboratory information systems. Furthermore, the global spread and relevance of antimicrobial resistance demands to incorporate international reference data in the analysis process. In this paper, we introduce the AMR package for R that aims at closing this gap by providing tools to simplify antimicrobial resistance data cleaning and analysis, while incorporating international guidelines and scientifically reliable reference data. The AMR package enables standardized and reproducible antimicrobial resistance analyses, including the application of evidence-based rules, determination of first isolates, translation of various codes for microorganisms and antimicrobial agents, determination of (multi-drug) resistant microorganisms, and calculation of antimicrobial resistance, prevalence and future trends. The AMR package works independently of any laboratory information system and provides several functions to integrate into international workflows (e.g., WHONET software provided by the World Health Organization).

microbiology↗

bioRxiv2019

Diagnosis of Centrocestus formosanus Infection in Zebrafish (Danio rerio): A Window on a New Globalization-Derived Invasive Parasite.

Centrocestus formosanusis a digenetic trematode with a complex life cycle, involving invertebrate and vertebrate hosts, humans included. In particular, it causes gill lesions and mortality in freshwater fish species, and gastrointestinal symptoms in infected humans. Here,we describe the occurrence of C. formosanus infection in zebrafish imported in Italy. Gill arches of 30 zebrafish were examined for the presence of encysted metacercariae under a stereomicroscope, and processed through molecular analyses targeting the ribosomal Internal Transcribed Sequence 2 (ITS2) using species-specific primers of C. formosanus. Encysted metacercariae were found on the gills of 20/30 zebrafish and all were identified as C. formosanus.\n\nDespite C. formosanus distribution was originally restricted to Asia, it has been subsequently reported in new countries, revealing itself as an invasive species, and raising important concerns for biodiversity, economy, animal and public health. Given the crucial role likely played by the ornamental fish industry in the spreading of this parasite, there is an urgent need for control measures to prevent the introduction and establishment of C. formosanus in non-endemic areas, Europe included. An adequate surveillance and health-monitoring program should be conducted in the development of microbiological and epidemiological approaches to diagnose and face these new globalization-derived invasive species.

microbiology↗

bioRxiv2019

Effective application of machine learning to microbiome-based classification problems

Machine learning (ML) modeling of the human microbiome has the potential to identify microbial biomarkers and aid in the diagnosis of many diseases such as inflammatory bowel disease, diabetes, and colorectal cancer. Progress has been made towards developing ML models that predict health outcomes using bacterial abundances, but inconsistent adoption of training and evaluation methods call the validity of these models into question. Furthermore, there appears to be a preference by many researchers to favor increased model complexity over interpretability. To overcome these challenges, we trained seven models that used fecal 16S rRNA sequence data to predict the presence of colonic screen relevant neoplasias (SRNs; n=490 patients, 261 controls and 229 cases). We developed a reusable open-source pipeline to train, validate, and interpret ML models. To show the effect of model selection, we assessed the predictive performance, interpretability, and training time of L2-regularized logistic regression, L1 and L2-regularized support vector machines (SVM) with linear and radial basis function kernels, decision trees, random forest, and gradient boosted trees (XGBoost). The random forest model performed best at detecting SRNs with an AUROC of 0.695 [IQR 0.651-0.739] but was slow to train (83.2 h) and not inherently interpretable. Despite its simplicity, L2-regularized logistic regression followed random forest in predictive performance with an AUROC of 0.680 [IQR 0.625-0.735], trained faster (12 min), and was inherently interpretable. Our analysis highlights the importance of choosing an ML approach based on the goal of the study, as the choice will inform expectations of performance and interpretability. ImportanceDiagnosing diseases using machine learning (ML) is rapidly being adopted in microbiome studies. However, the estimated performance associated with these models is likely over-optimistic. Moreover, there is a trend towards using black box models without a discussion of the difficulty of interpreting such models when trying to identify microbial biomarkers of disease. This work represents a step towards developing more reproducible ML practices in applying ML to microbiome research. We implement a rigorous pipeline and emphasize the importance of selecting ML models that reflect the goal of the study. These concepts are not particular to the study of human health but can also be applied to environmental microbiology studies.

microbiology↗

bioRxiv2019

New flat embedding method for transmission electron microscopy reveals an unknown mechanism of tetracycline

Transmission electron microscopy (TEM) is an important imaging technique in bacterial research and requires ultrathin sectioning of resin embedding of cell pellets. This method consumes milli- to deciliters of culture and results in sections of randomly orientated cells. For rod-shaped bacteria, this makes it exceedingly difficult to find longitudinally cut cells, which precludes large-scale quantification of morphological phenotypes. Here, we describe a new fixation method using either thin agarose layers or carbon-coated glass surfaces that enables flat embedding of bacteria. This technique allows for the observation of thousands of longitudinally cut rod-shaped cells per single section and requires only microliter culture volumes. We successfully applied this technique to Gram-positive Bacillus subtilis, Gram-negative Escherichia coli, the tuberculosis vaccine strain Mycobacterium bovis BCG, and the cell wall-lacking mycoplasma Acholeplasma laidlawii. To assess the potential of the technique to quantify morphological phenotypes, we examined cellular changes induced by a panel of different antibiotics. Surprisingly, we found that the ribosome inhibitor tetracycline causes significant deformations of the cell membrane. Further investigations showed that the presence of tetracycline in the cell membrane changes membrane organization and affects the peripheral membrane proteins MinD, MinC, and MreB, which are important for regulation of cell division and elongation. Importantly, we could show that this effect is not the result of ribosome inhibition but is a secondary antibacterial activity of tetracycline that has defied discovery for more than 50 years.\n\nSignificanceBacterial antibiotic resistance is a serious public health problem and novel antibiotics are urgently needed. Before a new antibiotic can be brought to the clinic, its antibacterial mechanism needs to be elucidated. Transmission electron microscopy is an important tool to investigate these mechanisms. We developed a flat embedding method that enables examination of many more bacterial cells than classical protocols, enabling large-scale quantification of phenotypic changes. Flat embedding can be adapted to most growth conditions and microbial species and can be employed in a wide variety of microbiological research fields. Using this technique, we show that even well-established antibiotics like tetracycline can have unknown additional antibacterial activities, demonstrating how flat embedding can contribute to finding new antibiotic mechanisms.

microbiology↗

bioRxiv2019

Frequency of tuberculosis in Farm Workers Exposed to dairy and beef livestock in Colombia

The objective of this study was to determine the frequency of tuberculosis (TB) in workers from dairy and beef livestock farms in the northern part of Colombia. Tuberculin skin test and an interferon-gamma release assay (IGRA) were used for diagnosis of latent tuberculosis; sputum samples were taken from respiratory symptomatic subjects, microbiological and molecular tests were done for diagnosis of active TB. Absolute frequencies, percentages, and crude prevalence ratios were calculated, and a robust Poisson Model with adjustment by municipality was made. In 674 farm workers, latent TB frequency was 35.8%. Variables such as having had pulmonary TB (PR 2.82, 95% CI 1.90 - 4.17), having been in contact with people with active TB (PR 1.57, 95% CI 1.24 - 1.98), and having performed some undergraduate or postgraduate study (PR 1.6, 95% CI 1.03 - 2.49), were significantly associated with latent TB. No active tuberculosis disease was confirmed in symptomatic respiratory patients. The exposure level to cattle was not significantly associated with latent TB infection. In conclusion, in the studied population exposure to cattle was not a risk factor for TB, other factors commonly found in general population exposed to human TB were demonstrated.\n\nAuthor summaryZoonotic TB is a disease caused by the transmission of the M. bovis bacteria that is part of the Mycobacterium tuberculosis Complex, through contact with cattle to humans, by the consumption of unpasteurized dairy products from infected animals or by inhalation of aerosols exhaled by sick animals.\n\nThis study investigated the frequency of TB in human population related to cattle, in order to determine if there were risk factors related to TB infection or disease. Finding that there was no significant relationship between being exposed to cattle and having latent TB. However, the results of this study together with other research reported in the literature suggest that research on zoonotic and bovine TB should be continued, especially about epidemiology, diagnostic methods, health systems and interventions coordinated with veterinary services.

microbiology↗

bioRxiv2019

ISOLATION AND SCREENING OF HEAVY METAL RESISTANT AMMONIA OXIDIZING BACTERIA FROM SOIL AND WASTE DUMP: A POTENTIAL CANDIDATES FOR BIOREMEDIATION OF HEAVY METALS

The study was undertaken to examine the response of ammonia oxidizing bacteria to different heavy metal salt in an elevated concentration. Surface soil samples at depth of 0-15 cm were collected at random from Akwa Ibom State University in Akwa Ibom State, soil sample from University of Nigeria, Nsukka and from solid waste disposal site in Uyo, Akwa Ibom State. The response of heavy metal salt on Ammonia Oxidizing bacteria(AOB) isolated from soil samples were investigated by supplementing different heavy metal salts namely, copper(Cu),nickel (Ni), lead (Pb) and cadmium (Cd) at four loading rates(100,200,500,1000 {micro}g/ml) in mineral salt broth with Ammonia Oxidizing Bacteria (AOB) isolate. The cultures were incubated for 7 days. Growth of AOB was measured by withdrawing samples from the medium every 24 hours and absorbance of the turbidity measured at 600 nanometre using spectrophotometer. All bacteria showed high tendency to decrease optical density while increasing metal concentration in the medium. Tolerance for the metal ions was dependent on concentration, time and the isolate tested. All the Ammonia oxidizing bacterial (AOB) showed a high level of tolerance for the metals tested, and exhibited good growth at all metal salt concentrations tested. These make the nitrifying bacteria attractive potential candidates for further investigations regarding their ability to remove metals from contaminated soil.\n\nIMPORTANCEThe aim of this study is tolerance of ammonia oxidizing bacteria growth to heavy metal. Nitrifying bacteria remain a good option for bioremediation of soil and waste dump, since it is regarded as eco-friendly and efficient in biosorption of heavy metal. The study is significant to the field of environmental microbiology by adding to knowledge in bioremediation.

microbiology↗

bioRxiv2025

micromorph: a Python toolkit for measurement of microbial morphology

Detection of morphological phenotypes from light microscopy is a key part of microbiology. Despite advances in automated morphological analysis, accurate measurements still often require significant user input. To address this, we have developed micromorph, a Python package to measure bacterial morphological properties from multiple types of light microscopy data. micromorph is available as a Python package with a documented API, or as napari plugin, thus appealing to a range of users regardless of their coding proficiency. The micromorph API allows easy integration into custom analysis pipelines. We demonstrate a range of scenarios in which micromorph demonstrates excellent performance and introduce a new approach for reliably measuring the widths and lengths of quasi-circular cells.

microbiology↗

bioRxiv2025

Poloxamer dilution as an on-demand alternative to agar dilution-based antimicrobial susceptibility testing

Agar dilution is a reference susceptibility testing method uniquely or preferentially recommended for certain antimicrobials. However, the effort required to pour individual agar plates spanning a doubling dilution range precludes its practical implementation in hospital clinical laboratories. Here, we describe an on-demand replacement for agar dilution (AD), specifically substituting poloxamer 407 (also known as Pluronic F-127) for Bacto Agar as the solidifying agent. Notably, 20% poloxamer 407 solutions (e.g., with Mueller-Hinton broth) remain liquid at refrigerated temperatures, but solidify upon warming, enabling facile set-up of poloxamer dilution testing (PD) in Petri dish or microwell format. For fosfomycin susceptibility testing, PD and reference agar dilution (AD) showed excellent categorical (CA) and essential (EA) agreement for E. coli (100% and 87%, respectively, n=31). For other Enterobacterales, excluding Klebsiella spp; CA and EA were both 82% (n=17, respectively). For P. aeruginosa, CA and EA were 60% and 100% (n=10), respectively, with the lower CA reflecting the large number of strains tested with MICs near categorical breakpoints. There were no very major errors, while major errors were only observed for Klebsiella spp. Additionally, PD substantially reduced the number of skipped dilutions 6-fold for E. coli (P < 0.0001) and inhibited swarming of Proteus species. We conclude that PD and AD, an imperfect gold standard, have essentially equivalent practical performance and that PD can therefore serve as an on-demand alternative testing methodology in clinical laboratories for fosfomycin testing of gram-negative pathogens. A broader exploration of PDs utility is thus warranted. ImportanceAccurate antibiotic susceptibility testing is essential for guiding treatment of bacterial infections. For the antibiotic fosfomycin, used to treat Escherichia coli urinary tract infections, the most reliable testing method requires solid media prepared by hand for each antibiotic concentration, which is too time consuming for most clinical laboratories to perform. Our study shows that replacing agar with an alternative temperature-sensitive gelling agent called poloxamer enables laboratories to prepare solid test plates rapidly without special equipment. This approach, which is essentially identical to traditional agar dilution, provides a practical alternative that can be implemented by hospital clinical microbiology laboratories for accurate fosfomycin testing near the point of patient care. This strategy may also be applicable to other drugs for which agar dilution is the preferred testing method, supporting expedited testing to inform treatment decisions for bacterial infections.

microbiology↗

bioRxiv2025

Differential Alterations of Gut Microbiota in Girls with Isolated Obesity, Isolated Precocious Puberty, and Their Comorbidity

BackgroundChildhood obesity and precocious puberty (PP) are escalating public health concerns with a significant comorbidity. Emerging evidence implicates gut microbiota dysbiosis in both conditions, yet microbial associations with their co-occurrence remain unexplored. MethodsA total of 68 girls (18 normal, 25 PP-only, 18 obesity-only, and 7 obesity-PP comorbidity) were enrolled. 16S rRNA sequencing was applied to determine gut microbiota of girls. Microbial diversity, composition, and differential abundant taxa were analyzed. ResultsWhile overall microbial diversity and community structure were similar across groups, obesity-only subjects exhibited significantly increased microbial richness (Chao1 index, P<0.01 vs. all groups), characterized by a higher Firmicutes/Bacteroidota ratio (P<0.001 vs. normal/PP), increased Proteobacteria (P<0.001 vs. normal), and enrichment of genera including Blautia, Faecalibacterium, Roseburia, Anaerostipes, Haemophilus, and Lachnospiraceae_NK4A136_group (P<0.05 vs. other groups). PP-only subjects showed reduced Verrucomicrobiota (P<0.001 vs. normal/obesity) and elevated Escherichia_Shigella (P<0.05 vs. normal). Most strikingly, the obesity-PP comorbid group exhibited a unique gut microbiota profile characterized by a dramatic depletion of Firmicutes compared to the obesity-only group (P<0.001) and a remarkable nearly 100-fold increase in Verrucomicrobiota abundance (P<0.001 vs. other groups), suggesting a distinct microbial ecotype potentially contributing to the pathophysiology of this comorbidity. ConclusionThis study delineated unique gut microbiota alterations associated with isolated obesity, isolated PP, and their comorbidity in girls. The distinct dysbiotic pattern in comorbid obesity-PP suggested complex microbiota-host interactions, providing a new microbiological perspective for understanding the mechanism of comorbidity between obesity and precocious puberty.

microbiology↗

bioRxiv2025

BioGeoFormer: A deep learning approach to classify unknown genes associated with critical biogeochemical cycles

Remote functional annotation continues to impede progress in microbial ecology, as alignment-based approaches still leave over one-third of microbial sequences functionally unresolved. In contrast, pre-trained natural-language-processing approaches have shown strong potential for inferring functions from diverse biological sequences, and here we introduce a protein language modeling approach allowing us to classify sequences into 37 defined key pathway categories involved in 4 major biogeochemical cycles (methane, sulfur, nitrogen and phosphorus cycles). To do so, we fine-tuned ESM2-8m using databases curated for biogeochemical cycling pathways. Our resultant BioGeochemical cycling transFormer (BioGeoFormer or BGF) was high-performing on validation and test sets, producing embeddings that exhibit an ability to infer protein function at a metabolic pathway level. BGF was applied to a dataset of metagenome-assembled genomes (MAGs) constructed from methane-fueled, deep-sea, "cold seep" environments to demonstrate its utility in contrast to current informatics approaches. We employed multiple gene assignments to identify gene function within these MAGs. A total of 1.05M genes were assigned biogeochemical functions, with BGF alone suggesting putative ecosystem roles for 0.49M (46%) of these at a confidence of 85% or greater; these genes were classified as unknown by the other approaches. Across the pathways of interest, BGF identified 6 times as many genes, on average, as Hidden Markov models (HMMs) as well as alignment-based approaches across the various pathways. BGF provides a novel tool that is capable of informing process-based hypotheses in diverse systems, highlighting cryptic proteins most notably linked to methane, nitrogen, and phosphorus cycling while uncovering the mysteries within microbial dark matter. Author summaryWhen investigating the function of microbes in the environment, scientists are often left with vast amounts of genes or proteins where no knowledge about their function is available. This represents a huge amount of information left to be discovered in many fields of biology. One recent approach that has shown significant potential in further understanding unknown proteins are protein language models, which are deep-learning methods leveraging large datasets to understand the language of proteins. We aimed to apply protein language modeling to further understand the function of proteins as they relate to large-scale environmental transformation of nutrients and carbon. Specifically, we designed our approach to further understand the metabolism of microbes that affect methane, nitrogen, phosphorus, and sulfur, all elements that are highly impactful to the planets function and health. We used our new approach on a deep-sea microbiology dataset, and showed the methods utility in further understanding the function of proteins and their impact on the environment. Overall, we found our method is an important new tool in the toolset of environmental scientists working to better understand the function of microbes and their proteins.

microbiology↗

bioRxiv2026

Emerging Pathogens in Urinary Tract Infections: Virulence and Phenotypic Characterization of Pseudomonas aeruginosa strains

Urinary tract infections (UTIs) affect a broad patient population and inflict a substantial financial burden on the U.S. healthcare system. While uropathogenic Escherichia coli (UPEC) causes the majority of cases, other pathogens are emerging. Analysis of patient data from our healthcare system in the Gulf Coast region of Alabama revealed that Pseudomonas aeruginosa accounted for 4.0% of UTI cases, roughly double the national average, prompting further investigation into this historically understudied uropathogen. Here, we performed whole-genome sequencing and phenotypic assays on 55 urinary P. aeruginosa isolates to identify key drivers of pathogenicity in the context of UTI. Multilocus sequence typing identified 19 novel sequence types, underscoring the uncharacterized diversity of urinary P. aeruginosa isolates. Serotype O6 was most common and enriched in patients with indwelling catheters, whereas O4 was linked to diabetes mellitus. Antibiotic susceptibility testing (AST) revealed high levofloxacin resistance (30.9%), with 23.6% multidrug-resistant (MDR) and 9.1% extensively drug-resistant (XDR) isolates. Resistance patterns correlated with demographics, including significantly higher meropenem and aztreonam resistance in isolates from African American patients. Phenotypic assays of growth, motility, and biofilm formation revealed negative correlations between antibiotic resistance and virulence. Specific virulence genes predicted enhanced iron acquisition, hemolysis, and colonization potential. Notably, motility and exotoxin profiles emerged as strong predictors of P. aeruginosa ascension in a murine UTI model. Together, these findings provide new biological and clinical insight into P. aeruginosa as a uropathogen and emphasize the need for continued research. IMPORTANCEPseudomonas aeruginosa is an emerging but understudied pathogen in urinary tract infections (UTIs). Given its resilience, adaptability, and the growing threat of multidrug resistance, P. aeruginosa remains a significant challenge in clinical microbiology and infection control. Our data reveal an increased prevalence of P. aeruginosa in our local patient population. In this study, we examined both genotypic and phenotypic traits of clinical isolates and correlated them with colonization in murine models and extensive patient metadata. We identified strong associations between antibiotic resistance patterns and patient demographics. Novel sequence type strains were linked to motility phenotypes in vitro. Additionally, specific flagellar alleles were associated with enhanced murine kidney colonization and recurrent UTIs in patients. These findings provide new insight into the evolutionary adaptations that contribute to P. aeruginosa uropathogenicity and support a more nuanced understanding of its clinical significance.

microbiology↗

bioRxiv2026

A next generation space antimicrobial: assessing microbial activity and reduction across the ISS

Microbial contamination in spacecraft poses a threat to crew health and operational integrity. Currently, microbial reduction aboard the International Space Station (ISS) relies on rigorous cleaning, which is time-consuming for astronauts. This study analyzes the ISS microbiome and applies a novel quaternized antimicrobial polymer coating to assess its effectiveness in mitigating microbial contamination and its durability over prolonged periods of interaction. The polymer was coated onto five material types representing common objects interacted with by the crew. Each material had six coupons placed on six placards in various ISS locations, while three placards remained on Earth to accumulate microbes from passive and direct transfer. The trial lasted six months, utilizing microbiological cell cultures and genomic analysis to assess bacterial communities and identify species. Among the six placards on the ISS, only one had sufficient viable bacteria to assess the polymers efficacy. On this placard, the polymer coating was shown to decrease culturable bacterial surface loads by 1.39 log10 (equal to a 95.9% reduction) compared with uncoated surfaces. Coated surfaces exhibited less genetic material and lower microbial species richness. Additionally, the polymer coating proved durable, persisting on ISS surfaces throughout the six-month period. This study demonstrates that the antimicrobial polymer coating can actively reduce microbial loads and remains on surfaces in zero gravity environments. Its application is likely beneficial when applied alongside other mitigation activities for viral and bacterial contamination, with potential uses in long-duration spaceflight to enhance crew health and maintain spacecraft integrity. We also generated data showing the lack of microbial presence on the ISS.

microbiology↗

bioRxiv2026

Enteroscape: An agent-based model for simulating microbial dynamics and host responses in the gut ecosystem

Understanding microbial interactions within the gut microbiome is challenging due to the sheer number of microorganisms present. Each additional microbe introduces new layers of complexity, especially when studying the conditions and dynamics of infectious progression. To address this challenge, we have developed Enteroscape: an in silico agent-based model, grounded in previous research, laboratory experiments, and in vivo assays. Enteroscape enables simulation of diverse microbial interactions within a nematode host environment. In this study, we used Enteroscape to model the progression of pathogenic yeast infection and evaluate the effects of probiotic yeast treatment in the Caenorhabditis elegans intestine. Drawing upon empirical and literature-based behavioral rules for individual microbes, Enteroscape simulations produced emergent behaviors of the microbial population that mirror experimentally observed infection dynamics, including visual patterns of infectious progression, as well as the effect of probiotic treatment on the host lifespan. Enteroscape demonstrates how computational models can generate testable predictions that deepen our insight into microbial community interactions and their impact on host biology. Author SummaryThe human microbiome is a complicated ecosystem, consisting of trillions of microbes of hundreds of different species that live in the human intestine. Understanding how different species interact to maintain a healthy balanced microbiological ecosystem that keeps pathogenic microbes at bay is critically important to human health. We have developed a simple experimental system that allows us to examine the interactions of just a few thousand microbes at a time in a living host, the microscopic nematode worm, C. elegans. In prior laboratory work, we have demonstrated how ingestion by the worm of the human pathogenic microbe C. albicans shortens the worms lifespan, while co-ingestion of various probiotic microbial species restores a longer lifespan. In this paper, we report the creation of a computational model of this system, which we named Enteroscape. We show that Enteroscape mimics the results of the experimental system, including the visual progression of infection by a pathogenic species, the effects on worm lifespan, and the amelioration of the pathogens effects by a probiotic species. Enteroscape will allow us to develop and test hypotheses about the mechanisms underlying remediation by probiotic microbes, with potential applications to better probiotic treatment of human infections.

microbiology↗

bioRxiv2026

Combined Lytic Score as a new metric to quantify phage-host specificity, tested in a screening study on a collection of Salmonella strains.

Bacteriophages are promising alternatives for antibiotics. One challenge of developing bacteriophage-based preparation for practical application is to understand expected host range of such products. We evaluated the host range and lytic activity of BAFASAL(R), a four-phage cocktail targeting Salmonella in poultry production. For that purpose, we developed a new composite metric, the Combined Lytic Score (CLS), integrating results from two in vitro assays: serial dilutions spot test on semisolid medium and spectrophotometric growth inhibition in liquid culture. Using this approach phage cocktail was tested against collection of 72 Salmonella strains, including 55 S. Enteritidis isolates representing diverse geographic origins and genomic backgrounds. Spot test patterns were transformed into a continuous scale using the Most Probable Number (MPN) approach to estimate the number of phages required for visible lysis. In parallel, growth inhibition was quantified as the area-under-curve-based inhibition score (ANS). Both metrics were normalized and combined into CLS as a projection onto the regression line describing their correlation (R{superscript 2} {approx} 0.82). More than 65% of S. Enteritidis strains, reached normalized CLS values higher 75%, indicating high susceptibility to BAFASAL(R) in vitro. Phage susceptibility did not correlate with either phenotypic antibiotic resistance or the number of resistance and virulence genes. CLS provides a quantitative method to integrate different experimental methods of determination of bacterial susceptibility to bacteriophages and to rank bacterial strains by phage susceptibility. This approach supports robust host range determination and may facilitate regulatory evaluation and rational design of phage-based interventions in food safety and animal production. IMPORTANCEAssessment of bacteriophage host range is an important step in characterization of bacteriophage strains both in basic and translational research, yet it is still commonly based on qualitative or poorly standardized assays. This lack of harmonization limits reproducibility and complicates comparisons across studies, laboratories, and application contexts. In this work, we propose the Combined Lytic Score (CLS) as a quantitative framework that integrates outcomes from two widely used experimental approaches: serial-dilution spot assays and microtiter-based growth inhibition kinetics. By converting spot-test results into a continuous, concentration-dependent metric and combining them with normalized kinetic inhibition data, CLS enables more consistent interpretation of phage-host interaction outcomes. Application of CLS to a diverse collection of Salmonella enterica strains demonstrates how this approach can support systematic, scalable host range analyses. The CLS framework provides a practical step toward improved standardization of phage susceptibility testing, facilitating clearer data interpretation and comparison in both environmental and applied microbiology research.

microbiology↗

bioRxiv2026

Temporal dynamics of microbiome communities within urban compost piles undergoing the heat process

Urban composting supports soil health but also intersects with food safety, where compost is produced near farms and communities. Here, we profiled temporal microbiome dynamics across a 6-week heat compost cycle from the urban compost piles using paired physicochemical panels and long-read metagenomics. Nutrient composition and pH shifted with compost age, coinciding with stage-structured microbial succession, including temperature-linked turnover of compost communities from mesophilic to thermotolerant taxa. Bacterial profiles included the presence of antimicrobial resistance genes and foodborne-associated genera early in the cycle, with reduced representation during the thermophilic phase. Analysis of previously unclassified long reads reveals an extensive repertoire of putative bacteriophages, including several complete genomes and candidates linked to foodborne bacteria, and their abundance is coupled to the host abundance. Together, these results support thermophilic composting as a key mitigation step for microbiological hazards in urban-adjacent systems and identify compost piles as a promising reservoir for discovering candidate lytic phages for downstream isolation and host-range testing.

microbiology↗