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bioRxiv2022

Omnicrobe, an open-access database of microbial habitats and phenotypes using a comprehensive text mining and data fusion approach

The dramatic increase in the amount of microbe descriptions in databases, reports and papers presents a two-fold challenge for accessing the information: integration of heterogeneous data in a standard ontology-based representation and normalization of the textual descriptions by semantic analysis. Recent text mining methods offer powerful ways to extract textual information and generate ontology-based representation. This paper describes the design of the Omnicrobe application that gathers comprehensive information on habitats, phenotypes and usages of microbes from scientific sources of high interest to the microbiology community. The Omnicrobe database contains around 1 million descriptions of microbe properties that are created by analyzing and combining six information sources of various kinds, i.e. biological resource catalogues, sequence database and scientific literature. The microbe properties are indexed by the Ontobiotope ontology and their taxa are indexed by an extended version of the taxonomy maintained by the National Center for Biotechnology Information. The Omnicrobe application covers all domains of microbiology. It provides an easy-to-use support in the resolution of scientific questions related to the habitats, phenotypes and uses of microbes through simple and complex ontology-based queries. We illustrate the potential of Omnicrobe with a use case from the food innovation domain.

bioinformatics↗

bioRxiv2023

16S rRNA phylogeny and clustering is not a reliable proxy for genome-based taxonomy in Streptomyces

Although Streptomyces is one of the most extensively studied genera of bacteria, their taxonomy remains contested and is suspected to contain significant species-level misclassification. Resolving the classification of Streptomyces would benefit many areas of study and applied microbiology that rely heavily on having an accurate ground truth classification of similar and dissimilar organisms, including comparative genomics-based searches for novel antimicrobials in the fight against the ongoing antimicrobial resistance (AMR) crisis. To attempt a resolution, we investigate taxonomic conflicts between 16S rRNA and whole genome classifications using all available 48,981 full-length 16S rRNA Streptomyces sequences from the combined SILVA, Greengenes, Ribosomal Database Project (RDP) and NCBI (National Center for Biotechnology Information) databases, and 2,276 publicly available Streptomyces genome assemblies. We construct a 16S gene tree for 14,239 distinct Streptomyces 16S rRNA sequences, identifying three major lineages of Streptomyces, and find that existing taxonomic classifications are inconsistent with the tree topology. We also use these data to delineate 16S and whole genome landscapes for Streptomyces, finding that 16S and whole-genome classifications of Streptomyces strains are frequently in disagreement, and in particular that 16S zero-radius Operational Taxonomic Units (zOTUs) are often inconsistent with Average Nucleotide Identity (ANI)-based taxonomy. Our results strongly imply that 16S rRNA sequence data does not map to taxonomy sufficiently well to delineate Streptomyces species reliably, and we propose that alternative markers should instead be adopted by the community for classification and metabarcoding. As much of current Streptomyces taxonomy has been determined or supported by historical 16S sequence data and may in parts be in error, we also propose that reclassification of the genus by alternative approaches is required. Impact StatementAccurate classification of microbes, usually in the form of taxonomic assignments, provides a fundamental ground truth or reference point for many aspects of applied microbiology including comparative genomics, identification of strains for natural product discovery, and dereplication of strains. Bacteria belonging to the genus Streptomyces are an important source of bioactive metabolites and enzymes in biotechnology, and proper understanding of their phylogeny aids understanding of the evolution of industrially important gene products and metabolites, and prioritization of strains for industrial exploitation. Taxonomic classification in the genus Streptomyces is complex and contested, and there are clear conflicts between taxonomies inferred from 16S rRNA and from whole genome sequences. Despite this, 16S sequence-based classifications are still widely used to infer taxonomic identity, to determine community composition, and to prioritise strains for study. We investigate a diverse and comprehensive set of Streptomyces genomes using whole-genome Average Nucleotide Identity (ANI) and 16S sequence analysis to delineate and compare classifications made using these approaches. We outline the genomic and 16S sequence landscape of Streptomyces, demonstrating that (i) distinct taxonomic species may share identical full-length 16S sequences, and (ii) in some instances, isolates representing the same taxonomic species do not share any common 16S rRNA sequence. Our results strongly imply that 16S rRNA sequence variation does not map to taxonomy sufficiently well to delineate Streptomyces species reliably, and that alternative markers should instead be adopted by the community. Much of current Streptomyces taxonomy has been determined or supported by historical 16S sequence data, and we therefore propose that reclassification within this group by alternative approaches is required. Data summaryAll code, raw and supporting data are publicly available from GitHub (https://github.com/kiepczi/Kiepas_et_al_2023_16S) and Zenodo (https://doi.org/10.5281/zenodo.8223787). The flowchart provided in Supplementary File 28 provides an overview of analysis steps and serves as a guide through Supplementary Files generated during reconstruction of the 16S phylogeny. The flowchart in Supplementary File 29 outlines the workflow processes and supplementary materials used for analysis of 16S rRNA sequences from Streptomyces genomes. Supplementary DataSupplementary File 1: Generate figures using Python and R. ZIP file containing all data, Python and R scripts to generate figures for this manuscript. (ZIP 40.9MB) Supplementary File 2: Raw 16S rRNA public databases. Zip file containing four separate txt files with sequence IDs for public 16S rRNA databases used in this manuscript, and an additional txt file with Greengenes sequence taxonomy information, and a Python script used to map taxonomy information to sequences found in Greengenes v13.5. (ZIP 34.8MB) Supplementary File 3: Filtration of 16S rRNA public databases. Zip file containing Python script used for filtration of the raw databases, and generated outputs. (ZIP 7.2MB) Supplementary File 4: Cleaning of the filtrated 16S rRNA local. Zip file containing all bash and Python scripts used to clean the local full-length 16S rRNA local databases by removing redundant and poor quality 16S rRNA sequences. (ZIP 9MB) Supplementary File 5: Sequence Clustering. Zip file containing a bash script used to cluster the full-length cleaned local 16S rRNA Streptomyces local databases at various thresholds, and provides txt files with accessions for representative sequences, and cluster members for each clustering threshold. (ZIP 40.8MB) Supplementary File 6: Analysis of taxonomic composition for each clustering threshold. Zip file containing Python scripts, NCBI taxonomy input and all outputs generated used to determine the taxonomic composition for each clustering threshold. (ZIP 49.6) Supplementary File 7. Cluster sizes. Empirical cumulative frequency plot showing cluster sizes generated for all clustering thresholds. (PDF 44KB) Supplementary File 8. Cluster taxID abundance. Empirical cumulative frequency plot for unique numbers of taxID present at all clustering thresholds. (PDF 9KB) Supplementary File 9. MSA. Zip file containing all Python and bash scripts, and additional data needed to generate and clean MSA for phylogenetic analysis. (ZIP 4.2MB) Supplementary File 10. Phylogenetic reconstruction. ZIP file containing bash scripts used for phylogenetic reconstruction, and all generated outputs and log files. (ZIP 16.8MB). Supplementary File 11. Collapse branches. ZIP file containing jupyter notebook used for collapsing branches with the same species names, and the collapsed tree in newick format. (ZIP 385KB) Supplementary File 12. Phylogenetic tree. PDF file showing collapsed phylogenetic tree with marked branches with transfer bootstrap expectation support of >= 50%. (PDF 224KB) Supplementary File 13. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces albus and Streptomyces griseus. (PDF 229KB) Supplementary File 14. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces albulus, Streptomyces lydicus and Streptomyces venezuelae. (PDF 228KB) Supplementary File 15. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces clavuligerus and Streptomyces coelicolor. (PDF 227KB) Supplementary File 16. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of Streptomyces lavendulae, Streptomyces rimosus and Streptomyces scabiei. (PDF 228KB) Supplementary File 17. Streptomyces genomes. Zip file containing bash scripts used to download Streptomyces genomes, and Python scripts used to check assembly status. The ZIP file also contains two separate txt files with Streptomyces genomes used in this manuscript: one file with all initial candidates, and a second file with replaced genomes. (ZIP 2.6MB) Supplementary File 18. Extraction of full-length and ambiguity free 16S rRNA sequences from Streptomyces genomes. Zip file containing all Python and bash scripts used to extract full-length sequences from the filtered Streptomyces genomes. A single FASTA file with all extracted 16S rRNA sequences, and a single FASTA file with filtered sequences. A txt file with accession of genomes retained in the analysis. (ZIP 742KB) Supplementary File 19. ANI analysis among Streptomyces genomes with identical 16S rRNA sequences. ZIP file containing all Bash and Python scripts used to determine taxonomic boundaries among Streptomyces genomes sharing identical full-length 16S rRNA sequences. All output and pyANI log files. (ZIP 37.1MB) Supplementary File 20. Network analysis of genomes based on shared 16S sequences. ZIP file containing jupyter notebook with NetworkX analysis and all associated output files including. bash script for pyANI analysis runs on each connected component and all associated matrices, heatmaps and log files. (ZIP 29.3MB) Supplementary File 21. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences with each node colour corresponding to the number of connections/degrees. (HTML 4.7MB) Supplementary File 22. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences showing clique (blue) and non-clique (green) components. (HTML 4.7MB) Supplementary File 23. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S sequences showing number of unique genera within each connected component. Each candidate genus is represented as a single node colour within a connected component. (HTML 4.7MB) Supplementary File 24. Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S rRNA sequences showing number of unique species within each connected component. Each candidate species is represented as a single node colour within a connected component. (HTML 4.7MB) Supplementary File 25 Interactive network graph. HTML file containing interactive network graph of genomes sharing common full-length 16S rRNA sequences showing number of unique NCBI names within each connected component. Each NCBI assigned name is represented as a single node colour within a connected component. Gray nodes represent genomes currently lacking assigned species names. (HTML 4.7MB) Supplementary file 26. Intragenomic 16S rRNA heterogeneity within 1,369 Streptomyces genomes which exclusively contain only full-length and ambiguity symbol-free 16S rRNA sequences. A total of 811 genomes containing single 16S rRNA sequences are not shown. (PDF 8KB) Supplementary File 27. Distribution of 16S copies per genome with a distinction between unique and total copies for genomes at assembly level complete and chromosome. (PDF 7KB) Supplementary File 28. Schematic workflow for construction of the full-length 16S rRNA Streptomyces phylogeny. Each arrow represents a process and is annotated with script used and corresponding supplementary file. Output/data files, and the number of remaining sequences after each step, are indicated by rectangles. The green shading represents a single processing step of collecting and collating 16S database sequences. (PDF 91KB) Supplementary File 29. Schematic representation of the pipeline used to filter publicly available Streptomyces genomes. (PDF 59KB) Supplementary File 30. Sankey plot showing counts of taxonomic names in source databases, assigned at ranks from phylum to genus, to sequences identified with a key word Streptomyces in the taxonomy field. Note that Actinobacteria and Actinobacteriota are synonyms in LPSN for the correct Phylum name Actinomycetota, but that Actinomycetales and Streptomycetales are not taxonomic synonyms for each other. Streptomycetales is synonymous in LPSN with the correct name Kitasatosporales; Actinomycetales is a distinct taxonomic Order. The parent order of the Family Streptomycetaceae in LPSN is Kitasatosporales. (PDF 64KB) Supplementary File 31. Rectangular phylogram of the comprehensive maximum-likelihood tree of the genus Streptomyces based on the 16S sequence diversity of all 5,064 full-length 16S rRNA sequences with 100 TBE values. (PDF 194KB) Supplementary file 32. Genomes sharing identical 16S rRNA sequences are assigned different names in NCBI. A total of 1,030 singleton clusters are not shown. (PDF 8KB) Supplementary File 33. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Acintacidiphila genus. (PDF 228KB) Supplementary File 34. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Phaeacidiphilus genus. (PDF 228KB) Supplementary File 35. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Mangrovactinospora genus. (PDF 228KB) Supplementary File 36. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Wenjunlia genus. (PDF 228KB) Supplementary File 37. Phylogenetic tree. PDF file showing collapsed phylogenetic tree showing distribution of members of the novel Streptantibioticus genus. (PDF 228KB)

genomics↗

bioRxiv2023

CD4+ T cells are homeostatic regulators during Mtb reinfection

Immunological priming - either in the context of prior infection or vaccination - elicits protective responses against subsequent Mycobacterium tuberculosis (Mtb) infection. However, the changes that occur in the lung cellular milieu post-primary Mtb infection and their contributions to protection upon reinfection remain poorly understood. Here, using clinical and microbiological endpoints in a non-human primate reinfection model, we demonstrate that prior Mtb infection elicits a long-lasting protective response against subsequent Mtb exposure and that the depletion of CD4+ T cells prior to Mtb rechallenge significantly abrogates this protection. Leveraging microbiologic, PET-CT, flow cytometric, and single-cell RNA-seq data from primary infection, reinfection, and reinfection-CD4+ T cell depleted granulomas, we identify differential cellular and microbial features of control. The data collectively demonstrate that the presence of CD4+ T cells in the setting of reinfection results in a reduced inflammatory lung milieu characterized by reprogrammed CD8+ T cell activity, reduced neutrophilia, and blunted type-1 immune signaling among myeloid cells, mitigating Mtb disease severity. These results open avenues for developing vaccines and therapeutics that not only target CD4+ and CD8+ T cells, but also modulate innate immune cells to limit Mtb disease.

immunology↗

bioRxiv2024

Inferring fungal growth rates from optical density data

Quantifying fungal growth underpins our ability to effectively treat severe fungal infections. Current methods quantify fungal growth rates from time-course morphology-specific data, such as hyphal length data. However, automated large-scale collection of such data lies beyond the scope of most clinical microbiology laboratories. In this paper, we propose a mathematical model of fungal growth to estimate morphology-specific growth rates from easy-to-collect, but indirect, optical density (OD600) data of Aspergillus fumigatus growth (filamentous fungus). Our method accounts for OD600 being an indirect measure by explicitly including the relationship between the indirect OD600 measurements and the calibrating true fungal growth in the model. Therefore, the method does not require de novo generation of calibration data. Our model outperformed reference models at fitting to and predicting OD600 growth curves and overcame observed discrepancies between morphology-specific rates inferred from OD600 versus directly measured data in reference models that did not include calibration. Author summaryQuantifying fungal growth is essential for antifungal drug discovery and monitoring antifungal resistance. As fungal growth is complex, with fungal morphology (shape) dynamically changing over time, previous studies have quantified fungal growth by estimating growth rates during specific fungal morphologies (morphology-specific growth rates) or by mathematically modelling fungal growth. However, collecting time-series data that captures the morphological information required for mathematical model fitting or estimating morphology-specific growth rates is prohibitively time consuming for large-scale drug testing in most microbiology laboratories. Alternatively, fungal growth can be quickly, although indirectly, quantified by measuring the optical density (OD) of a broth culture. However, changes in OD are not always reflective of true changes in fungal growth because OD is an indirect measure. This paper proposes a method to model fungal growth and estimate a morphology-specific growth rate from indirect OD600 measurements of the major mould pathogen, Aspergillus fumigatus. We explicitly model the relationship between measured indirect OD600 data and true fungal growth (calibration). The presented work serves as the much-needed foundation for estimating and comparing morphology-specific fungal growth rates in varying antifungal drug concentrations using only OD600 data.

systems biology↗

bioRxiv2024

Multisystemic inflammatory disease in Pheasantshell (Unionidae, Actinonaias pectorosa) associated with Yokenella regensburgei infection at sites experiencing seasonal mass mortality events

Freshwater mussels are integral components of riverine ecosystems, influencing water quality, nutrient cycling, and habitat characteristics. Enigmatic freshwater mussel declines, often characterized by sudden mass mortality events, pose significant challenges to conservation efforts. The Clinch River, a freshwater biodiversity hotspot, has experienced several enigmatic mass mortality events since 2016. Studies have reported bacteria associated with moribund Pheasantshell (Actinonaias pectorosa) during mortality events in the Clinch River, specifically Yokenella regensburgei. Despite reports of bacterial infection, little is known about their role as pathogens. Through a multiyear case-control study, combining in-situ experiments, field surveys, histology, bacterial isolation, and high-throughput sequencing, we assessed the role of bacteria in Pheasantshell (Actinonais pectorosa) mortality at two sites in the Clinch River. Between May 2021 and December 2023, we collected 29 wild moribund free-living A. pectorosa and 68 hatchery-reared A. pectorosa maintained in silos at the same sites and investigated differences in pathology and microbiology between groups. No silo mussels presented clinical signs of disease, or gross or microscopic lesions associated with pathological conditions leading to mortality. Our findings reveal a significant association between Yokenella regensburgei and severe multisystemic and multifocal infiltrative hemocytosis with necrosis, consistent with sepsis. Lesions associated with yokenellosis were of sufficient severity and physiological significance to explain mortality in infected hosts. Although our study does not explain the cause of these infections, it confirms that mussels at our study sites are ultimately dying from an infectious disease and that Y. regensburgei can be pathogenic in free-living mussels. Our results underscore the importance of considering bacterial diseases in wild mussel populations and emphasize the need for further research to elucidate the epidemiology and pathogenicity of Y. regensburgei. Overall, our study highlights the importance of integrated approaches combining pathology, microbiology, and epidemiology in freshwater mussel conservation efforts.

ecology↗

bioRxiv2025

Characterizing PET CT patterns and bacterial dissemination features of tuberculosis relapse in the macaque model

Tuberculosis (TB) relapse after appropriate drug treatment is poorly understood but critical to developing shorter treatment regimens. Using a cynomolgus macaque model of human TB, macaques with active TB disease were treated with a short course of isoniazid and rifampin and subsequently infected with SIV. Serial clinical, microbiologic, immunologic and position emission and computed tomography (PET CT) assessments were performed to identify risk factors of relapse. Of the 12 animals, eight developed radiologically defined relapse including four that had clinical and/or microbiologic signs. Greater gross pathology and bacterial burden were observed in relapse animals. PET CT characteristics before, during, and at the end of treatment were similar amongst relapse and non-relapse animals. We show that complete sterilization or very low Mtb burden is protective against SIV-induced TB relapse but cannot be predicted by PET CT. Using bar-coded M. tuberculosis, we found that Mtb dissemination during relapse originated from both lung and thoracic lymph nodes, underscoring the importance of lymph nodes as a reservoir. By matching bar-coded Mtb and serial PET CT, we also demonstrate that not every site of persistent Mtb growth after drug treatment is capable of dissemination and relapse, underscoring the complex nature of drug treatment and relapse.

immunology↗

bioRxiv2017

Trends in Escherichia coli bloodstream infection, urinary tract infections and antibiotic susceptibilities in Oxfordshire, 1998-2016: an observational study

BackgroundThe incidence of Escherichia coli bloodstream infections (EC-BSIs), particularly those caused by antibiotic-resistant strains, is increasing in the UK and internationally. This is a major public health concern but the evidence base to guide interventions is limited.\n\nMethodsIncidence of EC-BSIs and E. coli urinary tract infections (EC-UTIs) in one UK region (Oxfordshire) were estimated from anonymised linked microbiological and hospital electronic health records, and modelled using negative binomial regression based on microbiological, clinical and healthcare exposure risk factors. Infection severity, 30-day allcause mortality, and community and hospital co-amoxiclav use were also investigated.\n\nFindingsFrom 1998-2016, 5706 EC-BSIs occurred in 5215 patients, and 228376 EC-UTIs in 137075 patients. 1365(24%) EC-BSIs were nosocomial (onset >48h post-admission), 1863(33%) were community (>365 days post-discharge), 1346(24%) were quasi-community (31-365 days post-discharge), and 1132(20%) were quasi-nosocomial ([≤]30 days postdischarge). 1413(20%) EC-BSIs and 36270(13%) EC-UTIs were co-amoxiclav-resistant (41% and 30%, respectively, in 2016). Increases in EC-BSIs were driven by increases in community (10%/year (95% CI:7%-13%)) and quasi-community (8%/year (95% CI:7%-10%)) cases. Changes in EC-BSI-associated 30-day mortality were at most modest (p>0{middle dot}03), and mortality was substantial (14-25% across groups). By contrast, co-amoxiclav-resistant EC-BSIs increased in all groups (by 11%-19%/year, significantly faster than susceptible EC-BSIs, pheterogeneity<0{middle dot}001), as did co-amoxiclav-resistant EC-UTIs (by 13%-29%/year, pheterogeneity*0{middle dot}001). Co-amoxiclav use in primary-care facilities was associated with subsequent co-amoxiclav-resistant EC-UTIs (p=0{middle dot}03) and all EC-UTIs (p=0{middle dot}002).\n\nInterpretationCurrent increases in EC-BSIs in Oxfordshire are primarily community-associated, with high rates of co-amoxiclav resistance, nevertheless not impacting mortality. Interventions should target primary-care facilities with high co-amoxiclav usage.\n\nFundingNational Institute for Health Research.\n\nResearch in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for publications from inception up until October 26, 2017, with the terms \"Escherichia coli\", \"E. coli\", \"bacteraemia\", \"bloodstream infection\", restricting the search to English language articles, and also reviewed references from retrieved articles. Escherichia coli (E. coli) is the most common cause of bloodstream infection, and the incidence of E. coli bloodstream infection, and particularly antibiotic-resistant infections, is increasing in the UK and internationally. Although the UK government aims to reduce healthcare-associated E. coli bloodstream infection, there is only limited evidence to inform appropriate interventions.\n\nAdded value of this studyWe investigated potential drivers for these increases in incidence by exploiting available linked electronic health records over 19 years for ~5200 patients with E. coli bloodstream infection and ~140000 with E. coli urinary tract infection, together with community antimicrobial prescribing data for the most recent six years. Our study identified several findings with significant implications for health policy and patient care: O_LIIncreases in the incidence of E. coli bloodstream infections were driven mainly by non-hospital-associated cases; however, neither patients with previous urinary tract infections nor having previously had urine specimens sent from catheters appeared to be driving the increases\nC_LIO_LICo-amoxiclav-resistant bloodstream infections rose significantly faster than co-amoxiclav-susceptible bloodstream infections, with the greatest number of co-amoxiclav-resistant bloodstream infections in 2016 being in patients discharged more than a month previously (i.e. community-associated)\nC_LIO_LIHigher co-amoxiclav use in primary care was associated with higher rates of both co-amoxiclav-resistant E. coli urinary tract infections and E. coli urinary tract infections overall, supporting drives to reduce broad-spectrum and inappropriate antibiotic use in primary care\nC_LIO_LIDespite substantial increases in co-amoxiclav-resistant bloodstream infections there was no evidence that mortality was increasing in these cases; this does not support moving to broader empiric antibiotic prescribing in hospitals (i.e. carbapenems, piperacillin-tazobactam)\nC_LI\n\nImplications of all available adviceThis suggests that government strategies to effectively reduce E. coli bloodstream infections should target community settings, as well as healthcare-associated settings. The absence of an increased mortality signal suggests that co-amoxiclav resistant E. coli infections are either being successfully treated by dual empiric therapy in severe cases (e.g. with concomitant gentamicin), can be \"rescued\" once isolate susceptibilities become available, or currently deployed phenotypic susceptibility testing breakpoints do not adequately correlate with clinical outcome.

epidemiology↗

bioRxiv2018

The effectiveness of glass beads for plating cell cultures

Cell plating, the spreading out of a liquid suspension of cells on a surface followed by colony growth, is a common laboratory procedure in microbiology. Despite this, the exact impact of its parameters on colony growth has not been extensively studied. A common protocol involves the shaking of glass beads within a petri dish containing solid growth media. We investigated the effects of multiple parameters in this protocol - the number of beads, the shape of movement, and the number of movements. Standard suspensions of Escherichia coli were spread while varying these parameters to assess their impact on colony growth. Results were assessed by a variety of metrics - the number of colonies, the mean distance between closest colonies, and the variability and uniformity of their spatial distribution. Finally, we devised a mathematical model of shifting billiard to explain the heterogeneities in the observed spatial patterns. Exploring the parameters that affect the most fundamental techniques in microbiology allows us to better understand their function, giving us the ability to precisely control their outputs for our exact needs.

biophysics↗

bioRxiv2018

Integrating Host Response and Unbiased Microbe Detection for Lower Respiratory Tract Infection Diagnosis in Critically Ill Adults

Lower respiratory tract infections (LRTI) lead to more deaths each year than any other infectious disease category(1). Despite this, etiologic LRTI pathogens are infrequently identified due to limitations of existing microbiologic tests(2). In critically ill patients, non-infectious inflammatory syndromes resembling LRTI further complicate diagnosis. To address the need for improved LRTI diagnostics, we performed metagenomic next-generation sequencing (mNGS) on tracheal aspirates from 92 adults with acute respiratory failure and simultaneously assessed pathogens, the lung microbiome and the host transcriptome. To differentiate pathogens from respiratory commensals, we developed rules-based and logistic regression models (RBM, LRM) in a derivation cohort of 20 patients with LRTI or non-infectious acute respiratory illnesses. When tested in an independent validation cohort of 24 patients, both models achieved accuracies of 95.5%. We next developed pathogen, microbiome diversity, and host gene expression metrics to identify LRTI-positive patients and differentiate them from critically ill controls with non-infectious acute respiratory illnesses. When tested in the validation cohort, the pathogen metric performed with an AUC of 0.96 (95% CI = 0.86 - 1.00), the diversity metric with an AUC of 0.80 (95% CI = 0.63 - 0.98), and the host transcriptional classifier with an AUC of 0.91 (95% CI = 0.80 - 1.00). Combining all three achieved an AUC of 0.99 (95% CI = 0.97 - 1.00) and negative predictive value of 100%. This study suggests that a single streamlined protocol offering an integrated genomic portrait of pathogen, microbiome and host transcriptome may hold promise as a novel tool for LRTI diagnosis.\n\nSIGNIFICANCE STATEMENTLower respiratory tract infections (LRTI) are the leading cause of infectious disease-related death worldwide yet remain challenging to diagnose because of limitations in existing microbiologic tests. In critically ill patients, non-infectious respiratory syndromes that resemble LRTI further complicate diagnosis and confound targeted treatment. To address this, we developed a novel metagenomic sequencing-based approach that simultaneously interrogates three core elements of acute airway infections: the pathogen, lung microbiome and host response. We studied this approach in a prospective cohort of critically ill patients with acute respiratory failure and found that combining pathogen, microbiome and host gene expression metrics achieved accurate LRTI diagnosis and identified etiologic pathogens in patients with clinically identified infections but otherwise negative testing.\n\nFundingNHLBI K12HL119997 (Langelier C), NHLBI K23HL123778 (Christensen S), NIAID P01AI091575 and the Chan Zuckerberg Biohub (DeRisi JL), NHLBI K23 HL136844 (Moazed F), NHLBI R01HL110969, K24HL133390, R35HL140026 (Calfee C), Gladstone Institutes (Pollard KS).

genomics↗

bioRxiv2018

Selection of an Appropriate Empiric Antibiotic Regimen in Culture-Negative Hematogenous Vertebral Osteomyelitis

The aim of this study was to determine which antibiotic combinations are appropriate for culture-negative hematogenous vertebral osteomyelitis (HVO), based on the antibiotic-susceptibility pattern of organisms isolated from cases of culture-proven HVO. We conducted a retrospective chart review of adult patients with microbiologically proven HVO in five tertiary-care hospitals over a 7-year period. The appropriateness of empiric antibiotic regimens was assessed based on the antibiotic susceptibility profiles of isolated bacteria. In total, 358 cases of microbiologically proven HVO were identified. The main causative pathogens identified were methicillin-susceptible Staphylococcus aureus (33.5%), followed by methicillin-resistant S. aureus (MRSA) (24.9%), aerobic gram-negative bacteria (21.8%), and Streptococcus species (11.7%). Extended spectrum {beta}-lactamase (ESBL)-producing Enterobacteriaceae and anaerobes accounted for only 1.7% and 1.4%, respectively, of the causative pathogens. Based on the susceptibility results of isolated organisms, levofloxacin plus rifampicin was appropriate in 73.5%, levofloxacin plus clindamycin in 71.2%, and amoxicillin-clavulanate plus ciprofloxacin in 64.5% of cases. These oral combinations were more appropriate for treating community-acquired HVO (85.8%, 84.0%, and 80.4%, respectively) than healthcare-associated HVO (52.6%, 49.6%, and 37.6%, respectively). Vancomycin combined with ciprofloxacin, ceftriaxone, ceftazidime, or cefepime was similarly appropriate (susceptibility rates of 93.0%, 94.1%, 95.8%, and 95.8%, respectively). In conclusion, in a setting with a high prevalence of MRSA HVO, oral antibiotic combinations may be suboptimal for treatment of culture-negative HVO and should be used only in patients with community-acquired HVO. Vancomycin combined with fluoroquinolone or a broad-spectrum cephalosporin was appropriate in most cases of HVO in this study.

epidemiology↗

bioRxiv2018

The Integrated Rapid Infectious Disease Analysis (IRIDA) Platform

Whole genome sequencing (WGS) is a powerful tool for public health infectious disease investigations owing to its higher resolution, greater efficiency, and cost-effectiveness over traditional genotyping methods. Implementation of WGS in routine public health microbiology laboratories is impeded by a lack of user-friendly automated and semi-automated pipelines, restrictive jurisdictional data sharing policies, and the proliferation of non-interoperable analytical and reporting systems. To address these issues, we developed the Integrated Rapid Infectious Disease Analysis (IRIDA) platform (irida.ca), a user-friendly, decentralized, open-source bioinformatics and analytical web platform to support real-time infectious disease outbreak investigations using WGS data. Instances can be independently installed on local high-performance computing infrastructure, enabling private and secure data management and analyses according to organizational policies and governance. IRIDAs data management capabilities enable secure upload, storage and sharing of all WGS data and metadata. The core platform currently includes pipelines for quality control, assembly, annotation, variant detection, phylogenetic analysis, in silico serotyping, multi-locus sequence typing, and genome distance calculation. Analysis pipeline results can be visualized within the platform through dynamic line lists and integrated phylogenomic clustering for research and discovery, and for enhancing decision-making support and hypothesis generation in epidemiological investigations. Communication and data exchange between instances are provided through customizable access controls. IRIDA complements centralized systems, empowering local analytics and visualizations for genomics-based microbial pathogen investigations. IRIDA is currently transforming the Canadian public health ecosystem and is freely available at https://github.com/phac-nml/irida and www.irida.ca.\n\nImpact StatementWhole genome sequencing (WGS) is revolutionizing infectious disease analysis and surveillance due to its cost effectiveness, utility, and improved analytical power. To date, no \"one-size-fits-all\" genomics platform has been universally adopted, owing to differences in national (and regional) health information systems, data sharing policies, computational infrastructures, lack of interoperability and prohibitive costs. The Integrated Rapid Infectious Disease Analysis (IRIDA) platform is a user-friendly, decentralized, open-source bioinformatics and analytical web platform developed to support real-time infectious disease outbreak investigations using WGS data. IRIDA empowers public health, regulatory and clinical microbiology laboratory personnel to better incorporate WGS technology into routine operations by shielding them from the computational and analytical complexities of big data genomics. IRIDA is now routinely used as part of a validated suite of tools to support outbreak investigations in Canada. While IRIDA was designed to serve the needs of the Canadian public health system, it is generally applicable to any public health and multi-jurisdictional environment. IRIDA enables localized analyses but provides mechanisms and standard outputs to enable data sharing. This approach can help overcome pervasive challenges in real-time global infectious disease surveillance, investigation and control, resulting in faster responses, and ultimately, better public health outcomes.\n\nDATA SUMMARYO_LIData used to generate some of the figures in this manuscript can be found in the NCBI BioProject PRJNA305824.\nC_LI

bioinformatics↗

bioRxiv2018

Human microbiome aging clocks based on deep learning and tandem of permutation feature importance and accumulated local effects

Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=190 SRC="FIGDIR/small/507780_ufig1.gif" ALT="Figure 1"> View larger version (34K): org.highwire.dtl.DTLVardef@1ad6feaorg.highwire.dtl.DTLVardef@18e72a9org.highwire.dtl.DTLVardef@109f18aorg.highwire.dtl.DTLVardef@1d0c9b7_HPS_FORMAT_FIGEXP M_FIG C_FIG AbstractThe human gut microbiome is a complex ecosystem that both affects and is affected by its host status. Previous analyses of gut microflora revealed associations between specific microbes and host health and disease status, genotype and diet. Here, we developed a method of predicting biological age of the host based on the microbiological profiles of gut microbiota using a curated dataset of 1,165 healthy individuals (3,663 microbiome samples). Our predictive model, a human microbiome clock, has an architecture of a deep neural network and achieves the accuracy of 3.94 years mean absolute error in cross-validation. The performance of the deep microbiome clock was also evaluated on several additional populations. We further introduce a platform for biological interpretation of individual microbial features used in age models, which relies on permutation feature importance and accumulated local effects. This approach has allowed us to define two lists of 95 intestinal biomarkers of human aging. We further show that this list can be reduced to 39 taxa that convey the most information on their hosts aging. Overall, we show that (a) microbiological profiles can be used to predict human age; and (b) microbial features selected by models are age-related.

bioinformatics↗

bioRxiv2019

Microbiome data enhances predictive models of lung function in people with CF

BackgroundMicrobiome sequencing has brought increasing attention to the polymicrobial context of chronic infections. However, clinical microbiology continues to focus on canonical human pathogens, which may overlook informative, but non-pathogenic, biomarkers. We address this disconnect in lung infections in people with cystic fibrosis (CF). MethodsWe collected health information (lung function, age, BMI) and sputum samples from a cohort of 77 children and adults with CF. Samples were collected during a period of clinical stability and 16S rDNA sequenced for airway microbiome compositions. We use Elastic Net regularization to train linear models predicting lung function and extract the most informative features. ResultsModels trained on whole microbiome quantitation outperform models trained on pathogen quantitation alone, with or without the inclusion of patient metadata. Our most accurate models retain key pathogens as negative predictors (Pseudomonas, Achromobacter) along with established correlates of CF disease state (age, BMI, CF related diabetes). In addition, our models select non-pathogen taxa (Fusobacterium, Rothia) as positive predictors of lung health. ConclusionsThese results support a reconsideration of clinical microbiology pipelines to ensure the provision of informative data to guide clinical practice.

bioinformatics↗

bioRxiv2019

A Wild Yeast Laboratory Activity: From Isolation to Brewing

Microbial fermentation is a common form of metabolism that has been exploited by humans to great benefit. Industrial fermentation currently produces a myriad of products ranging from biofuels to pharmaceuticals. About one third of the worlds food is fermented, and the brewing of fermented beverages in particular has an ancient and storied history. Because fermentation is so intertwined with our daily lives, the topic is easily relatable to students interested in real-world applications for microbiology. Here, we describe the curriculum for an inquiry-based laboratory course that combines yeast molecular ecology and brewing. The rationale for the course is to compare commercial Saccharomyces cerevisiae yeast strains, which have been domesticated through thousands of generations of selection, with wild yeast, where there is growing interest in their potentially unique brewing characteristics. Because wild yeast are so easy to isolate, identify, and characterize, this is a great opportunity to present key concepts in molecular ecology and genetics in a way that is relevant and accessible to students. We organized the course around three main modules: isolation and identification of wild yeast, phenotypic characterization of wild and commercial ale yeast strains, and scientific design of a brewing recipe and head-to-head comparison of the performance of a commercial and wild yeast strain in the brewing process. Pre and post assessment showed that students made significant gains in the learning objectives for the course, and students enjoyed connecting microbiology to a real-world application.

scientific communication and education↗

bioRxiv2019

LncRNA and predictive model to improve the diagnosis of clinically diagnosed pulmonary tuberculosis

BackgroundClinically diagnosed pulmonary tuberculosis (PTB) patients lack Mycobacterium tuberculosis (MTB) microbiologic evidence, and misdiagnosis or delayed diagnosis often occurs as a consequence. We investigated the potential of lncRNAs and corresponding predictive models to diagnose these patients. MethodsWe enrolled 1372 subjects, including clinically diagnosed PTB patients, non-TB disease controls and healthy controls, in three cohorts (Screening, Selection and Validation). Candidate lncRNAs differentially expressed in blood samples of the PTB and healthy control groups were identified by microarray and qRT-PCR in the Screening Cohort. Logistic regression models were developed using lncRNAs and/or electronic health records (EHRs) from clinically diagnosed PTB patients and non-TB disease controls in the Selection Cohort. These models were evaluated by AUC and decision curve analysis, and the optimal model was presented as a Web-based nomogram, which was evaluated in the Validation Cohort. The biological function of lncRNAs was interrogated using ELISA, lactate dehydrogenase release analysis and flow cytometry. ResultsThree differentially expressed lncRNAs (ENST00000497872, n333737, n335265) were identified. The optimal model (i.e., nomogram) incorporated these three lncRNAs and six EHR variables (age, hemoglobin, weight loss, low-grade fever, CT calcification and TB-IGRA). The nomogram showed an AUC of 0.89, sensitivity of 0.86 and specificity of 0.82 in the Validation Cohort, which demonstrated better discrimination and clinical net benefit than the EHR model. ENST00000497872 may regulate inflammatory cytokine production, cell death and apoptosis during MTB infection. ConclusionsLncRNAs and the user-friendly nomogram could facilitate the early identification of PTB cases among suspected patients with negative MTB microbiologic evidence.

molecular biology↗

bioRxiv2026

Associations Between QuantiFERON-TB Gold Plus IFNγ Concentrations and Progression to Symptomatic Tuberculosis in Global High-Burden TB Settings

IntroductionPredictive biomarkers for symptomatic tuberculosis (TB) progression would transform targeted prevention efforts. Although interferon-gamma release assays (IGRAs), including QuantiFERON(R) TB-Gold Plus (QFT-Plus), have been studied for this purpose, systematic evaluation of the QFT-Plus TB1 and TB2 Interferon-Gamma (IFN{gamma}) concentrations remains limited, particularly in high-burden TB settings. MethodsBaseline TB1 and TB2 IFN{gamma} concentrations from 5,259 participants in TB-endemic regions were analyzed in relation to subsequent TB outcomes over a median of 525 days follow-up. Participants were categorized as controls (no TB), suspected TB (no microbiological confirmation), or laboratory-confirmed TB, including a subset meeting a stringent case definition ([≥]2 positive microbiologic tests). Associations between baseline IFN{gamma} concentrations and progression to symptomatic TB were assessed. ResultsIn the full cohort (IGRA+/-participants), baseline TB2 IFN{gamma} concentrations were significantly higher compared to controls among participants who developed suspected TB (p=0.01), laboratory-confirmed TB (p=0.01), or met the stringent case definition (p<0.0001). In IGRA+ participants, baseline TB2 concentrations were significantly higher than controls in suspected (p=0.01) and laboratory-confirmed (p=0.02) groups. Associations with baseline TB1 IFN{gamma} concentrations and TB progression were observed for participants meeting the stringent case definition within the full cohort (p=0.009). Among stringent definition cases, TB2 concentrations achieved an area under the Receiver Operating Characteristic curve of 0.84, with a sensitivity of 80% and specificity of 78%. ConclusionsQuantitative IFN{gamma} concentrations from QFT-Plus, particularly TB2, were associated with progression to symptomatic TB, met or exceeded WHO-recommended sensitivity and specificity thresholds for predictive biomarkers, and may support biomarker-based stratification in TB clinical research. SummaryIn an exploratory analysis from a global tuberculosis (TB) epidemiology study, higher QuantiFERON-TB Gold Plus IFN{gamma} concentrations were associated with progression to symptomatic TB and met WHO predictive biomarker thresholds, supporting their potential value for risk stratification in TB research.

immunology↗

bioRxiv2026

Analysing antimicrobial resistance mobility patterns using a diverse dataset of over 8000 bacterial species

2.Advances in genomics have enhanced surveillance of antimicrobial resistance (AMR), yet the factors governing resistance gene mobility, and therefore their risk of spread, remain poorly characterized. Here, we analyzed AMR gene distributions across thousands of bacterial genomes from NCBI RefSeq, encompassing clinical, agricultural and environmental isolates, to quantify associations with plasmids and predicted mobile genomic islands. AMR genes were identified using the Resistance Gene Identifier, and in addition to plasmid identification, mobile chromosomal elements were predicted using IslandViewer 4. Analysing both the full dataset and subsets of data with corrections for sampling bias, we show that known AMR genes are significantly enriched in mobile regions overall. However, stratification by resistance mechanism supported marked heterogeneity: certain drug classes and mechanisms are strongly associated with mobile elements, whereas others are predominantly chromosomal and non-mobile. Notably, mechanisms with specialized functions showed higher mobility, consistent with their role as "ecological public goods" that need not be present in all cells to confer community-level benefit. Differences were also observed across bacteria with distinct cell envelope structures. Together, these findings lay the groundwork for predictive models of AMR gene mobility and provide a framework for incorporating gene-level mobility into AMR risk assessment and antimicrobial stewardship policy. 3. Impact statementUnderstanding which antimicrobial resistance (AMR) genes are most likely to spread is central to managing resistance in clinical, agricultural, and environmental settings. This study reports a large-scale, systematic analysis of antimicrobial resistance (AMR) gene mobility across >8000 bacterial species, spanning diverse clinical and other ecological sources. By linking resistance mechanisms to their genomic location (plasmids, genomic islands, or the rest of chromosomes), our study demonstrates that AMR genes are overall enriched in mobile elements, but that mobility varies substantially by mechanism and bacterial context. In particular, functionally specialized resistance mechanisms are more frequently associated with mobile elements, consistent with an "ecological public goods" model for AMR dissemination. These findings extend beyond gene-specific case studies to identify generalizable patterns that help explain why some resistance determinants spread more readily than others. The breadth of relevance spans clinical microbiology, evolutionary biology, environmental microbiology, agri-foods, and public health. The work provides evidence supporting an improved framework for AMR mobility risk assessment, and antimicrobial stewardship strategies, with direct implications for forecasting the durability of existing and future antibiotics. 4. Data summaryThe complete dataset and analysis code used to generate the results can be accessed via the Open Science Framework (OSF) repository under DOI:10.17605/OSF.IO/WE3TX

genomics↗

bioRxiv2026

End-to-end mapping of membrane transport from chemical structure to microorganisms

Membrane transport is a fundamental biological process with profound implications for pharmacology, biotechnology, and microbiology. While computational approaches have largely adopted a protein-centric perspective to annotate transportomes, inferring transport function directly from the intrinsic properties of substrates remains a major challenge. Addressing transport at the compound level enables the systematic evaluation of whether molecules undergo active transport and by which mechanisms, independent of prior transporter annotation. Here, we introduce ChemProFlow, a comprehensive computational framework that redefines transport analysis from a substrate-centric perspective. By integrating geometric deep learning with orthology-based genomic mapping, ChemProFlow predicts molecular transportability, assigns transport mechanisms according to the Transporter Classification Database, and identifies the microorganisms encoding the corresponding transport systems. We show that this integrated pipeline enables scalable, end-to-end mapping of substrate-transporter-organism relationships, with broad applications in pharmacology for anticipating drug transport, in biotechnology for guiding strain engineering, and in microbiology for dissecting substrate utilization across diverse taxa. By capturing the chemical derminants of transportabiliy, ChemProFlow generalizes to previously unseen substrates and provides a high-throughput framework for systematic exploration of molecular transport across diverse biological contexts.

bioinformatics↗