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bioRxiv2022

Bridging the gap between bioinformatics and the clinical and public health microbiology laboratory: An ISO-accredited genomics workflow for antimicrobial resistance

Realising the promise of genomics to revolutionise routine AMR diagnosis and surveillance has been a long-standing challenge in clinical and public health microbiology. We have directly addressed this issue by creating and validating abritAMR, an ISO-accredited bioinformatics platform for genomics-based bacterial AMR gene detection. abritAMR utilises the NCBI AMRFinderPlus for detection of AMR genes and mutations, with additional features to classify AMR determinants into an antibiotic class. We validated abritAMR by comparing with multiplex PCR or gold-standard reference genomes, together representing 1500 different bacteria across 29 genera and covering 415 antibiotic resistance alleles. We also assessed inference of phenotypic resistance by comparing genomic predictions with agar dilution results for 864 Salmonella spp. Performance of abritAMR was excellent, detecting AMR genes with 99.9% accuracy (95% CI 99.9-99.9%), 97.9% sensitivity (95% CI 97.5-98.4%) and 100% specificity (100-100%). Phenotypic inference of resistance for Salmonella spp. was equally impressive, with 98.9% accuracy (98.7-99.1%). Validation data were submitted to the governing authority and ISO15189 accreditation was achieved. Implementation of abritAMR resulted in streamlined bioinformatics and reporting pathways, and it was readily updated and re-verified with database revisions or changes in reporting requirements. abritAMR is publicly and freely available to assist clinical and public health microbiology laboratories everywhere harness the power of AMR genomics in their professional practice.

microbiology↗

bioRxiv2023

Corynebacterium of the diphtheriae complex in companion animals: clinical and microbiological characterization of 64 cases from France

ObjectivesCorynebacteria of the diphtheriae complex (Cdc) can cause diphtheria in humans and have been reported from companion animals. We aimed to describe animal infection cases caused by Cdc isolates. Methods18 308 animals (dogs, cats, horses and small mammals) with rhinitis, dermatitis, non-healing wounds and otitis were sampled in metropolitan France (August 2019 to August 2021). Data on symptoms, age, breed, and the administrative region of origin were collected. Cultured bacteria were analyzed for tox gene presence, for production of the diphtheria toxin, for antimicrobial susceptibility, and genotyped by multilocus sequence typing. ResultsC. ulcerans was identified in 51 cases, 24 of which were toxigenic. Rhinitis was the most frequent presentation (18/51). Eleven cases (6 cats, 4 dogs, 1 rat) were mono-infections. Large breed dogs, especially German Shepherds (9 of 28 dogs; p < 0.00001) were overrepresented. C. ulcerans isolates were susceptible to all tested antibiotics. tox-positive C. diphtheriae was identified in 2 horses. Last, 11 infections cases (9 dogs, 2 cats; mostly chronic otitis, and 2 sores) had tox-negative C. rouxii, a recently defined species. C. rouxii and C. diphtheriae isolates were susceptible to most antibiotics tested, and almost all of these infections were polymicrobial. ConclusionsMonoinfections with C. ulcerans point towards a primary pathogenic potential to animals. C. ulcerans represents an important zoonotic risk, and C. rouxii may represent a novel zoonotic agent. This case series provides novel clinical and microbiological data on Cdc infections, and underlines the need for management of animals and their human contacts. ImportanceWe report on the occurrence, clinical and microbiological characteristics of infections caused by members of the Corynebacteria of the diphtheriae complex (Cdc) in companion animals. This is the first study based on the systematic analysis of a very large animal cohort (18 308 samples), which provides data on the frequency of Cdc isolates in various types of clinical samples from animals. Awareness of this zoonotic bacterial group remains low among veterinarians and veterinary laboratories, among which it is often considered a commensal bacteria of animals. We suggest that in case of Cdc detection in animals, the veterinary laboratories should be encouraged to send the samples to a reference laboratory for analysis of the presence of the tox gene. This work is relevant to the development of guidelines in case of Cdc infections in animals, and underlines their public health relevance given the zoonotic transmission risk.

microbiology↗

bioRxiv2023

Touching the (almost) untouchable: a minimally-invasive workflow for microbiological and biomolecular analyses of cultural heritage objects

Microbiological and biomolecular approaches to cultural heritage research have expanded the established research horizon -from the prevalent focus on the cultural objects conservation and human health protection to the relatively recent applications to provenance inquiry and assessment of environmental impacts on heritage objects in a global context of a changing climate. Standard microbiology and molecular biology methods were developed for other materials, specimens, disciplines and contexts. Although they could in principle be applied to cultural heritage research, certain characteristics common to several heritage objects - such as uniqueness, fragility, high value and restricted access, demand tailored approaches. In addition, samples from heritage objects often yield low microbial biomass, rendering them highly susceptible to cross-contamination. Therefore, dedicated methodology that addresses these material limitations and operational hurdles along all procedural steps are needed. Here were propose a step-by-step standardized laboratory and bioinformatic workflow to analyse the microbiome of cultural heritage objects. The methodology was developed targeting the challenging side of the spectrum of cultural heritage objects, such as the delicate written record, while retaining modularity and flexibility to adapt and/or upscale the proposed workflow to heritage artefacts of a more robust nature or larger dimensions. We hope this review and workflow will facilitate the interdisciplinary inquiry and interactions among the cultural heritage research community.

microbiology↗

bioRxiv2025

Development of an AI Algorithm for Automatic Classification of Gram Stain Images in Microbiology

This paper reflects on the development and performance of an advanced artificial intelligence (AI) algorithm for the automated processing and classification of Gram stain images obtained from actual microbiology samples used in clinical microbiology. The aim of the project was to effectively categorize non-standardized Gram stain images into the six most common categories: Gram-negative rods, Gram-positive cocci in chains, Gram-positive cocci in clusters, Gram-positive rods, Gram-negative cocci, and yeasts. The development and testing relied on 1,077 Gram stain images of varying sizes, originating from different laboratories and captured using diverse microscopes at different points in time, resulting in differences in image quality, scaling, color balance, and the presence of artifacts. The dataset was split into 80% training and 20% testing subsets, with the split performed in a stratified manner so that each object group was proportionally represented in both the training and testing sets. Preprocessing involved computer vision techniques to improve contrast and color balance, detect contours and object borders, and implement filtering mechanisms to remove unwanted artifacts. Morphological analysis of shapes was then performed to extract parameters characterizing each contour. Next, human-like classification criteria--based on gradient, morphological features (e.g., shape, size) and spatial arrangement that mimic microbiologists visual assessment--were established, achieving around 92% accuracy in image classification without using machine learning (ML) methods. However, any further improvements turned practically impossible, prompting the use of ML methods. Building on pre-obtained features, a random forest ML algorithm was employed to further refine the criteria, with three models trained and tested successively. The first model determined the Gram stain reaction (positive or negative) of each object. The second model classified objects into one of six predefined categories. The third model aggregated individual object classifications to generate an overall classification for each slide, based on the number of objects observed in each category and their occupied area. Overall, the ML solution was significantly more accurate, reaching 99.9% accuracy in classifying the images into one of the aforementioned groups. The algorithms limitations include inability to classify mixed cultures, as it primarily focuses on the dominant category. In cases where positive and negative objects coexist, the algorithm tends to prioritize Gram-positive objects. Additionally, the current morphological assessment is insufficient for yeast classification. Addressing these limitations is a crucial avenue for future research to enhance the algorithms versatility and accuracy.

microbiology↗

bioRxiv2026

Improving Turnaround Times with Artificial Intelligence in Microbiology

This dual-center study evaluated the impact of artificial intelligence (AI) on urine culture turnaround times in Canadian diagnostic laboratories employing full microbiology laboratory automation. Data were collected before and after the implementation of PhenoMATRIX (PM), an AI-based software designed to support culture sorting and result interpretation. In both a low-volume tertiary care hospital and a high-volume community laboratory, PM reduced the time to final culture reporting, with decreases of approximately 1.5 hours and 3.9 hours, respectively. Implementation of PM+, which automatically releases defined results to patient charts, further improved turnaround time. These findings indicate that microbiology laboratories with full laboratory automation can achieve further improvements in turnaround time by integrating AI-culture assessment and results release.

microbiology↗

bioRxiv2016

CLIMB (the Cloud Infrastructure for Microbial Bioinformatics): an online resource for the medical microbiology community

The increasing availability and decreasing cost of high-throughput sequencing has transformed academic medical microbiology, delivering an explosion in available genomes while also driving advances in bioinformatics. However, many microbiologists are unable to exploit the resulting large genomics datasets because they do not have access to relevant computational resources and to an appropriate bioinformatics infrastructure. Here, we present the Cloud Infrastructure for Microbial Bioinformatics (CLIMB) facility, a shared computing infrastructure that has been designed from the ground up to provide an environment where microbiologists can share and reuse methods and data.\n\nDATA SUMMARYThe paper describes a new, freely available public resource and therefore no data has been generated. The resource can be accessed at http://www.climb.ac.uk. Source code for software developed for the project can be found at http://github.com/MRC-CLIMB/\n\nI/We confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.\n\nIMPACT STATEMENTTechnological advances mean that genome sequencing is now relatively simple, quick, and affordable. However, handling large genome datasets remains a significant challenge for many microbiologists, with substantial requirements for computational resources and expertise in data storage and analysis. This has led to fragmentary approaches to software development and data sharing that reduce the reproducibility of research and limits opportunities for bioinformatics training. Here, we describe a nationwide electronic infrastructure that has been designed to support the UK microbiology community, providing simple mechanisms for accessing large, shared, computational resources designed to meet the bioinformatic needs of microbiologists.

Bioinformatics↗

bioRxiv2020

Proficiency Testing for bacterial whole genome sequencing in assuring the quality of microbiology diagnostics in clinical and public health laboratories

The adoption of whole genome sequencing (WGS) data over the past decade for pathogen surveillance, and decision-making for infectious diseases has rapidly transformed the landscape of clinical microbiology and public health. However, for successful transition to routine use of these techniques, it is crucial to ensure the WGS data generated meet defined quality standards for pathogen identification, typing, antimicrobial resistance detection and surveillance. Further, the ongoing development of these standards will ensure that the bioinformatic processes are capable of accurately identifying and characterising organisms of interest, and thereby facilitate the integration of WGS into routine clinical and public health laboratory setting. A pilot proficiency testing (PT) program for WGS of infectious agents was developed to facilitate widely applicable standardisation and benchmarking standards for WGS across a range of laboratories. The PT participating laboratories were required to generate WGS data from two bacterial isolates, and submit the raw data for independent bioinformatics analysis, as well as analyse the data with their own processes and answer relevant questions about the data. Overall, laboratories used a diverse range of bioinformatics tools and could generate and analyse high-quality data, either meeting or exceeding the minimum requirements. This pilot has provided valuable insight into the current state of genomics in clinical microbiology and public health laboratories across Australia. It will provide a baseline guide for the standardisation of WGS and enable the development of a PT program that allows an ongoing performance benchmark for accreditation of WGS-based test processes.

bioinformatics↗

bioRxiv2021

Ensuring that fundamentals of quantitative microbiology are reflected in microbial diversity analyses based on next-generation sequencing

Diversity analysis of amplicon sequencing data is mainly limited to plug-in estimates calculated using normalized data to obtain a single value of an alpha diversity metric or a single point on a beta diversity ordination plot for each sample. As recognized for count data generated using classical microbiological methods, read counts obtained from a sample are random data linked to source properties by a probabilistic process. Thus, diversity analysis has focused on diversity of (normalized) samples rather than probabilistic inference about source diversity. This study applies fundamentals of statistical analysis for quantitative microbiology (e.g., microscopy, plating, most probable number methods) to sample collection and processing procedures of amplicon sequencing methods to facilitate inference reflecting the probabilistic nature of such data and evaluation of uncertainty in diversity metrics. Types of random error are described and clustering of microorganisms in the source, differential analytical recovery during sample processing, and amplification are found to invalidate a multinomial relative abundance model. The zeros often abounding in amplicon sequencing data and their implications are addressed, and Bayesian analysis is applied to estimate the source Shannon index given unnormalized data (both simulated and real). Inference about source diversity is found to require knowledge of the exact number of unique variants in the source, which is practically unknowable due to library size limitations and the inability to differentiate zeros corresponding to variants that are actually absent in the source from zeros corresponding to variants that were merely not detected. Given these problems with estimation of diversity in the source even when the basic multinomial model is valid, sample-level diversity analysis approaches are discussed. HighlightsO_LIRandom error in amplicon sequencing method should be considered in diversity analysis C_LIO_LIClustering, amplification, and differential recovery distort sample diversity C_LIO_LIThe multinomial model for compositional count data is compromised by amplification C_LIO_LIThere are three types of zeros in amplicon sequencing data, including missing zeros C_LIO_LISource alpha diversity estimates are biased by unknown number of unique variants C_LI

bioinformatics↗

bioRxiv2022

Physico-chemical and microbiological characteristics of honey produced by stingless bees (Meliponula beccarii) from the Oromia Region, Ethiopia

The study was designed to characterize the Physico-chemical and microbiological properties of stingless bee honey (Meliponula beccarii L.) in the Oromia Region, Ethiopia. About thirty-nine (39) honey samples were collected from underground soils through an excavation of natural nests. The study revealed that the mean values of physicochemical parameters for honey samples were: moisture content (30.69{+/-}0.29), ash content (0.16{+/-}0.01), electrical conductivity (0.44{+/-}0.2), pH (3.30{+/-}0.03), free acidity (92.39{+/-}4.45), HMF (6.58{+/-}0.36), fructose (36.48{+/-}0.54), glucose (27.67{+/-}0.43),sucrose(1.24{+/-}0.18), maltose (1.2{+/-}0.18) and reducing sugar (64.15{+/-}0.75). Stingless bee honey in this study is characterized as possessing higher moisture content and free acidity, but a lower level of sugar as compared to Apis mellifera honey standard. The occurrence of microorganisms in the stingless honey sample ranged from 2.55 x 10 4 to 1.9 x 103 CFU/ml for Aerobic Mesophilic, 1.68 x104 to 9 x102 CFU/ml for yeast, and 1.8 x103 to 2 x102 CFU/ml for mold. The number of aerobic spore-forming bacteria was at non-detectable levels in all samples while Staphylococci species was detectable only in a sample obtained from Guduru. This indicated that there might be contamination during the harvesting, processing, and storage of stingless bee honey samples. The microbiological and physicochemical properties of stingless bee honey are different from those of A. mellifera honey and need to establish for specific quality standards to promote its commercialization.

animal behavior and cognition↗

bioRxiv2023

A Hypothesis-Based Hop Microbiology Laboratory Module Testing the Plausibility of the Mythical Origin of the India Pale Ale (IPA)

As one of the most famous fermented drinks in the world, beer is an especially relatable topic for microbiology courses. Here, we describe a short and easily adaptable module based on the antibacterial properties of hops used in brewing. By the 15th century, beer recipes included hops (the flower of the Humulus lupulus plant) as a bittering agent and antimicrobial. By the 19th century, the highly-hopped Indian Pale Ale (IPA) became popular, and a modern myth has emerged that IPAs were invented to survive long ocean voyages such as from Britain to India. With that myth in mind, we designed a hypothesis-driven microbiology lab module that tests the plausibility of this brewing myth-- namely that highly-hopped beers possess enough antibacterial activity to prevent spoilage, while lowly-hopped beers do not. The overall design of the module is to test the antimicrobial properties of hops using petri plates containing varying concentrations of hop extract. The module includes hypothesis generation and testing related to bacterial physiology and morphology (hops are not equally effective against gram-positive and gram-negative bacteria), and to mechanisms of antimicrobial resistance (as beer spoilage bacteria have repeatedly evolved hop resistance). Pre and post assessment showed that students made significant gains in the learning objectives for the module, which encourages critical thinking and hypothesis testing by linking microbial physiology and antimicrobial resistance to an important and topical real-world application.

scientific communication and education↗

bioRxiv2025

Assessing Mozambican Honey Quality: Differential Physical, Chemical and Microbiological Characteristics in Formal and Informal Markets

Honey is a natural product made by bees and is widely valued as a healthy food. Its quality can deteriorate due to microbial activity and physical or chemical changes. This study assessed honey from formal (n=12) and informal (n=12) markets in three Mozambican provinces: Maputo, Sofala, and Inhambane, to evaluate its quality and safety. Physicochemical parameters such as viscosity, water activity (Aw), pH, ash content, total soluble solids, and diastase activity were measured using standard AOAC methods. Microbiological quality was evaluated by counting aerobic mesophilic bacteria, moulds and yeasts, and by checking for pathogenic bacteria, including Escherichia coli and Salmonella species. The results showed marked differences. Honey from informal markets had a significantly higher ash content (0.613% {+/-} 0.079%), which is a recognized indicator of contamination (p < 0.05). In contrast, formal market samples demonstrated superior quality with greater total soluble solids (77.84 {degrees}Brix {+/-} 0.75{degrees}Brix) and viscosity (2.168 {+/-} 0.755 Pa.s). All honey samples from formal markets retained diastase activity, while 25% (3 out of 12) of informal market samples showed no enzymatic activity. From a microbiological perspective, all samples were within safe limits. In conclusion, the physicochemical irregularities observed in honey from informal markets underline the urgent need for better hygiene practices and stronger regulatory enforcement to protect consumers and strengthen the local honey industry.

biochemistry↗

bioRxiv2018

Bigger and Better? Representativeness of the Influenza A surveillance using one consolidated clinical microbiology laboratory data set as compared to the Belgian Sentinel Network of Laboratories

Infectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift towards laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014-2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralised CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. These results illustrate that centralised CML-derived data are not only credible but also advantageous to use for future surveillance and prediction purposes, especially for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.

epidemiology↗

bioRxiv2017

Preprinting Microbiology

The field of microbiology has experienced significant growth due to transformative advances in technology and the influx of scientists driven by a curiosity to understand how microbes sustain myriad biochemical processes that maintain the Earth. With this explosion in scientific output, a significant bottleneck has been the ability to rapidly disseminate new knowledge to peers and the public. Preprints have emerged as a tool that a growing number of microbiologists are using to overcome this bottleneck. Posting preprints can help to transparently recruit a more diverse pool of reviewers prior to submitting to a journal for formal peer-review. Although use of preprints is still limited in the biological sciences, early indications are that preprints are a robust tool that can complement and enhance peer-reviewed publications. As publishing moves to embrace advances in internet technology, there are many opportunities for preprints and peer-reviewed journals to coexist in the same ecosystem.

microbiology↗

bioRxiv2017

Discovery-oriented teaching: The use of chimeric proteorhodopsins for the development of a lab curriculum in marine microbiology and for the discovery of natural red-shifted proteorhodopsins

Student microbial ecology laboratory courses are often conducted as condensed courses in which theory and wet lab work are combined in a very intensive short time period. In last decades, the study of marine microbial ecology is increasingly reliant on molecular-based methods, and as a result many of the research projects conducted in such courses require sequencing that is often not available on site and may take more time than a typical course allows. In this work, we describe a protocol combining molecular and functional methods for analyzing proteorhodopsins (PRs), with visible results in only 4-5 days, that do not rely on sequencing. PRs were discovered in oceanic surface waters two decades ago, and have since been observed in different marine environments and diverse taxa, including the abundant alphaproteobacterial SAR11 group. PR subgroups are currently known to absorb green and blue light, and their distribution was previously explained by prevailing light conditions - green pigments at the surface and blue pigments in deeper waters, as blue light travels deeper in the water column. To detect PR in environmental samples, we created a chimeric plasmid suitable for direct expression of PRs using PCR amplification and functional analysis in Escherichia coli cells. Using this assay, we discovered several exceptional cases of PRs whose phenotypes differed from those predicted based on sequence only, including a previously undescribed yellow-light absorbing PRs. We applied this assay in two 10-days marine microbiology courses and found it to greatly enhance students laboratory experience, enabling them to gain rapid visual feedback and colorful reward for their work. Furthermore we expect this assay to promote the use of functional assays for the discovery of new rhodopsin variants.

microbiology↗

bioRxiv2020

Microrisk Lab: an online freeware for predictive microbiology

Microrisk Lab was designed as an interactive modeling freeware to realize parameter estimation and model simulation in predictive microbiology. This tool was developed based on the R programming language and Shinyapps.io server, and designed as a fully responsive interface to the internet-connected devices. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/ inactivation under static and non-isothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/ inactivation under static and non-isothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate dataset and/ or parameter setting. In this research, six case studies were reproduced in Microrisk Lab and compared in parallel to DMFit, GInaFiT, IPMP 2013/ GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling system in most cases. Microrisk Lab allowed for uniform user experience to implement microbial predictive modeling by its friendly interfaces, high-integration, and interconnectivity. It might become a useful tool for the microbial parameter determination and behavior simulation. Non-commercial users could freely access this application at https://microrisklab.shinyapps.io/english/.

microbiology↗

bioRxiv2020

Iterative Cochran's C test as a multivariate method to detect higher than expected variability: a microbiological inter-laboratory ring trial as a case-study

IntroductionAn interlaboratory calibration analysis was carried out to validate a methodology for a European standard for domestic laundry disinfection, using different doses of disinfectant and microorganisms. ISO 5725-2 and ISO 13528 form the basis of interlaboratory validations of quantitative methods, but there is a need for a simple graphical method to detect differences in laboratory behavior in terms of accuracy and variability. ObjectiveA novel multivariate method based on the classical Cochrans C test, as well as PCA and bootstrapping, which allows the inclusion of different correlated variables, was applied to identify higher variability than expected in factor (e.g. laboratories) levels, and the detection of multivariate outliers in a reduced space. MethodsThe proposed method is based on resampling, using the same sample many times but removing cases at random and performing Cochrans C test for all the variables together in a reduced space. ResultsThe method was tested by checking 7 laboratories for high variability in different parameters (logarithmic reduction (LR), cross contamination (RI), and wash water (WW)). After applying the proposed statistical analyses, no reasons were found to reject any of the participating laboratories. Multiple applications of the method are possible and we describe a case study in which the multivariant iterative Cochrans C test was used: variability detection with multiple microbiological parameters (with high variability) during an interlaboratory ring trial.

microbiology↗

bioRxiv2021

Heterogeneity of Staphylococcus epidermidis in prosthetic joint infections: Time to reevaluate microbiological criteria?

Prosthetic joint infection (PJI) is a feared complication after arthroplasty with Staphylococcus epidermidis as a major pathogen. One diagnostic criteria for PJI diagnosis is the finding of phenotypically identical organisms based on common laboratory tests in two or more periprosthetic microbial cultures. Because of phenotypical variation within a genetic clone, and clonal variation within a phenotype, the criteria may be ambiguous. Here, we investigate the extent of diversity among coagulase-negative staphylococci in PJI and characterize in detail S. epidermidis isolates from these infections. We performed a retrospective cohort study of 62 consecutive patients with PJI caused by coagulase-negative staphylococci (CoNS) from two hospitals in Northern Sweden. From 16 of the patients, two to nine S. epidermidis isolates were available for whole-genome analyses. Hospital-adapted multidrug-resistant genetic clones of S. epidermidis were identified in 40/62 (65%) of the PJIs using a combination of analysis by pulsed-field gel electrophoresis and multiple-locus sequence typing. Whole genome sequencing showed presence of multiple sequence types (STs) in seven (7/16, 44%) PJIs. Among isolates of the same ST, within-patient phenotypical variation in antibiotic susceptibility and/or whole-genome antibiotic resistance gene content was frequent (11/16, 69%). These results highlight the ambiguity of using phenotypical characterization of S. epidermidis as diagnostic criteria in PJI. The results call for larger systematic studies to determine the frequency of CoNS diversity in PJIs, the implications of such diversity for microbiological diagnostics, and for the therapeutic outcome in patients.

microbiology↗

bioRxiv2021

Adapting macroecology to microbiology: using occupancy modelling to assess functional profiles across metagenomes

Metagenomic sequencing provides information on the metabolic capacities and taxonomic affiliations for members of a microbial community. When assessing metabolic functions in a community, missing genes in pathways can occur in two ways: the genes may legitimately be missing from the community whose DNA was sequenced, or the genes were missed during shotgun sequencing or failed to assemble, and thus the metabolic capacity of interest is wrongly absent from the sequence data. Here, we borrow and adapt occupancy modelling from macroecology to provide mathematical context to metabolic predictions from metagenomes. We review the five assumptions underlying occupancy modelling through the lens of microbial community sequence data. Using the methane cycle, we apply occupancy modelling to examine the presence and absence of methanogenesis and methanotrophy genes from nearly 10,000 metagenomes spanning global environments. We determine that methanogenesis and methanotrophy are positively correlated across environments, and note that the lack of available standardized metadata for most metagenomes is a significant hindrance to large-scale statistical analyses. We present this adaptation of macroecologys occupancy modelling to metagenomics as a tool for assessing presence/absence of traits in environmental microbiological surveys. We further initiate a call for stronger metadata standards to accompany metagenome deposition, to enable robust statistical approaches in the future.

microbiology↗