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bioRxiv2022

DeepOM: Single-molecule optical genome mapping via deep learning

Efficient tapping into genomic information from a single microscopic image of an intact DNA molecule fragment is an outstanding challenge and its solution will open new frontiers in molecular diagnostics. Here, a new computational method for optical genome mapping utilizing Deep Learning is presented, termed DeepOM. Utilization of a Convolutional Neural Network (CNN), trained on simulated images of labeled DNA molecules, improves the success rate in alignment of DNA images to genomic references. The method is evaluated on acquired images of human DNA molecules stretched in nano-channels. The accuracy of the method is benchmarked against state-of-the-art commercial software Bionano Solve. The results show a significant advantage in alignment success rate for molecules shorter than 50 kb. DeepOM improves yield, sensitivity and throughput of optical genome mapping experiments in applications of human genomics and microbiology.

bioinformatics↗

bioRxiv2023

A defined community of core gut microbiota members promotes cognitive performance in honey bees

The composition of the gut microbiota has recently been identified as a cause of cognitive variability in humans and animals. Germ-free individuals and individuals exposed to an antibiotic treatment show severe alteration of their learning and memory performance measured in various cognitive tasks. While different species of bacteria are known to interact in the gut, their cumulative or synergistic effects on cognitive performance remain elusive. Here we established a defined bacterial community - composed of core members of the corbiculate bee microbiota - which enhances honey bees cognitive capacities. Honey bees colonized with this reconstituted community discriminated better two odours based on the presence or absence of a sucrose reward compared to germ-free individuals. They also memorized better these odour-food associations in the short-term. These cognitive improvements seem to constitute an emergent property of the community, because they could not be recapitulated by any of the community members when mono-associated in gnotobiotic bees and they were not explained by the total biomass in the gut. The identification of this community and its effect on bees open new avenues of research in neuroscience, microbiology, and ecology. Future research should help understanding how interactions between bacterial species in the community promote the hosts associative learning and memory performance.

animal behavior and cognition↗

bioRxiv2023

Facilitative interaction networks in experimental microbial community dynamics

Understanding potential roles of facilitative interactions between species is one of the major challenges in ecology and microbiology. However, we still have limited knowledge of entangled webs of facilitative interactions in ecosystems. By compiling whole-genome shotgun metagenomic data of an experimental microbial community, we tested the hypothesis that architecture of facilitative interaction networks could change through time. A metabolic modeling approach for estimating dependence between microbial genomes (species) allowed us to infer the network structure of potential facilitative interactions at 13 time points through the 110-day monitoring of experimental microbiomes. We then found that positive feedback loops, which were theoretically predicted to promote cascade breakdown of ecological communities, existed within the inferred networks of metabolic interactions prior to the drastic community-compositional shift observed in the microbiome time-series. We further applied "directed-graph" analyses to pinpoint potential keystone species located at the "upper stream" positions of such feedback loops. These analyses on facilitative interactions will help us understand key mechanisms causing catastrophic shifts in microbial community structure.

ecology↗

bioRxiv2023

Measuring single-cell susceptibility to antibiotics within monoclonal bacterial populations

Given the emergence of antimicrobial drug resistance, it is critical to understand the heterogeneity of response to an antibiotic within a population of cells. Since the drug can exert a selection pressure that leads to the emergence of resistant phenotypes. To date, neither bulk nor single-cell methods are able to link the heterogeneity of single-cell susceptibility to the population-scale response to antibiotics. Here we present a platform that measures the ability of individual E. coli cells to form small colonies at different ciprofloxacin concentrations, by using anchored microfluidic drops and an image and data analysis pipelines. The microfluidic results are benchmarked against classical microbiology measurements of antibiotic susceptibility, showing an agreement between the pooled microfluidic chip and replated bulk measurements. Further, the experimental likelihood of a single cell to form a colony is used to provide a probabilistic antibiotic susceptibility curve. In addition to the probabilistic viewpoint, the microfluidic format enables the characterization of morphological features over time for a large number of individual cells. This pipeline can be used to compare the response of different bacterial strains to antibiotics with different action mechanisms.

bioengineering↗

bioRxiv2023

One Health Assessment of an Urban Temporary Settlement Reveals Gut Microbiome Serving as Antimicrobial Resistance Gene Reservoir

Antimicrobial resistance (AMR) is an emerging and growing global health challenge that could result in 10.2 million deaths annually by 2050. The unrestricted and haphazard use of antibiotics is contributing to the rapid emergence and spread of AMR, and the problem is exacerbated by release of untreated waste water from high-risk sources like hospitals into rivers. Bacteria often develop resistance through horizontal gene transfer mechanism and gut flora can act as a source for new Antimicrobial Resistance Genes (ARG). Upcoming methods like metagenomics can identify the resistance profile (AMR) of gut microbiome, and detect bacterial infections that otherwise go unnoticed. Our study focused on understanding the presence of AMR mutations and gene transfer dynamics in human, animal and environmental samples collected in one of the temporary settlements of Kathmandu (Nepal) using One Health approach. Current AMR reporting based on clinical cases is limited and does not provide information on specific pathogen and associated AMR genes-our study is an effort to contribute information to fulfill this gap. Twenty-one samples were collected from a temporary settlement in Thapathali (Kathmandu), which included fecal samples from birds (n=3) and humans (n=14), and environmental samples (n=4). Microbiological assessment was carried out based on 16S sequence metagenomic analysis using MiSeq (Illumina, USA). Taxonomic classification on obtained 16S sequences were determined by using Metaphlan 2 and Qiime 2 bioinformatics tools. ShortBRED was used to classify ARG and virulence factors, and WAFFLE was used for horizontal gene transfer event prediction. The network analysis was carried out using Gephi v0.9 and the ResistoXplorer web tool to identify ARG in the collected samples. Prevotella spp. was the dominant gut microbiome in humans. We detected diverse phages and viruses, including Stx-2 converting phages. 72 virulence factors and 53 ARG subtypes were detected, with poultry samples having the highest number of subtypes. The cluster and network analysis showed a strong association between gut microbiome and ARG, which was also supported by Horizontal Gene Transfer (HGT) analysis. One-Health interface showed ARG dynamics and revealed gut microbiomes of humans and animals serving as a reservoir for the circulating ARG.

genomics↗

bioRxiv2023

Production of extracellular polymeric substances in granular sludge under selection for Accumulibacter and Competibacter

Granular sludge intensifies the removal of nutrients from wastewater. Granules structured by extracellular polymeric substances (EPS) can be recovered as biomaterial. Links between microbial selection and EPS formation during granulation need to get uncovered. We inoculated anaerobic-aerobic sequencing batch reactors with either flocs or granules to study the relationships between microbial selection, bioaggregation, exopolymer formation, and EPS composition. Selection for slow-growing organisms like the model polyphosphate- accumulating organism "Candidatus Accumulibacter" (max. 83% vs. amplicon sequencing read counts) and glycogen-accumulating organism "Ca. Competibacter" (max. 45%) sustained granulation. Gel-forming exopolymers were produced as high as above 40% of the volatile solids of the biomass by stepwise increase of the organic loading rate (0.3 to 2.0 g CODAc d-1 LR-1). Confocal laser scanning microscopy, FT-IR spectroscopy, and HPAE-PAD chromatography revealed the complex and dynamic chemical compositions of the structural EPS in relation to microbial population shifts along reactor regimes. The analysis of 20 representative genomes of "Ca. Accumulibacter" and "Ca. Competibacter" recovered from public databases revealed their functional potential to produce EPS among other representative wastewater microorganisms. The more than 40 functional gene categories annotated highlight the complexity of EPS metabolic networks from monomers processing to assembly, export, and epimerizations. The combination of ecological engineering principles and systems microbiology will help unravel and direct the production of EPS from wastewater, valorizing residual granular sludge into beneficial biomaterials for the circular economy. HighlightsO_LISelection for slow-growing organisms like PAOs and GAOs fostered a robust granulation. C_LIO_LIStructural EPS were produced above 40% of biomass volatile content under high loading. C_LIO_LIChemical composition of EPS evolved together with the microbial community composition. C_LIO_LIGenomic insights highlighted the genetic potential of PAOs and GAOs for EPS formation. C_LIO_LIMicrobial communities are complex; further are their EPS compositions and metabolisms. C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=130 SRC="FIGDIR/small/534144v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@a163e2org.highwire.dtl.DTLVardef@1a4ab94org.highwire.dtl.DTLVardef@1fc93d4org.highwire.dtl.DTLVardef@14d4b7f_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioengineering↗

bioRxiv2023

Deterministic and stochastic processes generating alternative states of microbiomes

The structure of microbial communities is often classified into discrete or semi-discrete compositions of taxa as represented by "enterotypes" of human gut microbiomes. Elucidating mechanisms that generate such "alternative states" of microbiome compositions has been one of the major challenges in ecology and microbiology. In a time-series analysis of experimental microbiomes, we here show that both deterministic and stochastic ecological processes drive divergence of alternative microbiome states. We introduced species-rich soil-derived microbiomes into eight types of culture media with 48 replicates, monitoring shifts in community compositions at six time points (8 media x 48 replicates x 6 time points = 2,304 community samples). We then confirmed that microbial community structure diverged into a few discrete states in each of the eight medium conditions as predicted in the presence of both deterministic and stochastic community processes. In other words, microbiome structure was differentiated into a small number of reproducible compositions under the same environment. This fact indicates not only the presence of selective forces leading to specific equilibria of community-scale resource use but also the influence of demographic drift (fluctuations) across basins of such equilibria. A reference-genome analysis further suggested that the observed alternative states differed in ecosystem-level functions. These findings will help us examine how microbiome structure and functions can be controlled by changing the "stability landscapes" of ecological community compositions.

ecology↗

bioRxiv2023

Homeostatic, repertoire and transcriptional relationships between colon T regulatory cell subsets

Foxp3+ regulatory T cells (Tregs) in the colon are key to promoting peaceful co-existence with symbiotic microbes. Differentiated in either thymic or peripheral locations, and modulated by microbes and other cellular influencers, colonic Treg subsets have been identified through key transcription factors (TF; Helios, Rorg, Gata3, cMaf), but their inter-relationships are unclear. Applying a multimodal array of immunologic, genomic, and microbiological assays, we find more overlap than expected between populations. The key TFs play different roles, some essential for subset identity, others driving functional gene signatures. Functional divergence was clearest under challenge. Single-cell genomics revealed a spectrum of phenotypes between the Helios+ and Ror{gamma}+ poles, different Treg-inducing bacteria inducing the same Treg phenotypes to varying degrees, not distinct populations. TCR clonotypes in monocolonized mice revealed that Helios+ and Ror{gamma}+ Tregs are related, and cannot be uniquely equated to tTreg and pTreg. We propose that rather than the origin of their differentiation, tissue-specific cues dictate the spectrum of colonic Treg phenotypes.

immunology↗

bioRxiv2023

The Size of Fields in Biomedical Sciences

Scientific research output has increased exponentially over the past few decades, but not equally across all fields of study, and we lack clear methods for estimating the size of any given field of research. Understanding how fields grow, change, and are organized is essential to understanding how human resources are allocated to the investigation of scientific problems. In this study we estimated the size of certain biomedical fields from the number of unique author names appearing in field relevant publications in the PubMed database. Focusing on microbiology, where the size of fields is often associated with those who work on a particular microbe, we find large differences in the size of its subfields. We found that plotting the number of unique investigators as a function of time can show changes consistent with growing or shrinking fields. We envision using unique author count to measure the strength of a workforce in any given field, analyze the overlap of workforce between fields, and compare how workforce correlates to available research funds and public health burden of a field.

systems biology↗

bioRxiv2023

Influence of DNA extraction methods on microbiome and resistome analysis in activated sludge

Amplicon sequencing, metagenomics, and quantitative polymerase chain reaction (qPCR) are commonly used techniques to analyse microorganisms and antibiotic resistance genes (ARGs) in activated sludge from wastewater treatment plants (WWTPs). However, the lack of workflow harmonisation poses challenges in comparing measurements across studies and research groups. To address this issue, we examined the impact of DNA extraction procedures on 16S rRNA gene amplicon sequencing, shotgun metagenomics, and qPCR analyses of activated sludge by combining two widely used DNA extraction kits (PowerSoil and FastDNA) and two commonly employed disruption instruments (bead-beater and vortex) through a 2x2 factorial experimental design involving four groups of three analysts performing DNA extractions in triplicates. Our findings revealed significant differences in DNA yield, purity, and reproducibility of amplicon sequencing profiles among the extraction kits. Operator variability also influenced the results. We compared microbiome profiles obtained by amplicon sequencing and metagenomics and observed that bead-beating introduced more variability among triplicates compared to vortexing. The combinations of extraction kits and disruption instruments impacted the relative abundances of specific phyla such as Actinobacteriota, Bacteroidota, and Nitrospirota. For resistome analysis, we employed metagenomics for high-resolution profiling and qPCR for high-sensitivity detection of ARGs. The compositions and diversities of resistome datasets were not significantly affected by the choice of extraction kits and disruption instruments. Although using the same method is ideal for accurate comparisons, our results suggest that acceptable reproducibility can still be achieved when using different methods. This finding encourages the implementation of ARG monitoring in wastewater treatment processes. However, it is important to consider biases introduced by DNA extraction workflows when designing analytical studies, interpreting their results, and comparing their findings. Striving for more harmonised molecular workflows is crucial in the field of wastewater microbiology and engineering. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=153 SRC="FIGDIR/small/546617v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@caa36eorg.highwire.dtl.DTLVardef@1afabfdorg.highwire.dtl.DTLVardef@448b9aorg.highwire.dtl.DTLVardef@296404_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

molecular biology↗

bioRxiv2023

A dose-response based model for statistical analysis of chemical genetic interactions in CRISPRi libraries

An important application of CRISPR interference (CRISPRi) technology is for identifying chemical-genetic interactions (CGIs). Discovery of genes that interact with exposure to antibiotics can yield insights to drug targets and mechanisms of action or resistance. The objective is to identify CRISPRi mutants whose relative abundance is suppressed (or enriched) in the presence of a drug when the target protein is depleted, reflecting synergistic behavior. Different sgRNAs for a given target can induce a wide range of protein depletion and differential effects on growth rate. The effect of sgRNA strength can be partially predicted based on sequence features. However, the actual growth phenotype depends on the sensitivity of cells to depletion of the target protein. For essential genes, sgRNA efficiency can be empirically measured by quantifying effects on growth rate. We observe that the most efficient sgRNAs are not always optimal for detecting synergies with drugs. sgRNA efficiency interacts in a non-linear way with drug sensitivity, producing an effect where the concentration-dependence is maximized for sgRNAs of intermediate strength (and less so for sgRNAs that induce too much or too little target depletion). To capture this interaction, we propose a novel statistical method called CRISPRi-DR (for Dose-Response model) that incorporates both sgRNA efficiencies and drug concentrations in a modified dose-response equation. We use CRISPRi-DR to re-analyze data from a recent CGI experiment in Mycobacterium tuberculosis to identify genes that interact with antibiotics. This approach can be generalized to non-CGI datasets, which we show via an CRISPRi dataset for E. coli growth on different carbon sources. The performance is competitive with the best of several related analytical methods. However, for noisier datasets, some of these methods generate far more significant interactions, likely including many false positives, whereas CRISPRi-DR maintains higher precision, which we observed in both empirical and simulated data. Author SummaryCRISPRi technology is revolutionizing research in various areas of the life sciences, including microbiology, affording the ability to partially deplete the expression of target proteins in a specific and controlled way. Among the applications of CRISPRi, it can be used to construct large (even genome-wide) libraries of knock-down mutants for profiling antibacterial inhibitors and identifying chemical-genetic interactions (CGIs), which can yield insights on drug targets and mechanisms of action and resistance. The data generated by these experiments (i.e., sgRNA counts from high throughput sequencing) is voluminous and subject to various sources of noise. The goal of statistical analysis of such data is to identify significant CGIs, which are genes whose depletion sensitizes cells to an inhibitor. In this paper, we show how to incorporate both sgRNA efficiency and drug concentration simultaneously in a model (CRISPRi-DR) based on an extension of the classic dose-response (Hill) equation in enzymology. This model has advantages over other analytical methods for CRISPRi, which we show using empirical and simulated data.

bioinformatics↗

bioRxiv2023

EasyFlow: An open source, user friendly cytometry analyzer with graphic user interface (GUI)

Flow cytometry enables quantitative measurements of fluorescence in single cells. The technique was widely used for immunology to identify populations with different surface protein markers. More recently, the usage of flow cytometry has been extended to additional readouts, including intracellular proteins and fluorescent protein transgenes, and is widely utilized to study development, systems biology, microbiology, and many other fields. A common file format (FCS format, defined by International Society for Advancement of Cytometry (ISAC)) has been universally adopted, facilitating data exchange between different machines. A diverse spectrum of software packages have been developed for analysis of flow cytometry data. However, those are either 1) costly proprietary softwares, 2) open source packages with prerequisite installation of R or Python and sometimes require users to have experience in coding or 3) online tools that are limiting for analysis of large data sets. Here we present EasyFlow, an open source flow cytometry analysis GUI based on Matlab or Python, that can be installed and run locally cross-platform-ly (Windows and MacOS), without prerequisite user having previous knowledge on coding. The python version (EasyFlowQ) is also developed on a popular plotting framework (Matplotlib) and modern user interface (UI) toolkit (Qt), allowing more advanced users to customize and keep contributing to the software, as well as its tutorials. Overall, EasyFlow serves as a simple-to-use tool for inexperienced users with little coding experience to use locally, as well as a platform for advanced users to further customize for their own needs.

molecular biology↗

bioRxiv2023

Changes in lactic acid concentrations in culture media and its implications in the inhibition of fungal development.

In the following research, a novel antifungal culture method was discussed with the aim of developing bacterial culture media in which fungal growth is inhibited and bacterial growth is stimulated. This technique is based on the use of lactic acid as an antifungal agent, due to its capacity to neutralise the electrochemical gradient of fungal cell membranes, thus inhibiting their development. Based on this principle, the Minimum Inhibitory Concentration (MIC) of lactic acid in the culture media was investigated by changing its concentrations. After the preparation of the solutions, microbiological seeding was carried out in the different experimental groups. Data were collected using a quadrat sampling technique for subsequent statistical analysis using the T-test coefficient. As a result, the minimum inhibitory concentration of lactic acid was found to be 20%, where fungal growth was inhibited, and bacterial growth was stimulated. RESUMENEn la presente investigacion, se discutio acerca de un novedoso metodo de cultivo antifungico con la finalidad de desarrollar medios de cultivo bacterianos; en los que el crecimiento de hongos se viera inhibido y el bacteriano estimulado. Esta tecnica se basa en el uso del acido lactico como agente antifungico, debido a la capacidad de este acido para neutralizar el gradiente electroquimico de las membranas celulares de los hongos, inhibiendo asi su desarrollo. A partir de esta premisa, se investigo la Concentracion Inhibitoria Minima (CIM) del acido lactico en los medios de cultivo mediante un cambio en sus concentraciones. Posterior a la preparacion de las soluciones, se procedio a la siembra microbiologica en los diferentes grupos experimentales. Los datos fueron recolectados mediante una tecnica de muestreo por cuadrantes para su posterior analisis estadistico mediante el coeficiente de la Prueba T. Como resultado, se encontro que la concentracion inhibitoria minima del acido lactico fue del 20%, donde el desarrollo fungico se vio inhibido y el bacteriano estimulado. Published ElectronicallyO_LIBackground and Aims Lactic acid has been shown to have a negative effect on the chemical activity of fungal cell membranes and is therefore proposed to be used as an antifungal agent in growth media. C_LIO_LIMethods In the current study, an 88% lactic acid solution was used to produce agar solutions for culture media to assess the extent to which lactic acid can inhibit fungal growth. Subsequently, a quadrant sampling technique and a statistical study were used to measure the extent to which lactic acid can inhibit the growth of fungi. C_LIO_LIKey Results An inversely proportional correlation was found where lactic acid inhibited fungal growth. Specifically, the minimum inhibitory concentration of lactic acid was 20% and the results were supported by mathematical correlation and a statistical T-test, where 95% reliability was assumed. C_LIO_LIConclusions These results are consistent with the limited research into the molecular pathways of lactic acid in fungal organisms, which establishes, as in this research, that lactic acid negatively affects fungi and their development in culture media. C_LI

cell biology↗

bioRxiv2023

DeepGEEP: Data-Driven Prediction of Bacterial Biofilm Gene Expression Profiles

Predicting the gene expression profile (GEEP) of bacterial biofilms in response to spatial, temporal, and concentration profiles of stimulus molecules holds significant potential across microbiology, biotechnology, and synthetic biology domains. However, the resource and time-intensive nature of experiments within Petri dishes presents significant challenges. Data-driven methods offer a promising avenue to replace or reduce such experiments given sufficient data. Through wellcrafted data generation techniques, the data scarcity issue can be effectively addressed. In this paper, an innovative methodology is presented for generating GEEP data over a Petri dish that results from a specific chemical stimulus release profile. A twodimensional convolutional neural network (2D-CNN) architecture is subsequently introduced to leverage the synthesized dataset to predict GEEP variations across bacterial biofilms within the Petri dish. The approach, coined DeepGEEP, is applied to data generated by a particle-based simulator (PBS) to enable a flexible evaluation of its efficacy. The proposed method attains a significant level of accuracy in comparison to established benchmark models such as Linear SVM, Radial Basis Function SVM, Decision Tree, and Random Forest.

bioinformatics↗

bioRxiv2023

An antimicrobial drug recommender system using MALDI-TOF MS and dual-branch neural networks

Timely and effective use of antimicrobial drugs can improve patient outcomes, as well as help safeguard against resistance development. Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) is currently routinely used in clinical diagnostics for rapid species identification. Mining additional data from said spectra in the form of antimicrobial resistance (AMR) profiles is, therefore, highly promising. Such AMR profiles could serve as a drop-in solution for drastically improving treatment efficiency, effectiveness, and costs. This study endeavours to develop the first machine learning models capable of predicting AMR profiles for the whole repertoire of species and drugs encountered in clinical microbiology. The resulting models can be interpreted as drug recommender systems for infectious diseases. We find that our dual-branch method delivers considerably higher performance compared to previous approaches. In addition, experiments show that the models can be efficiently fine-tuned to data from other clinical laboratories. MALDI-TOF-based AMR recommender systems can, hence, greatly extend the value of MALDI-TOF MS for clinical diagnostics. All code supporting this study is distributed on PyPI and is packaged under: https://github.com/gdewael/maldi-nn

bioinformatics↗

bioRxiv2023

Crystal structure of a Class A beta-lactamase from Nocardia cyriacigeorgica

Nocardia are gram-positive bacteria from the Actinobacteria phylum. Some Nocardia species can infect humans and are usually considered opportunist pathogens as they often infect immunocompromised patients. Albeit, their clinical incidence is low, many Nocardia species are nowadays considered emerging pathogens. Primary sites of infection of Nocardia are the skin or the lungs but dissemination to other body parts is very frequent. These disseminated infections are very difficult to treat and thus, tackled with multiple classes of antibiotics, on top of the traditional treatment targeting the folate pathway. {beta}-lactams are often included in the regimen but many Nocardia species present moderate or strong resistance to some of this drug class. Genomic, microbiological, and biochemical studies have reported the presence of class A {beta}-lactamases (ABL) in a handful of Nocardia species but no structural investigation of Nocardia {beta}-lactamases has been performed yet. In this study, we report the expression, purification, and preliminary biochemical characterization of the ABL from a Nocardia cyriacigeorgica (NCY-1) clinical strain. We describe, as well, the crystallization and the very high-resolution crystal structure of NCY-1. The protein sequence and structural analysis demonstrate that NCY-1 belongs to {beta}-lactamase of class A1 and attest to its very high conservation with ABL from other human pathogenic Nocardia. In addition, the presence of one molecule of citrate tightly bound in the catalytic site of the enzyme is described. This structure may provide a solid basis for future drug development to specifically target Nocardia spp. {beta}-lactamases. SynopsisThe crystal structure at high resolution of a class A {beta}-lactamase from a clinical strain of Nocardia cyriacigeorgica is reported.

biochemistry↗

bioRxiv2023

Bringing TB genomics to the clinic: A comprehensive pipeline to predict antimicrobial susceptibility from genomic data, validated and accredited to ISO standards.

BackgroundWhole genome sequencing (WGS) is increasingly contributing to the clinical management of tuberculosis. Whilst the availability of bioinformatic tools for analysis and clinical reporting of Mycobacterium tuberculosis sequence data is improving, However, there remains a need for accessible, flexible bioinformatic tools that can be easily tailored for clinical reporting needs in different settings and are suitable for accreditation to international standards. MethodsWe developed tbtAMR, a flexible yet comprehensive tool for analysis of Mycobacterium tuberculosis genomic data, including inference of phenotypic susceptibility and lineage calling. Validation was undertaken using local and publicly-available real-world data (phenotype and genotype) and synthetic genomic data to determine the appropriate quality control metrics and extensively validate the pipeline for clinical use. FindingstbtAMR accurately predicted lineages and phenotypic susceptibility for first- and second-line drugs, with equivalent computational and predictive performance compared to other bioinformatics tools currently available. tbtAMR is flexible with modifiable criteria to tailor results to users needs. InterpretationThe tbtAMR tool is suitable for use in clinical and public health microbiology laboratory settings, and can be tailored to specific local needs by non-programmers. We have accredited this tool to ISO standards in our laboratory, and it has been implemented for routine reporting of AMR from genomic sequence data in a clinically relevant timeframe. Reporting templates, validation methods and datasets are provided to offer a pathway for laboratories to adopt and seek their own accreditation for this critical test, to improve the management of tuberculosis globally. FundingVictorian Government Department of Health; Australian National Health and Medical Research Council and Medical Research Futures Fund. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed for studies using the search terms: "Mycobacterium tuberculosis", "clinical", "bioinformatics", "genomics", "drug resistance (OR antimicrobial)", published prior to 31st July 2024 without language restrictions (n=258). We considered all studies from this search that used genomics to infer likely drug-resistance in M. tuberculosis. Many of these studies highlight the challenges in making meaningful interpretations from genomic data for the purpose of inferring AMR for clinical applications. Despite the development of bioinformatics tools and compilation of catalogues of resistance conferring mutations, few of these studies directly address the challenges specific to implementation of a whole genome sequencing (WGS) and bioinformatics pipelines to deliver validated and accredited results for use in clinical applications and none provide sustainable solutions. Added value of this studyTo the best of our knowledge, this study is the first to detail practical solutions to challenges to validating and accrediting the routine inference of AMR from WGS data for clinical applications for treatment of M. tuberculosis. We developed, validated and accredited a bioinformatics tool, tbtAMR, which is robust and flexible, using a data-driven approach to inferring resistance, allowing for simple customisation of behaviour on a large collection of in-house data supplemented with publicly available data. Furthermore, we have made this pipeline and the accompanying data available for use by the wider public health community to aid others in implementing similar programs. Consultation with infectious disease clinicians, microbiologists and epidemiologists has allowed us to develop a robust workflow, that encompasses sequencing, analysis, interpretation, reporting, discrepancy resolution and ongoing verification. Implications of all the available evidenceThis study addressed the specific needs of reporting genomic AMR for M. tuberculosis in a clinical and public health laboratory. Furthermore, this work makes available a pipeline and data for laboratories to utilise to implement genomic AMR for M. tuberculosis. Through the use and further development of publicly available and open-source bioinformatics software, we have demonstrated the feasibility of clinical reporting of genomic AMR results for M. tuberculosis in a low-incidence high-income setting. We believe that this work lays the foundation for making reporting of genomic AMR for M. tuberculosis more accessible to public health and reference laboratories in general.

genomics↗

bioRxiv2023

Development and utilization of new O2-independent bioreporters

Fluorescent proteins have revolutionized science since their discovery in 1962. They have enabled imaging experiments to decipher the function of proteins, cells and organisms, as well as gene regulation. GFP and all its derivatives are now standard tools in cell biology, immunology, molecular biology and microbiology laboratories around the world. A common feature of these proteins is their O2-dependent maturation allowing fluorescence, which precludes their use in anoxic contexts. In this work, we report the development and in cellulo characterization of genetic circuits encoding the O2-independent KOFP-7 protein, a flavin-binding fluorescent protein. We have optimized the genetic circuit for high bacterial fluorescence at population and single-cell level, implemented this circuit in various plasmids differing in host range, and quantified their fluorescence under both aerobic and anaerobic conditions. Finally, we showed that KOFP-7 based constructions can be used to produce fluorescing cells of V. diazotrophicus, a facultative anaerobe, demonstrating the usefulness of the genetic circuits for various anaerobic bacteria. These genetic circuits can thus be modified at will, both to solve basic and applied research questions, opening a highway to shed light on the obscure anaerobic world. ImportanceFluorescent proteins are used since decades, and have allowed major discoveries in biology in a wide variety of fields, and are used in environmental as well as clinical contexts. GFP and all its derivatives share a common feature: they rely on the presence of O2 for protein maturation and fluorescence. This dependency precludes their use in anoxic environments. Here, we constructed a series of genetic circuits allowing production of KOFP-7, an O2-independant Flavin-Binding Fluorescent Protein. We demonstrated that Escherichia coli cells producing KOFP-7 are fluorescent, both at the population and single-cell levels. Importantly, we showed that, unlike cells producing GFP, cells producing KOFP-7 are fluorescent in anoxia. Finally, we demonstrated that Vibrio diazotrophicus NS1, a facultative anaerobe, is fluorescent in the absence of O2 when KOFP-7 is produced. Altogether, the development of new genetic circuits allowing O2-independent fluorescence will open new perspective to study anaerobic processes.

molecular biology↗