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bioRxiv2026

TFA-defluorinating microbial consortium achieved with analogue enrichment

Trifluoroacetate (TFA) is an ultra-short chain perfluoroalkyl substance (PFAS) and a pollutant of global significance; it is persistent and ubiquitous in the environment across compartments, including food, drinking water, animals, and humans. Concentrations in the environment are rising, concerns over the toxicity of the compound are increasing, and there are currently no efficient ways to remediate TFA pollution, as conventional methods are ineffective at scale under environmental conditions. Microorganisms possessing dehalogenating enzymes targeting the structurally analogous compound monofluoroacetate (MFA) present a potential solution to degrade TFA microbiologically. Due to previously demonstrated dehalogenation of difluoroacetate (DFA) by dehalogenases, the promiscuity of such enzymes was hypothesized to be extendable to TFA as well. In this study cultures obtained from an environmental sample of PFAS contaminated wastewater sludge were incubated in M9 minimal media supplied with 33 mM acetate and either 33 mM TFA, DFA, or MFA in an iterative manner, followed by testing in a 30-day experiment. Free fluoride (F-) was measured at 5 timepoints using a fluoride ion specific electrode (ISE). One culture designated MFA1C significantly degraded MFA, DFA, and TFA (two-way ANOVA mixed model, p < 0.05). MFA1C released 11.2 {+/-} 0.7 M F- which constitutes approximately 0.01% of the potential fluoride release from the total 33 mM TFA applied and with the greatest defluorination activity in the first 9 days of incubation. Fluoride release from TFA and formation of DFA and MFA was confirmed with 19F-NMR. Genetic sequencing and protein-ligand modelling identified two candidate dehalogenase enzymes from Achromobacter denitrificans and Stenotrophomonas sp., respectively, with potential capacity to degrade TFA. The findings of this study are a crucial first step towards future potential bioremediation of TFA, and are, to the best of our knowledge, the first to identify a TFA-degrading consortium of microorganisms.

microbiology↗

bioRxiv2026

A synthetic MALDI-TOF MS atlas for microbial classification across hospitals

Matrix-Assisted Laser Desorption/Ionization Time-of-Flight (MALDI-TOF) mass spectrometry (MS) is central to routine clinical microbiology for species identification. Its combination with machine learning is constrained by fragmented public datasets, species imbalance and acquisition-related variability. Synthetic datasets could provide more training data, but they must preserve the spectral information needed to make predictions on real samples. Here, we present an open, synthetic MALDI-TOF MS atlas comprising 4.3 million spectra across 43 clinically relevant bacterial species. To construct the atlas, we harmonised public repositories from three countries and trained a species- and institution-conditioned concept-bottleneck generative variational autoencoder (CBGM-VAE) on 179,086 measured spectra. We assessed the atlas as both a stand-alone training resource and a complement to real data, using bacterial species classification as a functional benchmark. Evaluation comprised 7,269 real spectra from more than 7 countries, 24 institutions and multiple acquisition platforms, covering: temporal, technical, biological and combined shifts. A neural network trained only on synthetic spectra achieved F1 scores ranging from 0.788 to 1.000 across external cohorts. When synthetic spectra were combined with real training data, classification performance improved or matched using real data only. These findings demonstrate that the atlas retains transferable information and can complement measured data. The openly released atlas provides a reusable resource for supervised learning and benchmarking. It can also be explored for pretraining representation-learning and foundation models in MALDI-TOF MS research.

microbiology↗

bioRxiv2026

Amoxicillin Worsens Mortality from Viral Respiratory Tract Infection

Influenza infections are common and result in substantial mortality, with secondary bacterial pneumonias contributing to worse outcomes. To prevent mortality associated with bacterial co-infections, antibiotics are often prescribed in the absence of microbiological data. Currently, there are conflicting society recommendations on antibiotic utilization in viral respiratory tract infections as we lack prospective studies to guide treatment. We developed an animal model to study the effects of antibiotic exposure during influenza infection. Our results suggest that amoxicillin started during influenza A infection increases mortality from influenza A and increases severity of secondary bacterial pneumonia, and this is more pronounced in males.

microbiology↗

bioRxiv2015

Characterizing and comparing phylogenies from their Laplacian spectrum

Phylogenetic trees are central to many areas of biology, ranging from population genetics and epidemiology to microbiology, ecology, and macroevolution. The ability to summarize properties of trees, compare different trees, and identify distinct modes of division within trees is essential to all these research areas. But despite wide-ranging applications, there currently exists no common, comprehensive framework for such analyses. Here we present a graph-theoretical approach that provides such a framework. We show how to construct the spectral density profiles of phylogenetic trees from their Laplacian graphs. Using ultrametric simulated trees as well as non-ultrametric empirical trees, we demonstrate that the spectral density successfully identifies various properties of the trees and clusters them into meaningful groups. Finally, we illustrate how the eigengap can identify modes of division within a given tree. As phylogenetic data continue to accumulate and to be integrated into various areas of the life sciences, we expect that this spectral graph-theoretical framework to phylogenetics will have powerful and long-lasting applications.

Systems Biology↗

bioRxiv2015

Role of autoregulation and relative synthesis of operon partners in alternative sigma factor networks

Despite the central role of alternative sigma factors in bacterial stress response and virulence their regulation remains incompletely understood. Here we investigate one of the best-studied examples of alternative sigma factors: the {sigma}B network that controls the general stress response of Bacillus subtilis to uncover widely relevant general design principles that describe the structure-function relationship of alternative sigma factor regulatory networks. We show that the relative stoichiometry of the synthesis rates of {sigma}B, its anti-sigma factor RsbW and the anti-anti-sigma factor RsbV plays a critical role in shaping the network behavior by forcing the {sigma}B network to function as an ultrasensitive negative feedback loop. We further demonstrate how this negative feedback regulation insulates alternative sigma factor activity from competition with the housekeeping sigma factor for RNA polymerase and allows multiple stress sigma factors to function simultaneously with little competitive interference.\n\nMajor Subject Areas: Computational and systems biology, Microbiology & Infectious disease\n\nResearch Organism: Bacillus subtilis

Systems Biology↗

bioRxiv2016

Next-generation sequencing in neuropathological diagnosis of infections of the nervous system

ObjectiveTo determine the feasibility of next-generation sequencing (NGS) microbiome approaches in the diagnosis of infectious disorders in brain or spinal cord biopsies in patients with suspected central nervous system (CNS) infections.\n\nMethodsIn a prospective-pilot study, we applied NGS in combination with a new computational analysis pipeline to detect the presence of pathogenic microbes in brain or spinal cord biopsies from ten patients with neurological problems indicating possible infection but for whom conventional clinical and microbiology studies yielded negative or inconclusive results.\n\nResultsDirect DNA and RNA sequencing of brain tissue biopsies generated 8.3 million to 29.1 million sequence reads per sample, which successfully identified with high confidence the infectious agent in three patients, identified possible pathogens in two more, and helped to understand neuropathological processes in three others, demonstrating the power of large-scale unbiased sequencing as a novel diagnostic tool. Validation techniques confirmed the pathogens identified by NGS in each of the three positive cases. Clinical outcomes were consistent with the findings yielded by NGS on the presence or absence of an infectious pathogenic process in eight of ten cases, and were non-contributory in the remaining two.\n\nConclusionsNGS-guided metagenomic studies of brain, spinal cord or meningeal biopsies offer the possibility for dramatic improvements in our ability to detect (or rule out) a wide range of CNS pathogens, with potential benefits in speed, sensitivity, and cost. NGS-based microbiome approaches present a major new opportunity to investigate the potential role of infectious pathogens in the pathogenesis of neuroinflammatory disorders.

Genomics↗

bioRxiv2016

Computational Pan-Genomics: Status, Promises and Challenges

Many disciplines, from human genetics and oncology to plant breeding, microbiology and virology, commonly face the challenge of analyzing rapidly increasing numbers of genomes. In case of Homo sapiens, the number of sequenced genomes will approach hundreds of thousands in the next few years. Simply scaling up established bioinformatics pipelines will not be sufficient for leveraging the full potential of such rich genomic datasets. Instead, novel, qualitatively different computational methods and paradigms are needed. We will witness the rapid extension of computational pan-genomics, a new sub-area of research in computational biology. In this paper, we generalize existing definitions and understand a pan-genome as any collection of genomic sequences to be analyzed jointly or to be used as a reference. We examine already available approaches to construct and use pan-genomes, discuss the potential benefits of future technologies and methodologies, and review open challenges from the vantage point of the above-mentioned biological disciplines. As a prominent example for a computational paradigm shift, we particularly highlight the transition from the representation of reference genomes as strings to representations as graphs. We outline how this and other challenges from different application domains translate into common computational problems, point out relevant bioinformatics techniques and identify open problems in computer science. With this review, we aim to increase awareness that a joint approach to computational pan-genomics can help address many of the problems currently faced in various domains.

Genomics↗

bioRxiv2016

NGMASTER: in silico Multi-Antigen Sequence Typing for Neisseria gonorrhoeae

Whole-genome sequencing (WGS) provides the highest resolution analysis for comparison of bacterial isolates in public health microbiology. However, although increasingly being used routinely for some pathogens such as Listeria monocytogenes and Salmonella enterica, the use of WGS is still limited for other organisms, such as Neisseria gonorrhoeae. Multi-antigen sequence typing (NG-MAST) is the most widely performed typing method for epidemiologic surveillance of gonorrhoea. Here, we present NGMASTER - a command-line software tool for performing in silico NG-MAST on assembled genome data. NGMASTER rapidly and accurately determined the NG-MAST of 630 assembled genomes, facilitating comparisons between WGS and previously published gonorrhoea epidemiological studies. The source code and user documentation are available at https://github.com/MDU-PHL/ngmaster.

Genomics↗

bioRxiv2016

Preliminary investigation of Incorporation and Viability of Crystal Violet in Aspergillus flavus

The abusive use of antimicrobials have caused resistance in the microorganisms. Therefore, inovations in research of modern microbiology developed the photodynamic inactivation that is non-toxic and do not promote microbian resistance since it has multiples action sites. When a photoactive molecule is activated by light, it becomes more toxic for the microorganism than for the human cells, increasing the phototoxic effect against such cells. We tested the incorporation of the photosensitizer crystal violet by Aspergillus flavus and its viability in different periods of time of incubation (6 and 18 hours), concentrations of crystal violet (4M, 10M e 15 M) and different conditions (resting and agitation). We also tested the photoinactivation of Aspergillus flavus containing crystal violet. The incorporation rate does not depend on the concentration of CV neither on the condition of incubation. It depends exclusively on the medium, since it presented higher values in lack ingredients medium. The assays of viability of fungi after exposure to crystal violet show that there is fungic growth when samples are maintained in the dark. However, when the culture were submitted to excitation via laser, there was a decrease in the biomass growth, which indicates that there was photodynamic inactivation of the fungi.

biochemistry↗

bioRxiv2016

A new improved and extended version of the multicell bacterial simulator gro

gro is a cell programming language developed in Klavins Lab for simulating colony growth and cell-cell communication. It is used as a synthetic biology prototyping tool for simulating multicellular biocircuits. In this work, we present several extensions made to gro that improve the performance of the simulator, make it easier to use and provide new functionalities. The new version of gro is between one and two orders of magnitude faster than the original version. It is able to grow microbial colonies with up to 105 cells in less than 20 minutes. A new library, CellEngine, accelerates the resolution of spatial physical interactions between growing and dividing cells by implementing a new shoving algorithm. A genetic library, CellPro, based on Probabilistic Timed Automata, simulates gene expression dynamics using simplified and easy to compute digital proteins. We also propose a more convenient language specification layer, ProSpec, based on the idea that proteins drive cell behavior. CellNutrient, another library, implements Monod-based growth and nutrient uptake functionalities. The intercellular signaling management was improved and extended in a library called CellSignals. Finally, bacterial conjugation, another local cell-cell communication process, was added to the simulator. To show the versatility and potential outreach of this version of gro, we provide studies and novel examples ranging from synthetic biology to evolutionary microbiology. We believe that the upgrades implemented for gro have made it into a powerful and fast prototyping tool capable of simulating a large variety of systems and synthetic biology designs.

synthetic biology↗

bioRxiv2017

Refractive index as an intrinsic imaging contrast for 3-D label-free live cell imaging

Live cell imaging provides essential information in the investigation of cell biology and related pathophysiology. Refractive index (RI) can serve as intrinsic optical imaging contrast for 3-D label-free and quantitative live cell imaging, and provide invaluable information to understand various dynamics of cells and tissues for the study of numerous fields. Recently significant advances have been made in imaging methods and analysis approaches utilizing RI, which are now being transferred to biological and medical research fields, providing novel approaches to investigate the pathophysiology of cells. To provide insight how RI can be used as an imaging contrast for imaging of biological specimens, here we provide the basic principle of RI-based imaging techniques and summarize recent progress on applications, ranging from microbiology, hematology, infectious diseases, hematology, and histopathology.

cell biology↗

bioRxiv2017

Key Issues Review: Evolution on rugged adaptive landscapes

Adaptive landscapes represent a mapping between genotype and fitness. Rugged adaptive landscapes contain two or more adaptive peaks: allele combinations that differ in two or more genes and confer higher fitness than intermediate combinations. How would a population evolve on such rugged landscapes? Evolutionary biologists have struggled with this question since it was first introduced in the 1930s by Sewall Wright.\n\nDiscoveries in the fields of genetics and biochemistry inspired various mathematical models of adaptive landscapes. The development of landscape models led to numerous theoretical studies analyzing evolution on rugged landscapes under different biological conditions. The large body of theoretical work suggests that adaptive landscapes are major determinants of the progress and outcome of evolutionary processes.\n\nRecent technological advances in molecular biology and microbiology allow experimenters to measure adaptive values of large sets of allele combinations and construct empirical adaptive landscapes for the first time. Such empirical landscapes have already been generated in bacteria, yeast, viruses, and fungi, and are contributing to new insights about evolution on adaptive landscapes.\n\nIn this Key Issues Review we will: (i) introduce the concept of adaptive landscapes; (ii) review the major theoretical studies of evolution on rugged landscapes; (iii) review some of the recently obtained empirical adaptive landscapes; (iv) discuss recent mathematical and statistical analyses motivated by empirical adaptive landscapes, as well as provide the reader with source code and instructions to implement simulations of adaptive landscapes; and (v) discuss possible future directions for this exciting field.

evolutionary biology↗

bioRxiv2017

Optimal Growth Of Microbes On Mixed Carbon Sources

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: in certain mixtures the bacteria consume the two carbon sources sequentially (diauxie) and in other mixtures the bacteria consume both sources simultaneously (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon and gene regulation in general. However, why microbes would bother to have different strategies of taking up nutrients remained a mystery. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources to achieve maximum growth quantitatively explains why and how the cell makes the choice when facing multiple carbon sources. Our work solves a long-standing puzzle and sheds light on microbes optimal growth in different nutrient conditions.

systems biology↗

bioRxiv2017

Plasmid Profiler: Comparative Analysis Of Plasmid Content In WGS Data

SummaryComparative analysis of bacterial plasmids from whole genome sequence (WGS) data generated from short read sequencing is challenging. This is due to the difficulty in identifying contigs harbouring plasmid sequence data, and further difficulty in assembling such contigs into a full plasmid. As such, few software programs and bioinformatics pipelines exist to perform comprehensive comparative analyses of plasmids within and amongst sequenced isolates. To address this gap, we have developed Plasmid Profiler, a pipeline to perform comparative plasmid content analysis without the need for de novo assembly. The pipeline is designed to rapidly identify plasmid sequences by mapping reads to a plasmid reference sequence database. Predicted plasmid sequences are then annotated with their incompatibility group, if known. The pipeline allows users to query plasmids for genes or regions of interest and visualize results as an interactive heat map.\n\nAvailability and ImplementationPlasmid Profiler is freely available software released under the Apache 2.0 open source software license. A stand-alone version of the entire Plasmid Profiler pipeline is available as a Docker container at https://hub.docker.com/r/phacnml/plasmidprofiler_0_1_6/.\n\nThe conda recipe for the Plasmid R package is available at: https://anaconda.org/bioconda/r-plasmidprofiler\n\nThe custom Plasmid Profiler R package is also available as a CRAN package at https://cran.r-project.org/web/packages/Plasmidprofiler/index.html\n\nGalaxy tools associated with the pipeline are available as a Galaxy tool suite at https://toolshed.g2.bx.psu.edu/repository?repository_id=55e082200d16a504\n\nThe source code is available at: https://github.com/phac-nml/plasmidprofiler\n\nThe Galaxy implementation is available at: https://github.com/phac-nml/plasmidprofiler-galaxy\n\nContactEmail: gary.vandomselaar@canada.ca\n\nAddress: National Microbiology Laboratory, Public Health Agency of Canada, 1015 Arlington Street, Winnipeg, Manitoba, Canada\n\nSupplementary informationDocumentation: http://plasmid-profiler.readthedocs.io/en/latest/

bioinformatics↗

bioRxiv2017

Exploring the Diversity of Bacillus whole genome sequencing projects using Peasant, the Prokaryotic Assembly and Annotation Tool

BackgroundThe persistent decrease in cost and difficulty of whole genome sequencing of microbial organisms has led to a dramatic increase in the number of species and strains characterized from a wide variety of environments. Microbial genome sequencing can now be conducted by small laboratories and as part of undergraduate curriculum. While sequencing is routine in microbiology, assembly, annotation and downstream analyses still require computational resources and expertise, often necessitating familiarity with programming languages. To address this problem, we have created a light-weight, user-friendly tool for the assembly and annotation of microbial sequencing projects.\n\nResultsThe Prokaryotic Assembly and Annotation Tool, Peasant, automates the processes of read quality control, genome assembly, and annotation for microbial sequencing projects. High-quality assemblies and annotations can be generated by Peasant without the need of programming expertise or high-performance computing resources. Furthermore, statistics are calculated so that users can evaluate their sequencing project. To illustrate the computational speed and accuracy of Peasant, the SRA records of 322 Illumina platform whole genome sequencing assays for Bacillus species were retrieved from NCBI, assembled and annotated on a single desktop computer. From the assemblies and annotations produced, a comprehensive analysis of the diversity of over 200 high-quality samples was conducted, looking at both the 16S rRNA phylogenetic marker as well as the Bacillus core genome.\n\nConclusionsPeasant provides an intuitive solution for high-quality whole genome sequence assembly and annotation for users with limited programing experience and/or computational resources. The analysis of the Bacillus whole genome sequencing projects exemplifies the utility of this tool. Furthermore, the study conducted here provides insight into the diversity of the species, the largest such comparison conducted to date.

bioinformatics↗

bioRxiv2017

Predicting Drug Resistance Evolution: Antimicrobial Peptides Vs. Antibiotics

Antibiotic resistance constitutes one of the most pressing public health concerns. Antimicrobial peptides of multicellular organisms are considered part of a solution to this problem, and AMPs produced by bacteria such as colistin are last resort drugs. Importantly, antimicrobial peptides differ from many antibiotics in their pharmacodynamic characteristics. Here we implement these differences within a theoretical framework to predict the evolution of resistance against antimicrobial peptides and compare it to antibiotic resistance. Our analysis of resistance evolution finds that pharmacodynamic differences all combine to produce a much lower probability that resistance will evolve against antimicrobial peptides. The finding can be generalized to all drugs with pharmacodynamics similar to AMPs. Pharmacodynamic concepts are familiar to most practitioners of medical microbiology, and data can be easily obtained for any drug or drug combination. Our theoretical and conceptual framework is therefore widely applicable and can help avoid resistance evolution if implemented in antibiotic stewardship schemes or the rational choice of new drug candidates.

evolutionary biology↗

bioRxiv2017

The SMuSh pathway is essential for survival during growth-induced compressive mechanical stress

Cells that proliferate within a confined environment build up mechanical compressive stress. For example, mechanical pressure emerges in the naturally space-limited tumor environment. However, little is known about how cells sense and respond to mechanical compression. We developed microfluidic bioreactors to enable the investigation of the effects of compressive stress on the growth of the genetically tractable model organism Saccharomyces cerevisiae. We used this system to determine that compressive stress is partly partly sensed through a module consisting of the mucin Msb2, and the cell wall protein Sho1, which act together as a sensor module in one of the two major osmosensing pathways in budding yeast. This signal is transmitted via the MAPKKK kinase Ste11. Thus, we term this mechanosensitive pathway the SMuSh pathway, for Ste11 through Mucin / Sho1 pathway. The SMuSh pathway delays cells in the G1 phase of the cell cycle and improves cell survival in response to growth-induced pressure. We also found that the Cell Wall Integrity (CWI) pathway contributes to the response to mechanical compressive stress. These latter results are confirmed in complimentary experiments in the accompanying manuscript from Mishra et al. When both the SMuSh and the CWI pathways are deleted, cells fail to adapt to compressive stress and all cells lyse at relatively low pressure when grown in confinement. Thus, we define a network that is essential for cell survival during growth under pressure. We term this new mechanosensory system the SCWISh (Survival through the CWI and SMuSh) network.\n\nSignificance StatementGrowth in confined environments leads to the build up of compressive mechanical stresses, which are relevant to diverse fields, from cancer to microbiology. In contrast to tensile stress, little is known about the molecular integration of compressive stresses. In this study, we elucidate the SMuSh pathway, which, together with the Cell Wall Integrity pathway, is essential for viability of the budding yeast S. cerevisiae when growing under mechanical pressure. Pressure-sensing requires the transmembrane mucin, Msb2, which is linked to the actin cortex. Our result raises the intriguing question of whether mucins, widely conserved in eukaryotes and frequently misregulated in cancers, might sense compressive stresses in other organisms, including humans.

cell biology↗

bioRxiv2017

Genome reconstruction and characterisation of extensively drug-resistant bacterial pathogens through direct metagenomic sequencing of human faeces

Whole-genome sequencing of microbial pathogens is revolutionising modern approaches to outbreaks of infectious diseases and is reliant upon organism culture. Culture-independent methods have shown promise in identifying pathogens, but high level reconstruction of microbial genomes from microbiologically complex samples for more in-depth analyses remains a challenge. Here, using metagenomic sequencing of a human faecal sample and analysis by tetranucleotide frequency profiling projected onto emergent self-organising maps, we were able to reconstruct the underlying populations of two extensively-drug resistant pathogens, Klebsiella pneumoniae carbapenemase (KPC)-producing Klebsiella pneumoniae and vancomycin-resistant Enterococcus faecium. From these genomes, we were able to ascertain molecular typing results, such as MLST, and identify highly discriminatory mutations in the metagenome to distinguish closely related strains. These proof-of-principle results demonstrate the utility of clinical sample metagenomics to recover sequences of important drug-resistant bacteria and application of the approach in outbreak investigations, independent of the need to culture the organisms.

genomics↗