Biology subjects

Bertrand, D.

Publications and source records attributed to Bertrand, D..

12 recordsLinked to original sources

bioRxiv2025

Structure of a pH-sensitive pentameric ligand-gated ion channel from the Sarcoptes scabies mite

Scabies is a skin infestation caused by the Sarcoptes scabiei mite. It produces a substantial global health burden, which is exacerbated by emerging resistance to prominent treatments, such as ivermectin. An anionic pentameric ligand-gated ion channel (pLGIC) identified in the mite, termed as SsCl, shows unique pH-sensitivity and is significantly modulated by ivermectin. Here, we use cryo-EM and electrophysiology to explore the pH-sensing mechanisms of SsCl and the impact of ivermectin on channel activity. Structures of SsCl were resolved under various pH conditions to capture a closed (pH 6.5) and desensitized (pH 9) state, alongside ivermectin-bound conformations. The desensitized structure adopts an unexpected "hourglass" conformation, suggesting the gating mechanisms may be more related to cation-selective channels. Structure-based analysis and mutagenesis highlighted extracellular histidine and glutamate residues which act as pH-sensors, revealing the protonation-dependent gating mechanism. Ivermectin binds at transmembrane subunit interfaces, stabilizing an open-pore conformation through hydrophobic interactions. Ivermectin bound-structures reveal a pH-dependent modulation, enhancing open-state prevalence at pH 9 and enabling atypical activation at pH 6.5, consistent with electrophysiological data. These structural and functional insights elucidate SsCls unique pH sensitivity and ivermectins mode of action, providing a foundation for designing next-generation therapeutics targeting this pathogen-related ion channel.

biochemistry↗

bioRxiv2022

Long-term ecological and evolutionary dynamics in the gut microbiomes of carbapenemase-producing Enterobacteriaceae colonized subjects

Long-term colonization of the gut microbiome by carbapenemase-producing Enterobacteriaceae (CPE) is a growing area of public health concern as it can lead to community transmission and rapid increase in cases of life-threatening CPE infections. Leveraging the observation that many subjects are decolonized without interventions within a year, we used longitudinal shotgun metagenomics (up to 12 timepoints) for detailed characterization of ecological and evolutionary dynamics in the gut microbiome of a cohort of CPE-colonized subjects and family members (n=46; 361 samples). Subjects who underwent decolonization exhibited a distinct ecological shift marked by recovery of microbial diversity, key commensals and anti-inflammatory pathways. In addition, colonization was marked by elevated but unstable Enterobacteriaceae abundances, which exhibited distinct strain-level dynamics for different species (Escherichia coli and Klebsiella pneumoniae). Finally, comparative analysis with whole genome sequencing data from CPE isolates (n=159) helped identify sub-strain variation in key functional genes and the presence of highly similar E. coli and K. pneumoniae strains with variable resistance profiles and plasmid sharing. These results provide an enhanced view into how colonization by multi-drug resistant bacteria associates with altered gut ecology and can enable transfer of resistance genes, even in the absence of overt infection and antibiotic usage.

genomics↗

bioRxiv2022

Genome-centric analysis of short and long read metagenomes reveals uncharacterized microbiome diversity in Southeast Asians

Despite extensive efforts to address it, the vastness of uncharacterized dark matter microbial genetic diversity can impact short-read sequencing based metagenomic studies. Population-specific biases in genomic reference databases can further compound this problem. Leveraging advances in long-read and Hi-C technologies, we deeply characterized 109 gut microbiomes from three ethnicities in Singapore to comprehensively reconstruct 4,497 medium and high-quality metagenome assembled genomes, 1,708 of which were missing in short-read only analysis and with >28x N50 improvement. Species-level clustering identified 70 (>10% of total) novel gut species out of 685, improved reference genomes for 363 species (53% of total), and discovered 3,413 strains that are unique to these populations. Among the top 10 most abundant gut bacteria in our study, one of the species and >80% of all strains were not represented in existing databases. Annotation of biosynthetic gene clusters (BGCs) uncovered more than 27,000 BGCs with a large fraction (36-88%) not represented in current databases, and with several unique clusters predicted to produce bacteriocins that could significantly alter microbiome community structure. These results reveal the significant uncharacterized gut microbial diversity in Southeast Asian populations and highlight the utility of hybrid metagenomic references for bioprospecting and disease-focused studies.

genomics↗

bioRxiv2022

Genomic patterns of divergence in the early and late steps of speciation of the deep-sea vent thermophilic worms of the genus Alvinella.

BackgroundThe transient and fragmented nature of the deep-sea hydrothermal environment made of ridge subduction, plate collision and the emergence of new rifts is currently acting to the separation of vent populations, local adaptation and is likely to promote bursts of speciation and species specialization. The tube-dwelling worms Alvinella pompejana called the Pompeii worm and its sister species A. caudata live syntopically on the hottest part of deep-sea hydrothermal chimneys along the East Pacific Rise. They are exposed to extreme thermal and chemical gradients, which vary greatly in space and time, and thus represent ideal candidates for understanding the evolutionary mechanisms at play in the vent fauna evolution. ResultsIn the present study, we explored genomic patterns of divergence in the early and late steps of speciation of these emblematic worms using transcriptome assemblies and the first draft genome to better understand the relative role of geographic isolation and habitat preference in their genome evolution. Analyses were conducted on allopatric populations of Alvinella pompejana (early stage of separation) and between A. pompejana and its syntopic species Alvinella caudata (late stage of speciation). We first identified divergent genomic regions and targets of selection as well as their position in the genome over collections of orthologous genes and, then, described the speciation dynamics by documenting the annotation of the most divergent and/or positively selected genes involved in the isolation process. Gene mapping clearly indicated that divergent genes associated with the early stage of speciation, although accounting for nearly 30% of genes, are highly scattered in the genome without any island of divergence and not involved in gamete recognition or mito-nuclear incompatibilities. By contrast, genomes of A. pompejana and A. caudata are clearly separated with nearly all genes (96%) exhibiting high divergence. This congealing effect however seems to be linked to habitat specialization and still allows positive selection on genes involved in gamete recognition, as a possible long-duration process of species reinforcement. ConclusionOur analyses pointed out the non-negligible role of natural selection on both the early and late stages of speciation in the emblematic thermophilic worms living on the walls of deep-sea hydrothermal chimneys. They shed ligth on the evolution of gene divergence during the process of speciation and species specialization over a very long period of time.

evolutionary biology↗

bioRxiv2021

Critical Assessment of Metagenome Interpretation - the second round of challenges

Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the community-driven initiative for the Critical Assessment of Metagenome Interpretation (CAMI). In its second challenge, CAMI engaged the community to assess their methods on realistic and complex metagenomic datasets with long and short reads, created from [~]1,700 novel and known microbial genomes, as well as [~]600 novel plasmids and viruses. Altogether 5,002 results by 76 program versions were analyzed, representing a 22x increase in results. Substantial improvements were seen in metagenome assembly, some due to using long-read data. The presence of related strains still was challenging for assembly and genome binning, as was assembly quality for the latter. Taxon profilers demonstrated a marked maturation, with taxon profilers and binners excelling at higher bacterial taxonomic ranks, but underperforming for viruses and archaea. Assessment of clinical pathogen detection techniques revealed a need to improve reproducibility. Analysis of program runtimes and memory usage identified highly efficient programs, including some top performers with other metrics. The CAMI II results identify current challenges, but also guide researchers in selecting methods for specific analyses.

bioinformatics↗

bioRxiv2021

A cationic lipid site at the outward transmembrane face of a pentameric ligand-gated ion channel

Pentameric ligand-gated ion channels (pLGICs) are crucial mediators of electrochemical signal transduction from bacteria to humans. Lipids play an important role in regulating pLGIC function, yet the structural basis for specific pLGIC-lipid interactions remains poorly understood. The bacterial channel ELIC recapitulates several properties of eukaryotic pLGICs, including activation by the neurotransmitter GABA and sensitivity to lipids, offering a simplified model system for structure-function studies. In this study, functional effects of non-canonical amino acid substitution of W206 at the top of the M1-helix, combined with detergent interactions observed in recent X-ray structures, are consistent with this region being the location of a lipid binding site on the outward face of the ELIC transmembrane domain. Coarse-grained and atomistic molecular dynamics simulations revealed preferential binding of lipids containing a positive charge, particularly involving interactions with residue W206 consistent with cation-{pi} binding. Polar contacts from the principal subunit, particularly M3 residue Q264, further supported lipid binding via headgroup ester linkages. Aromatic residues were identified at analogous sites in a handful of eukaryotic family members, including the human GABAA receptor subunit {varepsilon}, suggesting conservation of relevant interactions in other evolutionary branches. Further mutagenesis experiments indicated that mutations at this site in {varepsilon}-containing GABAA receptors can change the apparent affinity of the agonist response to GABA, consistent with a potential role of this site in channel gating. In conclusion, this work is a detailed case study in type-specific lipid interactions at an evolutionarily distinctive pLGIC site, with implications for lipid modulation and lipophilic drug design.

biophysics↗

bioRxiv2019

A functionally conserved mechanism of modulation via a vestibule site in pentameric ligand-gated ion channels

Pentameric ligand-gated ion channels (pLGICs) belong to a class of ion channels involved in fast synaptic signaling in the central and peripheral nervous systems. Molecules acting as allosteric modulators target binding sites that are remote from the neurotransmitter binding site, but functionally affect coupling of ligand binding to channel opening. Here, we investigated an allosteric binding site in the ion channel vestibule, which has converged from a series of studies on prokaryote and eukaryote channel homologs. We discovered single domain antibodies, called nanobodies, which are functionally active as allosteric modulators, and solved co-crystal structures of the prokaryote channel ELIC bound either to a positive (PAM) or a negative (NAM) allosteric modulator. We extrapolate the functional importance of the vestibule binding site to eukaryote ion channels, suggesting a conserved mechanism of allosteric modulation. This work identifies key elements of allosteric binding sites and extends drug design possibilities in pLGICs using nanobodies.

neuroscience↗

bioRxiv2019

Cartography of opportunistic pathogens and antibiotic resistance genes in a tertiary hospital environment

There is growing attention surrounding hospital acquired infections (HAIs) due to high associated healthcare costs, compounded by the scourge of widespread multi-antibiotic resistance. Although hospital environment disinfection is well acknowledged to be key for infection control, an understanding of colonization patterns and resistome profiles of environment-dwelling microbes is currently lacking. We report the first extensive genomic characterization of microbiomes (428), common HAI-associated microbes (891) and transmissible drug resistance cassettes (1435) in a tertiary hospital environment based on a 3-timepoint sampling (1 week and >1 year apart) of 179 sites from 45 beds. Deep shotgun metagenomic sequencing unveiled two distinct ecological niches of microbes and antibiotic resistance genes characterized by biofilm-forming and human microbiome influenced environments that display corresponding patterns of divergence over space and time. To study common nosocomial pathogens that were typically present at low abundances, a combination of culture enrichment and long-read nanopore sequencing was used to obtain thousands of high contiguity genomes (2347), phage sequences (1693) and closed plasmids (5910), a significant fraction of which (>60%) are not represented in current sequence databases. These high-quality assemblies and metadata enabled a rich characterization of resistance gene combinations, phage diversity, plasmid architectures, and the dynamic nature of hospital environment resistomes and their reservoirs. Phylogenetic analysis identified multidrug resistant strains as being more widely distributed and stably colonizing across hospital sites. Further genomic comparisons with clinical isolates across multiple species supports the hypothesis that multidrug resistant strains can persist in the hospital environment for extended periods (>8 years) to opportunistically infect patients. These findings highlight the importance of characterizing antibiotic resistance reservoirs in the hospital environment and establishes the feasibility of systematic genomic surveys to help target resources more efficiently for preventing HAIs.

microbiology↗

bioRxiv2018

Nanopore sequencing enables high-resolution analysis of resistance determinants and mobile elements in the human gut microbiome

The analysis of information rich whole-metagenome datasets acquired from complex microbial communities is often restricted by the fragmented nature of assembly from short-read sequencing. The availability of long-reads from third-generation sequencing technologies (e.g. PacBio or Oxford Nanopore) can help improve assembly quality in principle, but high error rates and low throughput have limited their application in metagenomics. In this work, we describe the first hybrid metagenomic assembler which combines the advantages of short and long-read technologies, providing an order of magnitude improvement in contiguity compared to short read assemblies, and high base-pair level accuracy. The proposed approach (OPERA-MS) integrates a novel assembly-based metagenome clustering technique with an exact scaffolding algorithm that can efficiently assemble repeat rich sequences. Based on evaluations with defined in vitro communities and virtual gut microbiomes, we show that it is possible to assemble near complete genomes from metagenomes with as little as 9x long read coverage, thus enabling high quality assembly of lowly abundant species (<1%). Furthermore, OPERA-MSs fine-grained clustering is able to deconvolute and assemble multiple genomes of the same species in a single sample, allowing us to study the complex dynamics of the human microbiome at the sub-species level. Applying nanopore sequencing to gut metagenomes of patients undergoing antibiotic treatment, we show that long reads can be obtained from stool samples in clinical studies to produce more meaningful metagenomic assemblies (up to 200x improvement over short-read assemblies), including the closed assembly of >80 putative plasmid/phage sequences and a 263kbp jumbo phage. Our results highlight that high-quality hybrid assemblies provide an unprecedented view of the gut resistome in these patients, including strain dynamics and identification of novel plasmid sequences.

bioinformatics↗

bioRxiv2018

A MinION-based pipeline for fast and cost-effective DNA barcoding

DNA barcodes are useful for species discovery and species identification, but obtaining barcodes currently requires a well-equipped molecular laboratory, is time-consuming, and/or expensive. We here address these issues by developing a barcoding pipeline for Oxford Nanopore MinION and demonstrate that one flowcell can generate barcodes for [~]500 specimens despite high base-call error rates of MinION. The pipeline overcomes the errors by first summarizing all reads for the same tagged amplicon as a consensus barcode. These barcodes are overall mismatch-free but retain indel errors that are concentrated in homopolymeric regions. We thus complement the barcode caller with an optional error correction pipeline that uses conserved amino-acid motifs from publicly available barcodes to correct the indel errors. The effectiveness of this pipeline is documented by analysing reads from three MinION runs that represent three different stages of MinION development. They generated data for (1) 511 specimens of a mixed Diptera sample, (2) 575 specimens of ants, and (3) 50 specimens of Chironomidae. The run based on the latest chemistry yielded MinION barcodes for 490 specimens which were assessed against reference Sanger barcodes (N=471). Overall, the MinION barcodes have an accuracy of 99.3%-100% and the number of ambiguities ranges from <0.01-1.5% depending on which correction pipeline is used. We demonstrate that it requires only 2 hours of sequencing to gather all information that is needed for obtaining reliable barcodes for most specimens (>90%). We estimate that up to 1000 barcodes can be generated in one flowcell and that the cost of a MinION barcode can be <USD 2.

bioinformatics↗

bioRxiv2017

Predicting Cancer Drug Response Using a Recommender System

MotivationAs we move towards an era of precision medicine, the ability to predict patient-specific drug responses in cancer based on molecular information such as gene expression data represents both an opportunity and a challenge. In particular, methods are needed that can accommodate the high-dimensionality of data to learn interpretable models capturing drug response mechanisms, as well as providing robust predictions across datasets.\n\nResultsWe propose a method based on ideas from \"recommender systems\" (CaDRReS) that predicts cancer drug responses for unseen cell-lines/patients based on learning projections for drugs and cell-lines into a latent \"pharmacogenomic\" space. Comparisons with other proposed approaches for this problem based on large public datasets (CCLE, GDSC) shows that CaDRReS provides consistently good models and robust predictions even across unseen patient-derived cell-line datasets. Analysis of the pharmacogenomic spaces inferred by CaDRReS also suggests that they can be used to understand drug mechanisms, identify cellular subtypes, and further characterize drug-pathway associations.\n\nAvailabilitySource code and datasets are available at https://github.com/CSB5/CaDRReS\n\nContactnagarajann@gis.a-star.edu.sg\n\nSupplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2017

ConsensusDriver Improves Upon Individual Algorithms For Predicting Driver Alterations In Different Cancer Types And Individual Patients -- A Toolbox For Precision Oncology

BackgroundIn recent years, several large-scale cancer genomics studies have helped generate detailed molecular profiling datasets for many cancer types and thousands of patients. These datasets provide a unique resource for studying cancer driver prediction methods and their utility for precision oncology, both to predict driver genetic alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nMethodsWe performed the most comprehensive assessment to date of 18 driver gene prediction methods, on more than 3,400 tumour samples, from 15 cancer types, to determine their suitability in guiding precision medicine efforts. These methods have diverse approaches, which can be classified into five categories: functional impact on proteins in general (FI) or specific to cancer (FIC), cohort-based analysis for recurrent mutations (CBA), mutations with expression correlation (MEC) and methods that use gene interaction network-based analysis (INA).\n\nResultsThe performance of driver prediction methods varies considerably, with concordance with a gold-standard varying from 9% to 68%. FI methods show relatively poor performance (concordance <22%) while CBA methods provide conservative results, but require large sample sizes for high sensitivity. INA methods, through the integration of genomic and transcriptomic data, and FIC methods, by training cancer-specific models, provide the best trade-off between sensitivity and specificity. As the methods were found to predict different subsets of drivers, we propose a novel consensus-based approach, ConsensusDriver, which significantly improves the quality of predictions (20% increase in sensitivity). This tool can be applied to predict driver alterations in patient subgroups (e.g. defined by histology or clinical phenotype) or even individual patients.\n\nConclusionExisting cancer driver prediction methods are based on very different assumptions and each of them can only detect a particular subset of driver events. Consensus-based methods, like ConsensusDriver, are thus a promising approach to harness the strengths of different driver prediction paradigms.

bioinformatics↗