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Derelle, R.

Publications and source records attributed to Derelle, R..

9 recordsLinked to original sources

kamino: proteome-wide variant calling for amino acid phylogenomics

Amino acid-based phylogenetics usually relies on first clustering and aligning orthologous proteins. This approach is powerful but computationally demanding. Here, we present kamino, a reference- and alignment-free method that rapidly builds amino acid phylogenomic alignments directly from proteomes. As with similar algorithms, homologous regions are identified through shared sequences flanking variable regions. The method uses local changes in recoded k-mer occupancy to efficiently identify variable positions within these homologous regions and extract the corresponding pseudo-aligned sequences. It generates phylogenetically informative alignments across diverse prokaryotic and eukaryotic datasets. Phylogenetic analyses show that it accurately recovers Mycobacterium tuberculosis lineages, most curated GTDB taxa, and relationships consistent with published Drosophila and mammalian phylogenies, while producing signals broadly similar to BUSCO-based approaches. Runtimes are comparable to genome-based alignment-free methods and several orders of magnitude faster than classical marker-based pipelines, with moderate memory requirements. The method performs well across a broad range of divergence levels, from within-species comparisons to family-level prokaryotic and phylum-level eukaryotic datasets. kamino therefore provides a fast and simple route from proteomes to phylogenomic alignments across a broad range of evolutionary scales. The program is implemented in Rust and freely available at https://github.com/rderelle/kamino.

bioinformatics↗

A comprehensive AMR genotype-phenotype database (CABBAGE)

Addressing the growing threat of antimicrobial resistance (AMR) requires the development of large-scale resources that link bacterial genomic data with phenotypic antimicrobial resistance profiles. Such datasets are essential for advancing genotype-based predictions of resistance to uncover novel resistance mechanisms, as well as identifying and tracking global trends. Here, we describe the development of the Comprehensive Assessment of Bacterial-Based AMR prediction from GEnotypes (CABBAGE) database, linking bacterial genomes to associated antibiotic susceptibility data and relevant metadata across WHO Bacterial Priority Pathogens, sourced from both publications and existing databases, and curated into a format that is compatible with, and extends, both NCBI and ENA formats. The resulting CABBAGE database, comprising over 170,000 unique sequenced isolates and approximately 1.7 million genome-phenotype pairs linked to extensive metadata, represents the largest database of its kind, consolidating existing AMR phenotype-genotype data into a single unified format. CABBAGE encompasses a broad range of antimicrobials, facilitating the analysis of global resistance trends as well as benchmarks of genotype-to-phenotype predictive methods, and empowering further research uses. The database is freely accessible at https://www.ebi.ac.uk/amr and is currently being integrated with the BioSample database, enabling easy access for the AMR research community.

genomics↗

skalo: using SKA split k-mers with coloured de Brujin graphs to genotype indels

The study of genomic variants is increasingly important for public health surveillance of pathogens. Traditional variant calling methods from whole-genome sequencing data rely on reference-based alignment, which can introduce biases and require significant computational resources. Alignment-free and reference-free approaches offer an alternative by leveraging k-mer-based methods, but existing implementations often suffer from sensitivity limitations, particularly in high mutation density genomic regions. Here, we present ska lo, a graph-based algorithm that aims to identify variants between pathogen whole-genome sequencing data by traversing a coloured De Bruijn graph and building variant groups (ie, sets of variant combinations). Through in-silico benchmarking and real-world dataset analyses, we demonstrate that ska lo achieves high sensitivity in SNP calls while also enabling the detection of insertions and deletions, as well as SNP positioning on a reference genome for recombination analyses. These findings highlight ska lo as a simple, fast and effective tool for pathogen genomic epidemiology, extending the range of reference-free variant calling approaches. ska lo is freely available as part of the SKA program (https://github.com/bacpop/ska.rust).

microbiology↗

Seamless, rapid and accurate analyses of outbreak genomic data using Split K-mer Analysis (SKA)

Sequence variation observed in populations of pathogens can be used for important public health and evolution genomic analyses, especially outbreak analysis and transmission reconstruction. Identifying this variation is typically achieved by aligning sequence reads to a reference genome, but this approach is susceptible to reference biases and requires careful filtering of called genotypes. Additionally, while the volume of bacterial genomes continues to grow, tools which can accurately and quickly call genetic variation between sequences have not kept pace. There is a need for tools which can process this large volume of data, providing rapid results, but remain simple so they can be used without highly trained bioinformaticians, expensive data analysis, and long term storage and processing of large files. Here we describe Split K-mer Analysis (SKA2), a method which supports both reference-free and reference-based mapping to quickly and accurately genotype populations of bacteria using sequencing reads or genome assemblies. SKA2 is highly accurate for closely related samples, and in outbreak simulations we show superior variant recall compared to reference-based methods, with no false positives. We also show that within bacterial strains, where it is possible to construct a clonal frame, SKA2 can also accurately map variants to a reference, and be used with recombination detection methods to rapidly reconstruct vertical evolutionary history. SKA2 is many times faster than comparable methods and can be used to add new genomes to an existing call set, allowing sequential use without the need to reanalyse entire collections. Given its robust implementation, inherent absence of reference bias and high accuracy, SKA2 has the potential to become the tool of choice for genotyping bacteria and can help expand the uses of genome data in evolutionary and epidemiological analyses. SKA2 is implemented in Rust and is freely available at https://github.com/bacpop/ska.rust.

bioinformatics↗

The macronuclear genomic landscape within Tetrahymena thermophila

The extent of intraspecific genomic variation is key to understanding species evolutionary history, including recent adaptive shifts. Intraspecific genomic variation remains poorly explored in eukaryotic microorganisms, especially in the nuclear dimorphic ciliates, despite their fundamental role as laboratory model systems and their ecological importance in many ecosystems. We sequenced the macronuclear genome of 22 laboratory strains of the oligohymenophoran Tetrahymena thermophila, a model species in both cellular biology and evolutionary ecology. We explored polymorphisms at the junctions of programmed eliminated sequences, and reveal their utility to barcode very closely related cells. As for other species of the genus Tetrahymena, we confirm micronuclear centromeres as gene diversification centres in T. thermophila, but also reveal a two-speed evolution in these regions. In the rest of the genome, we highlight recent diversification of genes encoding for extracellular proteins and cell adhesion. We discuss all these findings in relation with ciliates ecology and cellular characteristics. Impact StatementThis is the first study of population genomics in the ciliate Tetrahymena thermophila. This bacterivore species plays an important role in aquatic trophic chains and is widely used as a model in cell and molecular biology, ecology, evolution or toxicology. As all ciliates, it contains a germline micronucleus and a somatic macronucleus. Sequencing of the macronucleus reveals that the centromeric region of the micronucleus are simultaneously a region of new gene diversification, as observed in other Tetrahymena species, and a region containing highly conserved genes. The results also confirm that the formation of the macronucleus from the micronucleus is highly imprecise. Interestingly, this process generates a genomic barcode that can discriminate cells derived from a given sexual reproduction event, allowing to study more finely population dynamics/history in nature. Data summaryAll data are fully provided in Supplementary Materials. The raw data of the 22 Tetrahymena genomes have been deposited in the Sequence Read Archive (https://www-ncbi-nlm-nih-gov.inee.bib.cnrs.fr/bioproject/PRJNA1012331). Accession numbers are listed in Table S1 (available in the online version of this article).

genomics↗

fastlin: an ultra-fast program for Mycobacterium tuberculosis complex lineage typing

Lineage typing of the Mycobacterium tuberculosis complex (MTBC) has evolved from traditional phenotypic methods to advanced molecular and genomic techniques. In this study we present fastlin, a bioinformatics tool designed for rapid MTBC lineage typing. Fastlin utilises an ultra-fast alignment-free approach to detect previously identified barcode single nucleotide polymorphisms (SNPs) associated with specific MTBC lineages directly from fastq files. In a comprehensive benchmarking against existing tools, fastlin demonstrated high accuracy and significantly faster running times. Analysis of large MTBC datasets revealed fastlins capability not only to predict MTBC lineages, but also to detect mixed-lineage strain mixtures and estimate their proportions. Fastlin offers a user-friendly and efficient solution for MTBC lineage typing, complementing existing tools and facilitating large-scale analysis.

bioinformatics↗

100 years of anthropogenic impact causes changes in freshwater functional biodiversity

Despite efforts from scientists and regulators, biodiversity is declining at an alarming rate. Unless we find transformative solutions to preserve biodiversity, future generations may not be able to enjoy natures services. We have developed a conceptual framework that establishes the links between biodiversity dynamics and abiotic change through time and space using artificial intelligence. Here, we apply this framework to a freshwater ecosystem with a known history of human impact and study 100 years of community-level biodiversity, climate change and chemical pollution trends. We apply explainable network models with multimodal learning to community-level functional biodiversity measured with multilocus metabarcoding, to establish correlations with biocides and climate change records. We observed that the freshwater community assemblage and functionality changed over time without returning to its original state, even if the lake partially recovered in recent times. Insecticides and fungicides, combined with extreme temperature events and precipitation, explained up to 90% of the functional biodiversity changes. The community-level biodiversity approach used here reliably explained freshwater ecosystem shifts. These shifts were not observed when using traditional quality indices (e.g. Trophic Diatom Index). Our study advocates the use of high throughput systemic approaches on long-term trends over species-focused ecological surveys to identify the environmental factors that cause loss of biodiversity and disrupt ecosystem functions.

ecology↗

Unanticipated diversity, undiscovered ancient paralogs, and Asgard origins of the ARF GTPase protein family

The evolution of eukaryotic cellular complexity is interwoven with the extensive diversification of many protein families. One key family is the ARF GTPases that act in eukaryote-specific processes, including membrane traffic, tubulin assembly, actin dynamics, and cilia-related functions. Unfortunately, our understanding of the evolution of this family is limited. Sampling an extensive set of available genome and transcriptome sequences, we have assembled a dataset of over 2,000 manually curated ARF family genes from 114 eukaryotic species, including many deeply diverged protist lineages, and carried out comprehensive molecular phylogenetic analyses. These reconstructed as many as 16 ARF family members present in the last eukaryotic common ancestor (LECA), nearly doubling the previously inferred ancient system complexity. Evidence for the wide occurrence and ancestral origin of Arf6, Arl13 and Arl16 is presented for the first time. Moreover, Arl17, Arl18 and SarB, newly described here, are absent from well-studied model organisms and as a result their function(s) remain unknown. Analyses of our dataset revealed a previously unsuspected diversity of membrane association modes and domain architectures within the ARF family. We detail the step-wise expansion of the ARF family in the metazoan lineage, including discovery of several new animal-specific family members. Delving back to its earliest evolution in eukaryotes, the resolved relationship observed between the ARF family paralogs sets boundaries for scenarios of vesicle coat origins during eukaryogenesis. Altogether, our work fundamentally broadens the understanding of the diversity and evolution of a protein family underpinning the structural and functional complexity of the eukaryote cells. SignificanceARF Family GTPases are crucial regulations of a diversity of cellular compartments and processes and as such the extent of this system in eukaryotes reflects both cellular complexity in modern eukaryotes and its evolution. Strikingly, a comprehensive comparative genomic analysis of the protein family is lacking, leaving its recent and ancient evolution poorly resolved. We performed a comprehensive molecular evolutionary analysis, reconstructing a highly complex ARF family complement in the Last Eukaryotic Common Ancestor, including a number of paralogs never before identified as such, and we find resolved relationships between the paralogs. This work has implications for cellular evolution from eukaryogenesis to cellular complexity in metazoans.

evolutionary biology↗

Ancestral Mitochondrial Protein Secretion Machinery

The type 2 secretion system (T2SS) is present in some Gram-negative eubacteria and used to secrete proteins across the outer membrane. Here we report that certain representative heteroloboseans, jakobids, malawimonads and hemimastigotes unexpectedly possess homologues of core T2SS components. We show that at least some of them are present in mitochondria, and their behaviour in biochemical assays is consistent with the presence of a mitochondrial T2SS-derived system (miT2SS). We additionally identified 23 protein families co-occurring with miT2SS in eukaryotes. Seven of these proteins could be directly linked to the core miT2SS by functional data and/or sequence features, whereas others may represent different parts of a broader functional pathway, possibly also involving the peroxisome. Its distribution in eukaryotes and phylogenetic evidence together indicate that the miT2SS-centred pathway is an ancestral eukaryotic trait. Our findings thus have direct implications for the functional properties of the early mitochondrion.

evolutionary biology↗