Biology subjects

Dutilh, B. E.

Publications and source records attributed to Dutilh, B. E..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

Eco-evolutionary dynamics of massive, parallel bacteriophage outbreaks in compost communities

Bacteriophages are important drivers of microbial ecosystems, yet their dynamics in complex natural communities remain poorly understood compared to simplified laboratory systems. To address this gap, we analyzed viral dynamics across 20 compost-derived microbial communities propagated for one year in mesocosms. Communities clustered into two distinct types, each dominated by different cellulose-degraders and comprising hundreds of genera. In one type, we observed massive, parallel outbreaks of Theomophage, a previously undescribed bacteriophage, reaching 74% of metagenomic sequencing reads, the largest bacteriophage outbreak documented to date. Theomophage populations in isolated communities were composed of a single genotype that showed striking evolutionary stability throughout the experiment. In contrast, following experimental viral migration between mesocosms, all Theomophage populations showed rapid evolution via recombination between preexisting genotypes, replacement of ancestral lineages, and, upon successful migration to mesocosms of the alternate community type where Theomophage was initially absent, rapid acquisition of novel mutations that swept local populations and then spread to other mesocosms. Our study reveals the spatial and temporal scales at which bacteriophage microdiversity evolves in complex communities. It further shows that mixing of viral communities -- likely common in natural systems -- can rapidly accelerate bacteriophage evolution.

microbiology↗

bioRxiv2023

Improving genome-scale metabolic models of incomplete genomes with deep learning

Deciphering the metabolism of microbial species is crucial for understanding their function within complex ecosystems. Genome-scale metabolic models (GSMMs), which predict metabolic traits based on the enzymes encoded in a genome, are promising tools to study microbial ecosystems when genome sequences can be obtained. However, constructing GSMMs for uncultured bacteria is challenging, as metagenome-assembled genomes are typically incomplete, leading to metabolic reconstructions with numerous gaps. Existing methodologies often fill these gaps with the minimum set of reactions necessary to simulate an objective function such as growth. Here we introduce an artificial intelligence-based alternative: the Deep Neural Network Guided Imputation Of Reactomes (DNNGIOR). The DNNGIOR neural network learns weights for missing reactions in incomplete GSMMs from patterns in the presence and absence of metabolic reactions in genomes spanning the bacterial domain. We identified two important factors contributing to prediction accuracy: (1) the frequency of reaction across all bacteria, and (2) the phylogenetic distance between the query and the genomes in the training dataset. Reactions that occur in > 30% of the training genomes can be accurately predicted (Mean F1 score = 0.85). The weights generated by the DNNGIOR network improved the gap-filling of incomplete GSMMs, when assessed on a large and phylogenetically diverse testing dataset and a small set of high-quality manually curated models. The accuracy of DNNGIOR was on average 14 times greater than the standard unweighted gap-filling for draft reconstructions, and 2-9 times greater for manually curated models. DNNGIOR models could also simulate experimentally measured carbon usage profiles with similar accuracy as CarveMe. DNNGIOR is available at https://github.com/MGXlab/DNNGIOR or as a pip package (https://pypi.org/project/dnngior/).

bioinformatics↗

bioRxiv2023

Ultra-conserved bacteriophage genome sequence identified in 1300-year-old human paleofaeces

Bacteriophages are widely recognised as rapidly evolving biological entities. However, we discovered an ancient genome nearly identical to present-day Mushuvirus mushu, a phage that infects commensal microorganisms in the human gut ecosystem. The DNA damage patterns of this genome have confirmed its ancient origin, and, despite 1300 years of evolution, the ancient Mushuvirus genome shares 97.7% nucleotide identity with its modern counterpart, indicating a long-term relationship between the prophage and its host. We also reconstructed and authenticated 297 other phage genomes from the last 5300 years, including those belonging to unknown families. Our findings demonstrate the feasibility of reconstructing ancient phage genomes, expanding the known virosphere, and offering new insights into phage-bacteria interactions that cover several millennia.

microbiology↗

bioRxiv2023

Benchmarking Bioinformatic Virus Identification Tools Using Real-World Metagenomic Data across Biomes

As most viruses remain uncultivated, metagenomics is currently the main method for virus discovery. Detecting viruses in metagenomic data is not trivial. In the past few years, many bioinformatic virus identification tools have been developed for this task, making it challenging to choose the right tools, parameters, and cutoffs. As all these tools measure different biological signals, and use different algorithms and training/reference databases, it is imperative to conduct an independent benchmarking to give users objective guidance. We compared the performance of ten state-of-the-art virus identification tools in thirteen modes on eight paired viral and microbial datasets from three distinct biomes, including a new complex dataset from Antarctic coastal waters. The tools had highly variable true positive rates (0 - 68%) and false positive rates (0 - 15%). PPR-Meta best distinguished viral from microbial contigs, followed by DeepVirFinder, VirSorter2, and VIBRANT. Different tools identified different subsets of the benchmarking data and all tools, except for Sourmash, found unique viral contigs. Tools performance could be improved with adjusted parameter cutoffs, indicating that adjustment of parameter cutoffs before usage should be considered. Together, our independent benchmarking provides guidance on choices of bioinformatic virus identification tools and gives suggestions for parameter adjustments for viromics researchers.

bioinformatics↗

bioRxiv2023

Integration of taxonomic signals from MAGs and contigs improves read annotation and taxonomic profiling of metagenomes

AO_SCPLOWBSTRACTC_SCPLOWMetagenomic analysis typically includes read-based taxonomic profiling, assembly, and binning of metagenome-assembled genomes (MAGs). Here we integrate these steps in Read Annotation Tool (RAT), which uses robust taxonomic signals from MAGs and contigs to enhance read annotation. RAT reconstructs taxonomic profiles with high precision and sensitivity, outperforming other state-of-the-art tools. In high-diversity groundwater samples, RAT annotates a large fraction of the metagenomic reads, calling novel taxa at the appropriate, sometimes high taxonomic ranks. Thus, RAT integrative profiling provides an accurate and comprehensive view of the microbiome. CAT/BAT/RAT is available at https://github.com/MGXlab/CAT. The CAT pack now also supports GTDB annotations.

bioinformatics↗

bioRxiv2023

Identifying and tracking mobile elements in evolving compost communities yields insights into the nanobiome

Microbial evolution is driven by rapid changes in gene content mediated by horizontal gene transfer (HGT). While mobile genetic elements (MGEs) are important drivers of gene flux, the nanobiome - the zoo of Darwinian replicators that depend on microbial hosts - remains poorly characterised. New experimental approaches and analyses are necessary to advance our understanding beyond simple pairwise MGE-host interactions. To detect horizontal transfer, a bioinformatic pipeline (xenoseq) was developed to cross-compare metagenomic samples, which was then applied to metagenomic data from evolving compost communities. These communities were routinely exposed to an "MGE cocktail" derived from allopatric communities. We show that this results in horizontal transfer of a multitude of previously undetected MGEs, including bacteriophages, phage-plasmids, megaplasmids, and even nanobacteria. Sequences that spread from one community to another are shown to disproportionally carry characteristics of phages and insertion-sequences, i.e., traits of canonically parasitic MGEs. We also show that one particularly prolific mobile element - a 313 kb plasmid - correlates substantially with rates of ammonia production, which under nitrogen limitation is likely beneficial. Taken together, our data show that new experimental strategies combined with bioinformatic analyses of metagenomic data stand to provide insight into the drivers of microbial community evolution.

microbiology↗

bioRxiv2022

Copiotrophs dominate rhizosphere microbiomes and growth rate potential is a major factor explaining the rhizosphere effect

The structure and function of the root microbial community is shaped by plant root activity, enriching specific microbial taxa and functions from the surrounding soil as the plant root grows. Knowledge of bacterial rhizosphere competence traits are important for predictive microbiome modeling and the development of viable bioinoculants for sustainable agriculture solutions. In this work we compared growth rate potential, a complex trait that recently became predictable from bacterial genome sequences, to functional traits encoded by proteins. We analyzed 84 paired rhizosphere- and soil-derived 16S rRNA metabarcoding datasets from 18 different plants and soil types, performed differential abundance analyses and estimated growth rates for each bacterial genus. This analysis revealed that bacteria with a high growth rate potential consistently dominated the rhizosphere. Next, we analyzed the genome sequences of 3270 bacterial isolates and 6707 MAGs from 1121 plant- and soil-associated metagenomes, confirming this trend in different bacterial phyla. We next investigated which functional traits were enriched in the rhizosphere, expanding the catalog of rhizosphere-associated traits with hundreds of new functions. When we compared the importance of different functional categories to the predicted growth rate potential using a machine learning model, we found that growth rate potential was the main feature for differentiating rhizosphere and soil bacteria, revealing the broad importance of this factor for explaining the rhizosphere effect. Together, we contribute new understanding of the bacterial traits needed for rhizosphere competence. As this trait may be inferred from (meta-) genome data, our work has implications for understanding bacterial community assembly in the rhizosphere, where many uncultivated bacteria reside.

microbiology↗

bioRxiv2022

iPHoP: an integrated machine-learning framework to maximize host prediction for metagenome-assembled virus genomes

The extraordinary diversity of viruses infecting bacteria and archaea is now primarily studied through metagenomics. While metagenomes enable high-throughput exploration of the viral sequence space, metagenome-derived genomes lack key information compared to isolated viruses, in particular host association. Different computational approaches are available to predict the host(s) of uncultivated viruses based on their genome sequences, but thus far individual approaches are limited either in precision or in recall, i.e. for a number of viruses they yield erroneous predictions or no prediction at all. Here we describe iPHoP, a two-step framework that integrates multiple methods to provide host predictions for a broad range of viruses while retaining a low (<10%) false-discovery rate. Based on a large database of metagenome-derived virus genomes, we illustrate how iPHoP can provide extensive host prediction and guide further characterization of uncultivated viruses. iPHoP is available at https://bitbucket.org/srouxjgi/iphop, through a Bioconda recipe, and a Docker container.

microbiology↗

bioRxiv2022

On specialists and generalists: niche range strategies across the tree of life

Generalists can survive in many environments whereas specialists have a limited distribution. Although a classical concept in ecology, niche breadth has remained challenging to quantify because it depends on an objective definition of the environment. Here, by defining the environment of a microbe as the community it resides in, we integrated information from over 22 thousand environmental sequencing samples to derive a quantitative social niche breadth score for all microbial taxa. At the level of genera, we explored niche range strategies across the tree of life. We found that generalists include opportunists that stochastically dominate local communities, while specialists are stable but low in abundance. Generalists have a more diverse and open pan genome than specialists, but we found no global correlation between niche breadth and genome size. Instead, we observed two distinct evolutionary strategies, where specialists have relatively small genomes in habitats with low local diversity, but relatively large genomes in habitats with high local diversity. Together, our global analysis shines a new, data-driven light on microbial niche range strategies.

bioinformatics↗

bioRxiv2021

High viral abundance and low diversity are associated with increased CRISPR-Cas prevalence across microbial ecosystems

CRISPR-Cas are adaptive immune systems that protect their hosts against viruses and other parasitic mobile genetic elements. Consequently, selection from viruses and other genetic parasites is often assumed to drive the acquisition and maintenance of these immune systems in nature, but this remains untested. Here, we analyse the abundance of CRISPR arrays in natural environments using metagenomic datasets from 332 terrestrial, aquatic and host-associated ecosystems. For each metagenome we quantified viral abundance and levels of viral community diversity to test whether these variables can explain variation in CRISPR-Cas abundance across ecosystems. We find a strong positive correlation between CRISPR-Cas abundance and viral abundance. In addition, when controlling for differences in viral abundance, we found that the CRISPR-Cas systems are more abundant when viral diversity is low. We also found differences in relative CRISPR-Cas abundance among environments, with environmental classification explaining [~]24% of variation in CRISPR-Cas abundance. However, the correlations with viral abundance and diversity are broadly consistent across diverse natural environments. These results indicate that viral abundance and diversity are major ecological factors that drive the selection and maintenance of CRISPR-Cas in microbial ecosystems. Significance statementNumerous studies demonstrate that CRISPR-Cas immune systems can provide defence against bacteriophage and archaeal viruses, yet little is known about the ecological conditions where CRISPR-Cas immunity is favoured. Moreover, our knowledge is largely confined to laboratory studies and it is unknown if viruses are a key selective driver of CRISPR-Cas in nature. Using metagenomic data from diverse environments we find that both viral abundance and the abundance of CRISPR-Cas immune systems correlate positively across most environments. Furthermore, CRISPR-Cas systems are more prevalent when viral diversity is low. These results extend previous theoretical work by demonstrating that viruses are a key driver of selection of CRISPR-Cas immune systems across many natural ecosystems.

microbiology↗

bioRxiv2021

Members of a highly widespread bacteriophage family are hallmarks of metabolic syndrome gut microbiomes

There is significant interest in altering the course of cardiometabolic disease development via the gut microbiome. Nevertheless, the highly abundant phage members -which impact gut bacteria- of the complex gut ecosystem remain understudied. Here, we characterized gut phageome changes associated with metabolic syndrome (MetS), a highly prevalent clinical condition preceding cardiometabolic disease. MetS gut phageome populations exhibited decreased richness and diversity, but larger inter-individual variation. These populations were enriched in phages infecting Bacteroidaceae and depleted in those infecting Ruminococcaeae. Differential abundance analysis identified eighteen viral clusters (VCs) as significantly associated with either MetS or healthy phageomes. Among these are a MetS-associated Roseburia VC that is related to healthy control-associated Faecalibacterium and Oscillibacter VCs. Further analysis of these VCs revealed the Candidatus Heliusviridae, a highly widespread gut phage lineage found in 90+% of the participants. The identification of the temperate Ca. Heliusviridae provides a novel starting point to a better understanding of the effect that phages have on their bacterial hosts and the role that this plays in MetS.

microbiology↗

bioRxiv2021

A prioritized and validated resource of mitochondrial proteins in Plasmodium identifies leads to unique biology

Plasmodium species have a single mitochondrion that is essential for their survival and has been successfully targeted by anti-malarial drugs. Most mitochondrial proteins are imported into this organelle and our picture of the Plasmodium mitochondrial proteome remains incomplete. Many data sources contain information about mitochondrial localization, including proteome and gene expression profiles, orthology to mitochondrial proteins from other species, co-evolutionary relationships, and amino acid sequences, each with different coverage and reliability. To obtain a comprehensive, prioritized list of Plasmodium falciparum mitochondrial proteins, we rigorously analyzed and integrated eight datasets using Bayesian statistics into a predictive score per protein for mitochondrial localization. At a corrected false discovery rate of 25%, we identified 445 proteins with a sensitivity of 87% and a specificity of 97%. They include proteins that have not been identified as mitochondrial in other eukaryotes but have characterized homologs in bacteria that are involved in metabolism or translation. Mitochondrial localization of seven Plasmodium berghei orthologs was confirmed by epitope labeling and co-localization with a mitochondrial marker protein. One of these belongs to a newly identified apicomplexan mitochondrial protein family that in P. falciparum has four members. With the experimentally validated mitochondrial proteins and the complete ranked P. falciparum proteome, which we have named PlasmoMitoCarta, we present a resource to study unique proteins of Plasmodium mitochondria.

bioinformatics↗

bioRxiv2020

Nutrition or nature: disentangling the complex forces shaping prokaryote pan-genomes

Microbial pan-genomes are shaped by a complex combination of stochastic and deterministic forces. Even closely related genomes often exhibit extensive variation in their gene content. Understanding what drives this variation requires exploring the interactions of genes with each other and with their external environments. However, to date, conceptual models of pan-genome dynamics often represent genes as independent units and provide limited information about their mechanistic interactions. Here, we use pan-reactomes as proxies for pan-genomes since they can explicitly represent the interactions between the genes that code for metabolic reactions and simulate complex phenotypes that interact with the metabolic environment. We interpreted pan-reactomes as dynamic pools of metabolic reactions that are potentially gained or lost and simulated the routes along which different lineages lose reactions in alternative environments. We performed these simulations on the pan-reactomes of 46 bacterial and archaeal families covering a broad taxonomic range. These simulations allowed us to disentangle metabolic reactions whose presence does, and does not depend on the metabolite composition of the external environment, allowing us to identify reactions constrained "by nutrition" and "by nature", respectively. By comparing the frequency of reactions from the first group with their observed frequencies in bacterial and archaeal families, we predicted the metabolic niches that shaped the genomic composition of these lineages in their evolutionary past. Moreover, we found that the lineages that were shaped by a more diverse metabolic niche also occur in more diverse biomes as assessed by global environmental sequencing datasets. Together, we introduce a computational framework for analyzing and interpreting pan-reactomes that provides new insights into the ecological and evolutionary drivers of pan-genome composition.

evolutionary biology↗

bioRxiv2020

RaFAH: A superior method for virus-host prediction

Viruses of prokaryotes are extremely abundant and diverse. Culture-independent approaches have recently shed light on the biodiversity these biological entities1,2. One fundamental question when trying to understand their ecological roles is: which host do they infect? To tackle this issue we developed a machine-learning approach named Random Forest Assignment of Hosts (RaFAH), based on the analysis of nearly 200,000 viral genomes. RaFAH outperformed other methods for virus-host prediction (F1-score = 0.97 at the level of phylum). RaFAH was applied to diverse datasets encompassing genomes of uncultured viruses derived from eight different biomes of medical, biotechnological, and environmental relevance, and was capable of accurately describing these viromes. This led to the discovery of 537 genomic sequences of archaeal viruses. These viruses represent previously unknown lineages and their genomes encode novel auxiliary metabolic genes, which shed light on how these viruses interfere with the host molecular machinery. RaFAH is available at https://sourceforge.net/projects/rafah/.

bioinformatics↗

bioRxiv2020

Optimized DNA isolation method for microbiome analysis of human tissues

Recent advances in microbiome sequencing have rendered new insights into the role of the microbiome in human health with potential clinical implications. Unfortunately, developments in the field of tissue microbiomes have been hampered by the presence of host DNA in isolates which interferes with the analysis of the bacterial content. Here, we present a DNA isolation protocol from tissue samples including reduction of host DNA without distortion of microbial abundance profiles. We evaluated which concentrations of Triton and saponin lyse host cells and leave bacterial cells intact, which was combined with DNAse treatment to deplete released host DNA. We applied our protocol to extract microbial DNA from ex vivo and in vivo acquired human colon biopsies ([~]2-5 mm in size) and assessed the relative abundance of bacterial and human DNA by qPCR. Saponin at a concentration of 0.0125% in PBS lysed host cells, resulting in a 4.5-fold enrichment of bacterial DNA while preserving the relative abundance of Firmicutes, Bacteroidetes, {gamma}-Proteobacteria and Actinobacteria. Our protocol combined with shotgun metagenomic sequencing revealed a colon tissue microbiome profile with a Shannon diversity index of 3.2 and an UniFrac distance of 0.54, which is comparable to reported numbers based on amplicon sequencing. Hereby, we present the first protocol for enriching bacterial DNA from tissue biopsies that allows efficient isolation of rigid Gram-positive bacteria without depleting the more sensitive Gram-negative bacteria. Our protocol facilitates analysis of a wide spectrum of bacteria of clinical tissue samples improving their applicability for microbiome research.

microbiology↗

bioRxiv2020

Long identical sequences found in multiple bacterial genomes reveal frequent and widespread exchange of genetic material between distant species.

Horizontal transfer of genomic elements is an essential force that shapes microbial genome evolution. Horizontal Gene Transfer (HGT) occurs via various mechanisms and has been studied in detail for a variety of systems. However, a coarse-grained, global picture of HGT in the microbial world is still missing. One reason is the difficulty to process large amounts of genomic microbial data to find and characterise HGT events, especially for highly distant organisms. Here, we exploit the fact that HGT between distant species creates long identical DNA sequences in genomes of distant species, which can be found efficiently using alignment-free methods. We analysed over 90 000 bacterial genomes and thus identified over 100 000 events of HGT. We further developed a mathematical model to analyse the statistical properties of those long exact matches and thus estimate the transfer rate between any pair of taxa. Our results demonstrate that long-distance gene exchange (across phyla) is very frequent, as more than 8% of the bacterial genomes analysed have been involved in at least one such event. Finally, we confirm that the function of the transferred sequences strongly impact the transfer rate, as we observe a 3.5 order of magnitude variation between the most and the least transferred categories. Overall, we provide a unique view of horizontal transfer across the bacterial tree of life, illuminating a fundamental process driving bacterial evolution.

bioinformatics↗

bioRxiv2020

Prophages are associated with extensive, tolerated CRISPR-Cas auto-immunity

CRISPR-Cas systems require discriminating self from non-self DNA during adaptation and interference. Yet, multiple cases have been reported of bacteria containing self-targeting spacers (STS), i.e. CRISPR spacers targeting protospacers on the same genome. STS has been suggested to reflect potential auto-immunity as an unwanted side effect of CRISPR-Cas defense, or a regulatory mechanism for gene expression. Here we investigated the incidence, distribution, and evasion of STS in over 100,000 bacterial genomes. We found STS in all CRISPR-Cas types and in one fifth of all CRISPR-carrying bacteria. Notably, up to 40% of I-B and I-F CRISPR-Cas systems contained STS. We observed that STS-containing genomes almost always carry a prophage and that STS map to prophage regions in more than half of the cases. Despite carrying STS, genetic deterioration of CRISPR-Cas systems appears to be rare, suggesting a level of escape from the potentially deleterious effects of STS by other mechanisms such as anti-CRISPR proteins and CRISPR target mutations. We propose a scenario where it is common to acquire an STS against a prophage, and this may trigger more extensive STS buildup by primed spacer acquisition in type I systems, without detrimental autoimmunity effects. The mechanisms of auto-immunity evasion create tolerance to STS-targeted prophages, and contribute both to viral dissemination and bacterial diversification.

bioinformatics↗

bioRxiv2020

Quantifying the impact of Human Leukocyte Antigen on the human gut microbiome

ObjectiveThe gut microbiome is affected by a number of factors, including the innate and adaptive immune system. The major histocompatibility complex (MHC), or the human leukocyte antigen (HLA) in humans, performs an essential role in vertebrate immunity, and is very polymorphic in different populations. HLA determines the specificity of T lymphocyte and natural killer (NK) cell responses, including against the commensal bacteria present in the human gut. Thus, it is likely that our HLA molecules and thereby the adaptive immune response, can shape the composition of our microbiome. Here, we investigated the effect of HLA haplotype on the microbiome composition. ResultsWe performed HLA typing and microbiota composition analyses on 3,002 public human gut microbiome datasets. We found that (i) individuals with functionally similar HLA molecules (i.e. presenting similar peptides) are also similar in their microbiota, and (ii) HLA homozygosity correlated with microbiome diversity, suggesting that diverse immune responses limit microbiome diversity. ConclusionOur results show a statistical association between host HLA haplotype and gut microbiome composition. Because the HLA haplotype is a readily measurable parameter of the human immune system, these results open the door to incorporating the immune system into predictive microbiome models. IMPORTANCEThe microorganisms that live in the digestive tracts of humans, known as the gut microbiome, are essential for hosts survival as they support crucial functions. For example, they support the host in facilitating the uptake of nutrients and give colonization resistance against pathogens. The composition of the gut microbiome varies among humans. Studies have proposed multiple factors driving the observed variation, including; diet, lifestyle, and health condition. Another major influence on the microbiome is the hosts genetic background. We hypothesized the immune system to be one of the most important genetic factors driving the differences observed between gut microbiomes. Therefore, we are interested in linking the polymorphic molecules that play a role in human immune responses to the composition of the microbiome. HLA molecules are the most polymorphic molecules in our genome and therefore makes an excellent candidate to test such an association/link. To our knowledge for the first time, our results indicate a significant impact of the HLA on the human gut microbiome composition.

bioinformatics↗