Biology subjects

Quince, C.

Publications and source records attributed to Quince, C..

32 records · Page 2Linked to original sources

bioRxiv2021

Adaptive ecological processes and metabolic independence drive microbial colonization and resilience in the human gut

Changes in microbial community composition as a function of human health and disease states have sparked remarkable interest in the human gut microbiome. However, establishing reproducible insights into the determinants of microbial succession in disease has been a formidable challenge. Here we use fecal microbiota transplantation (FMT) as an in natura experimental model to investigate the association between metabolic independence and resilience in stressed gut environments. Our genome-resolved metagenomics survey suggests that FMT serves as an environmental filter that favors populations with higher metabolic independence, the genomes of which encode complete metabolic modules to synthesize critical metabolites, including amino acids, nucleotides, and vitamins. Interestingly, we observe higher completion of the same biosynthetic pathways in microbes enriched in IBD patients. These observations suggest a general mechanism that underlies changes in diversity in perturbed gut environments, and reveal taxon-independent markers of dysbiosis that may explain why widespread yet typically low abundance members of healthy gut microbiomes can dominate under inflammatory conditions without any causal association with disease.

microbiology↗

bioRxiv2021

Automated strain separation in low-complexity metagenomes using long reads

High-throughput short-read metagenomics has enabled large-scale species-level analysis and functional characterization of microbial communities. Microbiomes often contain multiple strains of the same species, and different strains have been shown to have important differences in their functional roles. Despite this, strain-level resolution from metagenomic sequencing remains challenging. Recent advances on long-read based methods enabled accurate assembly of bacterial genomes from complex microbiomes and an as-yet-unrealized opportunity to resolve strains. Here we present Strainberry, a metagenome assembly method that performs strain separation in single-sample low-complexity metagenomes and that relies uniquely on long-read data. We benchmarked Strainberry on mock communities and showed it consistently produces strain-resolved assemblies with near-complete reference coverage and 99.9% base accuracy. We also applied Strainberry on real datasets for which it improved assemblies generating 20-118% additional genomic material than conventional metagenome assemblies on individual strain genomes. Our results hence demonstrate that strain separation is possible in low-complexity microbiomes using a single regular long read dataset. We show that Strainberry is also able to refine microbial diversity in a complex microbiome, with complete separation of strain genomes. We anticipate this work to be a starting point for further methodological improvements aiming to provide better strain-resolved metagenome assemblies in environments of higher complexities.

bioinformatics↗

bioRxiv2020

Microbial community modelling and diversity estimation using the hierarchical Pitman-Yor process

BackgroundThe human microbiome comprises the microorganisms that inhabit the various locales of the human body and plays a vital role in human health. The composition of a microbial population is often quantified through measures of species diversity, which summarize the number of species along with their relative abundances into a single value. In a microbiome sample there will certainly be species missing from the target population which will affect the diversity estimates. MethodsWe employ a model based on the hierarchical Pitman-Yor (HPY) process to model the species abundance distributions over multiple populations. The model parameters are estimated using a Gibbs sampler. We also derive estimates of species diversity, conditional and unconditional on the observed data, as a function of the HPY parameters Finally, we derive a general formula for the Hill numbers in the HPY context. ResultsWe show that the Gibbs sampler for the HPY model performs well in simulations. We also show that the conditional estimates of diversity from the HPY model improve over naive estimates when species are missing. Similarly the conditional HPY estimates tend to perform better than the naive estimates especially when the number of individuals sampled from a population is small.

bioinformatics↗

bioRxiv2020

Metagenomics Strain Resolution on Assembly Graphs

We introduce a novel bioinformatics pipeline, STrain Resolution ON assembly Graphs (STRONG), which identifies strains de novo, when multiple metagenome samples from the same community are available. STRONG performs coassembly, followed by binning into metagenome assembled genomes (MAGs), but uniquely it stores the coassembly graph prior to simplification of variants. This enables the subgraphs for individual single-copy core genes (SCGs) in each MAG to be extracted. It can then thread back reads from the samples to compute per sample coverages for the unitigs in these graphs. These graphs and their unitig coverages are then used in a Bayesian algorithm, BayesPaths, that determines the number of strains present, their sequences or haplotypes on the SCGs and their abundances in each of the samples. Our approach both avoids the ambiguities of read mapping and allows more of the information on co-occurrence of variants in reads to be utilised than if variants were treated independently, whilst at the same time exploiting the correlation of variants across samples that occurs when they are linked in the same strain. We compare STRONG to the current state of the art on synthetic communities and demonstrate that we can recover more strains, more accurately, and with a realistic estimate of uncertainty deriving from the variational Bayesian algorithm employed for the strain resolution. On a real anaerobic digestor time series we obtained strain-resolved SCGs for over 300 MAGs that for abundant community members match those observed from long Nanopore reads.

bioinformatics↗

bioRxiv2020

Gene duplication drives genome expansion in Thaumarchaeota

Ammonia-oxidising archaea of the phylum Thaumarchaeota are keystone species in global nitrogen cycling. However, only three of the six known families of the terrestrially ubiquitous order Nitrososphaerales possess representative genomes. Here we provide genomes for the three remaining families and examine the impact of gene duplication, loss and transfer events across the entire phylum. Much of the genomic divergence in this phylum is driven by gene duplication and loss, but we also detected early lateral gene transfer that introduced considerable proteome novelty. In particular, we identified two large gene transfer events into Nitrososphaerales. The fate of gene families originating on these branches was highly lineage-specific, being lost in some descendant lineages, but undergoing extensive duplication in others, suggesting niche-specific roles within soil and sediment environments. Overall, our results suggest that lateral gene transfer followed by gene duplication drives Nitrososphaerales evolution, highlighting a previously under-appreciated mechanism of genome expansion in archaea.

evolutionary biology↗

bioRxiv2019

De Novo Growth Of Methanogenic Granules Indicates A Biofilm Life-Cycle With Complex Ecology

Methanogenic sludge granules are densely packed, small (diameter, approx. 0.5-2.0 mm) spherical biofilms found in anaerobic digesters used to treat industrial wastewaters, where they underpin efficient organic waste conversion and biogas production. A single digester contains millions of individual granules, each of which is a highly-organised biofilm comprised of a complex consortium of likely billions of cells from across thousands of species - but not all granules are identical. Whilst each granule theoretically houses representative microorganisms from all of the trophic groups implicated in the successive and interdependent reactions of the anaerobic digestion process, parallel granules function side-by-side in digesters to provide a meta-organism of sorts. Granules from a full-scale bioreactor were size-separated into small, medium and large granules. Laboratory-scale bioreactors were operated using only small (0.6-1 mm), medium (1-1.4 mm) or large (1.4-1.8 mm) granules, or unfractionated (naturally distributed) sludge. After >50 days of operation, the granule size distribution in each of the small, medium and large bioreactor types had diversified beyond - to both bigger and smaller than - the size fraction used for inoculation. New granules were analysed by studying community structure based on high-throughput 16S rRNA gene sequencing. Methanobacterium, Aminobacterium, Propionibacteriaceae and Desulfovibrio represented the majority of the community in new granules. H2-using, and not acetoclastic, methanogens appeared more important, and were associated with abundant syntrophic bacteria. Multivariate integration analyses identified distinct discriminant taxa responsible for shaping the microbial communities in different-sized granules, and along with alpha diversity data, indicated a possible biofilm life cycle.\n\nImportanceMethanogenic granules are spherical biofilms found in the built environment, where despite their importance for anaerobic digestion of wastewater in bioreactors, little is understood about the fate of granules across their entire life. Information on exactly how, and at what rates, methanogenic granules develop will be important for more precise and innovative management of environmental biotechnologies. Microbial aggregates also spark interest as subjects in which to study fundamental concepts from microbial ecology, including immigration and species sorting affecting the assembly of microbial communities. This experiment is the first, of which we are aware, to compartmentalise methanogenic granules into discrete, size-resolved fractions, which were then used to separately start up bioreactors to investigate the granule life cycle. The evidence, and extent, of de novo granule growth, and the identification of key microorganisms shaping new granules at different life-cycle stages, is important for environmental engineering and microbial ecology.

microbiology↗

bioRxiv2018

Diversity Converges During Community Assembly In Methanogenic Granules, Suggesting A Biofilm Life-Cycle

Anaerobic biological decomposition of organic matter is ubiquitous in Nature wherever anaerobic environments prevail, and is catalysed by hydrolytic, fermentative, acetogenic, methanogenic, and various other groups, including syntrophic bacteria. It is also harnessed in innovative ways in engineered systems that may rely on small (0.1-4.0 mm), spherical, anaerobic granules, which we have found to be highly-replicated, whole-ecosystems harbouring the entire community necessary to mineralise complex organics. We hypothesised distinct granule sizes correspond to stages in a biofilm life-cycle, in which small granules are young and larger ones are old. Here, granules were separated into 10 size fractions used for physico-chemical and ecological characterisation. Gradients of volatile solids, density, settleability, biofilm morphology, methanogenic activity, and EPS profiles were observed across size fractions. Sequencing of 16S rRNA genes indicated linear convergence of diversity during community assembly as granules increased in size. A total of 155 discriminant OTUs were identified, and correlated strongly with physico-chemical parameters. Community assembly across sizes was influenced by a niche effect, whereby Euryarchaeota dominated a core microbiome presumably as granules became more anaerobic. The findings indicate opportunities for precision management of environmental biotechnologies, and the potential of aggregates as playgrounds to study assembly and succession in whole microbiomes.

microbiology↗

bioRxiv2018

Recent mixing of Vibrio parahaemolyticus populations

BackgroundHumans have profoundly affected the ocean environment but little is known about anthropogenic effects on the distribution of microbes. Vibrio parahaemolyticus is found in warm coastal waters and causes gastroenteritis in humans and economically significant disease in shrimps.\n\nResultsBased on data from 1,103 genomes, we show that V. parahaemolyticus is divided into four diverse populations, VppUS1, VppUS2, VppX and VppAsia. The first two are largely restricted to the US and Northern Europe, while the others are found worldwide, with VppAsia making up the great majority of isolates in the seas around Asia. Patterns of diversity within and between the populations are consistent with them having arisen by progressive divergence via genetic drift during geographical isolation. However, we find that there is substantial overlap in their current distribution. These observations can be reconciled without requiring genetic barriers to exchange between populations if dispersal between oceans has increased dramatically in the recent past. We found that VppAsia isolates from the US have an average of 1.01% more shared ancestry with VppUS1 and VppUS2 isolates than VppAsia isolates from Asia itself. Based on time calibrated trees of divergence within epidemic lineages, we estimate that recombination affects about 0.017% of the genome per year, implying that the genetic mixture has taken place within the last few decades.\n\nConclusionsThese results suggest that human activity, such as shipping and aquatic products trade, are responsible for the change of distribution pattern of this marine species.

microbiology↗

bioRxiv2018

Systems genetic discovery of host-microbiome interactions reveals mechanisms of microbial involvement in disease

The role of the microbiome in health and disease involves complex networks of host genetics, genomics, microbes and environment. Identifying the mechanisms of these interactions has remained challenging. Systems genetics in the laboratory mouse enables data-driven discovery of network components and mechanisms of host-microbial interactions underlying multiple disease phenotypes. To examine the interplay among the whole host genome, transcriptome and microbiome, we mapped quantitative trait loci and correlated the abundance of cecal mRNA, luminal microflora, physiology and behavior in incipient strains of the highly diverse Collaborative Cross mouse population. The relationships that are extracted can be tested experimentally to ascribe causality among host and microbe in behavior and physiology, providing insight into disease. Application of this strategy in the Collaborative Cross population revealed experimentally validated mechanisms of microbial involvement in models of autism, inflammatory bowel disease and sleep disorder.\n\neTOC BlurbHost genetic diversity provides a variable selection environment and physiological context for microbiota and their interaction with host physiology. Using a highly diverse mouse population Bubier et al. identified a variety of host, microbe and potentially disease interactions.\n\nHighlights* 18 significant species-specific QTL regulating microbial abundance were identified\n* Cis and trans eQTL for 1,600 cecal transcripts were mapped in the Collaborative Cross\n* Sleep phenotypes were highly correlated with the abundance of B.P. Odoribacter\n* Elimination of sleep-associated microbes restored normal sleep patterns in mice.

genetics↗

bioRxiv2018

Machine learning based prediction of functional capabilities in metagenomically assembled microbial genomes

The increasing popularity of genome resolved meta genomics - the binning of genomes of potentially uncultured organisms direct from the environmental DNA - has resulted in a deluge of draft genomes. There is a pressing need to develop methods to interpret this data. Here, we used machine learning to predict functional and metabolic traits of microbes from their genomes. We collated an extensive database of 84 phenotypic traits associated with 9407 prokaryotic genomes and trained different machine learning models on this data. We found that a lasso logistic regression based on the frequency of gene orthologs had the best combination of functional prediction performance and interpretability. This model was able to classify 65 phenotypic traits with greater than 90

microbiology↗

bioRxiv2017

Accurate Reconstruction of Microbial Strains Using Representative Reference Genomes

Exploring the genetic diversity of microbes within the environment through metagenomic sequencing first requires classifying these reads into taxonomic groups. Current methods compare these sequencing data with existing biased and limited reference databases. Several recent evaluation studies demonstrate that current methods either lack sufficient sensitivity for species-level assignments or suffer from false positives, overestimating the number of species in the metagenome. Both are especially problematic for the identification of low-abundance microbial species, e. g. detecting pathogens in ancient metagenomic samples. We present a new method, SPARSE, which improves taxonomic assignments of metagenomic reads. SPARSE balances existing biased reference databases by grouping reference genomes into similarity-based hierarchical clusters, implemented as an efficient incremental data structure. SPARSE assigns reads to these clusters using a probabilistic model, which specifically penalizes non-specific mappings of reads from unknown sources and hence reduces false-positive assignments. Our evaluation on simulated datasets from two recent evaluation studies demonstrated the improved precision of SPARSE in comparison to other methods for species-level classification. In a third simulation, our method successfully differentiated multiple co-existing Escherichia coli strains from the same sample. In real archaeological datasets, SPARSE identified ancient pathogens with[≤] 0.02% abundance, consistent with published findings that required additional sequencing data. In these datasets, other methods either missed targeted pathogens or reported non-existent ones. SPARSE and all evaluation scripts are available at https://github.com/zheminzhou/SPARSE.

bioinformatics↗

bioRxiv2017

Nitrogen-Fixing Populations Of Planctomycetes And Proteobacteria Are Abundant In The Surface Ocean

Nitrogen fixation in the surface ocean impacts the global climate by regulating the microbial primary productivity and the sequestration of carbon through the biological pump. Cyanobacterial populations have long been thought to represent the main suppliers of the bio-available nitrogen in this habitat. However, recent molecular surveys of nitrogenase reductase gene revealed the existence of rare non-cyanobacterial populations that can also fix nitrogen. Here, we characterize for the first time the genomic content of some of these heterotrophic bacterial diazotrophs (HBDs) inhabiting the open surface ocean waters. They represent new lineages within Planctomycetes and Proteobacteria, a phylum never linked to nitrogen fixation prior to this study. HBDs were surprisingly abundant in the Pacific Ocean and the Atlantic Ocean northwest, conflicting with decades of PCR surveys. The abundance and widespread occurrence of non-cyanobacterial diazotrophs in the surface ocean emphasizes the need to re-evaluate their role in the nitrogen cycle and primary productivity.

microbiology↗

bioRxiv2017

Millennia of genomic stability within the invasive Para C Lineage of Salmonella enterica

Salmonella enterica serovar Paratyphi C is the causative agent of enteric (paratyphoid) fever. While today a potentially lethal infection of humans that occurs in Africa and Asia, early 20th century observations in Eastern Europe suggest it may once have had a wider-ranging impact on human societies. We recovered a draft Paratyphi C genome from the 800-year-old skeleton of a young woman in Trondheim, Norway, who likely died of enteric fever. Analysis of this genome against a new, significantly expanded database of related modern genomes demonstrated that Paratyphi C is descended from the ancestors of swine pathogens, serovars Choleraesuis and Typhisuis, together forming the Para C Lineage. Our results indicate that Paratyphi C has been a pathogen of humans for at least 1,000 years, and may have evolved after zoonotic transfer from swine during the Neolithic period.\n\nOne Sentence SummaryThe combination of an 800-year-old Salmonella enterica Paratyphi C genome with genomes from extant bacteria reshapes our understanding of this pathogens origins and evolution.

microbiology↗

bioRxiv2017

Critical Assessment of Metagenome Interpretation - a benchmark of computational metagenomics software

In metagenome analysis, computational methods for assembly, taxonomic profiling and binning are key components facilitating downstream biological data interpretation. However, a lack of consensus about benchmarking datasets and evaluation metrics complicates proper performance assessment. The Critical Assessment of Metagenome Interpretation (CAMI) challenge has engaged the global developer community to benchmark their programs on datasets of unprecedented complexity and realism. Benchmark metagenomes were generated from ~700 newly sequenced microorganisms and ~600 novel viruses and plasmids, including genomes with varying degrees of relatedness to each other and to publicly available ones and representing common experimental setups. Across all datasets, assembly and genome binning programs performed well for species represented by individual genomes, while performance was substantially affected by the presence of related strains. Taxonomic profiling and binning programs were proficient at high taxonomic ranks, with a notable performance decrease below the family level. Parameter settings substantially impacted performances, underscoring the importance of program reproducibility. While highlighting current challenges in computational metagenomics, the CAMI results provide a roadmap for software selection to answer specific research questions.

bioinformatics↗