Biology subjects

Marcelino, V. R.

Publications and source records attributed to Marcelino, V. R..

6 recordsLinked to original sources

bioRxiv2026

Accurate detection of metagenomic strain-level associations using average nucleotide identity with StrainSpy

Genetic variation among microbial strains of the same species can profoundly influence their phenotypes, ecological functions, and impacts on human health. Traditionally, the relative abundance of a species has been used to identify associations between the microbiome and disease. However, this approach overlooks intra-species genetic variation and is susceptible to spurious correlations arising from the compositional nature of abundance data and microbial load. Fast, k-mer-based algorithms can now accurately estimate strain-level Average Nucleotide Identity (ANI) in metagenomes. Despite its value as an orthogonal metric for strain-level analysis, methods for conducting ANI-based association studies remain limited. To address this, we developed StrainSpy, a statistical algorithm that identifies associations between containment ANI and variables of interest across a wide range of study designs, including longitudinal and multi-cohort designs. Re-analysis of a study examining gut microbiota recovery in 12 healthy adults following antibiotic exposure revealed novel strain-level associations, including a reduction in strain-level diversity despite species persistence. Applying StrainSpy to a multi-cohort analysis of 3,414 colorectal cancer metagenomes identified novel strain-level associations with colorectal cancer. However, in a separate collection of microbiome-immunotherapy studies, no individual strain was consistently associated across cohorts. Importantly, across both datasets, StrainSpy informed containment ANI-based machine learning models achieved comparable accuracy to traditional abundance-based methods. StrainSpy is publicly available as an R package github.com/gtonkinhill/strainspy.

microbiology↗

bioRxiv2024

Global picoplankton biogeography revealed by metagenomic and climatic data integration

Microbial plankton play fundamental roles in biogeochemical cycles, driving nutrient cycling that influences the global climate and supports life on Earth. Picoplankton are the smallest and most abundant planktonic organisms. The distribution and ecology of these organisms is determined by environmental factors and their biogeography is largely shaped by basin-scale patterns of physicochemical composition of ocean waters. The increased availability of high-throughput sequencing data of microbial communities has enabled the description of how the global oceans are partitioned into distinct microbial biogeographical provinces. However, the key attributes associated with such provinces are still unclear. Here we present a model of picoplankton biogeography based on 1454 metagenomes from multiple sampling consortia, resulting in the largest integrated surface ocean metagenome analysis to date. We identify ten distinct groups based on metagenomic dissimilarity, divided into three categories: polar (Arctic and Antarctic), temperate (coastal temperate, temperate/subtropical transition, oceanic temperate, Mediterranean-like) and tropical (tropical low nutrient, tropical high nutrient, subtropical oceanic gyres). Using machine learning and omics data integration techniques, we predict province areas across the surface oceans and describe their environmental, taxonomical, and functional features. We quantify the relationship between environmental factors and each biogeographical province, identify their main representative taxa and the importance of carbon degradation and antimicrobial resistance pathways in functional community composition, and discuss implications for establishing a model for global picoplankton biogeography.

ecology↗

bioRxiv2024

A widespread hydrogenase drives fermentative growth of gut bacteria in healthy people

Molecular hydrogen (H2) is among the most central, but least understood, metabolites in the human gastrointestinal tract (gut). H2 gas is produced in large quantities during bacterial fermentation and consumed as an energy source by bacteria and archaea. Disruption of H2 cycling is linked to gastrointestinal disorders, infections, and cancers, with H2 used as an indicator of gut dysfunction through breath tests. Despite this, the microorganisms, pathways, and enzymes mediating H2 production remain unresolved. Here we show that a previously uncharacterised enzyme, the group B [FeFe]-hydrogenase, drives most fermentative H2 production in the human gut. Analysis of stool, biopsy, and isolate (meta)genomes and (meta)transcriptomes show this hydrogenase is encoded by most gut bacteria and is highly expressed. Through analysis of 19 taxonomically diverse gut isolates, the group B [FeFe]-hydrogenase produces large amounts of H2 gas and supports fermentative growth of both Bacteroidetes and Firmicutes. Bacteroides particularly dominate H2 production. Biochemical and spectroscopic characterisation shows purified group B [FeFe]-hydrogenases are catalytically active and bind a di-iron active site. These hydrogenases are highly enriched in the guts of healthy individuals, but significantly depleted in favour of other fermentative hydrogenases in Crohns disease. Furthermore, we show that metabolically flexible respiratory bacteria are the most abundant H2 oxidizers in the gut, not sulfate reducers, methanogens, and acetogens as previously thought. This combination of enzymatic, cellular, and ecosystem-level analysis provides the first detailed understanding of H2 cycling in the human gut and reveals new links between microbiota function and gastrointestinal health.

microbiology↗

bioRxiv2023

Metaphor - A workflow for streamlined assembly and binning of metagenomes

Recent advances in bioinformatics and high-throughput sequencing have enabled the large-scale recovery of genomes from metagenomes. This has the potential to bring important insights as researchers can bypass cultivation and analyse genomes sourced directly from environmental samples. There are, however, technical challenges associated with this process, most notably the complexity of computational workflows required to process metagenomic data, which include dozens of bioinformatics software tools, each with their own set of customisable parameters that affect the final output of the workflow. At the core of these workflows are the processes of assembly - combining the short input reads into longer, contiguous fragments (contigs), and binning - clustering these contigs into individual genome bins. Both processes can be done for each sample separately or by pooling together multiple samples to leverage information from a combination of samples. Here we present Metaphor, a fully-automated workflow for genome-resolved metagenomics (GRM). Metaphor differs from existing GRM workflows by offering flexible approaches for the assembly and binning of the input data, and by combining multiple binning algorithms with a bin refinement step to achieve high quality genome bins. Moreover, Metaphor generates reports to evaluate the performance of the workflow. We showcase the functionality of Metaphor on different synthetic datasets, and the impact of available assembly and binning strategies on the final results. The workflow is freely available at https://github.com/vinisalazar/metaphor. Author summaryWe present Metaphor, a user-friendly, automated workflow for the recovery of genomes from metagenomes. Our tool offers flexible options for assembling and binning metagenomic contigs, that may be adjusted according to the characteristics of the input data and available computational resources, and a combination of binning algorithms, which improves the quantity and quality of resulting genome bins. We showcase the performance of Metaphor on synthetic benchmarking datasets and discuss the implication of methodological decisions regarding the strategy for assembling and binning metagenomic contigs.

bioinformatics↗

bioRxiv2021

Ten Ostreobium (Ulvophyceae) strains from Great Barrier Reef corals as a resource for algal endolith biology and genomics

Ostreobium is a genus of siphonous green algae that lives as an endolith in carbonate substrates under extremely limited light conditions and has recently been gaining attention due to its roles in reef carbonate budgets and its association with reef corals. Knowledge about this genus remains fairly limited due to the scarcity of strains available for physiological studies. Here, we report on 10 strains of Ostreobium isolated from coral skeletons from the Great Barrier Reef. Phenotypic diversity showed differences in the gross morphology and in few structures. Phylogenetic analyses of the tufA and rbcL put the strains in the context of the lineages identified previously through environmental sequencing. The chloroplast genomes of our strains are all around 80k bp in length and show that genome structure is highly conserved, with only a few insertions (some containing putative protein-coding genes) differing between the strains. The addition of these strains from the Great Barrier Reef to our toolkit will help develop Ostreobium as a model species for endolithic growth, low-light photosynthesis and coral-algal associations.

genetics↗

bioRxiv2020

Genomic adaptations to an endolithic lifestyle in the coral-associated alga Ostreobium

The green alga Ostreobium is an important coral holobiont member, playing key roles in skeletal decalcification and providing photosynthate to bleached corals that have lost their dinoflagellate endosymbionts. Ostreobium lives in the corals skeleton, a low-light environment with variable pH and O{square} availability. We present the Ostreobium nuclear genome and a metatranscriptomic analysis of healthy and bleached corals to improve our understanding of Ostreobiums adaptations to its extreme environment and its roles as a coral holobiont member. The Ostreobium genome has 10,663 predicted protein-coding genes and shows adaptations for life in low and variable light conditions and other stressors in the endolithic environment. This alga presents a rich repertoire of light-harvesting complex proteins but lacks many genes for photoprotection and photoreceptors. It also has a large arsenal of genes for oxidative stress response. An expansion of extracellular peptidases suggests that Ostreobium may supplement its energy needs by feeding on the organic skeletal matrix, and a diverse set of fermentation pathways allow it to live in the anoxic skeleton at night. Ostreobium depends on other holobiont members for vitamin B12, and our metatranscriptomes identify potential bacterial sources. Metatranscriptomes showed Ostreobium becoming a dominant agent of photosynthesis in bleached corals and provided evidence for variable responses among coral samples and different Ostreobium genotypes. Our work provides a comprehensive understanding of the adaptations of Ostreobium to its extreme environment and an important genomic resource to improve our comprehension of coral holobiont resilience, bleaching and recovery.

genomics↗