Haris Zafeiropoulos

Microbial growth curve data contain valuable information about microorganisms’ physiology and metabolism. However, they are currently reported in various formats scattered across publications. A generic resource that allows querying and comparing microbial growth curve data is currently missing. For this reason, we developed μGrowthDB, an open repository designed to store, visualize, analyze, and share quantitative measurements of species abundances and metabolite profiles from monocultures and communities of known composition. μGrowthDB is designed to accommodate a range of experimental designs and measurement techniques. In addition to querying microbial growth curves by organism or metabolite, μGrowthDB supports comparative visualisation of growth curves and offers a guided data submission process intended to make data upload as easy as possible. μGrowthDB is available at: https://mgrowthdb.gbiomed.kuleuven.be/ .

Microbial co-occurrence network inference is often hindered by low accuracy and tool dependency. We introduce microbetag, a comprehensive software ecosystem designed to annotate microbial networks. Nodes, representing taxa, are enriched with phenotypic traits, while edges are enhanced with metabolic complementarities, highlighting potential cross-feeding relationships. microbetag’s online version relies on microbetagDB, a database of 34,608 annotated representative genomes. microbetag can be applied to custom (metagenome-assembled) genomes via its stand-alone version. MGG, a Cytoscape app designed to support microbetag, offers a streamlined, user-friendly interface for network retrieval and visualization. microbetag effectively identified known metabolic interactions and serves as a robust hypothesis-generating tool.

Microbiome research has grown substantially over the past decade in terms of the range of biomes sampled, identified taxa, and the volume of data derived from the samples. In particular, experimental approaches such as metagenomics, metabarcoding, metatranscriptomics and metaproteomics have provided profound insights into the vast, hitherto unknown, microbial biodiversity. The ELIXIR Marine Metagenomics Community, initiated amongst researchers focusing on marine microbiomes, has concentrated on promoting standards around microbiome-derived sequence analysis, as well as understanding the gaps in methods and reference databases, and identifying solutions to the computational overheads of performing such analyses. Nevertheless, the methods used and the challenges faced are not confined to marine microbiome studies, but are broadly applicable to other biomes. Thus, expanding this Marine Metagenomics Community to a more inclusive ELIXIR Microbiome Community will enable it to encompass a broader range of biomes and link expertise across ‘omics technologies. Furthermore, engaging with a large number of researchers will improve the efficiency and sustainability of bioinformatics infrastructure and resources for microbiome research (standards, data, tools, workflows, training), which will enable a deeper understanding of the function and taxonomic composition of the different microbial communities.

Molecular methods such as DNA/eDNA metabarcoding have emerged as useful tools to document the biodiversity of complex communities over large spatio‐temporal scales. We established an international Marine Biodiversity Observation Network (ARMS‐MBON) combining standardised sampling using autonomous reef monitoring structures (ARMS) with metabarcoding for genetic monitoring of marine hard‐bottom benthic communities. Here, we present the data of our first sampling campaign comprising 56 ARMS units deployed in 2018–2019 and retrieved in 2018–2020 across 15 observatories along the coasts of Europe and adjacent regions. We describe the open‐access data set (image, genetic and metadata) and explore the genetic data to show its potential for marine biodiversity monitoring and ecological research. Our analysis shows that ARMS recovered more than 60 eukaryotic phyla capturing diversity of up to ~5500 amplicon sequence variants and ~1800 operational taxonomic units, and up to ~250 and ~50 species per observatory using the cytochrome c oxidase subunit I (COI) and 18S rRNA marker genes, respectively. Further, ARMS detected threatened, vulnerable and non‐indigenous species often targeted in biological monitoring. We show that while deployment duration does not drive diversity estimates, sampling effort and sequencing depth across observatories do. We recommend that ARMS should be deployed for at least 3–6 months during the main growth season to use resources as efficiently as possible and that post‐sequencing curation is applied to enable statistical comparison of spatio‐temporal entities. We suggest that ARMS should be used in biological monitoring programs and long‐term ecological research and encourage the adoption of our ARMS‐MBON protocols.

The importance of microbial communities in fish hatcheries for fish health and welfare has been recognized, with several studies mapping these communities during healthy rearing conditions and disease outbreaks. In this study, we analyzed the bacteriome of the live feeds, such as microalgae, rotifers, and Artemia, used in fish hatcheries that produce Mediterranean species. Our goal was to provide baseline information about their structure, emphasizing in environmental putative fish pathogenic bacteria. We conducted 16S rRNA amplicon Novaseq sequencing for our analysis, and we inferred 46,745 taxonomically annotated ASVs. Results showed that incoming environmental water plays a significant role in the presence of important taxa that constitute presumptive pathogens. Bio-statistical analyses revealed a relatively stable bacteriome among seasonal samplings for every hatchery but a diverse bacteriome between sampling stations and a distinct core bacteriome for each hatchery. Analysis of putative opportunistic fish pathogenic genera revealed some co-occurrence correlation events and a high average relative abundance of Vibrio, Tenacibaculum, and Photobacterium genera in live feeds, reaching a grand mean average of up to 7.3% for the hatchery of the Hellenic Center of Marine Research (HCMR), 12% for Hatchery A, and 11.5% for Hatchery B. Mapping the bacteriome in live feeds is pivotal for understanding the marine environment and distinct aquaculture practices and can guide improvements in hatchery management, enhancing fish health and sustainability in the Mediterranean region.

Molecular methods such as DNA/eDNA metabarcoding have emerged as useful tools to document biodiversity of complex communities over large spatio-temporal scales. We established an international Marine Biodiversity Observation Network (ARMS-MBON) combining standardised sampling using autonomous reef monitoring structures (ARMS) with metabarcoding for genetic monitoring of marine hard-bottom benthic communities. Here, we present the data of our first sampling campaign comprising 56 ARMS units deployed in 2018-2019 and retrieved in 2018-2020 across 15 observatories along the coasts of Europe and adjacent regions. We describe the open-access data set (image, genetic, and metadata) and explore the genetic data to show its potential for marine biodiversity monitoring and ecological research. Our analysis shows that ARMS recovered more than 60 eukaryotic phyla capturing diversity of up to ∼5,500 amplicon sequence variants and ∼1,800 operational taxonomic units, and up to ∼250 and ∼50 species per observatory using the cytochrome c oxidase subunit I (COI) and 18S rRNA marker genes, respectively. Further, ARMS detected threatened, vulnerable and non-indigenous species often targeted in biological monitoring. We show that while deployment duration does not drive diversity estimates, sampling effort and sequencing depth across observatories do. We recommend that ARMS should be deployed for at least three to six months during the main growth season to use resources as efficiently as possible and that post-sequencing curation is applied to enable statistical comparison of spatio-temporal entities. We suggest that ARMS should be used in biological monitoring programmes and long-term ecological research and encourage the adoption of our ARMS-MBON protocols.

Microbial co-occurrence network inference is often hindered by low accuracy and tool dependency. We introduce microbetag, a comprehensive software ecosystem designed to enhance network annotation. Nodes (taxa) are enriched with phenotypic traits, while edges represent metabolic complementarities, highlighting potential cross-feeding relationships. microbetag's online version relies on microbetagDB, a database of 34,608 high-quality genomes with detailed annotations. A stand-alone tool allows users to apply microbetag to custom reference genomes/bins/MAGs. Additionally, MGG, a CytoscapeApp, offers a streamlined, user-friendly interface for network retrieval and visualization. microbetag effectively identified known metabolic interactions and serves as a robust hypothesis-generating tool.

Molecular methods such as DNA/eDNA metabarcoding have emerged as useful tools to document biodiversity of complex communities over large spatio-temporal scales. We established an international Marine Biodiversity Observation Network (ARMS-MBON) combining standardised sampling using autonomous reef monitoring structures (ARMS) with metabarcoding for genetic monitoring of marine hard-bottom benthic communities. Here, we present the data of our first sampling campaign comprising 56 ARMS units deployed in 2018-2019 and retrieved in 2018-2020 across 15 observatories along the coasts of Europe and adjacent regions. We describe the open-access data set (image, genetic, and metadata) and explore the genetic data to show its potential for marine biodiversity monitoring and ecological research. Our analysis shows that ARMS recovered more than 60 eukaryotic phyla capturing diversity of up to ∼5,500 amplicon sequence variants and ∼1,800 operational taxonomic units, and up to ∼250 and ∼50 species per observatory using the cytochrome c oxidase subunit I (COI) and 18S rRNA marker genes, respectively. Further, ARMS detected threatened, vulnerable and non-indigenous species often targeted in biological monitoring. We show that while deployment duration does not drive diversity estimates, sampling effort and sequencing depth across observatories do. We recommend that ARMS should be deployed for at least three to six months during the main growth season to use resources as efficiently as possible and that post-sequencing curation is applied to enable statistical comparison of spatio-temporal entities. We suggest that ARMS should be used in biological monitoring programmes and long-term ecological research and encourage the adoption of our ARMS-MBON protocols.

Background: Given a genome-scale metabolic model (GEM) of a microorganism and criteria for optimization, flux balance analysis (FBA) predicts the optimal growth rate and its corresponding flux distribution for a specific medium. FBA has been extended to microbial consortia and thus can be used to predict interactions by comparing in-silico growth rates for co- and monocultures. Although FBA-based methods for microbial interaction prediction are becoming popular, a systematic evaluation of their accuracy has not yet been performed. Results: Here, we evaluate the accuracy of FBA-based predictions of human and mouse gut bacterial interactions using growth data from the literature. For this, we collected 26 GEMs from the semi-curated AGORA database as well as four previously published curated GEMs. We tested the accuracy of three tools (COMETS, Microbiome Modeling Toolbox and MICOM) by comparing growth rates predicted in mono- and co-culture to growth rates extracted from the literature and also investigated the impact of different tool settings and media. We found that except for curated GEMs, predicted growth rates and their ratios (i.e. interaction strengths) do not correlate with growth rates and interaction strengths obtained from in vitro data. Conclusions: Prediction of growth rates with FBA using semi-curated GEMs is currently not sufficiently accurate to predict interaction strengths reliably.