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Pavloudi, C.

Publications and source records attributed to Pavloudi, C..

6 recordsLinked to original sources

Hawaiian Geothermal Fumaroles Contain Diverse and Novel Viruses

Microbial communities of geothermal habitats are central to understanding the evolution of life on Earth. Metagenomics has provided insight into the role of viruses in shaping microbial diversity of complex environments. However, identification of novel viruses is constrained by lack of marker genes and low nucleotide similarities between related viral taxa. While microbial and viral diversity have been explored in terrestrial hot springs and hydrothermal vent systems, other volcanic features remain underexplored. Fumaroles (steam vents) are geothermal features that heat groundwater with magma, releasing steam and volcanic gases such as CO2 and H2S. Comparatively physicochemically dynamic to hot springs, fumarole temperatures and gas emissions rapidly fluctuate with volcanic activity. Here, we describe viruses identified metagenomically from microbial mats hosted near basaltic fumaroles on the Big Island of Hawaii. To our knowledge, this is the first systematic survey of fumarole viruses. Our utilization of a sensitive profile-based approach for identification reveals high viral diversity in fumaroles, resulting in estimation of two undescribed order-level clades of Caudoviricetes (tailed phages). Viral metabolic genes provide evidence of viral-mediated adaptation of microbes to fumarole conditions. We describe patterns of viral diversity that diverge from the Bank model of viral ecology, hinting at viral dispersal between biofilms and high viral richness and evenness. Lastly, we provide a description of the first terrestrial geothermal environment dominated by Microviridae, previously only described in viral communities of deep ocean hydrothermal vents. This study offers important findings for exploration of viral ecology in extreme environments.

microbiology↗

Amplicon and metagenomic data from fumarole-associated geothermal features of Hawaii

The Hawaiian islands are among the most geologically and volcanically active places on Earth. While the Hawaiian Archipelago is known for its animal and plant diversity, much less is known about microbial diversity in the areas diverse habitats. In this study, we focused on steam vent associated biofilms found on the most volcanically active island of Hawai{square}i, also known as the Big Island. From 46 samples from various biofilms and associated features around fumaroles emitting water steam, we generated amplicon and metagenomic sequences. Amplicon data showed that Chloroflexota and Cyanobacteriota are the numerically dominant phyla in these biofilm communities. We constructed 363 non-redundant medium to high-quality metagenome-assembled genomes (MAGs) that are at least 70% complete and with less than 5% contamination. Ten MAGs belong in the domain Archaea, and 353 belong in the domain Bacteria. This dataset could provide valuable insights into microbial diversity and ecology around volcanic features in Hawai i and elsewhere.

microbiology↗

microGalaxy: A gateway to tools, workflows, and training for reproducible and FAIR analysis of microbial data

The explosion of microbial omics data has outpaced the ability of many researchers to analyze it, with complex tools and limited computational resources creating barriers to discovery. To address this gap, we present the Microbiology Galaxy Lab: a free, globally accessible, community-supported platform that combines state-of-the-art analytical power with user-friendly accessibility. Supported by the Galaxy and global microbiology communities, this platform integrates over 315 tool suites and 115 curated workflows, enabling comprehensive metabarcoding, (meta)genomic, (meta)transcriptomic, and (meta)proteomic data analysis within a FAIR-aligned environment. It also supports research in the health and infectious disease sectors, as well as in environmental microbiology. The platforms utility is exemplified through various use cases, including antimicrobial resistance tracking, biomarker prediction, microbiome classification, and functional annotation of key microbes. Built on reproducibility and community engagement, it supports creation, sharing, and updating of best-practice workflows. Over 35 tutorials and learning paths empower scientists, fostering an ecosystem that keeps resources at the forefront of microbial science. The Microbiology Galaxy Lab enables collective analysis, democratising research, thereby accelerating discovery across the global microbiology community (microbiology.usegalaxy.org, microbiology.usegalaxy.eu, microbiology.usegalaxy.org.au, microbiology.usegalaxy.fr).

bioinformatics↗

A long-term ecological research data set from the marine genetic monitoring programme ARMS-MBON 2018-2020

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.

ecology↗

Bacteria are everywhere, even in your COI marker gene data!

The mitochondrial cytochrome C oxidase subunit I gene (COI) is commonly used in eDNA metabarcoding studies, especially for assessing metazoan diversity. Yet, a great number of COI operational taxonomic units or/and amplicon sequence variants are retrieved from such studies and referred to as "dark matter", and do not get a taxonomic assignment with a reference sequence. For a thorough investigation of this dark matter, we have developed the Dark mAtteR iNvestigator (DARN) software tool. A reference COI-oriented phylogenetic tree was built from 1,240 consensus sequences covering all the three domains of life, with more than 80% of those representing eukaryotic taxa. With respect to eukaryotes, consensus sequences at the family level were constructed from 183,330 retrieved from the Midori reference 2 database. Similarly, sequences from 559 bacterial genera and 41 archaeal were retrieved from the BOLD database. DARN makes use of the phylogenetic tree to investigate and quantify pre-processed sequences of amplicon samples to provide both a tabular and a graphical overview of phylogenetic assignments. To evaluate DARN, both environmental and bulk metabarcoding samples from different aquatic environments using various primer sets were analysed. We demonstrate that a large proportion of non-target prokaryotic organisms such as bacteria and archaea are also amplified in eDNA samples and we suggest bacterial COI sequences to be included in the reference databases used for the taxonomy assignment to allow for further analyses of dark matter. DARN source code is available on GitHub at https://github.com/hariszaf/darn and you may find it as a Docker at https://hub.docker.com/r/hariszaf/darn. Author summaryDARN is a software approach aiming to provide further insight in the COI amplicon data coming from environmental samples. Building a COI-oriented reference phylogeny tree is a challenging task especially considering the small number of microbial curated COI sequences deposited in reference databases; e.g ~4,000 bacterial and ~150 archaeal in BOLD. Apparently, as more and more such sequences are collated, the DARN approach improves. To provide a more interactive way of communicating both our approach and our results, we strongly suggest the reader to visit this Google Collab notebook where all steps are described step by step and also this GitHub page where our results are demonstrated. Our approach corroborates the known presence of microbial sequences in COI environmental sequencing samples and highlights the need for curated bacterial and archaeal COI sequences and their integration into reference databases (i.e. Midori, BOLD, etc). We argue that DARN will benefit researchers as a quality control tool for their sequenced samples in terms of distinguishing eukaryotic from non-eukaryotic OTUs/ASVs, but also in terms of understanding the unknown unknowns.

bioinformatics↗

PEMA: from the raw .fastq files of 16S rRNA and COI marker genes to the (M)OTU-table, a thorough metabarcoding analysis

BackgroundEnvironmental DNA (eDNA) and metabarcoding, allow the identification of a mixture of individuals and launch a new era in bio- and eco-assessment. A number of steps are required to obtain taxonomically assigned (Molecular) Operational Taxonomic Unit ((M)OTU) tables from raw data. For most of these, a plethora of tools is available; each tools execution parameters need to be tailored to reflect each experiments idiosyncrasy. Adding to this complexity, for such analyses, the computation capacity of High Performance Computing (HPC) systems is frequently required.\n\nSoftware containerization technologies ease the sharing and running of software packages across operating systems; thus, they strongly facilitate pipeline development and usage. Likewise are programming languages specialized for big data pipelines, incorporating features like roll-back checkpoints and on-demand partial pipeline execution.\n\nFindingsPEMA is a containerized assembly of key metabarcoding analysis tools with a low effort in setting up, running and customizing to researchers needs. Based on third party tools, PEMA performs reads pre-processing, clustering to (M)OTUs and taxonomy assignment for 16S rRNA and COI marker gene data. Due to its simplified parameterisation and checkpoint support, PEMA allows users to explore alternative algorithms for specific steps of the pipeline without the need of a complete re-execution. PEMA was evaluated against previously published datasets and achieved comparable quality results.\n\nConclusionsGiven its time-efficient performance and its quality results, it is suggested that PEMA can be used for accurate eDNA metabarcoding analysis, thus enhancing the applicability of next-generation biodiversity assessment studies.

bioinformatics↗