Biology subjects

Pachiadaki, M.

Publications and source records attributed to Pachiadaki, M..

5 recordsLinked to original sources

bioRxiv2022

MetaPredict: A machine learning-based tool for predicting metabolic modules in incomplete bacterial genomes

The reconstruction of complete microbial metabolic pathways using omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from KEGG module databases, MetaPathPredict employs neural network and XGBoost stacked ensemble models to reconstruct and predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as an R package, and both options are designed to be run locally or on a compute cluster. In our benchmarks, MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

bioinformatics↗

bioRxiv2022

Diverse secondary metabolites are expressed in particle-associated and free-living microorganisms of the permanently anoxic Cariaco Basin

Secondary metabolites play essential roles in ecological interactions and nutrient acquisition, and are of interest for their potential uses in medicine and biotechnology. Under laboratory conditions the discovery of new compounds is hindered by low production, high rediscovery rates, and detection evasion. Genome mining for biosynthetic gene clusters (BGCs) is a popular approach to address these limitations, thereby uncovering often cryptic secondary metabolic potential. Marine systems are often underrepresented in bulk genome mining efforts; and habitats with unique physicochemical characteristics such as oxygen-depleted and anoxic water columns remain virtually unexplored regarding BGCs. Here, we use genome mining and differential gene expression analyses to show that redox potential and particle-associated vs. free-living lifestyles both influence the composition and production of secondary metabolites through the stratified water column of the Cariaco Basin, Venezuela. Microbiota, including understudied clades, such as Planctomycetota, encode and express a wide range of secondary metabolites in these anoxic/euxinic waters.

bioinformatics↗

bioRxiv2021

Eukaryotic genomes from a global metagenomic dataset illuminate trophic modes and biogeography of ocean plankton

Metagenomics is a powerful method for interpreting the ecological roles and physiological capabilities of mixed microbial communities. Yet, many tools for processing metagenomic data are not designed to consider eukaryotes, nor are they built for an increasing amount of sequence data. EukHeist is an automated pipeline to retrieve eukaryotic and prokaryotic metagenome assembled genomes (MAGs) from large-scale metagenomic datasets. We developed the EukHeist workflow to specifically process large amounts of both metagenomic and/or metatranscriptomic sequence data in an automated and reproducible fashion. Here, we applied EukHeist to the large-size fraction data (0.8-2000{micro}m) from Tara Oceans to recover both eukaryotic and prokaryotic MAGs, which we refer to as TOPAZ (Tara Oceans Particle-Associated MAGs). The TOPAZ MAGs consisted of >900 environmentally-relevant eukaryotic MAGs and >4,000 bacterial and archaeal MAGs. The bacterial and archaeal TOPAZ MAGs expand the known marine phylogenetic diversity through the increase in coverage of likely particle- and host-associated taxa. We also demonstrate an approach to infer the putative functional mode of the recovered eukaryotic MAGs. A global survey of the TOPAZ MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. ImportanceDespite the ecological importance of single-celled eukaryotic organisms in marine environments, the majority are difficult to cultivate in the lab. Sequencing genetic material extracted from environmental samples enables researchers to document naturally-occurring protistan communities. However, conventional sequencing methodologies cannot separate out the genomes of individual organisms. To more completely capture the entire genomic content of mixed protistan community, we can create bins of sequences that represent the same organism. We developed a pipeline that enables scientists to bin individual organisms out of metagenomic reads, and show results that provide exciting insights into what protistan communities are present in the ocean and what roles they play in the ecosystem. Here, a global survey of both eukaryotic and prokaryotic MAGs enabled the identification of ecological cohorts, driven by specific environmental factors, and putative host-microbe associations. Accessible and scalable computational tools, such as EukHeist, are likely to accelerate the identification of meaningful genetic signatures from large datasets, ultimately expanding the eukaryotic tree of life.

microbiology↗

bioRxiv2020

Gene exchange networks define species-like units in marine prokaryotes

Post-submission note. Since the original submission of this manuscript to bioRxiv, we discovered that some of our results may be impacted by the limitations of some of the comparative genomics tools used in this study. We are working on a revised version of the manuscript. Although horizontal gene transfer is recognized as a major evolutionary process in Bacteria and Archaea, its general patterns remain elusive, due to difficulties tracking genes at relevant resolution and scale within complex microbiomes. To circumvent these challenges, we analyzed a randomized sample of >12,000 genomes of individual cells of Bacteria and Archaea in the tropical and subtropical ocean - a well-mixed, global environment. We found that marine microorganisms form gene exchange networks (GENs) within which transfers of both flexible and core genes are frequent, including the rRNA operon that is commonly used as a conservative taxonomic marker. The data revealed efficient gene exchange among genomes with <28% nucleotide difference, indicating that GENs are much broader lineages than the nominal microbial species, which are currently delineated at 4-6% nucleotide difference. The 42 largest GENs accounted for 90% of cells in the tropical ocean microbiome. Frequent gene exchange within GENs helps explain how marine microorganisms maintain millions of rare genes and adapt to a dynamic environment despite extreme genome streamlining of their individual cells. Our study suggests that sharing of pangenomes through horizontal gene transfer is a defining feature of fundamental evolutionary units in marine planktonic microorganisms and, potentially, other microbiomes.

microbiology↗

bioRxiv2018

Impacts of Deep-Sea Mining on Microbial Ecosystem Services

Interest in extracting mineral resources from the seafloor through deep-sea mining has accelerated substantially in the past decade, driven by increasing consumer demand for various metals like copper, zinc, manganese, cobalt and rare earth elements. While there are many on-going discussions and studies evaluating potential environmental impacts of deep-sea mining activities, these focus primarily on impacts to animal biodiversity. The microscopic spectrum of life on the seafloor and the services that this microbial realm provides in the deep sea are rarely considered explicitly. In April 2018, a community of scientists met to define the microbial ecosystem services that should be considered when assessing potential impacts of deep-sea mining, and to provide recommendations for how to evaluate these services. Here we show that the potential impacts of mining on microbial ecosystem services in the deep sea vary substantially, from minimal expected impact to complete loss of services that cannot be remedied by protected area offsets. We conclude by recommending that certain types of ecosystems should be \"off limits\" until initial characterizations can be performed, and that baseline assessments of microbial diversity, biomass, and biogeochemical function need to be considered in environmental impact assessments of all potential instances of deep-sea mining.

microbiology↗