Biology subjects

Zablocki, O.

Publications and source records attributed to Zablocki, O..

6 recordsLinked to original sources

bioRxiv2025

Scalable and systematic hierarchical virus taxonomy with vConTACT3

Viruses are key players in diverse ecosystems, but studying their impacts is technically and taxonomically challenging. Taxonomic complexities derive from undersampling, diverse DNA and RNA genomes with multiple evolutionary origins, and lack of a universal barcode gene. While virus ecogenomics has expanded access to and understanding of the virosphere, available classification tools poorly scale to modern discovery-based datasets, lack taxonomic resolution, and/or are unable to classify novel sequence space. Here we develop, benchmark, and release vConTACT3, a machine learning-based tool that improves scalability and accuracy, adds extensive user-requested features, expands classification to both eukaryote and prokaryote viruses for 4/6 officially recognized realms, and establishes accurate hierarchical taxonomy from genus to order. Application to 48,069 public virus genomes provided new taxonomy assignments for thousands of taxa, revealed support for fewer taxonomic ranks than currently available, and systematically identified taxonomically problematic areas across the virosphere.

bioinformatics↗

bioRxiv2022

Quantitative stable-isotope probing (qSIP) with metagenomics links microbial physiology and activity to soil moisture in Mediterranean-climate grassland ecosystems

The growth and physiology of soil microorganisms, which play vital roles in biogeochemical cycling, are likely dependent on current and prior soil moisture levels. Here, we developed and applied a genome-resolved metagenomic implementation of quantitative stable isotope probing (qSIP) to an H218O labeling experiment to determine which microbial community members, and with what capacities, are growing under in situ conditions. qSIP enabled measurement of taxon-specific growth because isotopic incorporation into microbial DNA requires production of new genome copies. We studied three Mediterranean grassland soils across a rainfall gradient to evaluate the hypothesis that historic precipitation levels are an important factor controlling trait selection. We used qSIP-informed genome-resolved metagenomics to resolve an active subset of soil community members and identify the ecophysiological traits that characterize them. Higher year-round precipitation levels correlated with higher activity and growth rates of flagellar motile microorganisms. In addition to bacteria that were heavily isotopically labeled, we identified abundant isotope-labeled phages suggesting phage-induced cell lysis likely contributed to necromass production at all three sites. Further, there was a positive correlation between phage activity and the activity of putative phage hosts. Contrary to our expectations, the capabilities to decompose the diverse complex carbohydrates common in soil necromass or oxidize methanol and carbon monoxide were broadly distributed across active and inactive bacteria in all three soils, implying that these traits are not highly selected for by historical precipitation.

microbiology↗

bioRxiv2020

VirION2: a short- and long-read sequencing and informatics workflow to study the genomic diversity of viruses in nature

Microbes play fundamental roles in shaping natural ecosystem properties and functions, but do so under constraints imposed by their viral predators. However, studying viruses in nature can be challenging due to low biomass and the lack of universal gene markers. Though metagenomic short-read sequencing has greatly improved our virus ecology toolkit-- and revealed many critical ecosystem roles for viruses -- microdiverse populations and fine-scale genomic traits are missed. Some of these microdiverse populations are abundant and the missed regions may be of interest for identifying selection pressures that underpin evolutionary constraints associated with hosts and environments. Though long-read sequencing promises complete virus genomes on single reads, it currently suffers from high DNA requirements and sequencing errors that limit accurate gene prediction. Here we introduce VirION2, an integrated short- and long-read metagenomic wet-lab and informatics pipeline that updates our previous method (VirION) to further enhance the utility of long-read viral metagenomics. Using a viral mock community, we first optimized laboratory protocols (polymerase choice, DNA shearing size, PCR cycling) to enable 76% longer reads (now median length of 6,965 bp) from 100-fold less input DNA (now 1 nanogram). Using a virome from a natural seawater sample, we compared viromes generated with VirION2 against other library preparation options (unamplified, original VirION, and short-read), and optimized downstream informatics for improved long-read error correction and assembly. VirION2 assemblies combined with short-read based data ( enhancedviromes), provided significant improvements over VirION libraries in the recovery of longer and more complete viral genomes, and our optimized error-correction strategy using long- and short-read data achieved 99.97% accuracy. In the seawater virome, VirION2 assemblies captured 5,161 viral populations (including all of the virus populations observed in the other assemblies), 30% of which were uniquely assembled through inclusion of long-reads, and 22% of the top 10% most abundant virus populations derived from assembly of long-reads. Viral populations unique to VirION2 assemblies had significantly higher microdiversity, which may explain why short-read virome approaches failed to capture them. These findings suggest the VirION2 sample prep and workflow (updated at protocols.io) can help researchers better investigate the virosphere, even from challenging low-biomass samples. Our new protocols are available to the research community on protocols.io as a living document to facilitate dissemination of updates to keep pace with the rapid evolution of long read sequencing technology. Taken together, the addition of long-reads uniquely enabled the recovery of 22% of the most abundant viruses--that would have been missed in short-read only assemblies.

microbiology↗

bioRxiv2020

Capturing in situ Virus-Host Range and Interaction Dynamics through Gene Fusion with epicPCR

Viruses impact microbial diversity, phenotype, and gene flow through virus-host interactions that in turn alter ecology and biogeochemistry. Though metagenomics surveys are rapidly cataloging viral diversity, capturing specific virus-host interactions in situ would identify hosts for novel viruses and reveal influential ecological or environmental factors. We leveraged metagenomics and a high-throughput, cultivation-independent gene fusion technique (epicPCR) to investigate viral diversity and virus-host interactions over time in a critical estuarine environment, the Chesapeake Bay. EpicPCR captured in situ virus-host interactions for viral clades with no closely related database representatives. Abundant freshwater Actinobacteria lineages were the most common hosts for these poorly characterized viruses, and observed viral interactions with one abundant Actinobacterial population (Rhodoluna) were correlated with environmental factors. Tracking virus-host interaction dynamics also revealed ecological differences between multi-host (generalist) and single-host (specialist) viruses. Generalist viruses had significantly longer periods with observed virus-host interactions but specialist viruses were observed interacting with hosts at lower minimum abundances, suggesting more efficient interactions. Together, these observations reveal ecological differences between generalist and specialist viruses that provide insight into evolutionary trade-offs. Capturing in situ interactions with epicPCR revealed environmental and ecological factors that shape virus-host interactions, highlighting epicPCR as a scalable new tool in viral ecology.

microbiology↗

bioRxiv2019

The human gut virome database

The gut microbiome profoundly impacts human health and disease, but viruses that infect these microbes are likely also important. Problematically, viral sequences are often missed due to insufficient reference viral genomes. Here we (i) built a human gut virome database, GVD, from 648 viral particle metagenomes or microbial metagenomes from 572 individuals previously searched for viruses, (ii) assessed its effectiveness, and (iii) conducted meta-analyses. GVD contains 13,203 unique viral populations (approximately species-level taxa) organized into 702 novel genera, which roughly doubles known phage genera and improves viral detection rates over NCBI viral RefSeq nearly 60-fold. Applying GVD, we assessed and rejected the idea of a core gut virome in healthy individuals, and found through meta-analyses that technical artifacts are more impactful than any treatment effect across the entire meta-study dataset. Together, this foundational resource and these findings will help human microbiome researchers better identify viral roles in health and disease.

bioinformatics↗

bioRxiv2019

Gene sharing networks to automate genome-based prokaryotic viral taxonomy

Viruses of bacteria and archaea are likely to be critical to all natural, engineered and human ecosystems, and yet their study is hampered by the lack of a universal or scalable taxonomic framework. Here, we introduce vConTACT 2.0, a network-based application to establish prokaryotic virus taxonomy that scales to thousands of uncultivated virus genomes, and integrates confidence scores for all taxonomic predictions. Performance tests using vConTACT 2.0 demonstrate near-identical correspondence to the current official viral taxonomy (>85% genus-rank assignments at 96% accuracy) through an integrated distance-based hierarchical clustering approach. Beyond "known viruses", we used vConTACT 2.0 to automatically assign 1,364 previously unclassified reference viruses to tentative taxa, and scaled it to modern metagenomic datasets for which the reference network was robust to adding 16,000 viral contigs. Together these efforts provide a systematic reference network and an accurate, scalable taxonomic analysis tool that is critically needed for the research community.

bioinformatics↗