Biology subjects

Duhaime, M. B.

Publications and source records attributed to Duhaime, M. B..

10 recordsLinked to original sources

bioRxiv2025

Novel Machine Learning-based Approach to Identify Viral Biomarkers of Human Respiratory Emissions from Oral and Nasal Metagenomes

Humans spend approximately 90% of their lives in built environments, making virus transmission indoors a key determinant of health. Environmental sampling of respiratory viral pathogens is often challenging because of frequent non-detect measurements. Non-detect measurements do not differentiate between samples containing low or no pathogens from samples that simply lack respiratory expulsions altogether. This ambiguity can be resolved by scanning samples for a biomarker of human respiratory emissions. To do so, reliable biomarkers for environmental monitoring need to be identified. Ideal biomarkers are prevalent across individuals, abundant, and unique to the human respiratory tract. Here, we present a new machine learning-based approach to query for suitable biomarker candidates from publicly available metagenomes and apply it to identify viral biomarkers of healthy oral and nasal microbiomes. Twelve viral biomarker candidates were selected from 1,232 curated viral operational taxonomic units. The viral biomarker candidates had as much as 63% prevalence across respiratory metagenomes and prevalence was further increased to 77-81% by combining two or three biomarkers. Quantitative PCR confirmed that these viral biomarkers were prevalent and abundant in nasal swabs and saliva samples. Notably, top candidate biomarkers remained stable and detectable through multiple lab purification steps, increasing confidence in their viral origins and demonstrating their suitability for environmental monitoring. These findings demonstrate that existing metagenomes can be used to identify effective biomarker candidates for environmental sampling. IMPORTANCEDeveloping non-pharmaceutical interventions to reduce virus transmission indoors relies on robust environmental monitoring methods. Monitoring viral pathogens is challenging because of frequent non-detect measurements that introduce uncertainty. For instance, a non-detect measurement could indicate either the absence of the pathogen or simply the lack of human respiratory activity and thus exposure. To aid in distinguishing these scenarios, this study identifies viruses from the human respiratory tract using publicly available sequencing data that can be incorporated into environmental monitoring as biomarkers of human respiratory activity. These viral biomarkers will improve indoor monitoring to help enact interventions to mitigate virus transmission. Furthermore, our approach to identify biomarkers from existing metagenomes can be adapted for future biomarker identification in any system.

microbiology↗

bioRxiv2024

Tracking Microcystis viruses and infection dynamics across distinct phases of a Microcystis-dominated bloom

Given the impact of viruses on microbial community composition and function, viruses have the potential to play a significant role in the fate of freshwater cyanobacterial harmful algal blooms (cHABs). Yet the role of viruses in cHABs remains poorly understood. We sought to address this knowledge gap with a metagenomic analysis of viruses of bloom-forming Microcystis aeruginosa across cHAB phases in the western basin of Lake Erie. Size-fractionation of the water allowed us to identify significant fraction-specific trends in viral diversity, which corresponded with Microcystis genetic diversity. Using a new machine-learning model, we predicted infections between viral and microbial host populations. We predicted hundreds of viral populations with infection histories including Microcystis and non-Microcystis hosts, suggesting extensive interconnectivity and the potential for virus-mediated cross-species exchange of genetic material within cHABs communities. Infection predictions revealed a broad host range for Lake Erie Microcystis viruses, challenging previous notions of "narrow" host-virus interactions in cHABs. Abundant viral genes belonging to predicted Microcystis viruses revealed their potential role in key metabolic pathways and adaptation to environmental changes. We observed significant turnover of predicted Microcystis virus populations across time. Viral diversity was highest in the viral fraction and lowest in the colony-associated fraction, suggesting that Microcystis colony formation and growth during cHABs leads to bottlenecks in viral diversity. These findings advance our understanding of uncultivated Microcystis virus diversity, their potential effects on host metabolism, potential influence on species interactions, and potential coevolutionary processes between microbial hosts and their viral predators within Microcystis-dominated cHABs. ImportanceUnderstanding interactions between viruses, their hosts, and environmental parameters may be key to identifying the mechanisms underlying the persistence and demise of cyanobacterial harmful algal blooms (cHABs). In this study we describe the viral diversity and host ranges of viruses predicted to infect Microcystis, describing the distribution of these properties across time, space, and different bloom-associated size fractions. Additionally, the study highlights abundant genes belonging to predicted Microcystis viruses and their potential roles in key metabolic pathways and adaptation to environmental changes. The observed turnover of Microcystis virus populations, with the highest diversity in viral fractions and the lowest in colony-associated fractions, suggests that Microcystis colony formation during blooms plays an important role in shaping viral diversity and community turnover. These findings contribute to a better understanding of the interplay between viruses, Microcystis, and their accompanying bacterial communities, shedding light on mechanisms driving bloom dynamics, species interactions, and coevolutionary processes.

microbiology↗

bioRxiv2023

Virus-Host Interactions Predictor (VHIP): machine learning approach to resolve microbial virus-host interaction networks

Viruses of microbes are ubiquitous biological entities that reprogram their hosts metabolisms during infection in order to produce viral progeny, impacting the ecology and evolution of microbiomes with broad implications for human and environmental health. Advances in genome sequencing have led to the discovery of millions of novel viruses and an appreciation for the great diversity of viruses on Earth. Yet, with knowledge of only "who is there?" we fall short in our ability to infer the impacts of viruses on microbes at population, community, and ecosystem-scales. To do this, we need a more explicit understanding "who do they infect?" Here, we developed a novel machine learning model (ML), Virus-Host Interaction Predictor (VHIP), to predict virus-host interactions (infection/non-infection) from input virus and host genomes. This ML model was trained and tested on a high-value manually curated set of 8849 virus-host pairs and their corresponding sequence data. The resulting dataset, Virus Host Range network (VHRnet), is core to VHIP functionality. Each data point that underlies the VHIP training and testing represents a lab-tested virus-host pair in VHRnet, from which features of coevolution were computed. VHIP departs from existing virus-host prediction models in its ability to predict multiple interactions rather than predicting a single most likely host or host clade. As a result, VHIP is the first virus-host range prediction tool able to reconstruct the complexity of virus-host networks in natural systems. VHIP has an 87.8% accuracy rate at predicting interactions between virus-host pairs at the species level and can be applied to novel viral and host population genomes reconstructed from metagenomic datasets. Through the reconstruction of complete virus-host networks from novel data, VHIP allows for the integration of multilayer network theory into microbial ecology and opens new opportunities to study ecological complexity in microbial systems. Author summaryThe ecology and evolution of microbial communities are deeply influenced by viruses. Metagenomics analysis, the non-targeted sequencing of community genomes, has led to the discovery of millions of novel viruses. Yet, through the sequencing process, only DNA sequences are recovered, begging the question: which microbial hosts do those novel viruses infect? To address this question, we developed a computational tool to allow researchers to predict virus-host interactions from such sequence data. The power of this tool is its use of a high-value, manually curated set of 8849 lab-verified virus-host pairs and their corresponding sequence data. For each pair, we computed signals of coevolution to use as the predictive features in a machine learning model designed to predict interactions between viruses and hosts. The resulting model, Virus-Host Interaction Predictor (VHIP), has an accuracy of 87.8% and can be applied to novel viral and host genomes reconstructed from metagenomic datasets. Because the model considers all possible virus-host pairs, it can resolve complete virus-host interaction networks and supports a new avenue to apply network thinking to viral ecology.

bioinformatics↗

bioRxiv2022

Evaluating limitations of quantitative metagenomics with synthetic dsDNA and ssDNA standards

Quantitative metagenomic methods are maturing but continue to lack clearly-defined analytical limits. Here, we developed a computational tool, QuantMeta, to determine the absolute abundance of targets in metagenomes spiked with synthetic DNA standards. The tool establishes (1) entropy-based detection thresholds to confidently determine the presence of targets, and (2) an approach to identify and correct read mapping or assembly errors and thus improve the quantification accuracy. Together this allows for an approach to confidently quantify absolute abundance of targets, be they microbial populations, genes, contigs, or metagenome assembled genomes (MAGs). We applied the approach to quantify single- and double-stranded DNA viruses in wastewater viral metagenomes, including pathogens and bacteriophages. Concentrations of total DNA viruses in wastewater influent and effluent were greater than 108 copies/mL using QuantMeta. Human-associated DNA viruses were detected and quantifiable with QuantMeta thresholds, including polyomavirus, papillomavirus, and crAss-like phages, at concentrations similar to previous reports that utilized quantitative PCR-based assays. Our results highlight the higher detection thresholds of quantitative metagenomics ([~]500 copies/L) as compared to PCR-based quantification ([~]10 copies/L) despite a sequencing depth of 200 million reads per sample. The QuantMeta approach, applicable to both viral and cellular metagenomes, advances quantitative metagenomics by improving the accuracy of measured target absolute abundances.

genomics↗

bioRxiv2021

Bacterial, phytoplankton, and viral dynamics of meromictic Lake Cadagno offer insights into the Proterozoic ocean microbial loop

Lake Cadagno, a permanently stratified high-alpine lake with a persistent microbial bloom in its anoxic chemocline, has long been considered a model for the low-oxygen, high-sulfide Proterozoic ocean where early microbial life gave rise to Earths oxygenated atmosphere. Although the lake has been studied for over 25 years, the absence of concerted study of the bacteria, phytoplankton, and viruses, together with primary and secondary production, has hindered a comprehensive understanding of its microbial food web. Here, the identities, abundances, and productivity of microbes were evaluated in the context of Lake Cadagno biogeochemistry. Photo-synthetic pigments and chloroplast 16S rRNA gene phylogenies suggested high abundances of eukaryotic phytoplankton, primarily Chlorophyta, through the water column. Of these, a close relative of Ankyra judayi, a high-alpine adapted chlorophyte, peaked with oxygen in the mixolimnion, while Closteriopsis-related chlorophytes peaked in the chemocline and monimolimnion. Anoxygenic phototrophic sulfur bacteria, Chromatium, dominated the chemocline along with Lentimicrobium, a newly observed genus of known fermenters. Secondary production peaked in the chemocline, suggesting anoxygenic primary producers depended on heterotrophic nutrient remineralization. Virus-to-microbe ratios spanned an order of magnitude, peaking with high phytoplankton abundances and at a minimum at the peak of Chromatium, dynamic trends that suggest viruses may play a role in the modulation of oxygenic and anoxygenic photo- and chemosynthesis in Lake Cadagno. Through the combined analysis of bacterial, eukaryotic, viral, and biogeochemical dynamics of Lake Cadagno, this study provides a new perspective on the biological and geochemical connections that comprised the food webs of the Proterozoic ocean. IMPORTANCEAs a window to the past, the study offers insights into the role of microbial guilds of Proterozoic ocean chemoclines in the production and recycling of organic matter of sulfur- and ammonia-containing ancient oceans. The new observations described here suggest that eukaryotic algae were persistent in the low oxygen upper-chemocline in association with purple and green sulfur bacteria in the lower half of the chemocline. Further, this study provides the first insights into Lake Cadagno viral ecology. High viral abundances suggested viruses may be essential components of the chemocline where their activity may result in the release and recycling of organic matter. The framework developed in this study through the integration of diverse geochemical and biological data types lays the foundation for future studies to quantitatively resolve the processes performed by discrete populations comprising the microbial loop in this early anoxic ocean analogue.

microbiology↗

bioRxiv2021

A Snapshot of the Global Drinking Water Virome: Diversity and Metabolic Potential Vary with Residual Disinfectant Use

Viruses are important drivers of microbial community ecology and evolution, influencing microbial mortality, metabolism, and horizontal gene transfer. However, the effects of viruses remain largely unknown in many environments, including in drinking water systems. Drinking water metagenomic studies have offered a whole community perspective of bacterial impacts on water quality, but have not yet considered the influences of viruses. In this study, we address this gap by mining viral DNA sequences from publicly available drinking water metagenomes from distribution systems in six countries around the world. These datasets provide a snapshot of the taxonomic diversity and metabolic potential of the global drinking water virome; and provide an opportunity to investigate the effects of geography, climate, and drinking water treatment practices on viral diversity. Both environmental conditions and differences in sample processing were found to influence the viral composition. Using free chlorine as the residual disinfectant was associated with clear differences in viral taxonomic diversity and metabolic potential, with significantly fewer viral populations and less even viral community structures than observed in distribution systems without residual disinfectant. Additionally, drinking water viruses carry antibiotic resistance genes (ARGs), as well as genes to survive oxidative stress and nitrogen limitation. Through this study, we have demonstrated that viral communities are diverse across drinking water systems and vary with the use of residual disinfectant. Our findings offer directions for future research to develop a more robust understanding of how virus-bacteria interactions in drinking water distribution systems affect water quality.

microbiology↗

bioRxiv2021

Microbial loop of Proterozoic ocean analogue

Meromictic Lake Cadagno, an ancient ocean analogue, is known for its permanent stratification and persistent anoxygenic microbial bloom within the chemocline. Although the anaerobic microbial ecology of the lake has been extensively studied for at least 25 years, a comprehensive picture of the microbial food web linking the bacterial layer to phytoplankton and viruses, with explicit measures of primary and secondary production, is still missing. This study sought to better understand the abundances and productivity of microbes in the context of nutrient biogeochemical cycling across the stratified zones of Lake Cadagno. Photosynthetic pigments and chloroplast 16S rRNA gene phylogenies suggested the presence of eukaryotic phytoplankton through the water column. Evidence supported high abundances of Ankyra judayi, a high-alpine adapted chlorophyte, in the oxic mixolimnion where oxygenic-primary production peaked. Through the no- and low-oxygen chemocline and monimolimnion, chlorophytes related to Closteriopsis acicularis, a known genus of meromictic lakes, and Parachlorella kessleri were observed. Chromatium, anoxygenic phototrophic sulfur bacteria, dominated the chemocline along with Lentimicrobium, a genus of known fermenters whose abundance was newly reported in Lake Cadagno. Secondary production peaked in the chemocline suggesting primary producers depend on hetrotrophs for nutrient remineralization. As previously observed, sulfur reducing bacteria (SRBs), especially Desulfocapsa and Desulfobulbus, were present in the chemocline and anoxic monimolimnion. Virus-to-microbe ratios (VMR) peaked in the zone of phytoplankton, yet were at a minimum at the peak of Chromatium. These dynamic trends suggest viruses may play a role in the modulation of oxygenic and anoxygenic photo- and chemosynthesis in Lake Cadagno and other permanently stratified systems. ImportanceAs a window to the past, the study offers insights into the role of microbial guilds of Proterozoic ocean chemoclines in the production and recycling of organic matter of sulfur- and ammonia-containing ancient oceans. The new observations described here suggest that eukaryotic algae were persistent in the low oxygen upper-chemocline in association with purple and green sulfur bacteria in the lower half of the chemocline. Further, this study provides the first insights into Lake Cadagno viral ecology. High viral abundances suggested viruses may be essential components of the chemocline where their activity may result in the release and recycling of organic matter. The framework developed in this study through the integration of diverse geochemical and biological data types lays the foundation for future studies to quantitatively resolve the processes performed by discrete populations comprising the microbial loop in this early anoxic ocean analogue.

microbiology↗

bioRxiv2020

Comparison of ultrafiltration and iron chloride flocculation in the preparation of aquatic viromes from contrasting sample types

Viral metagenomes (viromes) are a valuable untargeted tool for studying viral diversity and the central roles viruses play in host disease, ecology, and evolution. Establishing effective methods to concentrate and purify viral genomes prior to sequencing is essential for high quality viromes. Using virus spike-and-recovery experiments, we stepwise compared two common approaches for virus concentration, ultrafiltration and iron chloride flocculation, across diverse matrices: wastewater influent, wastewater secondary effluent, river water, and seawater. Viral DNA was purified by removing cellular DNA via chloroform cell lysis, filtration, and enzymatic degradation of extra-viral DNA. We found that viral genomes were concentrated 1-2 orders of magnitude more with ultrafiltration than iron chloride flocculation for all matrices and resulted in higher quality DNA suitable for amplification-free and long-read sequencing. Given its widespread use and utility as an inexpensive field method for virome sampling, we nonetheless sought to optimize iron flocculation. We found viruses were best concentrated in seawater with 5-fold higher iron concentrations than the standard used, inhibition of DNase activity reduced purification effectiveness, and five-fold more iron was needed to flocculate viruses from freshwater than seawater--critical knowledge for those seeking to apply this broadly used method to freshwater virome samples. Overall, our results demonstrated that ultrafiltration and purification performed better than iron chloride flocculation and purification in the tested matrices. Given that the method performance depended on the solids content and salinity of the samples, we suggest spike-and-recovery experiments be applied when concentrating and purifying sample types that diverge from those tested here. ImportanceViruses in diverse habitats numerically dominate microbial community members and are consequential for host fate and responsible for disease outbreaks. Metagenomics has provided novel insights into their presence, diversity, and ecosystem impacts. Yet the approach is hindered by the challenge that, despite their numbers, viral genomes comprise a minor portion of total community DNA in a sample. To comprehensively describe a viral community with metagenomics, viral genomes must be effectively concentrated and purified to capture low abundance and rare viruses amidst the overwhelming presence of cellular genomic DNA. We evaluated two commonly applied methods for concentrating and purifying viral DNA, ultrafiltration and iron chloride flocculation, across four aquatic sample types that differ in their solids content and salinity. Our findings demonstrated the importance of sample characteristics for method effectivity and provided performance measures for a variety of matrices. These findings contribute a more comprehensive understanding of the impacts of virome sample preparation across diverse matrices and are thus important for guiding future virome studies, given the wide range of habitats in which viruses are studied.

microbiology↗

bioRxiv2017

Viral and Bacterial Communities of Colorectal Cancer

Human viruses (those that infect human cells) have been associated with many cancers, largely due to their mutagenic and functionally manipulative abilities. Despite this, cancer microbiome studies have almost exclusively focused on bacteria instead of viruses. We began evaluating the cancer virome by focusing on colorectal cancer, a primary cause of morbidity and mortality throughout the world, and a cancer linked to altered colonic bacterial community compositions but with an unknown association with the gut virome. We used 16S rRNA gene, whole shotgun metagenomic, and purified virus metagenomic sequencing of stool to evaluate the differences in human colorectal cancer virus and bacterial community composition. Through random forest modeling we identified differences in the healthy and colorectal cancer virome. The cancer-associated virome consisted primarily of temperate bacteriophages that were also predicted to be bacteria-virus community network hubs. These results provide foundational evidence that bacteriophage communities are associated with colorectal cancer and potentially impact cancer progression by altering the bacterial host communities.

microbiology↗

bioRxiv2017

Biogeography & Environmental Conditions Shape Phage & Bacteria Interaction Networks Across The Human Microbiome

Viruses and bacteria are critical components of the human microbiome and play important roles in health and disease. Most previous work has relied on studying bacteria and viruses independently, thereby reducing them to two separate communities. Such approaches are unable to capture how these microbial communities interact, such as through processes that maintain community robustness or allow phage-host populations to co-evolve. We implemented a network-based analytical approach to describe phage-bacteria network diversity throughout the human body. We built these community networks using a machine learning algorithm to predict which phages could infect which bacteria in a given microbiome. Our algorithm was applied to paired viral and bacterial metagenomic sequence sets from three previously published human cohorts. We organized the predicted interactions into networks that allowed us to evaluate phage-bacteria connectedness across the human body. We observed evidence that gut and skin network structures were person-specific and not conserved among cohabitating family members. High-fat diets appeared to be associated with less connected networks. Network structure differed between skin sites, with those exposed to the external environment being less connected and likely more susceptible to network degradation by microbial extinction events. This study quantified and contrasted the diversity of virome-microbiome networks across the human body and illustrated how environmental factors may influence phage-bacteria interactive dynamics. This work provides a baseline for future studies to better understand system perturbations, such as disease states, through ecological networks.\n\nAuthor SummaryThe human microbiome, the collection of microbial communities that colonize the human body, is a crucial component to health and disease. Two major components of the human microbiome are the bacterial and viral communities. These communities have primarily been studied separately using metrics of community composition and diversity. These approaches have failed to capture the complex dynamics of interacting bacteria and phage communities, which frequently share genetic information and work together to maintain ecosystem homestatsis (e.g. kill-the-winner dynamics). Removal of bacteria or phage can disrupt or even collapse those ecosystems. Relationship-based network approaches allow us to capture this interaction information. Using this network-based approach with three independent human cohorts, we were able to present an initial understanding of how phage-bacteria networks differ throughout the human body, so as to provide a baseline for future studies of how and why microbiome networks differ in disease states.

microbiology↗