Biology subjects

Shaffer, M.

Publications and source records attributed to Shaffer, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Associations Between QuantiFERON-TB Gold Plus IFNγ Concentrations and Progression to Symptomatic Tuberculosis in Global High-Burden TB Settings

IntroductionPredictive biomarkers for symptomatic tuberculosis (TB) progression would transform targeted prevention efforts. Although interferon-gamma release assays (IGRAs), including QuantiFERON(R) TB-Gold Plus (QFT-Plus), have been studied for this purpose, systematic evaluation of the QFT-Plus TB1 and TB2 Interferon-Gamma (IFN{gamma}) concentrations remains limited, particularly in high-burden TB settings. MethodsBaseline TB1 and TB2 IFN{gamma} concentrations from 5,259 participants in TB-endemic regions were analyzed in relation to subsequent TB outcomes over a median of 525 days follow-up. Participants were categorized as controls (no TB), suspected TB (no microbiological confirmation), or laboratory-confirmed TB, including a subset meeting a stringent case definition ([≥]2 positive microbiologic tests). Associations between baseline IFN{gamma} concentrations and progression to symptomatic TB were assessed. ResultsIn the full cohort (IGRA+/-participants), baseline TB2 IFN{gamma} concentrations were significantly higher compared to controls among participants who developed suspected TB (p=0.01), laboratory-confirmed TB (p=0.01), or met the stringent case definition (p<0.0001). In IGRA+ participants, baseline TB2 concentrations were significantly higher than controls in suspected (p=0.01) and laboratory-confirmed (p=0.02) groups. Associations with baseline TB1 IFN{gamma} concentrations and TB progression were observed for participants meeting the stringent case definition within the full cohort (p=0.009). Among stringent definition cases, TB2 concentrations achieved an area under the Receiver Operating Characteristic curve of 0.84, with a sensitivity of 80% and specificity of 78%. ConclusionsQuantitative IFN{gamma} concentrations from QFT-Plus, particularly TB2, were associated with progression to symptomatic TB, met or exceeded WHO-recommended sensitivity and specificity thresholds for predictive biomarkers, and may support biomarker-based stratification in TB clinical research. SummaryIn an exploratory analysis from a global tuberculosis (TB) epidemiology study, higher QuantiFERON-TB Gold Plus IFN{gamma} concentrations were associated with progression to symptomatic TB and met WHO predictive biomarker thresholds, supporting their potential value for risk stratification in TB research.

immunology↗

bioRxiv2025

Modeling cetacean eDNA distribution along the Washington coast using metabarcoding from opportunistic samples and generalized additive models

Effective cetacean conservation depends on robust monitoring, yet traditional visual and passive-acoustic surveys have constraints. We evaluated environmental DNA (eDNA) metabarcoding coupled with species distribution modeling (SDM) as a tool to study habitat use of cetaceans along the Washington State coast, USA. Seawater was collected at the surface and at a 50 m depth from 43 sites (86 samples) during the 2019 U.S.-Canada Integrated Ecosystem & Acoustic-Trawl Survey. A partial section of the mitochondrial control region was amplified with cetacean - specific primers, sequenced on an Illumina MiSeq, and taxonomically assigned with a curated reference database. Nine species were detected; we modelled the three most frequent: Pacific white-sided dolphin (Lagenorhynchus obliquidens) (10 detections), humpback whale (Megaptera novaeangliae) (8 detections), and Rissos dolphin (Grampus griseus) (6 detections). Binomial generalized additive models related presence-absence to bathymetry, distance to shore, longitude, slope, and sea-surface temperature; model performance was assessed with stratified five-fold cross-validation. SDMs explained 17-51% of null deviance and presented high specificity ([≥] 0.80). The Pacific white-sided dolphin showed the highest eDNA presence probabilities offshore, beyond the shelf break. Humpback whale eDNA presence probabilities showed hotspots along the shelf break with secondary high-probability patches in near-shore waters. Rissos dolphin eDNA presence probabilities were elevated in offshore zones characterized by steep bathymetric gradients, particularly northwest of the sampled transect. These spatial patterns are consistent with historical visual-acoustic records, suggesting that eDNA-informed SDMs can capture cetacean habitat use. This proof of concept indicates that combining eDNA detections with flexible SDMs could provide a cost-effective, non-invasive complement to conventional surveys and may offer a scalable pathway for marine-mammal monitoring and spatial planning.

genomics↗

bioRxiv2025

Estimating Organism Abundance Using Within-Sample Haplotype Frequencies of eDNA Metabarcoding Data

Environmental DNA (eDNA) metabarcoding provides powerful insights into species presence and community composition, but remains limited in its ability to quantify species abundance or structure. Here, we show that deviation between observed haplotype frequencies within a given sample and the population haplotype frequencies can be used to infer the number of individual contributors to an eDNA sample. We also lay out the theory for how population haplotype frequencies can be approximated from eDNA data alone, enabling broad applicability even in the absence of tissue-based references. We then present an estimator to derive the number of individual contributors to a given eDNA sample and validate its performance using simulations with variable allele frequencies and noise. Our framework demonstrates that differences between expected and observed frequencies carry meaningful biological information in eDNA data. Our results show that the number of contributors can be recovered under a range of conditions, particularly with hypervariable markers and sufficient sampling. This approach complements existing molecular methods and opens a new avenue for inferring abundance from eDNA metabarcoding datasets.

molecular biology↗

bioRxiv2025

Differential Decay of Multiple eNA Components from a Cetacean

Environmental nucleic acids (eNA), such as DNA and RNA, are powerful tools for monitoring biodiversity. Yet, interpreting eNA detections requires understanding of their environmental persistence. We conducted a decay experiment using seawater from an open enclosure to track degradation of six eNA components derived from Tursiops truncatus: mitochondrial eDNA of varying lengths, ribosomal eRNA, and messenger eRNA. Targets were quantified over seven days via digital droplet PCR (ddPCR). Decay followed a biphasic exponential model with rapid initial loss ([~]24 hours at 15 {degrees}C), followed by slower degradation. Cytb messenger eRNA was least stable, disappearing within four hours. Ribosomal eRNA persisted longer but degraded slightly faster than its eDNA counterpart ({lambda} = 0.236 vs. 0.165 hr-{superscript 1}). Longest eDNA fragments decayed more rapidly ({lambda} = 0.190 hr-1) than shorter ones ({lambda} = 0.114 hr-1). These findings support the use of eDNA fragment length as a proxy for degradation state and reinforce that combining multiple eNA components with distinct stabilities can potentially provide a molecular clock for inferring eNA age. This approach improves the spatiotemporal resolution of eNA-based monitoring, particularly for rare marine-mammal that act as point sources. We also emphasize the importance of explicitly distinguishing between RNA types (ribosomal vs. messenger) in environmental studies, given their divergent stability and interpretability.

molecular biology↗

bioRxiv2024

Time resolved multi-omics reveals diverse metabolic strategies of Salmonella during diet-induced inflammation

With a rise in antibiotic resistance and chronic infection, the metabolic response of Salmonella enterica serovar Typhimurium to various dietary conditions over time remains an understudied avenue for novel, targeted therapeutics. Elucidating how enteric pathogens respond to dietary variation not only helps us decipher the metabolic strategies leveraged for expansion but also assists in proposing targets for therapeutic interventions. Here, we use a multi-omics approach to identify the metabolic response of Salmonella enterica serovar Typhimurium in mice on both a fibrous diet and high-fat diet over time. When comparing Salmonella gene expression between diets, we found a preferential use of respiratory electron acceptors consistent with increased inflammation of the high-fat diet mice. Looking at the high-fat diet over the course of infection, we noticed heterogeneity of samples based on Salmonella ribosomal activity, which separated into three infection phases: early, peak, and late. We identified key respiratory, carbon, and pathogenesis gene expression descriptive of each phase. Surprisingly, we identified genes associated with host-cell entry expressed throughout infection, suggesting sub-populations of Salmonella or stress-induced dysregulation. Collectively, these results highlight not only the sensitivity of Salmonella to its environment but also identify phase-specific genes that may be used as therapeutic targets to reduce infection. ImportanceIdentifying novel therapeutic strategies for Salmonella infection that occur in relevant diets and over time is needed with the rise of antibiotic resistance and global shifts towards Western diets that are high in fat and low in fiber. Mice on a high-fat diet are more inflamed compared to those on a fibrous diet, creating an environment that results in more favorable energy generation for Salmonella. Over time on a high-fat diet, we observed differential gene expression across infection phases. Together, these findings reveal the metabolic tuning of Salmonella to dietary and temporal perturbations. Research like this, exploring the dimensions of pathogen metabolic plasticity, can pave the way for rationally designed strategies to control disease.

microbiology↗

bioRxiv2024

Gut microbiome carbon and sulfur metabolisms support Salmonella during pathogen infection

Salmonella enterica serovar Typhimurium is a pervasive enteric pathogen and an ongoing global threat to public health. Ecological studies in the Salmonella impacted gut remain underrepresented in the literature, discounting the microbiome mediated interactions that may inform Salmonella physiology during colonization and infection. To understand the microbial ecology of Salmonella remodeling of the gut microbiome, here we performed multi-omics approaches on fecal microbial communities from untreated and Salmonella-infected mice. Reconstructed genomes recruited metatranscriptomic and metabolomic data providing a strain-resolved view of the expressed metabolisms of the microbiome during Salmonella infection. This data informed possible Salmonella interactions with members of the gut microbiome that were previously uncharacterized. Salmonella-induced inflammation significantly reduced the diversity of transcriptionally active members in the gut microbiome, yet increased gene expression was detected for 7 members, with Luxibacter and Ligilactobacillus being the most active. Metatranscriptomic insights from Salmonella and other persistent taxa in the inflamed microbiome further expounded the necessity for oxidative tolerance mechanisms to endure the host inflammatory responses to infection. In the inflamed gut lactate was a key metabolite, with microbiota production and consumption reported amongst transcriptionally active members. We also showed that organic sulfur sources could be converted by gut microbiota to yield inorganic sulfur pools that become oxidized in the inflamed gut, resulting in thiosulfate and tetrathionate that supports Salmonella respiration. Advancement of pathobiome understanding beyond inferences from prior amplicon-based approaches can hold promise for infection mitigation, with the active community outlined here offering intriguing organismal and metabolic therapeutic targets.

genomics↗

bioRxiv2023

Heat Inactivation of Aqueous Viable Norovirus and MS2 Bacteriophage

Human norovirus is a leading cause of acute gastroenteritis often associated with contaminated food or water exposure. Many studies have used morphologically similar viruses, such as MS2 bacteriophage, and molecular detection methods to study the environmental fate and inactivation characteristics, given the historical challenges to culture human norovirus. In this study, we used human intestinal enteroids (HIEs) to analyze the heat inactivation kinetics of viable human norovirus compared to the surrogate MS2 bacteriophage. Norovirus decay rates were 0.22 min-1, 0.68 min-1, and 1.11 min-1 for 50{degrees}C, 60{degrees}C, and 70{degrees}C, respectively, and MS2 bacteriophage decay rates were 0.0065 min-1, 0.045 min-1, and 0.16 min-1 for 50{degrees}C, 60{degrees}C, and 70{degrees}C, respectively. Norovirus and MS2 bacteriophage had significantly different decay rates at all tested temperatures (p = 0.002 - 0.007). No decrease of RNA titers as measured by realtime RT-PCR for both human norovirus and MS2 bacteriophage over time was observed, indicating molecular methods do not accurately depict viable human norovirus after heat inactivation. Overall, our data demonstrate that MS2 bacteriophage is a conservative surrogate to measure heat inactivation and potentially overestimates the infectious risk of norovirus. IMPORTANCENorovirus is the leading cause of epidemic and endemic acute gastroenteritis worldwide. Treatments to inactivate norovirus are critical to reducing the risk associated with contaminated food and water. The recent developments to replicate human norovirus in human intestinal enteroids (HIE) enables the evaluation of heat inactivation kinetics of viable norovirus. Historically, cultivable surrogate viruses, such as bacteriophage MS2, have been used to measure the environmental fate of human norovirus. Our findings indicate that compared to human norovirus, MS2 bacteriophage is a conservative surrogate to measure the effect of heat inactivation. Furthermore, this study corroborates that measuring viral RNA titers, as evaluated by PCR methods, does not correlate with persistence of viable norovirus.

microbiology↗

bioRxiv2023

Environmental Persistence and Disinfection of Lassa Virus

Lassa Fever, caused by Lassa virus (LASV), is endemic to West Africa, where approximately 300,000 illnesses and 5,000 deaths occur annually. LASV is primarily spread by infected multimammate rats via urine and fomites, highlighting the importance of understanding the environmental fate of LASV. This study evaluated the persistence of LASV strains on surfaces in aqueous solutions and with sodium hypochlorite disinfection. LASV strains (Josiah and Sauerwald) were more stable in DI water (k = 0.23 and 0.34 days-1) than primary influent wastewater (k = 1.3 and 1.9 days-1). The decay rates of LASV on HDPE (k = 4.3 and 2.3 days-1) and Stainless Steel (k = 5.3 and 2.7 days-1) were not significantly different for either strain. Sodium hypochlorite was highly effective at inactivating both strains of LASV. This work presents data for the environmental persistence of LASV to inform future risk assessment and management efforts.

microbiology↗

bioRxiv2023

Spatial and temporal metagenomics of river compartments reveals viral community dynamics in an urban impacted stream

Although river ecosystems comprise less than 1% of Earths total non-glaciated area, they are critical modulators of microbially and virally orchestrated global biogeochemical cycles. However, most studies either use data that is not spatially resolved or is collected at timepoints that do not reflect the short life cycles of microorganisms. As a result, the relevance of microbiome interactions and the impacts they have over time on biogeochemical cycles are poorly understood. To assess how viral and microbial communities change over time, we sampled surface water and pore water compartments of the wastewater-impacted River Erpe in Germany every 3 hours over a 48-hour period resulting in 32 metagenomes paired to geochemical and metabolite measurements. We reconstructed 6,500 viral and 1,033 microbial genomes and found distinct communities associated with each river compartment. We show that 17% of our vMAGs clustered to viruses from other ecosystems like wastewater treatment plants and rivers. Our results also indicated that 70% of the viral community was persistent in surface waters, whereas only 13% were persistent in the pore waters taken from the hyporheic zone. Finally, we predicted linkages between 73 viral genomes and 38 microbial genomes. These putatively linked hosts included members of the Competibacteraceae, which we suggest are potential contributors to carbon and nitrogen cycling. Together, these findings demonstrate that microbial and viral communities in surface waters of this urban river can exist as stable communities along a flowing river; and raise important considerations for ecosystem models attempting to constrain dynamics of river biogeochemical cycles.

microbiology↗

bioRxiv2023

Human-gut phages harbor sporulation genes

Spore-forming bacteria are prevalent in mammalian guts and have implications for host health and nutrition. The production of dormant spores is thought to play an important role in the colonization, persistence, and transmission of these bacteria. Spore formation also modifies interactions among microorganisms such as infection by phages. Recent studies suggest that phages may counter dormancy-mediated defense through the expression of phage-encoded sporulation genes during infection, which can alter the transitions between active and inactive states. By mining genomes and gut-derived metagenomes, we identified sporulation genes that are preferentially encoded by phages that infect spore-forming bacteria. These included genes involved in chromosome partitioning, DNA damage repair, and cell wall-associated functions. In addition, phages contained homologs of sporulation-specific transcription factors, notably spo0A, the master regulator of sporulation, which could allow phages to control the complex genetic network responsible for spore development. Our findings suggest that phages could influence the formation of bacterial spores with implications for the health of the human gut microbiome, as well as bacterial communities in other environments. SIGNIFICANCEPhages acquire bacterial genes and use them to alter host metabolism in ways that enhance their fitness. To date, most auxiliary genes replace or modulate enzymes that are used by the host for nutrition or energy production. However, phage fitness is affected by all aspects of host physiology, including decisions that reduce metabolic activity of the cell. Here we focus on endosporulation, a complex and ancient form of dormancy found among the Bacillota that involves hundreds of genes. By coupling homology searches with host classification, we identify 31 phage-encoded homologs of sporulation genes that are mostly limited to phages infecting spore-forming bacteria. Nearly one-third the homologs recovered were regulatory genes suggesting that phages may manipulate host genetic networks by tapping into their control elements. Our findings also suggest a mechanism by which phages can overcome the defensive strategy of dormancy, which may be involved in coevolutionary dynamics of spore-forming bacteria.

microbiology↗

bioRxiv2022

Non-invasive real-time genomic monitoring of the critically endangered kakapo

We used non-invasive real-time genomic approaches to monitor one of the last surviving populations of the critically endangered k[a]k[a]p[o] (Strigops habroptilus). We first established an environmental DNA metabarcoding protocol to identify the distribution of k[a]k[a]p[o] and other vertebrate species in a highly localized manner using soil samples. Harnessing real-time nanopore sequencing and the high-quality k[a]k[a]p[o] reference genome, we then extracted species-specific DNA from soil. We combined long read-based haplotype phasing with known individual genomic variation in the k[a]k[a]p[o] population to identify the presence of individuals, and confirmed these genomically informed predictions through detailed metadata on k[a]k[a]p[o] distributions. This study shows that individual identification is feasible through nanopore sequencing of environmental DNA, with important implications for future efforts in the application of genomics to the conservation of rare species, potentially expanding the application of real-time environmental DNA research from monitoring species distribution to inferring fitness parameters such as genomic diversity and inbreeding.

genomics↗

bioRxiv2022

Exposing New Taxonomic Variation with Inflammation-A Murine Model-Specific Genome Database for Gut Microbiome Researchers

BackgroundThe murine CBA/J mouse model widely supports immunology and enteric pathogen research. This model has illuminated Salmonella interactions with the gut microbiome since pathogen proliferation does not require disruptive pretreatment of the native microbiota, nor does it become systemic, thereby representing an analog to gastroenteritis disease progression in humans. Despite the value to broad research communities, microbiota in CBA/J mice are not represented in current murine microbiome genome catalogs. ResultsHere we present the first microbial and viral genomic catalog of the CBA/J murine gut microbiome. Using fecal microbial communities from untreated and Salmonella-infected, highly inflamed mice, we performed genomic reconstruction to determine the impacts on gut microbiome membership and functional potential. From high depth whole community sequencing (~42.4 Gbps/sample), we reconstructed 2,281 bacterial and 4,129 viral draft genomes. Salmonella challenge significantly altered gut membership in CBA/J mice, revealing 30 genera and 98 species that were conditionally rare and unsampled in non-inflamed mice. Additionally, inflamed communities were depleted in microbial genes that modulate host anti-inflammatory pathways and enriched in genes for respiratory energy generation. Our findings suggest decreases in butyrate concentrations during Salmonella infection corresponded to reductions in the relative abundance in members of the Alistipes. Strain-level comparison of CBA/J microbial genomes to prominent murine gut microbiome databases identified newly sampled lineages in this resource, while comparisons to human gut microbiomes extended the host relevance of dominant CBA/J inflammation resistant strains. ConclusionsThis CBA/J microbiome database provides the first genomic sampling of relevant, uncultivated microorganisms within the gut from this widely used laboratory model. Using this resource, we curated a functional, strain-resolved view on how Salmonella remodels intact murine gut communities, advancing pathobiome understanding beyond inferences from prior amplicon-based approaches. Salmonella-induced inflammation suppressed Alistipes and other dominant members, while rarer commensals like Lactobacillus and Enterococcus endure. The rare and novel species sampled across this inflammation gradient advance the utility of this microbiome resource to benefit the broad research needs of the CBA/J scientific community, and those using murine models for understanding the impact of inflammation on the gut microbiome more generally.

microbiology↗

bioRxiv2022

Targeted curation of the gut microbial gene content modulating human cardiovascular disease

Despite the promise of the gut microbiome to forecast human health, few studies expose the microbial functions underpinning such predictions. To comprehensively inventory gut microorganisms and their gene content that control trimethylamine induced cardiovascular disease, we mined over 200,000 gut-derived genomes from cultivated and uncultivated microbial lineages. Creating MAGICdb (Methylated Amine Gene Inventory of Catabolism database), we designated an atherosclerotic profile for 6,341 microbial genomes that encoded metabolisms associated with heart disease. We used MAGICdb to evaluate diverse human fecal metatranscriptome and metaproteome datasets, demonstrating how this resource eases the recovery of methylated amine gene content previously obscured in microbiome datasets. From the feces of healthy and diseased subjects, we show MAGICdb gene markers predicted cardiovascular disease as effectively as traditional blood diagnostics. This functional microbiome catalog is a public, exploitable resource, enabling a new era of microbiota-based therapeutics.

genomics↗

bioRxiv2022

Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments

Rivers have a significant role in global carbon and nitrogen cycles, serving as a nexus for nutrient transport between terrestrial and marine ecosystems. Although rivers have a small global surface area, they contribute substantially to global greenhouse gas emissions through microbially mediated processes within the river hyporheic zone. Despite this importance, microbial roles in these climatically relevant systems are mostly inferred from 16S rRNA amplicon surveys, which are not sufficiently resolved to inform biogeochemical models. To survey the metabolic potential and gene expression underpinning carbon and nitrogen biogeochemical cycling in river sediments, we collected an integrated dataset of over 30 metagenomes, metaproteomes, and paired metabolomes. We reconstructed over 500 microbial metagenome assembled genomes (MAGs), which we dereplicated into 55 unique genomes spanning 12 bacterial and archaeal phyla. We also reconstructed 2482 viral genomic contigs, which were dereplicated into 111 viral MAGs >10kb in size. As a result of integrating gene expression data with geochemical and metabolite data, we created a conceptual model that uncovers new roles for microorganisms in organic matter decomposition, carbon sequestration, nitrogen mineralization, nitrification, and denitrification. Integrated through shared resource pools of ammonium, carbon dioxide, and inorganic nitrogen we show how these metabolic pathways could ultimately contribute to carbon dioxide and nitrous oxide fluxes from hyporheic sediments. Further, by linking viral genomes to these active microbial hosts, we provide some of the first insights into viral modulation of river sediment carbon and nitrogen cycling. ImportanceHere we created HUM-V (Hyporheic Uncultured Microbial and Viral), an annotated microbial and viral genome catalog that captures the strain and functional diversity encoded in river sediments. Demonstrating its utility, this genomic inventory encompasses multiple representatives of the most dominant microbial and archaeal phyla reported in river sediments and provides novel viral genomes that can putatively infect these. Furthermore, we used HUM-V to recruit gene expression data to decipher the functional activities of these genomes and reconstruct their active roles in river sediment biogeochemical cycling. We show the power of genome resolved, multi-omics to uncover the organismal interactions and chemical handoffs shaping an intertwined carbon and nitrogen metabolic network and create a framework that can be extended to other river sediments. The accessible microbial and viral genomes in HUM-V will serve as a community resource to further advance more untargeted, activity-based measurements in these and related freshwater terrestrial-aquatic ecosystems.

microbiology↗

bioRxiv2022

kb_DRAM: Annotating and functional profiling of genomes with DRAM in KBase

SummaryAnnotation is predicting the location of and assigning function to genes in a genome. DRAM is a tool developed to annotate bacterial, archaeal and viral genomes derived from pure cultures or metagenomes. DRAM distills multiple gene annotations to summaries of functional potential. Despite these benefits, a downside of DRAM is processing requires large computation resources, which limits its accessibility, and it did not integrate with downstream metabolic modelling tools. To alleviate these constraints, DRAM and the viral counterpart, DRAM-v, are now available and integrated in the freely accessible KBase cyberinfrastructure. With kb_DRAM users can generate DRAM annotations and functional summaries from microbial or viral genomes in a point and click interface, as well as generate genome scale metabolic models from these DRAM annotations. Availability and ImplementationThe kb_DRAM software is available at https://github.com/shafferm/kb_DRAM. The kb_DRAM apps on KBase can be found in the catalog at https://narrative.kbase.us/#catalog/modules/kb_DRAM. A narrative with examples of running all KBase apps is available at https://kbase.us/n/88325/84/. ContactMichael Shaffer, michael.t.shaffer@colostate.edu; Kelly Wrighton, kelly.wrighton@colostate.edu Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2021

ORT: A workflow linking genome-scale metabolic models with reactive transport codes

MotivationNutrient and contaminant behavior in the subsurface are governed by multiple coupled hydrobiogeochemical processes which occur across different temporal and spatial scales. Accurate description of macroscopic system behavior requires accounting for the effects of microscopic and especially microbial processes. Microbial processes mediate precipitation and dissolution and change aqueous geochemistry, all of which impacts macroscopic system behavior. As omics data describing microbial processes is increasingly affordable and available, novel methods for using this data quickly and effectively for improved ecosystem models are needed. ResultsWe propose a workflow ( Omics to Reactive Transport - ORT) for utilizing metagenomic and environmental data to describe the effect of microbiological processes in macroscopic reactive transport models. This workflow utilizes and couples two open-source software packages: KBase (a software platform for systems biology) and PFLOTRAN (a reactive transport modeling code). We describe the architecture of ORT and demonstrate an implementation using metagenomic and geochemical data from a river system. Our demonstration uses microbiological drivers of nitrification and denitrification to predict nitrogen cycling patterns which agree with those provided with generalized stoichiometries. While our example uses data from a single measurement, our workflow can be applied to spatiotemporal metagenomic datasets to allow for iterative coupling between KBASE and PFLOTRAN. Availability and ImplementationInteractive models available at https://pflotranmodeling.paf.subsurfaceinsights.com/pflotran-simple-model/. Microbiological data available at NCBI via BioProject ID PRJNA576070. ORT Python code available at https://github.com/subsurfaceinsights/ort-kbase-to-pflotran. KBase narrative available at https://narrative.kbase.us/narrative/71260 or static narrative (no login required) at https://kbase.us/n/71260/258 Contactrebecca.rubinstein@subsurfaceinsights.com or roelof.versteeg@subsurfaceinsights.com Supplementary informationSupplementary data are available online.

bioinformatics↗

bioRxiv2020

SCNIC: Sparse Correlation Network Investigation for Compositional Data

BackgroundMicrobiome studies are often limited by a lack of statistical power due to small sample sizes and a large number of features. This problem is exacerbated in correlative studies of multi-omic datasets. Statistical power can be increased by finding and summarizing modules of correlated observations, which is one dimensionality reduction method. Additionally, modules provide biological insight as correlated groups of microbes can have relationships among themselves. ResultsTo address these challenges, we developed SCNIC: Sparse Cooccurrence Network Investigation for compositional data. SCNIC is open-source software that can generate correlation networks and detect and summarize modules of highly correlated features. Modules can be formed using either the Louvain Modularity Maximization (LMM) algorithm or a Shared Minimum Distance algorithm (SMD) that we newly describe here and relate to LMM using simulated data. We applied SCNIC to two published datasets and we achieved increased statistical power and identified microbes that not only differed across groups, but also correlated strongly with each other, suggesting shared environmental drivers or cooperative relationships among them. ConclusionsSCNIC provides an easy way to generate correlation networks, identify modules of correlated features and summarize them for downstream statistical analysis. Although SCNIC was designed considering properties of microbiome data, such as compositionality and sparsity, it can be applied to a variety of data types including metabolomics data and used to integrate multiple data types. SCNIC allows for the identification of functional microbial relationships at scale while increasing statistical power through feature reduction.

bioinformatics↗

bioRxiv2020

DRAM for distilling microbial metabolism to automate the curation of microbiome function

Microbial and viral communities transform the chemistry of Earths ecosystems, yet the specific reactions catalyzed by these biological engines are hard to decode due to the absence of a scalable, metabolically resolved, annotation software. Here, we present DRAM (Distilled and Refined Annotation of Metabolism), a framework to translate the deluge of microbiome-based genomic information into a catalog of microbial traits. To demonstrate the applicability of DRAM across metabolically diverse genomes, we evaluated DRAM performance on a defined, in silico soil community and previously published human gut metagenomes. We show that DRAM accurately assigned microbial contributions to geochemical cycles, and automated the partitioning of gut microbial carbohydrate metabolism at substrate levels. DRAM-v, the viral mode of DRAM, established rules to identify virally-encoded auxiliary metabolic genes (AMGs), resulting in the metabolic categorization of thousands of putative AMGs from soils and guts. Together DRAM and DRAM-v provide critical metabolic profiling capabilities that decipher mechanisms underpinning microbiome function.

microbiology↗