Biology subjects

Treangen, T.

Publications and source records attributed to Treangen, T..

12 recordsLinked to original sources

bioRxiv2026

Microfluidic Capture-Enrichment of individualized microbes from urban wastewater reveals a hidden reservoir of potential pre-emergent pathogens

Current environmental biosurveillance infrastructure excels at tracking known biological threats but is limited in its ability to proactively identify potential pre-emergent pathogens (PEPs). To address this gap, we developed a microfluidics-based Capture-Enrichment pipeline that isolates individual environmental cells via microdroplet encapsulation, preserving community biodiversity while enabling iterative selection against human sera. When applied to urban wastewater, this approach successfully recovered pathogens, commensals, and PEPs that fell below the detection limits of conventional amplicon sequencing. Genomic analysis revealed that wastewater-derived ESKAPE pathogens cluster closely with clinical isolates, showing that the pipeline captures clinically relevant threats. Furthermore, isolated PEPs exhibited known and emerging phenotypes associated with early-stage pathogenesis and antimicrobial resistance. These findings demonstrate that targeting PEPs can fundamentally expand the scope and quality of biosurveillance by providing a scalable strategy to identify, characterize, and close important gaps in our understanding of emerging microbial pathogens.

microbiology↗

bioRxiv2026

Kente: A Graph-based Pangenomic Approach for Horizontal Gene Transfer Detection in Microbiomes

MotivationHorizontal gene transfer (HGT) shapes bacterial evolution and microbial ecosystems, yet detecting HGT within microbiomes remains a challenge due to fragmented metagenomic assemblies, reference bias, reliance on gene boundaries, and limited ability to model structural mosaicism and patterns across genomes. MethodsWe present Kente, a novel pangenome graph-based framework designed for HGT detection that aligns metagenomic assembly contigs to a curated database of >600 genus-level bacterial pangenome graphs constructed using minigraph. Kente infers local taxonomic composition along contigs using alignment evidence and classifies candidate transfers using structured clade-transition topologies (e.g., A-B-A sandwich, open tips, and mosaic patterns). A complementary intra-genus module detects inter-species transfers within a single genus graph using segment-level clade annotations. ResultsAcross simulated intra- and inter-genus transfer scenarios, Kente achieves higher precision and comparable recall relative to existing gene-centric microbiome HGT detection approaches while reducing false positives from fragmented assemblies. Application to real human gut metagenomes (HMP2, n = 26) demonstrates Kentes ability to detect candidate cross-lineage transfer regions in complex microbial communities. Runtime profiling shows near-linear scaling with input size, enabling efficient analysis of large metagenomic assemblies. Availability and Implementationhttps://github.com/treangenlab/Kente

bioinformatics↗

bioRxiv2026

Leveraging spectrum of graph sheaf Laplacian as a genome-architecture-aware measure of microbiome diversity

MotivationMeasures of microbial diversity that can be derived directly from metagenomic sequencing data offer a valuable summary view of the underlying complex systems. Prior work has shown that both taxonomic composition and abundances that are captured by standard diversity measures (e.g., Shannon entropy), and structural variation within the metagenome due to gene duplications, losses and horizontal transfers (HGT), can correlate with the hosts health. However, there are no diversity measures available that simultaneously account for the genome architecture and taxonomic composition within the sample. Thus, in this work we propose the spectral energy of a graph sheaf Laplacian as such a measure, and justify its applicability through a simulation study and analysis of biological data. ResultsFirst, we describe a theoretical framework that allows us to combine the features of genome graphs with the taxonomic data. Then, we explore the sensitivity of the proposed diversity measure to genome rearrangements and HGT events in a simulation study. Finally, we explore applicability of our proposed measure to characterization of diversity of human gut metagenomes. We find our proposed measure to offer better discrimination between healthy controls and inflammatory bowel disease (IBD) patients samples (n = 403) in the cohorts analyzed. Availability and Implementationhttps://github.com/nsapoval/bd-gsl

bioinformatics↗

bioRxiv2025

tMHG-Finder: Tree-guided Maximal Homologous Group Finder for Bacterial Genomes

A maximal homologous group, or MHG, as a group of sequences with a shared evolutionary ancestry, shifts the focus from a genecentric view to a homology-centric view in comparative genomic studies. Each MHG is formed by identifying and grouping all homologous sequences, which ensures that evolutionary events, such as horizontal gene transfer, gene duplication and loss, or de novo sequence evolution, are encapsulated within the same MHG. However, the current MHG computation tool, MHG-Finder, faces challenges in scalability to handle large datasets and lacks the ability to provide detailed insights into intermediate MHGs involving subsets of input genomes. We present tMHG-Finder (https://github.com/yongze-yin/tMHG-Finder), a new method that improves our previous method, MHG-Finder, by utilizing a guide tree to significantly improve scalability and provide more informative biological results. We also introduce a new measure, fractionalization (available at https://github.com/yongze-yin/Fract-Calculator), to assess the accuracy of delineated MHGs compared to ground truth data. Our results show that tMHG-Finder scales linearly with the number of taxa, requiring a small fraction of the computational time of MHG-Finder. Furthermore, according to the fractionalization measure, tMHG-Finder outperforms four state-of-the-art whole-genome aligners on simulated data. Applying tMHG-Finder to a phylum of extreme-environment-resistant bacteria, we validated our results through the encapsulation of 16S rRNA sequences within MHGs. We further investigated how evolutionary rates change with phylogenetic distance and explored the functional roles of genes captured by conserved MHGs, demonstrating the broader utility of tMHG-Finder in uncovering evolutionary insights beyond MHG delineation and phylogenetic relationships.

evolutionary biology↗

bioRxiv2024

A near-tight lower bound on the density of forward sampling schemes

MotivationSampling k-mers is a ubiquitous task in sequence analysis algorithms. Sampling schemes such as the often-used random minimizer scheme are particularly appealing as they guarantee at least one k-mer is selected out of every w consecutive k-mers. Sampling fewer k-mers often leads to an increase in efficiency of downstream methods. Thus, developing schemes that have low density, i.e., have a small proportion of sampled k-mers, is an active area of research. After over a decade of consistent efforts in both decreasing the density of practical schemes and increasing the lower bound on the best possible density, there is still a large gap between the two. ResultsWe prove a near-tight lower bound on the density of forward sampling schemes, a class of schemes that generalizes minimizer schemes. For small w and k, we observe that our bound is tight when k {equiv} 1 (mod w). For large w and k, the bound can be approximated by [Formula]. Importantly, our lower bound implies that existing schemes are much closer to achieving optimal density than previously known. For example, with the current default minimap2 HiFi settings w = 19 and k = 19, we show that the best known scheme for these parameters, the double decycling-set-based minimizer of Pellow et al., is at most 3% denser than optimal, compared to the previous gap of at most 50%. Furthermore, when k {equiv} 1 (mod w) and the alphabet size{sigma} goes to {infty}, we show that mod-minimizers introduced by Groot Koerkamp and Pibiri achieve optimal density matching our lower bound. Availability and implementationMinimizer implementations: github.com/RagnarGrootKoerkamp/minimizers ILP and analysis: github.com/treangenlab/sampling-scheme-analysis

bioinformatics↗

bioRxiv2024

Antibiotic treatment induces microbiome dysbiosis and reduction of neuroinflammation following traumatic brain injury in mice.

Traumatic brain injury (TBI) induces both neuroinflammation and gut microbiome dysbiosis, yet the influence of antibiotics (ABX) on TBI-related neuropathology remains unclear. We administered a broad-spectrum oral ABX regimen to deplete the gut microbiome in single and repeated TBI mouse models. In male mice, ABX treatment significantly reduced neuroinflammation and neurodegeneration post-TBI, with no effects observed in uninjured controls. ABX also altered microbiome composition and decreased serum and fecal short-chain fatty acid levels, while intestinal damage and dysbiosis were further exacerbated by TBI severity. Notably, germ-free male mice exhibited heightened neuroinflammation and larger lesion volumes following TBI, underscoring the microbiomes essential role in recovery. Metagenomic analyses revealed Parasutterella excrementihominis and Lactobacillus johnsonii as potential ABX-resistant taxa post-injury. These findings suggest that short-term ABX treatment may attenuate TBI-induced neuroinflammation by reshaping the gut microbiome, offering directions for microbiome-targeted therapies in TBI.

neuroscience↗

bioRxiv2024

Probiotic treatment causes sex-specific neuroprotection after traumatic brain injury in mice

BackgroundRecent studies have shed light on the potential role of gut dysbiosis in shaping traumatic brain injury (TBI) outcomes. Changes in the levels and types of Lactobacillus bacteria present might impact the immune system disturbances, neuroinflammatory responses, anxiety and depressive-like behaviors, and compromised neuroprotection mechanisms triggered by TBI. ObjectiveThis study aimed to investigate the effects of a daily pan-probiotic (PP) mixture in drinking water containing strains of Lactobacillus plantarum, L. reuteri, L. helveticus, L. fermentum, L. rhamnosus, L. gasseri, and L. casei, administered for either two or seven weeks before inducing TBI on both male and female mice. MethodsMice were subjected to controlled cortical impact (CCI) injury. Short-chain fatty acids (SCFAs) analysis was performed for metabolite measurements. The taxonomic profiles of murine fecal samples were evaluated using 16S rRNA V1-V3 sequencing analysis. Histological analyses were used to assess neuroinflammation and gut changes post-TBI, while behavioral tests were conducted to evaluate sensorimotor and cognitive functions. ResultsOur findings suggest that PP administration modulates the diversity and composition of the microbiome and increases the levels of SCFAs in a sex-dependent manner. We also observed a reduction of lesion volume, cell death, and microglial and macrophage activation after PP treatment following TBI in male mice. Furthermore, PP-treated mice show motor function improvements and decreases in anxiety and depressive-like behaviors. ConclusionOur findings suggest that PP administration can mitigate neuroinflammation and ameliorate motor and anxiety and depressive-like behavior deficits following TBI. These results underscore the potential of probiotic interventions as a viable therapeutic strategy to address TBI-induced impairments, emphasizing the need for gender-specific treatment approaches.

neuroscience↗

bioRxiv2023

Minmers are a generalization of minimizers that enable unbiased local Jaccard estimation

1AbstractsO_ST_ABSMotivationC_ST_ABSThe Jaccard similarity on k-mer sets has shown to be a convenient proxy for sequence identity. By avoiding expensive base-level alignments and comparing reduced sequence representations, tools such as MashMap can scale to massive numbers of pairwise comparisons while still providing useful similarity estimates. However, due to their reliance on minimizer winnowing, previous versions of MashMap were shown to be biased and inconsistent estimators of Jaccard similarity. This directly impacts downstream tools that rely on the accuracy of these estimates. ResultsTo address this, we propose the minmer winnowing scheme, which generalizes the minimizer scheme by use of a rolling minhash with multiple sampled k-mers per window. We show both theoretically and empirically that minmers yield an unbiased estimator of local Jaccard similarity, and we implement this scheme in an updated version of MashMap. The minmer-based implementation is over 10 times faster than the minimizer-based version under the default ANI threshold, making it well-suited for large-scale comparative genomics applications. AvailabilityMashMap3 is available at https://github.com/marbl/MashMap Contactblk6@rice.edu, adam.phillippy@nih.gov

bioinformatics↗

bioRxiv2022

Systematic Analysis of Mobile Genetic Elements Mediating β-lactamase Gene Amplification in Non-Carbapenemase-Producing Carbapenem Resistant Enterobacterales Bloodstream Infections

Non-carbapenemase-producing carbapenem resistant Enterobacterales (non-CP-CRE) are increasingly recognized as important contributors to prevalent carbapenem resistant Enterobacterales (CRE) infections. However, there is limited understanding of mechanisms underlying non-CP-CRE causing invasive disease. Long- and short-read whole genome sequencing (WGS) was used to elucidate carbapenem non-susceptibility determinants in Enterobacterales bloodstream isolates at MD Anderson Cancer Center in Houston, Texas. We investigated carbapenem non-susceptible Enterobacterales (CNSE) mechanisms through a combination of phylogenetic analysis, antimicrobial resistant (AMR) gene detection/copy number quantification, porin assessment, and mobile genetic element (MGE) characterization. Most CNSE isolates sequenced were non-CP-CRE (41/79; 51.9%) whereas 25.3% (20/79) were carbapenem intermediate Enterobacterales (CIE) and 22.8% (18/79) were carbapenemase producing Enterobacterales (CPE). Statistically significant copy number variants (CNVs) of extended-spectrum {beta}-lactamase (ESBL) genes (Wilcoxon Test; p-value < 0.001) were present in both non-CP-CR E. coli (median CNV = 2.6X; n= 17) and K. pneumoniae (median CNV = 3.2X, n = 17). All non-CP-CR E. coli and K. pneumoniae had predicted reduced expression of at least one outer membrane porin gene (i.e., ompC/ompF or ompK36/ompK35). Completely resolved CNSE genomes revealed that IS26 and ISEcp1 structures harboring blaCTX-M variants along with other AMR elements were the primary drivers of gene amplification, occurring in mostly IncFIB/IncFII plasmid contexts. MGE mediated {beta}-lactamase gene amplifications resulted in either tandem arrays, primarily mediated by IS26 translocatable units, or segmental duplication, typically due to ISEcp1 transposition units. Non-CP-CRE strains were the most prevalent cause of CRE bacteremia with carbapenem non-susceptibility driven by concurrent porin loss and MGE-mediated amplification of blaCTX-M genes. IMPORTANCECarbapenem resistant Enterobacterales (CRE) are considered urgent antimicrobial resistance (AMR) threats. The vast majority of CRE research has focused on carbapenemase producing Enterobacterales (CPE) even though non-carbapenemase-producing CRE (non-CP-CRE) comprise 50% or more of isolates in some surveillance studies. Thus, carbapenem resistance mechanisms in non-CP-CRE remain poorly characterized. To address this problem, we applied a combination of short- and long-read sequencing technologies to a cohort of CRE bacteremia isolates and used these data to unravel complex mobile genetic element structures mediating {beta}- lactamase gene amplification. By generating complete genomes of 65 carbapenem non-susceptible Enterobacterales (CNSE) covering a genetically diverse array of isolates, our findings both generate novel insights into how non-CP-CRE overcome carbapenem treatments and provide researchers scaffolds for characterization of their own non-CP-CRE isolates. Improved recognition of mechanisms driving development of non-CP-CRE could assist with design and implementation of future strategies to mitigate the impact of these increasingly recognized AMR pathogens.

genomics↗

bioRxiv2021

Fecal microbiota transplantation derived from Alzheimer's disease mice worsens brain trauma outcomes in young C57BL/6 mice

Traumatic brain injury (TBI) causes neuroinflammation and neurodegeneration, both which increase the risk and accelerate the progression of Alzheimers disease (AD). The gut microbiome is an essential modulator of the immune system, impacting in the brain. AD has been related with reduced diversity and alterations in the community composition of the gut microbiota. This study aimed to determine whether the gut microbiota from AD mice exacerbates neurological deficits after TBI in control mice. We prepared fecal microbiota transplants from 18-24 months old 3xTg-AD (FMT-AD) and from healthy controls (FMT-young) mice. FMTs were administered orally to young control C57BL/6 (wild-type, WT) mice after they underwent controlled cortical impact (CCI) injury, as a model of TBI. Then, we characterized the microbiota composition of the fecal samples by full-length 16S rRNA gene sequencing analysis. We collected the blood, brain, and gut tissues for protein and immunohistochemical analysis. Our results showed that FMT-AD administration stimulates a higher relative abundance of the genus Muribaculum and a decrease in Lactobacillus johnsonii compared to FMT-young in WT mice. Furthermore, WT mice exhibited larger lesion, increased activated microglia/macrophages, and reduced motor recovery after FMT-AD compared to FMT-young one day after TBI. In summary, we observed gut microbiota from AD mice to have a detrimental effect and aggravate the neuroinflammatory response and neurological outcomes after TBI in young WT mice.

neuroscience↗

bioRxiv2021

High confidence identification of intra-host single nucleotide variants for person-to-person influenza transmission tracking in congregate settings

Influenza within-host viral populations are the source of all global influenza diversity and play an important role in driving the evolution and escape of the influenza virus from human immune responses, antiviral treatment, and vaccines, and have been used in precision tracking of influenza transmission chains. Next Generation Sequencing (NGS) has greatly improved our ability to study these populations, however, major challenges remain, such as accurate identification of intra-host single nucleotide variants (iSNVs) that represent within-host viral diversity of influenza virus. In order to investigate the sources and the frequency of called iSNVs in influenza samples, we used a set of longitudinal influenza patient samples collected within a University of Maryland (UMD) cohort of college students in a living learning community. Our results indicate that technical replicates aid in removal of random RT-PCR, PCR, and platform sequencing errors, while the use of clonal plasmids for removal of systematic errors is more important in samples of low RNA abundance. We show that the choice of reference for read mapping affects the frequency of called iSNVs, with the sample self-reference resulting in the lowest amount of iSNV noise. The importance of variant caller choice is also highlighted in our study, as we observe differential sensitivity of variant callers to the mapping reference choice, as well as the poor overlap of their called iSNVs. Based on this, we develop an approach for identification of highly probable iSNVs by removal of sequencing and bioinformatics algorithm-associated errors, which we implement in phylogenetic analyses of the UMD samples for a greater resolution of transmission links. In addition to identifying closely related transmission connections supported by the presence of highly confident shared iSNVs between patients, our results also indicate that the rate of minor variant turnover within a host may be a limiting factor for utilization of iSNVs to determine patient epidemiological links.

genomics↗

bioRxiv2020

PlasmidHawk: Alignment-based Lab-of-Origin Prediction of Synthetic Plasmids

With advances in synthetic biology and genome engineering comes a heightened awareness of potential misuse related to biosafety concerns. A recent study employed machine learning to identify the lab-of-origin of DNA sequences to help mitigate some of these concerns. Despite their promising results, this deep learning based approach had limited accuracy, is computationally expensive to train, and wasnt able to provide the precise features that were used in its predictions. To address these shortcomings, we have developed PlasmidHawk for lab-of-origin prediction. Compared to a machine learning approach, PlasmidHawk has higher prediction accuracy; PlasmidHawk can successfully predict unknown sequences depositing labs 63% of the time and 80% of the time the correct lab is in the top 10 candidates. In addition, PlasmidHawk can precisely single out the signature sub-sequences that are responsible for the lab-of-origin detection. In summary, PlasmidHawk represents a novel, explainable, accurate tool for lab-of-origin prediction of synthetic plasmid sequences. PlasmidHawk is available at https://gitlab.com/treangenlab/plasmidhawk.git

bioinformatics↗