Biology subjects

Woolums, A.

Publications and source records attributed to Woolums, A..

3 recordsLinked to original sources

bioRxiv2025

Target-enriched sequencing enables genomic characterization within diverse microbial populations - a preprint

Characterizing microbial genetic sequences and key variants is critical for understanding pathogen ecology, transmission, and clinical impact. Yet, conventional metagenomic sequencing often yields too few on-target reads to move beyond species-level identification. We developed a target-enriched (TE) metagenomic workflow, including bait design, an optimized TE shotgun protocol, and the VARIANT++ pipeline, to recover and classify reads at a clustered genomic sequence-variant (GSV) level (see Graphical abstract). The computational component clusters reference genomes by average nucleotide identity, builds a GSV database, and integrates Kraken2, Themisto, and mSWEEP to increase call confidence while reducing false positives. Using Mannheimia haemolytica (Mh), the primary cause of bovine respiratory disease, we designed 114,375 DNA baits targeting sequences across 70 reference genomes. TE libraries from nasopharyngeal swabs of feedlot cattle achieved >250-fold increases in on-target Mh reads ([~]2.5% of non-host reads on average) compared with conventional shotgun sequencing, despite using one-quarter the sequencing depth. This variant-level resolution revealed six GSVs; most samples contained at least two, indicating variant mixtures difficult to detect with culture- or shotgun-based surveys. Because the approach leverages available reference sequences, it can be reconfigured for other microbial targets. TE metagenomics paired with genome-similarity clustering provides a scalable approach to variant-level characterization from complex microbial populations. O_FIG O_LINKSMALLFIG WIDTH=159 HEIGHT=200 SRC="FIGDIR/small/684174v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@5a7247org.highwire.dtl.DTLVardef@4ccf1org.highwire.dtl.DTLVardef@12b0413org.highwire.dtl.DTLVardef@118a0ec_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO Overview of the components in our three-part workflow. C_FIG

genomics↗

bioRxiv2022

Hematological and gene co-expression network analyses of high-risk beef cattle defines immunological mechanisms and biological complexes involved in bovine respiratory disease and weight gain

Bovine respiratory disease (BRD), the leading disease complex in beef cattle production systems, remains highly elusive regarding diagnostics and disease prediction. Previous research has employed cellular and molecular techniques to describe hematological and gene expression variation that coincides with BRD development. Here, we utilized weighted gene co-expression network analysis (WGCNA) to leverage total gene expression patterns from cattle at arrival and generate hematological and clinical trait associations to describe mechanisms that may predict BRD development. Gene expression counts of previously published RNA-Seq data from 23 cattle (2017; n=11 Healthy, n=12 BRD) were used to construct gene co-expression modules and correlation patterns with complete blood count (CBC) and clinical datasets. Modules were further evaluated for cross-populational preservation of expression with RNA-Seq data from 24 cattle in an independent population (2019; n=12 Healthy, n=12 BRD). Genes within well-preserved modules were subject to functional enrichment analysis for significant Gene Ontology terms and pathways. Genes which possessed high module membership and association with BRD development, regardless of module preservation ("hub genes"), were utilized for protein-protein physical interaction network and clustering analyses. Five well-preserved modules of co-expressed genes were identified. One module ("steelblue"), involved in alpha-beta T-cell complexes and Th2-type immunity, possessed significant correlation with increased erythrocytes, platelets, and BRD development. One module ("purple"), involved in mitochondrial metabolism and rRNA maturation, possessed significant correlation with increased eosinophils, fecal egg count per gram, and weight gain over time. Fifty-two interacting hub genes, stratified into 11 clusters, may possess transient function involved in BRD development not previously described in literature. This study identifies co-expressed genes and coordinated mechanisms associated with BRD, which necessitates further investigation in BRD-prediction research. Author SummaryBovine respiratory disease (BRD), the leading disease in beef cattle, is a highly dynamic disease complex. Through simultaneous sequencing of thousands of genes active in the blood of cattle at arrival, we pursued the co-expression patterns of these genes to evaluate associations with BRD development and severity overtime. This approach allows for a better understanding of gene expression active in cattle at arrival, and the discovery of new molecules and biological complexes that may predict BRD before the onset of clinical signs. Our work provides evidence that genes related to T-cells, a type of immune cell, are strongly co-expressed when cattle arrive to beef production system, and correlate with increased red blood cell (RBC) factors and BRD development. Further analysis shows that genes involved in cellular energy production and the respiratory electron transport are strongly co-expressed when cattle arrive to beef production system, and correlate with increased eosinophils, a type of immune cell, and weight gain overtime. Additionally, using genes which strongly correlate with BRD development and severity overtime, we identify a novel protein interaction complex that may drive future research for discovering new ways to manage and treat BRD in beef cattle.

systems biology↗

bioRxiv2021

Time dependent proinflammatory responses shape virus interference during coinfections of influenza A virus and influenza D virus

Both influenza A virus (IAV) and influenza D virus (IDV) are enzootic in pigs. IAV causes approximately 100% morbidity with low mortality, whereas IDV leads to only mild respiratory diseases in pigs. In this study, we performed a series of coinfection experiments in vitro and in vivo to understand how IAV and IDV interact and cause pathogenesis during coinfection. Results showed that IAV inhibited IDV replication when infecting swine tracheal epithelial cells (STEC) with IAV 24- or 48-hours prior to IDV inoculation, and that IDV suppressed IAV replication when IDV preceded IAV inoculation by 48 hours. Virus interference was not identified during simultaneous IAV/IDV infections or with 6 hours between the two viral infections, regardless of their order. The interference pattern at 24- and 48-hours correlated with proinflammatory responses induced by the first infection, which was about 24-hours slower for IDV than IAV. The viruses did not interfere with each other if both infected the cells before proinflammatory responses were induced. Coinfection in pigs further demonstrated that IAV interfered both viral shedding and virus replication of IDV, especially in the upper respiratory tract. Clinically, coinfection of IDV and IAV did not show significant enhancement of disease pathogenesis, compared with the pigs infected with IAV alone. In summary, this study suggests that interference during coinfection of IAV and IDV is primarily due to the proinflammatory response and is therefore dependent on the time between infection, and the order of infection. ImportanceBoth IAV and IDV are enzootic in pigs, and feral pigs have a higher risk for both IAV and IDV exposures than IDV exposure alone. This study suggests that in coinfection with IAV and IDV either virus can interfere with the replication of the other virus by stimulating proinflammatory responses; however, the proinflammatory response was 24 hours slower for IDV than IAV. In vitro there was no interference during simultaneous coinfection, regardless of infection order. Coinfection of IDV and IAV in pigs did not show enhanced pathogenesis, compared with those infected only with IAV. This study can facilitate our understanding of virus epidemiology and pathogenesis associated with IAV and IDV coinfection.

microbiology↗