Biology subjects

Beigel, K.

Publications and source records attributed to Beigel, K..

5 recordsLinked to original sources

bioRxiv2025

SWANS: A highly configurable analysis pipeline for single-cell and single-nuclei RNA-sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-seq) is a powerful technique that enables the analysis of gene expression at the individual cell level. Bioinformatic tools for scRNA-seq data analysis have many different options throughout the typical scRNA-seq workflow (normalization, integration, annotation, clustering, and visualization), and the choice of method(s) and parameter(s) at each stage can impact results. ResultsHere, we introduce SWANS (v2.0), a configurable analysis pipeline that, in a single run, can employ multiple analysis methods, resolutions, and modifiable parameters. The resulting clustering arrangements, differential gene expression results, and other quantitative measurements can be dynamically visualized and compared in a Shiny interactive report to assist in choosing a single analysis schema for annotation and downstream analysis. Once a final approach is chosen, SWANS will perform differential gene expression (DGE) analysis based on experimental conditions and gene set enrichment analysis (GSEA) in addition to creating reports that display figures and interactive tables, quality control metrics, and benchmarking information. SWANS uses Snakemake as a workflow manager, Cell Ranger for alignment and gene expression quantification, Seurat for single cell data analysis, and additional single cell R packages for quality control and downstream single cell analysis. ConclusionSWANS is a tailorable pipeline that provides options for quality control, dimensionality reduction, clustering, differential gene expression analysis, gene set enrichment analysis, and trajectory analysis. Additionally, SWANS generates a series of reports that facilitate sharing large volumes of complex data in a clear and concise manner with other investigators.

bioinformatics↗

bioRxiv2025

Dietary manipulation of intestinal microbes prolongs survival in a mouse model of Hirschsprung disease

Enterocolitis is a common and potentially deadly manifestation of Hirschsprung disease (HSCR) but disease mechanisms remain poorly defined. Unexpectedly, we discovered that diet can dramatically affect the lifespan of a HSCR mouse model (Piebald lethal, sl/sl) where affected animals die from HAEC complications. In the sl/sl model, diet alters gut microbes and metabolites, leading to changes in colon epithelial gene expression and epithelial oxygen levels known to influence colitis severity. Our findings demonstrate unrecognized similarity between HAEC and other types of colitis and suggest dietary manipulation could be a valuable therapeutic strategy for people with HSCR. AbstractHirschsprung disease (HSCR) is a birth defect where enteric nervous system (ENS) is absent from distal bowel. Bowel lacking ENS fails to relax, causing partial obstruction. Affected children often have "Hirschsprung disease associated enterocolitis" (HAEC), which predisposes to sepsis. We discovered survival of Piebald lethal (sl/sl) mice, a well-established HSCR model with HAEC, is markedly altered by two distinct standard chow diets. A "Protective" diet increased fecal butyrate/isobutyrate and enhanced production of gut epithelial antimicrobial peptides in proximal colon. In contrast, "Detrimental" diet-fed sl/sl had abnormal appearing distal colon epithelium mitochondria, reduced epithelial mRNA involved in oxidative phosphorylation, and elevated epithelial oxygen that fostered growth of inflammation-associated Enterobacteriaceae. Accordingly, selective depletion of Enterobacteriaceae with sodium tungstate prolonged sl/sl survival. Our results provide the first strong evidence that diet modifies survival in a HSCR mouse model, without altering length of distal colon lacking ENS. HighlightsO_LITwo different standard mouse diets alter survival in the Piebald lethal (sl/sl) mouse model of Hirschsprung disease, without impacting extent of distal colon aganglionosis (the region lacking ENS). C_LIO_LIPiebald lethal mice fed the "Detrimental" diet had many changes in colon epithelial transcriptome including decreased mRNA for antimicrobial peptides and genes involved in oxidative phosphorylation. Detrimental diet fed sl/sl also had aberrant-appearing mitochondria in distal colon epithelium, with elevated epithelial oxygen that drives lethal Enterobacteriaceae overgrowth via aerobic respiration. C_LIO_LIElimination of Enterobacteriaceae with antibiotics or sodium tungstate improves survival of Piebald lethal fed the "Detrimental diet". C_LI Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=94 SRC="FIGDIR/small/637436v1_ufig1.gif" ALT="Figure 1"> View larger version (15K): org.highwire.dtl.DTLVardef@d95251org.highwire.dtl.DTLVardef@1ab58caorg.highwire.dtl.DTLVardef@5260b0org.highwire.dtl.DTLVardef@49ce42_HPS_FORMAT_FIGEXP M_FIG C_FIG

cell biology↗

bioRxiv2024

Rapid cyclic stretching induces synthetic, proinflammatory phenotypes in cultured human intestinal smooth muscle, with the potential to alter signaling to adjacent bowel cells

Background and AimsBowel smooth muscle experiences mechanical stress constantly during normal function, and pathologic mechanical stressors in disease states. We tested the hypothesis that pathologic mechanical stress could alter transcription to induce smooth muscle phenotypic class switching. MethodsPrimary human intestinal smooth muscle cells (HISMCs), seeded on electrospun aligned poly-{varepsilon}-caprolactone nano-fibrous scaffolds, were subjected to pathologic, high frequency (1 Hz) uniaxial 3% cyclic stretch (loaded) or kept unloaded in culture for 6 hours. Total RNA sequencing, qRT-PCR, and quantitative immunohistochemistry defined loading-induced changes in gene expression. NicheNet predicted how differentially expressed genes might impact HISMCs and other bowel cells. ResultsLoading induced differential expression of 4537 genes in HISMCs. Loaded HISMCs had a less contractile phenotype, with increased expression of synthetic SMC genes, proinflammatory cytokines, and altered expression of axon guidance molecules, growth factors and morphogens. Many differentially expressed genes encode secreted ligands that could act cell-autonomously on smooth muscle and on other cells in the bowel wall. DiscussionHISMCs demonstrate remarkably rapid phenotypic plasticity in response to mechanical stress that may convert contractile HISMCs into proliferative, fibroblast-like cells or proinflammatory cells. These mechanical stress-induced changes in HISMC gene expression may be relevant for human bowel disease.

bioengineering↗

bioRxiv2023

LETHAL COVID-19 ASSOCIATES WITH RAAS-INDUCED INFLAMMATION FOR MULTIPLE ORGAN DAMAGE INCLUDING MEDIASTINAL LYMPH NODES

Lethal COVID-19 outcomes are most often attributed to classic cytokine storm and attendant excessive immune signaling. We re-visit this question using RNA sequencing in nasopharyngeal and 40 autopsy samples from COVID-19-positive and negative individuals. In nasal swabs, the top 100 genes which significantly correlated with COVID-19 viral load, include many canonical innate immune genes. However, 22 much less studied "non-canonical" genes are found and despite the absence of viral transcripts, subsets of these are upregulated in heart, lung, kidney, and liver, but not mediastinal lymph nodes. An important regulatory potential emerges for the non-canonical genes for over-activating the renin-angiotensin-activation-system (RAAS) pathway, resembling this phenomenon in hereditary angioedema (HAE) and its overlapping multiple features with lethal COVID-19 infections. Specifically, RAAS overactivation links increased fibrin deposition, leaky vessels, thrombotic tendency, and initiating the PANoptosis death pathway, as suggested in heart, lung, and especially mediastinal lymph nodes, with a tightly associated mitochondrial dysfunction linked to immune responses. For mediastinal lymph nodes, immunohistochemistry studies validate the transcriptomic findings showing abnormal architecture, excess fibrin and collagen deposition, and pathogenic fibroblasts. Further, our findings overlap findings in SARS-CoV-2 infected hamsters, C57BL/6 and BALB/c mouse models, and importantly peripheral blood mononuclear cell (PBMC) and whole blood samples from COVID-19 patients infected with early variants and later SARS-CoV-2 strains. We thus present cytokine storm in lethal COVID-19 disease as an interplay between upstream immune gene signaling producing downstream RAAS overactivation with resultant severe organ damage, especially compromising mediastinal lymph node function.

immunology↗

bioRxiv2023

Petagraph: A large-scale unifying knowledge graph framework for integrating biomolecular and biomedical data

The use of biomedical knowledge graphs (BMKG) for knowledge representation and data integration has increased drastically in the past several years due to the size, diversity, and complexity of biomedical datasets and databases. Data extraction from a single dataset or database is usually not particularly challenging. However, if a scientific question must rely on integrative analysis across multiple databases or datasets, it can often take many hours to correctly and reproducibly extract and integrate data towards effective analysis. To overcome this issue, we created Petagraph, a large-scale BMKG that integrates biomolecular data into a schema incorporating the Unified Medical Language System (UMLS). Petagraph is instantiated on the Neo4j graph platform, and to date, has fifteen integrated biomolecular datasets. The majority of the data consists of entities or relationships related to genes, animal models, human phenotypes, drugs, and chemicals. Quantitative data sets containing values from gene expression analyses, chromatin organization, and genetic analyses have also been included. By incorporating models of biomolecular data types, the datasets can be traversed with hundreds of ontologies and controlled vocabularies native to the UMLS, effectively bringing the data to the ontologies. Petagraph allows users to analyze relationships between complex multi-omics data quickly and efficiently.

bioinformatics↗