Biology subjects

Sinnwell, J. P.

Publications and source records attributed to Sinnwell, J. P..

5 recordsLinked to original sources

bioRxiv2026

Maladaptive Piezo1 Mechanotransduction Drives Smooth Muscle Aging in the Gut

Age-related gastrointestinal dysfunction is common, but the mechanisms of aging-associated smooth muscle failure remain unclear. We show that aging in mice slows whole gut and colonic transit, increases regional stiffness, and reduces smooth muscle contractility. Inducible smooth muscle cell (SMC) specific deletion of Piezo1 preserved youthful transit and force generation, whereas Piezo1 activation in young mice phenocopied aging-associated transit delay. Single cell transcriptomics, RNA velocity, stiffness-controlled cell and tissue cultures, and pharmacologic studies revealed that Piezo1 couples increased stiffness to Ca2+, calcineurin, NFAT signaling, loss of contractile gene programs, leading to age-related contractile loss and contractile to synthetic SMC remodeling and gut wall stiffening. Human intestinal SMCs supported conservation of this pathway, and PIEZO1 gain of function carriers showed a trend toward delayed colonic transit. Thus, maladaptive SMC Piezo1 mechanotransduction is a targetable mechanism of aging-associated gut dysmotility.

physiology↗

bioRxiv2024

Gut Microbiome Signatures During Acute Infection Predict Long COVID

Long COVID (LC), manifests in 10-30% of non-hospitalized individuals post-SARS-CoV-2 infection leading to significant morbidity. The predictive role of gut microbiome composition during acute infection in the development of LC is not well understood, partly due to the heterogeneous nature of disease. We conducted a longitudinal study of 799 outpatients tested for SARS-CoV-2 (380 positive, 419 negative) and found that individuals who later developed LC harbored distinct gut microbiome compositions during acute infection, compared with both SARS-CoV-2-positive individuals who did not develop LC and negative controls with similar symptomatology. However, the temporal changes in gut microbiome composition between the infectious (0-1 month) and post-infectious (1-2 months) phases was not different between study groups. Using machine learning, we showed that microbiome composition alone more accurately predicted LC than clinical variables. Including clinical data only marginally enhanced this prediction, suggesting that microbiome profiles during acute infection may reflect underlying health status and immune responses thus, help predicting individuals at risk for LC. Finally, we identified four LC symptom clusters, with gastrointestinal and fatigue-only groups most strongly linked to gut microbiome alterations.

genomics↗

bioRxiv2022

Integration of multi-omics data shows downregulation of mismatch repair, purin, and tublin pathways in AR-negative triple-negative chemotherapy-resistant breast tumors

Triple negative breast cancer (TNBC) patients who fail to achieve a pathological complete response to neoadjuvant chemotherapy (NAC) will likely experience recurrence of the disease within 3-4 years. Prognostic assessment of early recurrence could facilitate clinical decisions and impact disease survival. This study investigated pre- and post-NAC multi-omics data from both an in-house and a public clinical study to identify biomarkers of NAC response. We observed significant transcriptional differences associated with response in the residual disease (post-NAC biopsies), which did not exist in the pre-NAC biopsies. We further refined the post-NAC transcriptional changes to a 17-gene signature and machine learning models were applied to evaluate the signatures diagnostic potential (area under the curve [≥] 0.8). Interestingly, the signature was enriched with down-regulated immune genes and several of these genes demonstrated prognostic potential in basal TNBC tumors. Our 17-gene signature provides additional insight into TNBC recurrence, which warrants further investigation.

cancer biology↗

bioRxiv2020

Identification of novel Alzheimer's disease genes co-expressed with TREM2

By analyzing whole-exome data from the Alzheimers disease sequencing project (ADSP), we identify a set of 4 genes that show highly significant association with Alzheimers disease (AD). These genes were identified within a human TREM2 co-expression network using a novel approach wherein prioritized polygenic score analyses were performed sequentially to identify significant polygenic components. Two of the 4 genes (TREM2, RIN3) have previously been linked to AD and two (ATP8B4, IL17RA) are novel. Like TREM2, the 2 novel AD genes are selectively expressed in human microglial cells. The most significant variants in ATP8B4 and IL17RA are non-synonymous variants with strong effects comparable to the APOE {varepsilon}4 and {varepsilon}2 alleles. These protein-altering variants will provide unique opportunities to further explore the biological role of microglial cells in AD and help inform future immune modulatory therapeutic development for AD.

genetics↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗