Biology subjects

Monaghan, K.

Publications and source records attributed to Monaghan, K..

4 recordsLinked to original sources

bioRxiv2025

Genetic Deletion of ASIC3 Alters Left Ventricular Remodeling and Autonomic Function After Myocardial Infarction in Mice

Chronic overactivation of neurohormonal systems is the principal driver of adverse cardiac remodeling following myocardial infarction (MI). Recent data suggest that ablating cardiac afferent neurons in rats attenuates left ventricular (LV) remodeling following MI by blocking this overactivation. Our lab has shown that acid-sensing ion channels (ASICs) are highly expressed in cardiac afferents and may sense myocardial acidosis. We hypothesized that genetic deletion of ASICs might abrogate disadvantageous remodeling after MI by disrupting afferent signaling pathways otherwise resulting in overactivation of neurohormonal responses. To test this hypothesis, we induced MI by coronary artery ligation in wild type (WT) and ASIC3-/- mice and assessed cardiac remodeling by serial echocardiography. We found that ASIC3-/- mice had less LV dilation relative to MI size, increased LV mass, and increased stroke volume compared to WT mice after MI. To investigate a potential role of the autonomic nervous system, we measured renal and splanchnic sympathetic nerve activity, heart rate and systolic blood pressure variability (sBPV), and hemodynamic responses to atropine and propranolol. In addition, we assessed baroreceptor-heart rate and baroreceptor-renal sympathetic nerve activity (RSNA) reflex function. Following MI, ASIC3-/- mice had lower baroreceptor-RSNA reflex sensitivity than WT mice, associated with elevated sBPV. Importantly, sBPV correlated significantly with post-MI changes in LV mass in ASIC3-/- but not WT mice. Our data shows that ASIC3 plays an important role in cardiac remodeling after MI potentially via modulation of baroreflex sensitivity and sBPV. ASIC3 may be further investigated as a potential therapeutic target in heart failure.

physiology↗

bioRxiv2024

APC coordinates GSK3 phosphorylation of SETD8 to suppress colorectal cancer

Colorectal cancer (CRC) is the second-leading cause of cancer-related deaths with increasing incidence globally. Mutations in the tumor suppressor APC initiate CRC at least in part by preventing the GSK3 kinase from phosphorylating {beta}-CATENIN, leading to its constitutive stabilization and transactivation of mitogenic target genes. While the importance of {beta}-CATENIN phosphorylation by GSK3 is well-established, APC regulation of GSK3 activity upon other targets with potential oncogenic relevance are not understood. Here, we identify the H4K20 methyltransferase SETD8 as target of APC-coordinated GSK3 phosphorylation in the intestinal epithelium. We found that phosphorylation by GSK3 restrains the oncogenic activity of SETD8, with loss of phosphorylation sensitizing mice to oncogenic insults. Mechanistically, phosphorylation alters the role of SETD8 in transcriptional regulation, most notably by preventing it from activating oncogenic YAP signaling and a fetal-like transcriptional program. These results underscore the importance of SETD8 in CRC and represent a novel {beta}-CATENIN -independent oncogenic consequence of APC loss. SignificanceGSK3 is thought to restrain colorectal cancer primarily by phosphorylation of {beta}-CATENIN. We show that GSK3 also phosphorylates SETD8, preventing SETD8 activation of oncogenic programs including YAP-driven fetal-like gene expression.

cancer biology↗

bioRxiv2023

Forward genetic screening in engineered colorectal cancer organoids identifies novel regulators of metastasis

Cancer is the second leading cause of death globally, due primarily to metastatic dissemination and colonization of distal sites. Recurrent genetic drivers of metastasis are elusive, suggesting that, unlike the stereotyped mutations promoting primary tumor development, drivers of metastasis may be variable. Here, we interrogate pathways governing metastasis through CRISPR/Cas9-based forward genetic screening in a genetically defined colorectal adenocarcinoma tumor organoid (tumoroid) model using ex vivo invasion screens and orthotopic, in vivo screens for gain of metastatic potential. We identify Ctnna1 and Bcl2l13 as bona fide metastasis suppressors. CTNNA1 loss promotes carcinoma cell invasion and migration through an atypical EMT-like mechanism, whereas BCL2L13 loss promotes cell survival after extracellular matrix detachment and non-cell-autonomous macrophage polarization. Ultimately, this study provides a proof-of-principle that high-content forward genetic screening can be performed in tumor-organoid models in vivo and identifies novel regulators of colon cancer metastasis.

cancer biology↗

bioRxiv2019

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

ObjectiveSystemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE.\n\nMethodsWe applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients with SLE and 29 healthy donors was contributed in this research; 141 patients with SLE and 51 healthy donors were analysed in total.\n\nResultsExamination of SLE clusters, as opposed to unstratified SLE patients, revealed underappreciated differences in the pattern of expression of disease-related genes relative to clinical presentation. Moreover, gene signatures correlated to flare activity were successfully identified.\n\nConclusionGiven that disease heterogeneity has confounded research studies and clinical trials, our approach addresses current unmet medical needs and provides a greater understanding of SLE heterogeneity in humans. Stratification of patients based on gene expression signatures may be a valuable strategy to harness disease heterogeneity and identify patient populations that may be at an increased risk of disease symptoms. Further, this approach can be used to understand the variability in responsiveness to therapeutics, thereby improving the design of clinical trials and advancing personalised therapy.

bioinformatics↗