Biology subjects

Adam, R. M.

Publications and source records attributed to Adam, R. M..

3 recordsLinked to original sources

bioRxiv2023

Integrated omics analysis unveils a DNA damage response to neurogenic injury

Spinal cord injury (SCI) evokes profound bladder dysfunction. Current treatments are limited by a lack of molecular data to inform novel therapeutic avenues. Previously, we showed systemic inosine treatment improved bladder function following SCI in rats. Here, we applied multi-omics analysis to explore molecular alterations in the bladder and their sensitivity to inosine following SCI. Canonical pathways regulated by SCI included those associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation. Upstream regulator analysis identified MYC as a key regulator, whereas causal network analysis predicted multiple regulators of DNA damage response signaling following injury, including PARP-1. Staining for both DNA damage ({gamma}H2AX) and PARP activity (poly-ADP-ribose) markers in the bladder was increased following SCI, and attenuated in inosine-treated tissues. Proteomics analysis suggested that SCI induced changes in protein synthesis-, neuroplasticity-, and oxidative stress-associated pathways, a subset of which were shown in transcriptomics data to be inosine-sensitive. These findings provide novel insights into the molecular landscape of the bladder following SCI, and highlight a potential role for PARP inhibition to treat neurogenic bladder dysfunction. Synopsis O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=100 SRC="FIGDIR/small/571015v1_ufig1.gif" ALT="Figure 1"> View larger version (21K): org.highwire.dtl.DTLVardef@7c3e79org.highwire.dtl.DTLVardef@e998f5org.highwire.dtl.DTLVardef@38fd4aorg.highwire.dtl.DTLVardef@17d3aba_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIEmployed a multi-omics approach, integrating both transcriptomic and proteomic analyses, to investigate the molecular response in a rat model of spinal cord injury (SCI) and the therapeutic effect of inosine. C_LIO_LIDiscovered multiple regulators of the DNA damage response, including PARP-1, using causal network analysis. C_LIO_LIObserved decreased markers of DNA damage and PARP activity in inosine-treated tissues, indicating the therapeutic potential of inosine in neurogenic dysfunction. C_LIO_LIIdentified significant alterations in molecular pathways associated with protein synthesis, neuroplasticity, wound healing, and neurotransmitter degradation after SCI, and their modulation by inosine, highlighting its neuroprotective effects beyond DNA damage repair. C_LI

cell biology↗

bioRxiv2023

Investigation of the impact of bromodomain inhibition on cytoskeleton stability and contraction

Injury to contractile organs such as the heart, vasculature, urinary bladder and gut can stimulate a pathological response that results in loss of normal contractility. PDGF and TGF{beta} are among the most well studied initiators of the injury response and have been shown to induce aberrant contraction in mechanically active cells of hollow organs including smooth muscle cells (SMC) and fibroblasts. However the mechanisms driving contractile alterations downstream of PDGF and TGF{beta} in SMC and fibroblasts are incompletely understood, limiting therapeutic interventions. To identify potential molecular targets, we have leveraged the analysis of publicly available data, comparing transcriptomic changes in mechanically active cells stimulated with PDGF and TGF{beta} and identified a shared molecular profile regulated by MYC and members of the AP-1 transcription factor complex. We also analyzed data sets from SMC and fibroblasts treated in the presence or absence of the MYC inhibitor JQ1. This analysis revealed a unique set of cytoskeleton-associated genes that were sensitive to MYC inhibition. JQ1 was also able to attenuate TGF{beta} and PDGF induced changes to the cytoskeleton and contraction of smooth muscle cells and fibroblasts in vitro. These findings identify MYC as a key driver of aberrant cytoskeletal and contractile changes in fibroblasts and SMC, and suggest that JQ1 could be used to restore normal contractile function in hollow organs.

cell biology↗

bioRxiv2022

MLcps: Machine Learning Cumulative Performance Score for classification problems

MotivationA performance metric is a tool to measure the correctness of a trained Machine Learning (ML) model. Numerous performance metrics have been developed for classification problems making it overwhelming to select the appropriate one since each of them represents a particular aspect of the model. Furthermore, selection of a performance metric becomes harder for problems with imbalanced and/or small datasets. Therefore, in clinical studies where datasets are frequently imbalanced and, in situations when the prevalence of a disease is low or the collection of patient samples is difficult, deciding on a suitable metric for performance evaluation of an ML model becomes quite challenging. The most common approach to address this problem is measuring multiple metrics and compare them to identify the best-performing ML model. However, comparison of multiple metrics is laborious and prone to user preference bias. Furthermore, evaluation metrics are also required by ML model optimization techniques such as hyperparameter tuning, where we train many models, each with different parameters, and compare their performances to identify the best-performing parameters. In such situations, it becomes almost impossible to assess different models by comparing multiple metrics. ResultsHere, we propose a new metric called Machine Learning Cumulative Performance Score (MLcps) as a Python package for classification problems. MLcps combines multiple pre-computed performance metrics into one metric that conserves the essence of all pre-computed metrics for a particular model. We tested MLcps on 4 different publicly available biological datasets and the results reveal that it provides a comprehensive picture of overall model robustness. AvailabilityMLcps is available at https://pypi.org/project/MLcps/ and cases of use are available at https://mybinder.org/v2/gh/FunctionalUrology/MLcps.git/main. Supplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗