Biology subjects

Chance, M.

Publications and source records attributed to Chance, M..

5 recordsLinked to original sources

bioRxiv2023

Exploring Temporal and Sex-Linked Dysregulation in Alzheimer's Disease Phospho-Proteome

This study aims to characterize dysregulation of phosphorylation for the 5XFAD mouse model of Alzheimers disease (AD). Employing global phosphoproteome measurements, we analyze temporal (3, 6, 9 months) and sex-dependent effects on mouse hippocampus tissue to unveil molecular signatures associated with AD initiation and progression. Our results indicate 1.9 to 4.4 times higher phosphorylation prevalence compared to protein expression across all time points, with approximately 4.5 times greater prevalence in females compared to males at 3 and 9 months. Moreover, our findings reveal consistent phosphorylation of known AD biomarkers APOE and GFAP in 5XFAD mice, alongside novel candidates BIG3, CLCN6 and STX7, suggesting their potential as biomarkers for AD pathology. In addition, we identify PDK1 as a significantly dysregulated kinase at 9 months in females, and the regulation of gap junction activity as a key pathway associated with Alzheimers disease across all time points. AD-Xplorer, the interactive browser of our dataset, enables exploration of AD-related changes in phosphorylation, protein expression, kinase activities, and pathways. AD-Xplorer aids in biomarker discovery and therapeutic target identification, emphasizing temporal and sex-specific nature of significant phosphoproteomic signatures. Available at: https://yilmazs.shinyapps.io/ADXplorer HighlightsO_LIPhosphorylation-level dysregulation surpasses protein expression C_LIO_LIHigher phospho-dysregulation in females, starting as early as 3-month time point C_LIO_LINovel candidates BIG3, CLCN6, and STX7 exhibit consistent phospho-dysregulation C_LIO_LIDeveloped AD-Xplorer: Online tool to explore Alzheimers disease phospho-proteome C_LI In BriefThis study investigates dysregulation of phospho-proteome in an Alzheimers disease (AD) mouse model, identifying consistent phosphorylation of established AD biomarkers APOE and GFAP, along with novel candidate biomarkers BIG3, CLCN6, and STX7. In addition, the study observes significant PDK1 dysregulation at 9 months, particularly in females. AD-Xplorer, our interactive tool for exploring temporal and sex-linked phosphorylation changes, protein expression, kinase activities, and pathway enrichment, empowers researchers to gain deeper insights into AD mechanisms and uncover novel biomarkers and therapeutic targets.

neuroscience↗

bioRxiv2021

Constructing Functional Networks of Phosphorylation Sites Using Co-Phosphorylation

Protein phosphorylation is a ubiquitous regulatory mechanism that plays a central role in cellular signaling. According to recent estimates, up to 70% of human proteins can be phosphorylated. Therefore, characterization of phosphorylation dynamics is critical for understanding a broad range of biological and biochemical processes. Technologies based on mass spectrometry are rapidly advancing to meet the needs for high-throughput screening of phosphorylation. These technologies enable untargeted quantification of thousands of phosphorylation sites in a given sample. Many labs are already utilizing these technologies to comprehensively characterize signaling landscapes by examining perturbations with drugs and knockdown approaches, or by assessing diverse phenotypes in cancers, neuro-degerenational diseases, infectious diseases, and normal development. Here, we comprehensively investigate the concept of "co-phosphorylation", defined as the correlated phosphorylation of a pair of phosphosites across various biological states. We integrate nine publicly available phospho-proteomics datasets for various diseases (including breast cancer, ovarian cancer and Alzhemiers disease) and utilize functional data related to sequence, evolutionary histories, kinase annotations, and pathway annotations to investigate the functional relevance of co-phosphorylation. Our results across a broad range of studies consistently show that functionally associated sites tend to exhibit significant positive or negative co-phosphorylation. Specifically, we show that co-phosphorylation can be used to predict with high precision the sites that are on the same pathway or that are targeted by the same kinase. Overall, these results establish co-phosphorylation as a useful resource for analyzing phospho-proteins in a network context, which can help extend our knowledge on cellular signaling and its dysregulation.

systems biology↗

bioRxiv2020

Robust Inference of Kinase Activity using Functional Networks

Mass spectrometry enables high-throughput screening of phospho-proteins across a broad range of biological contexts. When complemented by computational algorithms, phospho-proteomic data allows the inference of kinase activity, facilitating the identification of dysregulated kinases in various diseases including cancer, Alzheimers disease and Parkinsons disease. To enhance the reliability of kinase activity inference, we present a network-based framework, RoKAI, that integrates various sources of functional information to capture coordinated changes in signaling. Through computational experiments, we show that phosphorylation of sites in the functional neighborhood of a kinase are significantly predictive of its activity. The incorporation of this knowledge in RoKAI consistently enhances the accuracy of kinase activity inference methods while making them more robust to missing annotations and quantifications. This enables the identification of understudied kinases and will likely lead to the development of novel kinase inhibitors for targeted therapy of many diseases. RoKAI is available as web-based tool at http://rokai.io.

systems biology↗

bioRxiv2018

Metastable contacts and structural disorder in the estrogen receptor transactivation domain

The N-terminal transactivation domain (NTD) of estrogen receptor alpha, a well-known member of the family of intrinsically disordered proteins (IDPs), mediates the receptors transactivation function to regulate gene expression. However, an accurate molecular dissection of NTDs structure-function relationships remains elusive. Here, using small-angle X-ray scattering (SAXS), nuclear magnetic resonance (NMR), circular dichroism, and hydrogen exchange mass spectrometry, we show that NTD adopts a mostly disordered, unexpectedly compact conformation that undergoes structural expansion upon chemical denaturation. By combining SAXS, hydroxyl radical protein footprinting and computational modeling, we derive the ensemble-structures of the NTD and determine its ensemble-contact map that reveals metastable regional and long-range contacts, including interactions between residues I33 and S118. We show that mutation at S118, a known phosphorylation site, promotes conformational changes and increases coactivator binding. We further demonstrate via fluorine-19 (19F) NMR that mutations near residue I33 alter 19F chemical shifts at residue S118, confirming the proposed I33-S118 contact in the ensemble of structural disorder. These findings extend our understanding of IDPs structure-function relationship, and how specific metastable contacts mediate critical functions of disordered proteins.\n\nHighlightsO_LIA compact disorder is observed for the N-terminal domain (NTD) of estrogen receptor\nC_LIO_LIMulti-technique modeling elucidates the NTD ensemble structures\nC_LIO_LIEnsemble-based contact map reveals metastable contacts between I33 and S118\nC_LIO_LI19F-NMR data validate the proposed I33-S118 contact in the IDP\nC_LI

biophysics↗

bioRxiv2018

CoPhosK: A Method for Comprehensive Kinase Substrate Annotation Using Co-phosphorylation Analysis

We present CoPhosK to predict kinase-substrate associations for phosphopeptide substrates detected by mass spectrometry (MS). The tool utilizes a Naive Bayes framework with priors of known kinase-substrate associations (KSAs) to generate its predictions. Through the mining of MS data for the collective dynamic signatures of the kinases substrates revealed by correlation analysis of phosphopeptide intensity data, the tool infers KSAs in the data for the considerable body of substrates lacking such annotations. We benchmarked the tool against existing approaches for predicting KSAs that rely on static information (e.g. sequences, structures and interactions) using publically available MS data, including breast, colon, and ovarian cancer models. The benchmarking reveals that co-phosphorylation analysis can significantly improve prediction performance when static information is available (about 35% of sites) while providing reliable predictions for the remainder, thus tripling the KSAs available from the experimental MS data providing a to comprehensive and reliable characterization of the landscape of kinase-substrate interactions well beyond current limitations. Author SummaryKinases play an important role in cellular regulation and have emerged as an important class of drug targets for many diseases, particularly cancers. Comprehensive identification of the links between kinases and their substrates enhances our ability to understand the underlying mechanism of diseases and signalling networks to drive drug discovery. Most of the current computational methods for prediction of kinase-substrate associations use static information such as sequence motifs and physical interactions to generate predictions. However, phosphorylation is a dynamic process and these static predictions may overlook unique features of cellular context, where kinases may be rewired. In this manuscript, we propose a computational method, CoPhosK, which uses the mass spectrometry based phosphoproteomics data to predict the kinase for all identified phosphosites in the experiment. We show that our approach complements and extends existing approaches.

bioinformatics↗