Biology subjects

Chiang, W.-T.

Publications and source records attributed to Chiang, W.-T..

3 recordsLinked to original sources

bioRxiv2023

LipidSIM: inferring mechanistic lipid biosynthesis perturbations from lipidomics with a flexible, low-parameter, systematic Markov Modeling framework

Lipid metabolism is a complex and dynamic system involving numerous enzymes at the junction of multiple metabolic pathways. Disruption of these pathways leads to systematic dyslipidemia, a hallmark of many pathological developments, such as nonalcoholic steatohepatitis and diabetes. Recent advances in computational tools can provide insights into the dysregulation of lipid biosynthesis, but limitations remain due to the complexity of lipidomic data, limited knowledge of interactions among involved enzymes, and technical challenges in standardizing across different lipid types. In this study, we present a low-parameter, biologically interpretable framework named Lipid Synthesis Investigative Markov model (LipidSIM), which models and predicts the source of perturbations in lipid biosynthesis from lipidomic data. LipidSIM achieves this by accounting for the interdependency between the lipid species via the lipid biosynthesis network and generates testable hypotheses regarding changes in lipid biosynthetic reactions. This feature allows the integration of lipidomics with other omics types, such as transcriptomics, to elucidate the direct driving mechanisms of altered lipidomes due to treatments or disease progression. To demonstrate the value of LipidSIM, we first applied it to hepatic lipidomics following Keap1 knockdown and found changes in mRNA expression of the lipid pathways were consistent with the LipidSIM-predicted fluxes. Second, we used it to study lipidomic changes following intraperitoneal injection of CCl4 to induce fast NAFLD/NASH development and the progression of fibrosis and hepatic cancer. Finally, to show the power of LipidSIM for classifying samples with dyslipidemia, we used a Dgat2-knockdown study dataset. Thus, we show that as it demands no a priori knowledge of enzyme kinetics, LipidSIM is a valuable and intuitive framework for extracting biological insights from complex lipidomic data.

bioinformatics↗

bioRxiv2022

Preparing glycomics data for robust statistical analysis with GlyCompareCT

SummaryGlycomics data are rapidly increasing in scale and diversity. Complexities in glycan biosynthesis (hierarchy, competition, and compartmentalization) make preprocessing critical to address resulting sparsity (many similar glycosylation profiles may share few common glycans) and non-independence (substrate-competition in glycan biosynthesis results in non-independence incompatible with many statistical methods). Here, we present GlyCompareCT, a portable command-line tool, to address these challenges thereby facilitating downstream analyses. Given glycan abundances, GlyCompareCT conducts substructure decomposition to quantify hidden biosynthetic intermediate abundance and relationships between measured glycans. Thus, GlyComparCT mitigates sparsity and makes interdependence explicit thereby increasing statistical power. Ultimately, GlyComparCT is a user-friendly implementation of substructure analysis designed to increase accessibility, interoperability, and scope and consistency in glycomics analysis. Availability and implementationSource code, test data, and instructions for GlyCompareCT v1.1.0 are available at: https://github.com/LewisLabUCSD/GlyCompareCT Supplementary informationhttps://github.com/LewisLabUCSD/GlyCompareCT/raw/main/Supplementary%20Material.pdf

bioinformatics↗

bioRxiv2020

Dysregulation of the secretory pathway connects Alzheimer's disease genetics to aggregate formation

A hallmark of amyloid disorders, such as Alzheimers disease, is aggregation of secreted proteins. However, it is largely unclear how the hundreds of secretory pathway proteins contribute to amyloid formation. We developed a systems biology framework that integrates expression data with protein-protein interaction networks to successfully estimate a tissues fitness for producing specific secreted proteins. Using this framework, we analyzed the fitness of the secretory pathway of various brain regions and cell types for synthesizing the Alzheimers disease-associated amyloid-precursor protein (APP). While none of the key amyloidogenic pathway components were differentially expressed in AD brain, we found the deposition of A{beta} is associated with repressed expression of the secretory pathway components proximal to APP. Concurrently, we detected systemic up-regulation of the secretory pathway components proximal to {beta}- and {gamma}-secretases in AD brains. Our analyses suggest that perturbations from 3 high confidence AD risk genes cascade through the secretory machinery support network for APP and into the endocytosis pathway. Thus, we present a model where amyloidogenesis is associated with dysregulation of dozens of secretory pathway components supporting APP, which could yield novel therapeutic targets for the treatment of AD.

neuroscience↗