isolation-analysis-aortic-arch-root-lesions-an-atherosclerotic-mouse

Isolation and Analysis of Aortic Arch and Root Lesions in an Atherosclerotic Mouse Model

Atherosclerosis, a leading cause of cardiovascular diseases, necessitates a detailed examination of lesion development and progression. This study ...

evidence-based-knowledge-synthesis-hypothesis-validation-navigating

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases <em>via</em> Explainable AI and Agentic Systems

The scale of biomedical knowledge, spanning scientific literature and curated knowledge bases, poses a significant challenge for investigators in ...

Shanghai University of Sport

UCLA School of Medicine

University of California at Los Angeles (UCLA)

Obesity significantly contributes to the progression of cardiovascular diseases (CVDs) and elevates the risk of cardiovascular mortality. Atherosclerosis, the primary pathogenic process underlying CVDs, initiates with vascular endothelial dysfunction, serving as the cornerstone of vascular lesions. Adipokines, bioactive molecules secreted by adipose tissue that regulate metabolic and endocrine functions, play a pivotal role in modulating endothelial function during atherosclerosis. This review comprehensively examines the distinct roles of various adipokines in regulating endothelial function in atherosclerosis. We categorize these adipokines into two main groups: protective adipokines, including adiponectin, FGF21, CTRP9, PGRN, Omentin, and Vaspin, and detrimental adipokines such as leptin, Chemerin, Resistin, FABP4, among others. Targeting specific adipokines holds promise for novel clinical interventions in the management of atherosclerosis-related CVDs, thereby providing a theoretical foundation for cardiovascular disease treatment strategies.

Multifaced roles of adipokines in endothelial cell function.

We present a deep-learning-based platform, MIND-S, for protein post-translational modification (PTM) predictions. MIND-S employs a multi-head attention and graph neural network and assembles a 15-fold ensemble model in a multi-label strategy to enable simultaneous prediction of multiple PTMs with high performance and computation efficiency. MIND-S also features an interpretation module, which provides the relevance of each amino acid for making the predictions and is validated with known motifs. The interpretation module also captures PTM patterns without any supervision. Furthermore, MIND-S enables examination of mutation effects on PTMs. We document a workflow, its applications to 26 types of PTMs of two datasets consisting of ∼50,000 proteins, and an example of MIND-S identifying a PTM-interrupting SNP with validation from biological data. We also include use case analyses of targeted proteins. Taken together, we have demonstrated that MIND-S is accurate, interpretable, and efficient to elucidate PTM-relevant biological processes in health and diseases.

MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases.

Post-translational modifications (PTMs) serve as key regulatory mechanisms in various cellular processes; altered PTMs can potentially lead to human diseases. We present a protocol for using MIND-S (multi-label interpretable deep-learning approach for PTM prediction-structure version), to study PTMs. This protocol consists of step-by-step guide and includes three key applications of MIND-S: PTM predictions based on protein sequences, important amino acids identification, and elucidation of altered PTM landscape resulting from molecular mutations. For complete details on the use and execution of this protocol, please refer to Yan et al (2023)..

Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S.

Temporal proteomics data sets are often confounded by the challenges of missing values. These missing data points, in a time-series context, can lead to fluctuations in measurements or the omission of critical events, thus hindering the ability to fully comprehend the underlying biomedical processes. We introduce a Data Multiple Imputation (DMI) pipeline designed to address this challenge in temporal data set turnover rate quantifications, enabling robust downstream analysis to gain novel discoveries. To demonstrate its utility and generalizability, we applied this pipeline to two use cases: a murine cardiac temporal proteomics data set and a human plasma temporal proteomics data set, both aimed at examining protein turnover rates. This DMI pipeline significantly enhanced the detection of protein turnover rate in both data sets, and furthermore, the imputed data sets captured new representation of proteins, leading to an augmented view of biological pathways, protein complex dynamics, as well as biomarker-disease associations. Importantly, DMI exhibited superior performance in benchmark data sets compared to single imputation methods (DSI). In summary, we have demonstrated that this DMI pipeline is effective at overcoming challenges introduced by missing values in temporal proteome dynamics studies.

ArticleTotal : 2	Year
Publication title	2025
Publication title Cited by 1	2025

Article	Year
Multifaced roles of adipokines in endothelial cell function. Frontiers in endocrinology\| PubMed ID: 39558976	2024
MIND-S is a deep-learning prediction model for elucidating protein post-translational modifications in human diseases. Cell reports methods\| PubMed ID: 37056379	2023
Protocol for the prediction, interpretation, and mutation evaluation of post-translational modification using MIND-S. STAR protocols\| PubMed ID: 37979178	2023
Missing Values in Longitudinal Proteome Dynamics Studies: Making a Case for Data Multiple Imputation. Journal of proteome research\| PubMed ID: 39189460	2024

Yu Yan

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