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Qian, Wei-Jun (ORCID:0000000253932827)

Publications and source records attributed to Qian, Wei-Jun (ORCID:0000000253932827).

Biomarkers, Proteoforms, and Mass Spectrometry–Based Assays for Diabetes Clinical Research

Abstract The prevalence of diabetes, particularly type 2 diabetes, has reached epidemic proportions globally. The number of patients with type 1 diabetes (T1D) is also increasing rapidly. Despite advancements in understanding the pathogenesis of diabetes, the lack of circulating pancreatic biomarkers and reliable clinical-grade assays remains a major gap in diabetes research, often hindering the ability to adequately assess disease progression and therapeutic responses. This mini-review discusses emerging pancreatic biomarkers, with an emphasis on T1D, the limitations of current immunoassays, and the expanding role of mass spectrometry–based assays. Highlights include the recent work within the NIDDK-funded “Targeted Mass Spectrometry Assays for Diabetes and Obesity Research (TaMADOR)” consortium, which aims to develop robust, quantitative, and transferable assays for translational research. The review also emphasizes the importance of proteoform-specific assays for monitoring pancreatic function, including prohormone processing during disease progression or in responses to therapy.

Endocrinology & Metabolism↗

Artificial Intelligence Transforming Post-Translational Modification Research

Post-Translational Modifications (PTMs) are covalent changes to amino acids that occur after protein synthesis, including covalent modifications on side chains and peptide backbones. Many PTMs profoundly impact cellular and molecular functions and structures, and their significance extends to evolutionary studies as well. In light of these implications, we have explored how artificial intelligence (AI) can be utilized in researching PTMs. Initially, rationales for adopting AI and its advantages in understanding the functions of PTMs are discussed. Then, various deep learning architectures and programs, including recent applications of language models, for predicting PTM sites on proteins and the regulatory functions of these PTMs are compared. Finally, our high-throughput PTM-data-generation pipeline, which formats data suitably for AI training and predictions is described. We hope this review illuminates areas where future AI models on PTMs can be improved, thereby contributing to the field of PTM bioengineering.

59 BASIC BIOLOGICAL SCIENCES↗

Temporal dynamics of the multi-omic response to endurance exercise training

Regular exercise promotes whole-body health and prevents disease, but the underlying molecular mechanisms are incompletely understood. Here, the Molecular Transducers of Physical Activity Consortium profiled the temporal transcriptome, proteome, metabolome, lipidome, phosphoproteome, acetylproteome, ubiquitylproteome, epigenome and immunome in whole blood, plasma and 18 solid tissues in male and female Rattus norvegicus over eight weeks of endurance exercise training. The resulting data compendium encompasses 9,466 assays across 19 tissues, 25 molecular platforms and 4 training time points. Thousands of shared and tissue-specific molecular alterations were identified, with sex differences found in multiple tissues. Temporal multi-omic and multi-tissue analyses revealed expansive biological insights into the adaptive responses to endurance training, including widespread regulation of immune, metabolic, stress response and mitochondrial pathways. Many changes were relevant to human health, including non-alcoholic fatty liver disease, inflammatory bowel disease, cardiovascular health and tissue injury and recovery. The data and analyses presented in this study will serve as valuable resources for understanding and exploring the multi-tissue molecular effects of endurance training and are provided in a public repository (https://motrpac-data.org/).

59 BASIC BIOLOGICAL SCIENCES↗