Investigating the Mechanisms of Aging through Systems Biology
Our lab is dedicated to understanding the complex regulatory networks and metabolic mechanisms that drive aging and age-related diseases.
Research Focus
Aging is a key factor contributing to organismal decline and a variety of human diseases. However, the tissue distribution of senescent cells, key regulatory factors, and underlying mechanisms remain poorly understood. Our research group has made significant progress in the development of network-based approaches, construction of network models, and investigation of metabolic regulation mechanisms in aging.
Network Analysis
Developing novel co-expression network analysis methods to reveal network topology and refine gene modules.
Aging Networks
Constructing aging regulatory networks across 50 human tissues to identify key cell types and functional modules.
Metabolic Mechanisms
Elucidating metabolic mechanisms of aging and identifying driver genes associated with insulin resistance.
Our Team
Peng Xu
Professor
Linyu Hu
Research Assistant
Rongyao Huang
Master Student
Xiaobing Chen
Master Student
Wuye Zhao
Undergraduate
Selected Publications
Xu, P., Kong, Y., Palmer, N., Ng, M., Zhang, B., and Das, S.K. (2024). Integrated Multi-Omic Analyses Uncover the Effects of Aging on Cell-Type Regulation in Glucose-Responsive Tissues. Aging Cell, 23(8): e14199. (First and corresponding author)
Xu, P. and B. Zhang (2023). Multiscale network modeling reveals the gene regulatory landscape driving cancer prognosis in 32 cancer types. Genome Research 33(10): 1806-1817. (First and corresponding author, cover article)
Xu, P.+, Wang, M.+, Sharma, N.K.+, et al. (2023). Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations. Cell Systems 14, 41-57.e48. (+Co-first author)
Xu, P., Wang, M., Song, W.M., et al. (2022). The landscape of human tissue and cell type specific expression and co-regulation of senescence genes. Molecular Neurodegeneration 17, 5.
Wang, M.+, Song, W.M.+, Ming, C.+, Wang, Q.+, Zhou, X.+, Xu, P.+, et al. (2022). Guidelines for Bioinformatics of Single-Cell Sequencing Data Analysis in Alzheimer’s Disease: Review, Recommendation, Implementation, and Application. Molecular Neurodegeneration 17, 17. (+Co-first author)
Other Publications
Xu, P., Chen, Y., Gao, M., Chong, Z. (2021). ClipSV: Improving structural variation detection by read extension, spliced alignment, and tree-based decision rules. NAR Genomics and Bioinformatics 3, lqab003.
Xu, P., Kennell Jr, T., Gao, M., Consortium, H.G.S.V., Kimberly, R.P., and Chong, Z. (2020). MRLR: unraveling high-resolution meiotic recombination by linked reads. Bioinformatics 36, 10-16.
Kong, Y.+, Xu, P.+, Jing, X., Chen, L., Li, L., and Li, X. (2017). Decipher the ancestry of the plant-specific LBD gene family. BMC Genomics 18, 1-10. (+Co-first author)
Xu, P.+, Kong, Y.+, Song, D., Huang, C., Li, X., and Li, L. (2014). Conservation and functional influence of alternative splicing in wood formation of Populus and Eucalyptus. BMC Genomics 15, 1-12. (+Co-first author)
Xu, P., Kong, Y., Li, X., and Li, L. (2013). Identification of molecular processes needed for vascular formation through transcriptome analysis of different vascular systems. BMC Genomics 14, 1-11.
Huang, F., Xu, P., Yue, Z., Song, Y., Hu, K., Zhao, X., Gao, M., & Chong, Z. (2024). Body Weight Correlates with Molecular Variances in Patients with Cancer. Cancer Research, 84(5), 757-770.
Gonzales, M. M., Garbarino, V. R., Kautz, T. F., Palavicini, J. P., Lopez-Cruzan, M., Dehkordi, S. K., Mathews, J. J., Zare, H., Xu, P., Zhang, B., Franklin, C., Habes, M., Craft, S., Petersen, R. C., Tchkonia, T., Kirkland, J. L., Salardini, A., Seshadri, S., Musi, N., & Orr, M. E. (2023). Senolytic therapy in mild Alzheimer's disease: a phase 1 feasibility trial. Nature Medicine, 29(10), 2481-2488.
Choi, I., Wang, M., Yoo, S., Xu, P., Seegobin, S. P., Li, X., Han, X., Wang, Q., Peng, J., Zhang, B., & Yue, Z. (2023). Autophagy enables microglia to engage amyloid plaques and prevents microglial senescence. Nature Cell Biology, 25(7), 963-974.
Song, W. M., Elmas, A., Farias, R., Xu, P., Zhou, X., Hopkins, B., Huang, K. L., & Zhang, B. (2023). Multiscale protein networks systematically identify aberrant protein interactions and oncogenic regulators in seven cancer types. Journal of Hematology & Oncology, 16(1), 120.
Forst, C. V., Zeng, L., Wang, Q., Zhou, X., Vatansever, S., Xu, P., Song, W. M., Tu, Z., & Zhang, B. (2023). Multiscale network analysis identifies potential receptors for SARS‐CoV‐2 and reveals their tissue‐specific and age‐dependent expression. FEBS Letters, 597(10), 1384-1402.
Zhang, Z., Luo, L., Xing, C., Chen, Y., Xu, P., Li, M., Zeng, L., Li, C., Ghosh, S., Della Manna, D., et al. (2021). RNF2 ablation reprograms the tumor-immune microenvironment and stimulates durable NK and CD4+ T-cell-dependent antitumor immunity. Nature Cancer 2, 1018-1038.
Dehkordi, S. K., Walker, J., Sah, E., Bennett, E., Atrian, F., Frost, B., Woost, B., Bennett, R. E., Orr, T. C., Zhou, Y., Andhey, P. S., Colonna, M., Sudmant, P. H., Xu, P., Wang, M., Zhang, B., Zare, H., & Orr, M. E. (2021). Profiling senescent cells in human brains reveals neurons with CDKN2D/p19 and tau neuropathology. Nature Aging, 1(12), 1107-1116.
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