Faculty Profile
Xiaowei Zhu (朱曉維)

Prof. Xiaowei Zhu (朱曉維)

Assistant Professor, Department of Neuroscience (NS)

Postdoc (Stanford University)

PhD (Yale University)

Prof. Xiaowei Zhu obtained the BSc in the Special Class for the Gifted Young at the University of Science and Technology of China in 2002. Between 2002 and 2009, he studied bioinformatics and computational biology in Yale University, and received PhD with his research on mapping biological networks using genomic and proteomic approaches. Between 2010 and 2021, he joined the department of psychiatry and behavioral sciences at Stanford University, first as a postdoc and then as a research scientist. During this time, he participated in the Brain Somatic Mosaicism Consortium Network and led the research on the computational and functional analysis of somatic mutations in human brain development and neuropsychiatric disorders. Prof. Xiaowei Zhu joined City University of Hong Kong in 2022 as an assistant professor.

Research Interest

Psychiatric Disorders / Computational Biology / Genomics

The genetic basis for many psychiatric disorders remains elusive. We have previously identified that the highly repetitive mobile element (ME) sequences are actively jumping, and inserting into new genomic regions, during human brain development. These mobile element insertions (MEIs) therefore can disrupt genes with important brain functions and thus may contribute to the pathogenesis of neuropsychic disorders.

Due to the sequence repetitiveness and low frequency in the brain, the study of somatic MEI presents an extremely challenging signal-to-noise problem. The Zhu lab focuses on establishing a machine learning based approach, to accurately detect somatic MEIs using high throughput sequencing. We are also evaluating its application in the diagnostic genetic testing for neuropsychic disorders and other diseases such as cancer.

Furthermore, we aim to establish the definitive evidence that somatic MEI mutations can alter brain functions and contribute to disorders. We have identified highly deleterious MEI mutations in brains from patients with autism spectrum disorders, schizophrenia, and Tourette syndrome. We will also set out a large-scale screen to systematically study their perturbations in transcriptome and proteome. This research will improve our understanding for the genetic basis of neuropsychiatric disorders, which will then shed light on novel treatment approaches.

Position Availability

We are looking for motivated postgraduate students, research assistants or undergraduate students who are interested in computational biology and/or experimental biology. Please send your CV to: xiazhu@cityu.edu.hk.

Recent Publications

2025

  1. Tan M.*, Lin ZN.*, Chen ZF., Zhou HN., Park J., He ZT., Lee EA., Gao ZP., Zhu, X., Image-based DNA sequencing encoding for detecting low-mosaicism somatic mobile element insertions. Nat. Commun. 16, 9195 (2025).

2024

  1. Yu J., Wong S., Lin ZN., Shan ZM., Fan CY., Xia ZY., Cheung M., Zhu, X., Liu JA.*, Cheung CW.* High-Frequency Spinal Stimulation Suppresses Microglial Kaiso-P2X7R Axis-Induced Inflammation to Alleviate Neuropathic Pain in rats. Annals of Neurology. https://doi.org/10.1002/ana.26898 (2024 In press)
  2. Sun C., Kathuria K., Emery S.B., Kim B., Burbulis I.E., Shin J.H., Brain Somatic Mosaicism Network, including Zhu, X., Gleeson J.G., Weinberger D.R., Moran J.V., Kidd J.M., Mills R.E., McConnell M.J. Nature Communications (2024).

2023

  1. Xie, W., Chen, X., Zheng, Z., Wang, F., Zhu, X., Lin, Q., Sun, Y., & Wong, K-C. (2023). LncRNA-Top: Controlled Deep Learning Approaches for LncRNA Gene Regulatory Relationship Annotations across Different Platforms. iScience, 26(11), Article 108197. https://doi.org/10.1016/j.isci.2023.108197
  2. Yang, X., Xu, X., Breuss, M. W., Antaki, D., Ball, L. L., Chung, C., Shen, J., Li, C., George, R. D., Wang, Y., Bae, T., Cheng, Y., Abyzov, A., Wei, L., Alexandrov, L. B., Sebat, J. L., NIMH Brain Somatic Mosaicism Network, 102 authors, including, Zhu, X., & Gleeson, J. G. (2023). Control-independent mosaic single nucleotide variant detection with DeepMosaic. Nature Biotechnology, 41(6), 870–877. Advance online publication. https://doi.org/10.1038/s41587-022-01559-w
  3. 202 authors, including, Chung, C., Focal Cortical Dysplasia Neurogenetics Consortium, including, Brain Somatic Mosaicism Network, including, Zhu, X., & Gleeson, J. G. (2023). Comprehensive multi-omic profiling of somatic mutations in malformations of cortical development. Nature Genetics, 55(2), 209–220. Advance online publication. https://doi.org/10.1038/s41588-022-01276-9
  4. Akter, M., Ma, H., Hasan, M., Karim, A., Zhu, X., Zhang, L., & Li, Y. (2023). Exogenous L-lactate administration in rat hippocampus increases expression of key regulators of mitochondrial biogenesis and antioxidant defence. Frontiers in Molecular Neuroscience, 16, Article 1117146. Advance online publication. https://doi.org/10.3389/fnmol.2023.1117146
  5. Lin, Z., Huang, Y., Liu, S., Huang, Q., Zhang, B., Wang, T., Zhang, Z., Zhu, X., Liao, C., & Han, Q. (2023). Gene coexpression network during ontogeny in the yellow fever mosquito, Aedes aegypti. BMC Genomics, 24, Article 301. Advance online publication. https://doi.org/10.1186/s12864-023-09403-4

2022

  1. Breuss, MW, Yang, X, Schlachetzki, JCM, Antaki, D, Lana, AJ, Xu, X, Chung, C, Chai, G, Stanley, V, Song, Q, Newmeyer, TF, Nguyen, A, O’Brien, S, Hoeksema, MA, Cao, B, Nott, A, McEvoy-Venneri, J, Pasillas, MP, Barton, ST, Copeland, BR, Nahas, S, Van Der Kraan, L, Ding, Y, NIMH Brain Somatic Mosaicism Network, including, Zhu, X., Glass, CK & Gleeson, JG. Somatic mosaicism reveals clonal distributions of neocortical development, Nature, 604, 689–696 (2022).

2021

  1. Zhang, E. T., Hannibal, R. L., Badillo Rivera, K. M., Song, J. H. T., McGowan, K., Zhu, X., Meinhardt, G., Knöfler, M., Pollheimer, J., Urban, A. E., Folkins, A. K., Lyell, D. J., & Baker, J. C. (2021). PRG2 and AQPEP are misexpressed in fetal membranes in placenta previa and percreta. Biology of Reproduction, 105(1), 244–257. Advance online publication. https://doi.org/10.1093/biolre/ioab068
  2. Zhu, X., Zhou, B., Pattni, R., Gleason, K., Tan, C., Kalinowski, A., Sloan, S., Fiston-Lavier, A. S., Mariani, J., Petrov, D., Barres, B. A., Duncan, L., Abyzov, A., Vogel, H., Urban, A., Walsh, C., Ganz, J., et al. (2021) Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia. Nat. Neurosci. 24, 186–196.
  3. Rodin, R. E., Dou, Y., Kwon, M, Sherman, M. A., D’Gama, A., Doan, R. N., Rento, L., Girskis, K. M., Luquette, L. J., Gulhan, D. C., Brain Somatic Mosaicism Network, including, Zhu, X., Park, P. J., Walsh, C. A. (2021) The landscape of somatic mutation in cerebral cortex of autistic and neurotypical individuals revealed by ultra-deep whole-genome sequencing. Nature Neuroscience, 24(2), 176–185.

2019

  1. Vondra, S., Kunihs, V., Eberhart, T., Eigner, K., Bauer, R., Haslinger, P., Haider, S., Windsperger, K., Klambauer, G., Schütz, B., Mikula, M., Zhu, X., Urban, A. E., Hannibal, R. L., Baker, J., Knöfler, M., Stangl, H., Pollheimer, J., & Röhrl, C. (2019). Metabolism of cholesterol and progesterone is differentially regulated in primary trophoblastic subtypes and might be disturbed in recurrent miscarriages. Journal of Lipid Research, 60(11), 1922–1934.
  2. Zhou, B., Ho, S. S., Greer, S. U., Spies, N., Bell, J. M., Zhang, X., Zhu, X., Arthur, J. G., Byeon, S., Pattni, R., Saha, I., Huang, Y., Song, G., Perrin, D., Wong, W. H., Ji, H. P., Abyzov, A., & Urban, A. E. (2019). Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2. Nucleic Acids Research, 47(8), 3846–3861. https://doi.org/10.1093/nar/gkz169
  3. Zhou, B., Ho, S. S., Greer, S. U., Zhu, X., Bell, J. M., Arthur, J. G., Spies, N., Zhang, X., Byeon, S., Pattni, R., Ben-Efraim, N., Haney, M. S., Haraksingh, R. R., Song, G., Ji, H. P., Perrin, D., Wong, W. H., Abyzov, A., & Urban, A. E. (2019). Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562. Genome Research, 29(3), 472–484. https://doi.org/10.1101/gr.234948.118

Before 2019

  1. Zhang, X.*, Zhang, Y.*, Zhu, X.*, Purmann, C., Haney, M. S., Ward, T., Khechaduri, A., Yao, J., Weissman, S. M. & Urban, A. E. (2018) Local and global chromatin interactions are altered by large genomic deletions associated with human brain development. Nat. Commun. 9. * co-first author
  2. Zhou, B., Haney, M. S., Zhu, X., Pattni, R., Abyzov, A., & Urban, A. E. (2018). Detection and quantification of mosaic genomic DNA variation in primary somatic tissues using ddPCR: analysis of mosaic transposable-element insertions, copy-number variants, and single-nucleotide variants. In Methods in Molecular Biology (Vol. 1768, pp. 173–190). Humana Press Inc.
  3. Knowles, D. A., Davis, J. R., Edgington, H., Raj, A., Favé, M-J., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Levinson, D. F., Awadalla, P., Mostafavi, S., Montgomery, S. B., & Battle, A. (2017). Allele-specific expression reveals interactions between genetic variation and environment. Nature Methods, 14(7), 699–702. https://doi.org/10.1038/nmeth.4298
  4. Kukurba, K. R., Parsana, P., Balliu, B., Smith, K. S., Zappala, Z., Knowles, D. A., Favé, M-J., Davis, J. R., Li, X., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Kundaje, A., Levinson, D. F., Awadalla, P., Mostafavi, S., Battle, A., & Montgomery, S. B. (2016). Impact of the X chromosome and sex on regulatory variation. Genome Research, 26(6), 768–777.
  5. Holm, A., Lin, L., Faraco, J., Mostafavi, S., Battle, A., Zhu, X., Levinson, D. F., Han, F., Gammeltoft, S., Jennum, P., Mignot, E., & Kornum, B. R. (2015). EIF3G is associated with narcolepsy across ethnicities. European Journal of Human Genetics, 23(11), 1573–1580. https://doi.org/10.1038/ejhg.2015.4
  6. Mostafavi, S., Battle, A., Zhu, X., Potash, J. B., Weissman, M. M., Shi, J., Beckman, K., Haudenschild, C., Mccormick, C., Mei, R., Gameroff, M. J., Gindes, H., Adams, P., Goes, F. S., Mondimore, F. M., Mackinnon, D. F., Notes, L., Schweizer, B., Furman, D., … Levinson, D. F. (2014). Type I interferon signaling genes in recurrent major depression: Increased expression detected by whole-blood RNA sequencing. Molecular Psychiatry, 19(12), 1267–1274. https://doi.org/10.1038/mp.2013.161
  7. Davies, M. N., Krause, L., Bell, J. T., Gao, F., Ward, K. J., Wu, H., Lu, H., Liu, Y., Tsai, P-C., Collier, D. A., Murphy, T., Dempster, E., Mill, J., Battle, A., Mostafavi, S., Zhu, X., Henders, A., Byrne, E., Wray, N. R., … Wang, J. (2014). Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biology, 15(4), Article R56. https://doi.org/10.1186/gb-2014-15-4-r56
  8. Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., Urban, A. E., Montgomery, S. B., Levinson, D. F., & Koller, D. (2014). Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Research, 24(1), 14–24. https://doi.org/10.1101/gr.155192.113
  9. Mostafavi, S., Battle, A., Zhu, X., Urban, A. E., Levinson, D., Montgomery, S. B., & Koller, D. (2013). Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge. PLOS ONE, 8(7), Article e68141. https://doi.org/10.1371/journal.pone.0068141
  10. Mok, J., Zhu, X., & Snyder, M. (2011). Dissecting phosphorylation networks: Lessons learned from yeast. Expert Review of Proteomics, 8(6), 775–786. https://doi.org/10.1586/epr.11.64
  11. Zhu, X., Gerstein, M., & Snyder, M. (2007). Getting connected: Analysis and principles of biological networks. Genes and Development, 21(9), 1010-1024. https://doi.org/10.1101/gad.1528707
  12. Zhu, H.*, Hu, S.*, Jona, G.*, Zhu, X.*, Kreiswirth, N., Willey, B. M., Mazzulli, T., Liu, G., Song, Q., Chen, P., Cameron, M., Tyler, A., Wang, J., Wen, J., Chen, W., Compton, S. & Snyder, M. Severe acute respiratory syndrome diagnostics using a coronavirus protein microarray. Proc. Natl. Acad. Sci. U. S. A. 103, 4011-4016 (2006). * co-first author
  13. Royce, T. E., Rozowsky, J. S., Luscombe, N. M., Emanuelsson, O., Yu, H., Zhu, X., Snyder, M., & Gerstein, M. B. (2006). [15] Extrapolating Traditional DNA Microarray Statistics to Tiling and Protein Microarray Technologies. Methods in Enzymology, 411, 282-311. https://doi.org/10.1016/S0076-6879(06)11015-0
  14. Zhu, X., Gerstein, M., & Snyder, M. (2006). ProCAT: a data analysis approach for protein microarrays. Genome Biology, 7(11). https://doi.org/10.1186/gb-2006-7-11-r110
  15. Ptacek, J., Devgan, G., Michaud, G., Zhu, H., Zhu, X., Fasolo, J., Guo, H., Jona, G., Breitkreutz, A., Sopko, R., McCartney, R. R., Schmidt, M. C., Rachidi, N., Lee, S.-J., Mah, A. S., Meng, L., Stark, M. J. R., Stern, D. F., De Virgilio, C., … Snyder, M. (2005). Global analysis of protein phosphorylation in yeast. Nature, 438(7068), 679-684. https://doi.org/10.1038/nature04187
  16. Smith, M. G., Jona, G., Ptacek, J., Devgan, G., Zhu, H., Zhu, X., & Snyder, M. (2005). Global analysis of protein function using protein microarrays. Mechanisms of Ageing and Development, 126(1), 171-175. https://doi.org/10.1016/j.mad.2004.09.019
  17. Bertone, P., Stolc, V., Royce, T. E., Rozowsky, J. S., Urban, A. E., Zhu, X., Rinn, J. L., Tongprasit, W., Samanta, M., Weissman, S., Gerstein, M., & Snyder, M. (2004). Global identification of human transcribed sequences with genome tiling arrays. Science, 306(5705), 2242-2246. https://doi.org/10.1126/science.1103388
  18. Hall, D. A., Zhu, H., Zhu, X., Royce, T., Gerstein, M., & Snyder, M. (2004). Regulation of gene expression by a metabolic enzyme. Science, 306(5695), 482-484. https://doi.org/10.1126/science.1096773
  19. Yu, H., Greenbaum, D., Lu, H. X., Zhu, X., & Gerstein, M. (2004). Genomic analysis of essentiality within protein networks. Trends in Genetics, 20(6), 227-231. https://doi.org/10.1016/j.tig.2004.04.008
  20. Yu, H., Luscombe, N. M., Lu, H. X., Zhu, X., Xia, Y., Han, J.-D. J., Bertin, N., Chung, S., Vidal, M., & Gerstein, M. (2004). Annotation transfer between genomes: Protein-protein interrologs and protein-DNA regulogs. Genome Research, 14(6), 1107-1118. https://doi.org/10.1101/gr.1774904
  21. Yu, H., Zhu, X., Greenbaum, D., Karro, J., & Gerstein, M. (2004). TopNet: A tool for comparing biological sub-networks, correlating protein properties with topological statistics. Nucleic Acids Research, 32(1), 328-337. https://doi.org/10.1093/nar/gkh164

14 November 2025

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