Dr. Xiaowei Zhu (朱曉維博士)

PhD (Yale University)

Assistant Professor, Department of Neuroscience

Office: 2-310, 3/F, Block 2, To Yuen Building
Phone: +852 3442-4264
Email: xiazhu@cityu.edu.hk
Web: CityU Scholars

Dr. 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. Dr. 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.

List of Publications

  1. 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., Zhu, X., Zhou, B., Urban, A., Walsh, C., Ganz, J., et al. Machine learning reveals bilateral distribution of somatic L1 insertions in human neurons and glia. Nat. Neurosci. 24, 186–196 (2021).
  2. 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. Comprehensive, integrated, and phased whole-genome analysis of the primary ENCODE cell line K562. Genome Res. 29, 472–484 (2019).
  3. 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. Haplotype-resolved and integrated genome analysis of the cancer cell line HepG2. Nucleic Acids Res. 47, 3846–3861 (2019).
  4. 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. Metabolism of cholesterol and progesterone is differentially regulated in primary trophoblastic subtypes and might be disturbed in recurrent miscarriages. J. Lipid Res. (2019) doi:10.1194/jlr.P093427.
  5. Zhang, X.*, Zhang, Y.*, Zhu, X.*, Purmann, C., Haney, M. S., Ward, T., Khechaduri, A., Yao, J., Weissman, S. M. & Urban, A. E. Local and global chromatin interactions are altered by large genomic deletions associated with human brain development. Nat. Commun. 9, (2018). * co-first author
  6. Velicky, P., Meinhardt, G., Plessl, K., Vondra, S., Weiss, T., Haslinger, P., Lendl, T., Aumayr, K., Mairhofer, M., Zhu, X., Schütz, B., Hannibal, R. L., Lindau, R., Weil, B., Ernerudh, J., Neesen, J., Egger, G., Mikula, M., Röhrl, C., et al. Genome amplification and cellular senescence are hallmarks of human placenta development. PLoS Genet. 14, (2018).
  7. Zhou, B., Haney, M. S., Zhu, X., Pattni, R., Abyzov, A. & Urban, A. E. 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 Digital PCR: Methods and Protocols (eds. Karlin-neumann, G. & Francisco, B.) vol. 1768 173–190 (SPringer Science+Business Media, New York, 2018).
  8. 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. Allele-specific expression reveals interactions between genetic variation and environment. Nat. Methods 14, 699–702 (2017).
  9. 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. Impact of the X chromosome and sex on regulatory variation. Genome Res. 26, 768–777 (2016).
  10. 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. EIF3G is associated with narcolepsy across ethnicities. Eur. J. Hum. Genet. 23, 1573–1580 (2015).
  11. 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., et al. Type I interferon signaling genes in recurrent major depression: Increased expression detected by whole-blood RNA sequencing. Mol. Psychiatry (2014) doi:10.1038/mp.2013.161.
  12. 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. Characterizing the genetic basis of transcriptome diversity through RNA-sequencing of 922 individuals. Genome Res. (2014) doi:10.1101/gr.155192.113.
  13. 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., et al. Hypermethylation in the ZBTB20 gene is associated with major depressive disorder. Genome Biol. 15, (2014).
  14. Mostafavi, S., Battle, A., Zhu, X., Urban, A. E., Levinson, D., Montgomery, S. B. & Koller, D. Normalizing RNA-Sequencing Data by Modeling Hidden Covariates with Prior Knowledge. PLoS One (2013) doi:10.1371/journal.pone.0068141.
  15. Mok, J., Zhu, X. & Snyder, M. Dissecting phosphorylation networks: Lessons learned from yeast. Expert Review of Proteomics (2011) doi:10.1586/epr.11.64.
  16. Zhu, X., Gerstein, M. & Snyder, M. Getting connected: Analysis and principles of biological networks. Genes and Development (2007) doi:10.1101/gad.1528707.
  17. Royce, T. E., Rozowsky, J. S., Luscombe, N. M., Emanuelsson, O., Yu, H., Zhu, X., Snyder, M. & Gerstein, M. B. [15] Extrapolating Traditional DNA Microarray Statistics to Tiling and Protein Microarray Technologies. Methods in Enzymology (2006) doi:10.1016/S0076-6879(06)11015-0.
  18. Zhu, X., Gerstein, M. & Snyder, M. ProCAT: a data analysis approach for protein microarrays. Genome Biol. 7, (2006).
  19. 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
  20. Smith, M. G., Jona, G., Ptacek, J., Devgan, G., Zhu, H., Zhu, X. & Snyder, M. Global analysis of protein function using protein microarrays. in Mechanisms of Ageing and Development (2005). doi:10.1016/j.mad.2004.09.019.
  21. 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., et al. Global analysis of protein phosphorylation in yeast. Nature 438, 679–684 (2005).
  22. 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. Global identification of human transcribed sequences with genome tiling arrays. Science 306, 2242–2246 (2004).
  23. Hall, D. A., Zhu, H., Zhu, X., Royce, T., Gerstein, M. & Snyder, M. Regulation of gene expression by a metabolic enzyme. Science 306, 482–484 (2004).
  24. Yu, H., Greenbaum, D., Lu, H. X., Zhu, X. & Gerstein, M. Genomic analysis of essentiality within protein networks. Trends in Genetics (2004) doi:10.1016/j.tig.2004.04.008.
  25. Yu, H., Luscombe, N. M., Lu, H. X., Zhu, X., Xia, Y., Han, J. D. J., Bertin, N., Chung, S., Vidal, M. & Gerstein, M. Annotation transfer between genomes: Protein-protein interrologs and protein-DNA regulogs. Genome Res. 14, 1107–1118 (2004).
  26. Yu, H., Zhu, X., Greenbaum, D., Karro, J. & Gerstein, M. TopNet: A tool for comparing biological sub-networks, correlating protein properties with topological statistics. Nucleic Acids Res. 32, 328–337 (2004).