ZHU Yan----Tianjin Institute of Industrial Biotechnology Chinese Academy of Sciences

ZHU Yan, Ph.D.

Investigator, TIB, Tianjin, China

E-mail: zhuyan@tib.cas.cn

Professional Experience

Sep 2002 — Jul 2006 BSc in Biotechnology, Shandong University

Sep 2006 — Jan 2013 PhD in Microbiology, University of the Chinese Academy of Sciences

Education

May 2013 — Dec 2014 Postdoctoral Research Fellow, the University of Queensland, Brisbane, Australia

Jan 2015 — Nov 2022 Postdoctoral Research Fellow, Monash University, Melbourne, Australia

Nov 2022 — Professor, Tianjin Institute of Industrial Biotechnology, Chinese Academy of Sciences, Tianjin, China

Research Interests

The growing environmental concern of fossil-based chemical production urges the development of green biorefinery using microorganisms. However, biorefinery is often limited by low yield, poor productivity, and industrial harsh conditions. Systems biology offers unprecedented opportunities to dissect the complicated interplay of multiple biological pathways underlying the microbial metabolism and physiology. Using important chassis microorganisms (e.g. Corynebacterium glutamicum) as models, we aim to integrate cutting-edge multi-omics approaches to elucidate the mechanisms of cellular responses to stresses (e.g. low pH, heat, hyperosmolality, toxic substrates) and metabolic changes during fermentation, and develop virtual cell models to accurately simulate all cellular activities (e.g. DNA replication, transcription, translation, metabolism, macromolecule complexation) that occur in a single cell, thereby facilitating the development of effective engineering strategies to improve bioproduction.

Microbial communities play important roles in industrial production of food, beverages and probiotics. We are also interested in how community members interact with environment and with each other during these dynamic fermentation processes. Using meta-omics data, we aim to construct virtual microbial community models to decipher the mechanisms of these complicated interactions, which would open the gate for rational design of novel microbial consortia with improved performance.

Selected publications

Prof Zhu has published 85 peer-reviewed papers (20 as first, or/and senior author, with >1,500 cites, h-index 24), two book chapters. His first-authored research paper on polymyxin-dependence was featured on the front cover of Advanced Science in 2020. The novel synthetic lipopeptide antibiotic paper published on Nature Communications 2022 was selected as Editorials’ Highlights.

1. Chung WY, Abdul Rahim N, Mahamad Maifiah MH, Hawala Shivashekaregowda NK, Zhu Y*, Wong EH*. In silico genome-scale metabolic modeling and in vitro static time-kill studies of exogenous metabolites alone and with polymyxin B against Klebsiella pneumoniae. Front Pharmacol. 2022;13:880352.

2. Jin P, Dai J, Guo Y, Wang X, Lu J, Zhu Y*, Yu F*. Genomic Analysis of Mycobacterium abscessus Complex Isolates from Patients with Pulmonary Infection in China. Microbiol Spectr. 2022;10:e0011822.

3. Guo Y, Su L, Liu Q, Zhu Y*, Dai Z*, Wang Q. Dissecting carbon metabolism of Yarrowia lipolytica type strain W29 using genome-scale metabolic modelling. Comput Struct Biotechnol J. 2022;20:2503-2511.

4. Zhu Y*, Zhao J, Li J. Genome-scale metabolic modelling in antimicrobial pharmacology. Engineering Microbiology. 2022:100021.

5. Roberts KD^#, Zhu Y^#, Azad MAK^#, Han ML, Wang J, Wang L, Yu HH, Horne AS, Pinson JA, Rudd D, Voelcker NH, Patil NA, Zhao J, Jiang X, Lu J, Chen K, Lomovskaya O, Hecker SJ, Thompson PE, Nation RL, Dudley MN, Griffith DC, Velkov T, Li J. A synthetic lipopeptide targeting top-priority multidrug-resistant Gram-negative pathogens. Nat Commun. 2022;13:1625.

6. Bin Hafeez A, Jiang X, Bergen PJ, Zhu Y*. Antimicrobial Peptides: An Update on Classifications and Databases. Int J Mol Sci. 2021;22:11691.

7. Dai Z^#, Zhu Y^#, Dong H, Zhao C, Zhang Y, Li Y. Enforcing ATP hydrolysis enhanced anaerobic glycolysis and promoted solvent production in Clostridium acetobutylicum. Microb Cell Fact. 2021;20:149.

8. Zhu Y^#, Lu J^#, Han ML^#, Jiang X^#, Azad MAK, Patil NA, Lin YW, Zhao J, Hu Y, Yu HH, Chen K, Boyce JD, Dunstan RA, Lithgow T, Barlow CK, Li W, Schneider-Futschik EK, Wang J, Gong B, Sommer B, Creek DJ, Fu J, Wang L, Schreiber F, Velkov T, Li J. Polymyxins Bind to the Cell Surface of Unculturable Acinetobacter baumannii and Cause Unique Dependent Resistance. Adv Sci. 2020;7:2000704.

9. Zhao J, Zhu Y*, Han J, Lin YW, Aichem M, Wang J, Chen K, Velkov T, Schreiber F, Li J*. Genome-Scale Metabolic Modeling Reveals Metabolic Alterations of Multidrug-Resistant Acinetobacter baumannii in a Murine Bloodstream Infection Model. Microorganisms. 2020;8:1793.

10. Li M, Aye SM, Ahmed MU, Han ML, Li C, Song J, Boyce JD, Powell DR, Azad MAK, Velkov T, Zhu Y*, Li J*. Pan-transcriptomic analysis identified common differentially expressed genes of Acinetobacter baumannii in response to polymyxin treatments. Mol Omics. 2020;16:327-338.

11. Huang J, Chen L, Song J, Velkov T, Wang L, Zhu Y*, Li J*. Regulating Polymyxin Resistance in Gram-negative Bacteria: Roles of Two-Component Systems PhoPQ and PmrAB. Future Microbiol. 2020;15:445-459.

12. Zhu Y*, Lu J, Zhao J, Zhang X, Yu HH, Velkov T, Li J*. Complete genome sequence and genome-scale metabolic modelling of Acinetobacter baumannii type strain ATCC 19606. Int J Med Microbiol. 2020;310:151412.

13. Zhu Y^#*, Zhao J^#, Maifiah MHM, Velkov T, Schreiber F, Li J. Metabolic Responses to Polymyxin Treatment in Acinetobacter baumannii ATCC 19606: Integrating Transcriptomics and Metabolomics with Genome-Scale Metabolic Modeling. mSystems. 2019;4:e00157-18.

14. Zhu Y, Galani I, Karaiskos I, Lu J, Aye SM, Huang J, Yu HH, Velkov T, Giamarellou H, Li J. Multifaceted mechanisms of colistin resistance revealed by genomic analysis of multidrug-resistant Klebsiella pneumoniae isolates from individual patients before and after colistin treatment. J Infect. 2019;79:312-321.

15. Zhu Y, Czauderna T, Zhao J, Klapperstueck M, Maifiah MHM, Han ML, Lu J, Sommer B, Velkov T, Lithgow T, Song J, Schreiber F, Li J. Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa. Gigascience. 2018;7:giy021.

16. Zhu Y^#, Pham TH^#, Nhiep TH^#, Vu NM^#, Marcellin E, Chakrabortti A, Wang Y, Waanders J, Lo R, Huston WM, Bansal N, Nielsen LK, Liang ZX, Turner MS. Cyclic-di-AMP synthesis by the diadenylate cyclase CdaA is modulated by the peptidoglycan biosynthesis enzyme GlmM in Lactococcus lactis. Mol Microbiol. 2016;99:1015-27.

17. Zhu Y^#, Li D^#, Bao G, Wang S, Mao S, Song J, Li Y, and Zhang Y. Metabolic changes in Klebsiella oxytoca in response to low oxidoreduction potential, as revealed by comparative proteomic profiling integrated with flux balance analysis. Appl Environ Microbiol. 2014;80:2833-41.

18. Zhu Y, Song J, Xu Z, Sun J, Zhang Y, Li Y, and Ma Y. Development of thermodynamic optimum searching (TOS) to improve the prediction accuracy of flux balance analysis. Biotechnol Bioeng. 2013;110:914-23.

19. Zhu L^#, Zhu Y^#, Zhang Y, and Li Y. Engineering the robustness of industrial microbes through synthetic biology. Trends Microbiol. 2012;20:94-101.

20. Zhu Y, Zhang Y, and Li Y. Understanding the industrial application potential of lactic acid bacteria through genomics. Appl Microbiol Biotechnol. 2009;83:597-610.