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Tianmin Wang, PhDAssistant Professor

Email: wangtm@shanghaitech.edu.cn

Faculty KMS Profile

中文信息English
Systems Biology of Bacterial Drug Tolerance Group

Principal investigator

Name:

Tianmin WangAssistant Professor , PhD, Assistant Professor

Affiliation:

School of Life Science and Technology

Education Background:
  • 2008/09-2012/07, Peking University, Bachelor
  • 2013/09-2015/07, Tokyo Institute of Technology, Master (THU-TIT exchange program)
  • 2012/08-2018/07, Tsinghua University, Ph.D.
Working Experience:
  • 2019/01-2023/03, Tsinghua University, School of Medicine, Postdoctoral Researcher
  • 2023/05-Now, ShanghaiTech University, School of Life Science and Technology, Assistant Professor (Tenure-Track)

Group Introduction

Research Area:
Bacterial Drug Tolerance, Systems Biology, Spatiotemporal Multiomics, Bacterial Genomics, Biofilm, Microfluidics
Research Interests:

Antibiotics have been one of the most significant medical discoveries in the 20th century and form a crucial foundation for the functioning of modern healthcare systems. However, the current pace of discovering new antibiotics has slowed down, while pathogenic microorganisms are increasingly demonstrating enhanced resistance at both phenotypic and genotypic levels. This has led to the looming antibiotic crisis, which poses a major challenge to public health in this century. In this context, comprehending the molecular mechanisms underlying the generation, development, and evolution of bacterial resistance at different scales can maximize the killing efficacy of existing antibiotics and minimize the probability of resistance emergence. Furthermore, it can aid in the discovery of novel molecular targets to facilitate the design of new drugs, thereby providing a solid scientific foundation for addressing the antibiotic crisis.

 

Our research group is dedicated to systematically studying the molecular mechanisms of bacterial phenotypic tolerance and the dynamics of genetic resistance evolution at various scales, including the molecular, cellular, and community levels. The laboratory integrates cutting-edge experimental techniques (spatial transcriptomics, high-throughput genetic screening, microfluidics, single-cell technologies, time-lapse fluorescence microscopy, etc) and computational approaches (bacterial comparative genomics, machine learning, stochastic dynamical modeling, etc), aiming to understand the drug tolerance behaviors of diverse pathogens at different scales, with a particular emphasis on unveiling how tolerance (or susceptibility) emerges from the coordination (or dysregulation) of biological networks from a systems biology perspective.During this process, we will also utilize microfluidics, next-generation sequencing, and nanopore sequencing platforms to develop innovative technologies, aiming to provide tools for basic microbiology research, mining novel antimicrobials and enabling rapid diagnosis of drug-resistant bacteria in clinical settings.

Group Website:
Under construction

Research Achievement

  • We developed RAINBOW-seq to profile the spatial transcriptome of bacterial community with a resolution of 50 microns, uncovered rich coordination of metabolism within E. coli biofilm (Nat. Chem. Biol. 2023);


  • Using spatial transcriptomics, we discovered that, akin to spatially structured mammalian tissues, bacterial community actively maintains spatial homeostasis via latent regulations; and such preserved architecture is crucial for emergent drug tolerance in pathogenic biofilms (in preparation);


  • Use deep mutational scanning and stochastic modeling, we showed that how precisely coordinated dynamics of transcription-translation modularly tune bacterial indole signaling via TnaC sensor (Nat. Chem. Biol. 2020);


  • For the first time in bacteria, we established CRISPRi-seq method for functional genomics study in a pooled screen manner, which led to new biology such as tRNA essentiality and stress-tolerance-related gene atlas (Nat. Commun. 2018);


  • Combining high-throughput screen and machine learning, we decoded the relation of sgRNA sequence with on/off-target activity in CRISPR/Cas9 genome engineering in living bacterial cells, giving rise to predictive models to design active and specific sgRNA library in bacteria (Nucleic Acids Res. 2018, 2021).


Representative Publications (*First Author, # Corresponding Author)

Awards

  • 1. 2018/11, Distinguished Postdoctoral Fellowship, Tsinghua-Peking Center for Life Sciences
  • 2. 2019/04, National Postdoctoral Innovation Talent Support Program
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