Shijie Zhao, Ph.D.

Assistant Investigator, NIBS, Beijing

Email: zhaoshijie@nibs.ac.cn

Education

2019	Ph.D. in Computational and Systems Biology, Massachusetts Institute of Technology, Cambridge, MA, USA
2013	B.S. in Biological Sciences, Peking University, Beijing, China

Professional Experience

2026-present	Assistant Investigator, NIBS, Beijing
2023-2024	Postdoctoral Research Associate, Broad Institute of MIT and Harvard, Cambridge, MA, USA
2021-2022	Postdoctoral Research Associate, Columbia University, New York, NY, USA
2019-2021	Postdoctoral Research Associate, Massachusetts Institute of Technology, Cambridge, MA, USA

Research Description

Microbes carry out much of the chemistry that sustains life. They shape ecosystems, influence human health, and enable modern biotechnology. Advances in DNA sequencing now allow microbial genomes to be read at massive scale. Yet for most microbes, we still cannot reliably predict their functions, explain their behavior in communities, or engineer them. The central challenge is no longer data collection, but converting genomic information into mechanistic and predictive understanding.

Our research program addresses this challenge by building systematic experimental platforms to measure microbial behavior and applying quantitative modeling to extract general biological rules from these data. Because the space of genetic variation and environmental conditions is far too large to explore exhaustively, models guide experiment selection and hypothesis generation. The lab operates in an iterative cycle of experimentation, modeling, and validation, with the biological goal of understanding how microbial genetic variation shapes colonization, metabolism, and host interaction.

My past work established the key tools for this approach, including mapping microbial evolution within hosts, developing single-cell and spatial genomics to resolve strain organization in complex communities, and constructing large living strain collections for controlled perturbation. Together, these foundations support a research program that moves microbiome science from descriptive surveys toward causal and predictive biology. We pursue three integrated research directions:

1. Predictive maps of microbial behaviors

We will quantify how tens of thousands of microbial strains grow, interact, and adapt across diverse environmental and host-relevant conditions. These experiments will generate quantitative maps linking genetic variation to regulatory control, metabolic state, and ecological fitness. The outcome will be predictive frameworks for how strains and communities respond to perturbations such as dietary change, drug exposure, or host physiological shifts.

2. Discovery of molecular functions from nature’s dark matter

Microbial genomes contain vast numbers of genes whose functions cannot be reliably inferred from sequence alone, leaving much of microbial biochemistry unexplored. Testing this space with synthetic DNA libraries remains costly and limited in scale. We exploit natural strain diversity and large living strain collections as a cost-effective and information-rich alternative. By pairing these resources with functional screening, we aim to uncover new molecular activities in small-molecule biosynthesis, plasmid maintenance, phage resistance, and host-associated functions. These discoveries will define sequence–function relationships and provide mechanistic entry points for biochemical and genetic investigation.

3. Engineering previously intractable microbes

Many microbes that dominate human-associated ecosystems remain inaccessible to genetic manipulation. Progress requires not only developing new tools, but first uncovering the biological principles that govern how these organisms interact with foreign genetic material and regulate genome stability. We will therefore combine investigation of mobile genetic elements, plasmid compatibility, and host restriction systems with systematic development of DNA delivery and editing strategies. The outcome will be broadly applicable genetic platforms that enable direct testing of gene function and controlled perturbation of microbial pathways within complex communities.

Publications

1. Shijie Zhao, Tami Lieberman, M. Poyet, K. Kauffman, S. Gibbons, M. Groussin, Ramnik Xavier, Eric Alm. Adaptive evolution within the gut microbiome of healthy people. Cell Host & Microbe. 2019.

2.       Wenshan Zheng, Shijie Zhao, Yehang Yin, Huidan Zhang, …, Peter J. Lu, Eric J. Alm, David A. Weitz. High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome, Science. 2022

3. Shijie Zhao, C. Dai, Z. Lu, E. Evans, Eric Alm. Tracking strains predicts personal microbiomes and reveals adaptive evolution within adult twins. BioRxiv. 2020

4.       Miles Richardson, Shijie Zhao, Ravi Sheth, Thomas Moody, …, Harris H. Wang. SAMPL-seq reveals micron-scale spatial hubs in the human gut microbiome. Nature Microbiology, 2025.

5.       Yiming Huang, Ravi U. Sheth, Shijie Zhao, …, Harris H. Wang. High-throughput microbial culturomics using automation and machine learning. Nature Biotechnology, 2022.

6.       I. Brito, T. Gurry, Shijie Zhao, K. Huang, S. Young, T. Shea, W. Naisilisili, A. Jenkins, S. Jupiter, D. Gevers, Eric Alm. Transmission of human-associated microbiota along family and social networks. Nature Microbiology, 2019.

7.       J. Schnitzbauer, Y. Wang, Shijie Zhao, M. Bakalar, T. Nuwal, B. Chen, Bo Huang. Correlation analysis framework for localization-based super-resolution microscopy. PNAS, 2018

8.       F. Wu, A. Xiao, …, Shijie Zhao, J. Thompson, Eric J Alm, SARS-CoV-2 titers in wastewater foreshadow dynamics and clinical presentation of new COVID-19 cases, Science of The Total Environment, 2022

9.       An-Ni Zhang, Jeffry M Gaston, Chengzhen L Dai, Shijie Zhao, …, Eric J Alm, Tong Zhang. An omics-based framework for assessing the health risk of antimicrobial resistance genes. Nature Communications, 2021

10.   Xiaoqiong Gu, Jean XY Sim, …, Shijie Zhao, Zhanyi Lee, Janelle R Thompson, Eng Eong Ooi, Jenny G Low, Eric J Alm, Shirin Kalimuddin. Gut Ruminococcaceae Levels at Baseline Correlate with Risk of Antibiotic-associated Diarrhea. iScience, 2021

11.   Chenhao Li, Martin Stražar, Ahmed Mohamed, Julian Pacheco, Rebecca Walker, Shijie Zhao, …, Damian R Plichta, Ramnik J Xavier. Gut microbiome and metabolome profiling in Framingham heart study reveals cholesterol-metabolizing bacteria. Cell, 2024

12.   An-Ni Zhang, Jeffry Gaston, Pablo Cárdenas, Shijie Zhao, Xiaoqiong Gu, Eric J Alm. CRISPR spacer acquisition is a rare event in human gut microbiome. Cell Genomics, 2025.

13.   M. Poyet, M. Groussin, S. Gibbons, …, Shijie Zhao, et al. A library of human gut bacterial isolates paired with longitudinal multiomics data enables mechanistic microbiome research. Nature Medicine, 2019

14.   Y. Shen, N.C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, Shijie Zhao, H. Larochelle, D. Englund, M. Soljačić. Deep Learning with coherent nanophotonic circuits. Nature Photonics, 2017