Guojie Zhong

Columbia University

Dept. of Systems Biology

1130 St. Nicholas Avenue

New York, NY 10032

I am currently a PhD student in Dr. Yufeng Shen’s lab at Department of Systems Biology, Columbia University. My main research interests are in computational genomics and human genetics.

My current research is about developing deep learning tools to predict modes of action of missense variants with three aims:

Building efficient models for modes of action prediction from the representations of protein sequences, structures, and functions by protein language models, see project RESCVE.
Curating benchmark datasets on modes of action prediction from different domains, including clinical genetics, deep mutational scan experiments and protein engineering.
Applying the deep learning model to reveal the modes of action of missense variants on several diseases including neurodevelopmental disorders, autism, and congenital heart disease.

My previous PhD research experiences including:

Developing statistical learning methods that leverage large-scale exome sequencing and single cell sequencing data to understand human disease genetics, see project VBASS.
Cross-disease genetics of several developmental disorders including autism, congenital heart disease, congenital diaphragmatic hernia, etc.

I am always facinated by the application of novel machine learning algorithms to biological questions, especially in the genetics and genomics of human disease. These days I am exploring Transformer based Protein Language Models, Bayesian Graphical models, Variational Autoencoders.

Prior to Columbia, I got my B.S. in Integrated Science Program at Peking University with training mostly in biology, statistics and computer science. I joined Dr. Zemin Zhang’s Lab for my undergraduate thesis on developing computational methods to infer cellular spatial organization and cellular interaction from single cell genomics data, see project CSOmap.

I enjoy playing fingerstyle guitar and badminton in my free time, which both require a balance of power and control.

selected publications

MLSB 2022
Representation of missense variants for predicting modes of action

G. Zhong, and Y. Shen

Machine Learning in Structural Biology, Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS), 2022

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Accurate prediction of functional impact for missense variants is fundamental for genetic analysis and clinical applications. Current methods focused on generating an overall pathogenicity prediction score while overlooking the fact that variant effect should be multi-dimensional via different modes of action, such as gain or loss of function, and loss of folding stability or enzymatic activity. Recent breakthrough of high-capacity language models enabled ab initio prediction of protein structures as well as self-supervised representation learning of protein sequence and functions. Here we present RESCVE, a method to learn universal representation of sequence variation from protein context. We demonstrated the utility of the method predicting a range of modes of action for missense variants through transfer learning.
@article{RN10, author = {Zhong, G. and Shen, Y.}, title = {Representation of missense variants for predicting modes of action}, booktitle = {Machine Learning in Structural Biology, Workshop at the 36th Conference on Neural Information Processing Systems}, %type = {Conference Proceedings}, journal = {Machine Learning in Structural Biology, Workshop at the 36th Conference on Neural Information Processing Systems (NeurIPS),}, year = {2022}, }
Statistical models of the genetic etiology of congenital heart disease

G. Zhong, and Y. Shen

Curr Opin Genet Dev, 2022

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Congenital heart disease (CHD) is a collection of anatomically and clinically heterogeneous structure anomalies of heart at birth. Finding genetic causes of CHD can not only shed light on developmental biology of heart, but also provide basis for improving clinical care and interventions. The optimal study design and analytical approaches to identify genetic causes depend on the underlying genetic architecture. A few well-known syndromes with CHD as core conditions, such as Noonan and CHARGE, have known monogenic causes. The genetic causes of most of CHD patients, however, are unknown and likely to be complex. In this review, we highlight recent studies that assume a complex genetic architecture of CHD with two main approaches. One is genomic sequencing studies aiming for identifying rare or de novo risk variants with large genetic effect. The other is genome-wide association studies optimized for common variants with moderate genetic effect.
@article{RN9, author = {Zhong, G. and Shen, Y.}, title = {Statistical models of the genetic etiology of congenital heart disease}, journal = {Curr Opin Genet Dev,}, volume = {76}, pages = {101967}, issn = {1879-0380 (Electronic) 0959-437X (Linking)}, doi = {10.1016/j.gde.2022.101967}, url = {https://www.ncbi.nlm.nih.gov/pubmed/35939966}, year = {2022}, %type = {Journal Article}, }
Integration of gene expression data in Bayesian association analysis of rare variants

G. Zhong, Y. A. Choi, and Y. Shen

bioRxiv, 2022

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We present two new methods, xTADA and VBASS (Variational inference Bayesian ASSociation), that integrate expression data to improve power of rare variants association analysis. Optimized for bulk RNA-seq and single-cell transcriptomics data respectively, xTADA and VBASS model the association of disease risk as a function of expression profiles of relevant tissue or cell types in Bayesian frameworks. On simulated data, both methods show proper error rate control and better power than extTADA, the state-of-the-art Bayesian method. We applied the methods to published datasets and identified more candidate risk genes than extTADA with supports from literature or data from independent cohorts.
@article{RN8, author = {Zhong, G. and Choi, Y. A. and Shen, Y.}, title = {Integration of gene expression data in Bayesian association analysis of rare variants}, journal = {bioRxiv,}, pages = {2022.05.13.491893}, doi = {10.1101/2022.05.13.491893}, year = {2022}, %type = {Journal Article}, }
Identification and validation of candidate risk genes in endocytic vesicular trafficking associated with esophageal atresia and tracheoesophageal fistulas

G. Zhong*, P. Ahimaz*, N. A. Edwards*, J. J. Hagen, C. Faure, and 13 more authors

HGG Adv, 2022

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Esophageal atresias/tracheoesophageal fistulas (EA/TEF) are rare congenital anomalies caused by aberrant development of the foregut. Previous studies indicate that rare or de novo genetic variants significantly contribute to EA/TEF risk, and most individuals with EA/TEF do not have pathogenic genetic variants in established risk genes. To identify novel genetic contributions to EA/TEF, we performed whole genome sequencing of 185 trios (probands and parents) with EA/TEF, including 59 isolated and 126 complex cases with additional congenital anomalies and/or neurodevelopmental disorders. There was a significant burden of protein altering de novo coding variants in complex cases (p=3.3e-4), especially in genes that are intolerant of loss of function variants in the population. We performed simulation analysis of pathway enrichment based on background mutation rate and identified a number of pathways related to endocytosis and intracellular trafficking that as a group have a significant burden of protein altering de novo variants. We assessed 18 variants for disease causality using CRISPR-Cas9 mutagenesis in Xenopus and confirmed 13 with tracheoesophageal phenotypes. Our results implicate disruption of endosome-mediated epithelial remodeling as a potential mechanism of foregut developmental defects. This research may have implications for the mechanisms of other rare congenital anomalies.
@article{RN7, author = {Zhong*, G. and Ahimaz*, P. and Edwards*, N. A. and Hagen, J. J. and Faure, C. and Lu, Q. and Kingma, P. and Middlesworth, W. and Khlevner, J. and El Fiky, M. and Schindel, D. and Fialkowski, E. and Kashyap, A. and Forlenza, S. and Kenny, A. P. and Zorn, A. M. and Shen, Y. and Chung, W. K.}, title = {Identification and validation of candidate risk genes in endocytic vesicular trafficking associated with esophageal atresia and tracheoesophageal fistulas}, journal = {HGG Adv,}, volume = {3}, number = {3}, pages = {100107}, issn = {2666-2477 (Electronic) 2666-2477 (Linking)}, doi = {10.1016/j.xhgg.2022.100107}, url = {https://www.ncbi.nlm.nih.gov/pubmed/35519826}, year = {2022}, %type = {Journal Article}, }
Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly

X. Ren*, G. Zhong*, Q. Zhang, L. Zhang, Y. Sun, and 1 more author

Cell Res, 2020

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Single-cell RNA sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing unprecedented cellular and molecular throughputs, but spatial information of individual cells is lost during tissue dissociation. While imaging-based technologies such as in situ sequencing show great promise, technical difficulties currently limit their wide usage. Here we hypothesize that cellular spatial organization is inherently encoded by cell identity and can be reconstructed, at least in part, by ligand-receptor interactions, and we present CSOmap, a computational tool to infer cellular interaction de novo from scRNA-seq. We show that CSOmap can successfully recapitulate the spatial organization of multiple organs of human and mouse including tumor microenvironments for multiple cancers in pseudo-space, and reveal molecular determinants of cellular interactions. Further, CSOmap readily simulates perturbation of genes or cell types to gain novel biological insights, especially into how immune cells interact in the tumor microenvironment. CSOmap can be a widely applicable tool to interrogate cellular organizations based on scRNA-seq data for various tissues in diverse systems.
@article{RN3, author = {Ren*, X. and Zhong*, G. and Zhang, Q. and Zhang, L. and Sun, Y. and Zhang, Z.}, title = {Reconstruction of cell spatial organization from single-cell RNA sequencing data based on ligand-receptor mediated self-assembly}, journal = {Cell Res,}, volume = {30}, number = {9}, pages = {763-778}, issn = {1748-7838 (Electronic) 1001-0602 (Linking)}, doi = {10.1038/s41422-020-0353-2}, url = {https://www.ncbi.nlm.nih.gov/pubmed/32541867}, year = {2020}, %type = {Journal Article}, }
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma

Q. Zhang, Y. He, N. Luo, S. J. Patel, Y. Han, and 20 more authors

Cell, 2019

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The immune microenvironment of hepatocellular carcinoma (HCC) is poorly characterized. Combining two single-cell RNA sequencing technologies, we produced transcriptomes of CD45+ immune cells for HCC patients from five immune-relevant sites: tumor, adjacent liver, hepatic lymph node (LN), blood, and ascites. A cluster of LAMP3+ dendritic cells (DCs) appeared to be the mature form of conventional DCs and possessed the potential to migrate from tumors to LNs. LAMP3+ DCs also expressed diverse immune-relevant ligands and exhibited potential to regulate multiple subtypes of lymphocytes. Of the macrophages in tumors that exhibited distinct transcriptional states, tumor-associated macrophages (TAMs) were associated with poor prognosis, and we established the inflammatory role of SLC40A1 and GPNMB in these cells. Further, myeloid and lymphoid cells in ascites were predominantly linked to tumor and blood origins, respectively. The dynamic properties of diverse CD45+ cell types revealed by this study add new dimensions to the immune landscape of HCC.
@article{RN1, author = {Zhang, Q. and He, Y. and Luo, N. and Patel, S. J. and Han, Y. and Gao, R. and Modak, M. and Carotta, S. and Haslinger, C. and Kind, D. and Peet, G. W. and Zhong, G. and Lu, S. and Zhu, W. and Mao, Y. and Xiao, M. and Bergmann, M. and Hu, X. and Kerkar, S. P. and Vogt, A. B. and Pflanz, S. and Liu, K. and Peng, J. and Ren, X. and Zhang, Z.}, title = {Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma}, journal = {Cell,}, volume = {179}, number = {4}, pages = {829-845 e20}, issn = {1097-4172 (Electronic) 0092-8674 (Linking)}, doi = {10.1016/j.cell.2019.10.003}, url = {https://www.ncbi.nlm.nih.gov/pubmed/31675496}, year = {2019}, %type = {Journal Article}, }