ABI Innovation: Tools and databases for network-based plant systems biology with applications to understanding plant-virus interactions

PIs: Jianhua Ruan and Garry Sunter, The University of Texas at San Antonio

Link to NSF award detail page | Back to Ruan Lab page

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Abstract | Publications | Presentations | Software tools

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ABSTRACT
This project aims to improve the tools for making models for networks of interacting molecules in the small mustard plant, Arabidopsis. To demonstrate the effectiveness of these methods, the resources developed in this research will be used to study the plant's immunity to a virus infection under several conditions. Understanding how and when the virus overcomes the plants defenses gives us a handle on controlling an important pathogen of crop plants in the Southern US, with benefits such as better yield and less pesticide use. While biologists can measure the presence and amounts of tens of thousands of molecules in a cell all at once, understanding how they are connected, the 'networks' or 'systems' that lead to function, is a much harder problem. The huge amounts of measurements have to be properly managed so they are usable, and additional information has to be correctly added: it is important to track how amounts of one type of molecule change over time and not mix up different things. It is also important to understand which parts of the cell affect one another: those that belong to a functionally connected pathway (or network), and which are independent of each other. For example, plants have complicated mechanisms to defend themselves against biological and environmental stresses. Signaling pathways cause the plant's response, and they are influenced by internal genetic factors as well as the external ones that are more easily observed. If every protein (or other molecule) inside the cell that plays a part in carrying and interpreting the signal is known then very effective predictions about the final response are possible. However, plant researchers don't know nearly as much about the molecules in their organisms as is available to many researchers studying animals, which means there are a lot of missing nodes. That makes it hard to come up with a specific prediction that can be tested: this research aims to overcome this problem for selected plant pathways, to showcase what is possible when there is sufficient information. This project will actively engage students in interdisciplinary research, with a particular focus on recruiting underrepresented groups. The University of Texas at San Antonio (UTSA) is a Hispanic-Serving Institution.


This project has the following four specific aims: 1) to construct genome-wide transcriptional regulatory network in Arabidopsis with validation in immune-responsive genes; 2) to improve the prediction of protein-protein interactions and identification of defense subnetworks in Arabidopsis; 3) to perform network-based analysis of Arabidopsis immune-responsive network in order to decipher the role of plant viral RNA silencing suppressors in plant immunity; and 4) to provide online databases and analytic tools for network-based plant systems biology studies. This project promises to significantly improve network-based analysis with several innovative ideas. First, the proposed approaches focus on improving accuracy of predictions for individual genes by defining a network neighborhood for each node and testing for enrichment in the neighborhood for each node. This is in contrast to most existing approaches that make predictions on gene modules (within- or cross-species) and therefore lack quality control on an individual gene level. Second, combining protein-protein interaction network, gene co-expression network, and sample-sample network, this research provides an example to analyze such networks in a dynamic context automatically defined by the global transcriptomic landscape; as such, this study is expected to provide more specific predictions that can be experimentally tested. In addition, integration of computational tools to characterize defense-related network structure in this work will significantly improve the ability to study the role of co-regulated networks of genes in any number of processes, including but not limited to genes implicated in both plant and animal disease, cancer or stem cell biology, or tissue specificity of gene expression.

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PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

Gabriela Lacatus, Mary Berger, Jacqueline williams, Elizabeth Regedanz, Jianhua Ruan, David Bisaro and Garry Sunter. "The conserved late element of Cabbage leaf curl and Tomato golden mosaic viruses is required for infectivity and binds an Arabidopsis TCP transcription factor," In preparation.   

Maryam Zand and Jianhua Ruan. "Network-based single-cell RNA-seq data imputation enhances cell type identification," Genes, 2020, 11(4):377.   

Garry Sunter, Jennifer Guerrero, Elizabeth Ragendenz, Liu Lu, Jianhua Ruan and David Bisaro. "Manipulation of the plant host by the Geminivirus AC2/C2 protein, a Central Player in the Infection Cycle," Frontiers in Plant Science, 2020, 11:591   

Zhen Gao, Maryam Zand and Jianhua Ruan. " A Novel Multiple Classifier Generation and Combination Framework Based on Fuzzy Clustering and Individualized Ensemble Construction," IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2019, 231-240.   

Maryam Zand, Zhen Gao, Jinmao Wei, Garry Sunter and Jianhua Ruan. "An integrative approach to transcriptional co-regulatory network construction and characterization in Arabidopsis," Proceedings of the 8th IEEE International Conference on Computational Advances in Bio and Medical Sciences (ICCABS), 2018. 

Yuanyuan Xu, Jun Wang, Shuai An, Jinmao Wei and Jianhua Ruan, "Semi-Supervised Multi-Label Feature Selection by Preserving Feature-Label Space Consistency," Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018. 

Zhen Gao and Jianhua Ruan. "Computational modeling of in vivo and in vitro protein-DNA interactions by multiple instance learning," Bioinformatics, 2017. 

Zhen Gao, Lu Liu and Jianhua Ruan. "Logo2PWM: a tool to convert sequence logo to position weight matrix," BMC Genomics, 2017.

Lu Liu and Jianhua Ruan. "Utilizing networks for differential analysis of chromatin interactions," Journal of bioinformatics and computational biology, v.15, 2017, p. 1740008.

Lu Liu, Jinmao Wei, and Jianhua Ruan. "Pathway Enrichment Analysis with Networks," Genes, v.8, 2017, p. 246.

Lu Liu and Jianhua Ruan. "Network-based Differential Analysis of Hi-C Data," 9h International Conference on Bioinformatics and Computational Biology, 2017.

Jianhua Ruan, Md. Jamiul Jahid, Fei Gu, Chengwei Lei, Yi-Wen Huang, Ya-Ting Hsu, David G. Mutch, Chun-Liang Chen, Nameer B. Kirma, Tim H.-M. Huang. "A novel algorithm for network-based prediction of cancer recurrence," Genomics, 2016. 

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PRESENTATIONS

Network bioinformatics: from deeper knowledge discovery to personalized medicine, University of Texas Health Science Center, Texas. Oct 2018

Network bioinformatics: from accurate genome annotations to personalized medicine, Nankai University, Tianjin, China. July 2018

Regulation of Geminivirus CP Promoters, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing, China. May 2018

An integrative approach to transcriptional co-regulatory network construction and characterization in Arabidopsis, ASPB 2018

Network algorithms in biology: from accurate genome annotations to personalized medicine, talk in UTSA Computer Science department, Fall 2016

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SOFTWARE TOOLS

scQcut:A completely parameter-free method for graph-based single cell RNA-seq clustering Source code for our bioRxiv paper (Zand & Ruan 2021).

scSpatialMapping: An algorithm for scRNA-seq based spatial mapping algorithm Source code for our top-performing algorithm in recent DREAM challenge and the corresponding F1000 paper (Zand & Ruan 2020).

netImpute: a network-based scRNA-seq data imputation tool Source code for our Genes paper (Zand & Ruan 2020).

Multiple instance learning for Protein-DNA interaction modeling: Supplemental data and software for our bioinformatic paper (Gao & Ruan 2017).

LOGO2PWM: a tool to convert sequence logo to position weight matrix (Gao et. al. 2017).

Network-based differential analysis of Chromatin interactions: Software and data for our JBCB paper (Liu & Ruan 2017).

Network-based pathway enrichment analysis : Software and data for our Genes paper (Liu et. al. 2017).

Arabidopsis Co-expression Network-based Resource: Web-based tool to explore Arabidopsis gene co-expression network and modules, enriched functional modules, cis-regulatory elements for modules and individual genes.