NASA EPSCoR Rapid Response Research: Graph-based Network Analysis of Microgravity Regulated Gene Expression in Arabidopsis Thaliana

The goal of this proposal is to identify and characterize molecular pathways and mechanisms that underlie physiological changes in the Arabidopsis Thaliana plant grown in the International Space Station (ISS) using novel graph network analysis methods. As plants provide bioregenerative life support, understanding the gene expression of Arabidopsis in microgravity will enable planning future long term space missions. This plant has been named as one of the important well-established model organism in plant biology. Its genome structure has been well analyzed on ground. Graph based Gene Regulatory Networks (GRNs) will be implemented for the GeneLab datasets obtained from the Arabidopsis experiments conducted in space. Graphs are state-of-the-art biomolecular network analysis tools that can characterize omic datasets obtained by NASA and from other sources. GRNs have been implemented using graphs with no weights. In this project, we will develop GRNs using directed graphs with weighted edges to model connections with different strengths. Network analysis paradigms from electric circuits such as resistance distance, Kirchhoff index, and network criticality will be used to perform genome to phenome analysis. An algebraic approach will be taken to model complex networks that characterize Arabidopsis GRNs, with thousands of nodes and hundreds of thousands of interactions among the nodes. The network analysis will also involve use of bipartite graphs, and graph cuts where network interactions are between two groups of nodes. Analysis of Arabidopsis Thaliana will be done with graph combinatorial algorithms, as classical methods that are currently available would not be sufficient. The objectives for the first year of this NASA Rapid Response Project are: i) Perform directed and weighted graph based network analysis of the Arabidopsis Thaliana datasets in order to determine which genes are activated by gravity and other stress for regulating plant growth in space. ii) Develop a bioinformatics tool implementing the novel graph approach in Python for network analysis. The GeneLab datasets that would be used in this project are low atmospheric pressure stress (GLDS-136), gene expression profile in microgravity and ground based measurement (GLDS-44, 45), gene expression of the root exposed to high levels of magnesium sulfate (GLDS-22), and transcriptome analysis GLDS-7 from leaves, hypocotyls, and root. The graph based GRN analysis algorithms will be coded as a Python toolbox and provided to NASA. The data, tables and graphs generated by this project will be made available to the space biology research community. Dr. Jonathan Galazka, Scientist from NASA Space Biosciences Research Branch will be collaborating in this project as advisor. He and his team will enable GeneLab data understanding, connect the PIs with other scientists in this area, provide monthly feedback on research progress, evaluate the outcomes of the GRN analysis, and provide suggestions for improvement. This project will be conducted at the University of Puerto Rico (UPR), Mayaguez Campus and Rio Piedras Campus. The project team is interdisciplinary bringing together the PI, Dr. Manian a faculty in Electrical & Computer Engineering, and Bioengineering, and Co-PI, Dr. Janwa a faculty in Mathematics. This project is conducted in an Hispanic minority serving institution with 99% Hispanic students who will gain knowledge in the fields of space biology, mathematics, bioinformatics, and Python tool development. This project will directly support two graduate students in Space Biology research, and involve many undergraduate students through course work and workshops. They will be exposed to NASA space related research. This project will also enable Hispanic minority students to take up internships with NASA, and add to the workforce development for future NASA missions.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

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