19-BBSRC-NSF/BIO: Inference of isoform-level regulatory infrastructures with studies in steroid-producing cell

Cells are the fundamental units that provide all functions needed to sustain life in living organisms. Cellular functions are carried out by proteins, products of genes and the process of producing proteins from genes (i.e., gene expression) is mediated by a complex regulation system. Much remains unknown about the mechanism of gene regulation. Given all genes in a cell, the regulatory relationships between them can be represented by a network, called gene regulatory network. It has been a long-standing challenge to reconstruct these networks experimentally and computationally. A gene can express multiple isoforms (mRNA molecules), and hence produce multiple different proteins, which makes the underlying gene regulatory network more complicated. This project aims to computationally reconstruct accurate regulatory networks at the isoform-level from large-scale sequencing data. The project will first develop efficient approaches to identify the expressed isoforms and to determine their expression abundances, and then develop a network-reconstruction method which improves over the current status. The new computational methods will be validated and applied to the field of immunology--to study cellular mechanisms in steroid-producing cells. The project will contribute to both computer science and biology. The computational problems formulated consider an important problem in biology (infer the regulatory mechanisms of gene expression), and the formulated computational tasks bring new challenges to computer science and mathematics. The project will provide a toolkit to study the immune cell-mediated steroidogenesis pathway in cancer and reveal basic principles of steroid biosynthesis in tumour-infiltrating immune cells. The research can be integrated with educational activities and outreach, such as courses on topics of algorithms in computational biology. The software and data produced in this project will be made easily accessible to the public.

The recent advances in single-cell RNA-Sequencing (scRNA-Seq) has brought new opportunities in resolving high-quality regulatory networks, but also posed new computational challenges. This project will develop new algorithms to tackle some key computational problems. The algorithms exhibit two fundamental improvements over existing methods. First, the proposed methods for developing a scalable transcript assembler enables accurate determination and quantification of the expressed isoforms and makes it possible to build regulatory networks at the level of isoforms to reflect the possible difference in regulatory mechanisms for different isoforms. Second, many recently developed methods for network inference require the cells to be pre-ordered with trajectory inference or RNA-velocity methods to mimic time-series data. Errors in the cell ordering can mislead the network inference and lead to false predictions. The project proposes to perform cell ordering and network inference simultaneously, which is expected to provide better results for both cell ordering and network inference. The project will reconstruct transcript-level regulatory networks for different types of steroid-producing cells from both published and newly generated single-cell data. These networks and comparison among them will create new knowledge of immune cell-mediated steroidogenesis mechanisms.