Date of Award
8-2022
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Genetics and Biochemistry
Committee Chair/Advisor
Julia Frugoli
Committee Member
Alex Feltus
Committee Member
Hong Luo
Committee Member
Rajandeep Sekhon
Abstract
Use of chemical nitrogen fertilizers has environmental repercussions such as global warming, soil contamination, and aquatic eutrophication. Legumes form a symbiotic association with nitrogen-fixing bacteria (rhizobia sp.) to obtain atmospheric nitrogen through the formation of a specialized root structure called a nodule. Understanding the transcriptional reprogramming during nodulation is a powerful approach to decipher the genetic control of nodulation, with the goal of engineering nitrogen-fixing symbiosis into non-leguminous crops. This dissertation focuses on the analytics of bulk, tissue-specific, and single-cell RNA-seq technologies and how I utilized them to discover a collection of genes to aid in deciphering nodulation mechanisms in legumes and eventually reduce global usage of nitrogen fertilizer. Symbiotic nitrogen fixation results from a complex series of time-sensitive chemical and physical interactions between legumes and compatible rhizobia. To understand the dynamics of the transcriptional response in legumes, I developed a novel computational statistical and machine learning workflow named GeneShift for identifying condition-specific temporally differentially expressed genes. GeneShift identified genes that are regulated dynamically in both root and shoot during early nodulation from bulk RNA-seq data. Plant roots are composed of various tissues with distinct cellular identities. When inoculated with rhizobia, only a subset of
root inner cortical cells adjacent to the xylem poles form nodule primordia. With the advantage of the tissue specificity of laser capture microdissection (LCM) and the sensitivity of the GeneShift workflow, I identified 52 genes regulated differently in xylem adjacent and non adjacent inner cortical cells during early nodule development. I also identified time specific marker genes for individual tissues. In a follow-up study, these markers were used to discern 36028 root cells based on their single-cell RNAseq transcriptome profiles in two genotypes of M.truncatula during early nodulation signaling. I classified those root cells into different cell types, localized known nodulation-related genes, and discovered a novel collection of genes specifically expressed in nodule meristem cells.
Recommended Citation
Gao, Yueyao, "Deciphering Medicago Truncatula Nodulation Using Time-Series Transcriptomic Data at Multiple Levels of Resolution: Organ, Tissue, and Single-Cell" (2022). All Dissertations. 3106.
https://tigerprints.clemson.edu/all_dissertations/3106
Chapter3_Supplementary_Tables_1-3.xlsx (35 kB)
Chapter4_SuppTable4.1-4.25.xlsx (192 kB)
Author ORCID Identifier
0000-0002-7607-2938
Included in
Bioinformatics Commons, Computational Biology Commons, Genetics Commons, Genomics Commons