Date of Award

5-2025

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Genetics and Biochemistry

Committee Chair/Advisor

Dr. Rajandeep Sekhon

Committee Member

Dr. Nishanth Tharayil

Committee Member

Dr. Julia A. Frugoli

Committee Member

Dr. Hong Luo

Abstract

Maize (Zea mays L.) and sorghum (Sorghum bicolor (L.) Moench) are two important cereal crops cultivated worldwide for food, animal feed, and industrial products. Productivity of these crops is significantly impacted by various biotic and abiotic stresses, such as diseases, insect pests, drought, salinity, and heat. Among factors influencing crop performance under stress, the regulation of leaf longevity plays a critical role in determining yield. Leaf senescence is a highly regulated process essential for nutrient remobilization through the degradation of cellular components in aging leaves, but premature or accelerated senescence reduces photosynthetic efficiency and yield. Staygreen, a phenotype resulting from delayed senescence, is a critical adaptative trait influencing maize yield by regulating nutrient remobilization, photosynthetic efficiency, and stress adaptability. Despite its importance, the genetic and metabolic regulation of staygreen remains poorly resolved in important cereals, including maize and sorghum. Here, we leverage a multi-omics approach to identify genetic and metabolic factors underlying leaf senescence in these two C4 grasses. We integrated metabolomic and physiological data to dissect the regulatory networks controlling staygreen in maize to identify metabolites, pathways, and genes and used reverse genetics to validate gene function. We extended this analysis to leverage the genetic diversity of sorghum to identify metabolic pathways and genes that are species-specific or conserved across C4 grasses. Soil salinity is another major abiotic stress threatening global food security by disrupting essential physiological processes and causing significant yield losses. To address this challenge, we employed a combinatorial metabolomic and physiological approach to identify metabolites and genes associated with salinity tolerance in maize. Our integrative framework provides novel insights into the metabolic and genetic basis of these critical traits and identifies candidate genes for breeding and biotechnological interventions. Taken together, these findings contribute to a deeper understanding of stress-related traits and offer a foundation for developing climate-resilient maize and sorghum varieties to support global food security.

Comments

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Supplementary Dataset S2.1.xlsx (22 kB)
Phenotypic characterization of diverse inbred lines based on different physiological parameters

Supplementary Dataset S2.2.xlsx (106 kB)
Association of identified mass features with different physiological parameters

Supplementary Dataset S2.3.xlsx (21 kB)
Identification of genes encoding enzymes catalyzing the biosynthesis of different primary and secondary metabolites

Supplementary Dataset S3.1.xlsx (163 kB)
Phenotypic characterization of 20 sorghum lines based on different physiological phenotypes

Supplementary Dataset S3.2.xlsx (14 kB)
List of primary metabolites that showed significant association with different physiological phenotypes and differential abundance (DA) in non-staygreen (NSG) and cosmetic stagreen (CosSG) compared to staygreen (SG) lines

Supplementary Dataset S3.3.xlsx (137 kB)
List of secondary mass features that showed significant association with different physiological phenotypes

Supplementary Dataset S3.4.xlsx (156 kB)
List of identified specialized metabloites and their association with different physiological phenotypes and differential abundance (DA)in different comparisons

Supplementary Dataset S3.5.xlsx (20 kB)
List of putative genes regulating the biosynthesis of certain metabolites

Supplementary Dataset S4.1.xlsx (33 kB)
List of annotated mass features in leaves in NC326 and C68 inbred lines and differentially abundant (DA) metabolites under control and salinity conditions

Supplementary Dataset S4.2.xlsx (29 kB)

Author ORCID Identifier

0009-0008-1600-4520

Available for download on Sunday, May 31, 2026

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