Gene Coexpression Network Analysis Indicates that Hub Genes Related to Photosynthesis and Starch Synthesis Modulate Salt Stress Tolerance in Ulmus pumila

Abstract

Ulmus pumila L. is an excellent afforestation and biofuel tree that produces high-quality wood, rich in starch. In addition, U. pumila is highly adaptable to adverse environmental conditions, which is conducive to its utilization for vegetating saline soils. However, little is known about the physiological responses and transcriptional regulatory network of U. pumila under salt stress. In this study, we exposed five main cultivars in saline–alkali land (Upu2, 5, 8, 11, and 12) to NaCl stress. Of the five cultivars assessed, Upu11 exhibited the highest salt resistance. Growth and biomass accumulation in Upu11 were promoted under low salt concentrations (<150 mM). However, after 3 months of continuous treatment with 150 mM NaCl, growth was inhibited, and photosynthesis declined. A transcriptome analysis conducted after 3 months of treatment detected 7009 differentially expressed unigenes (DEGs). The gene annotation indicated that these DEGs were mainly related to photosynthesis and carbon metabolism. Furthermore, PHOTOSYNTHETIC ELECTRON TRANSFERH (UpPETH), an important electron transporter in the photosynthetic electron transport chain, and UpWAXY, a key gene controlling amylose synthesis in the starch synthesis pathway, were identified as hub genes in the gene coexpression network. We identified 25 and 62 unigenes that may interact with PETH and WAXY, respectively. Overexpression of UpPETH and UpWAXY significantly increased the survival rates, net photosynthetic rates, biomass, and starch content of transgenic Arabidopsis plants under salt stress. Our findings clarify the physiological and transcriptional regulators that promote or inhibit growth under environmental stress. The identification of salt-responsive hub genes directly responsible for photosynthesis and starch synthesis or metabolism will provide targets for future genetic improvements.

The Phylogeography of Deciduous Tree Ulmus macrocarpa (Ulmaceae) in Northern China

“…However, the genetic diversity and differentiation of U. macrocarpa was still unknown. Furthermore, owing to a lack of nuclear genome data, such research on elms is singularly lagging behind other species, with existing research limited to morphology, allozyme markers, molecular markers, and transcriptomes [10,22,[28][29][30][31][32][33][34][35][36]. The chloroplast genome was popularly utilized in plant population genetics, polymorphism investigations, ecological and evolutionary studies, and DNA barcoding owing to its characteristics of small genome size, relatively slow evolutionary rates, and maternal inheritance.…”

The relationship between atmospheric particulate matter, leaf surface microstructure, and the phyllosphere microbial diversity of Ulmus L.

“…comprises about 40 species, of which 25 species, six varieties, and three cultivars are recorded in China. As a perennial deciduous woody plant, Ulmus species are widely distributed in the temperate regions of the Northern Hemisphere; in China, they are mainly distributed and cultivated in the vast temperate region north of the Yangtze River [ 20 , 21 ]. Many Ulmus trees and shrubs are popular afforestation, timber, and landscaping species for their adaptability to different environmental conditions [ 22 – 24 ].…”

Genetic architecture of inter-specific and -generic grass hybrids by network analysis on multi-omics data

“…Thus, it would be of special interest to investigate the extent to which hub omic features can be significantly linked to biomass yield and other economically important phenotypes of fodder grasses. Researchers have found hub genes affecting biomass accumulation in other families of plants, for example, in Ulmus pumila L. [ 14 ] and Arabidopsis thaliana [ 15 ]. That being stated, one needs to first estimate the network to be able to explore its topological properties and this can be accomplished by leveraging graph theory and probability for modeling and representation of complex biological problems as probabilistic graphical models [ 12 ].…”