bioRxivPlant BiologyPreprint
Stress Resistance Heat Shock Protein 70 (HSP70) Analysis in Sorghum (Sorghum bicolor L.) at Genome-Wide Level
Abstract
Heat shock proteins (HSP70) play an important role in many biological processes. However, as typical in Sorghum bicolor, the systematic identification of the HSP70 gene is very limited, and the role of the Hsp70 gene in the evolution of Sorghum bicolor has not been described systematically a lot. To overcome the gap, Insilco analysis of HSP70 gene family was conducted.The investigation was utilizing the bioinformatics method to analyze the HSP70 gene family and it has been identified that 30 HSP70 genes from the genome sequence of Sorghum bicolor. A comprehensive analysis of these 30 identified genes undertaking the analysis of gene structure, phylogeny, and physicochemical properties, subcellular localization, and promoter region analysis. The gene structure visualization analyses revealed that 22 genes contains both 5 and 3 UTRS and one 5 and one 3 gene and 6 genes without UTR. The highest number of introns was recorded as 12 and those genes have shown that without in any intron. In the promoter region analysis, ten protein motifs are identified and characterized and 2219 cis-acting elements are identified. Among those, the promoter enhancer elements share the highest number (1411) and light-responsive elements share the next value (335). The physicochemical properties analysis revealed that 23 families have an acidic nature while four families are basic and the rests are neutral. In general, the different analyses performed disclosed their structural organization, subcellular localization, physicochemical properties, cis-acting elements, phylogenetic, and understress conditions. This study provides further information for the functional characterization of HSP70 and helps to understand the mechanisms of abiotic stress tolerance under diverse stress conditions in Sorghum bicolor.
- organism: plant (other)
- assay: DNA methylation
- assay: whole-genome sequencing
- research focus: gene regulatory network inference
- research focus: clustering
- research focus: phylogenetics / evolution
- approach: biophysical modeling
- approach: statistical model
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