Biology subjects

Valluru, R.

Publications and source records attributed to Valluru, R..

7 recordsLinked to original sources

bioRxiv2026

Computational predictions and evolutionary analysis of LrK10 kinase-related putative PSTOL1 gene homeologs in wheat and orthologs of its wild relatives.

Phosphorus Starvation Tolerance 1 in rice (OsPSTOL1, known as Phosphorus uptake 1, Pup1) is a receptor-like cytoplasmic protein kinase that confers tolerance to phosphorus deficiency. The OsPSTOL1 gene possesses a Ser/Thr kinase and shows high amino-acid sequence similarity with the leaf rust receptor-like kinase (OsLrK10). We hypothesize that the putative wheat TaPSTOL1 and TaLrK10 have a common ancestral origin and that putative TaPSTOL1 diverged recently acquiring new structural modifications and biological functions in the process. In this study, we identified all putative TaPSTOL1 homeologs and examine the evolutionary relationship between TaPSTOL1 and TaLrK10 in Triticum species. Our results indicate that the putative TaPSTOL1 diverged recently without possessing the amino-terminal domain, which is a typical characteristic of TaLrK10. We observed numerous conversions tracts between these two genes and the substitution pattern of randomly selected amino acids indicates that dynamic selection pressures acted on both genes. The putative TaPSTOL1 shows high nucleotide diversity compared to TaLrK10 within Triticum species. Further, a multiple-sequence analysis reveals that the third exon of TaLrK10 appears to have been duplicated and diverged as a putative single-exon based TaPSTOL1 in bread wheat. Overall, our comparative analysis indicates that both TaPSTOL1 and TaLrK10 appears to have diverged from a common ancestor, acquiring distinct structural organizations and biological functions.

genomics↗

bioRxiv2022

Allele mining, evolutionary genetic analysis of TaHKT1;5 gene and evaluation of salinity stress in selected lines of wheat.

This study reports the novel allelic diversity in HKT 1;5 gene (High Affinity Potassium Transporter) in bread wheat and its phylogenetic relationship among the paralogs/orthologs of in Triticum aestivum and its wild relatives. HKT 1;5 gene is a known and pivotal gene associated with the salinity tolerance in plants upon the discrimination of K+ over Na+ in leaves without change in Na+ concentration in root. This gene was sequenced in a diverse collection of bread wheat, durum wheat, wild relatives, and ditelosomic lines. Sequence analysis in bread wheat led to the identification of four alleles, which could be distinguished by number of SNPs. Sequence comparison between monocot bread wheat and dicot Arabidopsis thaliana revealed that the HKT1;5 gene is conserved at level of exonic regions; however, the presence of transposable elements especially in intronic regions is further intriguing towards evolutionary relatedness. Two paralogous or major alleles observed in Triticum monoccum and Aegilops tauschii were further categorized as sub-alleles based on their SNPs comparison. This gene was absent in T. urartu in accordance with existing evidence, while it was found in A. speltoides (an allelic variant) with a few base pairs insertion in the exon1 region causing a frameshift mutation with an altered amino acids and genomic database mining unveiled additional alleles in this species. Ditelosomic lines with 4DL and 4DS chromosomes revealed a higher similarity with bread and durum wheat respectively. Phylogenetic studies of HKT1;5 orthologs from different Poaceae species revealed the occurrence of five different ortholog groups with taxonomic consistency. Phenotyping based salinity stress experiment distinguished the unknown lines for salinity tolerance and sensitiveness in comparison with known reference lines and possible allelic comparison was made. The salinity stress analysis further revealed that some known drought/heat tolerance lines showed slightly better salinity tolerance with mean values and variability of traits than known saline tolerant wheat line at controlled ambient.

genetics↗

bioRxiv2021

Phenotyping stomatal closure by thermal imaging for GWAS and TWAS of water use efficiency-related genes

Stomata allow CO2 uptake by leaves for photosynthetic assimilation at the cost of water vapor loss to the atmosphere. The opening and closing of stomata in response to fluctuations in light intensity regulate CO2 and water fluxes and are essential to maintenance of water-use efficiency (WUE). However, little is known about the genetic basis for natural variation in stomatal movement, especially in C4 crops. This is partly because the stomatal response to a change in light intensity is difficult to measure at the scale required for association studies. High-throughput thermal imaging was used to bypass the phenotyping bottleneck and assess 10 traits describing stomatal conductance (gs) before, during and after a stepwise decrease in light intensity for a diversity panel of 659 sorghum accessions. Results from thermal imaging significantly correlated with photosynthetic gas-exchange measurements. gs traits varied substantially across the population and were moderately heritable (h2 up to 0.72). An integrated genome-wide and transcriptome-wide association study (GWAS/TWAS) identified candidate genes putatively driving variation in stomatal conductance traits. Of the 239 unique candidate genes identified with greatest confidence, 77 were orthologs of Arabidopsis genes related to functions implicated in WUE, including stomatal opening/closing (24 genes), stomatal/epidermal cell development (35 genes), leaf/vasculature development (12 genes), or chlorophyll metabolism/photosynthesis (8 genes). These findings demonstrate an approach to finding genotype-to-phenotype relationships for a challenging trait as well as candidate genes for further investigation of the genetic basis of WUE in a model C4 grass for bioenergy, food, and forage production. One sentence summaryRapid phenotyping of 659 accessions of Sorghum bicolor revealed heritable stomatal responses to a decrease in light. GWAS/TWAS was used to identify candidate genes influencing traits important to WUE.

plant biology↗

bioRxiv2020

Machine learning enabled phenotyping for GWAS and TWAS of WUE traits in 869 field-grown sorghum accessions

Sorghum is a model C4 crop made experimentally tractable by extensive genomic and genetic resources. Biomass sorghum is also studied as a feedstock for biofuel and forage. Mechanistic modelling suggests that reducing stomatal conductance (gs) could improve sorghum intrinsic water use efficiency (iWUE) and biomass production. Phenotyping for discovery of genotype to phenotype associations remain bottlenecks in efforts to understand the mechanistic basis for natural variation in gs and iWUE. This study addressed multiple methodological limitations. Optical tomography and a novel machine learning tool were combined to measure stomatal density (SD). This was combined with rapid measurements of leaf photosynthetic gas exchange and specific leaf area (SLA). These traits were then the subject of genome-wide association study (GWAS) and transcriptome-wide association study (TWAS) across 869 field-grown biomass sorghum accessions. SD was correlated with plant height and biomass production. Plasticity in SD and SLA were interrelated with each other, and productivity, across wet versus dry growing seasons. Moderate-to-high heritability of traits studied across the large mapping population supported identification of associations between DNA sequence variation, or RNA transcript abundance, and trait variation. 394 unique genes underpinning variation in WUE-related traits are described with higher confidence because they were identified in multiple independent tests. This list was enriched in genes whose orthologs in Arabidopsis have functions related to stomatal or leaf development and leaf gas exchange. These advances in methodology and knowledge will aid efforts to improve the WUE of C4 crops.

plant biology↗

bioRxiv2019

Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maize

Sorghum and maize share a close evolutionary history that can be explored through comparative genomics. To perform a large-scale comparison of the genomic variation between these two species, we analyzed 13 million variants identified from whole genome resequencing of 468 sorghum lines together with 25 million variants previously identified in 1,218 maize lines. Deleterious mutations in both species were prevalent in pericentromeric regions, enriched in non-syntenic genes, and present at low allele frequencies. A comparison of deleterious burden between sorghum and maize revealed that sorghum, in contrast to maize, departed from the \"domestication cost\" hypothesis that predicts a higher deleterious burden among domesticates compared to wild lines. Additionally, sorghum and maize population genetic summary statistics were used to predict a gene deleterious index with an accuracy higher than 0.5. This research represents a key step towards understanding the evolutionary dynamics of deleterious variants in sorghum and provides a comparative genomics framework to start prioritizing them for removal through genome editing and breeding.

genomics↗

bioRxiv2018

Evolutionarily informed deep learning methods: Predicting transcript abundance from DNA sequence

Deep learning methodologies have revolutionized prediction in many fields, and show potential to do the same in molecular biology and genetics. However, applying these methods in their current forms ignores evolutionary dependencies within biological systems and can result in false positives and spurious conclusions. We developed two novel approaches that account for evolutionary relatedness in machine learning models: 1) gene-family guided splitting, and 2) ortholog contrasts. The first approach accounts for evolution by constraining the models training and testing sets to include different gene families. The second, uses evolutionarily informed comparisons between orthologous genes to both control for and leverage evolutionary divergence during the training process. The two approaches were explored and validated within the context of mRNA expression level prediction, and have prediction auROC values ranging from 0.72 to 0.94. Model weight inspections showed biologically interpretable patterns, resulting in the novel hypothesis that the 3 UTR is more important for fine tuning mRNA abundance levels while the 5 UTR is more important for large scale changes.

molecular biology↗

bioRxiv2018

Leveraging mutational burden for complex trait prediction in sorghum

Sorghum (Sorghum bicolor (L.) Moench) is a major staple food cereal for millions of people worldwide. The sorghum genome, like other species, accumulates deleterious mutations, likely impacting its fitness. Though selection keeps deleterious mutations rare, their complete removal from the genome is impeded due to lack of recombination, drift, and their coupling with favorable loci. To study how deleterious mutations impact agronomic phenotypes, we identified putative deleterious mutations among ~5.5M segregating variants of 229 diverse sorghum lines. We provide the whole-genome estimate of the deleterious burden in sorghum, showing that about 33% of nonsynonymous substitutions are putatively deleterious. The pattern of mutation burden varies appreciably among racial groups; the caudatum shows higher mutation burden while the guinea has lower burden. Across racial groups, the mutation burden correlated negatively with biomass, plant height, Specific Leaf Area (SLA), and tissue starch content, suggesting deleterious burden decreases trait fitness. Putatively deleterious variants explain roughly half of the genetic variance. However, there is only moderate improvement in total heritable variance explained for biomass (7.6%) and plant height (5.2%). There is no advantage in total heritable variance for SLA and starch. The contribution of putatively deleterious variants to phenotypic diversity therefore appears to be dependent on the genetic architecture of traits. Overall, our results suggest that including putatively deleterious variants in models do not significantly improve breeding accuracy because of extensive linkage. However, knowledge of deleterious variants could be leveraged for sorghum breeding through genome editing.

genetics↗