Biology subjects

Khanal, R.

Publications and source records attributed to Khanal, R..

3 recordsLinked to original sources

bioRxiv2024

Identification of novel loci regulating dormancy in barley and association with hypoxia sensitivity

Low seed dormancy is an essential trait in malting barley since malting involves rapid and uniform induction of germination. At least two major QTLs on chromosome 5H, SD1 and SD2, regulate dormancy in multiple barley populations, and additional genetic regions are thought to be involved. To identify novel genetic loci that might be associated with dormancy, a panel of genotypes from diverse agro-ecosystems were evaluated alongside two Canadian biparental populations for germination rate. Association mapping revealed QTLs within the SD1 and SD2 loci in the Canadian populations, while neither of these loci were linked to dormancy in the diversity panel. The diversity panel identified 14 additional marker-trait associations, including novel genetic loci. An alanine aminotransferase (AlaAT1) underlies dormancy regulation at the SD1 allele and is thought to help mitigate the suppressive effects of hypoxia on respiration during grain fill. Additional testing with four genotypes carrying either dormant or non-dormant AlaAT1 alleles revealed that dormant alleles had increased hypoxia sensitivity and hypoxia-responsive gene expression during grain fill. Together, this work indicates that multiple genetic regions influence dormancy and suggests that hypoxia influences dormancy establishment in barley. HighlightDormancy is regulated by distinct genetic regions in North American barley compared to African barley. The SD1 locus influences dormancy in North American lines and genotypes with the dormant SD1 allele are more sensitive to hypoxia.

plant biology↗

bioRxiv2024

Lineage-resolved analysis of embryonic gene expression evolution in C. elegans and C. briggsae

What constraints govern the evolution of gene expression patterns across development remains a fundamental question of evolutionary biology. The advent of single-cell sequencing opens the possibility of learning these constraints by systematically profiling homologous cells across different organisms. The nematode C. elegans is a well-studied model for embryonic development, and its invariant lineage that is conserved with other Caenorhabditis species makes it an ideal model to directly compare gene expression between homologous progenitor and terminal cell types across evolution. We have measured the spatiotemporal divergence of gene expression across embryogenesis by collecting, annotating, and comparing the transcriptomes of homologous embryonic progenitors and terminal cell types, using a dataset comprising >200,000 C. elegans cells and >190,000 C. briggsae cells. We find a high level of similarity in gene expression programs between the species despite tens of millions of years of evolutionary divergence, consistent with their conserved developmental lineages. Even still, thousands of genes show divergence in their cell-type specific expression patterns, and these are enriched for categories involved in environmental response and behavior. Comparing the degree of expression conservation across cell types reveals that certain cell types such as neurons, have diverged more than others such as the intestine and body wall muscle. Taken together, this work identifies likely constraints on the evolution of developmental gene expression and provides a powerful resource for addressing diverse evolutionary questions.

evolutionary biology↗

bioRxiv2020

AIRBP: Accurate identification of RNA-binding proteins using machine learning techniques

MotivationIdentification of RNA-binding proteins (RBPs) that bind to ribonucleic acid molecules, is an important problem in Computational Biology and Bioinformatics. It becomes indispensable to identify RBPs as they play crucial roles in post-transcriptional control of RNAs and RNA metabolism as well as have diverse roles in various biological processes such as splicing, mRNA stabilization, mRNA localization, and translation, RNA synthesis, folding-unfolding, modification, processing, and degradation. The existing experimental techniques for identifying RBPs are time-consuming and expensive. Therefore, identifying RBPs directly from the sequence using computational methods can be useful to efficiently annotate RBPs and assist the experimental design. In this work, we present a method, called AIRBP, which is designed using an advanced machine learning technique, called stacking, to effectively predict RBPs by utilizing features extracted from evolutionary information, physiochemical properties, and disordered properties. Moreover, our method, AIRBP is trained on the useful feature-subset identified by the evolutionary algorithm (EA). ResultsThe results show that AIRBP attains Accuracy (ACC), F1-score, and MCC of 95.38%, 0.917, and 0.885, respectively, based on the benchmark dataset, using 10-fold cross-validation (CV). Further evaluation of AIRBP on independent test set reveals that it achieves ACC, F1-score, and MCC of 93.04%, 0.943, and 0.855, for Human test set; 91.60%, 0.942 and 0.789 for S. cerevisiae test set; and 91.67%, 0.953 and 0.594 for A. thaliana test set, respectively. These results indicate that AIRBP outperforms the current state-of-the-art method. Therefore, the proposed top-performing AIRBP can be useful for accurate identification and annotation of RBPs directly from the sequence and help gain valuable insight to treat critical diseases. AvailabilityCode-data is available here: http://cs.uno.edu/~tamjid/Software/AIRBP/code_data.zip

bioinformatics↗