Biology subjects

Dhiman, V.

Publications and source records attributed to Dhiman, V..

4 recordsLinked to original sources

bioRxiv2024

AP2/ERF transcription factors enriched in the drought-response transcriptome of the Thar desert tree Prosopis cineraria show higher copy number and greater DNA-binding affinity than orthologs in drought-sensitive species

We sequenced the drought-response transcriptome of the keystone tree species Prosopis cineraria from the Indian Thar desert to understand the key factors in its drought tolerance mechanism. We identified a network of genes activated in P. cineraria involved in osmotic stress response, phytohormone, calcium, and phosphorelay signal transduction. Of these, up-regulation of 54 APETALA2/Ethylene-Responsive Factor (AP2/ERF) transcription factor genes, validated by real-time PCR, suggests their key role in the drought tolerance of P. cineraria. We conducted a genome-wide study of the AP2/ERF superfamily in P. cineraria, classifying its 232 proteins into 15 clades and analyzing their protein structures, gene structure, and promoter organization. The P. cineraria genome contains more copies of AP2/ERF genes than drought-sensitive plants. Further, we identified sequence polymorphisms in AP2/ERF genes between Arabian and Indian cultivars of P. cineraria. We modeled the DNA-protein complex structures of AP2/ERFs from drought-tolerant and sensitive species using AlphaFold to compare their DNA binding ability. Though the DNA binding domain (DBD) is relatively conserved across species, the unstructured region of these proteins possesses different charge distributions, which might contribute differently to their DNA search and binding. Using all-atom molecular dynamics simulations, we teased out a higher number of specific DBD-DNA hydrogen bonds in P. cineraria, leading to a stronger DNA-binding affinity compared to drought-sensitive Arabidopsis thaliana. These results directly support copy number expansion of AP2/ERF transcription factors and the evolution of their structures for more efficient DNA search and binding as drought adaptation mechanisms in P. cineraria.

plant biology↗

bioRxiv2023

Deep learning-based method to identify disease-resistance proteins in Oryza sativa and relative species

Rice (Oryza sativa) is a significant agricultural crop consumed by more than half of the global population. Its demand is expected to increase due to rising consumption and a growing global population. Moreover, the rice plant is frequently exposed to disease-causing pathogens, such as bacteria, fungi, viruses, and nematodes. Thus, cultivating disease-resistant varieties is an efficient way of disease control compared to pesticide applications. However, the rice plant has a well-defined defense system to prevent the onset of disease, including Pathogen-associated molecular pattern (PAMP)-triggered immunity (PTI) and effector-triggered immunity (ETI). The defense system is controlled by various disease-resistance proteins, such as resistance (R) proteins and pathogen recognition receptors (PRRs). Therefore, the identification of disease-resistance proteins not only reduces the amount of pesticides used in rice fields but also increases their yield. Though some resistant proteins have been characterized, their rapid identification, precise diagnosis, and appropriate management are still lacking. However, few methods based on sequence-similarity and de novo prediction, such as Machine Learning (ML), usually have low prediction power. In this study, we built a state-of-the-art classifier based on Deep Learning (DL) for the early detection of disease-resistance proteins in rice and related species. We compared the DL-based Multi-layer Perceptron (MLP) model with the five well-established ML-based methods using a protein dataset of rice and its related species. The DL-based MLP model outperformed all of the five classifiers on 10-fold cross-validation. The accuracy, Area Under Receiving Operating Characteristic (ROC) curve (AUC), F1-score, precision, and recall were superior in the DL-based MLP model. In conclusion, the MLP model is an effective DL model for predicting disease-resistance proteins with high scores in performance metrics. This study will provide insight to the breeders in developing disease-resistant rice varieties and assist in transforming traditional rice farming practices into a new age of smart rice farming.

bioinformatics↗

bioRxiv2023

In silico characterization of five novel disease-resistance proteins in Oryza sativa sp. japonica against bacterial leaf blight and rice blast diseases

Oryza sativa sp. japonica is the most widely cultivated variety of rice. It has evolved several defense mechanisms, including PAMP-triggered immunity (PTI) and effector-triggered immunity (ETI), which provide resistance against different pathogens to overcome biotic stresses. Several disease-resistance genes and proteins, such as R genes and PRR proteins, have been reported in the scientific literature which shows resistance against Xanthomonas oryzae pv. oryzae (Xoo), a causative agent for bacterial leaf blight disease (BB), and Magnaporthe oryzae (M. oryzae), causing rice blast disease (RB). Although some of these resistance proteins have been studied, the functional characterization of resistance proteins in rice is not exhaustive. In the current study, we identified five novel resistance proteins against BB and RB diseases through gene network analysis. Structure and function prediction, disease-resistance domain identification, protein-protein interaction (PPI), and pathway analysis revealed that the five new proteins played a role in the disease resistance against BB and RB. In silico modeling, refinement, and model quality assessment were performed to predict the best structures of these five proteins, and submitted to ModelArchive for future use. The functional annotation of the proteins revealed their involvement in the bacterial disease resistance of rice. We predicted that the new resistance proteins could be localized to the nucleus and plasma membrane. This study provides insight into developing disease-resistant rice varieties by predicting novel candidate resistance proteins, which will pave the way for their future characterization and assist rice breeders in improving crop yield and addressing future food security.

bioinformatics↗

bioRxiv2019

An integrative ENCODE resource for cancer genomics

ENCODE comprises thousands of functional genomics datasets, and the encyclopedia covers hundreds of cell types, providing a universal annotation for genome interpretation. However, for particular applications, it may be advantageous to use a customized annotation. Here, we develop such a custom annotation by leveraging advanced assays, such as eCLIP, Hi-C, and whole-genome STARR-seq on a number of data-rich ENCODE cell types. A key aspect of this annotation is comprehensive and experimentally derived networks of both transcription factors and RNA-binding proteins (TFs and RBPs). Cancer, a disease of system-wide dysregulation, is an ideal application for such a network-based annotation. Specifically, for cancer-associated cell types, we put regulators into hierarchies and measure their network change (rewiring) during oncogenesis. We also extensively survey TF-RBP crosstalk, highlighting how SUB1, a previously uncharacterized RBP, drives aberrant tumor expression and amplifies the effect of MYC, a well-known oncogenic TF. Furthermore, we show how our annotation allows us to place oncogenic transformations in the context of a broad cell space; here, many normal-to-tumor transitions move towards a stem-like state, while oncogene knockdowns show an opposing trend. Finally, we organize the resource into a coherent workflow to prioritize key elements and variants, in addition to regulators. We showcase the application of this prioritization to somatic burdening, cancer differential expression and GWAS. Targeted validations of the prioritized regulators, elements and variants using siRNA knockdowns, CRISPR-based editing, and luciferase assays demonstrate the value of the ENCODE resource.

genomics↗