Advancements and challenges in gene editing for improvement of vegetatively propagated crops.

Gene editing technologies, particularly CRISPR-Cas9, have revolutionized agriculture by offering precise and efficient tools to enhance crop production. The vegetatively propagated crops, crucial for global food security, face challenges such as climate change, pests, and limited genetic diversity. CRISPR-Cas9 enables targeted modifications to improve traits like disease resistance, drought tolerance, and nutritional content, thereby boosting productivity and sustainability.

Application of CRISPR/Cas-based gene-editing for developing better banana.

Banana ( spp.), including plantain, is one of the major staple food and cash crops grown in over 140 countries in the subtropics and tropics, with around 153 million tons annual global production, feeding about 400 million people. Despite its widespread cultivation and adaptability to diverse environments, banana production faces significant challenges from pathogens and pests that often coexist within agricultural landscapes.

Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives.

Long non-coding RNAs (lncRNAs) play key roles in various epigenetic and post-transcriptional events in the cell, thereby significantly influencing cellular processes including gene expression, development and diseases such as cancer. Nuclear receptors (NRs) are a family of ligand-regulated transcription factors that typically regulate transcription of genes involved in a broad spectrum of cellular processes, immune responses and in many diseases including cancer. Owing to their many overlapping roles as modulators of gene expression, the paths traversed by lncRNA and NR-mediated signaling often cross each other; these lncRNA-NR cross-talks are being increasingly recognized as important players in many cellular processes and diseases such as cancer.

Q&A: Methods for estimating genetic gain in sub-Saharan Africa and achieving improved gains.

Regular measurement of realized genetic gain allows plant breeders to assess and review the effectiveness of their strategies, allocate resources efficiently, and make informed decisions throughout the breeding process. Realized genetic gain estimation requires separating genetic trends from nongenetic trends using the linear mixed model (LMM) on historical multi-environment trial data. The LMM, accounting for the year effect, experimental designs, and heterogeneous residual variances, estimates best linear unbiased estimators of genotypes and regresses them on their years of origin.