Transcriptional loss of domestication-driven cytoskeletal GhPRF1 gene causes defective floral and fiber development in cotton (Gossypium)

GhHDZ76, a cotton HD-Zip transcription factor, involved in regulating the initiation and early elongation of cotton fiber development in G. hirsutum

In this study, the whole HD-Zip family members of G. hirsutum were identified, and GhHDZ76 was classified into the HD-Zip IV subgroup. GhHDZ76 was predominantly expressed in the 0-5 DPA of fiber development stage and localized in the nucleus. Overexpression of GhHDZ76 significantly increased the length and density of trichomes in Arabidopsis thaliana. The fiber length of GhHDZ76 knockout lines by CRISPR/Cas9 was significantly shorter than WT at the early elongation and mature stage, indicating that GhHDZ76 positively regulate the fiber elongation. Scanning electron microscopy showed that the number of ovule surface protrusion of 0 DPA of GhHDZ76 knockout lines was significantly lower than WT, suggesting that GhHDZ76 can also promote the initiation of fiber development. The transcript level of GhWRKY16, GhRDL1, GhEXPA1 and GhMYB25 genes related to fiber initiation and elongation in GhHDZ76 knockout lines were significantly decreased. Yeast two-hybrid and Luciferase complementation imaging (LCI) assays showed that GhHDZ76 can interact with GhWRKY16 directly. As a transcription factor, GhHDZ76 has transcriptional activation activity, which could bind to L1-box elements of the promoters of GhRDL1 and GhEXPA1. Double luciferase reporter assay showed that the GhWRKY16 could enhance the transcriptional activity of GhHDZ76 to pGhRDL1, but it did not promote the transcriptional activity of GhHDZ76 to pGhEXPA1. GhHDZ76 protein may also promote the transcriptional activity of GhWRKY16 to the downstream target gene GhMYB25. Our results provided a new gene resource for fiber development and a theoretical basis for the genetic improvement of cotton fiber quality.

Genome-Wide Analysis of VILLIN Gene Family Associated with Stress Responses in Cotton (Gossypium spp.)

The VILLIN (VLN) protein plays a crucial role in regulating the actin cytoskeleton, which is involved in numerous developmental processes, and is crucial for plant responses to both biotic and abiotic factors. Although various plants have been studied to understand the VLN gene family and its potential functions, there has been limited exploration of VLN genes in Gossypium and fiber crops. In the present study, we characterized 94 VLNs from Gossypium species and 101 VLNs from related higher plants such as Oryza sativa and Zea mays and some fungal, algal, and animal species. By combining these VLN sequences with other Gossypium spp., we classified the VLN gene family into three distinct groups, based on their phylogenetic relationships. A more in-depth examination of Gossypium hirsutum VLNs revealed that 14 GhVLNs were distributed across 12 of the 26 chromosomes. These genes exhibit specific structures and protein motifs corresponding to their respective groups. GhVLN promoters are enriched with cis-elements related to abiotic stress responses, hormonal signals, and developmental processes. Notably, a significant number of cis-elements were associated with the light responses. Additionally, our analysis of gene-expression patterns indicated that most GhVLNs were expressed in various tissues, with certain members exhibiting particularly high expression levels in sepals, stems, and tori, as well as in stress responses. The present study potentially provides fundamental insights into the VLN gene family and could serve as a valuable reference for further elucidating the diverse functions of VLN genes in cotton.

Revolutionizing Biological Science: The Synergy of Genomics in Health, Bioinformatics, Agriculture, and Artificial Intelligence

With climate emergency, COVID-19, and the rise of planetary health scholarship, the binary of human and ecosystem health has been deeply challenged. The interdependence of human and nonhuman animal health is increasingly acknowledged and paving the way for new frontiers in integrative biology. The convergence of genomics in health, bioinformatics, agriculture, and artificial intelligence (AI) has ushered in a new era of possibilities and applications. However, the sheer volume of genomic/multiomics big data generated also presents formidable sociotechnical challenges in extracting meaningful biological, planetary health and ecological insights. Over the past few years, AI-guided bioinformatics has emerged as a powerful tool for managing, analyzing, and interpreting complex biological datasets. The advances in AI, particularly in machine learning and deep learning, have been transforming the fields of genomics, planetary health, and agriculture. This article aims to unpack and explore the formidable range of possibilities and challenges that result from such transdisciplinary integration, and emphasizes its radically transformative potential for human and ecosystem health. The integration of these disciplines is also driving significant advancements in precision medicine and personalized health care. This presents an unprecedented opportunity to deepen our understanding of complex biological systems and advance the well-being of all life in planetary ecosystems. Notwithstanding in mind its sociotechnical, ethical, and critical policy challenges, the integration of genomics, multiomics, planetary health, and agriculture with AI-guided bioinformatics opens up vast opportunities for transnational collaborative efforts, data sharing, analysis, valorization, and interdisciplinary innovations in life sciences and integrative biology.

Systematically and Comprehensively Understanding the Regulation of Cotton Fiber Initiation: A Review

Cotton fibers provide an important source of raw materials for the textile industry worldwide. Cotton fiber is a kind of single cell that differentiates from the epidermis of the ovule and provides a perfect research model for the differentiation and elongation of plant cells. Cotton fiber initiation is the first stage throughout the entire developmental process. The number of fiber cell initials on the seed ovule epidermis decides the final fiber yield. Thus, it is of great significance to clarify the mechanism underlying cotton fiber initiation. Fiber cell initiation is controlled by complex and interrelated regulatory networks. Plant phytohormones, transcription factors, sugar signals, small signal molecules, functional genes, non-coding RNAs, and histone modification play important roles during this process. Here, we not only summarize the different kinds of factors involved in fiber cell initiation but also discuss the mechanisms of these factors that act together to regulate cotton fiber initiation. Our aim is to synthesize a systematic and comprehensive review of different factors during fiber initiation that will provide the basics for further illustrating these mechanisms and offer theoretical guidance for improving fiber yield in future molecular breeding work.

Artificial Intelligence in Biological Sciences

Artificial intelligence (AI), currently a cutting-edge concept, has the potential to improve the quality of life of human beings. The fields of AI and biological research are becoming more intertwined, and methods for extracting and applying the information stored in live organisms are constantly being refined. As the field of AI matures with more trained algorithms, the potential of its application in epidemiology, the study of host–pathogen interactions and drug designing widens. AI is now being applied in several fields of drug discovery, customized medicine, gene editing, radiography, image processing and medication management. More precise diagnosis and cost-effective treatment will be possible in the near future due to the application of AI-based technologies. In the field of agriculture, farmers have reduced waste, increased output and decreased the amount of time it takes to bring their goods to market due to the application of advanced AI-based approaches. Moreover, with the use of AI through machine learning (ML) and deep-learning-based smart programs, one can modify the metabolic pathways of living systems to obtain the best possible outputs with the minimal inputs. Such efforts can improve the industrial strains of microbial species to maximize the yield in the bio-based industrial setup. This article summarizes the potentials of AI and their application to several fields of biology, such as medicine, agriculture, and bio-based industry.

Revealing Genetic Differences in Fiber Elongation between the Offspring of Sea Island Cotton and Upland Cotton Backcross Populations Based on Transcriptome and Weighted Gene Coexpression Networks

Fiber length is an important indicator of cotton fiber quality, and the time and rate of cotton fiber cell elongation are key factors in determining the fiber length of mature cotton. To gain insight into the differences in fiber elongation mechanisms in the offspring of backcross populations of Sea Island cotton Xinhai 16 and land cotton Line 9, we selected two groups with significant differences in fiber length (long-fiber group L and short-fiber group S) at different fiber development stages 0, 5, 10 and 15 days post-anthesis (DPA) for transcriptome comparison. A total of 171.74 Gb of clean data was obtained by RNA-seq, and eight genes were randomly selected for qPCR validation. Data analysis identified 6055 differentially expressed genes (DEGs) between two groups of fibers, L and S, in four developmental periods, and gene ontology (GO) term analysis revealed that these DEGs were associated mainly with microtubule driving, reactive oxygen species, plant cell wall biosynthesis, and glycosyl compound hydrolase activity. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated that plant hormone signaling, mitogen-activated protein kinase (MAPK) signaling, and starch and sucrose metabolism pathways were associated with fiber elongation. Subsequently, a sustained upregulation expression pattern, profile 19, was identified and analyzed using short time-series expression miner (STEM). An analysis of the weighted gene coexpression network module uncovered 21 genes closely related to fiber development, mainly involved in functions such as cell wall relaxation, microtubule formation, and cytoskeletal structure of the cell wall. This study helps to enhance the understanding of the Sea Island–Upland backcross population and identifies key genes for cotton fiber development, and these findings will provide a basis for future research on the molecular mechanisms of fiber length formation in cotton populations.