Phosphorus is a poorly bioavailable macronutrient that is essential for crop growth and yield. Overuse of phosphorus fertilizers results in low phosphorus use efficiency (PUE), has serious environmental consequences and accelerates the depletion of phosphorus mineral reserves. It has become extremely challenging to improve PUE while preserving global food supplies and maintaining environmental sustainability. Molecular and genetic analyses have revealed the primary mechanisms of phosphorus uptake and utilization and their relationships to phosphorus transporters, regulators, root architecture, metabolic adaptations, quantitative trait loci, hormonal signaling and microRNA. The ability to improve PUE requires a transition from this knowledge of molecular mechanisms and plant architecture to practical strategies. These could include: i) the use of arbuscular mycorrhizal fungal symbioses for efficient phosphorus mining and uptake; ii) intercropping with suitable crop species to achieve phosphorus activation and mobilization in the soil; and iii) tissue-specific overexpression of homologous genes with advantageous agronomic properties for higher PUE along with breeding for phosphorus-efficient varieties and introgression of key quantitative trait loci. More effort is required to further dissect the mechanisms controlling phosphorus uptake and utilization within plants and provide new insight into the means to efficiently improve PUE.

Single nucleotide polymorphisms (SNPs) are one of the most common determinants and potential biomarkers of human disease pathogenesis. SNPs could alter amino acid residues, leading to the loss of structural and functional integrity of the encoded protein. In humans, members of the minichromosome maintenance (MCM) family play a vital role in cell proliferation and have a significant impact on tumorigenesis. Among the MCM members, the molecular mechanism of how missense SNPs of minichromosome maintenance complex component 6 (MCM6) contribute to DNA replication and tumor pathogenesis is underexplored and needs to be elucidated. Hence, a series of sequence and structure-based computational tools were utilized to determine how mutations affect the corresponding MCM6 protein. From the dbSNP database, among 15,009 SNPs in the MCM6 gene, 642 missense SNPs (4.28%), 291 synonymous SNPs (1.94%), and 12,500 intron SNPs (83.28%) were observed. Out of the 642 missense SNPs, 33 were found to be deleterious during the SIFT analysis. Among these, 11 missense SNPs (I123S, R207C, R222C, L449F, V456M, D463G, H556Y, R602H, R633W, R658C, and P815T) were found as deleterious, probably damaging, affective and disease-associated. Then, I123S, R207C, R222C, V456M, D463G, R602H, R633W, and R658C missense SNPs were found to be highly harmful. Six missense SNPs (I123S, R207C, V456M, D463G, R602H, and R633W) had the potential to destabilize the corresponding protein as predicted by DynaMut2. Interestingly, five high-risk mutations (I123S, V456M, D463G, R602H, and R633W) were distributed in two domains (PF00493 and PF14551). During molecular dynamics simulations analysis, consistent fluctuation in RMSD and RMSF values, high Rg and hydrogen bonds in mutant proteins compared to wild-type revealed that these mutations might alter the protein structure and stability of the corresponding protein. Hence, the results from the analyses guide the exploration of the mechanism by which these missense SNPs of the MCM6 gene alter the structural integrity and functional properties of the protein, which could guide the identification of ways to minimize the harmful effects of these mutations in humans.

Ruminants and humans are unable to synthesize essential amino acids (EAAs) and conditionally essential amino acids (CEAAs) under normal conditions and need to acquire them from plant sources. Maize plays, as a major crop, a central role in global food security. However, maize is deficient in several EAAs and CEAAs. Genetic engineering has been successfully used to enrich the EAA content of maize to some extent, including the content of Lys, Trp, and Met. However, research on other EAAs is lacking. Genetic engineering provides several viable approaches for increasing the EAA content in maize, including transformation of a single gene, transformation of multiple genes in a single cassette, overexpression of putative amino acid transporters, engineering the amino acid biosynthesis pathway including silencing of feedback inhibition enzymes, and overexpression of major enzymes in this pathway. These challenging processes require a deep understanding of the biosynthetic and metabolic pathways of individual amino acids, and the interaction of individual amino acids with other metabolic pathways.

Type 2 diabetes (T2D) has earned widespread recognition as a primary cause of death, disability, and increasing healthcare costs. There is compelling evidence that hereditary factors contribute to the development of T2D. Clinical trials in T2D have mostly focused on genes and single nucleotide polymorphisms (SNPs) in protein-coding areas. Recently, it was revealed that SNPs located in noncoding areas also play a significant impact on disease vulnerability. It is required for cell type-specific gene expression. However, the precise mechanism by which T2D risk genes and SNPs work remains unknown. We integrated risk genes and SNPs from genome-wide association studies (GWASs) and performed comprehensive bioinformatics analyses to further investigate the functional significance of these genes and SNPs. We identified four intriguing transcription factors (TFs) associated with T2D. The analysis revealed that the SNPs are engaged in chromatin interaction regulation and/or may have an effect on TF binding affinity. The Gene Ontology (GO) study revealed high enrichment in a number of well-characterized signaling pathways and regulatory processes, including the STAT3 and JAK signaling pathways, which are both involved in T2D metabolism. Additionally, a detailed KEGG pathway analysis identified two major T2D genes and their prospective therapeutic targets. Our findings underscored the potential functional significance of T2D risk genes and SNPs, which may provide unique insights into the disease’s pathophysiology.