Background: Being sessile organisms, plants should adjust their metabolism to dynamic changes in their environment. Such adjustments need particular coordination in branched metabolic networks in which a given metabolite can be converted into multiple other metabolites via different enzymatic chains. In the present report, we developed a novel "Gene Coordination" bioinformatics approach and use it to elucidate adjustable transcriptional interactions of two branched amino acid metabolic networks in plants in response to environmental stresses, using publicly available microarray results.
Results: Using our "Gene Coordination" approach, we have identified in Arabidopsis plants two oppositely regulated groups of "highly coordinated" genes within the branched Asp-family network of Arabidopsis plants, which metabolizes the amino acids Lys, Met, Thr, Ile and Gly, as well as a single group of "highly coordinated" genes within the branched aromatic amino acid metabolic network, which metabolizes the amino acids Trp, Phe and Tyr. These genes possess highly coordinated adjustable negative and positive expression responses to various stress cues, which apparently regulate adjustable metabolic shifts between competing branches of these networks. We also provide evidence implying that these highly coordinated genes are central to impose intra- and inter-network interactions between the Asp-family and aromatic amino acid metabolic networks as well as differential system interactions with other growth promoting and stress-associated genome-wide genes.
Conclusion: Our novel Gene Coordination elucidates that branched amino acid metabolic networks in plants are regulated by specific groups of highly coordinated genes that possess adjustable intra-network, inter-network and genome-wide transcriptional interactions. We also hypothesize that such transcriptional interactions enable regulatory metabolic adjustments needed for adaptation to the stresses.
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http://dx.doi.org/10.1186/1752-0509-3-14 | DOI Listing |
J Chem Inf Model
January 2025
Department of Computational and Quantitative Medicine, Beckman Research Institute of the City of Hope, 1218 S 5th Ave, Monrovia, California 91016, United States.
Bayesian network modeling (BN modeling, or BNM) is an interpretable machine learning method for constructing probabilistic graphical models from the data. In recent years, it has been extensively applied to diverse types of biomedical data sets. Concurrently, our ability to perform long-time scale molecular dynamics (MD) simulations on proteins and other materials has increased exponentially.
View Article and Find Full Text PDFJ Org Chem
January 2025
Department of Chemistry, Indian Institute of Science Education and Research, Dr. Homi Bhabha Road, Pashan, Pune 411008, India.
β-Addition products are common in conjugate addition reactions consisting of α,β-unsaturated carbonyl compounds. Here, we are reporting an uncommon α-addition product as a major product in the thioacetic acid conjugate addition reaction on a peptide consisting of ()-α,β-unsaturated γ-amino acids. In addition, we observed highly diastereoselective β-addition products from the thiophenol and thioethanol conjugate addition reaction on peptides.
View Article and Find Full Text PDFRev Med Suisse
January 2025
Service de néphrologie, Département de médecine, Hôpitaux universitaires de Genève, Genève 14.
Certain molecules, such as GLP-1 agonists and endothelin antagonists, possess nephroprotective properties. When treating IgA nephropathy, endothelin antagonists and sibeprenlimab have shown effectiveness in slowing the progression of chronic kidney isease. Additionally, the infusion of amino acids can reduce the incidence of mild acute kidney injury following cardiac surgery.
View Article and Find Full Text PDFBioinform Adv
November 2024
Institute of Biochemistry and Molecular Medicine, University of Bern, Bern 3012, Switzerland.
Summary: Protein structure prediction aims to infer a protein's three-dimensional (3D) structure from its amino acid sequence. Protein structure is pivotal for elucidating protein functions, interactions, and driving biotechnological innovation. The deep learning model AlphaFold2, has revolutionized this field by leveraging phylogenetic information from multiple sequence alignments (MSAs) to achieve remarkable accuracy in protein structure prediction.
View Article and Find Full Text PDFFront Nutr
January 2025
School of Sports Training, Chengdu Sport University, Chengdu, China.
Background: Branched-chain amino acids (BCAAs) are widely used as sports nutrition supplements. However, their impact on the rate of force development (RFD), an indicator of explosive muscle strength, has not yet been validated. This study aimed to assess the impact of BCAA supplementation on the RFD in college basketball players during simulated games.
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