Small molecule metabolites are the product of many enzymatic reactions. Metabolomics thus opens a window into enzyme activity and function, integrating effects at the post-translational, proteome, transcriptome and genome level. In addition, small molecules can themselves regulate enzyme activity, expression and function both via substrate availability mechanisms and through allosteric regulation. Metabolites are therefore at the nexus of infectious diseases, regulating nutrient availability to the pathogen, immune responses, tropism, and host disease tolerance and resilience. Analysis of metabolomics data is however complex, particularly in terms of metabolite annotation. An emerging valuable approach to extend metabolite annotations beyond existing compound libraries and to identify infection-induced chemical changes is molecular networking. In this chapter, we discuss the applications of molecular networking in the context of infectious diseases specifically, with a focus on considerations relevant to these biological systems.
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http://dx.doi.org/10.1016/bs.mie.2021.09.018 | DOI Listing |
Mol Hortic
January 2025
Guangdong Provincial Key Laboratory of Applied Botany, Key Laboratory of South China Agricultural Plant Molecular Analysis and Genetic Improvement, South China Botanical Garden, Chinese Academy of Sciences, 510650, Guangzhou, China.
Banana is sensitive to cold stress and often suffers from chilling injury with browning peel and failure to normal ripening. We have previously reported that banana chilling injury is accompanied by a reduction of miR528 accumulation, alleviating the degradation of its target gene MaPPO and raising ROS levels that cause peel browning. Here, we further revealed that the miR528-MaPPO cold-responsive module was regulated by miR156-targeted SPL transcription factors, and the miR156c-MaSPL4 module was also responsive to cold stress in banana.
View Article and Find Full Text PDFJ Orthop Surg Res
January 2025
Department of Hand-Foot Microsurgery, Shenzhen Nanshan People's Hospital, The 6th Affiliated Hospital of Shenzhen University Health Science Center, Shenzhen, China.
Background: Steroid-induced osteonecrosis of the femoral head (SIONFH) is a universal hip articular disease and is very hard to perceive at an early stage. The understanding of the pathogenesis of SIONFH is still limited, and the identification of efficient diagnostic biomarkers is insufficient. This research aims to recognize and validate the latent exosome-related molecular signature in SIONFH diagnosis by employing bioinformatics to investigate exosome-related mechanisms in SIONFH.
View Article and Find Full Text PDFBMC Plant Biol
January 2025
Beijing Life Science Academy, Beijing, 102200, China.
Background: Fungal communities around plant roots play crucial roles in maintaining plant health. Nonetheless, the responses of fungal communities to bacterial wilt disease remain poorly understood. Here, the structure and function of fungal communities across four consecutive compartments (bulk soil, rhizosphere, rhizoplane and root endosphere) were investigated under the influence of bacterial wilt disease.
View Article and Find Full Text PDFCell Mol Biol Lett
January 2025
Enzymology and Metabolism Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.
Background: Metabolism is error prone. For instance, the reduced forms of the central metabolic cofactors nicotinamide adenine dinucleotide (NADH) and nicotinamide adenine dinucleotide phosphate (NADPH), can be converted into redox-inactive products, NADHX and NADPHX, through enzymatically catalyzed or spontaneous hydration. The metabolite repair enzymes NAXD and NAXE convert these damaged compounds back to the functional NAD(P)H cofactors.
View Article and Find Full Text PDFJ Cell Mol Med
January 2025
Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China.
This study aims to elucidate the potential genetic commonalities between metabolic syndrome (MetS) and rheumatic diseases through a disease interactome network, according to publicly available large-scale genome-wide association studies (GWAS). The analysis included linkage disequilibrium score regression analysis, cross trait meta-analysis and colocalisation analysis to identify common genetic overlap. Using modular partitioning, the network-based association between the two disease proteins in the protein-protein interaction set was divided and quantified.
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