Gestational diabetes mellitus (GDM) is a common metabolic disorder affecting approximately 16.5% of pregnancies worldwide and causing significant health concerns. GDM is a serious pregnancy complication caused by chronic insulin resistance in the mother and has been associated with the development of neurodevelopmental disorders in offspring. Emerging data support the notion that GDM affects both the maternal and fetal microbiome, altering the composition and function of the gut microbiota, resulting in dysbiosis. The observed dysregulation of microbial presence in GDM pregnancies has been connected to fetal neurodevelopmental problems. Several reviews have focused on the intricate development of maternal dysbiosis affecting the fetal microbiome. Omics data have been instrumental in deciphering the underlying relationship among GDM, gut dysbiosis, and fetal neurodevelopment, paving the way for precision medicine. Microbiome-associated omics analyses help elucidate how dysbiosis contributes to metabolic disturbances and inflammation, linking microbial changes to adverse pregnancy outcomes such as those seen in GDM. Integrating omics data across these different layers-genomics, transcriptomics, proteomics, metabolomics, and microbiomics-offers a comprehensive view of the molecular landscape underlying GDM. This review outlines the affected pathways and proposes future developments and possible personalized therapeutic interventions by integrating omics data on the maternal microbiome, genetics, lifestyle factors, and other relevant biomarkers aimed at identifying women at high risk of developing GDM. For example, machine learning tools have emerged with powerful capabilities to extract meaningful insights from large datasets.
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http://dx.doi.org/10.3389/fmolb.2024.1420664 | DOI Listing |
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Germplasm Bank of Wild Species & Yunnan Key Laboratory of Crop Wild Relatives Omics, Kunming Institute of Botany, Chinese Academy of Sciences, Kunming 650201, China.
The angiosperm family Elaeagnaceae comprises three genera and . 100 species distributed mainly in Eurasia and North America. Little family-wide phylogenetic and biogeographic research on Elaeagnaceae has been conducted, limiting the application and preservation of natural genetic resources.
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January 2025
Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Microbiome-metabolome association analysis is critical to reveal the key pairs of gut microbiota and metabolites for discovery of the microbial biomarkers in chronic diseases. However, the characteristics of microbiome data, such as zero inflation, over dispersion, may impair the confidence of association analysis between microbiome and metabolome data. The objectives of this study are to evaluate the strengths and weaknesses of existing statistical methods and to develop a computational framework tailored to the unique characteristics of microbiome data.
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June 2024
Department of Biostatistics, University of Florida, Gainesville, FL, USA.
Due to the tremendous heterogeneity of disease manifestations, many complex diseases that were once thought to be single diseases are now considered to have disease subtypes. Disease subtyping analysis, that is the identification of subgroups of patients with similar characteristics, is the first step to accomplish precision medicine. With the advancement of high-throughput technologies, omics data offers unprecedented opportunity to reveal disease subtypes.
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January 2024
CAS Key Lab of Bio-Medical Diagnostics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China.
Spatial monoomics has been recognized as a powerful tool for exploring life sciences. Recently, spatial multiomics has advanced considerably, which could contribute to clarifying many biological issues. Spatial monoomics techniques in epigenomics, genomics, transcriptomics, proteomics, and metabolomics can enhance our understanding of biological functions and cellular identities by simultaneously measuring tissue structures and biomolecule levels.
View Article and Find Full Text PDFJ Transl Med
January 2025
Department of Biochemistry and Pharmacology, Bio21 Molecular Science and Biotechnology Institute, University of Melbourne, Parkville, VIC, 3052, Australia.
Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS) is a complex and multifaceted disorder that defies simplistic characterisation. Traditional approaches to diagnosing and treating ME/CFS have often fallen short due to the condition's heterogeneity and the lack of validated biomarkers. The growing field of precision medicine offers a promising approach which focuses on the genetic and molecular underpinnings of individual patients.
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