Acta Biochimica et Biophysica Sinica•2026•DOI: 10.3724/abbs.2025199
Pregnancy induces profound physiological adaptations to meet the dynamic nutritional demands of fetal development, including a deliberate reduction in maternal insulin sensitivity to ensure fetal glucose availability. However, excessive insulin resistance may precipitate gestational diabetes mellitus (GDM), increasing the risk of both obstetric complications and long-term metabolic disorders in mothers and offspring. Although the role of adipose tissue in pregnancy-associated metabolic adaptation has been extensively studied, the contribution of skeletal muscle remains poorly understood. Here, we systematically characterize pregnancy-induced molecular and metabolic changes in maternal skeletal muscle through multi-omics profiling. We use transcriptomic, metabolomic, computational single-cell deconvolution, and qPCR validation in an established C57BL/6J mouse pregnancy model (8-week-old females). Pregnancy triggers remarkable skeletal muscle remodelling, featuring histological reorganization with myofiber depletion and expanded endothelial compartments. Concurrent metabolic disturbances include insulin resistance, dysregulated TCA cycle activity, and impaired ubiquinone biosynthesis. This study represents a multi-omics-based systematic elucidation of pregnancy-induced maternal skeletal muscle adaptations. Our findings demonstrate that pregnancy induces profound structural reorganization and metabolic reprogramming in maternal skeletal muscle, characterized by prioritized fetal nutrient provision at the expense of maternal tissue utilization. These observations not only reveal previously unrecognized mechanisms of pregnancy-specific metabolic regulation but also, more importantly, establish a critical theoretical foundation for developing skeletal muscle-targeted intervention strategies to prevent gestational diabetes mellitus.
Chinese Traditional and Herbal Drugs•2026•DOI: 10.7501/j.issn.0253-2670.2026.16.20261607
This study addresses the pharmacodynamic material basis of Houttuynia cordata volatile oil by integrating supramolecular "imprinting template" theory with matching frequency, total statistical moment, and factor rotation methods, coupled to in vitro antitumor activity. Fifty-eight batches (S1–S58) from different origins were fingerprinted by GC-MS. The matching frequency method reduced the imprinting template to 34 structural "material units" (A1–A34). Integration significantly decreased average peak count and information entropy (P < 0.01), while total zero-, first-, and second-order moments and information content remained unchanged. Factor rotation extracted eight common factors; comprehensive scores ranked S16 and S54 highest and S24 and S27 lowest. CCK-8 assays against human lung adenocarcinoma A549 cells yielded IC50 values from 73.6 to 269.9 nL/mL. Comprehensive scores negatively correlated with IC50 (r = −0.739, P < 0.01). High-contribution units A34, A31, and A7 were preliminarily identified as a potential pharmacodynamic component group. The model demonstrates utility for trend-level quality evaluation but does not provide one-to-one prediction of single-batch efficacy. Limitations include restriction to volatile constituents, single-cell-line validation, and lack of independent isolation and enrichment for the flagged units. The protocol offers a transferable statistical framework for linking chromatographic fingerprints to bioactivity in complex botanical oils.
Chinese Journal of Tissue Engineering Research•2026•DOI: 10.12307/2026.21235
BACKGROUND: Factors such as infection, limb ischemia, and histiocyte activation are involved in diabetic foot ulcers, but the key cell subpopulations influencing diabetic foot ulcer healing remain unclear, and specific biomarkers for diabetic foot ulcers have yet to be identified. Gene Expression Omnibus (GEO) is a publicly accessible database managed by the National Center for Biotechnology Information that stores high-throughput gene expression data, allowing users to freely submit, share, query, and analyze data. Secondary analysis of published data can save research costs and uncover new research targets and ideas. OBJECTIVE: To screen biomarkers for diabetic foot ulcers using single-cell transcriptome and conventional transcriptome bioinformatics analysis, high-dimensional weighted gene co-expression network analysis (hdWGCNA), and weighted gene co-expression network analysis (WGCNA). METHODS: The single-cell transcriptome dataset GSE165816, containing non-healing ulcer tissue samples from diabetic foot ulcer patients and foot skin samples from diabetic patients, was downloaded from GEO. After data quality control, dimensionality reduction, differential analysis, cell type annotation, and pseudotime analysis, cell types spanning the entire course of diabetic foot ulcers were identified, and differentially expressed genes (DEGs) were obtained. hdWGCNA identified gene modules highly correlated with diabetic foot ulcers. Conventional transcriptome datasets GSE68183 and GSE80178, containing non-healing ulcer tissue samples from diabetic foot ulcer patients and foot skin samples from diabetic patients, were downloaded for differential analysis to screen DEGs, and WGCNA was used to identify diabetic foot ulcer-related gene modules. The DEGs from single-cell transcriptome, DEGs from conventional transcriptome samples, and module genes from WGCNA and hdWGCNA were integrated to screen biomarkers for diabetic foot ulcers. The GSE134431 dataset was downloaded as a validation conventional transcriptome dataset, and the expression levels of diabetic foot ulcer biomarkers were compared in single-cell transcriptome and validation conventional transcriptome datasets. Diabetic and diabetic foot ulcer rat models were replicated, wound tissue was collected, and immunohistochemistry and western blot were used to detect biomarker expression levels. RESULTS AND CONCLUSION: Single-cell transcriptome data analysis showed that epithelial cell differentiation spanned the entire pathological process of diabetic foot ulcers. A total of 146 DEGs were obtained from single-cell transcriptome between groups, including 59 upregulated and 87 downregulated DEGs. hdWGCNA identified 19 gene modules related to diabetic foot ulcers, containing 476 core genes. Conventional transcriptome data analysis yielded a total of 913 DEGs, including 343 upregulated and 570 downregulated DEGs. WGCNA obtained 19 diabetic foot ulcer-related gene modules, containing 887 genes. Two biomarkers for diabetic foot ulcers were screened: S100A14 and SFN. The expression levels of these two genes in diabetic foot ulcer samples were higher than those in diabetic foot skin samples in both single-cell transcriptome and validation conventional transcriptome datasets. Animal experiments showed that the expression levels of S100A14 and SFN in wound tissue of diabetic foot ulcer rats were higher than those in back skin tissue of diabetic rats. The results indicate that the pathological process of diabetic foot ulcers involves multiple cell types, among which epithelial cells are the key cell subpopulation. S100A14 and SFN are significantly upregulated in diabetic foot ulcer samples and are potential targets for the treatment of diabetic foot ulcers.