Physiological genomics最新文献

MicroRNAs (miRNAs) are short noncoding RNAs that regulate gene expression in various cell types. Skeletal muscle consists of bundles of muscle fibers, which are classified as either slow-type or fast-type according to their properties. However, the roles of miRNAs in modulating physiological muscle phenotypes remain unclear. Here, we profiled fiber-type-enriched miRNAs to gain insight into differences in gene regulation between the two fiber types. To avoid cross-contamination, we used GFP-Myh7 mice, in which slow-type muscle fibers express green fluorescent protein (GFP), allowing easy discrimination between GFP-positive slow-type fibers and GFP-negative fast-type fibers under fluorescence microscopy. Here, we profiled miRNA expression in two muscle fiber types in GFP-Myh7 mice. Microarray analysis showed that 18 and 12 miRNAs were highly expressed in slow-type and fast-type fibers, respectively, with >2 log2 fold-change (log2FC) relative to their counterparts. These distinct miRNA expressions were largely consistent with polymerase chain reaction (PCR) results. Gene ontology analyses predicted that target genes of these miRNAs were mainly involved in "regulation of transcription" in slow-type muscle fibers, and in "extracellular matrix (ECM)"-related terms in fast-type fibers. Our results suggest that distinct miRNA expression patterns in each fiber type may participate in modulating fiber-type-specific intracellular and extracellular environments.NEW & NOTEWORTHY Skeletal muscle comprises fast and slow fiber types, which reflect physiological and metabolic features. Although identifying fiber types without PCR or antibody-based assays was challenging, we visually isolated slow- and fast-type fibers from GFP-Myh7 mice, in which slow-type fibers express green fluorescent protein (GFP). Using these mice, we successfully profiled miRNA expression in precisely distinguished slow- and fast-type fibers to capture fiber-type-dependent miRNA expression.

The heart undergoes significant molecular and functional adaptations throughout postnatal development. However, our understanding of these dynamic changes in the human heart is limited. Advances in pediatric cardiac research are often hindered by the lack of preclinical models. Guinea pigs may serve as a useful model for human cardiac research, as the guinea pig and human myocardium have similar ion channel expression and cardiovascular drug responsiveness. Yet, gene expression patterns during postnatal heart development have not been comprehensively investigated. In this study, we first characterized transcriptional changes in neonatal, juvenile, and adult guinea pig hearts. Neonatal hearts overexpressed cell-cycle (e.g., Cdk1, Cdk2) and glycogen energy metabolism genes (e.g., Irs1, Akt2), whereas adults overexpressed calcium signaling genes (e.g., Sln, Casq2). Second, we compared the transcriptional profile of right atria and left ventricular tissue; atrial maturation was enriched for sinoatrial node and conduction system pathways, whereas ventricular maturation was enriched for sarcomere organization and action potential regulation. Finally, we conducted a cross-species comparison of the right atrial transcriptome between humans and guinea pigs. This identified conserved maturation markers, including S100A1, SLN, and MYL4, suggesting shared temporal gene expression programs during postnatal cardiac development. Our findings provide a molecular framework for understanding age- and chamber-specific cardiac development, supporting the guinea pig as a promising preclinical model for studying human heart maturation. By identifying conserved gene programs and developmental markers across species, this study lays the groundwork for age-specific pharmacological strategies and computational models that can help to refine treatment decisions for pediatric patients.NEW & NOTEWORTHY Existing knowledge on postnatal heart development and cardiomyocyte maturation is limited. We investigated age-dependent transcriptional changes in neonatal, juvenile, and adult guinea pig hearts and then conducted a cross-species comparison to identify age-specific patterns that are conserved in the guinea pig and human atria. Expanding our knowledge of chamber- and age-specific gene expression patterns can inform and guide the selection of cardiovascular therapies in the pediatric population, where developmental differences are understudied.

The only comprehensive human genetic interaction map was constructed using increased gene copy numbers in radiation hybrid (RH) cells. Recently, a second map restricted to essential genes was created using CRISPR interference (CRISPRi)-induced loss-of-function alleles. Here, the two maps are compared to understand their similarities and differences. Both maps showed significant overlap with protein-protein interaction databases and identified a shared set of interacting genes, although the specific gene pairs differed between approaches. Notably, the RH map exhibited strong overlap with genome-wide association study (GWAS) networks, whereas the CRISPRi map did not. These findings demonstrate how gain- and loss-of-function alleles reveal distinct yet complementary genetic interaction landscapes.NEW & NOTEWORTHY This study compared two mammalian genetic interaction networks for cell growth: the radiation hybrid (RH) network used extra gene copies and the CRISPRi network used partial gene suppression. Both networks overlapped with protein-protein interaction data and identified common interacting genes, yet specific gene pair interactions differed dramatically. Only the RH network predicted genome-wide association study (GWAS) networks. As the first comparison of large-scale mammalian genetic interaction networks, this work reveals how gain- and loss-of-function variants capture diverse biological perspectives.

We developed a novel artificial intelligence (AI) approach based on machine learning to predict general health and food-intake parameters. This approach, named Transcriptome-driven Health status Transversal-predictor Analysis (THTA) is relevant for markers of diabesity and is based on a nontranscriptomic, mathematics-driven approach. The prediction was based on values derived from food consumption, dietary lipids and their bioactive metabolites, peripheral blood mononuclear cell (PBMC) mRNA-based transcriptome signatures, magnetic resonance imaging (MRI), energy metabolism measurements, microbiome analyses, and baseline clinical parameters, as determined in a cohort of 72 subjects. Our novel machine learning approach incorporated transcriptome data from PBMCs as a "one-method" approach to predict 77 general health status markers for the broad stratification of the diabesity phenotype. These markers would usually necessitate measurements using 16 different methods. The PBMC transcriptome was used to determine these 77 basic and background health markers with very high accuracy in a transversal-predictor establishment group (Pearson's correlations r = 0.98 ranging from 0.94 to 0.99). These collected variables provide valuable insides into which individual factor(s) are mainly target diabesity. Based on the "establishment group" prediction approach, a further "confirmation group" prediction approach was performed, achieving a predictive potential r = 0.59 (ranging from 0.19 to 0.98) for these 77 variables. This "one-method" approach enables the simultaneous monitoring of a large number of health-status variables relevant to diabesity and may facilitate the monitoring of therapeutic and preventive strategies. In summary, this novel technique, which is based on PBMC transcriptomics from human blood, can predict a wide range of health-related markers. ClinicalTrial.gov Identifier: NCT01684917.NEW & NOTEWORTHY We developed a novel AI approach based on machine learning to predict general health and food-intake parameters. This approach, named transcriptome-driven health status transversal-predictor analysis, is relevant for markers of diabesity and is based on a mathematics-driven approach. This "one-method" approach enables the simultaneous monitoring of a large number of health-status variables and may facilitate monitoring of therapeutic and preventive strategies. This PBMC transcriptomics-based technique from human blood offers prediction of a wide range of health-related markers.

Dorsal root ganglia (DRG) are essential for transmitting sensory information from visceral organs to the central nervous system. Sensory neuronal hyperactivity and glial reactivity have been reported in DRG in animal models of chronic pain, yet the molecular mechanisms contributing to the pathogenesis of visceral pain remain unclear. In this study, we performed transcriptome profiling of lumbosacral DRG in a mouse model of chronic pelvic pain, focusing on mapping the gene and signaling pathway changes associated with visceral hypersensitivity in lumbosacral DRG transmitting bladder afferent signals. Using the bulk RNA-sequencing method, we identified differentially expressed genes in the lumbosacral DRG between control mice and mice exhibiting visceral pain symptoms, with striking sex differences in identified genes. Hierarchical gene clustering analysis and Ingenuity Pathways Analysis both revealed sex-specific signaling pathway activation associated with visceral pain conditions, including glial activation and nociceptive sensitization in males and heightened immune activation in females. Interestingly, our data also showed enriched gene expression linked to extracellular matrix and immune functions in female control animals compared with male control animals, suggesting molecular sexual dimorphism in sensory ganglia. Finally, our data identified common genes and signaling pathway changes involved in visceral hypersensitivity in both sexes. This study is the first molecular and signaling pathway characterization in the lumbosacral DRG in the context of bladder-origin visceral pain. The sex differences in the molecular profile of lumbosacral DRG in healthy animals and in animals exhibiting visceral pain symptoms suggest sex-specific visceral pain etiology, despite similar symptoms.NEW & NOTEWORTHY This study examined transcriptomics in the lumbosacral DRG in a VEGF-induced visceral pain mouse model. Male and female mice underwent intravesical instillations of VEGF₁₆₅ or saline. Across the four experimental groups, we found significant sex differences in DRG transcriptome between control animals and VEGF-induced molecular changes, suggesting sex-specific visceral pain mechanisms. These findings provide insight into potential targets for alleviating visceral pain symptoms when considering sex as a biological variable.

Impaired lactose digestion, primarily resulting from lactase non-persistence (LNP), is widely observed across African and non-African populations; however, its prevalence differs according to genetic background and dietary practices. Although numerous pastoralist cultures in Africa have independently developed lactase persistence (LP), a sizable portion of the population experiences primary or secondary lactose malabsorption, either as a natural genetic trait or as a secondary impairment resulting from intestinal damage. This review summarizes the genetic variants and environmental contributors associated with lactose digestion in Africa, highlighting ancestry-specific variants and the underrepresentation of African populations in prior studies. A Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020-guided systematic review searched PubMed, African Journals Online, Wiley Online Library, and Google Scholar (1970 to June 2025) for studies on genetic and environmental contributors to lactose digestion in African groups. Inclusion focused on human studies reporting lactase persistence (LP)/LNP or secondary impaired lactose digestion. Data were extracted on variants, diagnostics, and outcomes. Twenty-eight studies were included, predominantly from East African pastoralists (53.3%), where LP alleles, including -13910T and -14010C in MCM6 intron 13, reached frequencies of 40%-43%.Southern or West/North African groups showed LNP rates >70%. Secondary impaired lactose digestion affected 65%-68% of malnourished/infected children, highlighting enteropathy and infections. Genotype-phenotype discrepancies were noted, with statistical associations due to linkage disequilibrium but not direct causation. Impaired lactose digestion in Africa reflects genetic adaptations in pastoralists and environmental stressors like malnutrition. Population-specific diagnostics and interventions are needed, integrating microbiome and dietary research for resource-limited settings.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine-metabolic disorder that adversely affects reproductive, metabolic, and cardiovascular health in females, leading to menstrual irregularities and an increased risk of endometrial malignancies. Emerging research evidence suggests that the gut and extra gastrointestinal microbiome dysbiosis may play a significant role in the pathophysiology of PCOS. This systematic review aims to elucidate the microbiome dysbiosis patterns in patients with PCOS compared with healthy controls. A systematic search was conducted across PubMed, Scopus, and Web of Science from inception until February 28, 2025, encompassing all original cross-sectional, cohort, or case-control studies that examined the gut, oral, blood, and lower genital tract (LGT) microbiomes of patients with PCOS (cases) against healthy females (controls). Of the 4,377 studies identified, 64 were assessed for eligibility through full-text screening, and ultimately, 29 studies met inclusion criteria and were included into the systematic review. The results revealed inconsistent patterns in alpha and beta diversity, with reports of increased, decreased, or unchanged microbial diversity across studies. Key alterations were observed at different taxonomic levels, such as phylum, family, genus, and species. The most significant bacterial alterations include changes in the relative abundance of various bacterial taxa such as Bacteroidetes, Firmicutes, Actinobacteria, Proteobacteria, Verrucomicrobia, Gammaproteobacteria, Fusobacteria, Eubacterium, Streptococcus, Paraprevotella, Tucibacter, and Tenericutes. These findings indicate that complex dysbiotic microbial shifts may be involved in the pathogenesis of PCOS. As per the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessment, the quality of evidence is low for most of the studies. This systematic review supports the role of microbial dysbiosis in PCOS pathogenesis; however, additional research is required to elucidate these interactions to guide the development of therapeutic strategies in the future.