Physiological genomics最新文献

Muscle strength decline is a hallmark of aging and contributes to frailty and bone deterioration, yet the genomic and epigenomic mechanisms predicting functional strength remain unclear. We applied a multi-omics approach to identify genetic and epigenetic signatures of muscle strength variability in postmenopausal women. A total of 141 women aged 50-70 years underwent functional tests, biochemical analysis, anthropometry, blood pressure assessment, and dual-energy X-ray absorptiometry. Participants were classified into higher and lower strength groups based on validated upper and lower limb tests. Genome-wide genotyping was performed with the Illumina Global Screening Array, and DNA methylation was measured using the Illumina EPIC 850K array. A polygenic risk score (PRS) was generated in a training cohort (n = 100) and validated in an independent group (n = 41). EpiScores were calculated using MethylDetectR, and four fitness-related epigenetic clocks (DNAmGrip, DNAmGait, DNAmVO2max, DNAmFitAge) were derived with the methylclock package. Twelve SNPs were associated with strength phenotypes, and the PRS predicted group classification with 51.2% accuracy. Epigenetic analysis revealed 12 differentially methylated regions, including higher BMP1 EpiScore levels in women with greater strength. Functional enrichment indicated pathways related to bone remodeling and vascular regulation. In the lower strength group, BMP1 EpiScore correlated inversely with femoral neck T-score (r = -0.66, p = 0.037). A meta-analysis of public muscle transcriptomes showed that resistance training increases BMP1 expression. These findings highlight molecular mechanisms linking genetic and epigenetic variation to musculoskeletal aging and functional decline in postmenopausal women.

Placental abnormalities are central to preeclampsia (PE), yet the cellular and molecular mechanisms underlying this dysfunction remain unclear. We applied a multi-layered, integrative approach to investigate placental tissue from patients with PE and matched controls. Single-cell RNA sequencing (scRNA-seq; GSE173193) and bulk RNA sequencing (bulk RNA-seq; GSE203507) datasets were obtained from the Gene Expression Omnibus. The scRNA-seq dataset included two PE and two control samples, whereas the bulk RNA-seq dataset focused on eight early-onset PE and five uncomplicated term births. Trophoblast subpopulations were identified via scRNA-seq, and pseudotime analysis was used to trace differentiation trajectories. Differential expression and pathway enrichment analyses were performed to elucidate molecular alterations. For metabolomic profiling, plasma samples from six patients with PE and six controls (3 replicates each) were analyzed. Transcriptomic and metabolomic data were integrated to investigate gene-metabolite interactions and their relevance to PE pathogenesis. Villous cytotrophoblasts (VCTs) and syncytiotrophoblasts (SCTs) were more abundant in PE placentas, whereas extravillous trophoblasts (EVTs) were reduced compared with controls. Five trophoblast subpopulations-SCT-VCT, Mix, EVT, VCT, and SCT-were characterized by distinct marker genes. Pseudotime analysis indicated differentiation from mixed states toward specific trophoblast lineages. Immune-related pathways were significantly enriched in PE. Integrated analysis highlighted key connections between metabolites, gene expression, and PE-related pathways, implicating oxidative stress, inflammation, metabolic dysregulation, and vascular dysfunction. Our study provides novel insights into placental dysfunction in PE, highlighting alterations in trophoblast subpopulations and immune pathways. These findings may inform strategies for early diagnosis, prevention, and therapeutic intervention in PE.NEW & NOTEWORTHY Our integrative multi-omics approach, spanning single-cell, bulk transcriptomic, and targeted metabolomic data, demonstrates convergent cellular and metabolic abnormalities in preeclampsia. We find disrupted trophoblast composition, altered differentiation patterns, and metabolic pathway shifts that jointly distinguish preeclamptic placentas from controls. These results advance understanding of placental dysfunction and underscore the value of multi-omics strategies for dissecting complex pregnancy disorders.

Hemorrhagic shock is a leading cause of preventable death among military and civilian trauma patients. Although caused by severe hypovolemia, the threshold of blood volume reduction that triggers recruitment of compensatory mechanisms varies markedly. Individuals have been classified as having low tolerance (LT) or high tolerance (HT) to hypovolemia; however, molecular features contributing to tolerance remain unclear. Here we investigate multiomics correlates of hypovolemia tolerance and molecular responses underlying physiological compensating mechanisms of blood loss. Healthy adult human subjects (n = 133) recruited from two sites underwent lower body negative pressure (LBNP) to simulate progressive hemorrhage. The primary outcome variable was hemodynamic instability accompanied by onset of decompensated shock defined by systolic blood pressure <80 mmHg. Participants were classified into HT (n = 90) and LT (n = 43) subjects using a cumulative stress index quantifying maximal LBNP tolerance. Genome-wide messenger ribonucleic acid (mRNA), microRNA, whole exome sequencing, and select protein abundances were assayed using blood samples collected immediately before and after LBNP procedure. LBNP produced extensive transcriptomic response at post- compared with pre-LBNP, including natural killer cell-mediated immunity activation and gas transport processes inhibition. Differentially expressed microRNAs (miRNAs) also regulated these enriched processes. Tolerance group-specific signals include alpha-beta T cell activation and major histocompatibility complex (MHC) class II protein complex assembly inhibition in HT group. Integrated analysis of multiple molecular layers demonstrated a role of cytokines and epigenetic regulators in molecular mechanisms of compensating for progressive hemorrhage. Overall, our results indicate that individual tolerances to central hypovolemia are associated with specific genomic mechanisms underlying the capacity to compensate for severe blood loss.NEW & NOTEWORTHY This study investigated the first multiomics data for association with tolerance to central hypovolemia in humans. Analyzing gene expression, proteins, and genetic variants in individuals with high and low tolerance to simulated hemorrhage, the findings show that severe blood loss causes widespread transcriptional changes, including the activation of innate immune system pathways and inhibition of gas transport processes. This research can help identify molecular factors that influence individuals' tolerance levels for progressive blood loss.

Placental ischemia (PI), a prenatal stressor, affects ∼1 in 10 human pregnancies worldwide and is associated with several pregnancy complications such as preeclampsia, placental abruption, and intrauterine growth restriction (IUGR). Both human and animal IUGR fetuses have an increased risk of developing hypertension (HTN) in adulthood, with males having a higher risk. Furthermore, multiple studies suggest that changes in brain function and molecular markers may contribute to HTN development. However, the alterations in brain mitochondrial dysfunction (MtDys), oxidative stress (OS), and epigenetic changes (miRNAs) in forebrain and midbrain collectively have not been investigated. Thus, we hypothesize that the sex difference in high blood pressure (HBP) is due to changes in miRNAs, brain MtDys, and increased OS in IUGR males (M) but not IUGR females (F). To test this hypothesis, IUGR and control (CON) M and F Sprague Dawley rats were evaluated at 16-18 wk (adulthood). IUGR adults were generated from PI dams, and CON adults from normal pregnant dams. Results identified 11 differentially expressed miRNAs in IUGR versus CON, with let-7d-3p miRNA being upregulated in IUGR M. IUGR M also displayed HBP, MtDys [decreased adenosine triphosphate (ATP)], and OS (∼50% increase in hydrogen peroxide). Conversely, mitochondrial G protein elongation factor (GFM-1), a protein regulated by let-7d-3p, and electron transport chain (ETC) proteins were increased with no changes in ATP production in IUGR F. In summary, our data suggest that increases in let-7d-3p will inhibit the compensatory increase in GFM-1 and ETC proteins needed to prevent HBP and cerebral OS in IUGR M. However, unchanged let-7d-3p may increase GFM-1 and ETC proteins in IUGR F to inhibit brain MtDys, OS, and HBP. Findings from this study provide insights into the mechanisms linking epigenetic changes to brain MtDys and OS along with HTN in adults born IUGR.NEW & NOTEWORTHY Adult IUGR male rodent offspring exposed to placental ischemia in utero have elevated mean arterial blood pressure with increased brain miRNA let-7d-3p expression, mitochondrial dysfunction, and oxidative stress, while adult IUGR females do not. Moreover, our results suggest that brain epigenetic changes may contribute to mitochondrial dysfunction and oxidative stress, eventually leading to hypertension. In conclusion, lifetime health begins in utero, and patients and healthcare providers should be aware of the consequences that prenatal stressors have on long-term health.

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.

Preeclampsia (PE) is a life-threatening pregnancy disorder strongly associated with maternal obesity, yet the mechanistic links between diet, microbiome, and disease risk remain unclear. The obese BPH/5 mouse, which spontaneously develops PE-like features, provides a model to investigate how maternal nutrition influences microbial and metabolic profiles. Here, we tested the effects of modest caloric restriction (pair-feeding= PF) initiated at embryonic day (e0.5) on maternal microbiota and circulating metabolites at embryonic day 7.5 (e7.5). Microbial communities were profiled by 16S rRNA sequencing across fecal, oral, and vaginal niches, and serum short-chain fatty acids (SCFAs) were quantified by gas chromatography mass spectrometry (GC-MS). The PF BPH/5 dams exhibited a markedly reduced Firmicutes-to-Bacteroidetes (F/B) ratio and increased abundance of Bacteroides and Lactobacillus in fecal samples, which are taxa associated with improved metabolic balance and gut barrier support. In contrast, PF increased Proteobacteria abundance in BPH/5 vaginal and oral sites, a shift linked to inflammation and barrier dysfunction. Serum acetic acid was significantly decreased in PF BPH/5 dams and their offspring, suggesting that restricted intake lowers systemic SCFA availability. These findings demonstrate that early pregnancy caloric restriction produces both beneficial and adverse microbial shifts, suggesting that high-fiber dietary interventions that enhance SCFA production may better support maternal-fetal health than caloric restriction alone.

Objectives: Glyoxalase 1 (Glo1) detoxifies reactive dicarbonyl compounds such as methylglyoxal, a precursor of advanced glycation end products (AGEs), which contribute to metabolic disorders. However, the contribution of AGE-independent mechanisms to Glo1-related metabolic dysfunction remains unclear. Methods: We conducted a longitudinal study in male and female Glo1 heterozygous knockdown (Glo1^+/-) mice (~50% Glo1 expression). Metabolic phenotypes, including body weight, adiposity, glycemic control, and plasma lipid levels, were assessed over time. Atherosclerotic burden, AGE levels, and gene expression profiles in liver, adipose, muscle, kidney, and aorta were examined to identify pathway alterations and regulatory genes affected by Glo1 reduction. Results: Partial Glo1 loss resulted in obesity, hyperglycemia, dyslipidemia, and altered lipid metabolism in an age- and sex-dependent manner, with most phenotypes emerging after ~14 weeks. Glo1^+/- females exhibited impaired glycemic control and elevated triglycerides, along with perturbations in adipogenesis, PPARγ signaling, insulin signaling, and fatty acid metabolism in liver and adipose tissue. Glo1^+/- males displayed increased skeletal muscle mass and visceral adiposity with changes in lipid metabolic pathways. Methylglyoxal-derived AGE accumulation was altered only in male skeletal muscle and did not explain broader phenotypes. Transcriptomic analyses suggest altered glucose and lipid metabolism may be partially driven by alternative detoxification of methylglyoxal to metabolites such as pyruvate. Transcription factor analysis identified Hnf4a (across tissues) and Arntl (in aorta, liver, and kidney) as female-biased regulators altered by Glo1 deficiency. Conclusions: Glo1 reduction disrupts metabolic health through sex- and age-dependent pathways largely independent of AGE accumulation, involving tissue-specific metabolic reprogramming and transcriptional regulation.

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.