Phenomics (Cham, Switzerland)最新文献

Sedentary behavior for two years during the coronavirus disease 2019 (COVID-19) pandemic contributes to weight gain. Gut microbiota and blood metabolome are related to body mass index (BMI) and indicate individual metabolic changes. Surgery and exercise are effective weight-loss methods. The precise plasma metabolites and gut microbiota biomarkers involved and the underlying mechanisms are still largely unclear. To address this issue, we analyzed weight gain and weight loss cohorts to identify biomarkers associated with obesity. In the sedentary cohort, 49 subjects were recruited in year 2019. After two years of sedentary behavior during the COVID-19 pandemic, the BMI of 24 subjects significantly increased (Weight gain group), while that of the remnant 25 subjects remained constant (Maintaining weight group). At baseline and two years post baseline, the gut microbiota and blood metabolome, as well as body composition and clinical indicators, were all collected. In weight loss studies, we analyze the plasma metabolome of the two cohorts, including individuals who underwent laparoscopic sleeve gastrectomy (LSG) surgery and exercise intervention. Weight gain through sedentary behavior contributed to the variation of the gut microbiota and plasma metabolites composition. Creatine, phenylalanine and tyrosine exhibited significant positive associations with BMI and fat mass. We further confirmed the association between BMI and plasma metabolites in two weight loss cohorts. By utilizing a linear regression model, we found that 10 metabolites including creatine were correlated with BMI in weight loss individuals. Based on receiver operating characteristic (ROC) curves, creatine exhibited a satisfactory classification performance in regard to predicting weight reduction (AUC_LSG = 0.890, AUC_{S ports} = 0.840). Moreover, some gut microbiota, including Bifidobacterium angulatum DSM 20098 = JCM 7096 and Rothia dentocariosa M567I could affect BMI through the mediating factor of creatine.

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Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00165-x.

The TRacing Etiology of Non-communicable Diseases (TREND) cohort is a prospective longitudinal cohort and biobank that is mainly based in Ma'anshan, Anhui Province, China. The primary aim of the study is to decipher comprehensive molecular characterization and deep phenotyping for a broad spectrum of chronic non-communicable diseases (NCDs), which focuses on providing mechanistic insights with diagnostic, prognostic and therapeutic implications. The recruitment was initiated in 2023 and is expected to complete in 2025 with 20,000 participants originated from urban and rural area. In the first phase, 3360 participants were recruited. Follow-up visits are scheduled annually and intervally for a total of 30 years. The cohort includes individuals aged over 18 years. Two participants with first-degree linkage were recruited from a household. The age distribution of recruited participants was stratified into four categories: 18-45, 45-55, 55-65, and ≥65 years, aligning with the population proportions of Ma'anshan. Meanwhile, the gender distribution was controlled by pairing men and women from the same household. Data collected at baseline includes socio-economic information, medical history, lifestyle and nutritional habits, anthropometrics, blood oxygen, electrocardiogram (ECG), heart sound, as well as blood, urine and feces tests results. Molecular profiling includes genome, proteome, metabolome, microbiome and extracellular vesicles -omics. Blood, urine and fecal samples are collected and stored at -80 °C in a storage facility for future research.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00196-4.

Polycystic ovarian syndrome (PCOS) is the most common reproductive metabolic disorder in women of reproductive age. However, the underlying mechanism is unclear, because the main symptoms vary with age and the pathogenesis is complex and multifactorial. In order to explore the gene expression and regulation networks, and identify potential biomarkers for diagnosis and treatment of PCOS, we conducted whole RNA sequencing of protein-coding genes, lncRNAs, and miRNAs in peripheral blood with case-control design. RNA sequencing and weighted gene co-expression network analysis (WGCNA) were performed on four pairs of PCOS cases and control peripheral blood samples. The results showed that there were significant differences in the expression levels of 341 mRNAs, 252 lncRNAs and 47 miRNAs between PCOS patients and control groups. Bioinformatics analysis showed that these differentially expressed genes (DEGs) were mainly involved in the metabolic, immune, endocrine, and nervous systems, and also identified potential WGCNA module related with PCOS. The DEGs of PCOS as reported in other published literatures were used to verify our DEGs in this study. These results suggest that the ceRNA regulatory relationship between miR-17-5p, LINC02213 and FCGR1A, the trans-regulatory relationship between RP11-405F3.4:IL1R1 and RP11-405F3.4:IL27, and a hub lncRNA of LINC02649 in core regulatory network, which have significant potential for PCOS research. We constructed the core WGCNA module of PCOS from the whole transcriptome of human peripheral blood and characterized the key gene characteristics of PCOS. These findings provide key insights into the candidate characteristics and mechanism elucidation of PCOS.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00183-9.

Lung cancer remains the leading cause of death among cancer patients, and the five-year survival rate is less than 25%. However, Methyl-CpG Binding Domain (MBD)4 polymorphism rs140693 predicts the prognosis of lung cancer patients still needs further verification. Primary lung cancer patients (n = 839) were collected from two hospitals, genomic DNA was extracted from blood, and genotyping was performed using SNPcan technology. Kaplan-Meier technique and multivariate Cox proportional hazards model were used to analyze the prognosis association between MBD4 and clinical characteristics. Significantly conferred a poorer prognosis was associated with the CT genotype (CT vs. CC; adjusted hazard ratio [HR] = 1.21, 95% CI: 1.03-1.43, p = 0.023) and dominant CT + TT genotype (CT + TT vs. CC; HR = 1.19, 95% CI: 1.02-1.39, p = 0.029) of MBD4 polymorphism rs140693 for all lung cancer patients, compared with the CC genotype. Stratified analysis showed that polymorphism rs140693 CT and dominant CT + TT genotype conferred a significantly poorer prognosis in female and lung adenocarcinoma (ADC) cancer patients, compared with the CC genotype. Non-small cell lung cancer (NSCLC) patients with the CT genotype had a poorer prognosis than those with the CC genotype. Additionally, the allele T of small cell lung cancer (SCLC) patients compared with the allele C was associated with a poor prognosis, and the CT and recessive TT genotype of SCLC patients conferred a significantly poor prognosis. The MBD4 polymorphism rs140693 is a significant prognostic genetic marker for predicting the prognosis of lung cancer patients.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00171-z.

In gynecological oncology, ovarian cancer (OC) remains the most lethal, highlighting its significance in public health. Our research focused on the role of long non-coding RNA (lncRNA) in OC, particularly senescence-related lncRNAs (SnRlncRNAs), crucial for OC prognosis. Utilizing data from the genotype-tissue expression (GTEx) and cancer genome Atlas (TCGA), SnRlncRNAs were discerned and subsequently, a risk signature was sculpted using co-expression and differential expression analyses, Cox regression, and least absolute shrinkage and selection operator (LASSO). This signature's robustness was validated through time-dependent receiver operating characteristics (ROC), and multivariate Cox regression, with further validation in the international cancer genome consortium (ICGC). Gene set enrichment analyses (GSEA) unveiled pathways intertwined with risk groups. The ROC, alongside the nomogram and calibration outcomes, attested to the model's robust predictive accuracy. Of particular significance, our model has demonstrated superiority over several commonly utilized clinical indicators, such as stage and grade. Patients in the low-risk group demonstrated greater immune infiltration and varied drug sensitivities compared to other groups. Moreover, consensus clustering classified OC patients into four distinct groups based on the expression of 17 SnRlncRNAs, showing diverse survival rates. In conclusion, these findings underscored the robustness and reliability of our model and highlighted its potential for facilitating improved decision-making in the context of risk assessment, and demonstrated that these markers potentially served as robust, efficacious biomarkers and prognostic tools, offering insights into predicting OC response to anticancer therapeutics.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00163-z.

Recently, there has been a debate regarding the association between polycystic ovary syndrome (PCOS) and pancreatic cancer (PC). In order to examine the causal relationship between PCOS and PC, we conducted a Mendelian randomization study, which utilized 12 single nucleotide polymorphisms (SNPs) identified from a genome-wide association study (GWAS) meta-analysis that included 10,074 PCOS cases and 103,164 controls of European ancestry as instrumental variables (IVs). The outcome data were obtained from the FinnGen database (including 605 cases and 218,187 controls). We demonstrate that genetically predicted PCOS is not causally associated with PC risk in Europeans (odds ratio = 0.99, 95% confidence interval (CI) = 0.72-1.36, p > 0.05). Sensitivity analysis showed horizontal pleiotropy (intercept p > 0.05), heterogeneity (Cochran Q p > 0.05), and the leave-one-out sensitivity test showed that individual SNP effects had no influence on the results. In conclusion, our study did not provide evidence of a causal link between PCOS and PC.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00156-y.

With thousands of years of application history, traditional Chinese medicine (TCM) has unique advantages in the prevention of various chronic diseases, and in recent years, the development of TCM has presented a situation where opportunities and challenges coexist. Phenomics is an emerging area of life science research, which has numerous similarities to the cognitive perspective of TCM. Thus, how to carry out the interdisciplinary research between TCM and phenomics deserves in-depth discussion. Diabetes is one of the most common chronic non-communicable diseases around the world, and TCM plays an important role in all stages of diabetes treatment, but the molecular mechanisms are difficult to elucidate. Phenomics research can not only reveal the hidden scientific connotations of TCM, but also provide a bridge for the confluence and complementary between TCM and Western medicine. Facing the challenges of the TCM phenomics research, we suggest applying the State-target theory (STT) to overall plan relevant researches, namely, focusing on the disease development, change trends, and core targets of each stage, and to deepen the understanding of TCM disease phenotypes and the therapeutic mechanisms of herbal medicine.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00146-6.

Currently, standard protocols for body imaging and corresponding image processing pipelines in population-based cohort studies are unavailable, limiting the applications of body imaging. Based on the China Phenobank Project (CHPP), the present study described a body imaging protocol for multiple organs, including cardiac structures, liver, spleen, pancreas, kidneys, lung, prostate, and uterus, and the corresponding image processing pipelines promoted its development. Briefly, the body imaging protocol comprised a 40-min cardiac magnetic resonance imaging (MRI) scan, a 5-min computed tomography (CT) scan, a 20-min abdominal MRI scan, and a 10-min pelvic MRI scan. The recommended image processing pipeline utilized deep learning segmentation models to facilitate the analysis of large amount of data. This study aimed to provide a reference for planning studies based on the CHPP platform.

Liph, a gut-enriched Lipase H encoding gene, shows decreased expression during gut aging in both fruit fly and mouse. However, whether such evolutionary conserved Liph plays a protective role in gut aging remains unknown. Here we report that knocking down CG6295, the Drosophila ortholog of the mammalian Liph, led to a shortened lifespan. Loss of CG6295 in adult fly whole body caused impaired gut integrity and function, as well as reduced gut lipid storage in Drosophila. Activation of the Toll/ immune deficiency (Imd) and Janus kinase/signal transducer and activator of transcription (JAK/STAT) immune pathways, and the release of pro-inflammatory cytokine Upd3 (IL-6) indicated immune responses in CG6295 knockdown samples. What's more, knockdown of Drosophila CG6295 specifically in enterocytes (ECs) led to enlarged and flattened ECs, suggesting a potential regulatory mechanism of CG6295 in gut aging. In addition, down-regulation of Liph induced senescence-associated cellular and molecular phenotypes in a rat intestine cell model, suggesting the evolutionary conserved role of Liph in gut aging. Together, we discovered Liph as a novel regulator for gut aging.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-024-00187-5.