BMC Medical Genomics最新文献

Background: Intrauterine growth restriction (IUGR) with rapid postnatal catch-up growth has been associated with adipose tissue inflammation and metabolic dysfunction. The long-term persistence of these abnormalities and their relationship with different catch-up growth patterns remain unclear.

Methods: To investigate the long-term metabolic consequences of IUGR in relation to different catch-up growth patterns. An experimental animal study using a rat model of IUGR induced by maternal protein restriction during gestation. Abdominal adipose tissue transcriptome profiles in male rats were analyzed at 3 and 9 months of age, considering variations in catch-up growth patterns. The primary outcomes included markers of adipose tissue inflammation and metabolic function.

Results: Among IUGR offspring, approximately 50% demonstrated slow catch-up growth and remained undernourished at 3 months of age. Transcriptome analysis revealed persistent adipose tissue inflammation and metabolic alterations that progressed with age. These abnormalities were present in both rapid and slow catch-up growth groups, although offspring with rapid catch-up growth exhibited more adverse manifestations.

Conclusion: IUGR was associated with long-term adipose tissue inflammation and metabolic dysfunction, independent of catch-up growth pattern. These findings suggest that IUGR may have lasting metabolic consequences regardless of postnatal growth trajectory.

Background: As the building blocks of proteins and precursors of many other important compounds, amino acids play a vital role in the biochemical processes needed to sustain life. The branched-chain amino acids (BCAAs) are unique in their structure and function, as they are metabolized in muscle tissue and play important roles in protein synthesis and energy production. However, despite their physiological importance, relatively little integrative research has been conducted into the direct relationships between this class of metabolites and their effect on risk for metabolic diseases.

Methods: Utilizing an integrative PheWAS approach using UK Biobank data, we were able to identify strong, high confidence, metabolite-disease correlations for the three BCAAs: leucine, isoleucine, and valine. Relationships were established through comparison of metabolite level-disease prevalence associations with polygenic scores for BCAAs, followed by Mendelian randomization analysis.

Results: All BCAAs studied demonstrated especially strong relationships with type II diabetes, and robust relationships with obesity, hypertension, sleep apnea, and chronic kidney disease. We illustrate this with a set of metabolite prevalence-disease risk plots that suggest differing potential for disease based on varying levels of branched-chain amino acid metabolites. Similar results are observed with polygenic scores for plasma BCAAs. Mendelian randomization shows positive effects of leucine and isoleucine on hypertension, and either reverse causality or no clear directional relationship for other associations, notably effects of obesity and type II diabetes on all three BCAAs, with limited or borderline evidence for other outcomes.

Conclusions: Overall, the results of our study highlight a relatively unexplored area of metabolite-disease associations and provide a blueprint for uncovering additional relationships using readily available biobank data.

Background: Stomach adenocarcinoma (STAD) poses a major public health challenge across various populations, necessitating the construction of robust models for prognostic prediction and effective clinical therapies. Dysregulation of lactylation, a key regulatory mechanism in cell metabolism and gene expression, can either impede or promote tumor growth and metastasis.

Methods: This study got into the bottom of TCGA-STAD-sourced transcriptome data to profile lactylation-related genes and construct a gene signature through LASSO regression. A nomogram was further created to assess the prognostic performance of this model. Our investigation primarily concentrated on the expression of Dehydrogenase/reductase 7 (DHRS7) in STAD, with the verification of its correlations with clinical characteristics, immune cell infiltration, and cellular signaling pathways.

Results: DHRS7 expressed lower in STAD tissues, and that modulating DHRS7 levels could either promote or inhibit malignant behaviors associated with STAD. In the later stages of tumor progression, DHRS7 appeared to facilitate tumor growth through mechanisms such as immune evasion and activation of PI3K/AKT/mTOR signaling pathways, ultimately contributing to an unfavorable prognosis.

Conclusions: DHRS7 has the potential to shift from acting as a tumor suppressor to functioning as an oncogene in modified TMEs, despite its lower expression levels in STAD tissues relative to normal tissues. This transformation accounts for the association between high DHRS7 expression in the later stages of STAD and a negative prognosis.

Background: Necroptosis, a regulated form of programmed cell death, exacerbates inflammatory responses by releasing damage-associated molecular patterns and inflammatory factors. However, the specific mechanisms underlying necroptosis in periodontitis remain largely unclear. This study integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data to identify core necroptosis-related genes (NRGs) and validated these findings using external datasets and periodontitis samples collected during our research.

Methods: Overlapping genes were identified through a comparative analysis of 114 NRGs sourced from GeneCards and marker genes specific to various cell types in the single-cell GSE171213 periodontitis dataset. Based on these genes, cells were categorized into high- and low-necroptosis score groups. Key NRGs were identified through intersection analysis of differentially expressed genes in the high necroptosis group using the GSE10334 bulk RNA-seq dataset, followed by Kyoto Encyclopedia of Genes and Genomes (KEGG)/ Gene Ontology (GO) enrichment analysis. Machine learning further identified hub genes associated with the inflammatory response in periodontitis. Consensus clustering analysis, clinical diagnostic model construction, gene set variation analysis, and gene set enrichment analysis were performed based on these hub genes. The model's predictive performance was validated using independent datasets and periodontitis tissue samples.

Results: We identified 10 cell types in periodontitis tissues and observed changes in the abundance of various cell populations in affected samples. Furthermore, we selected 35 NRGs differentially expressed in specific cell populations, with neutrophils and macrophages showing higher necroptosis scores. By integrating bulk RNA-seq data, we further identified 29 key NRGs. KEGG/GO analysis indicated their enrichment in inflammatory response signaling pathways. Machine learning highlighted six hub genes (CSF3R, CSF2RB, BTG2, CXCR4, GPSM3, and SSR4), all of which were highly expressed in periodontitis tissues. Consensus clustering based on these genes divided patients with periodontitis into two subgroups with distinct expression profiles. The clinical diagnostic model constructed based on these six key genes exhibited excellent diagnostic performance. Both external independent validation sets and clinical sample tests confirmed high expression of these six key genes in periodontitis tissues.

Conclusion: Our study identified six hub genes (CSF3R, CSF2RB, BTG2, CXCR4, GPSM3, and SSR4) highly expressed in periodontitis tissues and positively correlated with necroptosis. These genes may serve as therapeutic targets for inflammatory diseases like periodontitis.