Current Genomics最新文献

Background: Lysosomal dysfunction is significantly associated with tumor progression. This study aimed to identify and develop a new predictive panel for breast cancer (BRCA) and examine its relationship with the immune environment and therapeutical status.

Methods: We developed a prognostic panel employing lysosomal genes from The Cancer Genome Atlas Program (TCGA) and then validated and assessed it externally in the Gene Expression Omnibus (GEO). Furthermore, the disparities were identified between high and low-risk subgroups by examining the infiltration of microenvironment cells, gene expression of immune checkpoints, and small molecular compounds. Ultimately, the cancerous function and potential pathway of core LRG were verified using a series of in vitro tests.

Results and discussion: First, the predictive panel of lysosome-related genes (LRGs) was generated via the least absolute shrinkage and selection operator. High-risk populations showed the shortest survival times. Meanwhile, the area under the curves (AUC) for predicting 1-, 3-, and 5-year survival rates indicated good predictive performance across all cohorts. Subsequent extensive investigations revealed a strong correlation between the risk score and the pathological stage, drug sensitivity, and tumor mutation burden (TMB). Then, we discovered that the levels of GPLD1, PLA2G5, and STX7 were reduced in BRCA tissues, whereas the expressions of PLA2G10, LAMP3, EIF4EBP1, and LPCAT1 were elevated in BRCA tissues compared to paracancerous tissues. Patients exhibiting high EIF4EBP1 expression experienced a more unfavorable outcome compared to those with low expression. EIF4EBP1 disruption dramatically impeded BRCA cell growth and invasive capacity, as demonstrated by CCK8, wound healing, and transwell assays. Moreover, EIF4EBP1 silencing in BRCA cells significantly restricted the TGF-β pathway.

Conclusion: Our 9-LRG panel is a promising classifier for assessing the prognosis of BRCA. Notably, targeting EIF4EBP1 could potentially serve as a theoretical foundation for enhancing the prognosis of BRCA patients.

Introduction: DNA methylation is an important epigenetic modification associated with transcriptional repression and plays key roles in normal cell growth as well as oncogenesis. Among the three main DNA methyltransferases (DNMT1, DNMT3A, and DNMT3B), DNMT3A mediates de novo DNA methylation. However, the general effect of DNMT3A on cell proliferation, metabolism, and downstream gene regulation is still to be unveiled.

Methods: In this study, we successfully created DNMT3A-deficient HEK293 cells with frameshift mutations in the catalytic domain using CRISPR/Cas9 technology. The DNMT3A deficient cells showed a 21.5% reduction in global DNA methylation levels, leading to impaired cell proliferation as well as a blockage of MAPK and PI3K-Akt pathways in comparison with wild-type cells.

Results and discussion: RNA-seq analysis demonstrated that DNMT3A knockout resulted in the up-regulation of genes and pathways related to cell metabolism but down-regulation of those involved in ribosome function, potentially explaining the growth and signaling pathways inhibition. Furthermore, DNMT3A ablation reduced DNMT3B gene methylation, explaining the down-regulated profiles of genes.

Conclusion: Our findings suggest a complex epigenetic regulatory role for DNMT3A, and the compensatory upregulation of DNMT3B in response to DNMT3A deficiency warrants further investigation to be validated in future studies.

Background: Phthalic acid esters (PAEs) are widely used chemical compounds in various industries. However, PAEs are also a major source of pollution in soil and aquatic ecosystems, posing a significant environmental threat. Microbial degradation is a very effective way to remove phthalic acid esters from a polluted environment.

Objectives: The aims of this study were to investigate the ability of the strain Arthrobacter sp. SF27 (=VKM Ac-2063) to degrade PAEs (specifically, dibutyl phthalate (DBF)); to annotate the complete genome of the strain SF27 (GenBank accession number GCA_012952295); to identify genes (gene clusters) potentially involved in the degradation of DBF and its major degradation product, phthalic acid (PA).

Methods: The ability of the strain SF27 to use DBP as the only source of carbon and energy was determined by cultivating it on a mineral medium containing 0.5-4 g/L DBP. The evaluation of the bacterial decomposition of DBP was carried out by GC-MS. The genome was annotated using the JGI Microbial Genome Annotation Pipeline (MGAP) (https://jgi.doe.gov/). Functional annotation was performed using various databases: KEGG, COG, NCBI, and GO. The Mauve program was used to compare the strain SF27 genome and the genomes of the closest DBP-degrading strains.

Results: The strain Arthrobacter sp. SF27 is capable of growing on DBP as the sole source of carbon and energy at high concentrations (up to 4 g/L). The strain was able to degrade 60% of DBP (initial concentration of 1 g/L) and 20% of DBP (initial concentration of 3 g/L) within 72 hours. The genome analysis of the strain SF27 (GenBank accession number GCA_012952295) identified genes encoding hydrolases potentially involved in the initial stages of DBP degradation, leading to the formation of PA. Additionally, a cluster of pht genes encoding enzymes that are responsible for the transformation of PA into protocatechuic acid (PCA) has been identified and described in the genome. Based on genome analysis and cultural experiments, a complete pathway for the degradation of PA by the strain Arthrobacter sp. SF27 into basal metabolic compounds of the cell has been proposed.

Conclusion: Based on the conducted research, it can be stated that the strain Arthrobacter sp. SF27 is an efficient degrader of DBP, promising for the development of biotechnologies aimed at the restoration of ecosystems contaminated with DBP.

Rank aggregation (RA) is the process of consolidating disparate rankings into a single unified ranking. It holds immense potential in the field of genomics. RA has applications in diverse research areas, such as gene expression analysis, meta-analysis, gene prioritization, and biomarker discovery. However, there are many challenges in the application of the RA approach to biological data, such as dealing with heterogeneous data sources, rankings of mixed quality, and evaluating the consolidated rankings. In this review, we present an overview of the existing RA methods with an emphasis on those that have been tailored to the complexities of genomics research. These encompass a broad range of approaches, from distributional and heuristic methods to Bayesian and stochastic optimization algorithms. By examining these techniques, we aim to equip researchers with the background knowledge needed to navigate the intricacies of RA in genomics data integration effectively. We review the practical applications to highlight the relevance and impact of RA methods in advancing genomics research. As the field continues to evolve, we identify open problems and suggest future directions to enhance the effectiveness of rank aggregation in genomics, by addressing the challenges related to data heterogeneity, single-cell omics and spatial transcriptomics data, and the development of clear and consistent evaluation methods. In summary, RA stands as a powerful tool in genomics research, which can offer deeper insights and more comprehensive data integration solutions.

Background: Lactylation is increasingly recognized to play a crucial role in human health and diseases. However, its involvement in age-related macular degeneration (AMD) remains largely unclear.

Objectives: The aim of this study was to identify and characterize the pivotal lactylation-related genes and explore their underlying mechanism in AMD.

Methods: Gene expression profiles of AMD patients and control individuals were obtained and integrated from the GSE29801 and GSE50195 datasets. Differentially expressed genes (DEGs) were screened and intersected with lactylation-related genes for lactylation-related DEGs. Machine learning algorithms were used to identify hub genes associated with AMD. Subsequently, the selected hub genes were subject to correlation analysis, and reverse transcription quantitative real-time PCR (RT-qPCR) was used to detect the expression of hub genes in AMD patients and healthy control individuals.

Results: A total of 68 lactylation-related DEGs in AMD were identified, and seven genes, including HMGN2, TOP2B, HNRNPH1, SF3A1, SRRM2, HIST1H1C, and HIST1H2BD were selected as key genes. RT-qPCR analysis validated that all 7 key genes were down-regulated in AMD patients.

Conclusion: We identified seven lactylation-related key genes potentially associated with the progression of AMD, which might deepen our understanding of the underlying mechanisms involved in AMD and provide clues for the targeted therapy.

Background: Retinal Vein Occlusion (RVO) is a common and main cause of blindness. Causal, possible risk variables must be identified to develop preventative strategies for RVO. Thus, we decided to evaluate whether smoking, alcohol, obesity, sedentary behaviour, hypertension, and hyperglycemia are associated with increased risk of RVO.

Methods: The data sources of Mendelian Randomization (MR) study included FinnGen consortium and the original GWAS article. A total of 130,604 cases with RVO from FinnGen consortium and 12,136 cases with RVO from the original GWAS article. The exposures of this MR study included smoking, alcoholic consumption, obesity, sedentariness, hypertension, and hyperglycemia. The outcome of this MR study was RVO.

Results: Genetic predispositions to alcohol consumption (OR (odds ratio), 1.124; 95%CI, 1.007-1.254; P=0.037) and hyperglycemia (OR, 1.108; 95%CI, 1.023-1.200; P=0.012) were associated with increased risks of RVO in FinnGen. There were no significant associations of genetically predicted consumption of smoking (OR, 1.037; 95%CI, 0.341-3.155; P=0.949), obesity (OR, 1.045; 95%CI, 0.975-1.119; P=0.213), sedentariness (OR, 1.022; 95%CI, 0.753-1.38-; P=0.888), or hypertension (OR, 0.944; 95%CI, 0.848-1.051; P=0.290) with RVO.

Conclusion: This MR analysis provides genetic evidence that increased alcohol consumption and hyperglycemia may be causal risk factors for RVO. In addition, no genetic evidence in this MR analysis supported that there were causal associations between smoking, sedentariness, obesity and hypertension with RVO.

Analyzing prokaryotic codon usage trends has become a crucial topic of study with significant ramifications for comprehending microbial genetics, classification, evolution, and the control of gene expression. This review study explores the numerous facets of prokaryotic codon usage patterns, looking at different parameters like habitat and lifestyle across broad groups of prokaryotes by emphasizing the role of codon reprogramming in adaptive strategies and its integration into systems biology. We also explored the numerous variables driving codon usage bias, including natural selection, mutation, horizontal gene transfer, codon-anticodon interaction, and genomic composition in prokaryotes through a thorough study of current literature. Furthermore, a special session on codon usage on pathogenic prokaryotes and the role of codon usage in the phylogeny of prokaryotes has been discussed. We also looked at the various software and indices that have been recently applied to prokaryotic genomes. The promising directions that lay ahead to map the future of codon usage research on prokaryotes have been emphasized. Codon usage variations across prokaryotic communities could be better understood by combining environmental, metagenomic, and system biology approaches.

Aims: This study aimed to identify potential therapeutic targets in the progression from non-alcoholic fatty liver disease (NAFLD) to hepatocellular carcinoma (HCC), with a focus on genes that could influence disease development and progression.

Background: Hepatocellular carcinoma, significantly driven by non-alcoholic fatty liver disease, represents a major global health challenge due to late-stage diagnosis and limited treatment options. This study utilized bioinformatics to analyze data from GEO and TCGA, aiming to uncover molecular biomarkers that bridge NAFLD to HCC. Through identifying critical genes and pathways, our research seeks to advance early detection and develop targeted therapies, potentially improving prognosis and personalizing treatment for NAFLD-HCC patients.

Objectives: Identify key genes that differ between NAFLD and HCC; Analyze these genes to understand their roles in disease progression; Validate the functions of these genes in NAFLD to HCC transition.

Methods: Initially, we identified a set of genes differentially expressed in both NAFLD and HCC using second-generation sequencing data from the GEO and TCGA databases. We then employed a Cox proportional hazards model and a Lasso regression model, applying machine learning techniques to the large sample data from TCGA. This approach was used to screen for key disease-related genes, and an external dataset was utilized for model validation. Additionally, pseudo-temporal sequencing analysis of single-cell sequencing data was performed to further examine the variations in these genes in NAFLD and HCC.

Results: The machine learning analysis identified IGSF3, CENPW, CDT1, and CDC6 as key genes. Furthermore, constructing a machine learning model for CDT1 revealed it to be the most critical gene, with model validation yielding an ROC value greater than 0.80. The single-cell sequencing data analysis confirmed significant variations in the four predicted key genes between the NAFLD and HCC groups.

Conclusion: Our study underscores the pivotal role of CDT1 in the progression from NAFLD to HCC. This finding opens new avenues for early diagnosis and targeted therapy of HCC, highlighting CDT1 as a potential therapeutic target.

Introduction: Recent investigations have underscored the importance of long non-coding RNAs (lncRNAs), which exhibit more specific expression in tissues and cells than mRNA and are involved in gene regulation during development, pathology, and other processes through various mechanisms. Despite the predominant focus on the role of lncRNA Dio3os in cancer research, there has been relatively limited exploration of its potential involvement in glycolipid metabolism. Therefore, this study aims to consolidate existing knowledge on the function of Dio3os in glycolipid metabolism and calls for a broader investigation into its physiological roles.

Methods: This review synthesizes available literature to detail the gene characteristics of lncRNA Dio3os and its expression patterns. It also compiles recent insights and mechanisms pertaining to Dio3os's involvement in glycolipid metabolism, particularly its participation in the ceRNA regulatory network.

Results: Recent studies demonstrate that lncRNA Dio3os regulates glycolysis in cancer cells and impacts obesity, potentially serving as an indicator for diabetic peripheral neuropathy. Furthermore, its diminished expression has been noted in atherosclerotic plaques.

Conclusion: lncRNA Dio3os exerts a significant regulatory influence on glycolipid metabolism, with variations in its expression levels potentially affecting disease presentations. Further investigations are warranted to elucidate the precise relationship between lncRNA Dio3os and its associated pathologies.