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Epigenomes Jun 2024The post-genomic era has ushered in the extensive application of epigenetic editing tools, allowing for precise alterations of gene expression. The use of reprogrammable...
The post-genomic era has ushered in the extensive application of epigenetic editing tools, allowing for precise alterations of gene expression. The use of reprogrammable editors that carry transcriptional corepressors has significant potential for long-term epigenetic silencing for the treatment of human diseases. The ideal scenario involves precise targeting of a specific genomic location by a DNA-binding domain, ensuring there are no off-target effects and that the process yields no genetic remnants aside from specific epigenetic modifications (i.e., DNA methylation). A notable example is a recent study on the mouse gene, crucial for cholesterol regulation and expressed in hepatocytes, which identified synthetic zinc-finger (ZF) proteins as the most effective DNA-binding editors for silencing efficiently, specifically, and persistently. This discussion focuses on enhancing the specificity of ZF-array DNA binding by optimizing interactions between specific amino acids and DNA bases across three promoters containing CpG islands.
PubMed: 38920624
DOI: 10.3390/epigenomes8020023 -
Frontiers in Immunology 2024The global impact of the SARS-CoV-2 pandemic has been unprecedented, posing a significant public health challenge. Chronological age has been identified as a key... (Review)
Review
The global impact of the SARS-CoV-2 pandemic has been unprecedented, posing a significant public health challenge. Chronological age has been identified as a key determinant for severe outcomes associated with SARS-CoV-2 infection. Epigenetic age acceleration has previously been observed in various diseases including human immunodeficiency virus (HIV), Cytomegalovirus (CMV), cardiovascular diseases, and cancer. However, a comprehensive review of this topic is still missing in the field. In this review, we explore and summarize the research work focusing on biological aging markers, i.e., epigenetic age and telomere attrition in COVID-19 patients. From the reviewed articles, we identified a consistent pattern of epigenetic age dysregulation and shortened telomere length, revealing the impact of COVID-19 on epigenetic aging and telomere attrition.
Topics: Humans; COVID-19; Aging; SARS-CoV-2; Epigenesis, Genetic; Telomere; Telomere Shortening
PubMed: 38919619
DOI: 10.3389/fimmu.2024.1399676 -
Biology Direct Jun 2024Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients....
BACKGROUND
Prostate cancer (PCa) is the second leading cause of tumor-related mortality in men. Metastasis from advanced tumors is the primary cause of death among patients. Identifying novel and effective biomarkers is essential for understanding the mechanisms of metastasis in PCa patients and developing successful interventions.
METHODS
Using the GSE8511 and GSE27616 data sets, 21 metastasis-related genes were identified through the weighted gene co-expression network analysis (WGCNA) method. Subsequent functional analysis of these genes was conducted on the gene set cancer analysis (GSCA) website. Cluster analysis was utilized to explore the relationship between these genes, immune infiltration in PCa, and the efficacy of targeted drug IC50 scores. Machine learning algorithms were then employed to construct diagnostic and prognostic models, assessing their predictive accuracy. Additionally, multivariate COX regression analysis highlighted the significant role of POLD1 and examined its association with DNA methylation. Finally, molecular docking and immunohistochemistry experiments were carried out to assess the binding affinity of POLD1 to PCa drugs and its impact on PCa prognosis.
RESULTS
The study identified 21 metastasis-related genes using the WGCNA method, which were found to be associated with DNA damage, hormone AR activation, and inhibition of the RTK pathway. Cluster analysis confirmed a significant correlation between these genes and PCa metastasis, particularly in the context of immunotherapy and targeted therapy drugs. A diagnostic model combining multiple machine learning algorithms showed strong predictive capabilities for PCa diagnosis, while a transfer model using the LASSO algorithm also yielded promising results. POLD1 emerged as a key prognostic gene among the metastatic genes, showing associations with DNA methylation. Molecular docking experiments supported its high affinity with PCa-targeted drugs. Immunohistochemistry experiments further validated that increased POLD1 expression is linked to poor prognosis in PCa patients.
CONCLUSIONS
The developed diagnostic and metastasis models provide substantial value for patients with prostate cancer. The discovery of POLD1 as a novel biomarker related to prostate cancer metastasis offers a promising avenue for enhancing treatment of prostate cancer metastasis.
Topics: Humans; Male; Prostatic Neoplasms; Machine Learning; Immunotherapy; Neoplasm Metastasis; Biomarkers, Tumor; Prognosis; Molecular Docking Simulation; Gene Expression Regulation, Neoplastic
PubMed: 38918844
DOI: 10.1186/s13062-024-00494-x -
Scientific Reports Jun 2024The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated...
The benefits of breastfeeding for the health and wellbeing of both infants and mothers are well documented, yet global breastfeeding rates are low. One factor associated with low breast feeding is maternal body mass index (BMI), which is used as a measure of obesity. The negative relationship between maternal obesity and breastfeeding is likely caused by a variety of social, psychological, and physiological factors. Maternal obesity may also have a direct biological association with breastfeeding through changes in maternal DNA methylation. Here, we investigate this potential biological association using data from a UK-based cohort study, the Avon Longitudinal Study of Parents and Children (ALSPAC). We find that pre-pregnancy body mass index (BMI) is associated with lower initiation to breastfeed and shorter breastfeeding duration. We conduct epigenome-wide association studies (EWAS) of pre-pregnancy BMI and breastfeeding outcomes, and run candidate-gene analysis of methylation sites associated with BMI identified via previous meta-EWAS. We find that DNA methylation at cg11453712, annotated to PHTP1, is associated with pre-pregnancy BMI. From our results, neither this association nor those at candidate-gene sites are likely to mediate the link between pre-pregnancy BMI and breastfeeding.
Topics: Humans; DNA Methylation; Breast Feeding; Body Mass Index; Female; Pregnancy; Adult; Longitudinal Studies; Genome-Wide Association Study; United Kingdom; Obesity; Epigenesis, Genetic
PubMed: 38918574
DOI: 10.1038/s41598-024-65605-0 -
Scientific Reports Jun 2024PTBP1 is an oncogene that regulates the splicing of precursor mRNA. However, the relationship between PTBP1 expression and gene methylation, cancer prognosis, and tumor...
PTBP1 is an oncogene that regulates the splicing of precursor mRNA. However, the relationship between PTBP1 expression and gene methylation, cancer prognosis, and tumor microenvironment remains unclear. The expression profiles of PTBP1 across various cancers were derived from the TCGA, as well as the GTEx and CGGA databases. The CGGA mRNA_325, CGGA mRNA_301, and CGGA mRNA_693 datasets were utilized as validation cohorts. Immune cell infiltration scores were approximated using the TIMER 2.0 tool. Functional enrichment analysis for groups with high and low PTBP1 expression was conducted using Gene Set Enrichment Analysis (GSEA). Methylation data were predominantly sourced from the SMART and Mexpress databases. Linked-omics analysis was employed to perform functional enrichment analysis of genes related to PTBP1 methylation, as well as to conduct protein functional enrichment analysis. Single-cell transcriptome analysis and spatial transcriptome analysis were carried out using Seurat version 4.10. Compared to normal tissues, PTBP1 is significantly overexpressed and hypomethylated in various cancers. It is implicated in prognosis, immune cell infiltration, immune checkpoint expression, genomic variation, tumor neoantigen load, and tumor mutational burden across a spectrum of cancers, with particularly notable effects in low-grade gliomas. In the context of gliomas, PTBP1 expression correlates with WHO grade and IDH1 mutation status. PTBP1 expression and methylation play an important role in a variety of cancers. PTBP1 can be used as a marker of inflammation, progression and prognosis in gliomas.
Topics: Humans; Polypyrimidine Tract-Binding Protein; Heterogeneous-Nuclear Ribonucleoproteins; Prognosis; Biomarkers, Tumor; Glioma; Gene Expression Regulation, Neoplastic; Tumor Microenvironment; DNA Methylation; Gene Expression Profiling; Inflammation; Transcriptome; Brain Neoplasms; Disease Progression; Multiomics
PubMed: 38918441
DOI: 10.1038/s41598-024-64979-5 -
Scientific Reports Jun 2024Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the...
Type 2 diabetes (T2D) is the fastest growing non-infectious disease worldwide. Impaired insulin secretion from pancreatic beta-cells is a hallmark of T2D, but the mechanisms behind this defect are insufficiently characterized. Integrating multiple layers of biomedical information, such as different Omics, may allow more accurate understanding of complex diseases such as T2D. Our aim was to explore and use Machine Learning to integrate multiple sources of biological/molecular information (multiOmics), in our case RNA-sequening, DNA methylation, SNP and phenotypic data from islet donors with T2D and non-diabetic controls. We exploited Machine Learning to perform multiOmics integration of DNA methylation, expression, SNPs, and phenotypes from pancreatic islets of 110 individuals, with ~ 30% being T2D cases. DNA methylation was analyzed using Infinium MethylationEPIC array, expression was analyzed using RNA-sequencing, and SNPs were analyzed using HumanOmniExpress arrays. Supervised linear multiOmics integration via DIABLO based on Partial Least Squares (PLS) achieved an accuracy of 91 ± 15% of T2D prediction with an area under the curve of 0.96 ± 0.08 on the test dataset after cross-validation. Biomarkers identified by this multiOmics integration, including SACS and TXNIP DNA methylation, OPRD1 and RHOT1 expression and a SNP annotated to ANO1, provide novel insights into the interplay between different biological mechanisms contributing to T2D. This Machine Learning approach of multiOmics cross-sectional data from human pancreatic islets achieved a promising accuracy of T2D prediction, which may potentially find broad applications in clinical diagnostics. In addition, it delivered novel candidate biomarkers for T2D and links between them across the different Omics.
Topics: Humans; Diabetes Mellitus, Type 2; Machine Learning; DNA Methylation; Islets of Langerhans; Polymorphism, Single Nucleotide; Male; Female; Middle Aged; Biomarkers; Adult; Aged
PubMed: 38918439
DOI: 10.1038/s41598-024-64846-3 -
Poultry Science Apr 2024Magang geese are typical short-day breeders whose reproductive behaviors are significantly influenced by photoperiod. Exposure to a long-day photoperiod results in...
Magang geese are typical short-day breeders whose reproductive behaviors are significantly influenced by photoperiod. Exposure to a long-day photoperiod results in testicular regression and spermatogenesis arrest in Magang geese. To investigate the epigenetic influence of DNA methylation on the seasonal testicular regression in Magang geese, we conducted whole-genome bisulfite sequencing and transcriptome sequencing of testes across 3 reproductive phases during a long-day photoperiod. A total of 250,326 differentially methylated regions (DMR) were identified among the 3 comparison groups, with a significant number showing hypermethylation, especially in intronic regions of the genome. Integrating bisulfite sequencing with transcriptome sequencing data revealed that DMR-associated genes tend to be differentially expressed in the testes, highlighting a potential regulatory role for DNA methylation in gene expression. Furthermore, there was a significant negative correlation between changes in the methylation of CG DMRs and changes in the expression of their associated genes in the testes. A total of 3,359 DMR-associated differentially expressed genes (DEG) were identified; functional enrichment analyses revealed that motor proteins, MAPK signaling pathway, ECM-receptor interaction, phagosome, TGF-beta signaling pathway, and calcium signaling might contribute to the testicular regression process. GSEA revealed that the significantly enriched activated hallmark gene set was associated with apoptosis and estrogen response during testicular regression, while the repressed hallmark gene set was involved in spermatogenesis. Our study also revealed that methylation changes significantly impacted the expression level of vitamin A metabolism-related genes during testicular degeneration, with hypermethylation of STRA6 and increased calmodulin levels indicating vitamin A efflux during the testicular regression. These findings were corroborated by pyrosequencing and real-time qPCR, which revealed that the vitamin A metabolic pathway plays a pivotal role in testicular degeneration under long-day conditions. Additionally, metabolomics analysis revealed an insufficiency of vitamin A and an abnormally high level of oxysterols accumulated in the testes during testicular regression. In conclusion, our study demonstrated that testicular degeneration in Magang geese induced by a long-day photoperiod is linked to vitamin A homeostasis disruption, which manifests as the hypermethylation status of STRA6, vitamin A efflux, and a high level of oxysterol accumulation. These findings offer new insights into the effects of DNA methylation on the seasonal testicular regression that occurs during long-day photoperiods in Magang geese.
PubMed: 38917605
DOI: 10.1016/j.psj.2024.103769 -
EBioMedicine Jun 2024DNA methylation biomarkers in colorectal cancer (CRC) tissue hold potential as prognostic indicators. However, individual studies have yielded heterogeneous results, and...
BACKGROUND
DNA methylation biomarkers in colorectal cancer (CRC) tissue hold potential as prognostic indicators. However, individual studies have yielded heterogeneous results, and external validation is largely absent. We conducted a comprehensive external validation and meta-analysis of previously suggested gene methylation biomarkers for CRC prognosis.
METHODS
We performed a systematic search to identify relevant studies investigating gene methylation biomarkers for CRC prognosis until March 2024. Our external validation cohort with long-term follow-up included 2303 patients with CRC from 22 hospitals in southwest Germany. We used Cox regression analyses to assess associations between previously suggested gene methylation biomarkers and prognosis, adjusting for clinical variables. We calculated pooled hazard ratios (HRs) and their 95% confidence intervals (CIs) using random-effects models.
FINDINGS
Of 151 single gene and 29 multiple gene methylation biomarkers identified from 121 studies, 37 single gene and seven multiple gene biomarkers were significantly associated with CRC prognosis after adjustment for clinical variables. Moreover, the directions of these associations with prognosis remained consistent between the original studies and our validation analyses. Seven single biomarkers and two multi-biomarker signatures were significantly associated with CRC prognosis in the meta-analysis, with a relatively strong level of evidence for CDKN2A, WNT5A, MLH1, and EVL.
INTERPRETATION
In a comprehensive evaluation of the so far identified gene methylation biomarkers for CRC prognosis, we identified candidates with potential clinical relevance for further investigation.
FUNDING
The German Research Council, the Interdisciplinary Research Program of the National Center for Tumor Diseases (NCT), Germany, the German Federal Ministry of Education and Research.
PubMed: 38917511
DOI: 10.1016/j.ebiom.2024.105223 -
Database : the Journal of Biological... Jun 2024Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with...
Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.
Topics: Depressive Disorder, Major; Humans; Databases, Genetic; Polymorphism, Single Nucleotide; Genomics; DNA Methylation; Multiomics
PubMed: 38917209
DOI: 10.1093/database/baae042 -
BioRxiv : the Preprint Server For... Jun 2024Zinc knuckle (ZCCHC) motif-containing proteins are present in unicellular and multicellular eukaryotes and most ZCCHC proteins with known functions participate in the...
Zinc knuckle (ZCCHC) motif-containing proteins are present in unicellular and multicellular eukaryotes and most ZCCHC proteins with known functions participate in the metabolism of various classes of RNA, such as mRNAs, ribosomal RNAs, and microRNAs. The Arabidopsis () genome encodes 69 ZCCHC-containing proteins, but the functions of most remain unclear. One of these proteins is CAX-INTERACTING PROTEIN 4 (CXIP4), which has been classified as a PTHR31437 family member, along with human SREK1-interacting protein 1 (SREK1IP1), which is thought to function in pre-mRNA splicing and RNA methylation. Metazoan SREK1IP1-like and plant CXIP4-like proteins only share a ZCCHC motif, and their functions remain almost entirely unknown. We studied two loss-of-function alleles of Arabidopsis , the first mutations in PTHR31437 family genes described to date: is likely null and shows early lethality, and is hypomorphic and viable, with pleiotropic morphological defects. The mutant exhibited deregulation of defense genes and upregulation of transcription factor encoding genes, some of which might explain its developmental defects. This mutant also exhibited increased intron retention events, and the specific functions of misspliced genes, such as those involved in "gene silencing by DNA methylation" and "mRNA polyadenylation factor" suggest that CXIP4 has additional functions. The CXIP4 protein localizes to the nucleus in a pattern resembling nuclear speckles, which are rich in splicing factors. Therefore, is required for plant survival and proper development, and mRNA maturation.
PubMed: 38915646
DOI: 10.1101/2024.06.06.597795