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Nutrition & Diabetes Dec 2022Obesity is accompanied by excess adipose fat storage, which may lead to adipose dysfunction, insulin resistance, and type 2 diabetes (T2D). Currently, the tendency to...
BACKGROUND
Obesity is accompanied by excess adipose fat storage, which may lead to adipose dysfunction, insulin resistance, and type 2 diabetes (T2D). Currently, the tendency to develop T2D in obesity cannot be explained by genetic variation alone-epigenetic mechanisms, such as DNA methylation, might be involved. Here, we aimed to identify changes in DNA methylation and gene expression in visceral adipose tissue (VAT) that might underlie T2D susceptibility in patients with obesity.
METHODS
We investigated DNA methylation and gene expression in VAT biopsies from 19 women with obesity, without (OND = 9) or with T2D (OD = 10). Differences in genome-scale methylation (differentially methylated CpGs [DMCs], false discovery rate < 0.05; and differentially methylated regions [DMRs], p value < 0.05) and gene expression (DEGs, p value <0.05) between groups were assessed. We searched for overlap between altered methylation and expression and the impact of altered DNA methylation on gene expression, using bootstrap Pearson correlation. The relationship of altered DNA methylation to T2D-related traits was also tested.
RESULTS
We identified 11 120 DMCs and 96 DMRs distributed across all chromosomes, with the greatest density of epigenomic alterations at the MHC locus. These alterations were found in newly and previously T2D-related genes. Several of these findings were supported by validation and extended multi-ethnic analyses. Of 252 DEGs in the OD group, 68 genes contained DMCs (n = 88), of which 24 demonstrated a significant relationship between gene expression and methylation (p values <0.05). Of these, 16, including ATP11A, LPL and EHD2 also showed a significant correlation with fasting glucose and HbA1c levels.
CONCLUSIONS
Our results revealed novel candidate genes related to T2D pathogenesis in obesity. These genes show perturbations in DNA methylation and expression profiles in patients with obesity and diabetes. Methylation profiles were able to discriminate OND from OD individuals; DNA methylation is thus a potential biomarker.
Topics: Female; Humans; Adipose Tissue; Diabetes Mellitus, Type 2; DNA Methylation; Gene Expression Profiling; Obesity
PubMed: 36535927
DOI: 10.1038/s41387-022-00228-w -
BMC Gastroenterology Feb 2022Methylated SDC2 and TFPI2 are widely used for colorectal cancer (CRC) detection. However, they often miss some CRCs, which directly diminishes the sensitivity. Further...
BACKGROUND
Methylated SDC2 and TFPI2 are widely used for colorectal cancer (CRC) detection. However, they often miss some CRCs, which directly diminishes the sensitivity. Further investigations of the underlying mechanisms leading to the missed samples will facilitate developing more eligible methylation markers.
METHODS
CRC samples from TCGA and GEO datasets were divided into three groups, High-methylation/ High-methylation (HH), High-methylation/Low-methylation (HL), and Low-methylation/Low-methylation (LL) according to the methylation status of SDC2 and TFPI2 promoters. Variations in age, tumor location and microsatellite instable were then assessed between the three groups and verified in our custom cohort.
RESULTS
Samples of HL group preferred to derive from left-sided CRCs (P < 0.05). HH samples showed the highest microsatellite instability and mutation load (mean nonsynonymous mutations for HH/HL/LL: 10.55/3.91/7.02, P = 0.0055). Almost all mutations of BRAF, one of the five typical CpG island methylator phenotype (CIMP) related genes, were observed in HH group (HH/HL/LL: 51/0/1, P = 0.018). Besides, older patients were frequently found in HH group. Expression analysis identified 37, 84, and 22 group-specific differentially expressed genes (DEGs) for HH, HL, and LL, respectively. Functional enrichment analysis revealed that HH-specific DEGs were mainly related to transcription regulation, while LL-specific DEGs were enriched in the biological processes of extracellular matrix interaction and cell migration.
CONCLUSIONS
The current study revealed that the performance of methylation-based markers might be affected by tumor location, patient age, mutation load and MSI, and these respective sides should be considered when developing new methylation markers for CRC detection.
Topics: Colorectal Neoplasms; CpG Islands; DNA Methylation; Glycoproteins; Humans; Microsatellite Instability; Mutation; Phenotype; Proto-Oncogene Proteins B-raf; Syndecan-2
PubMed: 35227195
DOI: 10.1186/s12876-022-02175-3 -
Journal of Chemical Theory and... Jan 2023The methylation of the lysine residue can affect some fundamental biological processes, and specific biological effects of the methylations are often related to product...
Structural and Energetic Origin of Different Product Specificities and Activities for SETD3 and Its Mutants on the Methylation of the β-Actin H73K Peptide: Insights from a QM/MM Study.
The methylation of the lysine residue can affect some fundamental biological processes, and specific biological effects of the methylations are often related to product specificity of methyltransferases. The question remains concerning how active-site structural features and dynamics control the activity as well as the number (1, 2, or 3) of methyl groups on methyl lysine products. SET domain containing protein 3 (SETD3) has been identified recently as the β-actin histidine73-N methyltransferase, and also, it has a weak methylation activity on the H73K β-actin peptide for which the target H73 residue is mutated into K73. Interestingly, the K73 methylation activity of SETD3 increases significantly as a result of the N255 → A or N255 → F/W273 → A mutation, and the N255A product specificity also differs from that of wild-type. Here, we performed QM/MM molecular dynamics and potential of mean force (PMF) simulations for SETD3 and its mutants (N255A and N255F/W273A) to study how SETD3 and its mutants could have different product specificities and activities for the K73 methylation. The PMF simulations show that the barrier for the first methylation of K73 is higher compared to the barrier of the H73 methylation in SETD3. Moreover, the second methylation of K73 has been found to have a barrier from the free energy simulation that is higher by 2.2 kcal/mol compared to the barrier of the first methyl transfer to K73, agreeing with the suggestion that SETD3 is a monomethylase. For the first, second, and third methylations of K73 in the N255A mutant, the barriers obtained from the PMF simulations for transferring the second and third methyl groups are found to be lower relative to the barrier for the first methyl transfer. Thus, N255A can be considered as a trimethyl lysine methyltransferase. In addition, for the first K73 methylation, the activities from the PMF simulations follow the order of N255F/W273A > N255A > WT, in agreement with experiments. The examination of the structural and dynamic results at the active sites provides better understanding of different product specificities and activities for the K73 methylations in SETD3 and its mutants. It is demonstrated that the existence of well-balanced interactions at the active site leading to the near attack conformation is of crucial importance for the efficient methyl transfers. Moreover, the presence of potential interactions (e.g., the C-H···O and cation-π interactions) that are strengthening at the transition state can also be important. Furthermore, the activity as well as product specificity of the K73 methylation also seems to be controlled by certain active-site water molecules which may be released to provide extra space for the addition of more methyl groups on K73.
Topics: Methylation; Histone-Lysine N-Methyltransferase; Actins; Lysine; Molecular Dynamics Simulation; Peptides
PubMed: 36520638
DOI: 10.1021/acs.jctc.2c00668 -
International Journal of Molecular... Feb 2024Lysine methylation is a major post-translational protein modification that occurs in both histones and non-histone proteins. Emerging studies show that the methylated... (Review)
Review
Lysine methylation is a major post-translational protein modification that occurs in both histones and non-histone proteins. Emerging studies show that the methylated lysine residues in non-histone proteins provide a proteolytic signal for ubiquitin-dependent proteolysis. The SET7 (SETD7) methyltransferase specifically transfers a methyl group from S-Adenosyl methionine to a specific lysine residue located in a methylation degron motif of a protein substrate to mark the methylated protein for ubiquitin-dependent proteolysis. LSD1 (Kdm1a) serves as a demethylase to dynamically remove the methyl group from the modified protein. The methylated lysine residue is specifically recognized by L3MBTL3, a methyl-lysine reader that contains the malignant brain tumor domain, to target the methylated proteins for proteolysis by the CRL4 ubiquitin ligase complex. The methylated lysine residues are also recognized by PHF20L1 to protect the methylated proteins from proteolysis. The lysine methylation-mediated proteolysis regulates embryonic development, maintains pluripotency and self-renewal of embryonic stem cells and other stem cells such as neural stem cells and hematopoietic stem cells, and controls other biological processes. Dysregulation of the lysine methylation-dependent proteolysis is associated with various diseases, including cancers. Characterization of lysine methylation should reveal novel insights into how development and related diseases are regulated.
Topics: Humans; Proteolysis; Lysine; Methylation; Protein Processing, Post-Translational; Histones; Brain Neoplasms; Ubiquitins; Histone-Lysine N-Methyltransferase; Chromosomal Proteins, Non-Histone
PubMed: 38396925
DOI: 10.3390/ijms25042248 -
ELife Jan 2022Methyltransferase like-3 (METTL3) and METTL14 complex transfers a methyl group from -adenosyl-L-methionine to amino group of adenosine bases in RNA (mA) and DNA (mdA)....
Methyltransferase like-3 (METTL3) and METTL14 complex transfers a methyl group from -adenosyl-L-methionine to amino group of adenosine bases in RNA (mA) and DNA (mdA). Emerging evidence highlights a role of METTL3-METTL14 in the chromatin context, especially in processes where DNA and RNA are held in close proximity. However, a mechanistic framework about specificity for substrate RNA/DNA and their interrelationship remain unclear. By systematically studying methylation activity and binding affinity to a number of DNA and RNA oligos with different propensities to form inter- or intra-molecular duplexes or single-stranded molecules in vitro, we uncover an inverse relationship for substrate binding and methylation and show that METTL3-METTL14 preferentially catalyzes the formation of mdA in single-stranded DNA (ssDNA), despite weaker binding affinity to DNA. In contrast, it binds structured RNAs with high affinity, but methylates the target adenosine in RNA (mA) much less efficiently than it does in ssDNA. We also show that METTL3-METTL14-mediated methylation of DNA is largely restricted by structured RNA elements prevalent in long noncoding and other cellular RNAs.
Topics: DNA Methylation; DNA, Single-Stranded; Deoxyadenosines; Humans; Methyltransferases; RNA
PubMed: 35060905
DOI: 10.7554/eLife.67150 -
ACS Chemical Biology Jul 2020The methylation of amide nitrogen atoms can improve the stability, oral availability, and cell permeability of peptide therapeutics. Chemical -methylation of peptides is...
The methylation of amide nitrogen atoms can improve the stability, oral availability, and cell permeability of peptide therapeutics. Chemical -methylation of peptides is challenging. Omphalotin A is a ribosomally synthesized, macrocylic dodecapeptide with nine backbone -methylations. The fungal natural product is derived from the precursor protein, OphMA, harboring both the core peptide and a SAM-dependent peptide α--methyltransferase domain. OphMA forms a homodimer and its α--methyltransferase domain installs the methyl groups on the hydrophobic core dodecapeptide and some additional C-terminal residues of the protomers. These post-translational backbone -methylations occur in a processive manner from the N- to the C-terminus of the peptide substrate. We demonstrate that OphMA can methylate polar, aromatic, and charged residues when these are introduced into the core peptide. Some of these amino acids alter the efficiency and pattern of methylation. Proline, depending on its sequence context, can act as a tunable stop signal. Crystal structures of OphMA variants have allowed rationalization of these observations. Our results hint at the potential to control this fungal α--methyltransferase for biotechnological applications.
Topics: Agaricales; Amino Acid Sequence; Fungal Proteins; Methylation; Methyltransferases; Mutation; Peptides, Cyclic; Protein Domains; Protein Precursors; Protein Processing, Post-Translational; Substrate Specificity
PubMed: 32491837
DOI: 10.1021/acschembio.0c00237 -
Sheng Wu Gong Cheng Xue Bao = Chinese... Jun 2021Methyltransferases (MTs) constitute a large group of enzymes that catalyze the transfer of a methyl moiety, most frequently from S-adenosyl-L-methionine, to their... (Review)
Review
Methyltransferases (MTs) constitute a large group of enzymes that catalyze the transfer of a methyl moiety, most frequently from S-adenosyl-L-methionine, to their substrates. It plays an essential role in regulation of gene expression and synthesis of many natural compounds. Owing to its broad substrate spectrum, MTs make important contributions to diversify the spectrum of products through methylation modifications. Recently, great progress has been made in application of MTs for the biosynthesis of various natural products including phenylpropane compounds, fragrances, hormones and antibiotics, which is summarized in this review. Moreover, we highlighted the strategies of using MTs for efficient production and for expanding the diversity of these methylated natural products, and discussed the current challenges and future prospects in this area.
Topics: Biological Products; Methylation; Methyltransferases
PubMed: 34227282
DOI: 10.13345/j.cjb.200742 -
Computational and Mathematical Methods... 2022Coronary heart disease (CHD) is an ischemic heart disease involving a variety of immune factors. This study was aimed at investigating unique immune and m6A patterns in...
BACKGROUND
Coronary heart disease (CHD) is an ischemic heart disease involving a variety of immune factors. This study was aimed at investigating unique immune and m6A patterns in patients with CHD by gene expression in peripheral blood mononuclear cells (PBMCs) and at identifying novel immune biomarkers.
METHODS
The CIBERSORT algorithm and single-sample gene set enrichment analysis (ssGSEA) were applied to assess the population of specific infiltrating immunocytes. Weighted Gene Coexpression Network Analysis (WGCNA) was utilized on immune genes matching CHD. A prediction model based on core immune genes was constructed and verified by a machine learning model. Unsupervised cluster analysis identified various immune patterns in the CHD group according to the abundance of immune cells. Methylation of N6 adenosine- (m6A-) related gene was identified from the literature, and t-distributed stochastic neighbor embedding (t-SNE) analysis was used to determine the rationality of the m6A classification. The association between m6A-related genes and various immune cells was estimated using heat maps.
RESULTS
22/28 immune-associated cells differed between the CHD and normal groups, and a significant difference was detected in the expression of 21 m6A-related genes. The proportion of immune-related cells (activated CD4+ T cells and CD8+ T cells) in the peripheral blood of the CHD group was lower than that of the normal group. The immune genes were divided into four modules, of which the turquoise modules showed a significant association with coronary heart disease. Eight hub immune genes (, , , , , , , and ) can well distinguish the CHD group from the normal group. Two different immune patterns were identified in the CHD group. Interestingly, a significant association was detected between the m6A-related genes and immune cell abundance.
CONCLUSION
In conclusion, we identified different immune and m6A patterns in CHD. Thus, it could be speculated that the immune system plays a crucial role in CHD, and m6A is correlated with immune genes.
Topics: Biomarkers; Coronary Disease; Gene Regulatory Networks; Humans; Leukocytes, Mononuclear; Methylation
PubMed: 36060667
DOI: 10.1155/2022/2877679 -
Methods in Molecular Biology (Clifton,... 2023Arrays provide a cost-effective platform for the analysis of human DNA methylation. ShinyÉPICo is an interactive, web-based, and graphical tool that allows the user to...
Arrays provide a cost-effective platform for the analysis of human DNA methylation. ShinyÉPICo is an interactive, web-based, and graphical tool that allows the user to analyze Illumina DNA methylation arrays (450 k and EPIC), from the user's own computer or from a server. This tool covers the analysis entirely, from the raw data input to the final list of differentially methylated positions or regions. Here, we describe the steps of the analysis, the different parameters available, and useful information to understand and select the best options in each step.
Topics: Humans; Software; DNA Methylation; Data Interpretation, Statistical; CpG Islands
PubMed: 36723806
DOI: 10.1007/978-1-0716-2962-8_2 -
BMC Genomics Mar 2022The emerging epitranscriptome plays an essential role in female fertility. As the most prevalent internal mRNA modification, N6-methyladenine (mA) methylation regulate...
BACKGROUND
The emerging epitranscriptome plays an essential role in female fertility. As the most prevalent internal mRNA modification, N6-methyladenine (mA) methylation regulate mRNA fate and translational efficiency. However, whether mA methylation was involved in the aging-related ovarian reserve decline has not been investigated. Herein, we performed mA transcriptome-wide profiling in the ovarian granulosa cells of younger women (younger group) and older women (older group).
RESULTS
mA methylation distribution was highly conserved and enriched in the CDS and 3'UTR region. Besides, an increased number of mA methylated genes were identified in the older group. Bioinformatics analysis indicated that mA methylated genes were enriched in the FoxO signaling pathway, adherens junction, and regulation of actin cytoskeleton. A total of 435 genes were differently expressed in the older group, moreover, 58 of them were modified by mA. Several specific genes, including BUB1B, PHC2, TOP2A, DDR2, KLF13, and RYR2 which were differently expressed and modified by mA, were validated using qRT-PCR and might be involved in the decreased ovarian functions in the aging ovary.
CONCLUSIONS
Hence, our finding revealed the transcriptional significance of mA modifications and provide potential therapeutic targets to promote fertility reservation for aging women.
Topics: Aged; Female; Gene Expression Profiling; Granulosa Cells; Humans; Methylation; Ovarian Reserve; Transcriptome
PubMed: 35346019
DOI: 10.1186/s12864-022-08462-3