Cytokine levels reflect tic symptoms more prominently during mild phases.

Tic disorder is a neuropsychiatric condition that affects 3% of all children and can have a significant impact on their quality of life. Cytokines, interferons, interleukins, lymphokines, and tumor necrosis factors are involved in the neuroinflammatory circuitry of tic disorders. This study aimed to identify the cytokines involved in the pathogenesis of tic disorders. We enrolled 44 patients with tic disorder and 38 healthy controls. Patients were free of psychotropic medications for at least 3 weeks. Whole blood samples were analyzed using a Luminex® human cytokine multiplex assay kit. Patients were divided into groups with "mild tics" and "above moderate tics" based on Yale Global Tic Severity Scale (YGTSS) scores for comparison. The final analysis included 35 patients (28 male and 7 female) and 31 controls (20 male and 11 female). In the mild tic group, interleukin (IL)-12 p70 negatively correlated with motor tic scores. Granulocyte-macrophage colony-stimulating factor, IL-4, IL-8, and tumor necrosis factor (TNF)-α were positively correlated to phonic tic scores. IL-12 p40 and TNF-α were positively correlated to total tic scores. IL-12 p70 and IL-17a negatively correlated to impairment scores and total YGTSS scores. Tic disorder patients and healthy controls exhibit different cytokine profiles. Only patients with mild symptoms exhibit significant correlations, suggesting that the correlations between cytokine levels and tic symptoms are more relevant during the mild or remission phases. Our results present the importance of IL-1ß and TNF-α, among others, but the identification of key cytokines are still necessary.

Integrative Approaches of DNA Methylation Patterns According to Age, Sex and Longitudinal Changes.

Background: In humans, age-related DNA methylation has been studied in blood, tissues, buccal swabs, and fibroblasts, and changes in DNA methylation patterns according to age and sex have been detected. To date, approximately 137,000 samples have been analyzed from 14,000 studies, and the information has been uploaded to the NCBI GEO database. Methods: A correlation between age and methylation level and longitudinal changes in methylation levels was revealed in both sexes. Here, 20 public datasets derived from whole blood were analyzed using the Illumina BeadChip. Batch effects with respect to the time differences were correlated. The overall change in the pattern was provided as the inverse of the coefficient of variation (COV). Results: Of the 20 datasets, nine were from a longitudinal study. All data had age and sex as common variables. Comprehensive details of age-, sex-, and longitudinal change-based DNA methylation levels in the whole blood sample were elucidated in this study. ELOVL2 and FHL2 showed the maximum correlation between age and DNA methylation. The methylation patterns of genes related to mental health differed according to age. Age-correlated genes have been associated with malformations (anteverted nostril, craniofacial abnormalities, and depressed nasal bridge) and drug addiction (drug habituation and smoking). Conclusion: Based on 20 public DNA methylation datasets, methylation levels according to age and longitudinal changes by sex were identified and visualized using an integrated approach. The results highlight the molecular mechanisms underlying the association of sex and biological age with changes in DNA methylation, and the importance of optimal genomic information management.

Factors for risk stratification of patients with actinic keratosis using integrated analysis of clinicopathological features and gene expression patterns.

BACKGROUND: Actinic keratosis (AK) is considered as precursor lesion of invasive squamous cell carcinoma. Molecular studies on AK are limited because of too small size of the biopsy specimen to obtain enough DNA or RNA. METHODS: Twenty biopsy cases of AK, followed by second same-sited biopsies, were included. Ten cases were diagnosed with total regression (regression group), while the other 10 were diagnosed with invasive carcinoma (progression group) in the follow-up biopsies. Using digital spatial profiling (DSP) technology, whole-gene expression analysis defined by specific regions of interest was performed for all 20 cases. After the clinicopathological features were assessed, separate and integrated analyses of these features and gene expression patterns were performed using machine-learning technology. All analyses were performed on both lesion keratinocytes (KT) and infiltrated stromal lymphocytes (LC). RESULTS: Among the 18,667 genes assessed, 33 and 72 differentially expressed genes (DEGs) between the regression and progression groups were found in KT and LC respectively. The primary genes distinguishing the two groups were KRT10 for KT and CARD18 for LC. Clinicopathological features were weaker in risk stratification of AK progression than the gene expression patterns. Pathways associated with various cancers were upregulated in the progression group of KT, whereas the nucleotide-binding oligomerization domain (NOD)-like receptor signalling pathway was upregulated in the progression of LC. CONCLUSION: Gene expression patterns were effective for risk stratification of AK progression, and their distinguishing power was higher than that of clinicopathological features.

DNA Methylation Patterns According to Fatty Liver Index and Longitudinal Changes from the Korean Genome and Epidemiology Study (KoGES).

The role of differentially methylated regions (DMRs) in nonalcoholic fatty liver disease (NAFLD) is unclear. This study aimed to identify the role of DMR in NAFLD development and progression using the Korean Genome and Epidemiology Study (KoGES) cohort. We used laboratory evaluations and Illumina Methylation 450 k DNA methylation microarray data from KoGES. The correlation between fatty liver index (FLI) and genomic CpG sites was analyzed in 322 subjects. Longitudinal changes over 8 years were confirmed in 33 subjects. To identify CpG sites and genes related to FLI, we obtained enrichment terms for 6765 genes. DMRs were identified for both high (n = 128) and low (n = 194) groups on the basis of FLI 30 in 142 men and 180 women. To confirm longitudinal changes in 33 subjects, the ratio of follow-up and baseline investigation values was obtained. Correlations and group comparisons were performed for the 8 year change values. PITPNM3, RXFP3, and THRB were hypermethylated in the increased FLI groups, whereas SLC9A2 and FOXI3 were hypermethylated in the decreased FLI groups. DMRs describing NAFLD were determined, and functions related to inflammation were identified. Factors related to longitudinal changes are suggested, and blood circulation-related functions appear to be important in the management of NAFLD.

Demographic and Genome Wide Association Analyses According to Muscle Mass Using Data of the Korean Genome and Epidemiology Study.

BACKGROUND: Sarcopenia is commonly found in the elderly due to a decline in muscle mass. Many researchers have performed genome-wide association studies (GWAS) to find genetic risk factors of sarcopenia. Although many studies have discovered sarcopenia associated single nucleotide polymorphisms (SNPs), most of them are studies targeting Caucasians. The purpose of this study was to evaluate genetic correlation according to muscle mass in middle aged Koreans using data of the Korean Genome and Epidemiology Study (KOGES), a large population-based genomic cohort study. METHODS: Baseline participants were 10,030 subjects aged 40 to 69 years who were from Ansan or Anseong in Gyeonggi-do, South Korea. Among them, 9,351 subjects with laboratory data available were included in this study. To identify sarcopenia associated variants, those in the top 30% and bottom 30% of muscle mass index (MMI) were compared. A total of 7,452 people with an MMI of 30-70% were excluded. A total of 1,004 people were also excluded due to missing data. Finally, 895 people were selected for this study. The Korea Biobank Array generated 500,568 SNPs for this dataset. RESULTS: When subjects were divided into top 30% and bottom 30% of MMI, the top 30% had 169 men and 308 women and the bottom 30% had 220 men and 198 women. In men, age, body mass index (BMI), waist and hip were significantly (P < 0.005) different between top 30% and bottom 30% MMI groups. In women, age, BMI, waist, hip, and hypertension history were significantly different between the two MMI groups. There were 13 significant SNPs in men and 14 significant SNPs in women. Genes associated with variants in men based on the single-nucleotide polymorphism database (dbSNP) were LRP1B containing rs11679458 and RGS6 containing rs11848300. A gene associated with variants in women was Pi4K2A, which contained rs1189312 as a variant. In addition, rs11189312 was associated with expression quantitative trait loci (eQTL) of ZFYVE27 in skeletal muscles and other SNPs of ZFYVE27 (rs10882883, rs17108378, rs35077384) known to be associated with spastic paraplegia. The eQTL analysis revealed that rs11189312 was a variant associated with SNPs of ZFYVE27. CONCLUSIONS: In the demographic study, significant results were found in BMI, waist, hip, history of hyperlipidemia, and sedentary life status in male group, and significant results were found in BMI, waist, hip, and hypertension history in female group. Variant rs11189312 was found to be a novel variant affecting ZFYVE27 expressed in skeletal muscles, suggesting that rs11189312 might be related to sarcopenia as a novel discovery of this study. Further study is needed to determine the association between sarcopenia and ZFYVE27 known to be associated with spastic paraplegia.

A Genomic Information Management System for Maintaining Healthy Genomic States and Application of Genomic Big Data in Clinical Research.

Improvements in next-generation sequencing (NGS) technology and computer systems have enabled personalized therapies based on genomic information. Recently, health management strategies using genomics and big data have been developed for application in medicine and public health science. In this review, I first discuss the development of a genomic information management system (GIMS) to maintain a highly detailed health record and detect diseases by collecting the genomic information of one individual over time. Maintaining a health record and detecting abnormal genomic states are important; thus, the development of a GIMS is necessary. Based on the current research status, open public data, and databases, I discuss the possibility of a GIMS for clinical use. I also discuss how the analysis of genomic information as big data can be applied for clinical and research purposes. Tremendous volumes of genomic information are being generated, and the development of methods for the collection, cleansing, storing, indexing, and serving must progress under legal regulation. Genetic information is a type of personal information and is covered under privacy protection; here, I examine the regulations on the use of genetic information in different countries. This review provides useful insights for scientists and clinicians who wish to use genomic information for healthy aging and personalized medicine.

DNA Methylation Profiling for the Diagnosis and Prognosis of Patients with Nontuberculous Mycobacterium Lung Disease.

The incidence of nontuberculous Mycobacterium (NTM) lung disease is rapidly increasing; however, its diagnosis and prognosis remain unclear while selecting patients who will respond to appropriate treatment. Differences in DNA methylation patterns between NTM patients with good or poor prognosis could provide important therapeutic targets. We used the Illumina MethylationEPIC (850k) DNA methylation microarray to determine the pattern between differentially methylated regions (DMRs) in NTM patients with good or poor prognosis (n = 4/group). Moreover, we merged and compared 20 healthy controls from previous Illumina Methylation450k DNA methylation microarray data. We selected and visualized the DMRs in the form of heatmaps, and enriched terms associated with these DMRs were identified by functional annotation with the "pathfinder" package. In total, 461 and 293 DMRs (|Log2 fold change| > 0.1 and p < 0.03) were more methylated in patients with four poor and four good prognoses, respectively. Furthermore, 337 and 771 DMRs (|Log2 fold change| > 0.08 and p < 0.001) were more methylated in eight NTM patients and 20 healthy controls, respectively. TGFBr1 was significantly less methylated, whereas HLA-DR1 and HLA-DR5 were more methylated in patients with poor prognosis (compared to those with good prognosis). LRP5, E2F1, and ADCY3 were the top three less-methylated genes in NTM patients (compared with the controls). The mTOR and Wnt signaling pathway-related genes were less methylated in patients with NTM. Collectively, genes related to Th1-cell differentiation, such as TGFBr1 and HLA-DR, may be used as biomarkers for predicting the treatment response in patients with NTM lung disease.

RNA-sequencing identification and validation of genes differentially expressed in high-risk adenoma, advanced colorectal cancer, and normal controls.

Distinct gene expression patterns that occur during the adenoma-carcinoma sequence need to be determined to analyze the underlying mechanism in each step of colorectal cancer progression. Elucidation of biomarkers for colorectal polyps that harbor malignancy potential is important for prevention of colorectal cancer. Here, we use RNA sequencing to determine gene expression profile in patients with high-risk adenoma treated with endoscopic submucosal dissection by comparing with gene expression in patients with advanced colorectal cancer and normal controls. We collected 70 samples, which consisted of 27 colorectal polyps, 24 cancer tissues, and 19 normal colorectal mucosa. RNA sequencing was performed on an Illumina platform to select differentially expressed genes (DEGs) between colorectal polyps and cancer, polyps and controls, and cancer and normal controls. The Kyoto Gene and Genome Encyclopedia (KEGG) and gene ontology (GO) analysis, gene-concept network, GSEA, and a decision tree were used to evaluate the DEGs. We selected the most highly expressed genes in high-risk polyps and validated their expression using real-time PCR and immunohistochemistry. Compared to patients with colorectal cancer, 82 upregulated and 24 downregulated genes were detected in high-risk adenoma. In comparison with normal controls, 33 upregulated and 79 downregulated genes were found in high-risk adenoma. In total, six genes were retrieved as the highest and second highest expressed in advanced polyps and cancers among the three groups. Among the six genes, ANAX3 and CD44 expression in real-time PCR for validation was in good accordance with RNA sequencing. We identified differential expression of mRNAs among high-risk adenoma, advanced colorectal cancer, and normal controls, including that of CD44 and ANXA3, suggesting that this cluster of genes as a marker of high-risk colorectal adenoma.

Total Platelet Transcriptomics and Its Network Analysis by RNA-Seq and miRNA-Seq and PCA Application in Essential Thrombocythaemia.

Differentiating the aetiology of thrombocytosis is limited yet crucial in patients with essential thrombocythaemia (ET). MicroRNAs (miRNAs) regulate haematopoiesis and lineage commitment; aberrant expression of miRNAs plays an important role in myeloproliferative neoplasms. However, the miRNA profile has been poorly explored in ET patients compared to patients with reactive thrombocytosis (RT). A total of 9 samples, including 5 ET patient samples, 2 RT patient samples, and 2 healthy control samples, were analysed in this study. We produced 81.43 million reads from transcripts and 59.60 million reads from small RNAs. We generated a comprehensive miRNA-mRNA regulatory network and identified unique 14 miRNA expression patterns associated with ET. Among the 14 miRNAs, miR-1268a was downregulated in ET and showed an inverse correlation with its 8 putative target genes, including genes associated with thrombus formation and platelet activation (CDH6, EHD2, FUT1, KIF26A, LINC00346, PTPRN, SERF1A, and SLC6A9). Principal component analysis (PCA) showed ET and non-ET groups well clustered in space, suggesting each group had a distinctive expression pattern of mRNAs and miRNAs. These results suggest that the significant dysregulation of miR-1268a and its 8 target genes could be a unique expression of platelet mi-RNAs and miRNA/mRNA regulatory network in ET patients.

A Machine Learning-Based Identification of Genes Affecting the Pharmacokinetics of Tacrolimus Using the DMETTM Plus Platform.

Tacrolimus is an immunosuppressive drug with a narrow therapeutic index and larger interindividual variability. We identified genetic variants to predict tacrolimus exposure in healthy Korean males using machine learning algorithms such as decision tree, random forest, and least absolute shrinkage and selection operator (LASSO) regression. rs776746 (CYP3A5) and rs1137115 (CYP2A6) are single nucleotide polymorphisms (SNPs) that can affect exposure to tacrolimus. A decision tree, when coupled with random forest analysis, is an efficient tool for predicting the exposure to tacrolimus based on genotype. These tools are helpful to determine an individualized dose of tacrolimus.

Identification and expression analysis of human endogenous retrovirus Y (HERV-Y) in various human tissues.

Human endogenous retroviruses (HERVs) account for approximately 8% of the human genome. To date, several HERV families have been identified in the human genome, with some being valid biomarkers for specific disease states. In this study, we have identified three HERV-Y elements in the human genome and characterized their structure and expression in various human tissues. New HERV-Y elements (HERV-Y101, HERV-Y102, and HERV-Y103) were detected on human chromosomes 8 and 13. In a pol-based phylogenetic tree, HERV-Y elements were closely grouped with HERV-I, -T, -E, and -R. The HERV-Y pol gene was expressed ubiquitously in all examined tissues, and it was dominantly expressed in the pons among the 12 different brain regions investigated. These results will allow future studies to elucidate the potential functional roles of HERVs in the brain and other tissues.

Genome-wide analysis of DNA methylation patterns in horse.

BACKGROUND: DNA methylation is an epigenetic regulatory mechanism that plays an essential role in mediating biological processes and determining phenotypic plasticity in organisms. Although the horse reference genome and whole transcriptome data are publically available the global DNA methylation data are yet to be known. RESULTS: We report the first genome-wide DNA methylation characteristics data from skeletal muscle, heart, lung, and cerebrum tissues of thoroughbred (TH) and Jeju (JH) horses, an indigenous Korea breed, respectively by methyl-DNA immunoprecipitation sequencing. The analysis of the DNA methylation patterns indicated that the average methylation density was the lowest in the promoter region, while the density in the coding DNA sequence region was the highest. Among repeat elements, a relatively high density of methylation was observed in long interspersed nuclear elements compared to short interspersed nuclear elements or long terminal repeat elements. We also successfully identified differential methylated regions through a comparative analysis of corresponding tissues from TH and JH, indicating that the gene body regions showed a high methylation density. CONCLUSIONS: We provide report the first DNA methylation landscape and differentially methylated genomic regions (DMRs) of thoroughbred and Jeju horses, providing comprehensive DMRs maps of the DNA methylome. These data are invaluable resource to better understanding of epigenetics in the horse providing information for the further biological function analyses.

Survival Rate and Chronic Diseases of TCGA Cancer and KoGES Normal Samples by Clustering for DNA Methylation.

Insights from public DNA methylation data derived from cancer or normal tissues from cancer patients or healthy people can be obtained by machine learning. The goal is to determine methylation patterns that could be useful for predicting the prognosis for cancer patients and correcting lifestyles for healthy people. DNA methylation data were obtained from the DNA of 446 healthy participants from the Korean Genome Epidemiology Study (KoGES) and from the DNA of normal tissues or from cancer tissues of 11 types of carcinomas from The Cancer Genome Atlas (TCGA) database. To correct for the batch effect, R's ComBat function was used. Using the K-mean clustering (k = 3), the survival rates of the cancer patients and the incidence of chronic diseases were compared between the three clusters for TCGA and KoGES, respectively. Based on the public DNA methylation and clinical data of healthy participants and cancer patients, I present an analysis pipeline that integrates and clusters the methylation data from the two groups. As a result of clustering, CpG sites from gene or genomic regions, such as AFAP1, NINJ2, and HOOK2 genes, that correlated with survival rate and chronic disease are presented.

Factors related to tumor response rate from TCGA three omics data-variants, expression, methylation.

The Cancer Genome Atlas (TCGA) and its patient-derived multi-omics datasets have been the backbone of cancer research, and with novel approaches, it continues to shed new insight into the disease. In this study, we delved into a method of multi-omics integration of patient datasets and the association of biological pathways related to the disease. First, across thirty-three types of cancer present in TCGA, we merged genomic mutations and drug response datasets and filtered for the viable variant-drug response combinations available in TCGA, containing more than three samples for each drug response label with RNA sequencing (RNA-seq) and genomic methylation data available for each patient. We identified two distinct combinations in TCGA, one being pancreatic adenocarcinoma patients with/without rs121913529 variant in KRAS gene treated with gemcitabine, and the other low-grade glioma with/without rs121913500 variant in IDH1 gene administered with temozolomide. In these two groups, different patterns of gene expression were observed in the pathways often associated with cancer progression, such as mTOR and PDGF between the patients with complete response and progressive disease. Our result will provide yet another example of the relevance of these biological pathways in cancer drug response and a way for multi-omics integration in cancer datasets.

Four modeling approaches to study restrictions on everyday life and social activities due to chronic diseases with consequences of suicidal behavior.

The purpose of this study was to discover the association between disability in everyday life and social activities due to chronic diseases and suicidal ideation (SI), suicidal plan (SP), and suicidal attempt (SA) from the Korea National Health and Nutrition Examination Survey (KNHANES), considering the cross-sectional design of this study, 2016-2018 dataset. Variables for finding the associated factors of SI, SP, and SA were confirmed through random forest (RF), decision tree, generalized linear model (GLM), and support vector machine (SVM), and the performance of each model is listed. A total of 17,323 (males: 7,530, females: 9793) responders from the KNHANES from 2016 to 2018 were employed for the study. The relationship between restrictions on daily life, social activities, and three stages of suicidal behaviors due to diseases were analyzed using the R function (R version 4.2.0), randomForest, ctree, glm, and ksvm. The F1-score is a measure used to evaluate the accuracy of the performance of a model, in the binary classification. The score of 1 indicates good performance, whereas a score of 0 signifies poor performance. Due to chronic diseases, disability in everyday life and social activities lead to suicide behaviors. In our study, we examined the impact of limitations in daily living and social activities on suicidal behaviors among participants. Our findings revealed that for those experiencing such limitations, the odds ratios (ORs) for SIs were 6.10 (95% CI: 3.99-9.34) for males and 2.61 (1.79-3.81) for females. SPs were 3.69 (2.36-5.78) for males and 3.94 (2.70-5.75) for females. Similarly, the odds ratios for SAs were 5.04 (2.51-10.13) for males and 2.71 (1.48-4.98) for females, indicating a significant association between these limitations and increased suicidal behaviors, with variances observed between genders. These results underscore the necessity of addressing daily living and social activity restrictions when considering mental health interventions and suicide prevention strategies. In RF, GLM, and SVM, F1-score were 0.8192, 0.6887, and 0.9687 in SA, respectively. Among the patients with chronic disease, those with sequelae, low incomes, and low levels of education had limitations in daily activities and social activities, which increased the likelihood of suicidal thoughts, planning, and attempts.

Particulate matter-induced gene expression patterns in human-derived cells based on 11 public gene expression datasets.

BACKGROUND: Exposure to particulate matter (PM) and house dust mite (HDM) can change the expression patterns of inflammation-, oxidative stress-, and cell death-related genes. We investigated the changes in gene expression patterns owing to PM exposure. OBJECTIVE: This study examined the changes in gene expression patterns following PM exposure. METHODS: We searched for differentially expressed genes (DEGs) following PM exposure using five cell line-based RNA-seq or microarray datasets and six human-derived datasets. The enrichment terms of the DEGs were assessed. RESULTS: DEG analysis yielded two gene sets. Thus, enrichment analysis was performed for each gene set, and the enrichment terms related to respiratory diseases were presented. The intersection of six human-derived datasets and two gene sets was obtained, and the expression patterns following PM exposure were observed. CONCLUSIONS: Two gene sets were obtained for cells treated with PM and their expression patterns were presented following verification in human-derived cells. Our findings suggest that exposure to PM2.5 and HDM may reveal changes in genes that are associated with diseases, such as allergies, highlighting the importance of mitigating PM2.5 and HDM exposure for disease prevention.

Epigenome-wide Association Study for Tic Disorders in Children: A Preliminary Study in Korean Population.

Objective: : Tic disorders can affect the quality of life in both childhood and adolescence. Many factors are involved in the etiology of tic disorders, and the genetic and epigenetic factors of tic disorders are considered complex and heterogeneous. Methods: : In this study, the differentially methylated regions (DMRs) between normal controls (n = 24; aged 6-15; 7 females) and patients with tic disorders (n = 16; aged 6-15; 5 females) were analyzed. We performed an epigenome-wide association study of tic disorders in Korean children. The tics were assessed using Yale Global Tic Severity Scale. The DNA methylation data consisted of 726,945 cytosine phosphate guanine (CpG) sites, assessed using the Illumina Infinium MethylationEPIC (850k) BeadChip. The DNA methylation data of the 40 participants were retrieved, and DMRs between the four groups based on sex and tic disorder were identified. From 28 male and 16 female samples, 37 and 38 DMRs were identified, respectively. We analyzed the enriched terms and visualized the network, heatmap, and upset plot. Results: : In male, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed hypomethylated patterns in the ligand, receptor, and second signal transductors of the PI3K-Akt and MAPK signaling pathway (most cells were indicated as green color), and in female, the opposite patterns were revealed (most cells were indicated as red color). Five mental disorder-related enriched terms were identified in the network analysis. Conclusion: : Here, we provide insights into the epigenetic mechanisms of tic disorders. Abnormal DNA methylation patterns are associated with mental disorder-related symptoms.

Transposable Elements-Derived MicroRNA Expression Patterns in TCGA Dataset for 10 Species.

MicroRNAs (miRNAs) are a class of non-coding RNAs that act as regulators of disease. An evolutionary approach to the disease could reveal factors such as diagnosis, treatment, and prognosis prediction. The expression patterns of transposable element (TEs)-derived miRNAs could help elucidate diseases, and their evolutionary patterns are also valuable. The 34 miRNAs were compared in terms of stage survival and tumor status in 33 carcinomas from TCGA. Expression levels were compared using a t-test and presented as differentially expressed miRNAs (DEMs). For DEMs showing statistically specific expression patterns for 3 conditions (normal and cancer, early and advanced stage, and survival), interactions with related genes in 10 species, including humans, were compared. The enrichment term was discovered for the gene-miRNA interactions. In 18 out of the 33 carcinomas, at least one miRNA was retrieved with P < .05 and |fold change| >.05. A total of 128 DEMs for the 9 miRNAs were identified. Based on the TargetScan database, interactions between miRNAs and genes in 10 species, including humans, were confirmed. The evolutionarily conserved miR-130a was observed in all 10 species, whereas miR-151a was only observed in humans. GO terms of related genes were selected for the miRNAs commonly found in each species. Evolutionary analysis of TE-derived disease-associated miRNAs was performed, and the evolutionarily conserved miR-130a-related carcinomas included renal and thyroid cancers. Human and rhesus monkey-specific miR-625 is associated with various carcinomas.

Small RNA sequencing of small extracellular vesicles secreted by umbilical cord mesenchymal stem cells following replicative senescence.

BACKGROUND: Umbilical cord mesenchymal stem cells (UCMSC) are subsets of multipotent stem cells involved in immune modulation, tissue regeneration, and antimicrobial defense. Cellular senescence is associated with the onset of aging-related diseases and small extracellular vesicles (sEVs) are important mediators of senescence and aging. OBJECTIVE: However, little is known about the role and function of microRNAs (miRNAs) carried by UCMSC-derived sEVs. To analyze the expression profiles of miRNAs secreted by senescent UCMSC, small RNA sequencing of the miRNAs within the sEVs was performed in this study. METHODS: UCMSC cultures underwent serial passaging beyond passage number 20 to achieve replicative senescence, which was confirmed by various methods, including increased senescence-associated ß-gal staining and cytokine secretion levels. sEVs derived from non-senescent and senescent UCMSC were isolated and characterized by nanoparticle tracking analysis, transmission electron microscopy, and immunoblot analysis. RESULTS: Small RNA sequencing of the miRNAs within the sEVs revealed senescence-associated differences in the miRNA composition, as shown by the upregulation of miR-122-5p and miR-146a-5p, and downregulation of miR-125b-5p and miR-29-3p. In addition, total RNA sequencing analysis showed that PENK, ITGA8, and TSIX were upregulated, whereas AKR1B10, UNC13D, and IL21R were downregulated by replicative senescence in UCMSC. In sEVs, upregulated genes were linked to downregulated miRNAs, and vice versa. In the gene-concept network analysis, five gynecologic terms were retrieved. CONCLUSIONS: The study provides an insight into the cellular characteristics of UCMSC following replicative senescence and emphasizes the importance of monitoring passage numbers of UCMSC for further therapeutic use.

The Effect of DNA Methylation in the Development and Progression of Chronic Kidney Disease in the General Population: An Epigenome-Wide Association Study Using the Korean Genome and Epidemiology Study Database.

BACKGROUND: Although knowledge of the genetic factors influencing kidney disease is increasing, epigenetic profiles, which are associated with chronic kidney disease (CKD), have not been fully elucidated. We sought to identify the DNA methylation status of CpG sites associated with reduced kidney function and examine whether the identified CpG sites are associated with CKD development. METHOD: We analyzed DNA methylation patterns of 440 participants in the Korean Genome and Epidemiology Study (KoGES) with estimated glomerular filtration rates (eGFRs) ≥ 60 mL/min/1.73 m2 at baseline. CKD development was defined as a decrease in the eGFR of <60 at any time during an 8-year follow-up period ("CKD prediction" analysis). In addition, among the 440 participants, 49 participants who underwent a second methylation profiling were assessed for an association between a decline in kidney function and changes in the degree of methylation of CpG sites during the 8 years ("kidney function slope" analysis). RESULTS: In the CKD prediction analysis, methylation profiles of a total of 403,129 CpG sites were evaluated at baseline in 440 participants, and increased and decreased methylation of 268 and 189 CpG sites, respectively, were significantly correlated with the development of CKD in multivariable logistic regression. During kidney function slope analysis using follow-up methylation profiles of 49 participants, the percent methylation changes in 913 CpG sites showed a linear relationship with the percent change in eGFR during 8 years. During functional enrichment analyses for significant CpG sites found in the CKD prediction and kidney function slope analyses, we found that those CpG sites represented MAPK, PI3K/Akt, and Rap1 pathways. In addition, three CpG sites from three genes, NPHS2, CHCHD4, and AHR, were found to be significant in the CKD prediction analysis and related to a decline in kidney function. CONCLUSION: It is suggested that DNA methylation on specific genes is associated with the development of CKD and the deterioration of kidney function.