Comorbidity Genes of Alzheimer's Disease and Type 2 Diabetes Associated with Memory and Cognitive Function.

Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) are comorbidities that result from the sharing of common genes. The molecular background of comorbidities can provide clues for the development of treatment and management strategies. Here, the common genes involved in the development of the two diseases and in memory and cognitive function are reviewed. Network clustering based on protein-protein interaction network identified tightly connected gene clusters that have an impact on memory and cognition among the comorbidity genes of AD and T2DM. Genes with functional implications were intensively reviewed and relevant evidence summarized. Gene information will be useful in the discovery of biomarkers and the identification of tentative therapeutic targets for AD and T2DM.

Molecular Mechanisms of Endometriosis Revealed Using Omics Data.

Endometriosis is a gynecological disorder prevalent in women of reproductive age. The primary symptoms include dysmenorrhea, irregular menstruation, and infertility. However, the pathogenesis of endometriosis remains unclear. With the advent of high-throughput technologies, various omics experiments have been conducted to identify genes related to the pathophysiology of endometriosis. This review highlights the molecular mechanisms underlying endometriosis using omics. When genes identified in omics experiments were compared with endometriosis disease genes identified in independent studies, the number of overlapping genes was moderate. However, the characteristics of these genes were found to be equivalent when functional gene set enrichment analysis was performed using gene ontology and biological pathway information. These findings indicate that omics technology provides invaluable information regarding the pathophysiology of endometriosis. Moreover, the functional characteristics revealed using enrichment analysis provide important clues for discovering endometriosis disease genes in future research.

Urine Metabolite of Mice with Orientia tsutsugamushi Infection.

Scrub typhus is an acute febrile, mite-borne disease endemic to the Asia-Pacific region. In South Korea, it is a seasonal disease that occurs frequently in the autumn, and its incidence has increased steadily. In this study, we used a liquid chromatography and flow injection analysis-tandem mass spectrometry-based targeted urine metabolomics approach to evaluate the host response to Orientia tsutsugamushi infection. Balb/c mice were infected with O. tsutsugamushi Boryong, and their urine metabolite profile was examined. Metabolites that differed significantly between the experimental groups were identified using the Kruskal-Wallis test. Sixty-five differential metabolites were identified. The principal metabolite classes were acylcarnitines, glycerophospholipids, biogenic amines, and amino acids. An ingenuity pathway analysis revealed that several toxic (cardiotoxic, hepatotoxic, and nephrotoxic) metabolites are induced by scrub typhus infection. This is the first report of urinary metabolite biomarkers of scrub typhus infection and it enhances our understanding of the metabolic pathways involved.

Uncovering Oncogenic Mechanisms of Tumor Suppressor Genes in Breast Cancer Multi-Omics Data.

Tumor suppressor genes (TSGs) are essential genes in the development of cancer. While they have many roles in normal cells, mutation and dysregulation of the TSGs result in aberrant molecular processes in cancer cells. Therefore, understanding TSGs and their roles in the oncogenic process is crucial for prevention and treatment of cancer. In this research, multi-omics breast cancer data were used to identify molecular mechanisms of TSGs in breast cancer. Differentially expressed genes and differentially coexpressed genes were identified in four large-scale transcriptomics data from public repositories and multi-omics data analyses of copy number, methylation and gene expression were performed. The results of the analyses were integrated using enrichment analysis and meta-analysis of a p-value summation method. The integrative analysis revealed that TSGs have a significant relationship with genes of gene ontology terms that are related to cell cycle, genome stability, RNA processing and metastasis, indicating the regulatory mechanisms of TSGs on cancer cells. The analysis frame and research results will provide valuable information for the further identification of TSGs in different types of cancers.

Identification of a pleiotropic effect of ADIPOQ on cardiac dysfunction and Alzheimer's disease based on genetic evidence and health care records.

Observations of comorbidity in heart diseases, including cardiac dysfunction (CD) are increasing, including and cognitive impairment, such as Alzheimer's disease and dementia (AD/D). This comorbidity might be due to a pleiotropic effect of genetic variants shared between CD and AD/D. Here, we validated comorbidity of CD and AD/D based on diagnostic records from millions of patients in Korea and the University of California, San Francisco Medical Center (odds ratio 11.5 [8.5-15.5, 95% Confidence Interval (CI)]). By integrating a comprehensive human disease-SNP association database (VARIMED, VARiants Informing MEDicine) and whole-exome sequencing of 50 brains from individuals with and without Alzheimer's disease (AD), we identified missense variants in coding regions including APOB, a known risk factor for CD and AD/D, which potentially have a pleiotropic role in both diseases. Of the identified variants, site-directed mutation of ADIPOQ (268 G > A; Gly90Ser) in neurons produced abnormal aggregation of tau proteins (p = 0.02), suggesting a functional impact for AD/D. The association of CD and ADIPOQ variants was confirmed based on domain deletion in cardiac cells. Using the UK Biobank including data from over 500000 individuals, we examined a pleiotropic effect of the ADIPOQ variant by comparing CD- and AD/D-associated phenotypic evidence, including cardiac hypertrophy and cognitive degeneration. These results indicate that convergence of health care records and genetic evidences may help to dissect the molecular underpinnings of heart disease and associated cognitive impairment, and could potentially serve a prognostic function. Validation of disease-disease associations through health care records and genomic evidence can determine whether health conditions share risk factors based on pleiotropy.

MPI-GWAS: a supercomputing-aided permutation approach for genomewide association studies.

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 107 permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 107 permutations of ~30,000-50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.

A Genome-Wide Association Study of a Korean Population Identifies Genetic Susceptibility to Hypertension Based on Sex-Specific Differences.

Genome-wide association studies have expanded our understanding of the genetic variation of hypertension. Hypertension and blood pressure are influenced by sex-specific differences; therefore, genetic variants may have sex-specific effects on phenotype. To identify the genetic factors influencing the sex-specific differences concerning hypertension, we conducted a heterogeneity analysis of a genome-wide association study (GWAS) on 13,926 samples from a Korean population. Using the Illumina exome chip data of the population, we performed GWASs of the male and female population independently and applied a statistical test that identified heterogeneous effects of the variants between the two groups. To gain information about the biological implication of the genetic heterogeneity, we used gene set enrichment analysis with GWAS catalog and pathway gene sets. The heterogeneity analysis revealed that the rs11066015 of ACAD10 was a significant locus that had sex-specific genetic effects on the development of hypertension. The rs2074356 of HECTD4 also showed significant genetic heterogeneity in systolic blood pressure. The enrichment analysis showed significant results that are consistent with the pathophysiology of hypertension. These results indicate a sex-specific genetic susceptibility to hypertension that should be considered in future genetic studies of hypertension.

Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus.

Background: Most loci associated with type 2 diabetes mellitus (T2DM) discovered to date are within noncoding regions of unknown functional significance. By contrast, exonic regions have advantages for biological interpretation. Methods: We analyzed the association of exome array data from 14,026 Koreans to identify susceptible exonic loci for T2DM. We used genotype information of 50,543 variants using the Illumina exome array platform. Results: In total, 7 loci were significant with a Bonferroni adjusted P=1.03×10-6. rs2233580 in paired box gene 4 (PAX4) showed the highest odds ratio of 1.48 (P=1.60×10-10). rs11960799 in membrane associated ring-CH-type finger 3 (MARCH3) and rs75680863 in transcobalamin 2 (TCN2) were newly identified loci. When we built a model to predict the incidence of diabetes with the 7 loci and clinical variables, area under the curve (AUC) of the model improved significantly (AUC=0.72, P<0.05), but marginally in its magnitude, compared with the model using clinical variables (AUC=0.71, P<0.05). When we divided the entire population into three groups-normal body mass index (BMI; <25 kg/m2), overweight (25≤ BMI <30 kg/m2), and obese (BMI ≥30 kg/m2) individuals-the predictive performance of the 7 loci was greatest in the group of obese individuals, where the net reclassification improvement was highly significant (0.51; P=8.00×10-5). Conclusion: We found exonic loci having a susceptibility for T2DM. We found that such genetic information is advantageous for predicting T2DM in a subgroup of obese individuals.

Set-Wise Differential Interaction Between Copy Number Alterations and Gene Expressions of Lower-Grade Glioma Reveals Prognosis-Associated Pathways.

The integrative analysis of copy number alteration (CNA) and gene expression (GE) is an essential part of cancer research considering the impact of CNAs on cancer progression and prognosis. In this research, an integrative analysis was performed with generalized differentially coexpressed gene sets (gdCoxS), which is a modification of dCoxS. In gdCoxS, set-wise interaction is measured using the correlation of sample-wise distances with Renyi's relative entropy, which requires an estimation of sample density based on omics profiles. To capture correlations between the variables, multivariate density estimation with covariance was applied. In the simulation study, the power of gdCoxS outperformed dCoxS that did not use the correlations in the density estimation explicitly. In the analysis of the lower-grade glioma of the cancer genome atlas program (TCGA-LGG) data, the gdCoxS identified 577 pathway CNAs and GEs pairs that showed significant changes of interaction between the survival and non-survival group, while other benchmark methods detected lower numbers of such pathways. The biological implications of the significant pathways were well consistent with previous reports of the TCGA-LGG. Taken together, the gdCoxS is a useful method for an integrative analysis of CNAs and GEs.

KRGDB: the large-scale variant database of 1722 Koreans based on whole genome sequencing.

Since 2012, the Center for Genome Science of the Korea National Institute of Health (KNIH) has been sequencing complete genomes of 1722 Korean individuals. As a result, more than 32 million variant sites have been identified, and a large proportion of the variant sites have been detected for the first time. In this article, we describe the Korean Reference Genome Database (KRGDB) and its genome browser. The current version of our database contains both single nucleotide and short insertion/deletion variants. The DNA samples were obtained from four different origins and sequenced in different sequencing depths (10× coverage of 63 individuals, 20× coverage of 194 individuals, combined 10× and 20× coverage of 135 individuals, 30× coverage of 230 individuals and 30× coverage of 1100 individuals). The major features of the KRGDB are that it contains information on the Korean genomic variant frequency, frequency difference between the Korean and other populations and the variant functional annotation (such as regulatory elements in ENCODE regions and coding variant functions) of the variant sites. Additionally, we performed the genome-wide association study (GWAS) between Korean genome variant sites for the 30×230 individuals and three major common diseases (diabetes, hypertension and metabolic syndrome). The association results are displayed on our browser. The KRGDB uses the MySQL database and Apache-Tomcat web server adopted with Java Server Page (JSP) and is freely available at http://coda.nih.go.kr/coda/KRGDB/index.jsp. Availability: http://coda.nih.go.kr/coda/KRGDB/index.jsp.

Quantitative Evaluation of Liver Fibrosis on T1 Relaxometry in Comparison with Fibroscan.

Purpose: This study was performed to determine whether the T1 relaxation time of gadoxetic acid-enhanced liver MR imaging is useful for detecting and staging liver fibrosis in patients with chronic liver disease. Materials and Methods: One hundred and three patients with suspected focal liver lesion underwent MR imaging and Fibroscan. Fibroscan was chosen as the reference standard for classifying liver fibrosis. T1 relaxation times were acquired before (preT1), 20 minutes after (postT1) contrast administration, and reduction rate of T1 relaxation time (rrT1) on transverse 3D VIBE (volumetric interpolated breath-hold examination) sequence using 3T MR imaging. The optimal cut-off values for the fibrosis staging were determined with ROC analysis. Results: PreT1 and postT1 increased and rrT1 decreased constantly with increasing severity of liver fibrosis according to the METAVIR score (F0-F4). There were statistically significant differences between F2 and F3 in preT1 (F2, 836.0 ± 74.7 ms; F3, 888.6 ± 77.5 ms, p < 0.05) and between F3 and F4 in postT1 (F3, 309.0 ± 80.2 ms; F4, 406.6 ± 147.7 ms, p < 0.05) and rrT1 (F3, 65.4 ± 7.7%; F4, 57.3 ± 11.4%, p < 0.05). ROC analysis revealed that combination test (preT1 + postT1) was the best test for predicting liver fibrosis. Conclusion: PreT1 and postT1 increased constantly with increasing severity of liver fibrosis. T1 mapping in gadoxetic acid-enhanced liver MR imaging could be a helpful complementary sequence to determine the liver fibrosis stage.

Chronic heavy alcohol consumption influences the association between genetic variants of GCK or INSR and the development of diabetes in men: A 12-year follow-up study.

Chronic heavy alcohol consumption is a risk factor for diabetes, which is characterized by impaired ß-cell function and insulin resistance. We aimed to determine whether the longitudinal associations between genetic variants of glucokinase (GCK) and insulin receptor (INSR) and the risk of developing diabetes were influenced by chronic heavy alcohol consumption. Data were obtained from the Korean Genome and Epidemiology Study. To identify candidate variants, 1,520 subjects (726 non-drinkers and 794 heavy drinkers) were included in the baseline cross-sectional study. After excluding patients with diabetes at baseline and those with insufficient data on diabetes incidence, prospective analyses were conducted in 773 subjects (353 non-drinkers and 420 heavy drinkers). In the baseline cross-sectional study, one SNP (rs758989) in GCK and four SNPs (rs7245757, rs1035942, rs1035940, and rs2042901) in INSR were selected as candidate SNPs that interact with alcohol to affect prediabetes and diabetes. We identified that these GCK and INSR polymorphisms are affected by chronic heavy alcohol consumption and have an effect on the incidence of diabetes. The incidence of diabetes was increased in chronic heavy alcohol drinkers carrying the C allele of GCK compared with never-drinkers with the C allele (HR, 2.15; 95% CI 1.30-3.57), and was increased in chronic heavy alcohol drinkers who were not carrying the INSR haplotype (-/-) compared with never-drinkers carrying the AACT haplotype (HR, 1.98; 95% CI 1.24-3.18). Moreover, we observed that the aggravating effects on the late insulin secretion (I/G120 and I/G AUC 60-120) in individuals who were chronic heavy drinkers with C allele of GCK. In the INSR haplotype, chronic heavy drinkers not carrying AACT were associated with lower disposition index. These results potentially suggest that chronic heavy alcohol consumption induce ß-cell dysfunction partially mediated by decreased GCK expression or decline of insulin sensitivity via inhibition of INSR, thereby contributing to the development of diabetes.

Tracing diagnosis trajectories over millions of patients reveal an unexpected risk in schizophrenia.

The identification of novel disease associations using big-data for patient care has had limited success. In this study, we created a longitudinal disease network of traced readmissions (disease trajectories), merging data from over 10.4 million inpatients through the Healthcare Cost and Utilization Project, which allowed the representation of disease progression mapping over 300 diseases. From these disease trajectories, we discovered an interesting association between schizophrenia and rhabdomyolysis, a rare muscle disease (incidence < 1E-04) (relative risk, 2.21 [1.80-2.71, confidence interval = 0.95], P-value 9.54E-15). We validated this association by using independent electronic medical records from over 830,000 patients at the University of California, San Francisco (UCSF) medical center. A case review of 29 rhabdomyolysis incidents in schizophrenia patients at UCSF demonstrated that 62% are idiopathic, without the use of any drug known to lead to this adverse event, suggesting a warning to physicians to watch for this unexpected risk of schizophrenia. Large-scale analysis of disease trajectories can help physicians understand potential sequential events in their patients.

Identification of novel population clusters with different susceptibilities to type 2 diabetes and their impact on the prediction of diabetes.

Type 2 diabetes is one of the subtypes of diabetes. However, previous studies have revealed its heterogeneous features. Here, we hypothesized that there would be heterogeneity in its development, resulting in higher susceptibility in some populations. We performed risk-factor based clustering (RFC), which is a hierarchical clustering of the population with profiles of five known risk factors for type 2 diabetes (age, gender, body mass index, hypertension, and family history of diabetes). The RFC identified six population clusters with significantly different prevalence rates of type 2 diabetes in the discovery data (N = 10,023), ranging from 0.09 to 0.44 (Chi-square test, P < 0.001). The machine learning method identified six clusters in the validation data (N = 215,083), which also showed the heterogeneity of prevalence between the clusters (P < 0.001). In addition to the prevalence of type 2 diabetes, the clusters showed different clinical features including biochemical profiles and prediction performance with the risk factors. SOur results seem to implicate a heterogeneous mechanism in the development of type 2 diabetes. These results will provide new insights for the development of more precise management strategy for type 2 diabetes.

Asymmetric dimethylarginine (ADMA) is identified as a potential biomarker of insulin resistance in skeletal muscle.

To unravel metabolic determinats of insulin resistance, we performed a targeted metabolomics analysis in Korean Children-Adolescent Cohort Study (KoCAS, n = 430). Sixty-seven metabolites were associated with insulin resistance in adolescents and the association also found in an adult population (KoGES, n = 2,485). Functional interactions of metabolites with gene/proteins using biological pathway with insulin resistance were not identified biological significance and regulatory effects of asymmetric dimethylarginine (ADMA). However, ADMA showed a higher association with adolescent obesity (P < 0.001) and adult diabetes (P = 0.007) and decreased after obesity intervention program. Functional studies in cellular and mouse models demonstrated that an accumulation of ADMA is associated with the regulation of obesity-induced insulin resistance in skeletal muscle. ADMA treatment inhibited dimethylarginine-dimethylaminohydrolase (DDAH) activity and mRNA expression in insulin resistance muscle cell. Moreover, the treatment led to decrease of phosphorylation of insulin receptor (IR), AKT, and GLUT4 but increase of protein-tyrosine phosphatase 1B (PTP1B). Accordingly, increased ADMA significantly inhibited glucose uptake in myotube cell. We suggest that accumulation of ADMA is associated with modulation of insulin signaling and insulin resistance. ADMA might expand the possibilities of new therapeutic target for functional and clinical implications in the control of energy and metabolic homeostasis in humans.

COEX-Seq: Convert a Variety of Measurements of Gene Expression in RNA-Seq.

Next generation sequencing (NGS), a high-throughput DNA sequencing technology, is widely used for molecular biological studies. In NGS, RNA-sequencing (RNA-Seq), which is a short-read massively parallel sequencing, is a major quantitative transcriptome tool for different transcriptome studies. To utilize the RNA-Seq data, various quantification and analysis methods have been developed to solve specific research goals, including identification of differentially expressed genes and detection of novel transcripts. Because of the accumulation of RNA-Seq data in the public databases, there is a demand for integrative analysis. However, the available RNA-Seq data are stored in different formats such as read count, transcripts per million, and fragments per kilobase million. This hinders the integrative analysis of the RNA-Seq data. To solve this problem, we have developed a web-based application using Shiny, COEX-seq (Convert a Variety of Measurements of Gene Expression in RNA-Seq) that easily converts data in a variety of measurement formats of gene expression used in most bioinformatic tools for RNA-Seq. It provides a workflow that includes loading data set, selecting measurement formats of gene expression, and identifying gene names. COEX-seq is freely available for academic purposes and can be run on Windows, Mac OS, and Linux operating systems. Source code, sample data sets, and supplementary documentation are available as well.

Comorbidity of gynecological and non-gynecological diseases with adenomyosis and endometriosis.

OBJECTIVE: Adenomyosis and endometriosis are relatively common gynecological diseases that exhibit many common features. This study identified gynecological and non-gynecological diseases that exhibited comorbidity with adenomyosis and endometriosis in Korean women. METHODS: We used Health Insurance Review and Assessment data from 2009 to 2011 and searched for adenomyosis and endometriosis (coded as N80.1 and D25 in International Classification of Disease, 10th revision [ICD-10], respectively). We selected records from patients who had independent disease occurrences in each year, and comorbidities were estimated using Fisher's exact test. We computed each year's similarities and combined 3 years' results using Fisher's P-value summation method. RESULTS: A total of 61,516 patients' data were collected during the study period. The prevalence of adenomyosis and endometriosis were similar each year: 12.4% and 9.3% in 2009, 12.5% and 9.4% in 2010 and 13.3% and 9.1% in 2011, respectively. Meta-analysis revealed that 31 ICD-10 codes were significantly related with adenomyosis, and 44 ICD-10 codes were related with endometriosis. Gynecological diseases, such as leiomyoma and benign ovarian tumor, were significantly related to adenomyosis and endometriosis. Non-gynecological diseases, such as anemia and hypercholesterolemia, were also related to adenomyosis and endometriosis. CONCLUSION: We must monitor for the presence of gynecological and non-gynecological diseases with co-morbidities during evaluations and follow-up of patients with adenomyosis or endometriosis.

Mitochondrial DNA copy number augments performance of A1C and oral glucose tolerance testing in the prediction of type 2 diabetes.

Here, we tested the performance of the mitochondrial DNA copy number (mtDNA-CN) in predicting future type 2 diabetes (n = 1108). We used the baseline clinical data (age, sex, body mass index, waist-to-hip ratio, systolic and diastolic blood pressure) and the mtDNA-CN, hemoglobin A1c (A1C) levels and results of oral glucose tolerance test (OGTT) including fasting plasma glucose, 1-hour glucose, and 2-hour glucose levels, to predict future diabetes. We built a prediction model using the baseline data and the diabetes status at biannual follow-up of 8 years. The mean area under curve (AUC) for all follow-ups of the full model including all variables was 0.92 ± 0.04 (mean ± standard deviation), while that of the model excluding the mtDNA-CN was 0.90 ± 0.03. The sensitivity of the f4ull model was much greater than that of the model not including mtDNA-CN: the mean sensitivities of the model with and without mtDNA-CN were 0.60 ± 0.06 and 0.53 ± 0.04, respectively. We found that the mtDNA-CN of peripheral leukocytes is a biomarker that augments the predictive power for future diabetes of A1C and OGTT. We believe that these results could provide invaluable information for developing strategies for the management of diabetes.

HIA: a genome mapper using hybrid index-based sequence alignment.

BACKGROUND: A number of alignment tools have been developed to align sequencing reads to the human reference genome. The scale of information from next-generation sequencing (NGS) experiments, however, is increasing rapidly. Recent studies based on NGS technology have routinely produced exome or whole-genome sequences from several hundreds or thousands of samples. To accommodate the increasing need of analyzing very large NGS data sets, it is necessary to develop faster, more sensitive and accurate mapping tools. RESULTS: HIA uses two indices, a hash table index and a suffix array index. The hash table performs direct lookup of a q-gram, and the suffix array performs very fast lookup of variable-length strings by exploiting binary search. We observed that combining hash table and suffix array (hybrid index) is much faster than the suffix array method for finding a substring in the reference sequence. Here, we defined the matching region (MR) is a longest common substring between a reference and a read. And, we also defined the candidate alignment regions (CARs) as a list of MRs that is close to each other. The hybrid index is used to find candidate alignment regions (CARs) between a reference and a read. We found that aligning only the unmatched regions in the CAR is much faster than aligning the whole CAR. In benchmark analysis, HIA outperformed in mapping speed compared with the other aligners, without significant loss of mapping accuracy. CONCLUSIONS: Our experiments show that the hybrid of hash table and suffix array is useful in terms of speed for mapping NGS sequencing reads to the human reference genome sequence. In conclusion, our tool is appropriate for aligning massive data sets generated by NGS sequencing.