Search

To examine the effects of an educational intervention on patient-reported outcomes and all-cause mortality in heart failure (HF) patients METHODS: In this randomized controlled trial, we enrolled 122 hospitalized patients with HF. The intervention group (n = 60) received an individual nurse-led education session on HF self-management during hospitalization and three telephone calls after discharge. The control group (n = 62) received care as usual. Patient-reported outcomes were measured at baseline and at 3 and 6 months. Mortality status was determined using the National Death Records. Intervention effects were evaluated using the Cox proportional hazards regression model and linear mixed models.During the follow-up (median: 568 days), 7 deaths (12%) in the intervention group and 15 deaths (24%) in the control group occurred (adjusted hazard ratio, 0.40; 95% confidence interval, 0.16-0.98; P = .046). From baseline to 3 and 6 months, the intervention group showed greater improvements in HF knowledge (difference=6.14, P = .03; difference=5.76, P = .02, respectively), self-care (difference=-6.08, P .001; difference=-6.16, P .001, respectively), and health-related quality of life (difference=-11.90, P = .01; difference=-14.57, P = .004, respectively) than the control group.Educational intervention with telephone follow-up reduced all-cause mortality and improved patient-reported outcomes.Educational intervention should be considered as part of routine care for HF patients.

Supplementary Figures 1-5.

Purpose:To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance.Experimental Design:A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls.Results:A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets.Conclusions:This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

Purpose:Pancreatic ductal adenocarcinoma (PDAC) subtypes have been identified using various methodologies. However, it is a challenge to develop classification system applicable to routine clinical evaluation. We aimed to identify risk subgroups based on molecular features and develop a classification model that was more suited for clinical applications.Experimental Design:We collected whole dissected specimens from 225 patients who underwent surgery at Seoul National University Hospital [Seoul, Republic of Korea (South)], between October 2009 and February 2018. Target proteins with potential relevance to tumor progression or prognosis were quantified with robust quality controls. We used hierarchical clustering analysis to identify risk subgroups. A random forest classification model was developed to predict the identified risk subgroups, and the model was validated using transcriptomic datasets from external cohorts (N = 700), with survival analysis.Results:We identified 24 protein features that could classify the four risk subgroups associated with patient outcomes: stable, exocrine-like; activated, and extracellular matrix (ECM) remodeling. The “stable” risk subgroup was characterized by proteins that were associated with differentiation and tumor suppressors. “Exocrine-like” tumors highly expressed pancreatic enzymes. Two high-risk subgroups, “activated” and “ECM remodeling,” were enriched in terms such as cell cycle, angiogenesis, immunocompetence, tumor invasion metastasis, and metabolic reprogramming. The classification model that included these features made prognoses with relative accuracy and precision in multiple cohorts.Conclusions:We proposed PDAC risk subgroups and developed a classification model that may potentially be useful for routine clinical implementations, at the individual level. This clinical system may improve the accuracy of risk prediction and treatment guidelines.See related commentary by Thakur and Singh, p. 3272

Supplementary Materials

Supplementary materials

Supplementary Tables 1-8.

Supplementary table S1 to S14

Purpose: To develop and validate a protein-based, multi-marker panel that provides superior pancreatic ductal adenocarcinoma (PDAC) detection abilities with sufficient diagnostic performance. Experimental Design: A total of 959 plasma samples from patients at multiple medical centers were used. To construct an optimal, diagnostic, multi-marker panel, we applied data preprocessing procedure to biomarker candidates. The multi-marker panel was developed using a training set comprised of 261 PDAC cases and 290 controls. Subsequent evaluations were performed in a validation set comprised of 65 PDAC cases and 72 controls. Further validation was performed in an independent set comprised of 75 PDAC cases and 47 controls. Results: A multi-marker panel containing 14 proteins was developed. The multi-marker panel achieved AUCs of 0.977 and 0.953 for the training set and validation set, respectively. In an independent validation set, the multi-marker panel yielded an AUC of 0.928. The diagnostic performance of the multi-marker panel showed significant improvements compared with carbohydrate antigen (CA) 19-9 alone (training set AUC = 0.977 vs. 0.872, P < 0.001; validation set AUC = 0.953 vs. 0.832, P < 0.01; independent validation set AUC = 0.928 vs. 0.771, P < 0.001). When the multi-marker panel and CA 19-9 were combined, the diagnostic performance of the combined panel was improved for all sets. Conclusions: This multi-marker panel and the combined panel showed statistically significant improvements in diagnostic performance compared with CA 19-9 alone and has the potential to complement CA 19-9 as a diagnostic marker in clinical practice.

OBJECTIVE Older adults living alone face physical, emotional, and social health problems, and prefer to age in place (AIP) in their homes. A community-based integrated model for AIP is needed and few studies have identified its impact on older adults living alone. METHODS This was a non-randomized prospective study. Participants were 877 community-dwelling older adults living alone, aged above 65 years, in S* city in South Korea. The intervention group (n = 331) received a community-based integrated service (CBIS) model based on AIP for six months from October 2019 to April 2020. RESULTS Scores on frailty (β = -0.377, p

DNA methylation is a critical regulatory mechanism implicated in development, learning, memory, and disease in the human brain. Here we have elucidated DNA methylation changes during recent human brain evolution. We demonstrate dynamic evolutionary trajectories of DNA methylation in cell-type and cytosine-context specific manner. Specifically, DNA methylation in non-CG context, namely CH methylation, has increased (hypermethylation) in neuronal gene bodies during human brain evolution, contributing to human-specific down-regulation of genes and co-expression modules. The effects of CH hypermethylation is particularly pronounced in early development and neuronal subtypes. In contrast, DNA methylation in CG context shows pronounced reduction (hypomethylation) in human brains, notably in cis-regulatory regions, leading to upregulation of downstream genes. We show that the majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and harbors strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage since the divergence from the chimpanzee lineage and carries significant genetic risk for schizophrenia. Therefore, recent epigenetic evolution of human cortex has shaped the cellular regulatory landscape and contributed to the increased vulnerability to neuropsychiatric diseases., DNA methylation plays an important role in brain development and function. Here, the authors compare whole-genome methylation in neurons and oligodendrocytes in humans, chimpanzees and macaques to reconstruct evolution of DNA methylation at cell-type level, including in regions associated with schizophrenia heritability.

Background The importance of cell type-specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generate cell type-specific whole-genome methylomes (N = 95) and transcriptomes (N = 89) from neurons and oligodendrocytes obtained from brain tissue of patients with schizophrenia and matched controls. Results The methylomes of the two cell types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation differences between cases and controls are subtle compared to cell type differences, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell type differentially methylated sites, highlighting the significance of cell type-specific epigenetic dysregulation in a complex neuropsychiatric disorder. Conclusions Our results provide novel and comprehensive methylome and transcriptome data from distinct cell populations within patient-derived brain tissues. This data clearly demonstrate that cell type epigenetic-differentiated sites are preferentially targeted by disease-associated epigenetic dysregulation. We further show reduced cell type epigenetic distinction in schizophrenia. Electronic supplementary material The online version of this article (10.1186/s13059-019-1747-7) contains supplementary material, which is available to authorized users.

This longitudinal cohort correlational study aimed to confirm the relation among taekyo or traditional prenatal practice, prenatal depression, postpartum depression, maternal&ndash, fetal interaction, and infant temperament and colic using a prospective design. We recruited 212 women 16&ndash, 20 weeks pregnant from July 2017 to September 2018, they were followed up until six months postpartum. Data from 97 participants were used in the final analysis. We used the Edinburgh Postnatal Depression Scale, Cranley&rsquo, s Maternal&ndash, Fetal Attachment Scale, and What My Baby Is Like as measurement tools. We observed a significant correlation between prenatal maternal depression in the first to third trimesters and 6&ndash, 8 weeks and six months postpartum. In addition, infant temperament at six months old showed a significant negative correlation with prenatal and postpartum depression: the higher the prenatal and postpartum depression level, the more difficult the infant&rsquo, s temperament. Taekyo practice was significantly related to maternal&ndash, fetal attachment (r = 0.45&minus, 0.68, p &lt, 0.001). Difficult infants showed more colic episodes than any other type of infant (&chi, 2 = 18.18, p &lt, 0.001). Prenatal and postnatal maternal depression affected infants&rsquo, temperament and colic episodes. The management of mothers&rsquo, mental health before and after pregnancy is important for infants&rsquo, and mothers&rsquo, health.

Gene&ndash, environment interaction (G&times, E) studies are one of the most important solutions for understanding the &ldquo, missing heritability&rdquo, problem in genome-wide association studies (GWAS). Although many statistical methods have been proposed for detecting and identifying G&times, E, most employ single nucleotide polymorphism (SNP)-level analysis. In this study, we propose a new statistical method, Hierarchical structural CoMponent analysis of gene-based Gene&ndash, Environment interactions (HisCoM-G&times, E). HisCoM-G&times, E is based on the hierarchical structural relationship among all SNPs within a gene, and can accommodate all possible SNP-level effects into a single latent variable, by imposing a ridge penalty, and thus more efficiently takes into account the latent interaction term of G&times, E. The performance of the proposed method was evaluated in simulation studies, and we applied the proposed method to investigate gene&ndash, alcohol intake interactions affecting systolic blood pressure (SBP), using samples from the Korea Associated REsource (KARE) consortium data.

Cell-type specific epigenetic modifications are critical for brain development and neuropsychiatric diseases. Here we elucidate evolutionary origins of neuron- and oligodendrocyte-specific DNA methylation in human prefrontal cortex, and demonstrate dynamic and distinctive changes of CG and CH methylation. We show that the human brain has experienced pronounced reduction of CG methylation during evolution, which significantly contributed to cell-type specific active regulatory regions. On the other hand, a substantial increase of CH methylation occurred during human brain evolution, associated with fine-tuning expression in development and neuronal subtypes. The majority of differential CG methylation between neurons and oligodendrocytes originated before the divergence of hominoids and catarrhine monkeys, and carries strong signal for genetic risk for schizophrenia. Remarkably, a substantial portion of differential CG methylation between neurons and oligodendrocytes emerged in the human lineage and harbors additional genetic risk for schizophrenia, implicating epigenetic evolution of human cortex in increased vulnerability to neuropsychiatric diseases.

Shift work nurses experience multiple health problems due to irregular shifts and heavy job demands. However, the comorbidity patterns of nurses&rsquo, health problems and the association between health problems and turnover intention have rarely been studied. This study aimed to identify and cluster shift work nurses&rsquo, health problems and to reveal the associations between health problems and turnover intention. In this cross-sectional study, we analyzed data from 500 nurses who worked at two tertiary hospitals in Seoul, South Korea. Data, including turnover intention and nine types of health issues, were collected between March 2018 and April 2019. Hierarchical clustering and multiple ordinal logistic regressions were used for the data analysis. Among the participants, 22.2% expressed turnover intention and the mean number of health problems was 4.5 (range 0&ndash, 9). Using multiple ordinal logistic regressions analysis, it was shown that sleep disturbance, depression, fatigue, a gastrointestinal disorder, and leg or foot discomfort as a single health problem significantly increased turnover intention. After clustering the health problems, four clusters were identified and only the neuropsychological cluster&mdash, sleep disturbance, fatigue, and depression&mdash, significantly increased turnover intention. We propose that health problems within the neuropsychological cluster must receive close attention and be addressed simultaneously to decrease nurse&rsquo, s turnover intentions.

Big multi-omics data in bioinformatics often consists of a huge number of features and relatively small numbers of samples. In addition, features from multi-omics data have their own specific characteristics depending on whether they are from genomics, proteomics, metabolomics, etc. Due to these distinct characteristics, standard statistical analyses using parametric-based assumptions may sometimes fail to provide exact asymptotic results. To resolve this issue, permutation tests can be a way to exactly analyze multi-omics data because they are distribution-free and flexible to use. In permutation tests, p-values are evaluated by estimating the locations of test statistics in an empirical null distribution generated by random shuffling. However, the permutation approach can be infeasible when the number of features increases, because more stringent control of type I error is needed for multiple hypothesis testing, and consequently, much larger numbers of permutations are required to reach significance. To address this problem, we propose a well-organized strategy, “ENhanced Permutation tests via multiple Pruning (ENPP).” ENPP prunes the features in every permutation round if they are determined to be non-significant. In other words, if the feature statistics from the permuted datasets exceed the feature statistics from the original dataset, beyond a predetermined threshold, the feature is determined to be non-significant. If so, ENPP removes the feature and iterates the process without the feature in the next permutation round. Our simulation study showed that the ENPP method could remove about 50% of the features at the first permutation round, and, by the 100th permutation round, 98% of the features had been removed and only 7.4% of the computation time with the original unpruned permutation approach had elapsed. In addition, we applied this approach to a real data set (Korea Association REsource: KARE) of 327,872 SNPs to find association with a non-normally distributed phenotype (fasting plasma glucose), interpreted the results, and discussed the feasibility and advantages of the approach.

DNA methylation is one of the most extensively studied epigenetic modifications of genomic DNA. In recent years, sequencing of bisulfite-converted DNA, particularly via next-generation sequencing technologies, has become a widely popular method to study DNA methylation. This method can be readily applied to a variety of species, dramatically expanding the scope of DNA methylation studies beyond the traditionally studied human and mouse systems. In parallel to the increasing wealth of genomic methylation profiles, many statistical tools have been developed to detect differentially methylated loci (DMLs) or differentially methylated regions (DMRs) between biological conditions. We discuss and summarize several key properties of currently available tools to detect DMLs and DMRs from sequencing of bisulfite-converted DNA. However, the majority of the statistical tools developed for DML/DMR analyses have been validated using only mammalian data sets, and less priority has been placed on the analyses of invertebrate or plant DNA methylation data. We demonstrate that genomic methylation profiles of non-mammalian species are often highly distinct from those of mammalian species using examples of honey bees and humans. We then discuss how such differences in data properties may affect statistical analyses. Based on these differences, we provide three specific recommendations to improve the power and accuracy of DML and DMR analyses of invertebrate data when using currently available statistical tools. These considerations should facilitate systematic and robust analyses of DNA methylation from diverse species, thus advancing our understanding of DNA methylation.

BACKGROUND Colorectal cancer (CRC) develops through the alteration of several critical pathways. This study was aimed at evaluating the influence of critical pathways on survival outcomes for patients with CRC. METHODS Targeted next-generation sequencing of 40 genes included in the 5 critical pathways of CRC (WNT, P53, RTK-RAS, phosphatidylinositol-4,5-bisphosphate 3-kinase [PI3K], and transforming growth factor β [TGF-β]) was performed for 516 patients with stage III or high-risk stage II CRC treated with surgery followed by adjuvant fluoropyrimidine and oxaliplatin chemotherapy. The associations between critical pathway mutations and relapse-free survival (RFS) and overall survival were analyzed. The associations were further analyzed according to the tumor location. RESULTS The mutation rates for the WNT, P53, RTK-RAS, PI3K, and TGF-β pathways were 84.5%, 69.0%, 60.7%, 30.0%, and 28.9%, respectively. A mutation in the PI3K pathway was associated with longer RFS (adjusted hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.36-0.99), whereas a mutation in the RTK-RAS pathway was associated with shorter RFS (adjusted HR, 1.60; 95% CI, 1.01-2.52). Proximal tumors showed a higher mutation rate than distal tumors, and the mutation profile was different according to the tumor location. The mutation rates of Kirsten rat sarcoma viral oncogene homolog (KRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA), and B-Raf proto-oncogene serine/threonine kinase (BRAF) were higher in proximal tumors, and the mutation rates of adenomatous polyposis coli (APC), tumor protein 53 (TP53), and neuroblastoma RAS viral oncogene homolog (NRAS) were higher in distal tumors. The better RFS with the PI3K pathway mutation was significant only for proximal tumors, and the worse RFS with the RTK-RAS pathway mutation was significant only for distal tumors. CONCLUSIONS A PI3K pathway mutation was associated with better RFS for CRC patients treated with adjuvant chemotherapy, and an RTK-RAS pathway mutation was associated with worse RFS. The significance of the prognostic impact differed according to the tumor location. Cancer 2017;123:3513-23. © 2017 American Cancer Society.

// Hae Ryung Chang 1, 2, * , Seungyoon Nam 1, 3, * , Jinhyuk Lee 4, 5, * , Jin-Hee Kim 6, * , Hae Rim Jung 1 , Hee Seo Park 7 , Sungjin Park 1, 3 , Young Zoo Ahn 1 , Iksoo Huh 8 , Curt Balch 9 , Ja-Lok Ku 10 , Garth Powis 11 , Taesung Park 8 , Jin-Hyun Jeong 6 , Yon Hui Kim 1, 12 1 New Experimental Therapeutics Branch, National Cancer Center of Korea, Goyang-si, Republic of Korea 2 Research Institute of Women’s Health, Sookmyung Women’s University, Seoul, Republic of Korea 3 College of Medicine, Gachon University, Incheon, Republic of Korea 4 Korean Bioinformation Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, Republic of Korea 5 Department of Nanobiotechnology and Bioinformatics, Korea University of Science and Technology, Daejeon, Republic of Korea 6 College of Pharmacy, Yonsei Institute of Pharmaceutical Sciences, Yonsei University, Incheon, Republic of Korea 7 Animal Sciences Branch, National Cancer Center of Korea, Goyang-si, Republic of Korea 8 Department of Statistics, Seoul National University, Seoul, Republic of Korea 9 Bioscience Advising, Ypsilanti, MI, USA 10 Korean Cell Line Bank, Seoul National University, Seoul, Republic of Korea 11 Cancer Center, Sanford-Burnham Prebys Medical Discovery Institute, La Jolla, CA, USA 12 Discovery Biology, CrystalGenomics Inc., Seongnam-si, Republic of Korea * These authors have contributed equally to this work Correspondence to: Yon Hui Kim, email: yhkim@cgxinc.com Jin-Hyun Jeong, email: organicjeong@yonsei.ac.kr Taesung Park, email: tspark@stats.snu.ac.kr Keywords: gastric cancer, RHOA, G-protein, biomarker, therapeutic target Received: July 11, 2016 Accepted: October 17, 2016 Published: October 28, 2016 ABSTRACT Gastric cancer (GC) is a highly heterogeneous disease, in dire need of specific, biomarker-driven cancer therapies. While the accumulation of cancer “Big Data” has propelled the search for novel molecular targets for GC, its specific subpathway and cellular functions vary from patient to patient. In particular, mutations in the small GTPase gene RHOA have been identified in recent genome-wide sequencing of GC tumors. Moreover, protein overexpression of RHOA was reported in Chinese populations, while RHOA mutations were found in Caucasian GC tumors. To develop evidence-based precision medicine for heterogeneous cancers, we established a systematic approach to integrate transcriptomic and genomic data. Predicted signaling subpathways were then laboratory-validated both in vitro and in vivo , resulting in the identification of new candidate therapeutic targets. Here, we show: i) differences in RHOA expression patterns, and its pathway activity, between Asian and Caucasian GC tumors; ii) in vitro and in vivo perturbed RHOA expression inhibits GC cell growth in high RHOA-expressing cell lines; iii) inverse correlation between RHOA and RHOB expression; and iv) an innovative small molecule design strategy for RHOA inhibitors. In summary, RHOA, and its oncogenic signaling pathway, represent a strong biomarker-driven therapeutic target for Asian GC. This comprehensive strategy represents a promising approach for the development of “hit” compounds.

The importance of cell-type specific epigenetic variation of non-coding regions in neuropsychiatric disorders is increasingly appreciated, yet data from disease brains are conspicuously lacking. We generated cell-type specific whole-genome methylomes (N=95) and transcriptomes (N=89) from neurons and oligodendrocytes from brains of schizophrenia and matched controls. The methylomes of these two cell-types are highly distinct, with the majority of differential DNA methylation occurring in non-coding regions. DNA methylation difference between control and schizophrenia brains is subtle compared to cell-type difference, yet robust against permuted data and validated in targeted deep-sequencing analyses. Differential DNA methylation between control and schizophrenia tends to occur in cell-type differentially methylated sites, highlighting the significance of cell-type specific epigenetic dysregulation in a complex neuropsychiatric disorder. Our resource provides novel and comprehensive methylome and transcriptome data from distinct cell populations from schizophrenia brains, further revealing reduced cell-type epigenetic distinction in schizophrenia.

Supplementary Figures S1â S20. (PDF 1643 kb)

The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.

In a crossover design, individuals usually undergo all experimental conditions, and the measurements of biomarkers are repeatedly observed at serial time points for each experimental condition. To analyse time-dependent changing patterns of biomarkers, clustering algorithms are commonly used across time points to group together subjects having similar changing patterns. Among the clustering methods, hierarchical- and K-means clustering have been popularly used. However, since they are originally unsupervised approaches, they do not identify different changing patterns between experimental conditions. Therefore, we propose a new two-stage clustering method focusing on changing patterns. The first stage is to eliminate non-informative biomarkers using Euclidean distances, and the second stage is to allocate the remaining biomarkers to predefined patterns using a correlation-based distance. We demonstrate the advantages of our proposed method by simulation and real data analysis.

Significance The evolution of nonrecombining chromosomes such as sex chromosomes involves degeneration leading to loss of genetic information. We do not know, however, what happens during the incipient stages of such chromosomes, before appreciable degeneration. We studied this process in white-throated sparrows, a species that occurs in two alternative behavioral phenotypes determined by a nonrecombining autosomal rearrangement. We report that this rearrangement shows few signs of large-scale genetic degeneration. Instead, substantial changes have evolved at the level of gene expression, some of them consistent with adaptive evolution. Our work with this chromosome reveals that rapid changes in gene expression and dosage compensation, not necessarily large-scale genetic degeneration, characterize the early evolution of heteromorphic chromosomes and the associated divergent phenotypes., In the white-throated sparrow (Zonotrichia albicollis), the second chromosome bears a striking resemblance to sex chromosomes. First, within each breeding pair of birds, one bird is homozygous for the standard arrangement of the chromosome (ZAL2/ZAL2) and its mate is heterozygous for a different version (ZAL2/ZAL2m). Second, recombination is profoundly suppressed between the two versions, leading to genetic differentiation between them. Third, the ZAL2m version is linked with phenotypic traits, such as bright plumage, high aggression, and low parental behavior, which are usually associated with males. These similarities to sex chromosomes suggest that the evolutionary mechanisms that shape sex chromosomes, in particular genetic degeneration of the heterogametic version due to the suppression of recombination, are likely important in this system as well. Here, we investigated patterns of protein sequence evolution and gene expression evolution between the ZAL2 and ZAL2m chromosomes by whole-genome sequencing and transcriptome analyses. Patterns of protein evolution exhibited only weak signals of genetic degeneration, and few genes harbored signatures of positive selection. We found substantial evidence of transcriptome evolution, such as significant expression divergence between ZAL2 and ZAL2m alleles and signatures of dosage compensation for highly expressed genes. These results suggest that, early in the evolution of heteromorphic chromosomes, gene expression divergence and dosage compensation can prevail before large-scale genetic degeneration. Our results show further that suppression of recombination between heteromorphic chromosomes can lead to the evolution of alternative (sex-like) behavioral phenotypes before substantial genetic degeneration.

We put main results of motifs from the exhaustive search and LASSO approach.

The neuregulin 1 (NRG1) gene has been investigated as a candidate susceptibility gene for schizophrenia. A number of studies have also explored the genetic effect of NRG1 on cognitive deficits related to schizophrenia, and thus far generated inconsistent results. The current study aimed to determine whether genetic variations in NRG1 are associated with cognitive domains in schizophrenic patients and healthy individuals.Comprehensive neuropsychological tests composed of six cognitive domains were administered to 135 clinically stable patients with schizophrenia and 119 healthy individuals. On the basis of previous reports of positive association, a total of four single nucleotide polymorphisms were analyzed. In testing the genotype effect on cognitive domains, we used repeated-measure analysis for six cognitive domain scores of each individual as repeated measurements.An association of P-value less than 0.05 with at least one cognitive domain in patients and/or healthy individuals was observed for all of the single nucleotide polymorphisms. After applying the correction for multiple testing, the association remained statistically significant between rs6994992 and general cognitive ability (g) in the patient group and between rs2439272 and the 'working memory' domain in the group of healthy participants.This study suggests the involvement of NRG1 in the susceptibility for developing cognitive deficits in schizophrenic patients. For some cognitive domains, its genetic effect was also significant in generating interindividual variability within the normal functional range.

Seasonality is one of the key features in subjects with mood disorders and is involved in the multi-faceted nature of the clinical course. However, few studies have explored the clinical implications of seasonality in bipolar disorders. We examined the differential effects of seasonality on clinical variables between bipolar I and II disorder (BD I and II). Seasonality was assessed using the Seasonal Pattern Assessment Questionnaire (SPAQ) in 204 subjects with BD I and 308 with BD II. Following the comparisons between BD I and II groups, clinical characteristics related to seasonality were explored. Next, to predict the presence of seasonality, a logistic regression model was applied. The global seasonality score on the SPAQ was significantly higher in the BD II group than in the BD I group. In the BD I group, seasonality was associated with suicide attempt history. In the BD II group, on the other hand, seasonality was associated with female gender, depressive predominance, and premenstrual dysphoric disorder (PMDD). In the regression models, the presence of PMDD and female gender was significantly associated with seasonality in the BD II group. Our findings suggest that high seasonality tendency, a vulnerability maker for cyclic worsening, may contribute to a differential pattern of clinical characteristics in BD II.

Background Worldwide, gastric cancer (GC) is the fourth most common malignancy and the most common cancer in East Asia. Development of targeted therapies for this disease has focused on a few known oncogenes but has had limited effects. Objective To determine oncogenic mechanisms and novel therapeutic targets specific for GC by identifying commonly dysregulated genes from the tumours of both Asian-Pacific and Caucasian patients. Methods We generated transcriptomic profiles of 22 Caucasian GC tumours and their matched non-cancerous samples and performed an integrative analysis across different GC gene expression datasets. We examined the inhibition of commonly overexpressed oncogenes and their constituent signalling pathways by RNAi and/or pharmacological inhibition. Results Hepatocyte nuclear factor-4α (HNF4α) upregulation was a key signalling event in gastric tumours from both Caucasian and Asian patients, and HNF4α antagonism was antineoplastic. Perturbation experiments in GC tumour cell lines and xenograft models further demonstrated that HNF4α is downregulated by AMPKα signalling and the AMPK agonist metformin; blockade of HNF4α activity resulted in cyclin downregulation, cell cycle arrest and tumour growth inhibition. HNF4α also regulated WNT signalling through its target gene WNT5A, a potential prognostic marker of diffuse type gastric tumours. Conclusions Our results indicate that HNF4α is a targetable oncoprotein in GC, is regulated by AMPK signalling through AMPKα and resides upstream of WNT signalling. HNF4α may regulate ‘metabolic switch’ characteristic of a general malignant phenotype and its target WNT5A has potential prognostic values. The AMPKα-HNF4α-WNT5A signalling cascade represents a potentially targetable pathway for drug development.

Various statistical approaches can be applied to integrate traditional and omics biomarkers, allowing the discovery of prognostic markers to classify subjects into poor and good prognosis groups in terms of responses to nutritional interventions. Here, we performed a prototype study to identify metabolites that predict responses to an intervention against oxidative stress and inflammation, using a data set from a randomized controlled trial evaluating Korean black raspberry (KBR) in sedentary overweight/obese subjects. First, a linear mixed-effects model analysis with multiple testing correction showed that four-week consumption of KBR significantly changed oxidized glutathione (GSSG, q = 0.027) level, the ratio of reduced glutathione (GSH) to GSSG (q = 0.039) in erythrocytes, malondialdehyde (MDA, q = 0.006) and interleukin-6 (q = 0.006) levels in plasma, and seventeen NMR metabolites in urine compared with those in the placebo group. A subsequent generalized linear mixed model analysis showed linear correlations between baseline urinary glycine and N-phenylacetylglycine (PAG) and changes in the GSH:GSSG ratio (p = 0.008 and 0.004) as well as between baseline urinary adenine and changes in MDA (p = 0.018). Then, receiver operating characteristic analysis revealed that a two-metabolite set (glycine and PAG) had the strongest prognostic relevance for future interventions against oxidative stress (the area under the curve (AUC) = 0.778). Leave-one-out cross-validation confirmed the accuracy of prediction (AUC = 0.683). The current findings suggest that a higher level of this two-metabolite set at baseline is useful for predicting responders to dietary interventions in subjects with oxidative stress and inflammation, contributing to the emergence of personalized nutrition.

Colorectal cancer (CRC) develops through the alteration of several critical pathways. This study was aimed at evaluating the influence of critical pathways on survival outcomes for patients with CRC.Targeted next-generation sequencing of 40 genes included in the 5 critical pathways of CRC (WNT, P53, RTK-RAS, phosphatidylinositol-4,5-bisphosphate 3-kinase [PI3K], and transforming growth factor β [TGF-β]) was performed for 516 patients with stage III or high-risk stage II CRC treated with surgery followed by adjuvant fluoropyrimidine and oxaliplatin chemotherapy. The associations between critical pathway mutations and relapse-free survival (RFS) and overall survival were analyzed. The associations were further analyzed according to the tumor location.The mutation rates for the WNT, P53, RTK-RAS, PI3K, and TGF-β pathways were 84.5%, 69.0%, 60.7%, 30.0%, and 28.9%, respectively. A mutation in the PI3K pathway was associated with longer RFS (adjusted hazard ratio [HR], 0.59; 95% confidence interval [CI], 0.36-0.99), whereas a mutation in the RTK-RAS pathway was associated with shorter RFS (adjusted HR, 1.60; 95% CI, 1.01-2.52). Proximal tumors showed a higher mutation rate than distal tumors, and the mutation profile was different according to the tumor location. The mutation rates of Kirsten rat sarcoma viral oncogene homolog (KRAS), phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit α (PIK3CA), and B-Raf proto-oncogene serine/threonine kinase (BRAF) were higher in proximal tumors, and the mutation rates of adenomatous polyposis coli (APC), tumor protein 53 (TP53), and neuroblastoma RAS viral oncogene homolog (NRAS) were higher in distal tumors. The better RFS with the PI3K pathway mutation was significant only for proximal tumors, and the worse RFS with the RTK-RAS pathway mutation was significant only for distal tumors.A PI3K pathway mutation was associated with better RFS for CRC patients treated with adjuvant chemotherapy, and an RTK-RAS pathway mutation was associated with worse RFS. The significance of the prognostic impact differed according to the tumor location. Cancer 2017;123:3513-23. © 2017 American Cancer Society.

This study investigated the relationship between nurse staffing and quality of life (QOL) in Western New York State nursing homes. This was a cross-sectional, correlational study. The independent variables were hours per resident day (HPRD), skill mix, and turnover rate of nursing staff. The outcomes were measured using the self-reported QOL instrument. No coefficients were statistically significant with registered nurses’ (RNs) HPRD. Certified nursing assistant (CNA) HPRD had a statistically significant positive impact on the spiritual well-being domain. There was a statistically negative relationship between the amount of licensed practical nurse (LPN) HPRD and food enjoyment; and the ratio of more RNs to fewer LPNs and CNAs had a statistically significant negative influence on the meaningful-activity, food-enjoyment, and security domains. The turnover of RNs had a statistically negative relationship with the sum of each domain. None of the coefficients was statistically significant with LPN turnover.

Cytarabine arabinoside (ara-C) is the key agent for treating acute myeloid leukaemia (AML). Here, we genotyped 139 single nucleotide polymorphisms (SNPs) within the ara-C transport and metabolic pathway using the Illumina Golden Gate Assay in 97 patients with previously non-treated de novo AML other than M3. DCK rs4694362 (CC genotype) was a significant poor prognostic factor for overall survival (OS) (hazard ratio [HR], 33.202 [95% confidence interval (CI), 4.937–223.273], P P Bonferroni =0.017). SLC29A1 rs3734703 (AA or AC genotype) in combination with TYMS rs2612100 (AA genotype) was significantly associated with shorter relapse free survival (RFS) (HR, 17.630 [95% CI, 4.829–64.369], P P Bonferroni =0.021). These SNPs showed moderate or large interethnic divergence in allele frequencies from African or Caucasian populations. The results of our study suggest that a single SNP and SNP–SNP interactions may help to predict the drug response and provide a guide in developing individualised chemotherapy for AML patients receiving ara-C based chemotherapy.

This study aimed to investigate the association of multiple candidate genes with weight gain and appetite change during antipsychotic treatment.A total of 233 single nucleotide polymorphisms (SNPs) within 60 candidate genes were genotyped. BMI changes for up to 8 weeks in 84 schizophrenia patients receiving antipsychotic medication were analyzed using a linear mixed model. In addition, we assessed appetite change during antipsychotic treatment in a different group of 46 schizophrenia patients using the Drug-Related Eating Behavior Questionnaire.No SNP showed a statistically significant association with BMI or appetite change after correction for multiple testing. We observed trends of association (P0.05) between 19 SNPs of 11 genes and weight gain, and between 7 SNPs of 5 genes and appetite change. In particular, rs696217 in GHRL showed suggestive evidence of association with not only weight gain (P=0.001) but also appetite change (P=0.042). Patients carrying the GG genotype of rs696217 exhibited higher increase in both BMI and appetite compared to patients carrying the GT/TT genotype.Our findings suggested the involvement of a GHRL polymorphism in weight gain, which was specifically mediated by appetite change, during antipsychotic treatment in schizophrenia patients.

DNA methylation is one of the most important phenomena in epigenetics. Because the DNA methylation process regulates gene expression without altering the corresponding DNA sequences, it can explain many biological processes such as onset of cancer, aging, and differentiation of species. Therefore, detecting differentially methylated region (DMR) between groups has been considered one of important analyses in epigenetics. For the datasets generated by the methylated DNA immunoprecipitation (MeDIP) technique, DMR analysis has employed the previous methods developed for the microarray-based gene expression dataset. However, for the datasets generated by more recent bisulfite-sequencing (BS-seq) technologies, totally different approaches need to be used. Unfortunately, only a few methods are available for DMR analysis among which the Fisher's exact test is most widely used. We first propose a new DMR test for BS-seq data. Our approach employs Cochran-Mantel-Hantzel (CMH) test and extend it by incorporating correlation structures between adjacent CpG cytosine sites. Then we apply it to a real dataset of two subspecies of honeybee for detecting DMR regions.

There is an increasing interest in the pathway analysis of multiple genes and complex traits in association studies. Recently, a number of methods of pathway analysis have been developed to detect the novel pathways associated with human complex traits. In this paper, we propose a novel statistical approach for competitive pathway analysis based on Structural Equation Modeling (CPA-SEM), taking advantage of prior knowledge on existing relationships between genes in a pathway. Our CPA-SEM identifies pathways associated with traits of interest. The CPA-SEM approach is different from the previous SEM-based approaches in that it considers all possible sub-pathways into account and performs permutation based robust analysis. We applied the proposed CPA-SEM method to gene expression data of gastric cancer (GSE27342), and found that mTOR signaling pathway was significantly associated with gastric cancer. This pathway has previously been reported to be associated with gastric cancer. In conclusion, our CPA-SEM analysis provides a better understanding of biological mechanism by identifying pathways associated with a trait of interest.

Background Findings from family studies and recent genome-wide association studies have indicated overlap in the risk genes between schizophrenia and bipolar disorder (BD). After finding a linkage between the ST8SIA2 (ST8 alpha-N-acetyl-neuraminide alpha-2, 8-sicalyltransferase 2 gene) locus (15q26) and mixed families with schizophrenia and BD, several studies have reported a significant association between this gene and schizophrenia or BD. We investigated the genetic association between ST8SIA2 and both schizophrenia and BD in the Korean population. Methods A total of 582 patients with schizophrenia, 339 patients with BD, and 502 healthy controls were included. Thirty-one tag single nucleotide polymorphisms (SNPs) across the ST8SIA2 region and three other SNPs showing significant associations in previous studies were genotyped. The associations were evaluated by logistic regression analysis using additive, dominant, and recessive genetic models. Results Fourteen of 34 SNPs showed a nominally significant association (p < 0.05) with at least one diagnostic group. These association trends were strongest for the schizophrenia and combined schizophrenia and bipolar I disorder (BD-I) groups. The strongest association was observed in rs11637898 for schizophrenia (p = 0.0033) and BD-I (p = 0.0050) under the dominant model. The association between rs11637898 and the combined schizophrenia and BD-I group (p = 0.0006, under the dominant model) remained significant after correcting for multiple testing. Discussion We identified a possible role of ST8SIA2 in the common susceptibility of schizophrenia and BD-I. However, no association trend was observed for bipolar II disorder. Further efforts are needed to identify a specific phenotype associated with this gene crossing the current diagnostic categories.

This study assessed the relationship between thyroid-stimulating hormone (TSH) level and risk of depressive symptom in a population with no clinical or laboratory evidence of thyroid dysfunction.This retrospective cohort study included 13,017 subjects (7913 males and 5104 females), 17-84 years of age, who underwent health examinations at the hospital. Subjects had a Beck Depression Inventory (BDI) total score of ≤9 and fell within the normal range of free T4 levels at baseline. The association between gender-specific serum TSH tertile at baseline and the development of clinically significant depressive symptom (i.e., ≥19 BDI total score) on the follow-up visit was evaluated using the Cox proportional hazards model, with adjustment for demographic and life style factors.The risk of depressive symptom was increased among subjects with the highest tertile TSH level (adjusted hazard ratio [HR], 2.236; 95% confidence interval [CI], 1.443-3.466; p0.001) as compared with subjects with the lowest tertile in females, but not in males. Even among patients with normal TSH levels, females in the lowest-normal TSH tertile had a higher risk of depressive symptoms (adjusted HR, 2.279; 95% CI, 1.456-3.567; p0.001) than did those in the highest tertile. The TSH level as a continuous variable significantly predicted the depressive symptoms in females (adjusted HR, 1.402; 95% CI, 1.002-1.812; p=0.027).Our finding suggests that suboptimal thyroid function increases vulnerability to the occurrence of depressive symptom and represents a modifiable risk factor for depression in females.

Recent advances in genotyping technology have facilitated the use of genome-wide association studies (GWAS) to successfully identify genetic variants that are associated with common complex traits. Following the successes in identification of single variants, joint identification including gene-gene interaction has been studied vigorously and produced many novel results. However, most genome-wide association studies have been conducted by focusing on one trait of interest for identifying genetic variants associated with common complex traits. Since many complex diseases having severe influences on the public health are pleiotropic, simple univariate analysis focusing on a single trait does not well detect full genetic architecture of complex diseases. For example, hyperlipidemia is diagnosed by four multiple traits: Total cholesterol (Tchl), High density lipoprotein (HDL) cholesterol, Low density lipoprotein (LDL), and cholesterol and Triglycerides (TG). Surprisingly, however, only few studies handle multiple traits simultaneously so far. Therefore, in order to improve power and reflect biological association more expansively, we investigate a multivariate approach which considers multiple traits simultaneously. Especially for the gene-gene interaction analysis for the multiple traits, we extend original multifactor dimensionality reduction (MDR) to handle multiple traits. We then demonstrate its superiority to univariate analysis through simulation studies. We confirm that the multivariate approach provides more stable and precise accuracy measures compared to univariate analysis. We applied the multivariate MDR approach to a GWA dataset of 8,842 Korean individuals and detected genetic variants associated with hypertension traits using systolic blood pressure (SBP) and Diastolic blood pressure (DBP).