Familial Aggregation between the 14th and 21st Century and Type 2 Diabetes Risk in an Isolated Dutch Population

The development of type 2 diabetes results from an interaction of hereditary factors and environmental factors. This study aimed to investigate the contribution of interrelatedness to the risk of developing type 2 diabetes in an isolated Dutch population.A genealogical database from inhabitants living on the former island Urk between the 14th and 21st century was constructed. In a case-control study, effects of interrelatedness and the risk of type 2 diabetes were estimated with Kinship Coefficients (KCs). Relative risks in first, second, and third degree relatives and spouses of inhabitants with type 2 diabetes were compared to matched controls.Patients with type 2 diabetes were more interrelated, expressed by a higher KC compared to controls (7.2 vs. 5.2, p=0.001). First, second and third degree relatives had an increased risk of developing type 2 diabetes. Second degree relatives had a similar risk,1.7 (1.5-2.0) as third degree relatives,1.8 (1.5-2.2). Spouses of patients with diabetes had a 3.4 (2.7-4.4) higher risk of developing type 2 diabetes.Interrelatedness was higher among inhabitants with type 2 diabetes compared to controls. This differences extended beyond the nuclear family, thereby supporting the hypothesis that interrelatedness contributed to the development of type 2 diabetes on Urk. However, the size of this effect was small and the patterns of risk in first, second and third degree relatives suggested that factors other than interrelatedness were the main contributors to the development of type 2 diabetes on Urk

Evidence Based Selection of Housekeeping Genes

For accurate and reliable gene expression analysis, normalization of gene expression data against housekeeping genes (reference or internal control genes) is required. It is known that commonly used housekeeping genes (e.g. ACTB, GAPDH, HPRT1, and B2M) vary considerably under different experimental conditions and therefore their use for normalization is limited. We performed a meta-analysis of 13,629 human gene array samples in order to identify the most stable expressed genes. Here we show novel candidate housekeeping genes (e.g. RPS13, RPL27, RPS20 and OAZ1) with enhanced stability among a multitude of different cell types and varying experimental conditions. None of the commonly used housekeeping genes were present in the top 50 of the most stable expressed genes. In addition, using 2,543 diverse mouse gene array samples we were able to confirm the enhanced stability of the candidate novel housekeeping genes in another mammalian species. Therefore, the identified novel candidate housekeeping genes seem to be the most appropriate choice for normalizing gene expression data

Effectiveness of life skills training on increasing self-esteem of high school students

AbstractObjective This study designed to investigate effectiveness of training life skills on adolescents’ students. Method This study is a pseudo-experimental study which accomplished on 160 students in Karaj city. Subjects of the study selected randomly from list of students in all of the schools of Karaj; then they divided randomly in two groups. Trained counsellors taught the life skills to students of the study group, and 80 reminder subjects assigned as control group. After educating the training program, subjects administered Cooper Smith self-esteem questionnaire (58-items version). Results Findings of the study indicated that life skills training lead to significant increase of self-esteem in study group in contrast to control group subjects. Conclusion Psycho education and mental health programs such as life skills training could cause to increase the necessary skills in students and decline school and educational problems

A New Perspective on Transcriptional System Regulation (TSR): Towards TSR Profiling

It has been hypothesized that the net expression of a gene is determined by the combined effects of various transcriptional system regulators (TSRs). However, characterizing the complexity of regulation of the transcriptome is a major challenge. Principal component analysis on 17,550 heterogeneous human microarray experiments revealed that 50 orthogonal factors (hereafter called TSRs) are able to capture 64% of the variability in expression in a wide range of experimental conditions and tissues. We identified gene clusters controlled in the same direction and show that gene expression can be conceptualized as a process influenced by a fairly limited set of TSRs. Furthermore, TSRs can be linked to biological functions, as we demonstrate a strong relation between TSR-related gene clusters and biological functionality as well as cellular localization, i.e. gene products of similarly regulated genes by a specific TSR are located in identical parts of a cell. Using 3,934 diverse mouse microarray experiments we found striking similarities in transcriptional system regulation between human and mouse. Our results give biological insights into regulation of the cellular transcriptome and provide a tool to characterize expression profiles with highly reliable TSRs instead of thousands of individual genes, leading to a >500-fold reduction of complexity with just 50 TSRs. This might open new avenues for those performing gene expression profiling studies

Survival-Related Profile, Pathways, and Transcription Factors in Ovarian Cancer

Ate van der Zee and colleagues analyze the gene expression profiles of ovarian cancer samples from 157 patients, and identify an 86-gene expression profile that seems to predict overall survival

Unraveling the Regulatory Mechanisms Underlying Tissue-Dependent Genetic Variation of Gene Expression

It is known that genetic variants can affect gene expression, but it is not yet completely clear through what mechanisms genetic variation mediate this expression. We therefore compared the cis-effect of single nucleotide polymorphisms (SNPs) on gene expression between blood samples from 1,240 human subjects and four primary non-blood tissues (liver, subcutaneous, and visceral adipose tissue and skeletal muscle) from 85 subjects. We characterized four different mechanisms for 2,072 probes that show tissue-dependent genetic regulation between blood and non-blood tissues: on average 33.2% only showed cis-regulation in non-blood tissues; 14.5% of the eQTL probes were regulated by different, independent SNPs depending on the tissue of investigation. 47.9% showed a different effect size although they were regulated by the same SNPs. Surprisingly, we observed that 4.4% were regulated by the same SNP but with opposite allelic direction. We show here that SNPs that are located in transcriptional regulatory elements are enriched for tissue-dependent regulation, including SNPs at 3′ and 5′ untranslated regions (P = 1.84×10−5 and 4.7×10−4, respectively) and SNPs that are synonymous-coding (P = 9.9×10−4). SNPs that are associated with complex traits more often exert a tissue-dependent effect on gene expression (P = 2.6×10−10). Our study yields new insights into the genetic basis of tissue-dependent expression and suggests that complex trait associated genetic variants have even more complex regulatory effects than previously anticipated

Profiling studies in ovarian cancer:A review

Ovarian cancer is a heterogeneous disease with respect to histopathology, molecular biology, and clinical outcome. In advanced stages, surgery and chemotherapy result in an approximately 25% overall 5-year survival rate, pointing to a strong need to identify subgroups of patients that may benefit from targeted innovative molecular therapy. This review summarizes: (a) microarray research identifying gene-expression profiles in ovarian cancer; (b) the methodological flaws in the available microarray studies; and (c) applications of pathway analysis to define new molecular subgroups. Microarray technology now permits the analysis of expression levels of thousands of genes. So far seven studies have aimed to identify a genetic profile that can predict survival/clinical outcome and/or response to platinum-based therapy. To date, the clinical evidence of prognostic microarray studies has only reached the level of small retrospective studies, and there are other issues that may explain the nonreproducibility among the reported prognostic profiles, such as overfitting, technical platform differences, and accuracy of measurements. We consider pathway analysis a promising new strategy. The accumulation of small differential expressions within a meaningful molecular regulatory network might lead to a critical threshold level, resulting in ovarian cancer. Microarray technologies have already provided valuable expression data for classifying ovarian cancer and the first clues about which molecular changes in ovarian cancer could be exploited in new treatment strategies. Further improvements in technology as well as in study design, combined with pathway analysis, will allow us to detect even more subtle tumor expression differences among subgroups of ovarian cancer patients. Disclosure of potential conflicts of interest is found at the end of this article