The Tarsius gamma-globin gene: pseudogene or active gene?

We sequenced the approximately 5-kb long gamma-globin gene locus from Tarsius bancanus and compared it to the published gamma-globin gene sequence from the related species Tarsius syrichta. The T. syrichta gene's promoter lacks the distal CCAAT box and has a point mutation in the functionally important proximal CCAAT box (CCgAT). This previous finding had suggested that in tarsiers the gamma-globin gene might be a nonexpressed pseudogene. The two tarsier species show the same point mutation at the third nucleotide of the proximal CCAAT element and absence of the distal CCAAT element. Nevertheless, our results indicate that in tarsiers the gamma-globin gene is active, since all three coding regions show only synonymous substitutions and a much lower level of divergence than the noncoding regions. This is significantly different from what would be expected for a silent gene evading stabilizing selection. Thus, we hypothesize that the tarsier's gamma-globin gene locus is expressed even with the mutation in the proximal CCAAT box.     

Gene–gene and gene–environment interactions in ulcerative colitis

Genome-wide association studies (GWAS) have identified at least 133 ulcerative colitis (UC) associated loci. The role of genetic factors in clinical practice is not clearly defined. The relevance of genetic variants to disease pathogenesis is still uncertain because of not characterized gene–gene and gene–environment interactions. We examined the predictive value of combining the 133 UC risk loci with genetic interactions in an ongoing inflammatory bowel disease (IBD) GWAS. The Wellcome Trust Case–Control Consortium (WTCCC) IBD GWAS was used as a replication cohort. We applied logic regression (LR), a novel adaptive regression methodology, to search for high-order interactions. Exploratory genotype correlations with UC sub-phenotypes [extent of disease, need of surgery, age of onset, extra-intestinal manifestations and primary sclerosing cholangitis (PSC)] were conducted. The combination of 133 UC loci yielded good UC risk predictability [area under the curve (AUC) of 0.86]. A higher cumulative allele score predicted higher UC risk. Through LR, several lines of evidence for genetic interactions were identified and successfully replicated in the WTCCC cohort. The genetic interactions combined with the gene-smoking interaction significantly improved predictability in the model (AUC, from 0.86 to 0.89, P = 3.26E?05). Explained UC variance increased from 37 to 42 % after adding the interaction terms. A within case analysis found suggested genetic association with PSC. Our study demonstrates that the LR methodology allows the identification and replication of high-order genetic interactions in UC GWAS datasets. UC risk can be predicted by a 133 loci and improved by adding gene–gene and gene–environment interactions.     

The gene genie?

Gene therapy has the potential to cure currently incurable conditions, including some cancers and inherited disorders. It might even be used in the womb to prevent congenital abnormalities. The potential was greeted with great excitement ten years ago, when gene therapy first appeared to be viable, but little progress is perceived. Just how close are we to solving the obstacles in the way of successful gene implantation / replacement?     

A genome-wide screen of gene–gene interactions for rheumatoid arthritis susceptibility

The objective of the study was to identify interacting genes contributing to rheumatoid arthritis (RA) susceptibility and identify SNPs that discriminate between RA patients who were anti-cyclic citrullinated protein positive and healthy controls. We analyzed two independent cohorts from the North American Rheumatoid Arthritis Consortium. A cohort of 908 RA cases and 1,260 controls was used to discover pairwise interactions among SNPs and to identify a set of single nucleotide polymorphisms (SNPs) that predict RA status, and a second cohort of 952 cases and 1,760 controls was used to validate the findings. After adjusting for HLA-shared epitope alleles, we identified and replicated seven SNP pairs within the HLA class II locus with significant interaction effects. We failed to replicate significant pairwise interactions among non-HLA SNPs. The machine learning approach “random forest” applied to a set of SNPs selected from single-SNP and pairwise interaction tests identified 93 SNPs that distinguish RA cases from controls with 70% accuracy. HLA SNPs provide the most classification information, and inclusion of non-HLA SNPs improved classification. While specific gene–gene interactions are difficult to validate using genome-wide SNP data, a stepwise approach combining association and classification methods identifies candidate interacting SNPs that distinguish RA cases from healthy controls.     

Culture–gene coevolution of individualism–collectivism and the serotonin transporter gene

Culture–gene coevolutionary theory posits that cultural values have evolved, are adaptive and influence the social and physical environments under which genetic selection operates. Here, we examined the association between cultural values of individualism–collectivism and allelic frequency of the serotonin transporter functional polymorphism (5-HTTLPR) as well as the role this culture–gene association may play in explaining global variability in prevalence of pathogens and affective disorders. We found evidence that collectivistic cultures were significantly more likely to comprise individuals carrying the short (S) allele of the 5-HTTLPR across 29 nations. Results further show that historical pathogen prevalence predicts cultural variability in individualism–collectivism owing to genetic selection of the S allele. Additionally, cultural values and frequency of S allele carriers negatively predict global prevalence of anxiety and mood disorder. Finally, mediation analyses further indicate that increased frequency of S allele carriers predicted decreased anxiety and mood disorder prevalence owing to increased collectivistic cultural values. Taken together, our findings suggest culture–gene coevolution between allelic frequency of 5-HTTLPR and cultural values of individualism–collectivism and support the notion that cultural values buffer genetically susceptible populations from increased prevalence of affective disorders. Implications of the current findings for understanding culture–gene coevolution of human brain and behaviour as well as how this coevolutionary process may contribute to global variation in pathogen prevalence and epidemiology of affective disorders, such as anxiety and depression, are discussed.     

An ‘instant gene bank’ method for gene cloning by mutant complementation

We describe a new method of gene cloning by complementation of mutant alleles which obviates the need for construction of a gene library in a plasmid vector in vitro and its amplification in Escherichia coli. The method involves simultaneous transformation of mutant strains of the fungus Aspergillus nidulans with (i) fragmented chromosomal DNA from a donor species and (ii) DNA of a plasmid without a selectable marker gene, but with a fungal origin of DNA replication (‘helper plasmid’). Transformant colonies appear as the result of the Joining of chromosomal DNA fragments carrying the wild-type copies of the mutant allele with the helper plasmid. Joining may occur either by ligation (if the helper plasmid is in linear form) or recombination (if it is cccDNA). This event occurs with high efficiency in vivo, and generates an autonomously replicating plasmid cointegrate. Transformants containing Penicillium chrysogenum genomic DNA complementing A. nidulans niaD, nirA and argB mutations have been obtained. While some of these cointegrates were evidently rearranged or consisted only of unaltered replicating plasmid, in other cases plasmids could be recovered into E. coli and were subsequently shown to contain the selected gene. The utility of this “instant gene bank” technique is demonstrated here by the molecular cloning of the P. canescens trpC gene.     

A vitamin D pathway gene–gene interaction affects low-density lipoprotein cholesterol levels

Much evidence suggests an association between vitamin D deficiency and chronic diseases such as obesity and dyslipidemia. Although genetic factors play an important role in the etiology of these diseases, only a few studies have investigated the relationship between vitamin D-related genes and anthropometric and lipid profiles. The aim of this study was to investigate the association of three vitamin D-related genes with anthropometric and lipid parameters in 542 adult individuals. We analyzed the rs2228570 polymorphism in the vitamin D receptor gene (VDR), rs2134095 in the retinoid X receptor gamma gene (RXRG) and rs7041 in the vitamin D-binding protein gene (GC). Polymorphisms were genotyped by TaqMan allelic discrimination. Gene–gene interactions were evaluated by the general linear model. The functionality of the polymorphisms was investigated using the following predictors and databases: SIFT (Sorting Intolerant from Tolerant), PolyPhen-2 (Polymorphism Phenotyping v2) and Human Splicing Finder 3. We identified a significant effect of the interaction between RXRG (rs2134095) and GC (rs7041) on low-density lipoprotein cholesterol (LDL-c) levels (P=.005). Furthermore, our in silico analysis suggested a functional role for both variants in the regulation of the gene products. Our results suggest that the vitamin D-related genes RXRG and GC affect LDL-c levels. These findings are in agreement with other studies that consistently associate vitamin D and lipid profile. Together, our results corroborate the idea that analyzing gene–gene interaction would be helpful to clarify the genetic component of lipid profile.     

Language trees ≠ gene trees

Darwin saw similarities between the evolution of species and the evolution of languages, and it is now widely accepted that similarities between related languages can often be interpreted in terms of a bifurcating descent history (‘phylogenesis’). Such interpretations are supported when the distributions of shared and unshared traits (for example, in terms of etymological roots for elements of basic vocabulary) are analysed using tree-building techniques and found to be well-explained by a phylogenetic model. In this article, we question the demographic assumption which is sometimes made when a tree-building approach has been taken to a set of cultures or languages, namely that the resulting tree is also representative of a bifurcating population history. Using historical census data relating to Gaelic- and English-speaking inhabitants of Sutherland (Highland Scotland), we have explored the dynamics of language death due to language shift, representing the extreme case of lack of congruence between the genetic and the culture–historical processes. Such cases highlight the important role of selective cultural migration (or shifting between branches) in determining the extinction rates of different languages on such trees.     

Cancer gene therapy – state-of-the-art

A number of gene therapy clinical trials are being carried out the world over. Gene therapy is being applied in (I) cancer diseases, involving the largest number of patients, (II) monogenic diseases, (III) infectious diseases, (IV) vascular diseases, (V) autoimmune diseases and others. In the last decade, several strategies of cancer gene therapy have emerged due to a rapid development of gene delivery systems, both viral (recombinant retroviruses, adenoviruses, AAVs, herpes viruses) and nonviral (liposomes, gene guns, electroporation). To date four main strategies of cancer gene therapy have been evaluated in clinical trials: (I) immunogene therapy, (II) suicide gene therapy, (III) antiangiogenic gene therapy, (IV) and administration of tumour suppressor genes.These strategies mostly involve: malignant melanoma, prostate cancer, renal cell cancer, colon cancer, breast and ovarian cancers, lung cancers, neoplastic diseases of the blood and brain tumours.At the Department of Cancer Immunology at the GreatPoland Cancer Center Gene Modified Tumour Vaccine has been tested in malignant melanoma patients for more than six years. Due to encouraging results from phase I and II of clinical trials a phase III was designed and will be started in 2003.     

The Arabidopsis gene ATST4a is?not a typical brassinosteroids catabolic gene

Brassinosteroid (BR) homeostasis is maintained in part by this hormone’s catabolism. The presence of multiple BR-catabolic pathways in Arabidopsis demonstrates the importance of this process in growth and development. Previous biochemical analyses suggest that ATST4a has BR catalytic activity. We have used both overexpression and loss-of-function genetic approaches to further explore the role of ATST4a in Arabidopsis. Up to 1000-fold overexpression of the ATST4a gene did not result in any characteristic BR-deficient phenotypes. In addition, the T-DNA insertion null mutant atst4a1–1 did not display enhanced seedling hypocotyl growth in the presence or absence of the active BR brassinolide when grown in white light. This lack of hallmark characteristics for BR-inacitivion genes suggests that ATST4a encodes an atypical BR catabolic enzyme.     

Natural and orthogonal model for estimating gene–gene interactions applied to cutaneous melanoma

Epistasis, or gene–gene interaction, results from joint effects of genes on a trait; thus, the same alleles of one gene may display different genetic effects in different genetic backgrounds. In this study, we generalized the coding technique of a natural and orthogonal interaction (NOIA) model for association studies along with gene–gene interactions for dichotomous traits and human complex diseases. The NOIA model which has non-correlated estimators for genetic effects is important for estimating influence from multiple loci. We conducted simulations and data analyses to evaluate the performance of the NOIA model. Both simulation and real data analyses revealed that the NOIA statistical model had higher power for detecting main genetic effects and usually had higher power for some interaction effects than the usual model. Although associated genes have been identified for predisposing people to melanoma risk: HERC2 at 15q13.1, MC1R at 16q24.3 and CDKN2A at 9p21.3, no gene–gene interaction study has been fully explored for melanoma. By applying the NOIA statistical model to a genome-wide melanoma dataset, we confirmed the previously identified significantly associated genes and found potential regions at chromosomes 5 and 4 that may interact with the HERC2 and MC1R genes, respectively. Our study not only generalized the orthogonal NOIA model but also provided useful insights for understanding the influence of interactions on melanoma risk.     

Identification of glioblastoma gene prognosis modules based on weighted gene co-expression network analysis

Background

Glioblastoma multiforme, the most prevalent and aggressive brain tumour, has a poor prognosis. The molecular mechanisms underlying gliomagenesis remain poorly understood. Therefore, molecular research, including various markers, is necessary to understand the occurrence and development of glioma.

Method

Weighted gene co-expression network analysis (WGCNA) was performed to construct a gene co-expression network in TCGA glioblastoma samples. Gene ontology (GO) and pathway-enrichment analysis were used to identify significance of gene modules. Cox proportional hazards regression model was used to predict outcome of glioblastoma patients.

Results

We performed weighted gene co-expression network analysis (WGCNA) and identified a gene module (yellow module) related to the survival time of TCGA glioblastoma samples. Then, 228 hub genes were calculated based on gene significance (GS) and module significance (MS). Four genes (OSMR + SOX21?+?MED10?+?PTPRN) were selected to construct a Cox proportional hazards regression model with high accuracy (AUC?=?0.905). The prognostic value of the Cox proportional hazards regression model was also confirmed in GSE16011 dataset (GBM: n?=?156).

Conclusion

We developed a promising mRNA signature for estimating overall survival in glioblastoma patients.

     

An efficiency analysis of high-order combinations of gene–gene interactions using multifactor-dimensionality reduction

Background

Multifactor dimensionality reduction (MDR) is widely used to analyze interactions of genes to determine the complex relationship between diseases and polymorphisms in humans. However, the astronomical number of high-order combinations makes MDR a highly time-consuming process which can be difficult to implement for multiple tests to identify more complex interactions between genes. This study proposes a new framework, named fast MDR (FMDR), which is a greedy search strategy based on the joint effect property.

Results

Six models with different minor allele frequencies (MAFs) and different sample sizes were used to generate the six simulation data sets. A real data set was obtained from the mitochondrial D-loop of chronic dialysis patients. Comparison of results from the simulation data and real data sets showed that FMDR identified significant gene–gene interaction with less computational complexity than the MDR in high-order interaction analysis.

Conclusion

FMDR improves the MDR difficulties associated with the computational loading of high-order SNPs and can be used to evaluate the relative effects of each individual SNP on disease susceptibility. FMDR is freely available at http://bioinfo.kmu.edu.tw/FMDR.rar.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1717-8) contains supplementary material, which is available to authorized users.