Potential regulatory SNPs in promoters of human genes: a systematic approach

Role of a novel functional variant in the PPP2R1A promoter on the regulation of PP2A-Aalpha and the risk of hepatocellular carcinoma

Previously, we identified the genetic variant −241 (−/G) (rs11453459) in the PP2A-Aα gene (PPP2R1A) promoter and demonstrated that this variant influences the DNA-binding affinity of nuclear factor-kappa B (NF-κB). In this study, we further confirmed that the transcriptional activity of PPP2R1A may be regulated by NF-κB through the functional genetic variant −241 (−/G). Moreover, we also demonstrated that the methylation status of CpG islands in the promoter of PPP2R1A influences the activity of this gene promoter. Few studies have examined the role of this −241 (−/G) variant in genetic or epigenetic regulation in hepatocellular carcinoma (HCC). To investigate whether this functional variant in the PPP2R1A promoter is associated with the risk of HCC and confirm the function of the −241 (−/G) variant in the HCC population, we conducted a case-control study involving 251 HCC cases and 252 cancer-free controls from a Han population in southern China. Compared with the −241 (−−) homozygote, the heterozygous −241 (−G) genotype (adjusted OR  = 0.32, 95% confidence interval (CI)  = 0.17–0.58, P<0.001) and the −241 (−G)/(GG) genotypes (adjusted OR  = 0.38, 95% CI  = 0.22–0.67, P  = 0.001) were both significantly associated with a reduced risk of HCC. Stratification analysis indicated that the protective role of −241 (−G) was more pronounced in individuals who were ≤ 40 years of age, female and HBV-negative. Our data suggest that the transcriptional activity of PPP2R1A is regulated by NF-κB through the −241 (−/G) variant and by the methylation of the promoter region. Moreover, the functional −241 (−/G) variant in the PPP2R1A promoter contributes to the decreased risk of HCC. These findings contribute novel information regarding the gene transcription of PPP2R1A regulated by the polymorphism and methylation in the promoter region through genetic and epigenetic mechanisms in hepatocarcinogenesis.

Susceptibility and Resistance to Canine Leishmaniose is Associated to Polymorphisms of the Canine TNF-α Gene

The prevalence of canine leishmaniosis (CanL) infection in an enzootic area is considerably higher than the overall prevalence of the disease, suggesting a role of host genetics related to the outcome of the disease. It is accepted that one determining factor for the outcome of CanL is the type of the triggered immune response, which seems to be genetically determined. TNF-α is a cytokine which plays a crucial role during the immune response against Leishmania parasites. In the present study a case-control study with 20 resistant and 20 susceptible dogs was performed. The distribution of breeds was equal in both groups. By Sanger method the nucleotide sequence upstream the Open Reading Frame of the canine TNF-α gene was determined and four polymorphisms were identified (−40 C/A, −1134 T/G, −1150 T/C κα −1243 C/G). Statistical analysis showed that the polymorphism TNF-α −40 C/A is correlated with susceptibility to CanL, while the polymorphism TNF-α −1243 C/G is correlated with resistance to CanL. Further statistical analysis, regarding the possible correlation of gender as well as clinical manifestations of the disease with the above-mentioned polymorphisms of the TNF-α gene, showed no significant findings. Further analysis of the above polymorphisms, as well as identification of more polymorphisms in candidate genes, is required to provide a better understanding of the complex underlying immune response in CanL.

Detection and functional evaluation of -262A/T and -188A/G polymorphisms of SLAM gene in patients with systemic lupus erythematosus

OBJECTIVE

Signaling lymphocytic activation molecule (SLAM) has been related to the pathology of systemic lupus erythematosus (SLE) through regulation of T cell-dependent humoral immune responses. We investigated the functional associations of the -262A/T and -188A/G polymorphisms of SLAM in Chinese patients with SLE.

METHODS

Genotyping of -262A/T (rs2295614) and -188A/G (rs2295613) in SLAM was carried out in 248 cases and 278 controls. Promoter activities of haplotypes on the SLAM gene were evaluated with the dual-luciferase reporter system. The mRNA expressions of SLAM on peripheral blood mononuclear cells (PBMC) of SLE patients with different genotypes were determined by real-time polymerase chain reaction.

RESULTS

Frequencies of -262A allele and -188G allele were significantly higher in SLE patients than in controls. Haplotype analysis and multifactorial logistic regression analysis showed that individuals with the AG/AG haplotype had increased susceptibility to SLE (p = 0.002, OR 1.478, 95% CI 1.152-1.897). In response to PHA stimulation, the SLAM mRNA expression on PBMC of SLE patients was significantly higher in -262A-188G haplotype homozygotes compared with -262A-188G heterozygotes and individuals with other genotypes.

CONCLUSION

Our findings suggest that -262A-188G haplotype in the SLAM gene promoter contributes to the risk of SLE by increasing the expression of SLAM.

Quantifying the effect of sequence variation on regulatory interactions

The increasing amount of sequence data provides new opportunities and challenges to derive mechanistic models that can link sequence variations to phenotypic diversity. Here we introduce a new computational framework to suggest possible consequences of sequence variations on regulatory networks. Our method, called sTRAP (strap.molgen.mpg.de), analyses variations in the DNA sequence and predicts quantitative changes to the binding strength of any transcription factor for which there is a binding model. We have tested the method against a set of known associations between SNPs and their regulatory consequences. Our predictions are robust with respect to different parameters and model assumptions. Importantly we set an objective and quantifiable benchmark against which future improvements can be compared. Given the good performance of our method, we developed a publicly available tool that can serve as an important starting point for routine analysis of disease-associated sequence regions.

Computational identification of gene-social environment interaction at the human IL6 locus

To identify genetic factors that interact with social environments to impact human health, we used a bioinformatic strategy that couples expression array-based detection of environmentally responsive transcription factors with in silico discovery of regulatory polymorphisms to predict genetic loci that modulate transcriptional responses to stressful environments. Tests of one predicted interaction locus in the human IL6 promoter (SNP rs1800795) verified that it modulates transcriptional response to beta-adrenergic activation of the GATA1 transcription factor in vitro. In vivo validation studies confirmed links between adverse social conditions and increased transcription of GATA1 target genes in primary neural, immune, and cancer cells. Epidemiologic analyses verified the health significance of those molecular interactions by documenting increased 10-year mortality risk associated with late-life depressive symptoms that occurred solely for homozygous carriers of the GATA1-sensitive G allele of rs1800795. Gating of depression-related mortality risk by IL6 genotype pertained only to inflammation-related causes of death and was associated with increased chronic inflammation as indexed by plasma C-reactive protein. Computational modeling of molecular interactions, in vitro biochemical analyses, in vivo animal modeling, and human molecular epidemiologic analyses thus converge in identifying beta-adrenergic activation of GATA1 as a molecular pathway by which social adversity can alter human health risk selectively depending on individual genetic status at the IL6 locus.

Estimates of natural selection due to protein tertiary structure inform the ancestry of biallelic loci

We consider the inference of which of two alleles is ancestral when the alleles have a single nonsynonymous difference and when natural selection acts via protein tertiary structure. Whereas the probability that an allele is ancestral under neutrality is equal to its frequency, under selection this probability depends on allele frequency and on the magnitude and direction of selection pressure. Although allele frequencies can be well estimated from intraspecific data, small fitness differences have a large evolutionary impact but can be difficult to estimate with only intraspecific data. Methods for predicting aspects of phenotype from genotype can supplement intraspecific sequence data. Recently developed statistical techniques can assess effects of phenotypes, such as protein tertiary structure on molecular evolution. While these techniques were initially designed for comparing protein-coding genes from different species, the resulting interspecific inferences can be assigned population genetic interpretations to assess the effect of selection pressure, and we use them here along with intraspecific allele frequency data to estimate the probability that an allele is ancestral. We focus on 140 nonsynonymous single nucleotide polymorphisms of humans that are in proteins with known tertiary structures. We find that our technique for employing protein tertiary structure information yields some biologically plausible results but that it does not substantially improve the inference of ancestral human allele types.

In silico detection of sequence variations modifying transcriptional regulation

Identification of functional genetic variation associated with increased susceptibility to complex diseases can elucidate genes and underlying biochemical mechanisms linked to disease onset and progression. For genes linked to genetic diseases, most identified causal mutations alter an encoded protein sequence. Technological advances for measuring RNA abundance suggest that a significant number of undiscovered causal mutations may alter the regulation of gene transcription. However, it remains a challenge to separate causal genetic variations from linked neutral variations. Here we present an in silico driven approach to identify possible genetic variation in regulatory sequences. The approach combines phylogenetic footprinting and transcription factor binding site prediction to identify variation in candidate cis-regulatory elements. The bioinformatics approach has been tested on a set of SNPs that are reported to have a regulatory function, as well as background SNPs. In the absence of additional information about an analyzed gene, the poor specificity of binding site prediction is prohibitive to its application. However, when additional data is available that can give guidance on which transcription factor is involved in the regulation of the gene, the in silico binding site prediction improves the selection of candidate regulatory polymorphisms for further analyses. The bioinformatics software generated for the analysis has been implemented as a Web-based application system entitled RAVEN (regulatory analysis of variation in enhancers). The RAVEN system is available at http://www.cisreg.ca for all researchers interested in the detection and characterization of regulatory sequence variation.

DNA sequence variations (polymorphisms) that affect the expression levels of genes play important roles in the pathogenesis of many complex diseases. Compared with genetic variations that alter the amino acid sequences of encoded proteins, which are relatively easy to identify, sequence variants that affect the regulation of genes are difficult to pinpoint among the large amount of nonfunctional polymorphisms located in the vicinity of genes. Computational methods to distinguish functional from neutral variations could therefore prove useful to direct limited laboratory resources to sites most likely to exhibit a phenotypic effect. In this paper we present a Web-based tool for the identification of genetic variation in potential transcription factor binding sites. This tool can be used by any scientist interested in the characterization of regulatory polymorphisms. Using experimentally verified regulatory polymorphisms and background data collected from the literature, we evaluate the method's capacity to identify regulatory genetic variation, and we discuss the limitations of its application.