Gene-Gene Interaction and Functional Impact of Polymorphisms on Innate Immune Genes in Controlling <em>Plasmodium falciparum</em> Blood Infection Level

<div><p>Genetic variations in toll-like receptors and cytokine genes of the innate immune pathways have been implicated in controlling parasite growth and the pathogenesis of <em>Plasmodium falciparum</em> mediated malaria. We previously published genetic association of <em>TLR4</em> non-synonymous and <em>TNF-α</em> promoter polymorphisms with <em>P.falciparum</em> blood infection level and here we extend the study considerably by (i) investigating genetic dependence of parasite-load on interleukin-12B polymorphisms, (ii) reconstructing gene-gene interactions among candidate <em>TLR</em>s and cytokine loci, (iii) exploring genetic and functional impact of epistatic models and (iv) providing mechanistic insights into functionality of disease-associated regulatory polymorphisms. Our data revealed that carriage of AA (P = 0.0001) and AC (P = 0.01) genotypes of <em>IL12B</em> 3′UTR polymorphism was associated with a significant increase of mean log-parasitemia relative to rare homozygous genotype CC. Presence of <em>IL12B+1188</em> polymorphism in five of six multifactor models reinforced its strong genetic impact on malaria phenotype. Elevation of genetic risk in two-component models compared to the corresponding single locus and reduction of <em>IL12B</em> (2.2 fold) and lymphotoxin-α (1.7 fold) expressions in patients'peripheral-blood-mononuclear-cells under <em>TLR4Thr399Ile</em> risk genotype background substantiated the role of Multifactor Dimensionality Reduction derived models. Marked reduction of promoter activity of <em>TNF-α</em> risk haplotype (C-C-G-G) compared to wild-type haplotype (T-C-G-G) with (84%) and without (78%) LPS stimulation and the loss of binding of transcription factors detected <em>in-silico</em> supported a causal role of <em>TNF-1031</em>. Significantly lower expression of <em>IL12B+1188</em> AA (5 fold) and AC (9 fold) genotypes compared to CC and under-representation (P = 0.0048) of allele A in transcripts of patients' PBMCs suggested an Allele-Expression-Imbalance. Allele (<em>A+1188C</em>) dependent differential stability (2 fold) of <em>IL12B</em>-transcripts upon actinomycin-D treatment and observed structural modulation (P = 0.013) of RNA-ensemble were the plausible explanations for AEI. In conclusion, our data provides functional support to the hypothesis that de-regulated receptor-cytokine axis of innate immune pathway influences blood infection level in <em>P. falciparum</em> malaria.</p> </div

<i>IL12B</i> 3′UTR polymorphism based RNA ensemble structures.

<p>(A–D) display the SNPfold derived partition function heat maps generated for 101 nucleotide sequences harboring A (risk), G, U and C (non-risk) alleles respectively at the 51^st position. The partition function matrix illustrates the base-pairing probabilities represented by dots. We estimated the pairwise Pearson correlation coefficient with respect to wild-type A allele and P values to quantify the overall modulation in the RNA structural ensemble caused by a mutation. (*) indicates the P value to be statistically significant. (E & F) show the mountain plot diagrams for <i>IL12+1188</i>A and C allele for 101 bases using RNAfold. The upper panel demonstrates the height vs position graph in which the red, green and blue lines depict the minimum free energy structure, the partition function of all possible RNA secondary structures and ensemble centroid structure respectively. The lower panel represents the entropy vs position profile and arrowhead denotes the 51^st position, the location of <i>A+1188C</i>. (G & H) shows allele specific minimum free energy (MFE) conformations generated from RNA MFOLD. Numbers indicate the base position while the arrow directs the position of the polymorphic site.</p

Genetic and functional association of multifactor models obtained by Multifactorial Dimensionality Reduction analysis.

<p>(A) Forest plots presented the comparison of risk estimates in terms of odds ratio (OR) and 95% confidence interval (CI) for significant gene-gene interaction models and component single loci. The risk corresponding to each single and two factor models was denoted by a dot and the horizontal lines represented odds ratio and 95% CI respectively. The model IDs (A–J) and respective ORs (95% CI) were given at the left and right side of each dot in the forest plot. (B) <i>IL12B</i> and (C) <i>LTA</i> gene expression in patients' PBMCs classified according to <i>TLR4Thr399Ile</i> genotype status by real time quantitative PCR. Distribution of ΔC_t was plotted and compared between the genotypic groups. Statistical significance was determined by Mann Whitney U test. P values and corresponding fold changes obtained for each pairwise comparison were shown in the box plots. (*) indicates these differences to be statistically significant.</p

<i>IL12B</i> mRNA-microRNA interaction assay.

<p>(A) Schematic representation of reporter gene constructs for <i>IL12B</i> 3′UTR A and C alleles used for transfection assays. (B) Entire 3′UTR region was mapped for putative microRNA binding sites. The highlighted, boldfaced and underlined segments within <i>IL12B</i> sequence were the seed sequences for the miRNAs. (C) Schematic representation of the score and seed position of four miRNAs on target IL12B. (D & E) Normalized luciferase relative light units (RLU) in HepG2 cells were measured for <i>IL12B+1188</i> A and C allele containing pSiCHECK2 constructs with (+) and without (−) the effect of miRNAs. Co-transfection of the empty pRNAU6.1 (+) vector with pSiCHECK2-IL12B+1188 construct was set as 100% and reductions in luciferase expression in presence of four miRNAs were measured in relation to this. hsa-miR-23a and hsa-miR-23b resulted in significant reduction in luciferase activities for both pSiCHECK2-IL12B+1188A and pSiCHECK2-IL12B+1188C constructs. Statistical significance was measured with t-test. (*) indicates the P values and percentage reduction to be statistically significant.</p

<i>IL12B</i> 3′UTR based AEI analysis.

<p>(A) Representative electropherograms showing the peak heights of the sequence encompassing <i>IL12B+1188</i> locus in gDNA (lower panel) and cDNA (upper panel) extracted from the heterozygous (AC) patients' PBMC samples. The arrowhead indicates the +1188 site. Here A and C alleles are represented as green and blue peaks respectively. Difference in peak heights in sequences between cDNA and gDNA was evident. (B) Bar graph displays the A/C peak height ratios in individual heterozygous samples (N = 22). The dotted line indicates the baseline where the A/C ratio is one. (C) The departure of A/C peak height ratios from baseline in gDNA and cDNA were shown in the form of bar diagram. Each bar represents the mean height and corresponding standard deviation. The statistical difference of this distribution was measured by Sign test. P value has been indicated. (D) Dose-response and time-course assay of actinomycin-D treatment by real time PCR. (E) HepG2 cells (1×10⁵cells/ml) were treated with the optimum dose of actinomycin-D (5 µg/ml), harvested after 0 and 24 hours after treatment and <i>IL12B</i> mRNA levels were measured and corrected for <i>GAPDH</i> mRNA for both the wild-type and variant pSiCHECK2-3′UTR constructs. Statistical significance was measured by t-test. (*) indicates the P values to be statistically significant.</p

Genetic association of <i>IL12B</i> promoter and 3′UTR polymorphisms with parasitemia at genotypic level.

a<p> <i>MAF denotes minor allele frequency and SD denotes standard deviation.</i></p>*<p> <i>P-Value<0.05.</i></p

Association between <i>IL12B+1188</i> genotypes with blood infection level and <i>IL12B</i> gene-expression represented in Box plots.

<p>(A) Diagram represented the distribution of log-parasitemia across three genotypes: 11 (AA), 12 (AC) & 22 (CC) and (B) Diagram represented the comparison of log-parasitemia of minor homozygous genotype (CC) with AA and AC genotypic groups pooled. Statistical significance between pairwise comparisons was mentioned. (C) The ΔC_t distribution of <i>IL12B</i> gene expression across AA (N = 24), AC (N = 28) and CC (N = 12) genotypes and (D) comparison of gene expression between <i>IL12B+1188CC</i> genotype and with that of AA and AC individuals pooled together determined by quantitative real time PCR. Statistical significance was determined by the Mann Whitney U test. P values and fold changes obtained for each pairwise combination were appended in each plot. (*) indicates these differences to be statistically significant. The bottom, middle line, and top of each box correspond to the 25^th percentile, median, and the 75^th percentile, respectively. Bars extend to the lowest value and to the highest value of each group.</p

Best predictive gene-gene interaction models identified by multifactor dimensionality reduction analysis.

a<p> <i>The best model was selected as the one with the minimum prediction error and maximum CVC.</i></p>b<p> <i>TBA corresponds to the testing balanced accuracy defined as the prediction error (PE) = 1-TBA.</i></p>c<p> <i>CVC corresponds to cross validation consistency.</i></p>*<p> <i>P values for gene-gene interaction models were calculated after 10,000 permutations in MDRpt software.</i></p

Novel Point and Combo-Mutations in the Genome of Hepatitis B Virus-Genotype D: Characterization and Impact on Liver Disease Progression to Hepatocellular Carcinoma

<div><p>Background</p><p>The contribution of chronic hepatitis B virus (HBV) infection in the pathogenesis of hepatocellular carcinoma (HCC) through progressive stages of liver fibrosis is exacerbated by the acquisition of naturally occurring mutations in its genome. This study has investigated the prevalence of single and combo mutations in the genome of HBV-genotype D from treatment naïve Indian patients of progressive liver disease stages and assessed their impact on the disease progression to HCC.</p><p>Methods</p><p>The mutation profile was determined from the sequence analysis of the full-length HBV genome and compared with the reference HBV sequences. SPSS 16.0 and R software were used to delineate their statistical significance in predicting HCC occurrence.</p><p>Results</p><p>Age was identified as associated risk factor for HCC development in chronic hepatitis B (CHB) patients (p≤0.01). Beyond the classical mutations in basal core promoter (BCP) (A1762T/G1764A) and precore (G1862T), persistence of progressively accumulated mutations in enhancer-I, surface, HBx and core were showed significant association to liver disease progression. BCP_T1753C, core_T147C, surface_L213I had contributed significantly in the disease progression to HCC (p<0.05) in HBeAg positive patients whereas precore_T1858C, core_I116L, core_P130Q and preS1_S98T in HBeAg negative patients. Furthermore, the effect of individual mutation was magnified by the combination with A1762T/G1764A in HCC pathogenesis. Multivariate risk analysis had confirmed that core_P130Q [OR 20.71, 95% CI (1.64–261.77), p = 0.019] in B cell epitope and core_T147C [OR 14.58, 95% CI (1.17–181.76), p = 0.037] in CTL epitope were two independent predictors of HCC in HBeAg positive and negative patients respectively.</p><p>Conclusions</p><p>Thus distinct pattern of mutations distributed across the entire HBV genome may be useful in predicting HCC in high-risk CHB patients and pattern of mutational combinations may exert greater impact on HCC risk prediction more accurately than point mutations and hence these predictors may support the existing surveillance strategies in proper management of the patients.</p></div

A Study of Molecular Signals Deregulating Mismatch Repair Genes in Prostate Cancer Compared to Benign Prostatic Hyperplasia

<div><p>Prostate cancer is one of the leading causes of mortality among aging males. There is an unmet requirement of clinically useful biomarkers for early detection of prostate cancer to reduce the liabilities of overtreatment and accompanying morbidity. The present population-based study investigates the factors disrupting expression of multiple functionally related genes of DNA mismatch repair pathway in prostate cancer patients to identify molecular attributes distinguishing adenocarcinoma from benign hyperplasia of prostate. Gene expression was compared between tissue samples from prostate cancer and benign prostatic hyperplasia using real-time-PCR, western blot and immunohistochemistry. Assessment of genotypes of seven single-nucleotide-polymorphisms of three MMR genes was conducted using PCR-coupled RFLP and sequencing. Promoter methylation was interrogated by methylation-specific-PCR and bisulfite-sequencing. Interaction between microRNAs and MMR genes was verified by 3'UTR-based dual luciferase assays. Concurrent reduction of three MMR genes namely <i>hMLH1</i>, <i>hMSH6</i> and <i>hMSH2</i> (34-85%, P<0.05) was observed in prostate cancer tissues. <i>hMSH6</i> polymorphism rs1800932(Pro92Pro) conferred a borderline protection in cancer patients (OR = 0.33, 95% CI = 0.15-0.75). Relative transcript level of <i>hMLH1</i> was inversely related (r = -0.59, P<0.05) with methylation quotient of its promoter which showed a significantly higher methylation density (P = 0.008, Z = -2.649) in cancer patients. hsa-miR-155, hsa-miR-141 and hsa-miR-21 gene expressions were significantly elevated (66-85%, P<0.05) in tumor specimens and negatively correlated (r = -0.602 to -0.527, P<0.05) with that of MMR genes. hsa-miR-155 & hsa-miR-141 and hsa-miR-155 & hsa-miR-21 were demonstrated to bind to their putative seed sequences in <i>hMLH1</i> and <i>hMSH6</i> 3’UTRs respectively. Relatively higher expression of DNA methyl-transferases (<i>DNMT1</i> and <i>DNMT3b</i>) and <i>HIF-1α</i> genes (34-50%, P<0.05) were also detected in tumor tissues. This study provides statistical evidence that MMR deficiency is correlated with hypermethylation of <i>hMLH1</i> promoter and upregulation of hsa-miR-155, hsa-miR-141 and hsa-miR-21 in prostate cancer. This comparative study reflects that microRNA expression level, particularly hsa-miR-155, exhibits predictive signature of prostate adenocarcinoma.</p></div