Investigating the Role of Gene-Gene Interactions in TB Susceptibility

<div><p>Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB using a cohort recruited from a high TB incidence community from Cape Town, South Africa. Our discovery data set incorporates genotypes from a large a number of candidate gene studies as well as genome-wide data. After limiting our search space to pairs of putative TB susceptibility genes, as well as pairs of genes that have been curated in online databases as potential interactors, we use statistical modelling to identify pairs of interacting SNPs. We attempt to validate the top models identified in our discovery data set using an independent genome-wide TB case-control data set from The Gambia. A number of models were successfully validated, indicating that interplay between the <i>NRG1 - NRG3, GRIK1 - GRIK3</i> and <i>IL23R - ATG4C</i> gene pairs may modify susceptibility to TB. Gene pairs involved in the NF-κB pathway were also identified in the discovery data set (<i>SFTPD - NOD2, ISG15 - TLR8</i> and <i>NLRC5 - IL12RB1</i>), but could not be tested in the Gambian study group due to lack of overlapping data.</p></div

Allele combination frequencies in the Gambian study group.

<p>The frequencies of the four possible SNP pair allele combinations from models 1, 2, 4, 13 and 19 are depicted in this figure, per cases and controls. The frequencies were estimated using an EM-algorithm.</p

Schematic illustration depicting central nitrogen metabolism in the mycobacterium tuberculosis complex.

<p>Schematic illustration depicting central nitrogen metabolism in the mycobacterium tuberculosis complex.</p

The role(s) of enzymes related to central mycobacterial glutamate metabolism and metabolites in the resistance against acid and nitrosative stress.

<p>Glutamates is an important nitrogen source (i), carbon source (ii) protectant against acidic stress (iii) as well as against nitrosative stress (iv). Metabolites shown in red are responsible for resistance against either nitrosative stress or acidic stress. Enzymes marked with circles have been demonstrated to be required for resistance against cellular stress.</p

The effect of glutamate as a major carbon source on survival of <i>M</i>. <i>bovis</i> BCG wild type, mutant (<i>Δgdh</i> and <i>ΔgltBD</i>) and complemented strains.

<p>Exponential phase cultures were diluted to an OD₆₀₀ of 0.0005 into (A & B) 7H9 supplemented with glycerol, dextrose and Tween 80, (C & D) 7H9 without glycerol, dextrose, or Tween 80 (replaced with tyloxapol) or (E & F) 7H9 without glycerol, dextrose, or Tween80 and supplemented with cholesterol (0.25 mM). Symbols and error bars are means and standard errors calculated from triplicate plating obtained from two independent experiments. Data was analysed by a regular two-way ANOVA test with Bonferroni post-testing to compare mean CFU/ml of mutant (<i>Δgdh</i> and <i>ΔgltBD</i>) and wild type strains. * p < 0.05, ** p < 0.01, *** p < 0.001.</p

Effects in the SAC study group.

<p>The logits of genotype combinations from models 1, 2, 3, 4, 7, 13, 14 and 19 are depicted in this figure. Genotypes are ordered according to minor allele frequency, with the wildtype homozygote appearing first, and the rare homozygote appearing last. Non-parallel lines are indicative of interaction effects. The effects were estimated by absorbing the marginal effects of the SNPs into the SNP × SNP interaction term, and adjusting for the covariates included in the model by averaging over them.</p

The effect of pH on survival of <i>M</i>. <i>bovis</i> BCG wild type, mutant (<i>Δgdh</i> and <i>ΔgltBD</i>) and complemented strains.

<p>Exponential phase cultures were diluted to an OD₆₀₀ of 0.0005 into 7H9 of which the pH was adjusted to 4.5 (A & B) or 5.5 (C & D). Symbols and error bars are means and standard errors calculated from triplicate plating obtained from two independent experiments. Data was analysed by a regular two-way ANOVA test with Bonferroni post-testing to compare mean CFU/ml of mutant (<i>Δgdh</i> and <i>ΔgltBD</i>) and wild type strains. * p < 0.05, ** p < 0.01, *** p < 0.001.</p

Top twenty interaction models.

<p>This table summarizes the top twenty interaction models identified in the SAC study group. P-values reflect the overall significance of the association between the genotype combinations and having TB, after adjusting for the main effects of the SNPs and covariates. A model of type C indicates a candidate gene pair, and a model of type B indicates a biofilter gene pair. These models were validated in the Gambian study group set using multiple SNPs found within the same gene regions, and the SNP pairs and p-values of the highest scoring Gambian models are reported. For some of the models, no SNPs were available in the Gambian data set for one or both of the genes (blank entries).</p

Effects in the Gambian study group.

<p>The logits of genotype combinations from models 1, 2, 4, 13 and 19 are depicted in this figure. Genotypes are ordered according to minor allele frequency, with the wildtype homozygote appearing first, and the rare homozygote appearing last. Non-parallel lines are indicative of interaction effects. The effects were estimated by absorbing the marginal effects of the SNPs into the SNP × SNP interaction term, and adjusting for the covariates included in the model by averaging over them.</p

Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, using a SSM test sensitivity of 0.99.

<p>Distribution of delays since onset of symptoms to start of treatment for patients that do not drop out, using a SSM test sensitivity of 0.99.</p