Effect of multiple genetic polymorphisms on antigen presentation and susceptibility to Mycobacterium tuberculosis infection

A review of computational and mathematical modeling contributions to our understanding of Mycobacterium tuberculosis within-host infection and treatment

Tuberculosis (TB) is an ancient and deadly disease characterized by complex host-pathogen dynamics playing out over multiple time and length scales and physiological compartments. Computational modeling can be used to integrate various types of experimental data and suggest new hypotheses, mechanisms, and therapeutic approaches to TB. Here, we offer a first-time comprehensive review of work on within-host TB models that describe the immune response of the host to infection, including the formation of lung granulomas. The models include systems of ordinary and partial differential equations and agent-based models as well as hybrid and multi-scale models that are combinations of these. Many aspects of M. tuberculosis infection, including host dynamics in the lung (typical site of infection for TB), granuloma formation, roles of cytokine and chemokine dynamics, and bacterial nutrient availability have been explored. Finally, we survey applications of these within-host models to TB therapy and prevention and suggest future directions to impact this global disease.

Mice expressing human ERAP1 variants associated with ankylosing spondylitis have altered T-cell repertoires and NK cell functions, as well as increased in utero and perinatal mortality

Specific variants of endoplasmic reticulum-associated aminopeptidase 1 (ERAP1) identified by genome-wide association study modify the risk for developing ankylosing spondylitis. We previously confirmed that disease-associated ERAP1 variants have altered enzymatic abilities that can impact upon the production of pro-inflammatory cytokines from cells expressing the same ERAP1 variants. To determine if these ERAP1 variants also impacted immune responses in vivo, we generated two strains of transgenic mice expressing human ERAP1 genes containing non-synonymous single-nucleotide polymorphisms associated with an increased (ERAP1-High) or decreased (ERAP1-Low) risk for developing autoimmune disease. After vaccination with foreign antigens, ERAP1-High mice generated unique populations of antigen-specific T-cell clones. The expression of ERAP1-High also reduced MHC-I expression on the surface of multiple cell types, demonstrating a global impact on the MHC-I peptidome. ERAP1 variants also affected the innate immune system, because NK cells from murine ERAP1 (mERAP1) knockout mice and ERAP1-High/mERAP1-/- mice had decreased surface expression of the activating receptor NKG2D on their NK and T cells, and NK cells derived from mERAP1-/- mice or ERAP1-Low mice demonstrated more active NK cell killing than NK cells derived from wild-type or ERAP1-High mice. Finally, these studies were conducted in female mice, as all male ERAP1-High mice died in utero or shortly after birth, making ERAP1-High one of the only dominant lethal autosomal genes known in mammals. Together, these results present the first direct evidence that human disease-associated ERAP1 variants can greatly alter survival, as well as antigen presentation, T-cell repertoire and NK cell responses in vivo.

Multiscale modelling in immunology: a review

One of the greatest challenges in biomedicine is to get a unified view of observations made from the molecular up to the organism scale. Towards this goal, multiscale models have been highly instrumental in contexts such as the cardiovascular field, angiogenesis, neurosciences and tumour biology. More recently, such models are becoming an increasingly important resource to address immunological questions as well. Systematic mining of the literature in multiscale modelling led us to identify three main fields of immunological applications: host-virus interactions, inflammatory diseases and their treatment and development of multiscale simulation platforms for immunological research and for educational purposes. Here, we review the current developments in these directions, which illustrate that multiscale models can consistently integrate immunological data generated at several scales, and can be used to describe and optimize therapeutic treatments of complex immune diseases.

Heterozygosity at the A625C polymorphic site of the MyD88 gene is associated with Mycobacterium bovis infection in cattle

The study demonstrates that in cattle, animals heterozygous at the MyD88 A625C polymorphic marker have a 5-fold reduced risk for active pulmonary tuberculosis (odds ratio [OR] = 0.19; P = 6 × 10(-12)). The reduced risk, however, does not extend to animals with latent pulmonary tuberculosis (OR = 0.83; P = 0.40). Heterozygosity at the A625C single nucleotide polymorphism is associated with intermediate levels of tumor necrosis factor alpha, gamma interferon, and nitric oxide synthase (NOS). Accordingly, deficiency as well as overexpression of proinflammatory cytokines or NOS favor tuberculosis, while heterozygosity provides the animals with the optimal level of inflammation.

Association of LMP/TAP gene polymorphisms with tuberculosis susceptibility in Li population in China

Background

Tuberculosis (TB) is a contagious disease affected by multiple genetic and environmental factors. Several association studies have suggested that cellular immune response is vital for controlling and preventing of tuberculosis infection. Low molecular weight polypeptides (LMPs) and transporters with antigen processing (TAPs) are the main molecules in the processing and presentation pathway for intracellular antigens. This study was performed to elucidate whether these antigen-processing genes (LMP/TAP) polymorphisms could be associated with the risk of tuberculosis infection in China.

Methodology/Principal Findings

We recruited 205 active pulmonary tuberculosis patients and 217 normal controls from Li population for this study. Four polymorphisms of LMP/TAP genes were determined by PCR-RFLP assay and haplotypes were constructed by software PHASE 1.0. Of the total four polymorphisms, genotype frequencies of LMP7 AA homozygote and CA heterozygote were significantly greater among cases compared to controls, with odds ratio of 3.77 (95% CI: 1.60–8.89; P = 0.002) and 2.97 (95% CI: 1.80–4.90; P<0.0001), respectively. The genotypes of TAP1-2 GG homozygote and AG heterozygote were more frequent in subjects with TB than in controls, with odds ratio of 3.94 (95% CI: 1.82–8.53; P = 0.001) and 2.87 (95% CI: 1.75–4.71; P<0.0001), respectively. Similarly, we found that haplotype B which carried LMP7 and TAP1-2 variations significantly increased the susceptibility to TB (OR = 3.674, 95% CI: 2.254–5.988; P<0.0001). Moreover, it is noteworthy that the homozygote of wild haplotype A (A/A) may be a strong protection for TB infection.

Conclusions

Our findings suggested that LMP/TAP gene polymorphisms might be risk factors for TB infection among Li population in China.

A hybrid multi-compartment model of granuloma formation and T cell priming in tuberculosis

Tuberculosis is a worldwide health problem with 2 billion people infected with Mycobacterium tuberculosis (Mtb, the bacteria causing TB). The hallmark of infection is the emergence of organized structures of immune cells forming primarily in the lung in response to infection. Granulomas physically contain and immunologically restrain bacteria that cannot be cleared. We have developed several models that spatially characterize the dynamics of the host-mycobacterial interaction, and identified mechanisms that control granuloma formation and development. In particular, we published several agent-based models (ABMs) of granuloma formation in TB that include many subtypes of T cell populations, macrophages as well as key cytokine and chemokine effector molecules. These ABM studies emphasize the important role of T-cell related mechanisms in infection progression, such as magnitude and timing of T cell recruitment, and macrophage activation. In these models, the priming and recruitment of T cells from the lung draining lymph node (LN) was captured phenomenologically. In addition to these ABM studies, we have also developed several multi-organ models using ODEs to examine trafficking of cells between, for example, the lung and LN. While we can predict temporal dynamic behaviors, those models are not coupled to the spatial aspects of granuloma. To this end, we have developed a multi-organ model that is hybrid: an ABM for the lung compartment and a non-linear system of ODE representing the lymph node compartment. This hybrid multi-organ approach to study TB granuloma formation in the lung and immune priming in the LN allows us to dissect protective mechanisms that cannot be achieved using the single compartment or multi-compartment ODE system. The main finding of this work is that trafficking of important cells known as antigen presenting cells from the lung to the lymph node is a key control mechanism for protective immunity: the entire spectrum of infection outcomes can be regulated by key immune cell migration rates. Our hybrid multi-organ implementation suggests that effector CD4+ T cells can rescue the system from a persistent infection and lead to clearance once a granuloma is fully formed. This could be effective as an immunotherapy strategy for latently infected individuals.

Human leucocyte antigens and cytokine gene polymorphisms and tuberculosis

PURPOSE

Several genes encoding different cytokines and human leucocyte antigens (HLA) may play crucial roles in host susceptibility to tuberculosis (TB). Our objective was to investigate whether these genes might be associated with protection from or susceptibility to TB.

MATERIALS AND METHODS

Genomic DNA from patients with TB (n = 30) and ethnically matched controls (n = 30) was genotyped by using sequence-specific primers-polymerase chain reaction and sequence-specific oligonucletid methods.

RESULTS

Our results demonstrated that HLA-CwFNx0101 [P = 0.05, odds ration (OR) (95% confidence interval) = 2.269 (1.702-3.027)] allele frequency was significantly more common in TB patients than in healthy controls, and HLA-CwFNx0101 may be associated with susceptibility to TB. Analysis of cytokine allele frequencies showed that interleukin (IL)-10, -819 C and -592 C alleles was significantly more common in TB patients than in controls (pc: 0.038 and 0.017, respectively). From the IL-10 cluster, a positive significant difference was found at positions -1082 and -592 C/C (pc: 0.027 and 0.054, respectively) genotypes. Although these differences could be explained by the highest frequency of C/C and G/G homozygous patients with TB, in contrast to the control group, statistically significant differences for the C/C genotype however were lost after Bonferroni correction of the P-values.

CONCLUSION

Altogether, our results suggest that the polymorphisms in HLA (class I) and cytokine (IL-10) genes may affect the susceptibility to TB and increase the risk of developing the disease.

A multifaceted approach to modeling the immune response in tuberculosis

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium tuberculosis (Mtb). No available vaccine is reliable and, although treatment exists, approximately 2 million people still die each year. The hallmark of TB infection is the granuloma, a self-organizing structure of immune cells forming in the lung and lymph nodes in response to bacterial invasion. Protective immune mechanisms play a role in granuloma formation and maintenance; these act over different time/length scales (e.g., molecular, cellular, and tissue scales). The significance of specific immune factors in determining disease outcome is still poorly understood, despite incredible efforts to establish several animal systems to track infection progression and granuloma formation. Mathematical and computational modeling approaches have recently been applied to address open questions regarding host-pathogen interaction dynamics, including the immune response to Mtb infection and TB granuloma formation. This provides a unique opportunity to identify factors that are crucial to a successful outcome of infection in humans. These modeling tools not only offer an additional avenue for exploring immune dynamics at multiple biological scales but also complement and extend knowledge gained via experimental tools. We review recent modeling efforts in capturing the immune response to Mtb, emphasizing the importance of a multiorgan and multiscale approach that has tuneable resolution. Together with experimentation, systems biology has begun to unravel key factors driving granuloma formation and protective immune response in TB. WIREs Syst Biol Med 2011 3 479-489 DOI: 10.1002/wsbm.131

[Immunodiagnosis and biomarkers in tuberculosis]

Based on the tuberculin skin test it is estimated that latent tuberculosis infection is present in one-third of the world's population. The new strategies in public health and research are aimed to reduce and eradicate this enormous reservoir. However, the absence of effective biomarkers for diagnosis and treatment of latent tuberculosis limits the development of new drugs and vaccines. Some components are present in both, the PPD (used in the tuberculin skin test) and the BCG vaccine. This increases the number of false positives in vaccinated individuals. Nowadays, there is not an immune diagnostic method that can differentiate latent tuberculosis and tuberculosis disease. New studies have addressed some strategies including specific antibodies, new cytokines and / or antigens as candidates for biomarkers. However, the high costs of these studies, the low number of participants and their different methodology make difficult a future meta-analysis and more conclusive results.

IFNG +874 T>A single nucleotide polymorphism is associated with leprosy among Brazilians

Leprosy is a chronic infectious disease caused by Mycobacterium leprae, a low virulence mycobacterium, and the outcome of disease is dependent on the host genetics for either susceptibility per se or severity. The IFNG gene codes for interferon-γ (IFN-γ), a cytokine that plays a key role in host defense against intracellular pathogens. Indeed, single nucleotide polymorphisms (SNPs) in IFNG have been evaluated in several genetic epidemiological studies, and the SNP +874T>A, the +874T allele, more specifically, has been associated with protection against infectious diseases, especially tuberculosis. Here, we evaluated the association of the IFNG locus with leprosy enrolling 2,125 Brazilian subjects. First, we conducted a case-control study with subjects recruited from the state of São Paulo, using the +874 T>A (rs2430561), +2109 A>G (rs1861494) and rs2069727 SNPs. Then, a second study including 1,370 individuals from Rio de Janeiro was conducted. Results of the case-control studies have shown a protective effect for +874T carriers (OR(adjusted) = 0.75; p = 0.005 for both studies combined), which was corroborated when these studies were compared with literature data. No association was found between the SNP +874T>A and the quantitative Mitsuda response. Nevertheless, the spontaneous IFN-γ release by peripheral blood mononuclear cells was higher among +874T carriers. The results shown here along with a previously reported meta-analysis of tuberculosis studies indicate that the SNP +874T>A plays a role in resistance to mycobacterial diseases.

Tuberculosis: global approaches to a global disease

Mycobacterium tuberculosis is a remarkably successful human pathogen. The interaction with the human host is complex and much remains unknown. Recent advances in systems biology have allowed the integration of data from humans and animal models into computational approaches. For example, mathematical models provide a platform for in silico manipulation of host-pathogen interactions to gain insight into this infection across temporal and biologic scales. Here, we review recent studies on global approaches toward identifying comprehensive responses of both host and bacillus during infection, and the potential for incorporation of these data into many types of useful computational systems. Systems biology approaches provide a unique opportunity to study interventions that may improve therapy and vaccines against this major killer.

Identification of key processes that control tumor necrosis factor availability in a tuberculosis granuloma

Tuberculosis (TB) granulomas are organized collections of immune cells comprised of macrophages, lymphocytes and other cells that form in the lung as a result of immune response to Mycobacterium tuberculosis (Mtb) infection. Formation and maintenance of granulomas are essential for control of Mtb infection and are regulated in part by a pro-inflammatory cytokine, tumor necrosis factor-α (TNF). To characterize mechanisms that control TNF availability within a TB granuloma, we developed a multi-scale two compartment partial differential equation model that describes a granuloma as a collection of immune cells forming concentric layers and includes TNF/TNF receptor binding and trafficking processes. We used the results of sensitivity analysis as a tool to identify experiments to measure critical model parameters in an artificial experimental model of a TB granuloma induced in the lungs of mice following injection of mycobacterial antigen-coated beads. Using our model, we then demonstrated that the organization of immune cells within a TB granuloma as well as TNF/TNF receptor binding and intracellular trafficking are two important factors that control TNF availability and may spatially coordinate TNF-induced immunological functions within a granuloma. Further, we showed that the neutralization power of TNF-neutralizing drugs depends on their TNF binding characteristics, including TNF binding kinetics, ability to bind to membrane-bound TNF and TNF binding stoichiometry. To further elucidate the role of TNF in the process of granuloma development, our modeling and experimental findings on TNF-associated molecular scale aspects of the granuloma can be incorporated into larger scale models describing the immune response to TB infection. Ultimately, these modeling and experimental results can help identify new strategies for TB disease control/therapy.

Tuberculosis is a common and deadly infectious disease caused by a highly successful bacterium, Mycobacterium tuberculosis (Mtb). Multiple host immune factors control the formation of a self-organizing aggregate of immune cells termed a granuloma in the lungs after inhalation of Mtb. One such factor, tumor necrosis factor-α (TNF), is a protein that regulates inflammatory immune responses. Availability of TNF within a TB granuloma has been proposed to have a critical role in the protective immunity against TB. However, direct measurement of the level of TNF in a granuloma is not experimentally feasible. Therefore, we develop a mathematical model based on an experimental model of granuloma developed in mice to predict TNF availability in a granuloma. We measure values of critical model parameters and explore mechanisms that influence TNF availability in the granuloma. We find that cellular organization in a granuloma and intracellular trafficking of TNF control TNF availability in a granuloma. Further, our model analysis also highlights anti-TNF drug properties that determine their TNF neutralization power. Our findings complement and extend those of recent studies on the role of TNF in the immune response against TB.

Characterizing the dynamics of CD4+ T cell priming within a lymph node

Generating adaptive immunity postinfection or immunization requires physical interaction within a lymph node T zone between Ag-bearing dendritic cells (DCs) and rare cognate T cells. Many fundamental questions remain regarding the dynamics of DC-CD4+ T cell interactions leading to priming. For example, it is not known how the production of primed CD4+ T cells relates to the numbers of cognate T cells, Ag-bearing DCs, or peptide-MHCII level on the DC. To address these questions, we developed an agent-based model of a lymph node to examine the relationships among cognate T cell frequency, DC density, parameters characterizing DC-T cell interactions, and the output of primed T cells. We found that the output of primed CD4+ T cells is linearly related to cognate frequency, but nonlinearly related to the number of Ag-bearing DCs present during infection. This addresses the applicability of two photon microscopy studies to understanding actual infection dynamics, because these types of experiments increase the cognate frequency by orders of magnitude compared with physiologic levels. We found a trade-off between the quantity of peptide-major histocompatibility class II on the surface of individual DCs and number of Ag-bearing DCs present in the lymph node in contributing to the production of primed CD4+ T cells. Interestingly, peptide-major histocompatibility class II t(1/2) plays a minor, although still significant, role in determining CD4+ T cell priming, unlike the primary role that has been suggested for CD8+ T cell priming. Finally, we identify several pathogen-targeted mechanisms that, if altered in their efficiency, can significantly effect the generation of primed CD4+ T cells.

An automated framework for understanding structural variations in the binding grooves of MHC class II molecules

Background

MHC/HLA class II molecules are important components of the immune system and play a critical role in processes such as phagocytosis. Understanding peptide recognition properties of the hundreds of MHC class II alleles is essential to appreciate determinants of antigenicity and ultimately to predict epitopes. While there are several methods for epitope prediction, each differing in their success rates, there are no reports so far in the literature to systematically characterize the binding sites at the structural level and infer recognition profiles from them.

Results

Here we report a new approach to compare the binding sites of MHC class II molecules using their three dimensional structures. We use a specifically tuned version of our recent algorithm, PocketMatch. We show that our methodology is useful for classification of MHC class II molecules based on similarities or differences among their binding sites. A new module has been used to define binding sites in MHC molecules. Comparison of binding sites of 103 MHC molecules, both at the whole groove and individual sub-pocket levels has been carried out, and their clustering patterns analyzed. While clusters largely agree with serotypic classification, deviations from it and several new insights are obtained from our study. We also present how differences in sub-pockets of molecules associated with a pair of autoimmune diseases, narcolepsy and rheumatoid arthritis, were captured by PocketMatch₁₃.

Conclusion

The systematic framework for understanding structural variations in MHC class II molecules enables large scale comparison of binding grooves and sub-pockets, which is likely to have direct implications towards predicting epitopes and understanding peptide binding preferences.

Mathematical and computational approaches can complement experimental studies of host-pathogen interactions

In addition to traditional and novel experimental approaches to study host-pathogen interactions, mathematical and computer modelling have recently been applied to address open questions in this area. These modelling tools not only offer an additional avenue for exploring disease dynamics at multiple biological scales, but also complement and extend knowledge gained via experimental tools. In this review, we outline four examples where modelling has complemented current experimental techniques in a way that can or has already pushed our knowledge of host-pathogen dynamics forward. Two of the modelling approaches presented go hand in hand with articles in this issue exploring fluorescence resonance energy transfer and two-photon intravital microscopy. Two others explore virtual or 'in silico' deletion and depletion as well as a new method to understand and guide studies in genetic epidemiology. In each of these examples, the complementary nature of modelling and experiment is discussed. We further note that multi-scale modelling may allow us to integrate information across length (molecular, cellular, tissue, organism, population) and time (e.g. seconds to lifetimes). In sum, when combined, these compatible approaches offer new opportunities for understanding host-pathogen interactions.

A methodology for performing global uncertainty and sensitivity analysis in systems biology

Accuracy of results from mathematical and computer models of biological systems is often complicated by the presence of uncertainties in experimental data that are used to estimate parameter values. Current mathematical modeling approaches typically use either single-parameter or local sensitivity analyses. However, these methods do not accurately assess uncertainty and sensitivity in the system as, by default, they hold all other parameters fixed at baseline values. Using techniques described within we demonstrate how a multi-dimensional parameter space can be studied globally so all uncertainties can be identified. Further, uncertainty and sensitivity analysis techniques can help to identify and ultimately control uncertainties. In this work we develop methods for applying existing analytical tools to perform analyses on a variety of mathematical and computer models. We compare two specific types of global sensitivity analysis indexes that have proven to be among the most robust and efficient. Through familiar and new examples of mathematical and computer models, we provide a complete methodology for performing these analyses, in both deterministic and stochastic settings, and propose novel techniques to handle problems encountered during these types of analyses.