Background Cutaneous leishmaniasis is a vector-borne disease that is in Ethiopia mainly caused by the parasite Leishmania aethiopica . This neglected tropical disease is common in rural areas and causes serious morbidity. Persistent nonhealing cutaneous leishmaniasis has been associated with poor T cell mediated responses; however, the underlying mechanisms are not well understood. Methodology/Principal Findings We have recently shown in an experimental model of cutaneous leishmaniasis that arginase-induced L-arginine metabolism suppresses antigen-specific T cell responses at the site of pathology, but not in the periphery. To test whether these results translate to human disease, we recruited patients presenting with localized lesions of cutaneous leishmaniasis and assessed the levels of arginase activity in cells isolated from peripheral blood and from skin biopsies. Arginase activity was similar in peripheral blood mononuclear cells (PBMCs) from patients and healthy controls. In sharp contrast, arginase activity was significantly increased in lesion biopsies of patients with localized cutaneous leishmaniasis as compared with controls. Furthermore, we found that the expression levels of CD3ζ, CD4 and CD8 molecules were considerably lower at the site of pathology as compared to those observed in paired PBMCs. Conclusion Our results suggest that increased arginase in lesions of patients with cutaneous leishmaniasis might play a role in the pathogenesis of the disease by impairing T cell effector functions.

The CD8+ T cell effector mechanisms that mediate control of HIV-1 and SIV infections remain poorly understood. Recent work suggests that the mechanism may be primarily non-lytic. This is in apparent conflict with the observation that SIV and HIV-1 variants that escape CD8+ T cell surveillance are frequently selected. Whilst it is clear that a variant that has escaped a lytic response can have a fitness advantage compared to the wild-type, it is less obvious that this holds in the face of non-lytic control where both wild-type and variant infected cells would be affected by soluble factors. In particular, the high motility of T cells in lymphoid tissue would be expected to rapidly destroy local effects making selection of escape variants by non-lytic responses unlikely. The observation of frequent HIV-1 and SIV escape poses a number of questions. Most importantly, is the consistent observation of viral escape proof that HIV-1- and SIV-specific CD8+ T cells lyse infected cells or can this also be the result of non-lytic control? Additionally, the rate at which a variant strain escapes a lytic CD8+ T cell response is related to the strength of the response. Is the same relationship true for a non-lytic response? Finally, the potential anti-viral control mediated by non-lytic mechanisms compared to lytic mechanisms is unknown. These questions cannot be addressed with current experimental techniques nor with the standard mathematical models. Instead we have developed a 3D cellular automaton model of HIV-1 which captures spatial and temporal dynamics. The model reproduces in vivo HIV-1 dynamics at the cellular and population level. Using this model we demonstrate that non-lytic effector mechanisms can select for escape variants but that outgrowth of the variant is slower and less frequent than from a lytic response so that non-lytic responses can potentially offer more durable control.

Multidisciplinary techniques, in particular the combination of theoretical and experimental immunology, can address questions about human immunity that cannot be answered by other means. From the turnover of virus-infected cells in vivo, to rates of thymic production and HLA class I epitope prediction, theoretical techniques provide a unique insight to supplement experimental approaches. Here we present our opinion, with examples, of some of the ways in which mathematics has contributed in our field of interest: the efficiency of the human CD8+ T cell response to persistent viruses.

BackgroundVariations in the pattern of molecular associations are observed during disease development. The comprehensive analysis of molecular association patterns and their changes in relation to different physiological conditions can yield insight into the biological basis of disease-specific phenotype variation.MethodologyHere, we introduce a formal statistical method for the differential analysis of molecular associations via network representation. We illustrate our approach with extensive data on lipoprotein subclasses measured by NMR spectroscopy in 4,406 individuals with normal fasting glucose, and 531 subjects with impaired fasting glucose (prediabetes). We estimate the pair-wise association between measures using shrinkage estimates of partial correlations and build the differential network based on this measure of association. We explore the topological properties of the inferred network to gain insight into important metabolic differences between individuals with normal fasting glucose and prediabetes.Conclusions/SignificanceDifferential networks provide new insights characterizing differences in biological states. Based on conventional statistical methods, few differences in concentration levels of lipoprotein subclasses were found between individuals with normal fasting glucose and individuals with prediabetes. By performing the differential analysis of networks, several characteristic changes in lipoprotein metabolism known to be related to diabetic dyslipidemias were identified. The results demonstrate the applicability of the new approach to identify key molecular changes inaccessible to standard approaches.

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results. INVERSION polymorphisms are a copy-neutral form of structural variant, which although largely uncataloged in human populations, are likely a common feature in the human genome (Kidd et al. 2008). The study of inversions in human populations is in its infancy but early indications suggest that their influence on genetic variation (Stefansson et al. 2005) and phenotypic traits (Tantisira et al. 2008) may be significant. One of the first large inversions (1.1 Mb) to have been discovered in the human genome is located at the MAPT locus on chromosome 17 (Boettger et al. 2012;Steinberg et al. 2012). The inverted sequence was found to be associated with elevated fertility in women and an increased recombination rate genome-wide (Stefansson et al. 2005). Another study found that three of five detected inversions were located at sites of recurrent microdeletion associated with human disease (Kidd et al. 2008), adding to several studies linking inversions with susceptibility to disease (Lakich et al. 1993;Osborne et al. 2001;Koolen et al. 2006;Tantisira et al. 2008;Antonacci et al. 2009). Supporting Information, Figure S1 shows the distribution of known human inversions according to the TCAG database.To complement the investigation of the biological impact of inversions, it is important to establish their effect on methods that exploit genetic variation data to...