Analysis of host responses to Mycobacterium tuberculosis antigens in a multi-site study of subjects with different TB and HIV infection states in sub-Saharan Africa.

BACKGROUND: Tuberculosis (TB) remains a global health threat with 9 million new cases and 1.4 million deaths per year. In order to develop a protective vaccine, we need to define the antigens expressed by Mycobacterium tuberculosis (Mtb), which are relevant to protective immunity in high-endemic areas. METHODS: We analysed responses to 23 Mtb antigens in a total of 1247 subjects with different HIV and TB status across 5 geographically diverse sites in Africa (South Africa, The Gambia, Ethiopia, Malawi and Uganda). We used a 7-day whole blood assay followed by IFN-γ ELISA on the supernatants. Antigens included PPD, ESAT-6 and Ag85B (dominant antigens) together with novel resuscitation-promoting factors (rpf), reactivation proteins, latency (Mtb DosR regulon-encoded) antigens, starvation-induced antigens and secreted antigens. RESULTS: There was variation between sites in responses to the antigens, presumably due to underlying genetic and environmental differences. When results from all sites were combined, HIV- subjects with active TB showed significantly lower responses compared to both TST(-) and TST(+) contacts to latency antigens (Rv0569, Rv1733, Rv1735, Rv1737) and the rpf Rv0867; whilst responses to ESAT-6/CFP-10 fusion protein (EC), PPD, Rv2029, TB10.3, and TB10.4 were significantly higher in TST(+) contacts (LTBI) compared to TB and TST(-) contacts fewer differences were seen in subjects with HIV co-infection, with responses to the mitogen PHA significantly lower in subjects with active TB compared to those with LTBI and no difference with any antigen. CONCLUSIONS: Our multi-site study design for testing novel Mtb antigens revealed promising antigens for future vaccine development. The IFN-γ ELISA is a cheap and useful tool for screening potential antigenicity in subjects with different ethnic backgrounds and across a spectrum of TB and HIV infection states. Analysis of cytokines other than IFN-γ is currently on-going to determine correlates of protection, which may be useful for vaccine efficacy trials

What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation

What makes a problem easy or hard for a genetic algorithm (GA)? This question has become increasingly important as people have tried to apply the GA to ever more diverse types of problems. Much previous work on this question has studied the relationship between GA performance and the structure of a given fitness function when it is expressed as a Walsh polynomial . The work of Bethke, Goldberg, and others has produced certain theoretical results about this relationship. In this article we review these theoretical results, and then discuss a number of seemingly anomalous experimental results reported by Tanese concerning the performance of the GA on a subclass of Walsh polynomials, some members of which were expected to be easy for the GA to optimize. Tanese found that the GA was poor at optimizing all functions in this subclass, that a partitioning of a single large population into a number of smaller independent populations seemed to improve performance, and that hillelimbing outperformed both the original and partitioned forms of the GA on these functions. These results seemed to contradict several commonly held expectations about GAs.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46892/1/10994_2004_Article_BF00993046.pd

Feasibility, reliability and validity of a questionnaire on healthcare consumption and productivity loss in patients with a psychiatric disorder (TiC-P)

Background: Patient self-report allows collecting comprehensive data for the purpose of performing economic evaluations. The aim of the current study was to assess the feasibility, reliability and a part of the construct validity of a commonly applied questionnaire on healthcare utilization and productivity losses in patients with a psychiatric disorder (TiC-P). Methods. Data were derived alongside two clinical trials performed in the Netherlands in patients with mental health problems. The response rate, average time of filling out the questionnaire and proportions of missing values were used as indicators of feasibility of the questionnaire. Test-retest analyses were performed including Cohen's kappa and intra class correlation coefficients to assess reliability of the data. The construct validity was assessed by comparing patient reported data on contacts with psychotherapists and reported data on long-term absence from work with data derived from registries. Results: The response rate was 72%. The mean time needed for filling out the first TiC-P was 9.4 minutes. The time needed for filling out the questionnaire was 2.3 minutes less for follow up measurements. Proportions of missing values were limited (< 2.4%) except for medication for which in 10% of the cases costs could not be calculated. Cohen's kappa was satisfactory to almost perfect for most items related to healthcare consumption and satisfactory for items on absence from work and presenteeism. Comparable results were shown by the ICCs on variables measuring volumes of medical consumption and productivity losses indicating good reliability of the questionnaire. Absolute agreement between patient-reported data and data derived from medical registrations of the psychotherapists was satisfactory. Accepting a margin o

Bayesian approaches to reverse engineer cellular systems: a simulation study on nonlinear Gaussian networks

BACKGROUND. Reverse engineering cellular networks is currently one of the most challenging problems in systems biology. Dynamic Bayesian networks (DBNs) seem to be particularly suitable for inferring relationships between cellular variables from the analysis of time series measurements of mRNA or protein concentrations. As evaluating inference results on a real dataset is controversial, the use of simulated data has been proposed. However, DBN approaches that use continuous variables, thus avoiding the information loss associated with discretization, have not yet been extensively assessed, and most of the proposed approaches have dealt with linear Gaussian models. RESULTS. We propose a generalization of dynamic Gaussian networks to accommodate nonlinear dependencies between variables. As a benchmark dataset to test the new approach, we used data from a mathematical model of cell cycle control in budding yeast that realistically reproduces the complexity of a cellular system. We evaluated the ability of the networks to describe the dynamics of cellular systems and their precision in reconstructing the true underlying causal relationships between variables. We also tested the robustness of the results by analyzing the effect of noise on the data, and the impact of a different sampling time. CONCLUSION. The results confirmed that DBNs with Gaussian models can be effectively exploited for a first level analysis of data from complex cellular systems. The inferred models are parsimonious and have a satisfying goodness of fit. Furthermore, the networks not only offer a phenomenological description of the dynamics of cellular systems, but are also able to suggest hypotheses concerning the causal interactions between variables. The proposed nonlinear generalization of Gaussian models yielded models characterized by a slightly lower goodness of fit than the linear model, but a better ability to recover the true underlying connections between variables.Italian Ministry of University and Scientific Research; National Institutes of Health & National Human Genome Research Institute (HG003354-01A2); Collegio Ghislieri, Pavia Italy fellowshi

Vaccination against Human Influenza A/H3N2 Virus Prevents the Induction of Heterosubtypic Immunity against Lethal Infection with Avian Influenza A/H5N1 Virus

Annual vaccination against seasonal influenza viruses is recommended for certain individuals that have a high risk for complications resulting from infection with these viruses. Recently it was recommended in a number of countries including the USA to vaccinate all healthy children between 6 and 59 months of age as well. However, vaccination of immunologically naïve subjects against seasonal influenza may prevent the induction of heterosubtypic immunity against potentially pandemic strains of an alternative subtype, otherwise induced by infection with the seasonal strains. Here we show in a mouse model that the induction of protective heterosubtypic immunity by infection with a human A/H3N2 influenza virus is prevented by effective vaccination against the A/H3N2 strain. Consequently, vaccinated mice were no longer protected against a lethal infection with an avian A/H5N1 influenza virus. As a result H3N2-vaccinated mice continued to loose body weight after A/H5N1 infection, had 100-fold higher lung virus titers on day 7 post infection and more severe histopathological changes than mice that were not protected by vaccination against A/H3N2 influenza. The lack of protection correlated with reduced virus-specific CD8+ T cell responses after A/H5N1 virus challenge infection. These findings may have implications for the general recommendation to vaccinate all healthy children against seasonal influenza in the light of the current pandemic threat caused by highly pathogenic avian A/H5N1 influenza viruses

Cross-Reactive T Cells Are Involved in Rapid Clearance of 2009 Pandemic H1N1 Influenza Virus in Nonhuman Primates

In mouse models of influenza, T cells can confer broad protection against multiple viral subtypes when antibodies raised against a single subtype fail to do so. However, the role of T cells in protecting humans against influenza remains unclear. Here we employ a translational nonhuman primate model to show that cross-reactive T cell responses play an important role in early clearance of infection with 2009 pandemic H1N1 influenza virus (H1N1pdm). To “prime” cellular immunity, we first infected 5 rhesus macaques with a seasonal human H1N1 isolate. These animals made detectable cellular and antibody responses against the seasonal H1N1 isolate but had no neutralizing antibodies against H1N1pdm. Four months later, we challenged the 5 “primed” animals and 7 naive controls with H1N1pdm. In naive animals, CD8+ T cells with an activated phenotype (Ki-67+ CD38+) appeared in blood and lung 5–7 days post inoculation (p.i.) with H1N1pdm and reached peak magnitude 7–10 days p.i. In contrast, activated T cells were recruited to the lung as early as 2 days p.i. in “primed” animals, and reached peak frequencies in blood and lung 4–7 days p.i. Interferon (IFN)-γ Elispot and intracellular cytokine staining assays showed that the virus-specific response peaked earlier and reached a higher magnitude in “primed” animals than in naive animals. This response involved both CD4+ and CD8+ T cells. Strikingly, “primed” animals cleared H1N1pdm infection significantly earlier from the upper and lower respiratory tract than the naive animals did, and before the appearance of H1N1pdm-specific neutralizing antibodies. Together, our results suggest that cross-reactive T cell responses can mediate early clearance of an antigenically novel influenza virus in primates. Vaccines capable of inducing such cross-reactive T cells may help protect humans against severe disease caused by newly emerging pandemic influenza viruses

On State-Space Reduction in Multi-Strain Pathogen Models, with an Application to Antigenic Drift in Influenza A

Many pathogens exist in phenotypically distinct strains that interact with each other through competition for hosts. General models that describe such multi-strain systems are extremely difficult to analyze because their state spaces are enormously large. Reduced models have been proposed, but so far all of them necessarily allow for coinfections and require that immunity be mediated solely by reduced infectivity, a potentially problematic assumption. Here, we suggest a new state-space reduction approach that allows immunity to be mediated by either reduced infectivity or reduced susceptibility and that can naturally be used for models with or without coinfections. Our approach utilizes the general framework of status-based models. The cornerstone of our method is the introduction of immunity variables, which describe multi-strain systems more naturally than the traditional tracking of susceptible and infected hosts. Models expressed in this way can be approximated in a natural way by a truncation method that is akin to moment closure, allowing us to sharply reduce the size of the state space, and thus to consider models with many strains in a tractable manner. Applying our method to the phenomenon of antigenic drift in influenza A, we propose a potentially general mechanism that could constrain viral evolution to a one-dimensional manifold in a two-dimensional trait space. Our framework broadens the class of multi-strain systems that can be adequately described by reduced models. It permits computational, and even analytical, investigation and thus serves as a useful tool for understanding the evolution and ecology of multi-strain pathogens

Surprisingly High Specificity of the PPD Skin Test for M. tuberculosis Infection from Recent Exposure in The Gambia

BACKGROUND: Options for intervention against Mycobacterium tuberculosis infection are limited by the diagnostic tools available. The Purified Protein Derivative (PPD) skin test is thought to be non-specific, especially in tropical settings. We compared the PPD skin test with an ELISPOT test in The Gambia. METHODOLOGY/PRINCIPAL FINDINGS: Household contacts over six months of age of sputum smear positive TB cases and community controls were recruited. They underwent a PPD skin test and an ELISPOT test for the T cell response to PPD and ESAT-6/CFP10 antigens. Responsiveness to M. tuberculosis exposure was analysed according to sleeping proximity to an index case using logistic regression. 615 household contacts and 105 community controls were recruited. All three tests assessed increased significantly in positivity with increasing M. tuberculosis exposure, the PPD skin test most dramatically (OR 15.7; 95% CI 6.6–35.3). While the PPD skin test positivity continued to trend downwards in the community with increasing distance from a known case (61.9% to 14.3%), the PPD and ESAT-6/CFP-10 ELISPOT positivity did not. The PPD skin test was more in agreement with ESAT-6/CFP-10 ELISPOT (75%, p = 0.01) than the PPD ELISPOT (53%, p<0.0001). With increasing M. tuberculosis exposure, the proportion of ESAT-6/CFP-10 positive contacts who were PPD skin test positive increased (p<0.0001), and the proportion of ESAT-6/CFP-10 negative contacts that were PPD skin test negative decreased (p<0.0001); the converse did not occur. CONCLUSIONS/SIGNIFICANCE: The PPD skin test has surprisingly high specificity for M. tuberculosis infection from recent exposure in The Gambia. In this setting, anti-tuberculous prophylaxis in PPD skin test positive individuals should be revisited

Evolutionary approaches for the reverse-engineering of gene regulatory networks: A study on a biologically realistic dataset

<p>Abstract</p> <p>Background</p> <p>Inferring gene regulatory networks from data requires the development of algorithms devoted to structure extraction. When only static data are available, gene interactions may be modelled by a Bayesian Network (BN) that represents the presence of direct interactions from regulators to regulees by conditional probability distributions. We used enhanced evolutionary algorithms to stochastically evolve a set of candidate BN structures and found the model that best fits data without prior knowledge.</p> <p>Results</p> <p>We proposed various evolutionary strategies suitable for the task and tested our choices using simulated data drawn from a given bio-realistic network of 35 nodes, the so-called insulin network, which has been used in the literature for benchmarking. We assessed the inferred models against this reference to obtain statistical performance results. We then compared performances of evolutionary algorithms using two kinds of recombination operators that operate at different scales in the graphs. We introduced a niching strategy that reinforces diversity through the population and avoided trapping of the algorithm in one local minimum in the early steps of learning. We show the limited effect of the mutation operator when niching is applied. Finally, we compared our best evolutionary approach with various well known learning algorithms (MCMC, K2, greedy search, TPDA, MMHC) devoted to BN structure learning.</p> <p>Conclusion</p> <p>We studied the behaviour of an evolutionary approach enhanced by niching for the learning of gene regulatory networks with BN. We show that this approach outperforms classical structure learning methods in elucidating the original model. These results were obtained for the learning of a bio-realistic network and, more importantly, on various small datasets. This is a suitable approach for learning transcriptional regulatory networks from real datasets without prior knowledge.</p

Antibody landscapes after influenza virus infection or vaccination.

We introduce the antibody landscape, a method for the quantitative analysis of antibody-mediated immunity to antigenically variable pathogens, achieved by accounting for antigenic variation among pathogen strains. We generated antibody landscapes to study immune profiles covering 43 years of influenza A/H3N2 virus evolution for 69 individuals monitored for infection over 6 years and for 225 individuals pre- and postvaccination. Upon infection and vaccination, titers increased broadly, including previously encountered viruses far beyond the extent of cross-reactivity observed after a primary infection. We explored implications for vaccination and found that the use of an antigenically advanced virus had the dual benefit of inducing antibodies against both advanced and previous antigenic clusters. These results indicate that preemptive vaccine updates may improve influenza vaccine efficacy in previously exposed individuals.This is the author’s version of the work. It will be under embargo for 6 months following publication. It is posted here by permission of the AAAS for personal use, not for redistribution. The final version is available from AAAS in Science at http://www.sciencemag.org/content/346/6212/996.long