Luminex-based virtual crossmatching for renal transplantation in South Africa

Background. Current practice in the Johannesburg renal transplantation programme is to perform a transplant when the patient’s complement-dependent cytotoxicity and flow cytometric crossmatches are negative. However, even in patients with negative crossmatches early graft rejections have occurred. We retrospectively evaluated the use of Luminex anti-human leukocyte antigen (HLA) antibody detection technology, often termed ‘virtual crossmatching’, compared with the flow cytometric crossmatch, for predicting graft outcome in renal transplant patients. Methods. Sixty-four recipients were crossmatched against multiple donors during their routine work-up for transplant (111 crossmatches); 17 of these patients received transplants during the study period. Anti-HLA antibody detection was performed using Luminex technology and the results were compared with the flow cytometric crossmatch results and with short-term graft success. Results. Compared with flow cytometric crossmatch results, the sensitivity and specificity of Luminex virtual crossmatching was 85.7% and 90.7% for the T-cell crossmatch and 100% and 87.2% for the B-cell crossmatch. Both the sensitivity and specificity of Luminex for predicting short-term graft success were 100%. Conclusions. Strong evidence is provided that single-antigen assays provide improved sensitivity to detect clinically relevant anti-HLA antibodies and can reliably be used to predict shortterm graft success. We recommend incorporation of single-antigen Luminex methodology into the routine work-up algorithm of renal transplant recipients in South Africa.S Afr Med J, 2012;102:40-4

Multi-analyte profiling of ten cytokines in South African HIV-infected patients with Immune Reconstitution Inflammatory Syndrome (IRIS)

Abstract Background Immune reconstitution inflammatory syndrome (IRIS) is an important complication of HAART in sub-Saharan Africa, where opportunistic infections (OIs) including mycobacteria and cryptococcus are common. The immune system's role in HIV infected patients is complex with cytokine expression strongly influencing HIV infection and replication. Methods We determined the expression patterns of 10 cytokines by Luminex multi-analyte profiling in 17 IRIS nested case-control pairs participating in a prospective South African cohort initiating anti-retroviral therapy. Results Interferon-gamma (IFN-γ) expression was significantly elevated in IRIS cases compared to controls (median 9.88 pg/ml versus 2.68 pg/ml, respectively, P = 0.0057), while other cytokines displayed non-significant differences in expression. Significant correlation was observed between IL-6, IL-10, and IFN-γ expression in the IRIS patients. Conclusions Significantly increased expression levels of IFN-γ suggest that this cytokine possibly plays a role in IRIS pathology and is a potential diagnostic marker

Distinguishing regional from within-codon rate heterogeneity in DNA sequence alignments

We present an improved phylogenetic factorial hidden Markov model (FHMM) for detecting two types of mosaic structures in DNA sequence alignments, related to (1) recombination and (2) rate heterogeneity. The focus of the present work is on improving the modelling of the latter aspect. Earlier papers have modelled different degrees of rate heterogeneity with separate hidden states of the FHMM. This approach fails to appreciate the intrinsic difference between two types of rate heterogeneity: long-range regional effects, which are potentially related to differences in the selective pressure, and the short-term periodic patterns within the codons, which merely capture the signature of the genetic code. We propose an improved model that explicitly distinguishes between these two effects, and we assess its performance on a set of simulated DNA sequence alignments

Pulmonary Immune-Compartment-Specific Interferon Gamma Responses in HIV-Infected Individuals with Active Tuberculosis (TB) in an Area of High TB Prevalence

There is a paucity of data on the pulmonary immune-compartment interferon gamma (IFNγ) response to M. tuberculosis, particularly in settings of high tuberculosis (TB) prevalence and in HIV-coinfected individuals. This data is necessary to understand the diagnostic potential of commercially available interferon gamma release assays (IGRAs) in both the pulmonary immune-compartment and peripheral blood. We used intracellular cytokine staining by flow cytometry to assess the IFNγ response to purified protein derivative (PPD) and early secretory antigen 6 (ESAT6) in induced sputa (ISp) and blood samples from HIV-infected, smear-negative, TB suspects. We found that individuals with active TB disease produced significantly less IFNγ in response to PPD in their induced sputa samples than individuals with non-active TB (control group). This difference was not reflected in the peripheral blood, even within the CD27− CD4+ memory T lymphocyte population. These findings suggest that progression to active TB disease may be associated with the loss of IFNγ secretion at the site of primary infection. Our findings highlight the importance of studying pulmonary immune-compartment M. tuberculosis specific responses to elucidate IFNγ secretion across the spectrum of TB disease

Fully Bayesian tests of neutrality using genealogical summary statistics

<p>Abstract</p> <p>Background</p> <p>Many data summary statistics have been developed to detect departures from neutral expectations of evolutionary models. However questions about the neutrality of the evolution of genetic loci within natural populations remain difficult to assess. One critical cause of this difficulty is that most methods for testing neutrality make simplifying assumptions simultaneously about the mutational model and the population size model. Consequentially, rejecting the null hypothesis of neutrality under these methods could result from violations of either or both assumptions, making interpretation troublesome.</p> <p>Results</p> <p>Here we harness posterior predictive simulation to exploit summary statistics of both the data and model parameters to test the goodness-of-fit of standard models of evolution. We apply the method to test the selective neutrality of molecular evolution in non-recombining gene genealogies and we demonstrate the utility of our method on four real data sets, identifying significant departures of neutrality in human influenza A virus, even after controlling for variation in population size.</p> <p>Conclusion</p> <p>Importantly, by employing a full model-based Bayesian analysis, our method separates the effects of demography from the effects of selection. The method also allows multiple summary statistics to be used in concert, thus potentially increasing sensitivity. Furthermore, our method remains useful in situations where analytical expectations and variances of summary statistics are not available. This aspect has great potential for the analysis of temporally spaced data, an expanding area previously ignored for limited availability of theory and methods.</p

Choosing among Partition Models in Bayesian Phylogenetics

Bayesian phylogenetic analyses often depend on Bayes factors (BFs) to determine the optimal way to partition the data. The marginal likelihoods used to compute BFs, in turn, are most commonly estimated using the harmonic mean (HM) method, which has been shown to be inaccurate. We describe a new more accurate method for estimating the marginal likelihood of a model and compare it with the HM method on both simulated and empirical data. The new method generalizes our previously described stepping-stone (SS) approach by making use of a reference distribution parameterized using samples from the posterior distribution. This avoids one challenging aspect of the original SS method, namely the need to sample from distributions that are close (in the Kullback–Leibler sense) to the prior. We specifically address the choice of partition models and find that using the HM method can lead to a strong preference for an overpartitioned model. In contrast to the HM method and the original SS method, we show using simulated data that the generalized SS method is strikingly more precise (repeatable BF values of the same data and partition model) and yields BF values that are much more reasonable than those produced by the HM method. Comparisons of HM and generalized SS methods on an empirical data set demonstrate that the generalized SS method tends to choose simpler partition schemes that are more in line with expectation based on inferred patterns of molecular evolution. The generalized SS method shares with thermodynamic integration the need to sample from a series of distributions in addition to the posterior. Such dedicated path-based Markov chain Monte Carlo analyses appear to be a cost of estimating marginal likelihoods accurately

Determinants of dengue virus dispersal in the Americas

Dengue viruses (DENVs) are classified into four serotypes, each of which contains multiple genotypes. DENV genotypes introduced into the Americas over the past five decades have exhibited different rates and patterns of spatial dispersal. In order to understand factors underlying these patterns, we utilized a statistical framework that allows for the integration of ecological, socioeconomic, and air transport mobility data as predictors of viral diffusion while inferring the phylogeographic history. Predictors describing spatial diffusion based on several covariates were compared using a generalized linear model approach, where the support for each scenario and its contribution is estimated simultaneously from the data set. Although different predictors were identified for different serotypes, our analysis suggests that overall diffusion of DENV-1, -2, and -3 in the Americas was associated with airline traffic. The other significant predictors included human population size, the geographical distance between countries and between urban centers and the density of people living in urban environments