Tax Gene Characterization of Human T-Lymphotropic Virus Type 1 Strains from Brazilian HIV-Coinfected Patients

The tax gene of human T-lymphotropic virus type 1 (HTLV-1) diverges among isolates according to geographic regions and has been classified into two genotypes: taxA and taxB. In Brazil, taxA is the most prevalent genotype in symptomatic and asymptomatic carriers. Few studies have been conducted in HIV-infected patients. The present study characterized the tax gene (1059 bp) in 13 Brazilian HIV-1/HTLV-1-coinfected patients from the south and southeast regions. The results confirmed the transcontinental HTLV-1 subgroup A of the Cosmopolitan subtype and showed high nucleotide similarity both among Brazilian sequences and in relation to the ATK prototype (99.5% and 99.2%, respectively). Six nucleotide substitutions were highly conserved among isolates, ranging from 76.9% to 100%: C7401T, T7914C, C7920T, C7982T, G8231A, and A8367C. The presence of the Brazilian molecular signature of genotype taxA was confirmed in all of the isolates, and they clustered into two Latin American clusters, which confirms the double introduction of HTLV-1 in Brazil.

Geographic differentiation of polymorphism in the Plasmodium falciparum malaria vaccine candidate gene SERA5

SERA5 is regarded as a promising malaria vaccine candidate of the most virulent human malaria parasite Plasmodium falciparum. SERA5 is a 120 kDa abundantly expressed blood-stage protein containing a papain-like protease. Since substantial polymorphism in blood-stage vaccine candidates may potentially limit their efficacy, it is imperative to fully investigate polymorphism of the SERA5 gene (sera5). In this study, we performed evolutionary and population genetic analysis of sera5. The level of inter-species divergence (kS = 0.076) between P. falciparum and Plasmodium reichenowi, a closely related chimpanzee malaria parasite is comparable to that of housekeeping protein genes. A signature of purifying selection was detected in the proenzyme and enzyme domains. Analysis of 445 near full-length P. falciparum sera5 sequences from nine countries in Africa, Southeast Asia, Oceania and South America revealed extensive variations in the number of octamer repeat (OR) and serine repeat (SR) regions as well as substantial level of single nucleotide polymorphism (SNP) in non-repeat regions (2562 bp). Remarkably, a 14 amino acid sequence of SERA5 (amino acids 59-72) that is known to be the in vitro target of parasite growth inhibitory antibodies was found to be perfectly conserved in all 445 worldwide isolates of P. falciparum evaluated. Unlike other major vaccine target antigen genes such as merozoite surface protein-1, apical membrane antigen-1 or circumsporozoite protein, no strong evidence for positive selection was detected for SNPs in the non-repeat regions of sera5. A biased geographical distribution was observed in SNPs as well as in the haplotypes of the sera5 OR and SR regions. In Africa, OR- and SR-haplotypes with low frequency (<5%) and SNPs with minor allele frequency (<5%) were abundant and were mostly continent-specific. Consistently, significant genetic differentiation, assessed by the Wright's fixation index (FST) of inter-population variance in allele frequencies, was detected for SNPs and both OR- and SR-haplotypes among almost all parasite populations. The exception was parasite populations between Tanzania and Ghana, suggesting frequent gene flow in Africa. The present study points to the importance of investigating whether biased geographical distribution for SNPs and repeat variants in the OR and SR regions affect the reactivity of human serum antibodies to variants. (C) 2011 Elsevier Ltd. All rights reserved.

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

Abstract Background An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. Results We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. Conclusion Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site.

A feature selection approach for identification of signature genes from SAGE data

Abstract Background One goal of gene expression profiling is to identify signature genes that robustly distinguish different types or grades of tumors. Several tumor classifiers based on expression profiling have been proposed using microarray technique. Due to important differences in the probabilistic models of microarray and SAGE technologies, it is important to develop suitable techniques to select specific genes from SAGE measurements. Results A new framework to select specific genes that distinguish different biological states based on the analysis of SAGE data is proposed. The new framework applies the bolstered error for the identification of strong genes that separate the biological states in a feature space defined by the gene expression of a training set. Credibility intervals defined from a probabilistic model of SAGE measurements are used to identify the genes that distinguish the different states with more reliability among all gene groups selected by the strong genes method. A score taking into account the credibility and the bolstered error values in order to rank the groups of considered genes is proposed. Results obtained using SAGE data from gliomas are presented, thus corroborating the introduced methodology. Conclusion The model representing counting data, such as SAGE, provides additional statistical information that allows a more robust analysis. The additional statistical information provided by the probabilistic model is incorporated in the methodology described in the paper. The introduced method is suitable to identify signature genes that lead to a good separation of the biological states using SAGE and may be adapted for other counting methods such as Massive Parallel Signature Sequencing (MPSS) or the recent Sequencing-By-Synthesis (SBS) technique. Some of such genes identified by the proposed method may be useful to generate classifiers.