HIV Types, Groups, Subtypes and Recombinant Forms: Errors in Replication, Selection Pressure and Quasispecies

HIV-1 is a chimpanzee virus which was transmitted to humans by several zoonotic events resulting in infection with HIV-1 groups M P, and in parallel transmission events from sooty mangabey monkey viruses leading to infections with HIV-2 groups A H. Both viruses have circulated in the human population for about 80 years. In the infected patient, HIV mutates, and by elimination of some of the viruses by the action of the immune system individual quasispecies are formed. Along with the selection of the fittest viruses, mutation and recombination after superinfection with HIV from different groups or subtypes have resulted in the diversity of their patterns of geographic distribution. Despite the high variability observed, some essential parts of the HIV genome are highly conserved. Viral diversity is further facilitated in some parts of the HIV genome by drug selection pressure and may also be enhanced by different genetic factors, including HLA in patients from different regions of the world. Viral and human genetic factors influence pathogenesis. Viral genetic factors are proteins such as Tat, Vif and Rev. Human genetic factors associated with a better clinical outcome are proteins such as APOBEC, langerin, tetherin and chemokine receptor 5 (CCR5) and HLA B27, B57, DRB1{*}1303, KIR and PARD3B. Copyright (C) 2012 S. Karger AG, Base

ShoRAH: estimating the genetic diversity of a mixed sample from next-generation sequencing data

<p>Abstract</p> <p>Background</p> <p>With next-generation sequencing technologies, experiments that were considered prohibitive only a few years ago are now possible. However, while these technologies have the ability to produce enormous volumes of data, the sequence reads are prone to error. This poses fundamental hurdles when genetic diversity is investigated.</p> <p>Results</p> <p>We developed ShoRAH, a computational method for quantifying genetic diversity in a mixed sample and for identifying the individual clones in the population, while accounting for sequencing errors. The software was run on simulated data and on real data obtained in wet lab experiments to assess its reliability.</p> <p>Conclusions</p> <p>ShoRAH is implemented in C++, Python, and Perl and has been tested under Linux and Mac OS X. Source code is available under the GNU General Public License at <url>http://www.cbg.ethz.ch/software/shorah</url>.</p

Selection at a single locus leads to widespread expansion of toxoplasma gondii lineages that are virulent in mice

The determinants of virulence are rarely defined for eukaryotic parasites such as T. gondii, a widespread parasite of mammals that also infects humans, sometimes with serious consequences. Recent laboratory studies have established that variation in a single secreted protein, a serine/threonine kinase known as ROPO18, controls whether or not mice survive infection. Here, we establish the extent and nature of variation in ROP18among a collection of parasite strains from geographically diverse regions. Compared to other genes, ROP18 showed extremely high levels of diversification and changes in expression level, which correlated with severity of infection in mice. Comparison with an out-group demonstrated that changes in the upstream region that regulates expression of ROP18 led to an historical increase in the expression and exposed the protein to diversifying selective pressure. Surprisingly, only three atypically distinct protein variants exist despite marked genetic divergence elsewhere in the genome. These three forms of ROP18 are likely adaptations for different niches in nature, and they confer markedly different virulence to mice. The widespread distribution of a single mouse-virulent allele among geographically and genetically disparate parasites may have consequences for transmission and disease in other hosts, including humans

Defining functional distances over Gene Ontology

<p>Abstract</p> <p>Background</p> <p>A fundamental problem when trying to define the functional relationships between proteins is the difficulty in quantifying functional similarities, even when well-structured ontologies exist regarding the activity of proteins (i.e. 'gene ontology' -GO-). However, functional metrics can overcome the problems in the comparing and evaluating functional assignments and predictions. As a reference of proximity, previous approaches to compare GO terms considered linkage in terms of ontology weighted by a probability distribution that balances the non-uniform 'richness' of different parts of the Direct Acyclic Graph. Here, we have followed a different approach to quantify functional similarities between GO terms.</p> <p>Results</p> <p>We propose a new method to derive 'functional distances' between GO terms that is based on the simultaneous occurrence of terms in the same set of Interpro entries, instead of relying on the structure of the GO. The coincidence of GO terms reveals natural biological links between the GO functions and defines a distance model <it>D</it>_{<it>f </it>}which fulfils the properties of a Metric Space. The distances obtained in this way can be represented as a hierarchical 'Functional Tree'.</p> <p>Conclusion</p> <p>The method proposed provides a new definition of distance that enables the similarity between GO terms to be quantified. Additionally, the 'Functional Tree' defines groups with biological meaning enhancing its utility for protein function comparison and prediction. Finally, this approach could be for function-based protein searches in databases, and for analysing the gene clusters produced by DNA array experiments.</p