HIV superinfection (reinfection) has been reported in several settings, but no study has been designed and powered to rigorously compare its incidence to that of initial infection. Determining whether HIV infection reduces the risk of superinfection is critical to understanding whether an immune response to natural HIV infection is protective. This study compares the incidence of initial infection and superinfection in a prospective seroincident cohort of high-risk women in Mombasa, Kenya. A next-generation sequencing-based pipeline was developed to screen 129 women for superinfection. Longitudinal plasma samples at <6 months, >2 years and one intervening time after initial HIV infection were analyzed. Amplicons in three genome regions were sequenced and a median of 901 sequences obtained per gene per timepoint. Phylogenetic evidence of polyphyly, confirmed by pairwise distance analysis, defined superinfection. Superinfection timing was determined by sequencing virus from intervening timepoints. These data were combined with published data from 17 additional women in the same cohort, totaling 146 women screened. Twenty-one cases of superinfection were identified for an estimated incidence rate of 2.61 per 100 person-years (pys). The incidence rate of initial infection among 1910 women in the same cohort was 5.75 per 100 pys. Andersen-Gill proportional hazards models were used to compare incidences, adjusting for covariates known to influence HIV susceptibility in this cohort. Superinfection incidence was significantly lower than initial infection incidence, with a hazard ratio of 0.47 (CI 0.29-0.75, p?=?0.0019). This lower incidence of superinfection was only observed >6 months after initial infection. This is the first adequately powered study to report that HIV infection reduces the risk of reinfection, raising the possibility that immune responses to natural infection are partially protective. The observation that superinfection risk changes with time implies a window of protection that coincides with the maturation of HIV-specific immunity.
When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this article, we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Around Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, whereas PAM only gives a solution for the prespecified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, whereas the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.
In microbial ecology studies, the most commonly used ways of investigating alpha (within-sample) diversity are either to apply non-phylogenetic measures such as Simpsons index to Operational Taxonomic Unit (OTU) groupings, or to use classical phylogenetic diversity (PD), which is not abundance-weighted. Although alpha diversity measures that use abundance information in a phylogenetic framework do exist, they are not widely used within the microbial ecology community. The performance of abundance-weighted phylogenetic diversity measures compared to classical discrete measures has not been explored, and the behavior of these measures under rarefaction (sub-sampling) is not yet clear. In this paper we compare the ability of various alpha diversity measures to distinguish between different community states in the human microbiome for three different datasets. We also present and compare a novel one-parameter family of alpha diversity measures, BWPD?, that interpolates between classical phylogenetic diversity (PD) and an abundance-weighted extension of PD. Additionally, we examine the sensitivity of these phylogenetic diversity measures to sampling, via computational experiments and by deriving a closed form solution for the expectation of phylogenetic quadratic entropy under re-sampling. On the three datasets, a phylogenetic measure always performed best, and two abundance-weighted phylogenetic diversity measures were the only measures ranking in the top four across all datasets. OTU-based measures, on the other hand, are less effective in distinguishing community types. In addition, abundance-weighted phylogenetic diversity measures are less sensitive to differing sampling intensity than their unweighted counterparts. Based on these results we encourage the use of abundance-weighted phylogenetic diversity measures, especially for cases such as microbial ecology where species delimitation is difficult.
Classifying individual bacterial species comprising complex, polymicrobial patient specimens remains a challenge for culture-based and molecular microbiology techniques in common clinical use. We therefore adapted practices from metagenomics research to rapidly catalog the bacterial composition of clinical specimens directly from patients, without need for prior culture. We have combined a semiconductor deep sequencing protocol that produces reads spanning 16S ribosomal RNA gene variable regions 1 and 2 (?360 bp) with a de-noising pipeline that significantly improves the fraction of error-free sequences. The resulting sequences can be used to perform accurate genus- or species-level taxonomic assignment. We explore the microbial composition of challenging, heterogeneous clinical specimens by deep sequencing, culture-based strain typing, and Sanger sequencing of bulk PCR product. We report that deep sequencing can catalog bacterial species in mixed specimens from which usable data cannot be obtained by conventional clinical methods. Deep sequencing a collection of sputum samples from cystic fibrosis (CF) patients reveals well-described CF pathogens in specimens where they were not detected by standard clinical culture methods, especially for low-prevalence or fastidious bacteria. We also found that sputa submitted for CF diagnostic workup can be divided into a limited number of groups based on the phylogenetic composition of the airway microbiota, suggesting that metagenomic profiling may prove useful as a clinical diagnostic strategy in the future. The described method is sufficiently rapid (theoretically compatible with same-day turnaround times) and inexpensive for routine clinical use.
The execution of a software application or pipeline using various combinations of parameters and inputs is a common task in bioinformatics. In the absence of a specialized tool to organize, streamline and formalize this process, scientists must write frequently complex scripts to perform these tasks. We present nestly, a Python package to facilitate running tools with nested combinations of parameters and inputs. nestly provides three components. First, a module to build nested directory structures corresponding to choices of parameters. Second, the nestrun script to run a given command using each set of parameter choices. Third, the nestagg script to aggregate results of the individual runs into a CSV file, as well as support for more complex aggregation. We also include a module for easily specifying nested dependencies for the SCons build tool, enabling incremental builds.
Heterochromatin is the gene-poor, satellite-rich eukaryotic genome compartment that supports many essential cellular processes. The functional diversity of proteins that bind and often epigenetically define heterochromatic DNA sequence reflects the diverse functions supported by this enigmatic genome compartment. Moreover, heterogeneous signatures of selection at chromosomal proteins often mirror the heterogeneity of evolutionary forces that act on heterochromatic DNA. To identify new such surrogates for dissecting heterochromatin function and evolution, we conducted a comprehensive phylogenomic analysis of the Heterochromatin Protein 1 gene family across 40 million years of Drosophila evolution. Our study expands this gene family from 5 genes to at least 26 genes, including several uncharacterized genes in Drosophila melanogaster. The 21 newly defined HP1s introduce unprecedented structural diversity, lineage-restriction, and germline-biased expression patterns into the HP1 family. We find little evidence of positive selection at these HP1 genes in both population genetic and molecular evolution analyses. Instead, we find that dynamic evolution occurs via prolific gene gains and losses. Despite this dynamic gene turnover, the number of HP1 genes is relatively constant across species. We propose that karyotype evolution drives at least some HP1 gene turnover. For example, the loss of the male germline-restricted HP1E in the obscura group coincides with one episode of dramatic karyotypic evolution, including the gain of a neo-Y in this lineage. This expanded compendium of ovary- and testis-restricted HP1 genes revealed by our study, together with correlated gain/loss dynamics and chromosome fission/fusion events, will guide functional analyses of novel roles supported by germline chromatin.
Bacterial vaginosis (BV) is a common condition that is associated with numerous adverse health outcomes and is characterized by poorly understood changes in the vaginal microbiota. We sought to describe the composition and diversity of the vaginal bacterial biota in women with BV using deep sequencing of the 16S rRNA gene coupled with species-level taxonomic identification. We investigated the associations between the presence of individual bacterial species and clinical diagnostic characteristics of BV.
Resistance commonly arises in infants exposed to single-dose nevirapine (sdNVP) for prevention of mother to child transmission. Although K103N and Y181C are common following sdNVP, multiple other mutations also confer NVP resistance. It remains unclear whether specific NVP resistance mutations or combinations of mutations predict virologic failure in infants when present at low frequencies before NVP-based treatment.
The human SAMHD1 protein potently restricts lentiviral infection in dendritic cells and monocyte/macrophages but is antagonized by the primate lentiviral protein Vpx, which targets SAMHD1 for degradation. However, only two of eight primate lentivirus lineages encode Vpx, whereas its paralog, Vpr, is conserved across all extant primate lentiviruses. We find that not only multiple Vpx but also some Vpr proteins are able to degrade SAMHD1, and such antagonism led to dramatic positive selection of SAMHD1 in the primate subfamily Cercopithecinae. Residues that have evolved under positive selection precisely determine sensitivity to Vpx/Vpr degradation and alter binding specificity. By overlaying these functional analyses on a phylogenetic framework of Vpr and Vpx evolution, we can decipher the chronology of acquisition of SAMHD1-degrading abilities in lentiviruses. We conclude that vpr neofunctionalized to degrade SAMHD1 even prior to the birth of a separate vpx gene, thereby initiating an evolutionary arms race with SAMHD1.
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