HIV-1 integrase modulates the interaction of the HIV-1 cellular cofactor LEDGF/p75 with chromatin

<p>Abstract</p> <p>Background</p> <p>Chromatin binding plays a central role in the molecular mechanism of LEDGF/p75 in HIV-1 DNA integration. Conflicting results have been reported in regards to the relevance of the LEDGF/p75 chromatin binding element PWWP domain in its HIV-1 cofactor activity.</p> <p>Results</p> <p>Here we present evidence that re-expression of a LEDGF/p75 mutant lacking the PWWP domain (ΔPWWP) rescued HIV-1 infection in cells verified to express background levels of endogenous LEDGF/p75 that do not support efficient HIV-1 infection. The HIV-1 cofactor activity of LEDGF/p75 ΔPWWP was similar to that of LEDGF/p75 wild type (WT). A possible molecular explanation for the nonessential role of PWWP domain in the HIV-1 cofactor activity of LEDGF/p75 comes from the fact that coexpression of HIV-1 integrase significantly restored the impaired chromatin binding activity of LEDGF/p75 ΔPWWP. However, integrase failed to promote chromatin binding of a non-chromatin bound LEDGF/p75 mutant that lacks both the PWWP domain and the AT hook motifs (ΔPWWP/AT) and that exhibits negligible HIV-1 cofactor activity. The effect of integrase on the chromatin binding of LEDGF/p75 requires the direct interaction of these two proteins. An HIV-1 integrase mutant, unable to interact with LEDGF/p75, failed to enhance chromatin binding, whereas integrase wild type did not increase the chromatin binding strength of a LEDGF/p75 mutant lacking the integrase binding domain (ΔIBD).</p> <p>Conclusions</p> <p>Our data reveal that the PWWP domain of LEDGF/p75 is not essential for its HIV-1 cofactor activity, possibly due to an integrase-mediated increase of the chromatin binding strength of this LEDGF/p75 mutant.</p

Transcriptome reconstruction and annotation of cynomolgus and African green monkey

Non-human primates (NHPs) and humans share major biological mechanisms, functions, and responses due to their close evolutionary relationship and, as such, provide ideal animal models to study human diseases. RNA expression in NHPs provides specific signatures that are informative of disease mechanisms and therapeutic modes of action. Unlike the human transcriptome, the transcriptomes of major NHP animal models are yet to be comprehensively annotated. In this manuscript, employing deep RNA sequencing of seven tissue samples, we characterize the transcriptomes of two commonly used NHP animal models: Cynomolgus macaque (Macaca fascicularis) and African green monkey (Chlorocebus aethiops). We present the Multi-Species Annotation (MSA) pipeline that leverages well-annotated primate species and annotates 99.8% of reconstructed transcripts. We elucidate tissue-specific expression profiles and report 13 experimentally validated novel transcripts in these NHP animal models. We report comprehensively annotated transcriptomes of two non-human primates, which we have made publically available on a customized UCSC Genome Browser interface. The MSA pipeline is also freely available

Viral diversity and clonal evolution from unphased genomic data

Background: Clonal expansion is a process in which a single organism reproduces asexually, giving rise to a diversifying population. It is pervasive in nature, from within-host pathogen evolution to emergent infectious disease outbreaks. Standard phylogenetic tools rely on full-length genomes of individual pathogens or population consensus sequences (phased genotypes). Although high-throughput sequencing technologies are able to sample population diversity, the short sequence reads inherent to them preclude assessing whether two reads originate from the same clone (unphased genotypes). This obstacle severely limits the application of phylogenetic methods and investigation of within-host dynamics of acute infections using this rich data source. Methods: We introduce two measures of diversity to study the evolution of clonal populations using unphased genomic data, which eliminate the need to construct full-length genomes. Our method follows a maximum likelihood approach to estimate evolutionary rates and times to the most recent common ancestor, based on a relaxed molecular clock model; independent of a growth model. Deviations from neutral evolution indicate the presence of selection and bottleneck events. Results: We evaluated our methods in silico and then compared it against existing approaches with the well-characterized 2009 H1N1 influenza pandemic. We then applied our method to high-throughput genomic data from marburgvirus-infected non-human primates and inferred the time of infection and the intra-host evolutionary rate, and identified purifying selection in viral populations. Conclusions: Our method has the power to make use of minor variants present in less than 1% of the population and capture genomic diversification within days of infection, making it an ideal tool for the study of acute RNA viral infection dynamics

Nomenclature- and Database-Compatible Names for the Two Ebola Virus Variants that Emerged in Guinea and the Democratic Republic of the Congo in 2014

In 2014, Ebola virus (EBOV) was identified as the etiological agent of a large and still expanding outbreak of Ebola virus disease (EVD) in West Africa and a much more confined EVD outbreak in Middle Africa. Epidemiological and evolutionary analyses confirmed that all cases of both outbreaks are connected to a single introduction each of EBOV into human populations and that both outbreaks are not directly connected. Coding-complete genomic sequence analyses of isolates revealed that the two outbreaks were caused by two novel EBOV variants, and initial clinical observations suggest that neither of them should be considered strains. Here we present consensus decisions on naming for both variants (West Africa: “Makona”, Middle Africa: “Lomela”) and provide database-compatible full, shortened, and abbreviated names that are in line with recently established filovirus sub-species nomenclatures

Evolution and Spread of Ebola Virus in Liberia, 2014–2015

The 2013–present Western African Ebola virus disease (EVD) outbreak is the largest ever recorded with >28,000 reported cases. Ebola virus (EBOV) genome sequencing has played an important role throughout this outbreak; however, relatively few sequences have been determined from patients in Liberia, the second worst-affected country. Here, we report 140 EBOV genome sequences from the second wave of the Liberian outbreak and analyze them in combination with 782 previously published sequences from throughout the Western African outbreak. While multiple early introductions of EBOV to Liberia are evident, the majority of Liberian EVD cases are consistent with a single introduction, followed by spread and diversification within the country. Movement of the virus within Liberia was widespread and reintroductions from Liberia served as an important source for the continuation of the already ongoing EVD outbreak in Guinea. Overall, little evidence was found for incremental adaptation of EBOV to the human host.Organismic and Evolutionary Biolog

Ebola virus epidemiology, transmission, and evolution during seven months in Sierra Leone

The 2013-2015 Ebola virus disease (EVD) epidemic is caused by the Makona variant of Ebola virus (EBOV). Early in the epidemic, genome sequencing provided insights into virus evolution and transmission and offered important information for outbreak response. Here, we analyze sequences from 232 patients sampled over 7 months in Sierra Leone, along with 86 previously released genomes from earlier in the epidemic. We confirm sustained human-to-human transmission within Sierra Leone and find no evidence for import or export of EBOV across national borders after its initial introduction. Using high-depth replicate sequencing, we observe both host-to-host transmission and recurrent emergence of intrahost genetic variants. We trace the increasing impact of purifying selection in suppressing the accumulation of nonsynonymous mutations over time. Finally, we note changes in the mucin-like domain of EBOV glycoprotein that merit further investigation. These findings clarify the movement of EBOV within the region and describe viral evolution during prolonged human-to-human transmission

Genomic Variability of Monkeypox Virus among Humans, Democratic Republic of the Congo

Monkeypox virus is a zoonotic virus endemic to Central Africa. Although active disease surveillance has assessed monkeypox disease prevalence and geographic range, information about virus diversity is lacking. We therefore assessed genome diversity of viruses in 60 samples obtained from humans with primary and secondary cases of infection from 2005 through 2007. We detected 4 distinct lineages and a deletion that resulted in gene loss in 10 (16.7%) samples and that seemed to correlate with human-to-human transmission (p = 0.0544). The data suggest a high frequency of spillover events from the pool of viruses in nonhuman animals, active selection through genomic destabilization and gene loss, and increased disease transmissibility and severity. The potential for accelerated adaptation to humans should be monitored through improved surveillance

Reduced evolutionary rate in reemerged Ebola virus transmission chains

On 29 June 2015, Liberia’s respite from Ebola virus disease (EVD) was interrupted for the second time by a renewed outbreak (“flare-up”) of seven confirmed cases. We demonstrate that, similar to the March 2015 flare-up associated with sexual transmission, this new flare-up was a reemergence of a Liberian transmission chain originating from a persistently infected source rather than a reintroduction from a reservoir or a neighboring country with active transmission. Although distinct, Ebola virus (EBOV) genomes from both flare-ups exhibit significantly low genetic divergence, indicating a reduced rate of EBOV evolution during persistent infection. Using this rate of change as a signature, we identified two additional EVD clusters that possibly arose from persistently infected sources. These findings highlight the risk of EVD flare-ups even after an outbreak is declared over

Virus genomes reveal factors that spread and sustained the Ebola epidemic.

The 2013-2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic 'gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics