The HIV-1 Capsid-Targeted Inhibitor GSK878 Alters Selection of Target Sites for HIV DNA Integration.

Decades of effort have yielded highly effective antiviral agents to treat HIV, but viral strains have evolved resistance to each inhibitor type, focusing attention on the importance of developing new inhibitor classes. A particularly promising new target is the HIV capsid, the function of which can be disrupted by highly potent inhibitors that persist long term in treated subjects. Studies with such inhibitors have contributed to an evolving picture of the role of capsid itself-the inhibitors, like certain capsid protein (CA) amino acid substitutions, can disrupt intracellular trafficking to alter the selection of target sites for HIV DNA integration in cellular chromosomes. In this study, we compare effects on HIV integration targeting for two potent inhibitors-a new molecule targeting CA, GSK878, and the previously studied lenacapavir (LEN, formerly known as GS-6207). We find that both inhibitors reduce integration in active transcription units and near epigenetic marks associated with active transcription. A careful study of integration near repeated sequences indicated frequencies were also altered for integration within multiple repeat classes. One notable finding was increased integration in centromeric satellite repeats in the presence of LEN and GSK878, which is of interest because proviruses integrated in centromeric repeats have been associated with transcriptional repression, inducibility, and latency. These data add to the picture that CA protein remains associated with preintegration complexes through the point in infection during which target sites for integration are selected, and specify new aspects of the consequences of disrupting this mechanism.

Type I Interferon Signaling via the EGR2 Transcriptional Regulator Potentiates CAR T Cell-Intrinsic Dysfunction.

Chimeric antigen receptor (CAR) T cell therapy has shown promise in treating hematologic cancers, but resistance is common and efficacy is limited in solid tumors. We found that CAR T cells autonomously propagate epigenetically programmed type I interferon signaling through chronic stimulation, which hampers antitumor function. EGR2 transcriptional regulator knockout not only blocks this type I interferon-mediated inhibitory program but also independently expands early memory CAR T cells with improved efficacy against liquid and solid tumors. The protective effect of EGR2 deletion in CAR T cells against chronic antigen-induced exhaustion can be overridden by interferon-ß exposure, suggesting that EGR2 ablation suppresses dysfunction by inhibiting type I interferon signaling. Finally, a refined EGR2 gene signature is a biomarker for type I interferon-associated CAR T cell failure and shorter patient survival. These findings connect prolonged CAR T cell activation with deleterious immunoinflammatory signaling and point to an EGR2-type I interferon axis as a therapeutically amenable biological system. SIGNIFICANCE: To improve CAR T cell therapy outcomes, modulating molecular determinants of CAR T cell-intrinsic resistance is crucial. Editing the gene encoding the EGR2 transcriptional regulator renders CAR T cells impervious to type I interferon pathway-induced dysfunction and improves memory differentiation, thereby addressing major barriers to progress for this emerging class of cancer immunotherapies. This article is highlighted in the In This Issue feature, p. 1501.

No Evidence Known Viruses Play a Role in the Pathogenesis of Onchocerciasis-Associated Epilepsy. An Explorative Metagenomic Case-Control Study.

Despite the increasing epidemiological evidence that the Onchocerca volvulus parasite is strongly associated with epilepsy in children, hence the name onchocerciasis-associated epilepsy (OAE), the pathophysiological mechanism of OAE remains to be elucidated. In June 2014, children with unprovoked convulsive epilepsy and healthy controls were enrolled in a case control study in Titule, Bas-Uélé Province in the Democratic Republic of the Congo (DRC) to identify risk factors for epilepsy. Using a subset of samples collected from individuals enrolled in this study (16 persons with OAE and 9 controls) plasma, buffy coat, and cerebrospinal fluid (CSF) were subjected to random-primed next-generation sequencing. The resulting sequences were analyzed using sensitive computational methods to identify viral DNA and RNA sequences. Anneloviridae, Flaviviridae, Hepadnaviridae (Hepatitis B virus), Herpesviridae, Papillomaviridae, Polyomaviridae (Human polyomavirus), and Virgaviridae were identified in cases and in controls. Not unexpectedly, a variety of bacteriophages were also detected in all cases and controls. However, none of the identified viral sequences were found enriched in OAE cases, which was our criteria for agents that might play a role in the etiology or pathogenesis of OAE.

NCBI's Virus Discovery Hackathon: Engaging Research Communities to Identify Cloud Infrastructure Requirements.

A wealth of viral data sits untapped in publicly available metagenomic data sets when it might be extracted to create a usable index for the virological research community. We hypothesized that work of this complexity and scale could be done in a hackathon setting. Ten teams comprised of over 40 participants from six countries, assembled to create a crowd-sourced set of analysis and processing pipelines for a complex biological data set in a three-day event on the San Diego State University campus starting 9 January 2019. Prior to the hackathon, 141,676 metagenomic data sets from the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) were pre-assembled into contiguous assemblies (contigs) by NCBI staff. During the hackathon, a subset consisting of 2953 SRA data sets (approximately 55 million contigs) was selected, which were further filtered for a minimal length of 1 kb. This resulted in 4.2 million (Mio) contigs, which were aligned using BLAST against all known virus genomes, phylogenetically clustered and assigned metadata. Out of the 4.2 Mio contigs, 360,000 contigs were labeled with domains and an additional subset containing 4400 contigs was screened for virus or virus-like genes. The work yielded valuable insights into both SRA data and the cloud infrastructure required to support such efforts, revealing analysis bottlenecks and possible workarounds thereof. Mainly: (i) Conservative assemblies of SRA data improves initial analysis steps; (ii) existing bioinformatic software with weak multithreading/multicore support can be elevated by wrapper scripts to use all cores within a computing node; (iii) redesigning existing bioinformatic algorithms for a cloud infrastructure to facilitate its use for a wider audience; and (iv) a cloud infrastructure allows a diverse group of researchers to collaborate effectively. The scientific findings will be extended during a follow-up event. Here, we present the applied workflows, initial results, and lessons learned from the hackathon.