Deep-learning in situ classification of HIV-1 virion morphology

Oligomeric HIV-1 Integrase Structures Reveal Functional Plasticity for Intasome Assembly and RNA Binding

Integrase (IN) performs dual essential roles during HIV-1 replication. During ingress, IN functions within an oligomeric "intasome" assembly to catalyze viral DNA integration into host chromatin. During late stages of infection, tetrameric IN binds viral RNA and orchestrates the condensation of ribonucleoprotein complexes into the capsid core. The molecular architectures of HIV-1 IN assemblies that mediate these distinct events remain unknown. Furthermore, the tetramer is an important antiviral target for allosteric IN inhibitors. Here, we determined cryo-EM structures of wildtype HIV-1 IN tetramers and intasome hexadecamers. Our structures unveil a remarkable plasticity that leverages IN C-terminal domains and abutting linkers to assemble functionally distinct oligomeric forms. Alteration of a newly recognized conserved interface revealed that both IN functions track with tetramerization in vitro and during HIV-1 infection. Collectively, our findings reveal how IN plasticity orchestrates its diverse molecular functions, suggest a working model for IN-viral RNA binding, and provide atomic blueprints for allosteric IN inhibitor development.

Machine learning for cross-scale microscopy of viruses

Despite advances in virological sciences and antiviral research, viruses continue to emerge, circulate, and threaten public health. We still lack a comprehensive understanding of how cells and individuals remain susceptible to infectious agents. This deficiency is in part due to the complexity of viruses, including the cell states controlling virus-host interactions. Microscopy samples distinct cellular infection stages in a multi-parametric, time-resolved manner at molecular resolution and is increasingly enhanced by machine learning and deep learning. Here we discuss how state-of-the-art artificial intelligence (AI) augments light and electron microscopy and advances virological research of cells. We describe current procedures for image denoising, object segmentation, tracking, classification, and super-resolution and showcase examples of how AI has improved the acquisition and analyses of microscopy data. The power of AI-enhanced microscopy will continue to help unravel virus infection mechanisms, develop antiviral agents, and improve viral vectors.

Multimodal Functionalities of HIV-1 Integrase

Integrase is the retroviral protein responsible for integrating reverse transcripts into cellular genomes. Co-packaged with viral RNA and reverse transcriptase into capsid-encased viral cores, human immunodeficiency virus 1 (HIV-1) integrase has long been implicated in reverse transcription and virion maturation. However, the underlying mechanisms of integrase in these non-catalytic-related viral replication steps have remained elusive. Recent results have shown that integrase binds genomic RNA in virions, and that mutational or pharmacological disruption of integrase-RNA binding yields eccentric virion particles with ribonucleoprotein complexes situated outside of the capsid shell. Such viruses are defective for reverse transcription due to preferential loss of integrase and viral RNA from infected target cells. Parallel research has revealed defective integrase-RNA binding and eccentric particle formation as common features of class II integrase mutant viruses, a phenotypic grouping of viruses that display defects at steps beyond integration. In light of these new findings, we propose three new subclasses of class II mutant viruses (a, b, and c), all of which are defective for integrase-RNA binding and particle morphogenesis, but differ based on distinct underlying mechanisms exhibited by the associated integrase mutant proteins. We also assess how these findings inform the role of integrase in HIV-1 particle maturation.