Identification of HIV-1 Envelope Mutations that Enhance Entry Using Macaque CD4 and CCR5

popDMS infers mutation effects from deep mutational scanning data

SUMMARY

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

AVAILABILITY AND IMPLEMENTATION

popDMS is implemented in Python and Julia, and is freely available on GitHub at https://github.com/bartonlab/popDMS.

SHIV-C109p5 NHP induces rapid disease progression in elderly macaques with extensive GI viral replication

CCR5-tropic simian/human immunodeficiency viruses (SHIV) with clade C transmitted/founder envelopes represent a critical tool for the investigation of HIV experimental vaccines and microbicides in nonhuman primates, although many such isolates lead to spontaneous viral control post infection. Here, we generated a high-titer stock of pathogenic SHIV-C109p5 by serial passage in two rhesus macaques (RM) and tested its virulence in aged monkeys. The co-receptor usage was confirmed before infecting five geriatric rhesus macaques (four female and one male). Plasma viral loads were monitored by reverse transcriptase-quantitative PCR (RT-qPCR), cytokines by multiplex analysis, and biomarkers of gastrointestinal damage by enzyme-linked immunosorbent assay. Antibodies and cell-mediated responses were also measured. Viral dissemination into tissues was determined by RNAscope. Intravenous SHIV-C109p5 infection of aged RMs leads to high plasma viremia and rapid disease progression; rapid decrease in CD4+ T cells, CD4+CD8+ T cells, and plasmacytoid dendritic cells; and wasting necessitating euthanasia between 3 and 12 weeks post infection. Virus-specific cellular immune responses were detected only in the two monkeys that survived 4 weeks post infection. These were Gag-specific TNFα+CD8+, MIP1β+CD4+, Env-specific IFN-γ+CD4+, and CD107a+ T cell responses. Four out of five monkeys had elevated intestinal fatty acid binding protein levels at the viral peak, while regenerating islet-derived protein 3α showed marked increases at later time points in the three animals surviving the longest, suggesting gut antimicrobial peptide production in response to microbial translocation post infection. Plasma levels of monocyte chemoattractant protein-1, interleukin-15, and interleukin-12/23 were also elevated. Viral replication in gut and secondary lymphoid tissues was extensive.IMPORTANCESimian/human immunodeficiency viruses (SHIV) are important reagents to study prevention of virus acquisition in nonhuman primate models of HIV infection, especially those representing transmitted/founder (T/F) viruses. However, many R5-tropic SHIV have limited fitness in vivo leading to many monkeys spontaneously controlling the virus post acute infection. Here, we report the generation of a pathogenic SHIV clade C T/F stock by in vivo passage leading to sustained viral load set points, a necessity to study pathogenicity. Unexpectedly, administration of this SHIV to elderly rhesus macaques led to extensive viral replication and fast disease progression, despite maintenance of a strict R5 tropism. Such age-dependent rapid disease progression had previously been reported for simian immunodeficiency virus but not for R5-tropic SHIV infections.

popDMS infers mutation effects from deep mutational scanning data

Deep mutational scanning (DMS) experiments provide a powerful method to measure the functional effects of genetic mutations at massive scales. However, the data generated from these experiments can be difficult to analyze, with significant variation between experimental replicates. To overcome this challenge, we developed popDMS, a computational method based on population genetics theory, to infer the functional effects of mutations from DMS data. Through extensive tests, we found that the functional effects of single mutations and epistasis inferred by popDMS are highly consistent across replicates, comparing favorably with existing methods. Our approach is flexible and can be widely applied to DMS data that includes multiple time points, multiple replicates, and different experimental conditions.

Learning from prepandemic data to forecast viral escape

Effective pandemic preparedness relies on anticipating viral mutations that are able to evade host immune responses to facilitate vaccine and therapeutic design. However, current strategies for viral evolution prediction are not available early in a pandemic-experimental approaches require host polyclonal antibodies to test against1-16, and existing computational methods draw heavily from current strain prevalence to make reliable predictions of variants of concern17-19. To address this, we developed EVEscape, a generalizable modular framework that combines fitness predictions from a deep learning model of historical sequences with biophysical and structural information. EVEscape quantifies the viral escape potential of mutations at scale and has the advantage of being applicable before surveillance sequencing, experimental scans or three-dimensional structures of antibody complexes are available. We demonstrate that EVEscape, trained on sequences available before 2020, is as accurate as high-throughput experimental scans at anticipating pandemic variation for SARS-CoV-2 and is generalizable to other viruses including influenza, HIV and understudied viruses with pandemic potential such as Lassa and Nipah. We provide continually revised escape scores for all current strains of SARS-CoV-2 and predict probable further mutations to forecast emerging strains as a tool for continuing vaccine development ( evescape.org ).

Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs

Deep mutational scanning (DMS) is a high-throughput experimental technique that measures the effects of thousands of mutations to a protein. These experiments can be performed on multiple homologs of a protein or on the same protein selected under multiple conditions. It is often of biological interest to identify mutations with shifted effects across homologs or conditions. However, it is challenging to determine if observed shifts arise from biological signal or experimental noise. Here, we describe a method for jointly inferring mutational effects across multiple DMS experiments while also identifying mutations that have shifted in their effects among experiments. A key aspect of our method is to regularize the inferred shifts, so that they are nonzero only when strongly supported by the data. We apply this method to DMS experiments that measure how mutations to spike proteins from SARS-CoV-2 variants (Delta, Omicron BA.1, and Omicron BA.2) affect cell entry. Most mutational effects are conserved between these spike homologs, but a fraction have markedly shifted. We experimentally validate a subset of the mutations inferred to have shifted effects, and confirm differences of > 1,000-fold in the impact of the same mutation on spike-mediated viral infection across spikes from different SARS-CoV-2 variants. Overall, our work establishes a general approach for comparing sets of DMS experiments to identify biologically important shifts in mutational effects.