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Host cell glycosylation selects for infection with CCR5- versus CXCR4-tropic HIV-1

Nature Microbiology volume 9, pages 2985–2996 (2024) Cite this article

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Abstract

Human immunodeficiency virus type 1 (HIV-1) infection involves a selection bottleneck that leads to transmission of one or a few variants. C–C motif chemokine receptor 5 (CCR5) or C–X–C motif chemokine receptor 4 (CXCR4) can act as coreceptors for HIV-1 viral entry. However, initial infection mostly occurs via CCR5, despite abundant expression of CXCR4 on target cells. The host factors that influence HIV-1 susceptibility and selection during transmission are unclear. Here we conduct CRISPR–Cas9 screens and identify SLC35A2 (a transporter of UDP–galactose expressed in target cells in blood and mucosa) as a potent and specific CXCR4-tropic restriction factor in primary target CD4+ T cells. SLC35A2 inactivation, which resulted in truncated glycans, not only increased CXCR4-tropic infection levels but also decreased those of CCR5-tropic strains consistently. Single-cycle infections demonstrated that the effect is cell-intrinsic. These data support a role for a host protein that influences glycan structure in regulating HIV-1 infection. Host cell glycosylation may, therefore, affect HIV-1 selection during transmission in vivo.

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Fig. 1: SLC35A2 is an X4-specific hit in a CRISPR-KO screen for HIV-1 restriction factors in primary CD4+ T cells.
The alternative text for this image may have been generated using AI.
Fig. 2: SLC35A2 KO differentially impacts X4 and CCR5-tropic HIV-1.
The alternative text for this image may have been generated using AI.
Fig. 3: SLC35A2 inactivation causes truncated glycans on host cells and impacts HIV-1 entry in a cell-intrinsic manner.
The alternative text for this image may have been generated using AI.
Fig. 4: SLC35A2 does not impact HIV-1 receptor or coreceptor expression.
The alternative text for this image may have been generated using AI.
Fig. 5: Working model of the impact of CD4+ T cell glycosylation on HIV-1 infection.
The alternative text for this image may have been generated using AI.

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Data availability

Illumina sequencing reads from HIV-CRISPR screens can be accessed via the NCBI Sequence Read Archive (SRA BioProject ID PRJNA1111960). Data for all figures are provided as source data. Source data are provided with this paper.

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Acknowledgements

We thank the Fred Hutch Shared Resources Genomics and Bioinformatics Cores, particularly A. Dawson and E. Jensen for performing Illumina and Sanger sequencing, respectively; the Fred Hutch Center for Data Visualization, especially M. Zager, N. Thorpe and S. Minot, for their support with MAGeCK analysis; M. Emerman, M. Ohainle, C. Stoddard and A. Willcox for helpful discussions and technical assistance. We also thank M. Emerman (Division of Human Biology, Fred Hutchinson Cancer Center) for sharing LAI and VSV-G envelope plasmids with us. Figures 1a, 3a and 5 were generated with BioRender. This work was supported by the following NIH grants: NICHD R01 HD103571 to J.O. and NIGMS T32 GM007270 and NIAID F31 AI165168 to H.L.I. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. Daryl Humes

    Present address: Tr1X Inc, La Jolla, CA, USA

Authors and Affiliations

  1. Molecular and Cellular Biology PhD Program, University of Washington, Seattle, WA, USA

    Hannah L. Itell

  2. Division of Human Biology, Fred Hutchinson Cancer Center, Seattle, WA, USA

    Hannah L. Itell, Jamie Guenthoer, Daryl Humes, Nell E. Baumgarten & Julie Overbaugh

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Contributions

J.O. conceived the project and J.O., H.L.I., J.G. and D.H. designed the methodology of the study. H.L.I., J.G., D.H. and N.E.B. performed experiments. H.L.I., J.G. and D.H. conducted the data analysis, with the supervision of J.O. H.L.I. generated data visualizations. J.O. and H.L.I. wrote the paper with input from J.G., D.H. and N.E.B.

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Correspondence to Julie Overbaugh.

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Nature Microbiology thanks Nuria Izquierdo-Useros and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 SLC35A2 KO has opposite effects on two HIV-1 strains that utilize different coreceptors, related to Figures 2b, 2c, and 3e.

a. Independent experimental replicates for data depicted in Fig. 2b. Reverse transcriptase (RT) activity, relative (rel) to CD19 KO for each independent primary CD4+ T cell experiment, at 2 dpi (MOI=0.02). Knockouts were performed in five donors across independent knockout and infection experiments as indicated. b. Independent experimental replicates for data depicted in Fig. 2c. Percentage of CD4+ T cells staining positive for HIV-Gag at 3 dpi (MOI=1). Donor letters correspond with those in Panel A. c. Independent experimental replicates for data depicted in Fig. 3e. Percentage of GFP+ CD4+ T cells, relative to CD19 KO, after 2 days of infection with GFP-expressing HIV-1 pseudoviruses (MOI=1). Infection data from all panels are shown as the mean of 2-3 technical replicates, as indicated by individual data points.

Source data

Extended Data Fig. 2 SLC35A2 KO differentially impacts CXCR4-tropic and CCR5-tropic HIV-1, related to Figure 2e.

a. Percentage of CD4+ T cells staining positive for HIV-Gag, relative (rel) to CD19 KO, at 6 dpi (MOI=0.02, same infections as depicted in Fig. 2e, different measurement of HIV-1 infection). b. RT activity over time from spreading infections (MOI=0.02) in primary CD4+ T cells from two donors to determine coreceptor tropism. Results are separated by expected coreceptor tropism. Donor numbering is consistent within this figure and with Fig. 2e. Additional information on the strains used for infections is presented in Extended Data Table 1.

Source data

Extended Data Fig. 3 Expression of SLC35A2 and the HIV-1 coreceptors in CD4+ T cells from the blood and common HIV-1 transmission sites.

RNA expression levels for (a) SLC35A2, (b) CXCR4, and (c) CCR5 in CD4+ T cells isolated from different anatomical compartments from four donors. Shapes denote donors.

Source data

Extended Data Table 1 Characteristics of the HIV-1 strains used for infections
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Supplementary Table 1 (download XLSX )

Supplementary Tables.

Supplementary Data 1 (download XLSX )

Supplementary Data 1. Gene-level MAGeCK analysis results for HIV-CRISPR screens, related to Fig. 1b–d. Four biologically independent screens were conducted in total: two HIV-1 strains (Q23.BG505, LAI) using CD4+ T cells from two donors. Each tab contains the raw MAGeCK analysis ‘rra.gene_summary’ results as well as a column called ‘MAGeCK Score’, which equals the −log10(pos|score). One-sided P values are independently reported for positive and negative enrichment. For MAGeCK analysis, genomic DNA reads were considered as the ‘control’ condition, and viral RNA-derived counts were considered as ‘treatment’. Therefore, ‘pos’ indicates enrichment in the viral RNA. All enrichment scores reported in figures utilize the computed ‘MAGeCK Score’. For more detailed information on this analysis pipeline, please refer to the MAGeCKFlute publication (Wang, B. et al. Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute. Nat. Protoc. 14, 756–780, doi: 10.1038/s41596-018-0113-7 (2019)).

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Itell, H.L., Guenthoer, J., Humes, D. et al. Host cell glycosylation selects for infection with CCR5- versus CXCR4-tropic HIV-1. Nat Microbiol 9, 2985–2996 (2024). https://doi.org/10.1038/s41564-024-01806-7

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