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Rebound HIV-1 in cerebrospinal fluid after antiviral therapy interruption is mainly clonally amplified R5 T cell-tropic virus

Nature Microbiology volume 8, pages 260–271 (2023)Cite this article

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Abstract

HIV-1 persists as a latent reservoir in people receiving suppressive antiretroviral therapy (ART). When ART is interrupted (treatment interruption/TI), rebound virus re-initiates systemic infection in the lymphoid system. During TI, HIV-1 is also detected in cerebrospinal fluid (CSF), although the source of this rebound virus is unknown. To investigate whether there is a distinct HIV-1 reservoir in the central nervous system (CNS), we compared rebound virus after TI in the blood and CSF of 11 participants. Peak rebound CSF viral loads vary and we show that high viral loads and the appearance of clonally amplified viral lineages in the CSF are correlated with the transient influx of white blood cells. We found no evidence of rebound macrophage-tropic virus in the CSF, even in one individual who had macrophage-tropic HIV-1 in the CSF pre-therapy. We propose a model in which R5 T cell-tropic virus is released from infected T cells that enter the CNS from the blood (or are resident in the CNS during therapy), with clonal amplification of infected T cells and virus replication occurring in the CNS during TI.

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Fig. 1: HIV-1 rebound populations in participants with high CSF viral load.
Fig. 2: HIV-1 in CSF is replicating and adapted to growth in T cells.
Fig. 3: HIV-1 rebound populations compared to pre-therapy viruses.
Fig. 4: Inflammatory biomarkers in treatment interruption.

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Data availability

The sequences of the full-length env amplicons are available in GenBank (accession numbers ON599411–ON59572). The deep sequencing data are available in the Sequencing Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA880316). Source data are provided with this paper.

Code availability

Illumina MiSeq data were analysed using TCS pipeline version 2.5.1 (https://www.primer-id.org/?from_old).

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Acknowledgements

We thank the many participants who donated the specimens that were analysed in this study, and the UNC High Throughput Sequencing Facility for their assistance in generating the sequence data. This work was supported by NIH grant R01 NS094067 (R.W.P.), the UNC Center for AIDS Research (NIH award P30 AI050410 to R.S.), the UNC Lineberger Comprehensive Cancer Center (NIH award P30 CA16068 to R.S.) and by the Swedish state, under an agreement between the Swedish government and the county councils (ALF agreement ALFGBG-717531 to M.G.). H.Z. is a Wallenberg Scholar supported by grants from the Swedish Research Council (no. 2018-02532), the European Research Council (no. 681712) and the Swedish State Support for Clinical Research (no. ALFGBG-720931).

Author information

Authors and Affiliations

  1. Department of Microbiology and Immunology, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Laura P. Kincer, Sarah Beth Joseph & Shuntai Zhou

  2. Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Laura P. Kincer, Sarah Beth Joseph, Maria M. Gilleece, Blake M. Hauser, Sabrina Sizemore, Shuntai Zhou & Ronald Swanstrom

  3. UNC HIV Cure Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sarah Beth Joseph

  4. UNC Center for AIDS Research, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Sarah Beth Joseph & Ronald Swanstrom

  5. Biogen, Research Triangle Park, NC, USA

    Maria M. Gilleece

  6. Ragon Institute of MGH, MIT and Harvard, Cambridge, MA, USA

    Blake M. Hauser

  7. Vitalant Research Institute, San Francisco, CA, USA

    Clara Di Germanio

  8. Department of Laboratory Medicine, University of California San Francisco, San Francisco, CA, USA

    Clara Di Germanio

  9. Department of Psychiatry and Neurochemistry, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Mölndal, Sweden

    Henrik Zetterberg

  10. Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden

    Henrik Zetterberg

  11. Department of Neurodegenerative Disease, UCL Institute of Neurology, Queen Square, London, UK

    Henrik Zetterberg

  12. UK Dementia Research Institute at UCL, London, UK

    Henrik Zetterberg

  13. Hong Kong Center for Neurodegenerative Diseases, Clear Water Bay, Hong Kong, China

    Henrik Zetterberg

  14. Division of Biological Chemistry, Biocenter, Innsbruck Medical University, Innsbruck, Austria

    Dietmar Fuchs

  15. Division of HIV, Infectious Diseases, and Global Medicine, Zuckerberg San Francisco General Hospital, University of California San Francisco, San Francisco, CA, USA

    Steven G. Deeks

  16. Department of Neurology, Yale University School of Medicine, New Haven, CT, USA

    Serena Spudich

  17. Department of Infectious Diseases, Sahlgrenska Academy at the University of Gothenburg, Gothenburg, Sweden

    Magnus Gisslen

  18. Region Västra Götaland, Sahlgrenska University Hospital, Department of Infectious Diseases, Gothenburg, Sweden

    Magnus Gisslen

  19. Department of Neurology, University of California San Francisco, San Francisco, CA, USA

    Richard W. Price

  20. Department of Biochemistry and Biophysics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Ronald Swanstrom

Authors
  1. Laura P. Kincer
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Contributions

R.S., R.W.P., M.G., H.Z. and S.G.D. conceived the study. S.Z. developed the methodology. L.P.K., M.M.G., B.M.H., S. Sizemore, C.D.G. and D.F. conducted the investigations. L.P.K., S.B.J., S.Spudich., M.G., R.W.P. and R.S. analysed the data. R.S. and S.B.J. wrote the manuscript with input from R.W.P., M.G., S. Spudich and L.P.K.

Corresponding author

Correspondence to Ronald Swanstrom.

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UNC is pursuing IP protection for Primer ID and R.S. has received nominal royalties.

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Extended data

Extended Data Fig. 1 Statistical analysis of the link between CSF viral load and pleocytosis.

The ratio of CSF viral load to blood viral load was calculated for each time point shown in Fig. 1. This was done to normalize the CSF viral load as a fraction of the blood viral load thus reducing the variability in blood set point viral load; in this analysis the larger the value the closer the CSF viral load approached that of the blood. These values were then grouped as coming from time points where the white blood cell count was greater than 5/μl or less than or equal to 5/μl. The values of these two groups were compared using the two-sided Mann-Whitney test, with the p value included in the graph. In this analysis we did not correct for the fact that multiple values were collected for each participant.

Source data

Extended Data Fig. 2 Deep sequencing of HIV-1 populations from participant 51126.

Neighbor-joining phylogenetic tree containing a large sampling of template consensus sequences (TCS) from MiSeq/Primer ID sequencing from the pretherapy and TI timepoints. Sequences from the blood plasma pretherapy are shown in pink (1,734 TCS). Sequences from the CSF pretherapy are shown in light blue (1,734 TCS). Sequences from the blood plasma post TI are shown in red (2,233 TCS). Sequences from the CSF post TI are shown in dark blue (2,233 TCS). Also included in gray are 50 sequences from the blood and CSF from each of the two intermediate decay timepoints. On the right the tree is expanded to show the portion where the macrophage-tropic virus lineage was found in the CSF pretherapy.

Extended Data Fig. 3 Marker analysis during TI.

Further analysis of markers was done for the five participants shown in Fig. 4. Two additional graphs are presented in vertical columns for each of the participants. In the top graph is shown the level of NfL as a function of time post TI/enrollment (purple circles). Also included is the QNPZ4 score test of neurocognition (tan diamonds). The vertical dashed line is drawn at the time point of the initial peak CSF viral load for each participant (Fig. 1). In the lower graph of the pair, the values of sCD163 are shown (light blue circles), and sCD14 (orange squares).

Extended Data Fig. 4 Statistical analysis of links between biomarkers and pleocytosis during TI.

Selected biomarker data presented in Fig. 4 and Extended Data Fig. 3 were pooled based on the presence or absence of pleocytosis (WBC count greater than or less than 5/μl, respectively) in the CSF. These two groups were compared for IP-10 (a), MMP9 (b), neopterin (c), NfL (d), or the neurocognitive score QNPZ4 (e). The groups were compared using the two-sided Mann-Whitney test, with the p values indicated on each graph. A Bonferroni correction for multiple comparisons indicates a significant p value cutoff of 0.01. No corrections were made for the fact that multiple values were included for each participant.

Source data

Extended Data Table 1 Extended information about sample collection, viral loads, cell counts, sequencing results, and compartmentalization tests
Full size table
Extended Data Table 2 Diagnosis and treatment summary
Full size table
Extended Data Table 3 Amplification and sequencing primers
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Supplementary information

Source data

Source Data Fig. 2

Infectivity data including biological replicates.

Source Data Fig. 3

Infectivity data including biological replicates.

Source Data Extended Data Fig. 1

Statistical source data for VL ratio comparison.

Source Data Extended Data Fig. 4

Statistical source data for biomarker comparisons.

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Kincer, L.P., Joseph, S.B., Gilleece, M.M. et al. Rebound HIV-1 in cerebrospinal fluid after antiviral therapy interruption is mainly clonally amplified R5 T cell-tropic virus. Nat Microbiol 8, 260–271 (2023). https://doi.org/10.1038/s41564-022-01306-6

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