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. 2021 May 24;16(5):e0251969.
doi: 10.1371/journal.pone.0251969. eCollection 2021.

Geospatial HIV-1 subtype C gp120 sequence diversity and its predicted impact on broadly neutralizing antibody sensitivity

Jyoti Sutar  1   2 , Suprit Deshpande  1 , Ranajoy Mullick  1   2 , Nitin Hingankar  1 , Vainav Patel  3 , Jayanta Bhattacharya  1   2
Affiliations

Geospatial HIV-1 subtype C gp120 sequence diversity and its predicted impact on broadly neutralizing antibody sensitivity

Jyoti Sutar et al. PLoS One. .

Abstract

Evolving diversity in globally circulating HIV-1 subtypes presents a formidable challenge in defining and developing neutralizing antibodies for prevention and treatment. HIV-1 subtype C is responsible for majority of global HIV-1 infections. In the present study, we examined the diversity in genetic signatures and attributes that differentiate region-specific HIV-1 subtype C gp120 sequences associated with virus neutralization outcomes to key bnAbs having distinct epitope specificities. A total of 1814 full length HIV-1 subtype C gp120 sequence from 37 countries were retrieved from Los Alamos National Laboratory HIV database (www.hiv.lanl.gov). The amino acid sequences were assessed for their phylogenetic association, variable loop lengths and prevalence of potential N-linked glycosylation sites (pNLGS). Responses of these sequences to bnAbs were predicted with a machine learning algorithm 'bNAb-ReP' and compared with those reported in the CATNAP database. Subtype C sequences from Asian countries including India differed phylogenetically when compared with that from African countries. Variable loop lengths and charges within Indian and African clusters were also found to be distinct from each other, specifically for V1, V2 and V4 loops. Pairwise analyses at each of the 25 pNLG sites indicated distinct country specific profiles. Highly significant differences (p<0.001***) were observed in prevalence of four pNLGS (N130, N295, N392 and N448) between South Africa and India, having most disease burden associated with subtype C. Our findings highlight that distinctly evolving clusters within global intra-subtype C gp120 sequences are likely to influence the disparate region-specific sensitivity of circulating HIV-1 subtype C to bnAbs.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Phylogenetic Analysis of HIV-1 subtype C gp120 amino acid sequence.
A. A maximum likelihood tree depicting phylogenetic association of 1837 HIV-1 subtype C amino acid sequences depicted radially. The legend describes the country codes as well as the sequence distribution among the countries. B. A maximum likelihood subtree detailing phylogenetic association between sequences from South Africa and Those from India, China and Nepal. The black dots indicate nodes with corresponding SH-aLRT and bootstrap values.
Fig 2
Fig 2. Assessment of Variable region characteristics.
A. variable region length: gp120 variable region (V1, V2, V1+V2, V4, V5) as well as entire gp120 lengths have been plotted on the Y axis against the Countries of origin indicated on the X axis. B. Variable region charge: gp120 variable region Charges for V1, V2 and V1+V2 as well as hypervariable regions within them have been plotted on the Y axis against the Countries of origin indicated on the X axis. P values have been indicated following a statistical analysis by Kruskal-Wallis test followed by Dunn’s multiple comparison.
Fig 3
Fig 3. Assessment of potential N-linked glycosylation sites.
A heatmap comparison of abundance of pNLG sites plotted on Y axis against countries plotted on X axis. Each pixel represents 1 pNLG site data from 1 country. Specific domains of pNLGs have been indicated along the Y axis. Color key represents correlation of color intensity with abundance of pNLGs ranging from 0 to 100.
Fig 4
Fig 4. Entropy analysis.
A. Shannon entropy (bits) at key sites for bnAbs VRC01, VRC03, VRC07, VRC13, CAP256:VRC26.25, PGDM1400, PG9, PG16, PGT121 and PGT128 excluding positions in hypervariable regions have been plotted for overall Subtype C, South Africa (ZA), India (IN), Malawi (MW), Botswana (BW), Zambia (ZM) and Tanzania (TZ). Statistical comparison of entropy distribution through p values has been performed following application of Mann-Whitney test. B. Shannon Entropy difference (H(background)-H(query) (unit: bits) has been plotted on Y axis against each amino acid position on X axis, where ZA dataset was the background while IN dataset was the query. Different domains of gp120 have been indicated. Bars with red color indicate positions with statistically significant entropy differences. Bars above 0 indicate higher entropy in South Africa while those below 0 indicate higher entropy in India. C. Variable entropy positions are plotted on prefusion gp120 envelope model derived from PDB:5U7O. Residue position highlighted in red indicate statistically significantly higher entropy in India compared to South Africa while those highlighted in blue indicate statistically significantly higher entropy in South Africa compared to India (C).
Fig 5
Fig 5. Abundance of bnAb resistance associated residues.
A circos heatmap depicting abundance of bnAb resistance associated residues was plotted for 11 bnAbs (VRC01, VRC07, PGT121, PGT128, PGT145, PG9, PG16, VRC26.25, 3BNC117, 10–1074 and N6) wherein each track indicates the country of origin. Each pixel on the circular track indicates a specific residue position colored as per abundance of resistance causing residues at that position as per the color key.
Fig 6
Fig 6. Prediction of bnAb sensitivity across different countries.
Each panel indicates available country-wise CATNAP data plotted next to country-wise prediction data for available sequences for 3BNC117, VRC01, VRC03, VRC07, VRC13, CAP256:VRC26.25, PGDM1400, PG9, PG16, PGT145, PGT121, and PGT128 and 10–1074. In the CATNAP data panels, country-wise violin plots have been inlayed with boxplots against reported IC50 (μg/mL) values. Black dots indicate outliers while red dots indicate median values. Red background zone indicates bnAb resistance (IC50 >50 μg/mL) while green background zone indicates bnAb sensitivity (IC50 < 50 μg/mL). In the bNAb-ReP data panels, country-wise violin plots have been inlayed with boxplots against probability of neutralization as predicted by bNAb-ReP. Black dots indicate outliers while red dots indicate median values. Red background zone indicates probable bnAb resistance (Neutralization probability < 0.5) while green background zone indicates probable bnAb sensitivity (Neutralization probability > 0.5).
Fig 7
Fig 7. Assessment of predicted bnAb sensitivity over time.
Each panel indicates cumulative prediction data for available sequences for 3BNC117, VRC01, VRC03, VRC07, VRC13, CAP256:VRC26.25, PGDM1400, PG9, PG16, PGT145, PGT121, and PGT128 and 10–1074 plotted against 3-time periods as follows: 1986–2000 (N = 244), 2001–2010 (N = 1187) and 2011–2019 (N = 333). P values indicate trend analysis performed by Jonckheere-Terpstra test. P values < 0.05 were considered statistically significant and have been indicated with ‘*’.
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