Human gut microbiota is associated with HIV-reactive immunoglobulin at baseline and following HIV vaccination


Autoři: Jacob A. Cram aff001;  Andrew J. Fiore-Gartland aff001;  Sujatha Srinivasan aff001;  Shelly Karuna aff003;  Giuseppe Pantaleo aff004;  Georgia D. Tomaras aff005;  David N. Fredricks aff001;  James G. Kublin aff003
Působiště autorů: Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America aff001;  University of Maryland Center for Environmental Science, Cambridge, Maryland, United States of America aff002;  HIV Vaccine Trials Network, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Research Center, Seattle, Washington, United States of America aff003;  Service of Immunology and Allergy, and Swiss Vaccine Research Institute, Lausanne University Hospital (CHUV), Lausanne, Switzerland aff004;  Duke Human Vaccine Institute, Duke University School of Medicine, Durham, North Carolina, United States of America aff005
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225622

Souhrn

Antibodies that recognize commensal microbial antigens may be cross reactive with a part of the human immunodeficiency virus (HIV) envelope glycoprotein gp41. To improve understanding of the role of the microbiota in modulating the immune response to HIV vaccines, we studied the associations of the gut microbiota composition of participants in the HIV Vaccine Trials Network 096 clinical trial with their HIV-specific immune responses in response to vaccination with a DNA-prime, pox virus boost strategy designed to recapitulate the only efficacious HIV-vaccine trial (RV144). We observed that both levels of IgG antibodies to gp41 at baseline and post-vaccination levels of IgG antibodies to the Con.6.gp120.B, ZM96.gp140 and gp70 B.CaseA V1-V2 antigens were associated with three co-occurring clusters of family level microbial taxa. One cluster contained several families positively associated with gp41-specific IgG and negatively associated with vaccine-matched gp120, gp140 and V1-V2-specific IgG responses. A second cluster contained families that negatively associated with gp41 and positively associated with gp120, gp140 and V1-V2-specific IgG responses. A third cluster contained microbial groups that did not correlate with any immune responses. Baseline and post-vaccination levels of gp41 IgG were not significantly correlated, suggesting that factors beyond the microbiome that contribute to immune response heterogeneity. Sequence variant richness was positively associated with gp41, p24, pg140 and V1-V2 specific IgG responses, gp41 and p24 IgA responses, and CD4+ T cell responses to HIV-1 proteins. Our findings provide preliminary evidence that the gut microbiota may be an important predictor of vaccine response.

Klíčová slova:

Antibodies – Antigens – Community structure – HIV vaccines – HIV-1 – Immune response – Microbiome – Vaccines


Zdroje

1. Collins N, Belkaid Y. Do the microbiota influence vaccines and protective immunity to pathogens?: engaging our endogenous adjuvants. Cold Spring Harb Perspect Biol. 2017 Apr 21;a028860.

2. Littman DR. Do the microbiota influence vaccines and protective immunity to pathogens?: If so, is there potential for efficacious microbiota-based vaccines? Cold Spring Harb Perspect Biol. 2017 Apr 21;a029355.

3. Macpherson AJ. Do the microbiota influence vaccines and protective immunity to pathogens?: Issues of sovereignty, federalism, and points-testing in the prokaryotic and eukaryotic spaces of the host-microbial superorganism. Cold Spring Harb Perspect Biol. 2017 Apr 21;a029363.

4. Tomaras GD, Yates NL, Liu P, Qin L, Fouda GG, Chavez LL, et al. Initial B-Cell Responses to Transmitted Human Immunodeficiency Virus Type 1: Virion-Binding Immunoglobulin M (IgM) and IgG Antibodies Followed by Plasma Anti-gp41 Antibodies with Ineffective Control of Initial Viremia. J Virol. 2008 Dec 15;82(24):12449–63. doi: 10.1128/JVI.01708-08 18842730

5. Williams WB, Han Q, Haynes BF. Cross-reactivity of HIV vaccine responses and the microbiome. Curr Opin HIV AIDS. 2018 Jan;13(1):9–14. doi: 10.1097/COH.0000000000000423 29035947

6. Trama AM, Moody MA, Alam SM, Jaeger FH, Lockwood B, Parks R, et al. HIV-1 Envelope gp41 Antibodies Can Originate from Terminal Ileum B Cells that Share Cross-Reactivity with Commensal Bacteria. Cell Host Microbe. 2014 Aug 13;16(2):215–26. doi: 10.1016/j.chom.2014.07.003 25121750

7. Williams WB, Liao H-X, Moody MA, Kepler TB, Alam SM, Gao F, et al. Diversion of HIV-1 vaccine–induced immunity by gp41-microbiota cross-reactive antibodies. Science. 2015 Jul 30;aab1253.

8. Liao H-X, Chen X, Munshaw S, Zhang R, Marshall DJ, Vandergrift N, et al. Initial antibodies binding to HIV-1 gp41 in acutely infected subjects are polyreactive and highly mutated. J Exp Med. 2011 Oct 24;208(11):2237–49. doi: 10.1084/jem.20110363 21987658

9. Harris VC, Armah G, Fuentes S, Korpela KE, Parashar U, Victor JC, et al. Significant correlation between the infant gut microbiome and rotavirus vaccine response in rural ghana. J Infect Dis. 2017 Jan 1;215(1):34–41. doi: 10.1093/infdis/jiw518 27803175

10. Huda MN, Lewis Z, Kalanetra KM, Rashid M, Ahmad SM, Raqib R, et al. Stool microbiota and vaccine responses of infants. Pediatrics. 2014 Aug 1;134(2):e362–72. doi: 10.1542/peds.2013-3937 25002669

11. Zimmermann P, Curtis N. The influence of probiotics on vaccine responses—A systematic review. Vaccine. 2018 Jan;36(2):207–13. doi: 10.1016/j.vaccine.2017.08.069 28923425

12. Rerks-Ngarm S, Pitisuttithum P, Nitayaphan S, Kaewkungwal J, Chiu J, Paris R, et al. Vaccination with ALVAC and AIDSVAX to Prevent HIV-1 Infection in Thailand. N Engl J Med. 2009 Dec 3;361(23):2209–20. doi: 10.1056/NEJMoa0908492 19843557

13. Haynes BF, Gilbert PB, McElrath MJ, Zolla-Pazner S, Tomaras GD, Alam SM, et al. Immune-Correlates Analysis of an HIV-1 Vaccine Efficacy Trial. N Engl J Med. 2012 Apr 5;366(14):1275–86. doi: 10.1056/NEJMoa1113425 22475592

14. Yates NL, Liao H-X, Fong Y, deCamp A, Vandergrift NA, Williams WT, et al. Vaccine-Induced Env V1–V2 IgG3 Correlates with Lower HIV-1 Infection Risk and Declines Soon After Vaccination. Sci Transl Med. 2014 Mar 19;6(228):228ra39. doi: 10.1126/scitranslmed.3007730 24648342

15. Zolla-Pazner S, deCamp A, Gilbert PB, Williams C, Yates NL, Williams WT, et al. Vaccine-Induced IgG Antibodies to V1V2 Regions of Multiple HIV-1 Subtypes Correlate with Decreased Risk of HIV-1 Infection. PLOS ONE. 2014 Feb 4;9(2):e87572. doi: 10.1371/journal.pone.0087572 24504509

16. Robb ML, Rerks-Ngarm S, Nitayaphan S, Pitisuttithum P, Kaewkungwal J, Kunasol P, et al. Risk behaviour and time as covariates for efficacy of the HIV vaccine regimen ALVAC-HIV (vCP1521) and AIDSVAX B/E: a post-hoc analysis of the Thai phase 3 efficacy trial RV 144. Lancet Infect Dis. 2012 Jul 1;12(7):531–7. doi: 10.1016/S1473-3099(12)70088-9 22652344

17. Pantaleo G, Janes H, Karuna S, Grant S, Ouedraogo L, Allen M, et al. Co-administration of HIV Env protein with DNA and/or NYVAC vaccines in humans results in earlier and potent generation of anti-Env antibody responses. Lancet HIV. In Press;

18. Pantaleo G, Janes H, Tomaras GD, Montefiori DC, Frahm N, Grant S, et al. Comparing Different Priming Strategies to Optimize HIV Vaccine Antibody Responses: Results from HVTN 096/EV04 (NCT01799954) [Internet]. HIV Research for Prevention 2016; 2016 Oct 19; Chicago, IL. https://www.liebertpub.com/doi/pdf/10.1089/aid.2016.5000.abstracts

19. NYVAC-HIV Vaccine Used in the HVTN 092 and HVTN 096 Clinical Trials | NIH: National Institute of Allergy and Infectious Diseases [Internet]. [cited 2019 Mar 28]. https://webcache.googleusercontent.com/search?q=cache:TYpc9WpAsbcJ:https://www.niaid.nih.gov/news-events/nyvac-hiv-vaccine-used-hvtn-092-and-hvtn-096-clinical-trials+&cd=1&hl=en&ct=clnk&gl=us&client=ubuntu

20. Sui Y, Lewis GK, Wang Y, Berckmueller K, Frey B, Dzutsev A, et al. Mucosal vaccine efficacy against intrarectal SHIV is independent of anti-Env antibody response. J Clin Invest. 2019 Mar 1;129(3):1314–28. doi: 10.1172/JCI122110 30776026

21. Li F, Malhotra U, Gilbert PB, Hawkins NR, Duerr AC, McElrath JM, et al. Peptide selection for human immunodeficiency virus type 1 CTL-based vaccine evaluation. Vaccine. 2006 Nov 17;24(47–48):6893–904. doi: 10.1016/j.vaccine.2006.06.009 16890329

22. Srinivasan S, Hoffman NG, Morgan MT, Matsen FA, Fiedler TL, Hall RW, et al. Bacterial Communities in Women with Bacterial Vaginosis: High Resolution Phylogenetic Analyses Reveal Relationships of Microbiota to Clinical Criteria. PLOS ONE. 2012 Jun 18;7(6):e37818. doi: 10.1371/journal.pone.0037818 22719852

23. Lin L, Finak G, Ushey K, Seshadri C, Hawn TR, Frahm N, et al. COMPASS identifies T-cell subsets correlated with clinical outcomes. Nat Biotechnol. 2015 Jun;33(6):610–6. doi: 10.1038/nbt.3187 26006008

24. Zhao N, Chen J, Carroll IM, Ringel-Kulka T, Epstein MP, Zhou H, et al. Testing in Microbiome-Profiling Studies with MiRKAT, the Microbiome Regression-Based Kernel Association Test. Am J Hum Genet. 2015 May 7;96(5):797–807. doi: 10.1016/j.ajhg.2015.04.003 25957468

25. Willis A, Martin BD, Trinh P, Barger K, Bunge J. breakaway: Species Richness Estimation and Modeling [Internet]. 2018. https://adw96.github.io/breakaway/

26. Lovell D, Pawlowsky-Glahn V, Egozcue JJ, Marguerat S, Bähler J. Proportionality: A Valid Alternative to Correlation for Relative Data. PLOS Comput Biol. 2015 Mar 16;11(3):e1004075. doi: 10.1371/journal.pcbi.1004075 25775355

27. Benjamini Y, Hochberg Y. Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. J R Stat Soc Ser B Methodol. 1995;57(1):289–300.

28. Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP. Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research. 2016 Jun 24;5:1492. doi: 10.12688/f1000research.8986.1 27508062

29. Storey JD. A direct approach to false discovery rates. J R Stat Soc Ser B Stat Methodol. 2002 Aug 1;64(3):479–98.

30. Hooper LV, Littman DR, Macpherson AJ. Interactions between the microbiota and the immune system. Science. 2012 Jun 8;336(6086):1268–73. doi: 10.1126/science.1223490 22674334

31. Jovel J, Patterson J, Wang W, Hotte N, O’Keefe S, Mitchel T, et al. Characterization of the Gut Microbiome Using 16S or Shotgun Metagenomics. Front Microbiol [Internet]. 2016 [cited 2019 May 27];7. https://www.frontiersin.org/articles/10.3389/fmicb.2016.00459/full

32. Bálint M, Bahram M, Eren AM, Faust K, Fuhrman JA, Lindahl B, et al. Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes. FEMS Microbiol Rev. 2016 Sep 1;40(5):686–700. doi: 10.1093/femsre/fuw017 27358393

33. Wang X, Li H, Bezemer TM, Hao Z. Drivers of bacterial beta diversity in two temperate forests. Ecol Res. 2016 Jan;31(1):57–64.

34. Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2010 Sep 9;5:169–72. doi: 10.1038/ismej.2010.133 20827291

35. Lagos R, Fasano A, Wasserman SS, Prado V, San Martin O, Abrego P, et al. Effect of Small Bowel Bacterial Overgrowth on the Immunogenicity of Single-Dose Live Oral Cholera Vaccine CVD 103-HgR. J Infect Dis. 1999;180(5):1709–12. doi: 10.1086/315051 10515838


Článek vyšel v časopise

PLOS One


2019 Číslo 12