Letter to the Editor - Journal of Virology - American Society for

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Caution for Microarray Studies. Human immunodeficiency virus (HIV) is capable of infect- ing diverse blood leukocytes, through which interaction be- tween HIV ...
JOURNAL OF VIROLOGY, Oct. 2008, p. 10326–10327 0022-538X/08/$08.00⫹0 doi:10.1128/JVI.01386-08 Copyright © 2008, American Society for Microbiology. All Rights Reserved.

Vol. 82, No. 20

Letter to the Editor Transitory Viremic Surges in a Human Immunodeficiency Virus-Positive Elite Controller Can Shift the Cellular Transcriptome Profile: a Word of Caution for Microarray Studies䌤 Human immunodeficiency virus (HIV) is capable of infecting diverse blood leukocytes, through which interaction between HIV and cellular transcriptome can be unveiled. Recently, the transcriptional profiles of primary CD4⫹ and CD8⫹ T cells from HIV-positive individuals have been shown to distinguish progression from nonprogression (3). This study, together with our study on cell surface antigens using a protein microarray (8), has demonstrated that the CD8⫹ T-cell transcriptome and its surface are more informative in differentiating HIV disease groups than the CD4⫹ T cells. This is consistent with our recent findings, which showed that the whole-genome transcriptome in primary CD8⫹ T cells was able to segregate HIV-positive viremic (VIR) patients on highly active antiretroviral therapy (HAART) from therapy-naı¨ve long-term nonprogressors (LTNPs) (7). Here, we wish to highlight how a subtle surge in plasma viremia in HIVpositive LTNPs can affect the clustering pattern of the cellular transcriptome, which should be an imperative consideration for genome-wide microarray studies on primary cells from HIV-positive patients. We used the Illumina Sentrix Human-6 Expression BeadChip to generate genome-wide expression profiles of CD8⫹ T cells at two time points over 12 months from four LTNPs along with the VIR group and negative controls (Table 1). Cluster analysis at time point 1 revealed a distinct cluster of LTNPs away from the VIR group (Fig. 1A). Strikingly, 1 year later, the cluster analysis at time point 2 segregated all LTNPs but one (L4), which coincidently grouped with the VIR group (Fig. 1B). Retrospective tracking of L4 plasma viremia revealed 1,000 copies/ml. Interestingly, a similar exception was also noted in Hyrcza’s study (3), where one nonprogressor patient (L4) with 269 copies/ml formed a distinct cluster with a progressor (C4). These unexpected findings from two independent studies clearly provide several lessons which may prove to be pertinent to all microarray studies. First, plasma viremia is a key determinant of the clustering pattern between the VIR and LTNP groups, as evident from our study that the low viremic surge could shift the whole transcriptome profile despite the therapy effects. This shift could represent the effects of viral replication resulting in the change of certain CD8⫹ T-cell subsets (1), which was reflected at the transcriptome level. This shift also highlighted the greater effects of viral replication on the cellular transcriptome as opposed to other factors, including HAART (2, 5). Second, the most current viral loads (VLs) should be determined prior to microarray studies, as intermittent viremia surges do occur in HIV-positive patients responding well to HAART (4) and therapy-naive LTNPs, and they should be seriously considered. Third, stringent clinical criteria should be adopted for selecting and/or defining HIV-positive LTNPs for microarray studies, as suggested recently (6). Finally, a VL threshold may exist, above which the transcriptome profile can shift. This postulation came from our and Hyrcza’s data that the transcriptome profile from LTNPs with VLs of ⬍50 copies/ml clustered together, whereas the transcriptome profile in nonprogressors with VLs

TABLE 1. Clinical details of study patients Time point and participanta

Time point 1 VIR VI VII VIII LTNP L1 L2 L3 L4 Time point 2 VIR V1 V2 V3 V4 V5 LTNP L1 L2 L3 L4

Viral load (copies/ml)

CD4 count (cells/␮l)

98 11,500 53,900

617 230 350

688 560 900

6/6/06 6/14/06 6/14/06

⬍50 ⬍50 ⬍50 ⬍50

690 817 880 760

650 513 860 1,800

6/28/06 6/28/06 7/18/06 7/21/06

334 2,130 4,940 668,000 713,000

282 296 387 86 40

2,281 905 1,239 335 70

5/29/07 5/30/07 7/11/07 8/17/07 8/17/07

⬍50 ⬍50 ⬍50 1,000

630 714 920 670

579 476 900 1,600

7/30/07 7/30/07 7/19/07 7/2/07

CD8 count (cells/␮l)

Collection date (mo/day/yr)

a The VIR group (VI to VIII and V1 to V5) represents viremic patients who received two nucleoside reverse transcriptase inhibitors in association with one or two protease inhibitors, except V3, who received one nonnucleoside reverse transcriptase inhibitor, etravirine, one integrase inhibitor, raltegravir, and two protease inhibitors. The nucleoside reverse transcriptase inhibitors the patients received included zidovudine, lamivudine, stavudine, emtricitabine, and tenofovir; the protease inhibitors included darunavir, ritonavir, indinavir, saquinavir, and atazanavir. The LTNP group (L1 to L4) represents therapy-naı¨ve patients with ⬎21 years of HIV infection, CD4 counts of ⬎630 cells/␮l, and viremia below detection (⬍50 copies of HIV RNA/ml). These LTNPs, according to the current definition, can be grouped as elite controllers.

of 269 to 1,000 copies/ml shifted (3). Should this threshold be determined, it may serve as a reference guide for future microarray studies. REFERENCES 1. Agrati, C., C. Gioia, F. Soldani, F. Martini, A. Antinori, and F. Poccia. 2006. T cell selection and differentiation in AIDS disease: the model of HIV-discordant monozygotic twins. J. Biol. Regul. Homeost. Agents 20:24–28. 2. Gray, C., J. Schapiro, M. Winters, and T. Merigan. 1998. Changes in CD4⫹ and CD8⫹ T cell subsets in response to highly active antiretroviral therapy in HIV type 1-infected patients with prior protease inhibitor experience. AIDS Res. Hum. Retrovir. 14:561–569. 3. Hyrcza, M., C. Kovacs, M. Loutfy, R. Halpenny, L. Heisler, S. Yang, O. Wilkins, M. Ostrowski, and S. Der. 2007. Distinct transcriptional profiles in ex vivo CD4⫹ and CD8⫹ T cells are established early in human immunodeficiency virus type 1 infection and are characterized by a chronic interferon response as well as extensive transcriptional changes in CD8⫹ T cells. J. Virol. 81:3477–3486. 4. Jones, L., and A. Perelson. 2007. Transient viremia, plasma viral load, and

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FIG. 1. Clustering analysis of global gene expression profiles of CD8⫹ T cells from LTNPs at two time points over 12 months, along with those from the viremic patients and negative controls. Panels A and B show time points 1 and 2, respectively. Groups are designated as follows: VI to VIII and V1 to V5, VIR patients on HAART; L1 to L4, LTNP group, therapy-naı¨ve, ⬎21 years of HIV infection, CD4 counts of ⬎630 cells/␮l, and viremia below detection (⬍50 copies/ml); N1 to N5, HIV-negative controls. Note that the members of the LNTP group according to the current definition can be grouped as elite controllers. At time point 1 (A), L4 clustered together with all of the other three LTNPs (L1 to L3), whereas at time point 2 (B), L4 experienced a subtle viremia surge, which led to the clustering of L4 with all the VIR patients (V1 to V5). Peripheral blood mononuclear cells from a single blood sample (10 ml in EDTA) were isolated immediately by Ficoll gradient centrifugation. CD8⫹ T cells were obtained by positive isolation (Dynal Biotech, Oslo, Norway). Total RNA was isolated using the RNeasy minikit (Qiagen Pty. Ltd., Clifton Hill, Victoria, Australia) with an integrated step of on-column DNase treatment. RNA quality was checked by Agilent Bioanalyzer and RNA integrity scores of ⬎7. cRNAs were amplified and hybridized to the Sentrix Human-6 Expression BeadChip. Following quality assessment, the raw data were normalized using cubic spline function. Similarities in the gene expression patterns among individuals were evaluated and visualized with the BeadStudio v3 Cluster Analysis tool. The algorithm used is named Correlation, which computes the Pearson correlation using a 1 ⫺ r distance measure. The distance on the x axis represents the similarity relationships among samples.

reservoir replenishment in HIV-infected patients on antiretroviral therapy. J. Acquir. Immune Defic. Syndr. 45:483–493. 5. Plana, M., C. Martinez, F. Garcia, M. Maleno, J. Barcelo, A. Garcia, M. Lejeune, C. Vidal, A. Cruceta, J. Miro, T. Pumarola, T. Gallart, and J. Gatell. 2002. Immunologic reconstitution after 1 year of highly active antiretroviral therapy, with or without protease inhibitors. J. Acquir. Immune Defic. Syndr. 29:429–434. 6. Saksena, N., B. Rodes, B. Wang, and V. Soriano. 2007. Elite HIV controllers: myth or reality? AIDS Rev. 9:195–207.

7. Wu, J. Q., D. E. Dwyer, W. B. Dyer, Y. H. Yang, B. Wang, and N. K. Saksena. 9 August 2008. Transcriptional profiles in CD8⫹ T cells from HIV⫹ progressors on HAART are characterized by coordinated up-regulation of oxidative phosphorylation enzymes and interferon responses. Virology doi:10.1016/j.virol.2008.06.039. 8. Wu, J., B. Wang, L. Belov, J. Chrisp, J. Learmont, W. Dyer, J. Zaunders, A. Cunningham, D. Dwyer, and N. Saksena. 2007. Antibody microarray analysis of cell surface antigens on CD4⫹ and CD8⫹ T cells from HIV⫹ individuals correlates with disease stages. Retrovirology 4:83.

Jing Qin Wu Bin Wang Nitin K. Saksena* Retroviral Genetics Division Center for Virus Research Westmead Millennium Institute Westmead, NSW 2145 Sydney, Australia *Phone: 612 9845 9119 Fax: 612 9845 9103 E-mail: [email protected]

Published ahead of print on 13 August 2008.