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1 Nature Medicine Article Title Corresponding Authors Supplementary Information CCL3L1-CCR5 genotype influences durability of immune recovery during antiretroviral therapy of HIV-1 infected individuals Sunil K. Ahuja and Matthew J. Dolan Acknowledgements 2 Supplementary Methods and Note 3 A. Supplementary Methods 3 A1. HAART during chronic HIV infection -WHMC cohort 3 A2. HAART during early infection - AIEDRP cohort from UCSF 6 A3. HAART during acute and early HIV infection - AIEDRP cohort from UCSD 6 A4. CCL3L1, CCR5 genotyping and CCL3L1-CCR5 genetic risk groups (GRG) 7 A5. HLA genotyping in the WHMC HIV + cohort 8 A6. DTH skin testing in the WHMC HIV + cohort 9 A7. Statistical methods. 9 B. Supplementary Note 12 B1. Supplementary Results 12 B2. Number of subjects and measurements of CD4 + T cell counts and viral load (VL) for the 13 analyses shown in this paper Supplementary Figures 18 Supplementary Figure 1 Supplementary Figure 2 Supplementary Figure 3 Influence of CCL3L1, CCR5 and CCL3L1-CCR5 GRG on recovery of CD4 + T cell counts and effect of HAART-induced VL suppression on disease course in HIV + subjects from the WHMC cohort. Influence of CCL3L1-CCR5 GRG on post-haart CD4 + T cell trajectories assessed using mixed models. Impact of GRG on CD4 + T cell and VL trajectory in HIV + subjects from the WHMC and AIEDRP cohorts and association of the HLA-A*68 and HLA-C*16 alleles with surrogate markers of HIV disease progression in the WHMC cohort. Supplementary Tables 23 Supplementary Table 1 Supplementary Table 2 Supplementary Table 3 Effects of CCL3L1-CCR5 GRG status on CD4 + cell recovery in VL suppressors (data complementing that shown in Table 1 of the main text). Replication of the results shown in Table 3 models 1 and 2 in the main text in therapy naïve subjects. Likelihood of HAART-induced viral load suppression in subjects from the HIV + WHMC cohort who possessed a moderate or high GRG compared to those with a low GRG. Supplementary Table 4 Pre-HAART CD4 and VL distribution by GRGs in subjects from the HIV + WHMC cohort who received HAART during chronic infection. References

2 ACKNOWLEDGEMENTS We thank S. Wegner and other members of IDCRP for critical support of this work; G. Crawford for invaluable programmatic support at UTHSCSA; members of the Ahuja lab and E. Wright for critical reading of the manuscript; R. Bosch for statistical advice; S. Anderson, W. Bradley, and R. Sanchez for technical assistance; L. Song and B. Shah for outstanding dedication to the graphic work; and A. S. Ahuja for forbearance. This work was supported by the Veterans Administration Center on AIDS and HIV infection of the South Texas Veterans Health Care System, and a MERIT (R ) and other awards (AI and MH069270) from the NIH to S.K.A. S.K.A. is a recipient of the Elizabeth Glaser Scientist Award and the Burroughs Wellcome Clinical Scientist Award in Translational Research. Support for the WHMC cohort was provided by the Infectious Disease Clinical Research Program (IDCRP) of the Uniformed Services University of the Health Sciences (USUHS), of which the Tri-Service AIDS Clinical Consortium (TACC) is a component. The IDCRP is a Department of Defense tri-service program executed through USUHS and the Henry M. Jackson Foundation for the Advancement of Military Medicine (HJF), in collaboration with HHS/NIH/NIAID/DCR through Interagency Agreement HU The cohort studied from UCSF was supported by an NIH award (AI41531) to FRH. This work was supported in part by grants AI043638, and AI47745 from the National Institutes of Health, the UCSD Center for AIDS Research (AI36214) and the San Diego Veterans Affairs Healthcare System. This work was also supported in part by grants from the NIAID (AI052745, AI to SGD), the UCSF/ Gladstone CFAR (P30 MH59037, P30 AI27763), and the General Clinical Research Center at San Francisco General Hospital (5-MO1-RR00083). We are indebted to the subjects who enrolled in these studies and who made this work possible. The opinions or assertions contained herein are the private views of the authors, and are not to be construed as official, or as reflecting the views of the Departments of the Army, Navy, Air Force, or the Department of Defense. For space constraints we regret our inability to cite additional excellent work. 2

3 SUPPLEMENTARY METHODS AND NOTE A. Supplementary Methods A1. HAART during chronic HIV infection - WHMC cohort The WHMC cohort is a prospective observational cohort that is a component of the United States Military s Tri-Service AIDS Clinical Consortium (TACC) Natural History Study. This racially and ethnically balanced single-site cohort, whose baseline characteristics have been previously described 1,2, is one of the largest cohorts of HIV-positive patients followed prospectively at a single medical center. In our previous genotype-phenotype association studies reported to date, we analyzed 1,132 subjects from the study period beginning January 14, 1986 and ending December 31, 1999 (Refs. 1-3). Extensive details of these 1,132 subjects, including their recruitment dates and follow-up periods, have been described 1-4. In this study to determine the association between the CCL3L1-CCR5 GRGs and reconstitution of CD4 + T cell counts, we extended the study period to November 16, 2005, examining a total of 1,279 patients, including the 1,132 subjects studied previously. These 1,279 Received HAART N = 502 GRG known N = 445 Post-HAART VL available N = 441 VL suppressors N = 298 Sustained VL suppressors N = 144 Entire Cohort N = 1,279 Did not receive HAART N = 777 GRG unknown N = 57 Post-HAART VL not available N = 4 VL non-suppressors N = 143 Transient VL suppressors N = 154 subjects were 54% European Americans, 37% African Americans, 6% Hispanic Americans and 3% of other ethnic origins. This study of 1,279 subjects represents 7,748.9 person-years of followup. At the end of this study period, 36% of these patients had progressed to AIDS (1987 criteria), and 34% had died. Average age at seropositivity in the cohort was 30.1 years (standard deviation 7.4), and 94% were males. Following the introduction of protease inhibitors in 1996 and non-nucleoside reverse transcriptase inhibitors in 1997, HAART usage in the cohort increased quickly from 50% in December 1997 to a peak of 80% in 1999, subsequently declining to a stable plateau of ~70%. These characteristics of this subset of subjects from the WHMC cohort who received HAART is similar 3

4 to that reported recently 4. A schematic description of the WHMC cohort members based on their therapy status and viral load (VL) following HAART is shown above. Of the 502 patients that received HAART, 219 (43.6%) were therapy-naïve and 283 (56.4%) had received ART previously. The patients who received HAART had a similar ethnic and gender composition as the overall cohort. Of those who received HAART, 13 progressed to AIDS and one died. Median follow-up after initiation of HAART was for 4.27 years with an interquartile range of 2.01 to 7.19 years, and there were no differences in the length of the follow-up by genotype. In the subjects with low CCL3L1-CCR5 GRG the median (IQR) length of follow-up was 4.19 ( ) years while in subjects with a moderate-high GRG it was 4.50 ( ) years (Mann- Whitney P for difference = ). The maximum length of follow-up was 9.75 years, and total follow-up was for person-years. Approximately 80%, 62% and 50% of patients commenced HAART when their CD4 cell counts were >200, >300 and >350 cells/mm 3, respectively. The table below shows that the number of subjects with the indicated CCL3L1-CCR5 GRG from the WHMC HIV + cohort according to the HAART initiation CD4 + count was similar (Numbers in the parenthesis are the percentages. Overall difference, χ 2 = 0.31, P = 0.853). CCL3L1-CCR5 GRG Pre-HAART CD4 + (cells/mm 3 ) Total < Low 80 (62%) 82 (59%) 162 (60%) Moderate 44 (34%) 52 (37%) 96 (36%) High 5 (4%) 5 (4%) 10 (4%) Total Age at initiation of HAART was 36 years (standard deviation of 6.9 years). The table below shows that the mean age (standard errors) of subjects from the HIV + WHMC cohort at time of initiation of HAART according to pre-haart CD4 + (cells/mm 3 ) and VL was also similar by GRGs (P by Mann-Whitney test). Low GRG Moderate-High GRG P Pre-HAART CD (1.03) 34.2 (1.50) < (0.84) 35.2 (0.96) < (0.87) 35.1 (1.13) to < (1.55) 36.7 (1.64) < (0.97) 34.2 (1.16) < (1.07) 34.2 (1.45) Pre-HAART VL < 20, (1.05) 36.5 (1.46) ,000 to < 55, (1.40) 35.1 (3.88) , (1.05) 34.2 (1.12)

5 Additionally, the number of subjects from the WHMC HIV + cohort who initiated HAART during chronic infection by CCL3L1-CCR5 GRG and calendar year was similar [In the table below, the significance value for differences in distribution of the GRGs by the calendar year in which HAART was initiated (assessed by ANOVA) was as follows: between low and moderate GRG, P = ; between low and high GRG, P = Number in parenthesis reflects the percentage of subjects with the indicated GRG]. Year Low GRG Moderate GRG High GRG Total N (%) N (%) N (%) N (%) (1.2) 1 (0.6) 0 (0.0) 4 (0.9) (26.7) 57 (33.9) 5 (22.7) 130 (29.2) (32.2) 44 (26.2) 5 (22.7) 131 (29.4) (15.7) 25 (14.9) 3 (13.6) 68 (15.3) (8.6) 10 (6.0) 2 (9.1) 34 (7.6) (3.1) 11 (6.7) 0 (0.0) 19 (4.3) (4.3) 8 (4.8) 0 (0.0) 19 (4.3) (3.9) 6 (3.6) 3 (13.6) 19 (4.3) (3.1) 4 (2.4) 1 (4.6) 13 (2.9) (1.2) 1 (0.6) 1 (4.6) 5 (1.1) (0.0) 1 (0.6) 2 (9.1) 3 (0.7) Total 255 (100.0) 168 (100.0) 22 (100.0) 445 (100.0) Finally, the number of subjects who initiated HAART in each calendar year in the WHMC cohort by pre-haart CD4 levels were also similar [P by analysis of covariance = Pre- HAART CD4 refers to the average CD4 cell count during the three months prior to initiation of HAART]. Year 450 N (%) < 450 N (%) Pre-HAART CD4 T cell count (cells/mm 3 ) < 350 N (%) 300 to < 450 N (%) < 300 N (%) < 200 N (%) Total N (%) (10.3) 58 (32.0) 45 (34.4) 18 (22.2) 40 (40.0) 26 (48.1) 67 (25.0) (36.8) 49 (27.1) 31 (23.7) 26 (32.1) 23 (23.0) 11 (20.4) 81 (30.2) (17.2) 28 (15.5) 21 (16.0) 12 (14.8) 16 (16.0) 9 (16.7) 43 (16.0) (14.9) 7 (3.9) 4 (3.0) 4 (4.9) 3 (3.0) 1 (1.9) 20 (7.5) (5.7) 8 (4.4) 5 (3.8) 6 (7.4) 2 (2.0) 2 (3.7) 13 (4.9) (8.0) 7 (3.9) 5 (3.8) 4 (4.9) 3 (3.0) 2 (3.7) 14 (5.2) (3.4) 10 (5.5) 10 (7.6) 4 (4.9) 6 (6.0) 2 (3.7) 13 (4.9) (1.1) 8 (4.4) 7 (5.3) 4 (4.9) 4 (4.0) 0 (0.0) 9 (3.4) (1.1) 3 (1.7) 2 (1.5) 1 (1.2) 2 (2.0) 1 (1.9) 4 (1.5) (1.1) 3 (1.7) 1 (0.8) 2 (2.5) 1 (1.0) 0 (0.0) 4 (1.5) Total 87 (100.0) 181 (100.0) 131 (100.0) 81 (100.0) 100 (100.0) 54 (100.0) 268 (100.0) 5

6 The voluntary, fully informed consent of the subjects used in this research was obtained as required by Air Force Regulation and additional approval from the Institutional Review Board (IRB) of the University of Texas Health Science Center, San Antonio, TX. A2. HAART during early infection - AIEDRP cohort from University of California San Francisco (UCSF) In this prospectively recruited cohort, 315 HIV-infected patients were enrolled in the University of California San Francisco (UCSF) Options project; this cohort is part of the Acute Infection and Early Disease Research Program (AIEDRP) sponsored by the National Institute of Allergy and Infectious Diseases, Division of AIDS, National Institutes of Health (NIH). Subjects with signs or symptoms of an acute retroviral syndrome or evidence of recent HIV infection were evaluated for study entry. Acute HIV-1 infection was defined by a detectable HIV RNA (>5,000 copies/ml) at baseline in the presence of a negative HIV enzyme immunoassay (EIA) and followed by subsequent HIV seroconversion or a positive HIV EIA but indeterminate Western Blot. Recent or early HIV infection was defined by a positive HIV EIA and an HIV-1 DT-EIA1 of 1.0 (defined as sample OD-negative control OD/positive control OD), in the presence of a CD4 cell count > 200/mm 3 or CD4% > 14 or a documented negative HIV EIA in the days prior to the date of HIV EIA seroconversion. Subjects were excluded if they had received more than 7 days of antiretroviral therapy at any time prior to study entry. All subjects were offered 3- drug combination therapy immediately upon entry. However, the decision to initiate antiretroviral therapy was voluntary, and those who chose not to start therapy were followed in a manner identical to those who chose to start therapy. In the current analyses only subjects in whom HAART was initiated during early infection are included. Additional details of the cohort studied are on the next page. A3. HAART during acute and early HIV infection - AIEDRP cohort from University of California San Diego (UCSD) This cohort was also part of the AIEDRP sponsored by NIH. The entry and exclusion criteria were similar to that for the UCSF component of the AIEDRP cohort. Some of the features of the subjects enrolled in the UCSD site for the AIEDRP have been described previously 5-8. The UCSD had a substantially larger number of subjects who were recruited during acute infection, 6

7 and for this reason a separate analysis for these individuals is shown in Fig. 2a. Details of the subjects studied herein from the AIEDRP cohort are listed below. Additional details regarding the AIEDRP can be obtained from Dr. Susan Little (slittle@ ucsd.edu) for the UCSD site and Dr. Rick Hecht (rhecht@php.ucsf.edu) for the UCSF site. Additional features of the AIEDRP program are available at Following is a summary of the characteristics of the study subjects in the AIEDRP cohort who received HAART during acute or early HIV infection. CCL3L1-CCR5 GRG data was available only in a subset of these subjects. Characteristic UCSF UCSD N Ethnicity (N, %) European Americans (EA) African Americans (AA) Hispanic Americans (HA) Others 225 (71.4%) 16 (5.1%) 34 (10.8%) 40 (12.7%) 177 (74.4%) 16 (6.7%) 36 (15.1%) 9 (3.8%) Subjects receiving HAART (N, %) 209 (66.3%) 174 (73.1%) Maximum follow-up post HAART (years) Median (IQR) follow-up post-haart (years) 2.29 (3.04) 2.04 (2.20) Total follow-up (person years) Age at cohort entry (mean ± SD years) 40.8 ± ± 9.0 Males (N, %) 299 (94.9%) 225 (94.5%) Stage of HIV infection at the time of recruitment (N, %) Acute Early 0 (0.0%) 315 (100.0%) 36 (15.1%) 202 (84.9%) CD4 + T cell count at HAART initiation (mean ± SE cells/mm 3 ) ± ± 13.4 Log 10 VL at HAART initiation (mean ± SE copies/ml) 4.29 ± ± 0.07 Distribution of CCL3L1-CCR5 GRGs (N, %) Low Moderate High 195 (73.6%) 64 (24.2%) 6 (2.3%) 154 (67.8%) 68 (30.0%) 5 (2.2%) A4. CCL3L1, CCR5 genotyping and CCL3L1-CCR5 genetic risk groups (GRG) Methods for determining the copy number of CCL3L1 and CCR5 haplotype pairs (genotypes) as well as their categorization into four CCL3L1-CCR5 GRGs are as described previously 1,2. Briefly, we found recently that a copy number of CCL3L1 lower than the average copy number of 2 in HIV-infected European Americans or 3 in HIV-infected African Americans was associated with a rapid rate of disease progression. These were designated as CCL3L1 low, while copy numbers 2 or 3 in European- and African-Americans, respectively, were designated as CCL3L1 high. We designated CCR5 genotypes associated with a rapid rate of progression to AIDS 7

8 and death as CCR5 det (detrimental CCR5 genotypes) and the remaining genotypes as CCR5 nondet (Non-detrimental CCR5 genotypes; Ref. 2). Based on possession of a CCL3L1 low or CCL3L1 high, and CCR5 det or CCR5 nondet, variations in CCL3L1 and CCR5 segregate into four GRGs 2. By using the critical χ 2 test and Akaike information criterion, we found that a three-group GRG system, in which the genotypic groups CCL3L1 high -CCR5 det and CCL3L1 low -CCR5 nondet were combined into a single category, provided the most predictive information for prognosticating the risk of AIDS progression 4. Based on the association of these groups with a low (CCL3L1 high -CCR5 nondet ), moderate (CCL3L1 high -CCR5 det or CCL3L1 low -CCR5 nondet ) or high (CCL3L1 low -CCR5 det ) risk of progressing to AIDS or death 2,4, we labeled patients CCL3L1-CCR5 genotype as a low, moderate or high GRG (shown above Fig. 1d). For some analyses, we combined the moderate and high GRGs into a single category, and given the low prevalence of a high GRG, the data for the combined analyses reflects, in large part the effects of the moderate GRG. A5. HLA genotyping in the WHMC HIV + cohort HLA genotyping: Genomic DNA was isolated from PBMCs by using QIAamp columns containing a silica membrane (Qiagen Inc, Valencia, CA) from the HIV-1 infected individuals recruited at Wilford Hall Medical Center, San Antonio, TX. HLA alleles were genotyped at the molecular level by using a combination of PCR-SSOP methods. Briefly, genomic DNA was amplified by using specific primers for HLA-A, HLA-B, HLA-C and HLA-DRB1 provided by the manufacturer (Tepnel Lifecodes, Stamford, CT) to obtain a product spanning exons 2-3 for class I and exon 2 for class II alleles in a 100 µl PCR reaction. Five microliter of the amplified product were run on the agarose gel to confirm the amplification and the remaining was applied to a series of positively charged nylon membranes (Amersham Hybond, Piscataway, NJ). After chemical denaturation with NaOH and neutralization, the membranes were hybridized with locus-specific probes labeled with alkaline phosphatase and the positive hybridization signal was revealed with Lumiphos 480 substrate (Tepnel Lifecodes) and a permanent radiographic record was obtained. Patterns of hybridization for allele assignment were by QuickType An additional degree of resolution was reached at by using a fluorescent bead-based assay using the Luminex platform (Luminex, Austin, TX) for confirmation and enhanced discrimination of the HLA alleles. In brief, the LIFEMATCH System (Tepnel Lifecodes) for HLA typing is based on the simultaneous detection of multicolored beads in suspension. Identically sized polystyrene beads have two fluorophores. Each different colored bead set can carry a different sequence-specific 8

9 oligonucleotide probe. The LIFEMATCH fluoroanalyzer is a minidigital processing flow analyzer that uses two lasers and is capable of discriminating up to 100 different colored beads in a single reaction. In our study, one tube reaction containing the PCR amplified specific HLA product was hybridized with a set of probes attached to the fluorescent beads and further discrimination of positive hybridization was allowed by the use of Streptavidin-Phycoerythrin binding to PCR products carrying original biotin-labeled primers. HLA allele assignation was performed by using the LIFEMATCH program. A6. DTH skin testing in the WHMC HIV + cohort The protocols for conducting the DTH skin tests in the WHMC HIV + cohort are highly standardized and are as described 4,9-11. Each patient at enrollment and then prospectively received the standard Mantoux type of intradermal skin test. The protocols for conducting the DTH skin tests in the WHMC HIV + cohort are highly standardized and were as described previously The antigens and concentrations used were as follows: mumps (Connaught), 40 colony-forming units per milliliter full strength until unavailability as of July 2003; trichophyton (Holister-Stier), 1:500 dilution until removed from the market by the FDA in June 1996; candida (Walter Reed Army Institute of Research, 200 PNU/mL), 1:100 dilution; and tetanus toxoid (Lederle, 1.6 Lf/mL), 1:100 dilution. Test results were assessed at 48 hours. Skin test results were considered positive when the diameter of induration was 5mm. As Trichophyton was unavailable after 1996, the DTH responses during the HAART era which coincided with the calendar year 1996 were for a threeantigen panel. However, because subjects had differences in duration of HIV infection at cohort enrollment, the best DTH response was chosen to provide a standardized measure of CMI status. 1,030 (91.47%) subjects had four DTH antigens placed while 96 (8.53%) had three. From time of enrollment into the HIV + WHMC cohort, the median and mean time to the best DTH responses detected during disease course was 48 days, and 1.34 years (95% confidence interval (CI): yrs), respectively. Additional details are as described previously 4. A7. Statistical methods Time trends in CD4 + T cell counts before and after accounting for the GRGs were computed at the level of the entire cohort during the calendar years from 1986 to 2005 as well as from the time of initiation of HAART. The calendar years were divided into eras depending on whether ART or HAART (mono- and dual therapy era, and HAART era) was available or not. Time 9

10 trends were also determined in subjects in whom the HAART-initiating CD4 + T cell counts were < or 350 cells/mm 3. Unless otherwise stated, the pre-haart CD4 + T cell count levels were defined as the average of all CD4 + T cell count measurements within one year prior to initiation of HAART. We applied a similar definition to compute the pre-haart viral load. Nonlinear or linear generalized estimating equations (GEEs) were used to model the time trends in CD4 + T cell counts in subjects from the WHMC cohort. For the nonlinear GEEs, we used spline-smoothing based on knots at annual intervals and then constructed 95% confidence bands. The regression coefficients estimated using GEE models were compared across the GRGs using Student s t-test. The goodness-of-fit of the GEE models were assessed using the Wald statistic. Since the splinesmoothed curves indicate the population-level dynamics of CD4 + T cell counts, they do not reflect the cross-sectional measurements of these parameters at the level of an individual. As the GEE estimates may be biased by a potential difference in the duration of follow-up after initiation of HAART, we used linear mixed models to estimate the monthly rates of changes in CD4 + T cell counts. Since the results for the CCL3L1-CCR5 GRG status shown in Supplementary Fig. 2 were concordant with those shown in Fig. 1e and Table 1, we used the GEE models for the rest of the analyses. Time trends in the viral load and CD4 + T cell counts in subjects from the AIEDRP cohorts were at times also graphically represented by Loess curves (e.g., Fig. 2a-c). In Fig. 2a (lower panel), to account for repeated measures of CD4 + counts per individual, mean and 95% confidence interval were generated by linear GEE, and the resulting coefficients were compared using Student s t-test. To determine the association between the GRGs and viral load suppression in subjects from the WHMC cohort, we used a clustered logistic regression model where the identifier for each subject was used as the clustering variable, thereby accounting for the intra-individual correlation among viral load estimates. Since square-root transformed CD4 + T cell counts are known to follow the normality assumption more faithfully than raw CD4 + T cell counts, we also replicated key analyses using the square-root transformed CD4 + T cell counts as the outcome variable. Again, these results (Supplementary Fig. 1f and Supplementary Table 1) were concordant with their corresponding analyses. For this reason, we have presented the data using raw CD4 + T cell counts for the purposes of ease of description. Rationale for using 2 years as the cut-point for segmented regression analyses of post- HAART CD4 trajectories. Overall, the trajectory of CD4 trend after initiation of HAART at the level of cohort showed a non-linear profile with a visible hump close to two years before which 10

11 the gain was faster and after which the gain was slower (Fig. 1a). To statistically corroborate this visual observation, we made use of breakpoint regression using the program SegRegW ( which predicts the breakpoint. However, since we had used the GEE method for the cohort-level analysis, we first predicted the overall non-linear trend using Stata program xtgee. To these predicted values, we applied the SegRegW program. The program predicted a single breakpoint at 2.32 years. To make the analysis simple and clinically meaningful, we chose 2 years as the cutpoint for segmented regression analyses presented in the paper in Fig. 1f. Statistical approaches to test the association of HLA alleles with AIDS progression rates: We used two approaches to assess the association of the HLA alleles with early events in HIV disease course. In the first approach, we used backward elimination stepwise multivariate Cox proportional hazards regression analyses to shortlist alleles at each HLA locus (i.e. HLA-A, HLA- B, HLA-C and HLA-DRB1) that are associated with variable rates of disease progression. The alleles from these four HLA loci that were found to be significantly associated with variable rates of disease progression were then simultaneously included in another stepwise Cox multivariate model to derive at a final list of HLA alleles that were significantly associated with HIV disease course. Finally, we adjusted the effects of these HLA alleles for the disease-modifying effects of baseline CD4 + T cell count, initial viral load, the best DTH response during disease, nadir CD4 + T cell count, percentage of CD4 + T cells and recruitment in therapy era. The description and nature of the these covariates in the WHMC cohort are as described previously 4. We observed that this final model contained only 3 HLA alleles HLA-A*68, HLA-B*57 and HLA-C*16 (Fig. 3a). We then examined the association of each of these three alleles with HIV-AIDS more extensively (Fig. 3a and Supplementary Fig. 3b). First using Student s t test we assessed the association of these three alleles with parameters that reflect early immune damage, i.e., baseline CD4 + T cell count, steady-state viral load, and best DTH response as well as cumulative CD4 + T cell count. We found that only HLA-B*57 influenced all three parameters reflective of early immune damage and cumulative CD4 + T cell counts (Fig. 3b and Supplementary Fig. 3b). Based on this data, we then determined the CD4 + T cell count trajectories from time of initiation of HAART for the B*57 allele (Fig. 3c). Since the three HLA alleles demonstrated a strong association for disease progression, we estimated the strength of association of CCL3L1- CCR5 GRGs with rates of CD4 + T cell recovery after adjusting (using multivariate GEE models) for the effects of these three alleles (shown in Model 3 in Table 3). 11

12 In the second and complementary statistical approach, we dichotomized the WHMC cohort into subjects possessing rare versus common class I HLA alleles since possession of common class I HLA alleles have been shown to be associated with a faster rate of disease progression We adapted the methods of Brumme et al 14. For these analyses, each subject was assigned a HLA allele frequency score reflecting the sum of the HLA allele frequencies at each individual locus, as well as the sum of allele frequencies across combined loci. The allele frequency used for the calculation of the scores was conducted in a race-specific manner. For example, an African American subject with *23/*30, *51/*52 and *04/*16 genotypes for the HLA-A, HLA-B and HLA-C loci, respectively received scores of ( ), 4.46 ( ) and ( ) for the locus A, B and C, respectively. The total HLA allele frequency score for the abovementioned example was The scores reflect the race-specific allele frequency of the observed HLA alleles in the WHMC cohort. After scoring all the cohort subjects, we generated two HLA groups: those possessing common alleles (HLA allele frequency score median score of the specific racial/ethnic group) and rare alleles (HLA allele frequency score < median score of the specific racial/ethnic group). We assessed the association of this dichotomous variable with the surrogate markers of HIV disease progression (Fig 3d). An advantage of this approach was that it permitted us to model the contribution of all HLA alleles without the need to create a short list of significant alleles as used in the first statistical approach. As the HLA allele frequency score was significantly associated with the markers of early immune damage, the effect of the CCL3L1- CCR5 GRGs on recovery of CD4 + T cell counts was also determined after adjustment for the HLA allele frequency scores (Table 3, model 4). B. Supplementary Note B1. Supplementary Results. The specificity of the effects of GRG on CD4 + recovery was assessed by accounting for the following potential parameters and covariates. The impact of GRG status on CD4 + cell recovery in the WHMC cohort was unlikely to be solely due to the high GRG as subjects with this genotype comprise a small proportion of the cohort (~8%) and when this group is removed from the comparative analyses, it is evident that the majority of the effects were due to differences in CD4 + recovery between those possessing a low vs moderate GRG (Table 2 and data not shown). Additionally, the influence of GRG status on CD4 + cell recovery was unlikely to be due to significant 12

13 inter-grg differences in the viral loads during HAART, as (i) among subjects from the WHMC cohort, CCL3L1-CCR5 GRG did not influence the ability to attain viral loads below detectable limits (Supplementary Table 3) and GRG status stratified the rate of change in CD4 + cell counts, regardless of whether subjects had sustained or transient viral load suppression (Table 1); (ii) the trajectories of the decline in viral load after initiation of HAART did not differ by GRG status in subjects from the AIEDRP cohort (Supplementary Fig. 3c); and (iii) the time to attain viral load suppression did not differ by GRG status in subjects from the WHMC cohort (Supplementary Fig. 3d). We also excluded other factors that might confound the association between GRG status and variable recovery of CD4 + cell counts during HAART among subjects from the WHMC cohort. We found that: (i) the number of subjects who initiated HAART < or 350 CD4-cells/ mm 3 did not differ by GRG status (see Table in Supplementary Methods section); (ii) the age at which HAART was initiated did not differ by GRG status (see Table in Supplementary Methods section); (iii) the number of subjects who initiated HAART in each of the calendar years did not differ by GRG status (low vs moderate GRG, P = ; low vs high GRG, P = assessed by ANOVA, and see Table in Supplementary Methods section); (iv) the CD4 + cell count at which the HAART was initiated did not differ as a function of the calendar year in which HAART was initiated (P using analysis of covariance = , and see Table in Supplementary Methods section); (v) there were no significant differences in the CD4 + cell counts or VL according to both GRG and the pre-haart CD4 + cell count or viral load (Supplementary Table 4); (vi) the pre- HAART viral load did not influence the effects of the GRGs on CD4 + cell count recovery (data not shown); and (vii) after square-root transformation of the raw CD4 + T cell counts concordant associations for GRG status and recovery of CD4 + cell counts were detected (Supplementary Fig. 1f and Supplementary Table 1). B2. Number of subjects and measurements of CD4 + T cell counts and viral load (VL) for the analyses shown in this paper Figures or Tables Group Number of CD4 + count subjects measurements Fig. 1a All subjects 1,235 18,741 Fig. 1b CCL3L1 high 753 7,758 CCL3L1 low ,983 Fig. 1c CCR5 non-det 1,023 15,912 CCR5 det 212 2,829 VL measurements 13

14 Low GRG ,570 Fig. 1d Moderate GRG 517 7,273 High GRG Fig. 1e left CCL3L1 high 315 4,040 CCL3L1 low 130 1,474 Fig. 1e middle CCR5 non-det 378 4,667 CCR5 det Low GRG 255 3,173 Fig.1e right Moderate GRG 168 2,172 High GRG Fig. 1f left CCL3L1 high 315 4,040 CCL3L1 low 130 1,474 Fig. 1f middle CCR5 non-det 378 4,667 CCR5 det Low GRG 255 3,173 Fig.1f right Moderate GRG 168 2,172 High GRG Fig. 2a top Low GRG Moderate-High GRG 7 97 Fig. 2a bottom Low GRG Moderate-High GRG 7 97 Fig. 2b top VL suppressed 238 2,422 VL not suppressed Fig. 2b bottom Low GRG 168 1,746 Moderate-High GRG Fig. 2c top CCL3L1 high 202 2,061 CCL3L1 low Fig. 2c bottom CCR5 non-det 202 2,076 CCR5 det Fig. 2d top Low GRG 108 1,352 Moderate-High GRG Fig. 2d bottom Low GRG 126 1,000 Moderate-High GRG 83 1,199 Fig. 2e top CCL3L1 high CCL3L1 low Fig. 2e bottom CCL3L1 high CCL3L1 low Fig. 3a Numbers given in figure legend Fig. 3b HLA-B*57 present 52 (Steady-state VL) HLA-B*57 absent 697 Fig. 3b HLA-B*57 present 75 (Baseline CD4) HLA-B*57 absent 941 Fig. 3b HLA-B*57 present 75 (DTH) HLA-B*57 absent 937 Fig. 3b HLA-B*57 present 75 (Cumulative CD4) HLA-B*57 absent 927 Fig. 3c Absent 369 4,087 Present

15 Fig. 3d Common 342 (Steady-state VL) Rare 396 Fig. 3d Common 512 (Baseline CD4) Rare 484 Fig. 3d Common 511 (DTH) Rare 481 Fig. 3d Common 505 (Cumulative CD4) Rare 476 Fig. 3e Common 212 2,366 Rare 179 1,937 Table 1 Numbers correspond to those in Figure 1 Low GRG Table 2 Moderate-High GRG (<350) Moderate GRG Table 2 (< 300) Table 2 (< 250) Table 2 (< 200) Table 2 (< 150) Table 2 (< 100) Table 2 (< 50) Table 3 model 1&2 (First two year 350) Table 3 model 1&2 (First two year < 350) Table 3 model 1&2 (After two year 350) Table 3 model 1&2 (After two year < 350) Table 3 model 3&4 (First two year 350) Table 3 model 3&4 (First two year < 350) Table 3 model 3&4 (After two year 350) Table 3 model 3&4 (After two year < 350) Low GRG Moderate-High GRG Moderate GRG Low GRG Moderate-High GRG Moderate GRG Low GRG Moderate-High GRG Moderate GRG Low GRG Moderate-High GRG Moderate GRG Low GRG Moderate-High GRG Moderate GRG Low GRG 9 87 Moderate-High GRG 7 65 Moderate GRG 7 65 Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG Low GRG Moderate-High GRG

16 CCL3L1 = SOM Fig. 1a (EA) CCL3L1 = CCL3L1 = ,141 CCL3L1 > CCL3L1 = 0/ SOM Fig. 1a (AA) CCL3L1 = CCL3L1 = 3/ CCL3L1 > Overall 445 5,514 SOM Fig. 1b left and 1c left VL suppressed 298 4,501 VL not suppressed SOM Fig. 1b right and 1c right Sustained 144 1,679 Transient 154 2,822 Low GRG 171 2,585 SOM Fig. 1d left Moderate GRG 112 1,770 High GRG Low GRG SOM Fig. 1d right Moderate GRG High GRG Null SOM Fig. 1e Half Full 153 1,718 Higher 78 1,028 SOM Fig. 1f Numbers correspond to those in Table 2 SOM Fig. 2 Numbers correspond to those in Figure 1d SOM Fig. 3a top left Low GRG Moderate-High GRG SOM Fig. 3a top right Low GRG Moderate-High GRG SOM Fig. 3a bottom left Low GRG Moderate-High GRG SOM Fig. 3a bottom right Low GRG 72 1,031 Moderate-High GRG SOM Fig. 3b (Steady-state VL) SOM Fig. 3b (Baseline CD4) SOM Fig. 3b (DTH) SOM Fig. 3b (Cumulative CD4) HLA-A*68 present 76 HLA-A*68 absent 681 HLA-C*16 present 95 HLA-C*16 absent 661 HLA-A*68 present 115 HLA-A*68 absent 911 HLA-C*16 present 123 HLA-C*16 absent 904 HLA-A*68 present 114 HLA-A*68 absent 908 HLA-C*16 present 123 HLA-C*16 absent 900 HLA-A*68 present 114 HLA-A*68 absent 896 HLA-C*16 present 123 HLA-C*16 absent

17 SOM Fig. 3c left Low GRG Moderate-High GRG SOM Fig. 3c right Low GRG 233 2,468 Moderate-High GRG 99 1,040 Low GRG 188 SOM Fig. 3d Moderate GRG 121 High GRG 14 SOM Table 1 Numbers correspond to those for Table 1 and for Table 3, model 1 SOM Table 2 Low GRG (First two year 350) Moderate-High GRG SOM Table 2 Low GRG (First two year < 350) Moderate-High GRG SOM Table 2 Low GRG (After two year 350) Moderate-High GRG SOM Table 2 Low GRG (After two year < 350) Moderate-High GRG All subjects 155 SOM Table 3 ART-naïve 71 ART-experienced 84 SOM Table 4 Low GRG 80 (Pre-HAART CD4 <350) Moderate-High GRG 50 SOM Table 4 Low GRG 64 (Pre-HAART CD4 <300) Moderate-High GRG 36 SOM Table 4 Low GRG 45 (Pre-HAART CD4 300 to <450) Moderate-High GRG 33 SOM Table 4 Low GRG 53 (Pre-HAART CD4 450) Moderate-High GRG 38 SOM Table 4 Low GRG 41 (Pre-HAART VL <10000) Moderate-High GRG 18 SOM Table 4 Low GRG 58 (Pre-HAART VL to <55000) Moderate-High GRG 28 SOM Table 4 Low GRG 47 (Pre-HAART VL 55000) Moderate-High GRG 41 17

18 SUPPLEMENTARY FIGURES Supplementary Figure 1. Influence of CCL3L1, CCR5 and CCL3L1-CCR5 GRG on recovery of CD4 + T cell counts and effect of HAART-induced VL suppression on disease course in HIV + subjects from the WHMC cohort. (a) Influence of the CCL3L1 gene dose on the rates of change in CD4 + T cell counts after HAART. The diamonds and error bars represent the mean (diamond) and 95% confidence interval (error bars) estimates of the overall rate of change in CD4 + T cell counts per month. These estimates were obtained using linear GEE models. Categorization of the average CCL3L1 copy number was based on our previous observations of phenotypic equivalence of gene dose in European Americans (EA) and African Americans (AA, Ref. 2). (b) Time trends in the left panel are for CD4 + cell counts (by non-linear GEE) from time-of-initiation of HAART before (black) and after accounting for viral load (VL) suppression status, i.e., VL suppressors (orange) and non-suppressors (brown). Time trends of CD4 + cell counts on the right are for those with VL suppression (transient or sustained). (c) Influence of the depth of HAART-induced suppression of viral load on rates of disease progression to AIDS (1987 criteria). Kaplan-Meier plots on the left are for the rates of progression to AIDS (1987 criteria) in subjects who did or did not attain viral load suppression. Kaplan-Meier plots on the right are for rates of progression to AIDS (1987 criteria) in those with transient or sustained viral load suppression. RH, relative hazard; CI, confidence interval. (d) Time trends of CD4 + T cell counts (by non-linear GEE) according to CCL3L1-CCR5 GRG status in those who attained sustained VL suppression (left) and those who had non-sustained VL suppression (right). Blue, green and red plots denote those with a low, moderate and high GRG status, respectively. (e) Additive effects of CCL3L1 dose and CCR5 genotype on recovery of CD4 + T cell counts. *Null, half, full, and higher CCL3L1 dose indicates 0, 1, 2, >2 copies in European Americans, respectively, and 0 or 1 (combined), 2, 3 or 4 (combined), and >4 copies in African Americans, respectively 2. Rate from time-of-initiation of HAART was determined by estimating the regression coefficients by linear GEE; numbers in parenthesis are SE. There were no subjects who were null for CCL3L1 who also had CCR5 det. (f) Replication of the results shown in Table 2 in the main text using square-root transformed CD4 + T cell counts. Pre-HAART CD4 + T cell count was the average of all CD4 + T cell counts during the three months prior to initiation of HAART. 18

19 a Rate of CD4 count change (cells/month) Copy # EA AA >2 0/1 2 3/4 >4 CCL3L1 low CCL3L1 high CCL3L1 low CCL3L1 high b CD4 + count (cells/mm 3 ) 1000 Overall VL non-suppressors VL suppressors CD4 + count (cells/mm 3 ) VL suppression Transient Sustained Time after HAART (years) Time after HAART (years) c AIDS-free (proportion) VL non-suppressors VL suppressors RH 95% CI P Time (Years) AIDS-free (proportion) VL suppression Transient Sustained RH 95% CI P Time (Years) d CD4 + count (cells/mm 3 ) CD4 + count (cells/mm 3 ) GRG Low Moderate High Time after HAART (years) Time after HAART (years) e CCL3L1 dose* CCR5 non-det CCR5 det Null 1.02 (0.51) - Half 0.71 (0.26) (1.03) Full 2.95 (0.21) 2.24 (0.35) Higher 2.98 (0.22) 2.64 (0.55) P f Pre-HAART CD4 count Low GRG Moderate-High GRG Difference between Low and Moderate-High GRGs < (0.0051) (0.0065) (0.0083) < (0.0054) (0.0082) (0.0098) < (0.0064) (0.0092) (0.0112) < (0.0068) (0.0123) (0.0141) < (0.0086) (0.0133) (0.0158) < (0.0096) (0.0149) (0.0177) < (0.0146) (0.0270) (0.0307) P 6.9 x x x x x x x

20 Supplementary Figure 2. Influence of CCL3L1-CCR5 GRG on post-haart CD4 + T cell trajectories assessed using mixed models. (a) Each panel shows the individual level trends (shown using light colors) and the average trend (thick lines) in CD4 + T cell changes post-haart is superimposed. The blue, green and red colored lines correspond to data for subjects with a low, moderate and high GRG, respectively. The average rates (Rate) of change in CD4 + T cell counts and the standard errors (SE) are shown at the bottom right. The P values denote the significance values for the difference between the rates of change in CD4 + T cell counts compared to the reference group which was the low GRG. (b) Monthly rates of change in CD4 + T cell counts (cells/ month (standard error)) after HAART initiation during chronic HIV infection in the WHMC cohort assessed by mixed models. VL suppression status is as defined in Methods (main text). a CD4 + count (cells/mm 3 ) Low GRG Time after HAART (months) Moderate GRG Time after HAART (months) CD4 + count (cells/mm 3 ) High GRG Time after HAART (months) Rates of CD4 cell recovery (cells/month) Rate (SE) P b CCL3L1-CCR5 GRG VL suppressors VL suprression Sustained Transient Low Moderate High P low-vs-moderate P low-vs-high 3.26 (0.17) 1.43 (0.19) (0.81) 5.25 (0.27) 3.66 (0.35) 0.52 (1.39) 2.66 (0.21) 0.67 (0.22) (0.95)

21 Supplementary Figure 3. Impact of GRG on CD4 + T cell and VL trajectory in HIV + subjects from the WHMC and AIEDRP cohorts and association of the HLA-A*68 and HLA-C*16 alleles with surrogate markers of HIV disease progression in the WHMC cohort. (a) CD4 + T cell trajectory by GRG status in subjects from the WHMC cohort who initiated HAART during chronic infection at < (left column) or 350 (right column) CD4 + T cells/mm 3. The plots show the time course of CD4 + cell counts (modeled using nonlinear GEE) as mean (bold lines) and 95% confidence bands for subjects possessing a low (blue) and moderate or high (brown) GRG and according to the CD4 + T cell count at which HAART was initiated. The color codes for the plots are shown in the upper right panel. In these analyses, we combined the moderate and high GRG status into a single category and the data is for ART-naïve (top row) and -experienced (bottom row) subjects. Pre-HAART CD4 + T cell count was the average of all CD4 + T cell counts during the one year prior to initiation of HAART. (b) Association of the HLA-A*68 and HLA-C*16 alleles with surrogate markers of HIV disease progression in the WHMC cohort Diamonds and error bars indicate the mean and 95% confidence intervals, respectively. P, significance value estimated using Student s t test. (c) Rate of decline in plasma viral load (VL) after initiation of HAART by GRG in subjects from the AIEDRP cohort. Plasma VL changes after initiation of HAART during acute (left) or early (right) infection in subjects possessing the low (blue) and moderate or high (brown) GRGs. Among all HIV-positive patients who received HAART during acute infection (left panel), there was a rapid decline in the VL, and the rate of this decline did not differ by GRG [-0.34 log 10 (copies/ ml) per month in subjects with low GRG; log 10 (copies/ml) per month in subjects with a moderate-high GRG; P = ]. Similarly, among HIV-positive patients who received HAART during early infection (right panel), the rapid decline in the VL did not differ by GRG [-0.15 log 10 (copies/ml) per month in subjects with a low GRG; log 10 (copies/ml) per month in subjects with a moderate-high GRG; P = ]. (d) Influence of CCL3L1-CCR5 GRG status on time to viral load suppression among HIV + subjects from the WHMC cohort. Kaplan-Meier plots are for time to viral load suppression. P and relative hazard (RH) and 95% CI were determined by Cox proportional hazard models. Reference category was a low GRG. 21

22 a CD4 + count (cells/mm 3 ) CD4 + count (cells/mm 3 ) c VL (log 10 copies per ml) d Proportion of unsuppressed VL Time after HAART (years) GRG Low Moderate-High GRG Low Moderate-High Time since HAART (months) Time since HAART (months) GRG Low Moderate High RH 95% CI P Time (years) b Baseline CD4 Viral Load Cumulative CD4 DTH Allele present Allele absent HLA-A*68 HLA-C*16 P = P = VL (Log 10 copies per ml) P = P = CD4+ count (cells/mm 3 ) P = P = Positive skin test P = P = ccd4 (cells-days/mm 3 (x10 5 )) 22

23 SUPPLEMENTARY TABLES Supplementary Table 1. Effects of CCL3L1-CCR5 GRG status on CD4 + cell recovery in VL suppressors (data complementing that shown in Table 1 of the main text). GRG VL suppression All VL suppression Sustained Transient Analysis using square-root transformed CD4 + T cell counts a Low (0.0031) (0.0054) (0.0039) Moderate (0.0044) (0.0063) (0.0056) High (0.0145) (0.0129) (0.0145) P low-vs-moderate P low-vs-high Segmented regression analysis Rate of change in CD4 + T cell counts/month (95% CI) in the first two years after initiating HAART Low 7.31 ( ) 9.97 ( ) 5.38 ( ) Moderate 7.38 ( ) 9.30 ( ) 5.35 ( ) High 4.21 ( 0.96 to 9.38) 8.47 ( ) 4.15 ( 9.90 to 1.60) P low-vs-moderate P low-vs-high Rate of change in CD4 + T cell counts/month (95% CI) after two years of initiating HAART Low 1.23 ( ) 3.47 ( ) 0.38 ( 0.14 to 0.90) Moderate 0.59 ( ) Inestimable 0.30 ( 0.78 to 0.19) High 3.90 ( 6.10 to 1.70) Inestimable 4.87 ( 7.16 to 2.58) P low-vs-moderate P low-vs-high a Values reflect the mean rate of change in CD4 + T cell counts/month (SE). Rate of change of CD4 + T cell counts was determined by estimating the regression coefficients by GEE; 95% CI, confidence interval around the regression coefficient; and inestimable, GEE models were unable to estimate rate of change of CD4 + T cell counts in these subjects. The results shown under Analysis using square-root transformed CD4 + T cell counts refers to data similar to that shown in Table 1 (main text) except using square-root transformed CD4 + T cell counts. The results shown under segmented regression analysis refers to data similar to that shown in Table 1 except that segmented regression analyses was used to determine rates of change in CD4 + counts during and after two years of HAART. 23

24 Supplementary Table 2. Replication of the results shown in Table 3 models 1 and 2 in the main text in therapy naïve subjects. Pre-HAART CD4 Low GRG Moderate-High GRG P Model 1: unadjusted During first two years of HAART 350 a 8.84 (1.19) 7.14 (1.64) < (0.98) 5.05 (1.33) After two years of HAART (0.40) 0.65 (0.62) < (0.35) 0.12 (0.34) Model 2: adjusted for covariates During first two years of HAART (2.01) 3.09 (1.89) < (1.83) --- b --- After two years of HAART (1.22) 2.45 (0.88) < (0.70) 2.80 (1.33) a CD4 + cell counts in cells/mm 3. Pre-HAART CD4 + T cell count was the average of all CD4 + T cell counts during the three months prior to initiation of HAART. b ---, GEE models were unable to estimate rate of change of CD4 + T cell counts in these subjects. 24

25 Supplementary Table 3. Likelihood of HAART-induced viral load suppression in subjects from the HIV + WHMC cohort who possessed a moderate or high GRG compared to those with a low GRG. Group Pre-HAART CD4 <350 OR (95% CI), P Pre-HAART CD4 350 OR (95% CI), P All subjects 0.63 ( ), ( ), ART-naïve 0.41 ( ), ( ), ART-experienced 0.75 ( ), ( ), The odds ratio (OR) and their 95% confidence intervals (CI) were estimated using clustered logistic regression analyses. The reference category is the low GRG (OR = 1). The moderate and high GRGs were combined into a single category for these analyses. Pre-HAART CD4 + T cell refers to the HAART-initiating CD4 + T cell count, and is the average CD4 + T cell count during the one year preceding initiation of HAART. The likelihood of HAART-induced viral load suppression among all subjects from the WHMC HIV + cohort with a moderate-high GRG relative to those with a low GRG (reference category) who initiated HAART during chronic infection at <350, <300, <250, <200, <150, <100, <50 CD4 + T cells/mm 3 was [odds ratio (95% CI), P value]: 0.93 ( ), 0.919; 0.64 ( ), 0.524; 0.43 ( ), 0.299; 0.50 ( ), 0.457; 0.46 ( ), 0.487; 0.45 ( ), 0.497; and 0.74 ( ), 0.826, respectively. 25