Introduction
A complex set of host genetic factors has been shown to regulate the transmission and pathogenesis of HIV-1 infection [1-4]. Consistent associations with contrasting clinical (e.g., onset of AIDS), immunological, and virological outcomes following HIV-1 seroconversion have confirmed the importance of genetic polymorphisms in the human major histocompatibility complex (MHC or HLA) [5-10] and the CC (β) chemokine receptor (CCR) system [11-22]. Many of the epidemiological findings related to HLA and CCR variants are also consistent with experimental evidence that collectively reveals the fine mechanisms in natural [23-25] and adaptive [26-36] immune control of HIV/AIDS in different ethnic groups. The emerging consensus now suggests that host genetic profiles defined by HLA and CCR polymorphisms can strongly and independently predict HIV-1 disease progression in chronically infected patients not taking antiretroviral therapy [4,6-8].
HLA and CCR genotyping has not been adopted for clinical decision-making because its value in patient care and disease prognosis has not been defined. More specifically, the prognostic contribution of an HLA and CCR typing profile beyond combinations of other conventional laboratory assays (e.g., measurements of HIV-1 RNA and CD4 T lymphocyte counts) awaits formal evaluation. Clinical application of genetic data will rely either on a single carefully constructed robust algorithm that incorporates both the genetic effects and those of non-genetic host factors such as ethnic background, age, sex, and exposure to antiretroviral therapy or on multiple models individualized for particular population and host characteristics. Earlier attempts to demonstrate the predictive capability and the limitations of genetic data have addressed the relative effects of genetic variants and their frequencies within cohorts/ethnic groups [19,22,37], methods for summarizing the genetic profile within individuals [4,8,37], dominant and recessive models for genetic effects [3,4,18], gender-specific effects [7], and race-specific effects [18,19,22]. These efforts were undertaken primarily in cohorts of adult Caucasians, Hispanics, and African-Americans. Cohorts with different mixes of race, age, and gender should help to refine host genetic contributions to HIV-1 infection and disease progression.
CCR2-CCR5 haplotypes (haplogroups) were initially analyzed [38] in the Reaching for Excellence in Adolescent Care and Health (REACH) cohort [39] of the Adolescent Medicine and HIV/AIDS Research Network. Differences in haplotype occurrence had only limited impact on variability in plasma HIV-1 RNA concentration [viral load (VL)], a major predictor of disease progression in both REACH and another adolescent cohort [40]. Here we describe more comprehensive analyses of the individual and joint effects of HLA and CCR2-CCR5 variants on HIV-1 RNA levels and CD4 T cell counts in adolescents with early chronic infection.
Materials and methods
Subject selection
The REACH Study enrolled teenagers (13-18 years; mean, 17) at 15 locations in 13 United States cities (four in the New York/New Jersey area, three in the mid-Atlantic region, six in the southeast, one in Chicago, and one in Los Angeles) [39,41]. The REACH study design and assessment measures have been described elsewhere [39,41,42]; they conformed to the procedures for informed consent approved by local and/or sponsoring institutional review boards. Longitudinal data were collected at quarterly intervals from 1996 to 2000 for an initially enrolled group of 550 subjects, of whom 530 had a sample suitable for DNA amplification. Subjects eligible for genetic analyses included 183 HIV-1-seronegative population controls and 228 HIV-1 seropositives with both a known history of, and adherence to, therapeutic regimen and at least two measurements of plasma HIV-1 RNA concentration and two consecutive measurements of CD4 T cell counts during the first 3 years of follow-up. Overall, the 228 seropositives (ages 13-18; 75% females; 71% African-Americans) eligible for detailed analysis were representative of the entire REACH cohort in their age (at enrollment), gender, and racial distribution [38,39,41].
Immunological and virological outcome measurements in HIV-1-seropositive participants
CD4 T lymphocytes were quantified in NIAID-certified laboratories at the individual clinical sites [43] with flow cytometry using standard protocols recommended by the AIDS Clinical Trials Group (ACTG). HIV-1 RNA level in plasma was measured in a centralized laboratory on frozen specimens using either nucleic acid sequence-based amplification (NASBA) or NucliSens assays (Organon Teknika, Durham, North Carolina, USA) as previously described [44]. The respective lower limits of detection for the NASBA and Nuclisens assays were 400 and 80 copies/ml. In earlier analyses, HIV-1 RNA level was predictive of advances in disease progression [40]. For this study, the multiple HIV-1 RNA measurements (transformed to log10) and CD4 cell counts were taken within the 1-year interval (two to four visits at 6-12 months) following enrollment for therapy-naive patients or following discontinuation of therapy for those initially receiving treatment. Twelve (∼ 6%) of the HIV-1-seropositive individuals had a viral RNA level < 400 copies/ml for a total of 16 visits; a value of 20 (for which the log10 value of 1.3 is half of that of log10 for 400) was assigned for these visits. Seventeen (∼ 8%) additional subjects had a viral RNA < 80 copies/ml for a combined total of 35 visits, and a value of 9 (for which log10 value of 0.95 is half of that of log10 for 80) was assigned. Average HIV-1 RNA concentrations with and without log10 transformation and absolute CD4 T cell counts in each subject were calculated by summing all measurements for all available visits within the study period and dividing by the number of visits.
DNA extraction and HLA typing
High-molecular-weight genomic DNA was extracted from whole blood using the QIAamp Blood Kit and protocols recommended by the manufacturer (Qiagen, Valencia, California, USA). All DNA samples were stored at 4°C in TE buffer (10 mmol/l Tris-HCl, pH 8.0, 2 mmol/l ethylenediaminetetraacetic acid) before HLA typing. HLA class I alleles were first typed by polymerase chain reaction (PCR) with sequence-specific primers (SSP) in a commercial kit (Pel-Freez Clinical Systems, Brown Deer, Wisconsin, USA), which defined alleles largely to their 2-digit specificities. Individuals with apparent homozygosity at any class I locus and those carrying HLA-B*57 were further defined by sequencing-based typing (SBT) following locus-specific PCR amplification [45] and solid-phase direct sequencing [46] on the ALFexpress automated sequencer (Amersham Pharmacia Biotech Inc., Piscataway, New Jersey, USA). Automated reference-strand conformation analyses (RSCA) (Pel-Freez Clinical Systems) were applied to resolve ambiguities in PCR-SSP and SBT [47].
CCR2 and CCR5 genotyping
PCR-based CCR typing [38,48] differentiated and often linked the dimorphic variants at nine sites: the 190G←A SNP in CCR2, which encodes V64I; seven SNP [-2733A←G (A29G or A58755G), -2554G←T (G208T), -2459G←A (G303A), -2135T←C (T627C), -2132C←T (C630T), -2086A←G (A676G), and -1835C←T (C927T)] in or adjacent to the cis-regulatory or promoter region of CCR5; and the 32 base pair deletion (Δ32) in CCR5. Haplotyping was facilitated by PCR with 12 combinations of SSP described originally [48] along with four additional SSP reactions consisting of -2733A/G-specific primers (sense orientation) paired with -2554G/T-specific primers (antisense orientation). Combinations of variants within the CCR2-CCR5 region form nine haplotypes (haplogroups) according to the nomenclature of the Tri-Service HIV-1 Natural History Study (TSS) [16].
Genetic scoring algorithm
Individual HLA class I [6,7,10,37,49] and CCR2-CCR5 [16,20-22,38] variants consistently (repeatedly) associated with either a favorable or an unfavorable effect on HIV-1 viremia and/or disease progression in large (n > 200) cohort studies with sufficient numbers of study outcomes (e.g., AIDS) were assigned a value of +1 or -1. A composite score was produced for each individual by simple addition/subtraction for each contributing HLA and CCR2-CCR5 variant. Two exceptions were considered in alternative analyses. The first, CCR2-64I, is frequent in all ethnic groups and has been associated with modest retardation of the HIV-1 pathogenetic process, with relative risks of AIDS and/or death between 0.74 and 0.76 according to an international meta-analysis [21]. However, the effect of CCR2-64I as part of the entire HHF*2 haplotype on HIV-1 viremia was negligible in REACH participants [38]. The second serologically or molecularly defined HLA specificity, HLA-B*08, is primarily found commonly in Caucasians in the A*01-B*08-Cw*07 haplotype and was initially associated with accelerated disease progression in several cohorts of Caucasian seroconverters [6,37,50,51]. More recent analyses of similar HLA data in certain of these and other cohorts of Caucasian and African ancestry have not reproduced the HLA-B*08 effect [7,8] (R. A. Kaslow et al., unpublished data). As a result, neither CCR2-64I nor HLA-B*08 was retained as an independent marker in the genetic scoring algorithm here, although their effects were examined separately.
Statistical analyses
Statistical routines in SAS (Statistical Analysis Software, version 8.0; SAS Institute, Cary, North Carolina, USA) were applied to (i) describe and compare general characteristics (gender, race, and treatment) using χ2 tests; (ii) explore the pairwise linear regression between age, VL and CD4 T cell counts; (iii) compare age, mean log10 HIV-1 VL and absolute CD4 cell counts in HIV-1-seropositive subjects using t-tests and F tests; (iv) convert VL and T cell counts to categorical variables for χ2 tests, (v) tabulate (by direct counting) distributions of HLA and CCR variants in the entire cohort and in subsets chosen for comparison; (vi) assess the effects of individual HLA and CCR variants and of the algorithm score on HIV-1 VL and CD4 T cells using generalized linear model and logistic regression statistics with adjustment for race, gender, age, and patient group (number of eligible follow-up visits and treatment history); and (vii) perform multivariable analyses that summarize the association of all host genetic and non-genetic variables with immunological and/or virological outcomes.
Results
Distribution of individual HLA class I and CCR2-CCR5 variants included in the genetic scoring algorithm
Successful genotyping in 227 of the 228 HIV-1-seropositive and all 183 seronegative predominantly female and African-American adolescents identified five HLA and two CCR2-CCR5 markers (Table 1) consistently found to modify HIV-1 disease progression and/or virus-host equilibration in other studies [6,10,16,20-22,37,38]. Within the range of expected frequencies, overlapping between HLA and CCR2-CCR5 markers was found in 3.7% of African-Americans and 4.5% of other subjects with different ethnic backgrounds. By comparison, the majority (56%) of the 227 HIV-1-seropositive individuals had one or more HLA and CCR markers, with algorithm scores ranging from -4 to +2. The distribution of genetic scores for the 227 seropositive subjects was similar (P = 0.24) to those found in the 183 seronegatives (data not shown), although the frequency of HLA-B*27 was increased among non-African-American seronegatives compared with seropositives (P = 0.001).
Virological and immunological outcomes and their correlation with individual genetic markers
HIV-1-seropositive subjects generally fell into four groups based on their clinical profiles including history of antiretroviral therapy and the number of visits with laboratory data. Group I subjects (n = 82) had taken no antiretroviral therapy at study entry and remained off antiretroviral therapy for three to four study visits (9-12 months). Group II (n = 79) initially received antiretroviral therapy but stopped for three to four consecutive visits. Group III (n = 46) resembled groups I and II but had fewer (two) consecutive visits without therapy. Group IV (n = 21) had at least two CD4 T cell count < 200 × 106 cells/l within the follow-up period and met the 1993 Centers for Disease Control AIDS definition. To assess genetic effects on overall virological and immunological status, all four groups were analyzed with statistical adjustment for group membership. For detailed assessment of the genetic contribution to host-virus equilibration (RNA 'setpoints') relatively early in the course of infection, only AIDS-free subjects (groups I-III) were analyzed.
The mean VL in the entire cohort (n = 227) and in AIDS-free participants (n = 207) was 3.77 ± 0.96 and 3.73 ± 1.02 log10 copies/ml, respectively. In univariate analyses of individual genetic markers (Table 2), the mean VL was consistently higher in HLA-B*35, HLA-B*53, and CCR2-CCR5 HHE/HHE-positive subjects (P = 0.018-0.145) and lower in those with HLA-B*57 (P < 0.0001). HLA and CCR markers that did not have a statistically significant association with VL in this cohort nonetheless showed trends consistent with their previously reported relationship with HIV-1 pathogenesis. For example, HLA-B*27 was associated with a lower VL (mean, 3.53 log10 copies/ml), and HLA class I homozygosity (including single, double, and triple loci) was associated with a somewhat increased VL (mean, 3.84-3.94 log10 copies/ml). Relationships between individual genetic markers and CD4 T cell counts in the REACH participants generally followed the patterns seen with VL (data not shown).
Genetic scores and HIV-1 viremia
Genetic scores, both individually and jointly, were strongly associated with variability in HIV-1 VL both in the entire cohort (n = 227) and in the 207 AIDS-free participants (Table 3). Subjects with more extreme genetic scores (≤ -2 versus ≥ +1) differed in their VL by 1.0 log10 copies/ml (P < 0.0001) (Table 3). On average, VL changed by 0.31-0.33 log10 copies/ml (SD = 0.07) per unit change in genetic score (P < 0.0001) before and after adjustment for non-genetic host factors (age, gender, race, and patient group) in AIDS-free participants. When skewed VL data in the 20 individuals with AIDS were included as part of the cohort, the linear relationship weakened only slightly (0.24-0.27 ± 0.06 log10 copies/ml decrease in VL per +1 score; P = 0.0001) but remained independent of differences in non-genetic host factors including gender (adjusted P = 0.30 in multivariable analyses), race (P = 0.59), age (P = 0.20), and patient group (P < 0.0001).
HLA and CCR markers in patients categorized by combinations of virological and immunological outcomes
At the two to four follow-up visits in the 227 seropositive REACH participants when no antiretroviral therapy was taken, the average VL and CD4 T cell counts as continuous variables were not ideal for F tests or t-tests because neither conformed to a normal distribution (P < 0.01 for both in Kolmogorov-Smirnov normality test). In alternative analyses with VL and CD4 T cells treated as three- and two-level categorical variables, respectively, 37 subjects were categorized as 'controllers' with a favorable combination of VL (< 1000 copies/ml in the lowest category) and CD4 T cell count (> 450 × 106 cells/l, in the upper category). A second group (`non-controllers') of 56 subjects had unfavorable outcomes (VL > 16 000 copies/ml in the highest category and CD4 cells < 450 × 106 cells/l). The remainder (n = 134) showed intermediate CD4 T cell and VL profiles. VL in controllers (2.22 ± 0.69 log10 copies/ml) was 2.5 log10 copies/ml lower than in non-controllers (4.74 ± 0.52 log10 copies/ml). CD4 cell counts were nearly three times higher in controllers (795 ± 245 × 106 cells/l) than in non-controllers (289 ± 132 × 106 cells/l). Likewise, controllers and non-controllers each differed from the intermediate group (VL, 3.80 ± 0.50 log10 copies/ml; CD4 cell count, 539 ± 210 × 106 cells/l).
Seropositive participants in the three defined categories of disease control were evaluated for their HLA and CCR marker distribution (Table 4). HLA-B*35, HLA-B*53, HLA-B*57, and class I homozygosity each showed significant effects (P < 0.0001-0.049) across patient categories after adjustment for non-genetic factors. Trends for differential distribution were also detected for HLA-B*27, HHG*2, and HHE/HHE, consistent with earlier analyses of virological outcomes alone (Table 2).
Controllers consistently had more positive (≥ +1) than negative (≤ -2 to -1) scores compared with either non-controllers or intermediates (P < 0.0001 for the model) (Table 5). When scores were collapsed to three levels and patients with neutral score served as the reference category, the association was equally strong in African-American and other patients (P < 0.001 and < 0.01, respectively), and close association of genetic profiles with combinations of virological and immunological outcomes persisted (P < 0.0001) after adjustment for differences in gender (P = 0.045), race (P = 0.71), and patient group (P = 0.004).
Alternative analyses including two additional markers [HLA-B*08 and CCR2-64I (HHF*2)]
HLA-B*08 as an unfavorable (-1) and CCR2-64I on the HHF*2 haplotype as a favorable (+1) marker have been recognized in several studies [6,21,37,50,51], but HHF*2 showed no appreciable effect on viral load in the REACH cohort [38]. Individually, HLA-B*08 was found in five (13.5%) controllers, eight (6.0%) intermediates, and five (8.9%) non-controllers (P > 0.20). The 10 participants (all African-Americans) with homozygous HHF*2 genotype included one (2.7%) controller, eight (6.0%) intermediates, and one (1.8%) non-controller (P > 0.50). HHF*2 heterozygosity (n = 80) as the most common marker further failed to show any clear trend (35.1, 37.3, and 30.4%, respectively) across the three categories of seropositive individuals (P > 0.50). Inclusion of HLA-B*08 and CCR2-CCR5 HHF*2 as part of the genetic scoring algorithm decreased the magnitudes of the resulting relationships between genetic scores and patient categories: the overall trend remained detectable (P = 0.004, likelihood ratio χ2 test) in the cohort of 227, but the trend was heavily dependent on the scores of ≤ -2 (n = 25; adjusted P = 0.003), especially in the small group of non-African-Americans (adjusted P = 0.018) (data not shown but available upon request).
Discussion
The search for host genetic determinants of HIV-1 infection and pathogenesis has clearly demonstrated that polymorphisms in at least two genetic systems, CCR and HLA, account for some of the variation in the ultimate clinical outcomes (AIDS and/or death) during the course of HIV-1 infection. Because most studies of genes governing natural and acquired immune responses have relied heavily on cohorts with prolonged follow-up, the timing and evolution of early effects are not well understood. Certain of these genetic effects on late disease manifestations may actually reflect pathophysiological mechanisms for virological and immunological equilibration operating relatively early in infection [5,22]. In our analyses of seven previously established HLA and CCR markers, it was also evident that composite genetic scores were more advantageous than individual markers in predicting the virological and immunological outcomes. The unequivocal host genetic contribution to variability in plasma viral RNA concentration and CD4 T cell counts confirmed not only the importance of HLA and CCR polymorphisms relatively early in HIV-1 infection but also the validity of several immunogenetic determinants as reliable benchmarks of host genetic influence on the natural HIV-1 subtype B infection in primarily African-American adolescents.
Favorable and unfavorable HLA markers on chromosome 6 and CCR markers on chromosome 3 can often be found in the same individuals, especially in Caucasians with the highest frequency of CCR5-Δ32 (on the HHG*2 haplotype). Simultaneous analyses of major HLA, CCR2, and CCR5 genotyping data from several Caucasian cohorts have shown independent HLA effects on HIV-1 disease progression [6]. Our more restricted analyses here revealed fewer than 5% of participants with one or more HLA markers in combination with at least one more CCR marker. At the same time, however, over 50% of these participants had genetic scores as a consequence of one or more predictive HLA and CCR markers. For those with two or more, the effect of a single marker can be obscured by the effect of another. Accordingly, it is important both biologically and statistically to obtain some composite measure suitable for modeling the multigenic contribution to the natural history of HIV/AIDS. The scoring algorithm tested here effectively captured the genetic effects on virological and immunological outcomes, independeny of non-genetic host factors. In particular, the algorithm appears capable of providing a robust assessment of host genetic contribution in settings where stratifications and precision of risk estimates are limited by sample size, for example in the small (n = 66) group of non-African-American REACH participants.
Our semi-quantitative scoring algorithm offers a simple empirical approach to summarizing well-established genetic influences on HIV/AIDS. As contributions of known genetic effects are further elucidated and new genetic determinants discovered, statistical techniques will undoubtedly advance to accommodate effects of background genetic heterogeneity, specific non-genetic characteristics, temporal changes, and other factors. First, individual markers may be weighted differently according to the strengths and consistency of their associations with specific outcomes [4]. Second, outcome-specific and race-specific models may be constructed using meta-analysis of highly comparable data from different cohorts [21]. Third, interactions with non-genetic host factors such as age, gender, and treatment may be incorporated more fully into the models rather than being used as separate adjustment factors. Fourth, alternative classification of HLA class I variants according to their specific peptide-binding profile [49,52], shared serological specificity [7], and perhaps other motifs governing natural killer cell activities [53-55] may provide additional specificity of HLA effects. Finally, confirmation of any of the growing number of reports on effects of polymorphisms at loci encoding HLA class II products [6,7,34], stromal cell-derived factor-1 [15,56,57], RANTES [19,58,59], interleukin-4 [60,61], interleukin-10 [62], CX3CR1 [63,64], and MIP-1α [19], independent of HLA class I and CCR variants will inevitably improve the measurement of genetic influence on HIV/AIDS.
Systematic quantification of major host genetic effects on HIV/AIDS is likely to benefit the decision-making processes in both experimental and clinical settings. Genetic profiling is likely to become useful in selecting patients for mapping cytotoxic T lymphocyte epitopes presented by favorable or unfavorable HLA variants and in evaluating the association of these epitopes with immunological control and escape during HIV-1 infection (A. Bansal and M.J. Mulligan et al., in preparation). Reports of CCR5-Δ32- and 59029A/A-mediated effects during potent antiretroviral therapy in children as well as adults [17,65-67] have suggested that genetic typing can aid in assessing differential therapeutic responses. It remains to be seen whether the predictive capacity of fixed heritable genetic variants proves sufficiently reliable to supplement or diminish the frequent use of virological and immunological measurements now considered essential to the clinical management and care of HIV patients [68]. Reduced cost and complexity, coupled with improved prognostic capability of genotyping, may eventually help to bring new, clinically relevant genetic information from the bench to the bedside.
Examination of joint HLA and CCR effects in new cohorts like REACH should benefit our understanding of HIV-host evolution in affected populations. From a biological perspective, adolescents differ from adults at least in their preservation of CD8 naive T cells [69], rates of CD4 T cell depletion, and progression to clinical AIDS [70]. More importantly, HIV-1 genotypes/phenotypes evolve rapidly in infected individuals [71,72], and directional (non-random) viral evolution is often driven by HLA [73,74] and CCR products [75]. In successive generations of viral isolates, those with increasing ability to escape immunity and expand coreceptor usage may no longer be restricted by the same HLA [73] or CCR variants that played prominent roles during the early course of the HIV/AIDS pandemic. Despite these concerns, the addition of largely confirmatory findings here suggests that at least some of those host genetic factors in the HLA and CCR systems continue to account for variable virological and immunological outcomes during chronic HIV-1 infection.
Acknowledgments
The authors are deeply grateful to Dr Patricia D'Souza for her exceptional conceptualization of this project, to Dr Bonnie Mathieson for her coordination of the necessary resources, to Drs James Bradac and Sandra Bridges for critical review of the manuscript, to Dr Charles A. Rivers for valuable contribution to various aspects of genotyping-related work, to Margaret Schaen for data management, to the members of the Community Advisory Board for their insight and counsel, and to the youths participating in the REACH study for their cooperation and informed consent.
Sponsorship: The Adolescent Medicine HIV/AIDS Research Network is supported by the National Institute of Child Health and Human Development, with supplemental funding from the National Institutes on Drug Abuse, Allergy and Infectious Diseases, and Mental Health and the Health Resources and Services Administration. Partial support by NIAID grant AI41951 (RAK, JT) is also acknowledged. Note: Data derived from this study were presented in part at the Ninth Conference on Retroviruses and Opportunistic Infections. Seattle, February 2002 [abstract 325-w].
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Appendix
Additional investigators on the REACH project, listed in order of the numbers of subjects enrolled, are from the following institutions. University of Miami: L. Friedman, L. Pall, D. Maturo, A. Pasquale; Montefiore Medical Center: D. Futterman, D. Monte, M. Alovera-DeBellis, N. Hoffman, S. Jackson; University of Pennsylvania and the Children's Hospital of Philadelphia: D. Schwarz, B. Rudy; Children's Hospital of Philadelphia: M. Tanney, A. Feldman; Children's Hospital of Los Angeles: M. Belzer, D. Tucker, C. Hosmer, K. Chung; Tulane Medical Center: S. E. Abdalian, L. Green, C. McKendall, L. Wenthold; Children's National Medical Center: L. J. D'Angelo, C. Trexler, C. Townsend-Akpan, R. Hagler, J. A. Morrissy; University of Maryland: L. Peralta, C. Ryder, S. Miller, S. Calianno; Cook County Hospital/University of Chicago: L. Henry-Reid, R. Camacho; Children's Hospital, Birmingham: M. Sturdevant, A. Howell, J. E. Johnson; Children's Diagnostic and Treatment Center: A. Puga, D. Cruz, P. McLendon; Emory University: M. Sawyer, J. Tigner, A. Simmonds; St Jude Children's Research Hospital: P. Flynn, K. Lett, J. Dewey, S. Discenza; Mt Sinai Medical Center: L. Levin, M. Geiger; University of Medicine and Dentistry of New Jersey: P. Stanford, F. Briggs; SUNY Health Science Center at Brooklyn: J. Birnbaum, M. Ramnarine, V. Guarino. Investigators responsible for the basic science agenda include: C. Holland, Center for Virology, Immunology, and Infectious Disease, Children's Research Institute, Children's National Medical Center; A.B. Moscicki, University of California at San Francisco; D. A. Murphy, University of California at Los Angeles; S. H. Vermund, University of Alabama at Birmingham; P. Crowley-Nowick, The Fearing Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston; S. D. Douglas, University of Pennsylvania and the Children's Hospital of Philadelphia. Network operations and analytic support are provided by C. M. Wilson, C. Partlow at University of Alabama at Birmingham; S. J. Durako, J. H. Ellenberg, B. Hobbs, A. Bennett, M. Camarca, K. Clingan, J. Houser, V. Junankar, O. Leytush, L. Muenz, Y. Ma, R. Mitchell, T. Myint, P. Ohan, L. Paolinelli, M. Rakheja, M. Sarr, A. Soloviov at Westat, Inc. Program Officers from sponsoring agencies include A. Rogers and A. Willoughby from NICHD, K. Davenny and V. Smeriglio from NIDA, E. Matzen from NIAID, and B. Vitiello from NIMH. Cited Here...
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