| Literature DB >> 24961156 |
Stefania Bellino, Antonella Tripiciano, Orietta Picconi, Vittorio Francavilla, Olimpia Longo, Cecilia Sgadari, Giovanni Paniccia, Angela Arancio, Gioacchino Angarano, Nicoletta Ladisa, Adriano Lazzarin, Giuseppe Tambussi, Silvia Nozza, Carlo Torti, Emanuele Focà, Guido Palamara, Alessandra Latini, Laura Sighinolfi, Francesco Mazzotta, Massimo Di Pietro, Giovanni Di Perri, Stefano Bonora, Vito S Mercurio, Cristina Mussini, Andrea Gori, Massimo Galli, Paolo Monini, Aurelio Cafaro, Fabrizio Ensoli, Barbara Ensoli1.
Abstract
BACKGROUND: Tat is a key HIV-1 virulence factor, which plays pivotal roles in virus gene expression, replication, transmission and disease progression. After release, extracellular Tat accumulates in tissues and exerts effects on both the virus and the immune system, promoting immune activation and virus spreading while disabling the host immune defense. In particular, Tat binds Env spikes on virus particles forming a virus entry complex, which favors infection of dendritic cells and efficient transmission to T cells via RGD-binding integrins. Tat also shields the CCR5-binding sites of Env rendering ineffective virus neutralization by anti-Env antibodies (Abs). This is reversed by the anti-Tat Abs present in natural infection or induced by vaccination.Entities:
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Year: 2014 PMID: 24961156 PMCID: PMC4087126 DOI: 10.1186/1742-4690-11-49
Source DB: PubMed Journal: Retrovirology ISSN: 1742-4690 Impact factor: 4.602
Characteristics at baseline of the study participants
| Age (years) | 38 (32–42) | 38 (32–43) | 38 (32–41) |
| Male (%) | 90.2 | 95.0 | 87.8 |
| Female (%) | 9.8 | 5.0 | 12.2 |
| CD4+ (cells/μl) | 544 (463–678) | 546 (500–702) | 541 (454–640) |
| CD4+ (%) | 29.0 (25.0-34.0) | 29.0 (25.0-33.0) | 28.5 (25.5-34.5) |
| Viral load (log10 copies/ml) | 4.2 (3.7-4.5) | 4.2 (3.7-4.6) | 4.2 (3.9-4.4) |
| Years from diagnosis of HIVb | 1.3 (0.9-3.2) | 1.0 (0.7-4.0) | 1.6 (1.0-2.7) |
| HAART initiation since HIV+ (years)c | 3.6 (2.9-5.0) | 3.6 (3.2-5.4) | 3.5 (2.6-5.0) |
| HAART initiation since study entry (months)c | 22 (14–19) | 30 (28–31) | 17 (13–22) |
Data are expressed as median with interquartile range (IQR).
aEight individuals became anti-Tat Ab-positive during follow-up; bBased on 52 individuals.
cBased on data from 13 individuals, 4 anti-Tat Ab-positive with low and transient anti-Tat Abs (3 IgM and 1 IgG) and 9 anti-Tat Ab-negative.
Anti-Tat, anti-Env and anti-Gag antibody responses.
| | | | |
| IgM and IgG positive | 3 (27%) | 0 (0%) | 0 (0%) |
| IgG and IgA positive | 1 (9%) | 0 (0%) | 0 (0%) |
| IgM positive | 0 (0%) | 6 (67%) | 0 (0%) |
| IgG positive | 6 (55%) | 3 (33%) | 0 (0%) |
| IgA positive | 1 (9%) | 0 (0%) | 0 (0%) |
| 11 (100%) | 9 (100%) | 41 (100%) | |
| 11 (100%) | 9 (100%) | 41 (100%) | |
| | | | |
| IgM anti-Tat | 25 (25–25) | 25 (25–25) | <25 |
| IgG anti-Tat | 600 (200–12800) | 100 (100–100) | <100 |
| IgA anti-Tat | 100 (25–200) | <25 | <25 |
| IgG anti-Env | 12,800 (800–51,200) | 3,200 (400–12,800) | 6,400 (800–38,400) |
| IgG anti-Gag | 102,400 (4,000-2,457,600) | 12,800 (1,600-819,200) | 19,200 (200–3,276,800) |
Figure 1Kaplan-Meier curves, CD4T-cell number and viral load stratified by anti-Tat Abs. (A) Cumulative probability to remain naïve to therapy according to the presence (n = 20) or absence (n = 41) of anti-Tat Abs, and (B) for subjects with high titers of anti-Tat Abs (n = 11) versus subjects with low/no anti-Tat Abs (n = 50). (C) Baseline values and changes from baseline values of CD4+ T-cell counts and (D) viral load levels at years 1, 2 and 3, according to the presence or absence of anti-Tat Abs, respectively, in subjects naïve to therapy (anti-Tat Ab-positive n = 16 year 1, n = 10 year 2, n = 10 year 3; anti-Tat Ab-negative n = 32 year 1, n = 17 year 2, n = 9 year 3). A longitudinal analysis for data arising from repeated measures, adjusted for baseline values, was applied, using the generalized estimating equations method, where the measurements were assumed to be multivariate normal. Data are presented as mean values with standard error.
Risk of starting antiretroviral therapy
| | | | |
| Anti-Tat Ab + vs Anti-Tat Ab- | 0.16 | 0.03 – 0.84 | 0.0305 |
| IgG anti-Env (log10 titers) | 0.19 | 0.05 – 0.73 | 0.0148 |
| IgG anti-Gag (log10 titers) | 0.78 | 0.41 – 1.48 | 0.4573 |
| Years from diagnosis of HIV | 1.27 | 0.91 – 1.76 | 0.1631 |
| CD4+ T cells/μl at baseline | 1.00 | 0.99 – 1.00 | 0.3309 |
| Viral load (log10 copies/ml) at baseline | 2.53 | 0.75 – 8.52 | 0.1344 |
| | | | |
| IgG anti-Env (log10 titers) | 0.41 | 0.06 – 2.67 | 0.3545 |
| IgG anti-Gag (log10 titers) | 1.57 | 0.27 – 9.04 | 0.6101 |
| Years from diagnosis of HIV | 1.00 | 0.46 – 2.20 | 0.9935 |
| CD4+ T cells/μl at baseline | 0.99 | 0.98 – 1.01 | 0.3445 |
| Viral load (log10 copies/ml) at baseline | 24.20 | 0.22 – 2671.23 | 0.1843 |
A Cox proportional hazards model with time-dependent repeated measurements was used to estimate the effects of the presence of anti-Tat Abs, or of anti-Env or anti-Gag IgG titers on the risk of starting HAART, after adjusting for years from HIV diagnosis, CD4+ T cells and viral load at baseline. Anti-Env and anti-Gag Abs were assessed at baseline and at 6, 18 and 36 months.
Figure 2Changes over time of CD4T-cell number and viral load by anti-Tat Abs in individuals naïve to therapy. (A) CD4+ T-cell counts and (B) viral load in subjects who remained naive to therapy were analyzed over time, according to the presence or absence of anti-Tat Abs, by applying a random-effect regression model. The decrease from baseline of CD4+ T cells/μl was -1.1 (95% CI -3.7; 1.5) per month in the anti-Tat Ab-positive subjects and -5.9 (95% CI -8.7; -3.1, p < 0.0001) per month in the anti-Tat Ab-negative individuals, respectively. The difference between the coefficients of regression was statistically significant (p = 0.0060). Similarly, the increase of viral load was 0.003 log10 copies/ml (95% CI -0.004; 0.010) per month in anti-Tat Ab-positive patients and 0.015 (95% CI 0.009; 0.022, p < 0.0001) per month in anti-Tat Ab-negative subjects, respectively. The difference between the slopes was statistically significant (p = 0.0105). All longitudinal samples from 48 individuals were included in the analysis.
Changes from baseline of CD4 T cells and viral load by a longitudinal analysis.
| | | | |
| Change per month in Anti-Tat Ab+ | -1.6 | -6.2; 3.0 | 0.4768 |
| Change per month Anti-Tat Ab– | -8.9 | -13.8; -3.9 | 0.0011 |
| IgG anti-Env (log10 titers) anti-Tat Ab+ | 133.4 | -130.0; 396.8 | 0.3070 |
| IgG anti-Env (log10 titers) anti-Tat Ab– | 150.0 | -40.7; 340.6 | 0.1178 |
| IgG anti-Gag (log10 titers) anti-Tat Ab+ | -15.6 | -141.6; 110.5 | 0.8014 |
| IgG anti-Gag (log10 titers) anti-Tat Ab– | -56.0 | -177.8; 65.9 | 0.3533 |
| | | | |
| Change per month in Anti-Tat Ab+ | 0.005 | -0.007; 0.018 | 0.3898 |
| Change per month Anti-Tat Ab– | 0.011 | 0.001; 0.021 | 0.0401 |
| IgG anti-Env (log10 titers) anti-Tat Ab+ | 0.26 | -0.64; 1.16 | 0.5560 |
| IgG anti-Env (log10 titers) anti-Tat Ab– | 0.08 | -0.67; 0.83 | 0.8249 |
| IgG anti-Gag (log10 titers) anti-Tat Ab+ | -0.19 | -0.57; 0.18 | 0.3006 |
| IgG anti-Gag (log10 titers) anti-Tat Ab– | -0.15 | -0.55; 0.26 | 0.4632 |
Multivariate analysis for repeated measures of longitudinal samples from 41 individuals (random-effect regression model). Anti-Env and anti-Gag Abs were assessed at baseline and at 6, 18 and 36 months.