Changes in the HIV-1 mutational profile before first-line HAART in the RESINA cohort.

Sporadic observations have shown changing patterns of transmitted drug resistance mutations (TDRMs) in HIV infection even without selection pressure by antiretroviral treatment (ART). Repeated genotypic resistance analyses in treatment-naïve patients were performed, in order to analyze intraindividual variances of resistance patterns over time. Between the years 2001 and 2008 two genotypic resistance tests were performed at different time-points in 49 treatment-naïve HIV-positive patients aged >18 years. Wild-type virus was found at baseline and during follow-up in 31 patients (group A, median time between resistance tests 146 days), while resistance mutations were found either at baseline or during follow-up in 18 patients (group B, median time between resistance tests 297 days). In group B, the pattern of resistance changed in eight out of 18 patients over time, with three patients showing decreasing numbers and five patients showing increasing numbers of resistance mutations. The pattern of resistance mutations remained unchanged in 10 out of 18 patients. The mutational pattern in untreated HIV infection may change over time and a single resistance analysis may underestimate the true prevalence of preserved resistance mutations. If these findings can be confirmed in a larger number of patients, it would be prudent to perform genotypic resistance testing both at baseline and prior to the start of ART in order to capture a more complete picture of preserved mutations before initiating ART.

Prevalence of key resistance mutations K65R, K103N, and M184V as minority HIV-1 variants in chronically HIV-1 infected, treatment-naïve patients.

BACKGROUND AND OBJECTIVES: Minority drug-resistant HIV-1 variants, undetected by conventional genotyping, may impair the outcome of antiretroviral therapy (ART). Thus, we retrospectively analyzed the prevalence of minority drug-resistant HIV-1 variants before ART in chronically HIV-1 infected patients initiating first-line therapy and assessed the impact on clinical outcome in the prospective German Truvada cohort. STUDY DESIGN: Samples from 146 antiretroviral treatment-naïve patients were collected between April 2005 and August 2006. K65R, K103N, and M184V variants at low frequencies were detected by allele-specific real-time PCR. RESULTS: Minority drug-resistant HIV-1 variants were detected in 20/146 patients (13.7%): the M184V mutation in 12/146 patients (8.2%), the K103N mutation in 8/146 patients (5.5%), and the K65R mutation in 4/146 patients (2.7%). Four patients with the M184V mutation also harbored the K65R or the K103N mutation. The 12- and 24 months virological efficacy data revealed that the rate of treatment failure was not increased in the group of patients harboring minority drug-resistant HIV-1 variants prior to ART. CONCLUSIONS: Minority drug-resistant HIV-1 variants can be frequently detected in treatment-naïve, chronically HIV-1 infected patients. Despite the presence of those mutations as minority variants before initiating ART, most of the patients were successfully treated.

Improved prediction of HIV­1 coreceptor usage with sequence information from the second hypervariable loop of gp120.

BACKGROUND: Human immunodeficiency virus type 1 (HIV­1) uses the CD4 receptor and a coreceptor to gain cell entry. Coreceptor usage is mainly determined by the V3 loop of gp120. Therefore, coreceptor usage is currently inferred from the genotype on the basis of V3 alone. However, several mutations outside V3 have been repeatedly reported to influence coreceptor usage. In this study, the impact of the V2 loop on coreceptor usage prediction was analyzed. METHODS: Sequences were analyzed for differences at specific positions and position­independent features with the Fisher exact and Student t tests. Prediction models were trained with support vector machines and evaluated in cross­validation on clonal data. Models trained on the clonal data set were validated on 2 clinical data sets. RESULTS: Several mutations and position­independent features within V2 were statistically significantly different between R5 and X4 viruses. Cross­validation on the clonal data set revealed a statistically significantly higher area under the receiver operating characteristic curve if features of both loops were used, compared with those using only V2 or V3 alone. Similar results were found with clinically derived data sets. CONCLUSIONS: The ability of the V2 loop to improve coreceptor usage prediction has been shown in a large data set. Utilization of this information can lead to considerable improvements in the prediction of coreceptor use both on clonal data sets and on clinically derived data sets.

Short communication: selection of thymidine analogue resistance mutational patterns in children infected from a common HIV type 1 subtype G source.

In HIV-1, thymidine analogue mutations (TAMs) cluster in one of two groups (215Y, 41L, 210W, or 215F, 219E/Q), representing two independent mutational patterns (T215Y and T215F cluster, respectively). The mechanisms by which these pathways are selected are not fully understood. To investigate possible factors driving the selection of the TAMs, we analyzed the TAM patterns with regard to the respective treatment, viral load, and HLA in 18 children all infected from a common source of HIV-1 clade G virus and initially treated with zidovudine. The HIV reverse transcriptase sequences of 14/18 children carried at least one TAM. At first sampling date, the T215Y-linked pattern was observed in five cases and the T215F cluster was seen in nine. During the follow-up period, three patients changed their patterns. Children treated with identical NRTI combinations at the first sampling date developed different pathways. Under AZT/d4T therapies, an association was found between the HLA B*13 (in combination with HLA DRB1*0701) and the mutation T215Y. The mutation T215Y reverted in three out of four patients who discontinued AZT/d4T treatment. We speculate that in the context of these subtype G viruses, the development of the T215Y mutation may be strongly disfavored whereas the presence of HLA B*13 may counteract this effect and permit its development.

Evolution of raltegravir resistance during therapy.

OBJECTIVES: We investigated the prevalence of raltegravir resistance-associated mutations at baseline and their evolution during raltegravir therapy in patients infected with different HIV-1 subtypes. METHODS: At pre-treatment screening, the integrase gene from plasma samples from patients infected with subtype B and non-B viruses was analysed. Raltegravir resistance evolution was further evaluated in 10 heavily pre-treated patients. RESULTS: Two hundred and nine plasma samples from 94 subtype B and 115 non-B patients were sequenced. No signature/primary raltegravir resistance mutations were detected at baseline. The secondary mutations L74M, T97A, V151I and G163R were observed with a frequency of <4%. The primary mutations N155H, Q148R/H or Q143R were observed during raltegravir therapy. The Q148R/H was detected only in subtype B. A switch of the primary mutation during raltegravir treatment was not restricted to the subtype B viruses. The prevalence of each primary mutation varied depending on the length of the raltegravir therapy. The Q148R/H was mostly detected after short exposure to raltegravir, while the Y143R was observed only after prolonged raltegravir exposure. We detected an association between the presence of the T206S in the baseline genotype and the absence of the primary Q148R/H mutation or any secondary mutation accompanying the N155H following raltegravir failure. CONCLUSIONS: A number of secondary and additional mutations were found in baseline genotypes. During therapy, when the virus was not optimally suppressed, resistance mutations developed, which were dependent on subtype and time on raltegravir.

Papillomavirus binding factor binds to SAP30 and represses transcription via recruitment of the HDAC1 co-repressor complex.

Papillomavirus binding factor, PBF, identical to the Huntington's disease binding protein 2, HDBP2, is a nuclear-cytoplasmic shuttling factor with the ability to inhibit cell growth. It has been identified by its ability to bind to GC-rich sequence elements within upstream promoter regions of certain human papillomavirus (HPV) types and of the Huntingtin protein, respectively. Here, we show that PBF acts as a repressor of HPV transcription. This repression requires the DNA-binding activity of PBF, which we mapped to two C-terminal four-amino acids motifs conserved to the so-called e-tail of certain T-cell factors. Moreover, we show that PBF directly binds to SAP30 (Sin3-associated polypeptide of 30kDa) a component of the mSIN3A-HDAC1 complex, via amino acids 263-312. The addition of Trichostatin A, an inhibitor of HDACs, alleviated PBF-mediated repression. Thus, PBF-mediated repression of transcription involves specific DNA-binding and the recruitment of the SIN3A-HDAC1 complex.

Papillomavirus binding factor (PBF)-mediated inhibition of cell growth is regulated by 14-3-3beta.

The cellular factor, papillomavirus (PV)-binding factor (PBF)/Huntington's disease binding protein 2 (HDBP2), was identified by its ability to bind regulatory sequences of certain papillomavirus types as well as the Huntington's disease gene. PBF is thought to be a novel nuclear-shuttling transcription factor with unknown function. To further characterize PBF, we identified 14-3-3beta as an interaction partner. We demonstrated that PBF binds to 14-3-3beta using two motifs. Akt-kinase and an unidentified kinase that are activated by the PI3K-signaling pathway were able to phosphorylate these motifs, allowing PBF to associate with 14-3-3beta. This interaction may contribute to the control of the subcellular localization of PBF, which migrated into the nucleus in the absence of growth factors. Over-expression of PBF resulted in the inhibition of cell growth, which was enhanced using a 14-3-3 binding-deficient PBF mutant. Thus, our experiments characterized PBF as a new cellular factor mediating the effects of PI3K/Akt signaling and 14-3-3 on cell growth.