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Journal of Clinical Microbiology, November 2008, p. 3856-3859, Vol. 46, No. 11
0095-1137/08/$08.00+0 doi:10.1128/JCM.01122-08
Copyright © 2008, American Society for Microbiology. All Rights Reserved.
| CASE REPORT |

Section of Microbiology, Department of Molecular Biology, University of Siena, Siena, Italy,1 Division of Infectious Diseases, University of Perugia, Perugia, Italy2
Received 12 June 2008/ Returned for modification 15 August 2008/ Accepted 14 September 2008
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FIG. 1. Temporal course of HIV-1 RNA load (gray line, right vertical axis in log scale) and CD4 cell counts (black line, left vertical axis) for patients M and F. Horizontal axis indicates months of follow-up with respect to the first available laboratory measurement. The horizontal black lines below the graphs show the antiretroviral treatments used during the same follow-up period. 3TC, lamivudine; AZT, zidovudine; D4T, stavudine; EFV, efavirenz; FTC, emtricitabine; LPV, lopinavir; TDF, tenofovir; rtv, ritonavir (boosting dose); TPV, tipranavir.
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TABLE 1. HIV-1 protease and reverse transcriptase resistance-related mutations in the virus population harbored by patients M and F at different time pointsa
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FIG. 2. Maximum likelihood phylogenetic tree showing the close relationship between patient F and M sequences. Phylogenetic analysis was conducted on a data set of 53 HIV-1 polymerase sequences aligned through ClustalX and including the seven and six sequences available from patients F and M, respectively, as well as an additional 40 sequences with drug resistance mutations obtained from the same geographic area during the same time period and randomly selected from the Siena HIV Monitoring Service database. Patient F and M sequences are indicated by F and M followed by the follow-up month number. The most appropriate model of evolution selected using ModelTest 3.7 was TVM+I+ , implemented into PAUP*v4b10 to estimate a maximum likelihood tree for the data set. The parameters associated with the selected model of evolution were as follows: base frequencies, A = 0.3898, C = 0.1577, G = 0.2196, and T = 0.2329; rate of substitution for A to C = 1.9457, A to G = 6.9768, A to T = 0.6498, C to G = 1.0882, C to T = 6.9768, and G to T = 1.0000; an alpha value for the gamma shape distribution of 0.7780; and a proportion of invariable sites of 0.4104. Numbers at tree nodes indicate the bootstrap values.
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The only relevant changes in virus genotype during the observation period included the switch from T215D to T215Y and the selection of M184V in reverse transcriptase and the appearance of M46I in protease in the last available resistance test for patient M. However, some of the previously detected NRTI resistance mutations were not present in the last genotype obtained from the two patients (two TAMs and M184V in patient M and M184V in patient F), despite ongoing NRTI treatment. This suggests a suboptimal level of adherence to treatment in both patients at least at these later time points. However, no therapeutic drug monitoring data were available. Gradual replacement of the drug resistance T215Y with the revertant T215D reverse transcriptase mutation in patient F at the last two analyses is also in line with this hypothesis.
Development of drug resistance is a major concern for successful treatment of HIV-1 infection. The clinical impact of drug resistance has been shown to be highly relevant, particularly when susceptibility to multiple drug classes is lowered (11). HIV-1 drug-resistant variants are commonly selected under suboptimal therapy (acquired or secondary resistance) and can be transmitted to newly infected subjects (transmitted or primary resistance). Large surveys have estimated that 10 to 25% of drug-naive infected patients harbor HIV-1 with mutations for resistance to at least one antiretroviral class, although resistance to all three major classes (NRTIs, NNRTIs, and PIs) is much less common (4). Subjects newly infected with drug-resistant virus can potentially infect other individuals, further spreading drug resistance among untreated patients (2).
Detection of closely related drug-resistant HIV-1 variants in epidemiologically linked drug-naive subjects has been occasionally documented (9, 10). This is the first report of likely onward transmission of a three-class-resistant HIV-1 from one to another drug-naive patient. There were no laboratory or anamnestic data indicating whether transmission occurred from patient F to patient M or vice versa. The clinical presentation of patient F and the CD4 cell counts of both patients suggest that they were not acutely infected. The lower CD4 cell counts in patient M at admission to hospital may favor the hypothesis of M-to-F transmission, but both patients had probably been long infected, decreasing the confidence in this assumption. Theoretically, the possibility remains that both patients were infected by a third subject. However, both patients F and M were strictly heterosexual and had been in a relationship for more than 10 years, had never had blood transfusions, and denied use of intravenous drugs, making exposure to a common third source of infection quite unlikely. Indeed, the only risk factor declared by patients F and M was occasional heterosexual contacts with other partners before starting their relationship. While phylogenetic analysis clearly indicated a common origin for the viruses, there were subtle differences in drug resistance mutations in the first samples available from the two patients, including D67N in reverse transcriptase and M46I in protease. Diverse major histocompatibility complex-restricted recognition of viral epitopes may have driven a slightly different virus evolution in the absence of treatment in the two infected hosts (6). However, mutation M46I did appear in patient M in the last available genotype test while he was on therapy. Although M46I may have independently evolved at a late stage in patient M, its presence in the closely related patient F virus since her earliest genotypic test raises the possibility that an M46I-containing low-frequency virus population was also acquired by patient M and later contributed to the last mutational pattern detected under lopinavir-ritonavir pressure. Clonal analysis or ultrasensitive genotyping of the first patient M sample could shed light on this hypothesis.
Overall, there was not much evolution in the HIV-1 genome under therapy. While the revertant T215D mutation changed into the T215Y NRTI resistance mutation, both NNRTI and PI resistance mutations were remarkably stable during the whole treatment period. This implies that the three-class-resistant virus had acquired a constellation of compensatory mutations preserving its function at a limited cost in replicative capacity. Resistance to NNRTI is believed to impact viral fitness minimally (3). By contrast, several reports have indicated decreased replicative capacity in the context of major PI resistance mutations (1). However, compensatory mutations can rescue the original viral fitness even for highly resistant proteases (7). In line with this possibility, it is noteworthy that HIV-1 protease maintained its full complement of resistance mutations in the last available patient M genotype concomitantly with a possible decrease in adherence to treatment suggested by a lack of some of the resistance mutations previously detected in reverse transcriptase.
Notably, treatment appeared to work quite well despite the extensive resistance pattern. Even the first-line therapy, started without knowledge of genotype and actually including partially or completely ineffective drugs, yielded a significant decrease in viral load in both patients. Following the first genotypic testing, the boosted PI-based treatment lines were not completely successful primarily because of adherence issues. Nevertheless, this case highlights the potential for spreading of multiple-class-resistant HIV variants and reinforces the need for surveillance of transmitted resistance in newly infected subjects.
Published ahead of print on 24 September 2008. ![]()
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