- Short report
- Open Access
Structure of HIV-1 quasi-species as early indicator for switches of co-receptor tropism
© Dybowski et al; licensee BioMed Central Ltd. 2010
- Received: 22 September 2010
- Accepted: 30 November 2010
- Published: 30 November 2010
Deep sequencing is able to generate a complete picture of the retroviral quasi-species in a patient. We demonstrate that the unprecedented power of deep sequencing in conjunction with computational data analysis has great potential for clinical diagnostics and basic research. Specifically, we analyzed longitudinal deep sequencing data from patients in a study with Vicriviroc, a drug that blocks the HIV-1 co-receptor CCR5. Sequences covered the V3-loop of gp120, known to be the main determinant of co-receptor tropism. First, we evaluated this data with a computational model for the interpretation of V3-sequences with respect to tropism, and we found complete agreement with results from phenotypic assays. Thus, the method could be applied in cases where phenotypic assays fail. Second, computational analysis led to the discovery of a characteristic pattern in the quasi-species that foreshadows switches of co-receptor tropism. This analysis could help to unravel the mechanism of tropism switches, and to predict these switches weeks to months before they can be detected by a phenotypic assay.
- Phenotypic Assay
- Probability Plane
- Deep Sequencing Data
- Seed Strain
Human Immunodeficiency Virus 1 (HIV-1) enters cells in a complex process involving interactions of viral envelope protein gp120 with the cellular receptor CD4 and a co-receptor, typically one of the chemokine receptors CCR5 or CXCR4 . According to their co-receptor usage or "tropism", viruses are classified as "R5" (interacting with CCR5) or "X4" (interacting with CXCR4). Additionally, there are dual-tropic "R5X4" strains that use both co-receptors for cell entry. Tropism is mainly determined by the sequence of the variable loop 3 (V3) of gp120. In initial infection, R5 viruses dominate the viral quasi-species . As the disease progresses, about 50% of the patients develop X4 virus . CCR5 blocking drugs, such as Maraviroc or Vicriviroc [4, 5] are ineffective against X4 virus, and thus it is advisable to test tropism prior to treatment with these drugs. The current state-of-the-art is testing by phenotypic assays such as Trofile® (Monogram Biosciences, CA)  or enhanced sensitivity Trofile® assay (ESTA) . However, their restriction to specialized laboratories, high cost and long turn-around are limiting availability. Moreover, phenotypic assays have been reported to fail in delivering any result in more than 15% of the cases . An alternative for routine diagnostics is genotypic testing: the genomic sequence of V3 from a patient is interpreted using computational models that relate V3 sequence and tropism. These models are typically derived by machine learning methods from a training set of V3 sequences and corresponding phenotypic test results [9–14]. Genotypic predictions can be made available via the Internet, and they are fast and cheap. Failure rates have been estimated to be around 7.5% . In clinical settings with tropism predictions based on single sequences from bulk sequencing, genotypic methods tend to perform less well , which is mostly attributed to low detection rates of X4 minorities by bulk sequencing . Genotypic testing based on so-called "next generation sequencing" or "deep sequencing" methods may not suffer from this limitation  as they provide detailed data for the whole viral quasi-species. In fact, Vandenbroucke et al. have demonstrated that a combination of deep sequencing of V3 with a genotype interpretation algorithm [11, 19] can be used for determination of tropism even in cases where phenotypic testing fails. In their study, the error rate of prediction methods was a limiting factor.
Unique V3 sequences
We next exploited the property of T-CUP to provide in the first level two independent tropism predictions based on physical properties (electrostatics and hydropathy) of V3. The corresponding probabilities span a plane ("probabilities plane") in which every V3 sequence is represented by a point and the quasi-species by a cloud of such points. Figure 2 shows this plane for all twelve datasets from Ref.  with the points colored according to frequency of the respective sequence in the deep sequencing data.
The dynamics of the quasi-species in the probabilities plane has several remarkable features. First, all sequences in week 0 cluster in the lower left corner of the plane as is expected for a quasi-species that is R5 tropic. Second, the movement of the clouds indicates the dynamics of tropism. For Subjects 07 and 18 the clouds move towards the upper right, i.e. to more X4 tropism. For Subject 19 this movement is also seen for the first two time points but then reverts again to the lower left, i.e. to more R5 tropism. Subject 47 shows no marked movement to the upper right but remains localized in the lower left, in agreement with a quasi-species that remains R5 tropic. Third, for the patients 07, 18, and 19 where a co-receptor switch had been observed, there is only one clearly dominating X4 strain in the probabilities plane, and this strain is already present at therapy start with considerable frequency (bright spots with green arrows). This "X4 seed strain" is specific for each of the patients - the seed strains for different patients are clearly located in different regions of the probabilities plane. Additionally, the X4 seed strain is accompanied from the beginning by a growing halo of local minor variants. Note that Subject 47 who remains R5 tropic throughout all time points does not have such a cluster.
Development of X4 variants causing tropism switch
Fraction of population at
Although the high cost of deep sequencing will probably prevent its use in routine diagnostics in the near future, the combination of this powerful method with accurate predictions could be applied when phenotype testing fails and to study evolution of viral quasi-species under selective pressure, and thus contribute to the development of sustainably effective treatments.
This work was funded by BMBF grant 01ES0709 and DFG TRR 60/A6. The authors thank Hauke Walter for fruitful discussions, and Tsibris et al. for making their data available to the public.
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