“Dynamic Range” of Inferred Phenotypic HIV Drug Resistance Values in Clinical Practice

Background: ‘Virtual ’ or inferred phenotypes (vPhenotypes) are commonly used to assess resistance to antiretroviral agents in patients failing therapy. In this study, we provide a clinical context for understanding vPhenotype values. Methods: All HIV-infected persons enrolled in the British Columbia Drug Treatment Program with a baseline plasma viral load (pVL) and follow-up genotypic resistance and pVL results were included up to October 29, 2008 (N = 5,277). Change from baseline pVL was determined as a function of Virco vPhenotype, and the ‘‘dynamic range’ ’ (defined here by the 10th and 90th percentiles for fold-change in IC50 amongst all patients) was estimated from the distribution of vPhenotye foldchanges across the cohort. Results: The distribution of vPhenotypes from a large cohort of HIV patients who have failed therapy are presented for all available antiretroviral agents. A maximum change in IC50 of at least 13-fold was observed for all drugs. The dideoxy drugs, tenofovir and most PIs exhibited small ‘‘dynamic ranges’ ’ with values of,4-fold change observed in.99 % of samples. In contrast, zidovudine, lamivudine, emtricitabine and the non-nucleoside reverse transcriptase inihibitors (excluding etravirine) had large dynamic ranges. Conclusion: We describe the populational distribution of vPhenotypes such that vPhenotype results can be interprete

Results of external quality assessment for proviral DNA testing of HIV tropism in the Maraviroc switch collaborative study

The Maraviroc Switch collaborative study (MARCH) is a study in aviremic patients on stable antiretroviral therapy and utilizes population-based sequencing of proviral DNA to determine HIV tropism and susceptibility to maraviroc. An external quality assessment (EQA) program was implemented to ensure competency in assessing the tropism of clinical samples conducted by MARCH laboratories (n = 14). The MARCH EQA has three prestudy phases assessing V3 loop sequencing and tropism determination using the bioinformatic algorithm geno2pheno, which generates a false-positive rate (FPR). DNA sequences with low FPRs are more likely to be from CXCR4-using (X4) viruses. Phase 1 of the EQA involved chromatogram interpretation. Phases 2, 2/3, and 3 involved patient and clonal samples. Clinical samples used in these phases were from treatment-experienced HIVinfected volunteers; 18/20 had viral loads o

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives

Biological Earth observation with animal sensors

Space-based tracking technology using low-cost miniature tags is now delivering data on fine-scale animal movement at near-global scale. Linked with remotely sensed environmental data, this offers a biological lens on habitat integrity and connectivity for conservation and human health; a global network of animal sentinels of environmen-tal change

Reconstructing the Dynamics of HIV Evolution within Hosts from Serial Deep Sequence Data

At the early stage of infection, human immunodeficiency virus (HIV)-1 predominantly uses the CCR5 coreceptor for host cell entry. The subsequent emergence of HIV variants that use the CXCR4 coreceptor in roughly half of all infections is associated with an accelerated decline of CD4+ T-cells and rate of progression to AIDS. The presence of a 'fitness valley' separating CCR5- and CXCR4-using genotypes is postulated to be a biological determinant of whether the HIV coreceptor switch occurs. Using phylogenetic methods to reconstruct the evolutionary dynamics of HIV within hosts enables us to discriminate between competing models of this process. We have developed a phylogenetic pipeline for the molecular clock analysis, ancestral reconstruction, and visualization of deep sequence data. These data were generated by next-generation sequencing of HIV RNA extracted from longitudinal serum samples (median 7 time points) from 8 untreated subjects with chronic HIV infections (Amsterdam Cohort Studies on HIV-1 infection and AIDS). We used the known dates of sampling to directly estimate rates of evolution and to map ancestral mutations to a reconstructed timeline in units of days. HIV coreceptor usage was predicted from reconstructed ancestral sequences using the geno2pheno algorithm. We determined that the first mutations contributing to CXCR4 use emerged about 16 (per subject range 4 to 30) months before the earliest predicted CXCR4-using ancestor, which preceded the first positive cell-based assay of CXCR4 usage by 10 (range 5 to 25) months. CXCR4 usage arose in multiple lineages within 5 of 8 subjects, and ancestral lineages following alternate mutational pathways before going extinct were common. We observed highly patient-specific distributions and time-scales of mutation accumulation, implying that the role of a fitness valley is contingent on the genotype of the transmitted variant. Citation: Poon AFY, Swenson LC, Bunnik EM, Edo-Matas D, Schuitemaker H, et al. (2012) Reconstructing the Dynamics of HIV Evolution within Hosts from Serial Deep Sequence Data. PLoS Comput Biol 8(11): e1002753. doi:10.1371/journal.pcbi.100275