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Measuring integrated HIV DNA ex vivo and in vitro provides insights about how reservoirs are formed and maintained

Overview of attention for article published in Retrovirology, February 2018
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Title
Measuring integrated HIV DNA ex vivo and in vitro provides insights about how reservoirs are formed and maintained
Published in
Retrovirology, February 2018
DOI 10.1186/s12977-018-0396-3
Pubmed ID
Authors

Marilia Rita Pinzone, Una O’Doherty

Abstract

The identification of the most appropriate marker to measure reservoir size has been a great challenge for the HIV field. Quantitative viral outgrowth assay (QVOA), the reference standard to quantify the amount of replication-competent virus, has several limitations, as it is laborious, expensive, and unable to robustly reactivate every single integrated provirus. PCR-based assays have been developed as an easier, cheaper and less error-prone alternative to QVOA, but also have limitations. Historically, measuring integrated HIV DNA has provided insights about how reservoirs are formed and maintained. In the 1990s, measuring integrated HIV DNA was instrumental in understanding that a subset of resting CD4 T cells containing integrated HIV DNA were the major source of replication-competent virus. Follow-up studies have further characterized the phenotype of these cells containing integrated HIV DNA, as well as shown the correlation between the integration levels and clinical parameters, such as duration of infection, CD4 count and viral load. Integrated HIV DNA correlates with total HIV measures and with QVOA. The integration assay has several limitations. First, it largely overestimates the reservoir size, as both defective and replication-competent proviruses are detected. Since defective proviruses are the majority in patients on ART, it follows that the number of proviruses capable of reactivating and releasing new virions is significantly smaller than the number of integrated proviruses. Second, in patients on ART clonal expansion could theoretically lead to the preferential amplification of proviruses close to an Alu sequence though longitudinal studies have not captured this effect. Proviral sequencing combined with integration measures is probably the best estimate of reservoir size, but it is expensive, time-consuming and requires considerable bioinformatics expertise. All these reasons limit its use on a large scale. Herein, we review the utility of measuring HIV integration and suggest combining it with sequencing and total HIV measurements can provide insights that underlie reservoir maintenance.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 76 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 76 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 20 26%
Researcher 13 17%
Student > Bachelor 7 9%
Student > Doctoral Student 6 8%
Student > Master 6 8%
Other 10 13%
Unknown 14 18%
Readers by discipline Count As %
Immunology and Microbiology 16 21%
Medicine and Dentistry 16 21%
Biochemistry, Genetics and Molecular Biology 11 14%
Agricultural and Biological Sciences 7 9%
Engineering 2 3%
Other 6 8%
Unknown 18 24%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 06 March 2018.
All research outputs
#13,580,944
of 23,023,224 outputs
Outputs from Retrovirology
#630
of 1,108 outputs
Outputs of similar age
#171,434
of 330,704 outputs
Outputs of similar age from Retrovirology
#19
of 30 outputs
Altmetric has tracked 23,023,224 research outputs across all sources so far. This one is in the 39th percentile – i.e., 39% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,108 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one is in the 41st percentile – i.e., 41% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 330,704 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 46th percentile – i.e., 46% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 30 others from the same source and published within six weeks on either side of this one. This one is in the 26th percentile – i.e., 26% of its contemporaries scored the same or lower than it.