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HIV-1 latency and virus production from unintegrated genomes following direct infection of resting CD4 T cells

Overview of attention for article published in Retrovirology, January 2016
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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9 X users
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1 patent

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91 Mendeley
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Title
HIV-1 latency and virus production from unintegrated genomes following direct infection of resting CD4 T cells
Published in
Retrovirology, January 2016
DOI 10.1186/s12977-015-0234-9
Pubmed ID
Authors

Chi N. Chan, Benjamin Trinité, Caroline S. Lee, Saurabh Mahajan, Akanksha Anand, Dominik Wodarz, Steffanie Sabbaj, Anju Bansal, Paul A. Goepfert, David N. Levy

Abstract

HIV-1 integration is prone to a high rate of failure, resulting in the accumulation of unintegrated viral genomes (uDNA) in vivo and in vitro. uDNA can be transcriptionally active, and circularized uDNA genomes are biochemically stable in non-proliferating cells. Resting, non-proliferating CD4 T cells are prime targets of HIV-1 infection and latently infected resting CD4 T cells are the major barrier to HIV cure. Our prior studies demonstrated that uDNA generates infectious virions when T cell activation follows rather than precedes infection. Here, we characterize in primary resting CD4 T cells the dynamics of integrated and unintegrated virus expression, genome persistence and sensitivity to latency reversing agents. Unintegrated HIV-1 was abundant in directly infected resting CD4 T cells. Maximal gene expression from uDNA was delayed compared with integrated HIV-1 and was less toxic, resulting in uDNA enrichment over time relative to integrated proviruses. Inhibiting integration with raltegravir shunted the generation of durable latency from integrated to unintegrated genomes. Latent uDNA was activated to de novo virus production by latency reversing agents that also activated latent integrated proviruses, including PKC activators, histone deacetylase inhibitors and P-TEFb agonists. However, uDNA responses displayed a wider dynamic range, indicating differential regulation of expression relative to integrated proviruses. Similar to what has recently been demonstrated for latent integrated proviruses, one or two applications of latency reversing agents failed to activate all latent unintegrated genomes. Unlike integrated proviruses, uDNA gene expression did not down modulate expression of HLA Class I on resting CD4 T cells. uDNA did, however, efficiently prime infected cells for killing by HIV-1-specific cytotoxic T cells. These studies demonstrate that contributions by unintegrated genomes to HIV-1 gene expression, virus production, latency and immune responses are inherent properties of the direct infection of resting CD4 T cells. Experimental models of HIV-1 latency employing directly infected resting CD4 T cells should calibrate the contribution of unintegrated HIV-1.

X Demographics

X Demographics

The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 91 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 2 2%
Denmark 1 1%
United States 1 1%
Unknown 87 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 24%
Student > Ph. D. Student 16 18%
Student > Bachelor 12 13%
Student > Master 10 11%
Student > Postgraduate 5 5%
Other 9 10%
Unknown 17 19%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 24 26%
Immunology and Microbiology 17 19%
Medicine and Dentistry 17 19%
Agricultural and Biological Sciences 12 13%
Computer Science 1 1%
Other 3 3%
Unknown 17 19%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 17 October 2019.
All research outputs
#3,740,756
of 23,308,124 outputs
Outputs from Retrovirology
#177
of 1,113 outputs
Outputs of similar age
#64,154
of 395,820 outputs
Outputs of similar age from Retrovirology
#4
of 20 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. Compared to these this one has done well and is in the 83rd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,113 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done well, scoring higher than 84% of its peers.
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 395,820 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 83% of its contemporaries.
We're also able to compare this research output to 20 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.