For example, assuming integrations are Poisson distributed with a

For example, assuming integrations are Poisson distributed with an MOI of 0. 1, 90. 5% of cells will not contain a provirus, www.selleckchem.com/products/epz-5676.html 9% of cells will contain one proviral integration and 0. 5% of cells will con tain multiple integrations. The cells without an integration are not amplified by HIV targeted PCR leaving only Inhibitors,Modulators,Libraries 9. 5% of the total cells. Of these cells actually under study, Inhibitors,Modulators,Libraries 4. 9% will contain multiple integrations. Thus the signal from expressed proviruses may be muted by the presence of latent proviruses in the expressed population. The replication cycle of HIV is error prone, and a sig nificant proportion of virions contain mutated genomes. In studies that do not check for inducibility, mutant proviruses integrated in regions of the genome other wise favorable to proviral expression can be sorted into the latent pool due to mutational inactivation.

This prob lem of inactivated provirus is worse when latent provirus are rare and exacerbated further when looking at latency in the cells of HIV patients due to selective enrichment of inactivated proviruses incapable of spreading infection. Here, the effects of mutation are minimized Inhibitors,Modulators,Libraries in the datasets that required inducible viral expression but may be a confounder in the two datasets that were sorted based on lack of viral expression only. Inaccurate staining or leaky markers may also result in misclassification of proviruses. False positives and false negatives will result in incorrectly sorted latent and expressed integrations. For example, if 5% of cells not con taining Gag are labeled Inhibitors,Modulators,Libraries as Gag and there are an equal amount of latent and expressed integration sites, then 4.

8% of integrations labeled expressed will actually be latent. If a category is rare, false staining Inhibitors,Modulators,Libraries has even greater potential to cause error. For example, if only 5% of sites are latent and a Gag stain has a false negative rate of 5%, then we would expect 48. 7% of sites classified as latent to actually be mislabeled expressed integrations. Attempts to induce latent proviruses in patients have so far focused on using histone deacetylase inhibitors, raising interest in associations with histone acetylation in these data. An important caveat in results from these genome wide data is that histone modification near the integrated provirus may not be representative of modifi cation within the provirus at the key Nuc 1 nucleosome of the transcription start site, though local correla tions in chromatin states are well established from stud ies of position effect variegation.

We found that some histone acetylation marks were significantly asso http://www.selleckchem.com/products/FTY720.html ciated with viral expression in some but not all samples. This lack of association may be due to a lack of power in these studies, but the confidence intervals suggest that any correlations between acetyla tions and latency are unlikely to be strong.

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