Genome-wide analysis of chromosomal features repressing human immunodeficiency virus transcription - PubMed (original) (raw)
Genome-wide analysis of chromosomal features repressing human immunodeficiency virus transcription
M K Lewinski et al. J Virol. 2005 Jun.
Abstract
We have investigated regulatory sequences in noncoding human DNA that are associated with repression of an integrated human immunodeficiency virus type 1 (HIV-1) promoter. HIV-1 integration results in the formation of precise and homogeneous junctions between viral and host DNA, but integration takes place at many locations. Thus, the variation in HIV-1 gene expression at different integration sites reports the activity of regulatory sequences at nearby chromosomal positions. Negative regulation of HIV transcription is of particular interest because of its association with maintaining HIV in a latent state in cells from infected patients. To identify chromosomal regulators of HIV transcription, we infected Jurkat T cells with an HIV-based vector transducing green fluorescent protein (GFP) and separated cells into populations containing well-expressed (GFP-positive) or poorly expressed (GFP-negative) proviruses. We then determined the chromosomal locations of the two classes by sequencing 971 junctions between viral and cellular DNA. Possible effects of endogenous cellular transcription were characterized by transcriptional profiling. Low-level GFP expression correlated with integration in (i) gene deserts, (ii) centromeric heterochromatin, and (iii) very highly expressed cellular genes. These data provide a genome-wide picture of chromosomal features that repress transcription and suggest models for transcriptional latency in cells from HIV-infected patients.
Figures
FIG. 1.
Acquisition of cells containing stably expressed and inducible proviruses. (A) Tat-transducing HIV-based vector used in this study. Tat, HIV-encoded transcriptional activator; IRES, internal ribosome entry site. Transcription initiates within the left LTR. (B) Acquisition of cells containing stably expressed and inducible proviruses by FACS. Cells were infected at a multiplicity of about 0.1 and sorted for GFP-positive and -negative cells (left side). GFP-positive cells were collected and then sorted a second time to isolate a stably bright fraction. The GFP-negative (dark) population was sorted twice, and the dark cells were collected each time. The stably dark cells were then treated with TNF-α, and the resulting bright cells were collected (right side).
FIG. 2.
Primary sequences surrounding the stably expressed and inducible proviruses. The weak consensus sequence seen at the stably expressed (top) and inducible (bottom) proviruses was rendered so that the degree of conservation is proportional to the height of each letter, using LOGO (
http://weblogo.Berkeley.edu/logo.cgi
). The y axis reflects the information content at each base, so that perfect conservation would have a score of 2 bits. The points of joining between the HIV and human DNA lie between −1 and 0 (for the sequenced HIV DNA end) and between 4 and 5 on the other strand for the other end of the HIV DNA. Thus, the points of joining, and the integration consensus sequence, are symmetric around position 2 (arrow).
FIG. 3.
Frequency of stably expressed or inducible proviruses in intergenic regions of different lengths. Shorter intergenic regions are shown to the left, and longer ones are to the right. Genscan genes were used for this analysis, though the conclusions were similar for other gene sets as well (see p. 67-79 of the statistical information provided in the supplement material). The P value is obtained from the logistic regression of event type (stable or inducible) on a cubic B-spline basis (i.e., a third-order polynomial) for intergenic distance. The units on the x axis indicate lengths of intergenic regions, in base pairs. Lengths of intergenic regions for each category were defined by the following boundaries (from left to right, in bp): 1,627, 6,135, 10,506, 14,900, 21,907, 28,989, 36,333, 43,531, 62,837, 104,802, and 3,182,720. The inducible proviruses in the rightmost five bins accounted for 14% of all inducible proviruses.
FIG. 4.
Inducible proviruses are found more commonly in very highly active genes. Expression levels were assayed in Jurkat cells (three independent Affymetrix HU133A microarrays for each condition) and scored using the Affymetrix Microarry suite 5.1 software package. To classify the expression levels of genes hosting integration events, class boundaries were first generated by dividing all the genes on the array into eight classes according to their relative level of expression. Genes that hosted integration events were then distributed into the classes defined by these boundaries, summed, and expressed as a percentage of the total number of integration sites in genes on the array. The leftmost class in each panel contains the 1/8 most weakly expressed genes, and the rightmost class contains the 1/8 most highly expressed. The highest signal value represented in each expression bin (for untreated Jurkat cells) was as follows: bin 1, 9.2; bin 2, 20.6; bin 3, 38.6; bin 4, 66; bin 5, 117; bin 6, 227; bin 7, 488; bin 8, 12050. Integration sites were analyzed using data from untreated Jurkat cells (A), TNF-treated Jurkat cells (B), or HIV-Tat-GFP-infected Jurkat cells (C) (P < 0.003; chi-square test). Inducible proviruses in the eighth class (most highly expressed) accounted for about 17% of the total.
FIG. 5.
Tat down-modulates host cell genes important in signal transduction and immune responses. Signal intensities from Affymetrix HU133A microarrays were analyzed by SAM (
http://www-stat.Stanford.EDU/∼tibs/SAM/
) to identify significantly affected genes and then clustered according to gene ontology using EASE (
http://david.niaid.nih.gov/david/ease.htm
). The three left columns show results from uninfected cells, and the three right columns show results from cells infected with the Tat-transducing HIV-based vector. A large set of Tat-repressed genes (115 probe sets corresponding to 108 different genes) was identified as overrepresented compared to all genes queried by the microarray in the “signal transducer activity” category (P = 1.16 × 10−5; Fisher exact test with Bonferroni correction for multiple comparisons). Expression values were normalized by dividing by the mean. In cases where multiple probe sets queried the activities of a single gene, the values were found to be closely similar and a single representative probe set was used for the figure. Gray tiles indicate negative values. All genes called by EASE in the “signal transducer activity” category are shown, except for six olfactory receptors and one taste receptor.
FIG. 6.
Clustering of transcriptional profiles from Jurkat cells with human leukocytes. Data for human tissues are from reference . All analyses used Affymetrix HU133A microarrays. Transcription signal values were averaged between replicates and ranked prior to clustering. Squared Euclidean distance and unweighted pair-group average linkage (also know as UPGMA) cluster analysis of the transcriptional profiles was carried out using Statistica 7.0.
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