T-cell commitment is believed to occur early during activation and therefore changes in gene expression during the earliest stages of induction are of particular interest. An experimental model using human Jurkat T cells activated with PMA and ionomycin was used to investigate early changes in gene expression (up to one hour of stimulation), focusing on both changes in mRNA transcription rates as well as polyA mRNA levels. One hour was chosen in order to examine gene regulatory events occurring immediately after activation and to avoid, for the most part, the influence of secondary gene regulatory mechanisms taking place at later time points (e.g., increased synthesis of transcription factors). NRO RNA was prepared from isolated cell nuclei (Methods) and polyA mRNA from intact cells. Figure shows an example of filter images obtained after hybridization of arrays using either NRO RNA or polyA mRNA. Cells stimulated for 30 minutes exhibited moderate changes in gene expression in the mRNA arrays. In contrast, NRO RNA arrays revealed rapid and robust changes in transcription that were evident as early as 5 minutes following induction. Unexpectedly, a careful analysis of all significant changes in gene expression across the time course revealed that these early-response genes (as identified by NRO) were a relatively small subset of all of the genes shown to be regulated (see below).
Figure 1 Hybridization images of polyA mRNA and nuclear run-on (NRO) RNA. Depicted are signals in fields of arrays corresponding to untreated (time 0), as well as 5, and 30 min after induction of Jurkat cells using 40 ng/ml PMA and 1 μM Ionomycin (P+I). (more ...)
In all, a total of 4608 genes, including sets of genes enriched for immune response and signal transduction function, were polled (Fig. ). Of these genes, 2386 showed significant regulation (p < 0.001, or Z ratio > ± 1.5) during the time course of one hour post activation by either changing gene transcription or polyA mRNA levels. These significantly regulated genes were chosen for further analysis.
Figure 2 Distributions of significantly regulated genes in both polyA mRNA and nuclear run-on (NRO) RNA. For this analysis, a gene was considered to be up- or down-regulated in either polyA mRNA RNA or NRO RNA (Altered Gene Expression) if it was significantly (more ...)
The distribution and direction (increase, decrease, or no change) of significant changes in gene expression either at the transcriptional level (NRO) or at the level of polyA mRNA (whole-cell) are displayed in the table in Figure . The single most common expression event (55.2 %) was an up or down regulation at one or more time points as measured in mRNA without a corresponding (either up or down) regulation as measured by transcription at any time point. The second largest group of regulated genes (27.4%) showed changes in transcription with no corresponding change in polyA mRNA. Examples of genes dramatically up-regulated in this second class included CD69, a type II transmembrane receptor involved in lymphocyte proliferation and a classic marker of early T cell activation; PPP3C, the catalytic subunit of the calmodulin-activated phosphatase, calcineurin, which plays a central role in signal transduction from the T cell receptor to the nucleus; as well as several members of the JUN family of transcription factor immediate early response genes (Fig. ).
Figure 3 Comparison between polyA mRNA and nuclear run-on RNA of immediate early gene activation in Jurkat T cells. A.1 Heatmap of relative gene expression intensities (Z scores). A.2 Graphical representation of the same data illustrating an immediately apparent (more ...)
The final, relatively minor groups of regulated genes included genes which were regulated at both the transcriptional and the polyA mRNA levels in either the same (8.4%) or opposite directions (9%). The relatively low concordance between transcriptional production of mRNA and its measured appearance in polyA mRNA levels was somewhat surprising, although clear examples of coordinated step-wise production were noted for some key genes, as for example, the early response genes EGR1
(previously shown to be induced at 30 minutes by phorbol ester treatment of a human promyelocytic leukemia cell line [16
] and, the apoptosis-related genes DAP
(death-associated protein, mediator of interferon-gamma-induced apoptosis) and CASP3
, as well as the immune response signal transducer and activator of transcription (STAT) 6
Dramatic activation of immune response, immediate-early response genes, and apoptosis-related genes was observed in the nuclear run-on RNA as early as 5 minutes following activation (Fig. ). This group of genes included immediate-early response genes commonly up-regulated during cellular activation (ETR101, Myb, Myc, and genes of the JUN and EGR families), genes specifically associated with an early response in immune cells (IL6, IL8, STAT4, STAT6, PPP3C, NFKBIA, IRF5, and CD69), as well as genes involved in regulating apoptosis (BCL2A1, CASP3, CASP9, CASP10, and DAP). Many of these same genes were eventually up-regulated in polyA mRNA later in the time course and their increase in expression after one hour was independently validated by single end-point PCR validation using GS320 technology (Fig. ).
A particularly interesting example of the dichotomy between transcription and changes in polyA mRNA levels was seen in the production of the mRNAs encoding NFKB1 (NF-kappa B), a key mediator of the transcriptional control of genes involved in the immune response and acute phase reactions, and its inhibitor, NFKBIA (NF-kappa B inhibitor A). Both NFKB1 and NFKBIA have previously been shown by microarray analysis to be significantly induced in polyA mRNA between 3–4 hrs following phorbol or lectin activation of either Jurkat or human peripheral blood lymphocytes [7
]. As demonstrated here (Fig. ), the production of NFKB1 mRNA clearly increases between 30 minutes and one hour at the transcriptional level without a detectable corresponding increase in polyA mRNA during that time (subsequent PCR analysis did show some increase at the steady-state level between 0 and 60 minutes for the NFKB1 gene but this increase failed to meet the significance thresholds set for the microarray analysis). NFKBIA, on the other hand, is rapidly induced transcriptionally to a maximum level by 30 minutes, returning essentially to baseline within one hour. Meanwhile, NFKBIA steady-state levels can be seen to gradually rise across the first hour of the time course. Analysis of the dynamics of gene expression for NFKB1 and its inhibitor as deduced from conventional microarrays might suggest that by one hour NFKB1 production had not yet begun (contradicted by the NRO data here), and also that the mRNA for the inhibitor of NKFKB1 is steadily increasing (when, in fact, it is clear from NRO data that virtually all increases in the production of NFKBIA have concluded by one hour). These data provide a clear example of how information from nuclear run-on microarrays can enhance studies of gene activation and feedback mechanisms.
Consistency at each time point for genes regulated by activation-induced changes in mRNA stability can be seen in a graph of the Z ratio differences (Methods) between NRO and polyA mRNA calculated for all genes at all time points (Fig. ). These putative stability-regulated genes exhibited a very high degree of consistency at all the time points measured. This replicability is further illustrated in the heat map of clustered gene expression in Figure in which the data for each gene has been independently normalized to its baseline (0 time) level. Large numbers of genes as measured in the polyA mRNA are consistently and strongly regulated following activation. Some of these changes are mirrored by changes in gene transcription (NRO) but most are not.
Figure 4 Global comparison of gene expression changes in polyA mRNA and NRO RNA. A. At each time period indicated columns correspond to the values derived by subtracting the Z ratio of NRO RNA from the Z ratio of polyA mRNA for every gene. Equivalency between (more ...)
In order to compare changes in gene expression patterns at the transcriptional and polyA mRNA levels in a systematic fashion, a simple barcode of 1, -1, or 0 was applied to all significant changes in gene expression indicating up, down, or no change, respectively. In addition, a value of -1, 0, or 1 (low, moderate, or high) was assigned to each gene according to its relative intensity at baseline (0 time). An analysis of the distribution of these gene expression patterns (Fig. ) revealed that both at the transcriptional and the steady-state levels two thirds of all genes were restricted to just one of 20 patterns (out of a possible number of 729) and that half of these patterns were shared between the two groups. An interesting distinction between the two groups was that whereas up-regulation from a moderately high level of baseline expression was highly favored for new gene synthesis, down-regulation from a moderately high level of baseline expression was very highly favored during polyA mRNA regulation (Fig. ). In fact, down-regulation, as a consistent trend, was much less common among transcriptional-regulated than steady-state-regulated genes, with implications (see below) as to the roles these modes of regulation play in concert for the control of gene expression.
Figure 5 Frequency distribution of gene expression patterns generated from polyA mRNA or NRO RNA. The top 20 patterns for each method is shown. Significant changes in gene expression were assigned a 1, -1, or 0 for up, down, or no change, respectively. In addition, (more ...)
Figure 6 Differential distribution of transcriptional and polyA mRNA up- (A) or down- (B) regulated gene expression patterns during Jurkat T cell activation. The number of genes consistently regulated (up or down at every time point) are correlated with their (more ...)
In order to confirm that stability regulation was in fact a reasonable explanation for the observation that the expression levels of large numbers of genes were changing at the whole cell but not the transcriptional level, a series of experiments were carried out in which activation of Jurkat cells was carried out in the presence or absence of the transcription inhibitor Actinomycin D. Analysis of polyA mRNA demonstrated the strong inhibition of mRNA levels for genes previously shown to be transcriptionally up-regulated (Fig. ). In contrast, large numbers of genes which were significantly regulated in polyA mRNA but not in NRO RNA were not affected by Actinomycin D treatment (Fig. &). Among these unaffected genes there was a bias towards presumptively de-stablized genes (Fig. ) consistent with the earlier conclusion (Fig. ) that down-regulation is the predominant motif in overall polyA mRNA levels.
Figure 7 Persistence of stability-regulated changes in gene expression in the presence of Actinomycin D. A. Effect of Actinomycin D on the (P+I)-induced changes in the expression patterns of gene deemed to be transcriptionally regulated. Z ratio comparisons are (more ...)
Although an examination of the functional classifications of stability regulated versus transcriptionally regulated genes yielded no obvious trends, some biological pathways appear to be differentially, and sometimes, exhaustively regulated by each type of expression event. One example of this can be seen in the apoptotic pathways, which are comprehensively regulated during the Jurkat activation scenario [16
]. As can be seen from the pathway schematic illustrated in Figure , some major effectors of apoptosis such as CASP 3
(up-regulated) and BCL2
(down-regulated), are controlled by new gene synthesis while other factors such as CASP 1
(up-regulated) and BCL2L2
(down-regulated) appear to be regulated by stability processes alone. Regardless of the cellular rationale for regulating at the level of new gene synthesis or mRNA stability, it is clear from these data that there is a strong internal coherence: genes regulated by one mechanism do not crossover to the other within the time frame investigated.
Figure 8 Regulation of apoptotic pathways during T cell activation involves changes in gene expression by both mRNA transcriptional and mRNA stability mechanisms. Genes colored in red and blue were up- or down-regulated in polyA mRNA only. Genes colored in yellow (more ...)
Common patterns of transcription factor binding sites in the upstream promoter regions of groups of genes whose transcription was significantly up-regulated were detected. An enrichment of transcription factor-binding sites for both NFAT and NFκB was found in the promoter regions of genes most significantly up-regulated at 1 hour as shown in Figure . The frequency with which both NFAT- and NFκB-binding sites were found in the promoters of this group of genes was significantly greater than that seen for other genes in the array [e.g., genes down-regulated at this time point or genes selected at random [supplemental data]. The discovery that NFAT- and NFκB-binding sites were enriched in the promoters of these genes was not unexpected, since these transcription factors, along with AP-1 components Fos and Jun, constitute the major transcription factors involved in the early stages of T-cell activation. Indeed, the frequency of genes significantly up-regulated in NRO RNA and enriched for AP-1 binding sites peaks during the time course (supplemental data). Many of the genes that are transcriptionally upregulated at 1 hour, such as CD69, are elevated throughout the time examined, although a few genes including PTEN, DUSP5, and NFκB1 itself, show elevated transcription only after 60 minutes. The simultaneous increase of NFκB1 gene transcription (between 30 minutes and 1 hour) combined with a noticeable increase in the transcription of genes containing NFκB-binding sites (at one hour) demonstrates the synchronous relationship between the appearance of this transcription factor and its downstream targets at the indicated time points.
Figure 9 Enrichment of NFAT and NFκB transcription factor-binding sites in the promoter regions of genes upregulated after 60 min of treatment of human Jurkat T cells with PMA + Ionomycin. Color gradient from bright red to bright green is directly proportional (more ...)