Analysis of post-transcriptional regulations by a functional, integrated, and quantitative method

Abstract

In the past 10 years, transcriptome and proteome analyses have provided valuable data on global gene expression and cell functional networks. However, when integrated,these analyses revealed partial correlations between mRNA expression levels and protein abundance thus suggesting that post-transcriptional regulations may be in part responsible for this discrepancy. In the present work, we report the development of a functional, integrated, and quantitative method to measure post-transcriptional regulations that we named FunREG. This method enables (i) quantitative measure of post-transcriptional regulations mediated by selected 3-untranslated regions and exogenous small interfering-RNA or micro-RNAs and (ii) comparison of these regulatory processes in physiologically relevant systems (e.g. cancer versus primary untransformed cells). We applied FunREG to the study of liver cancer, and we demonstrate for the first time the differential regulatory mechanisms controlling gene expression at a post-transcriptional level in normal and tumoral hepatic cells. As an example, translation efficiency mediated by heparin-binding epidermal growth factor 3-untranslated region was increased 3-fold in liver cancer cells compared with normal hepatocytes, whereas stability of an mRNA containing a portion of Cyclin D1 3-untranslated region was increased more than 2-fold in HepG2 cells compared with normal hepatocytes. Consequently we believe that the method presented herein may become an important tool in fundamental and medical research. This approach is convenient and easy to perform, accessible to any investigator, and should be adaptable to a large number of cell type, functional and chemical screens, as well as genome scale analyses. Finally FunREG may represent a helpful tool to reconcile transcriptome and proteome data.

Fig. 1.

FunREG. A schematic representation of the FunREG experimental pipeline is shown. The three major steps are depicted by boxes as indicated. Boxes are recognized by a title (top panel), and some, corresponding to the three major steps of the method, contain the material used (middle panel) and the biological or experimental outcomes (bottom panel), as indicated. Step 1, target cells are transduced with lentiviral particles (as illustrated) containing either the transgene of interest or the reference. Following transduction, the transgenes are integrated into the host genome, and eGFP is expressed. At this stage, the objective is either to study the functioning of known or putative cisARSs as post-transcriptional elements in selected cells, evaluate the involvement of transRFs in a cisARS-mediated post-transcriptional regulation, or perform both analyses in parallel (cisARS and transRF regulations boxes as indicated). Step 2, molecular and cellular analyses are done by measuring the “TCN” and the amounts of “M” by qPCR and qRT-PCR using genomic DNA and total RNA extracted from the whole transduced cell population, respectively. The amount of “P” is measured by flow cytometry using eGFP-expressing live cells (as illustrated). Step 3, the three ratios, P/TCN, M/TCN, and P/M, are calculated. These ratios are indicative of the global post-transcriptional regulation, the relative mRNA stability, and the relative translation efficiency, respectively. Finally the values obtained with every transgene or in every condition are compared.

Fig. 2.

EGFP protein expression correlates with the transgene copy number in the different cell types. MG63 (A), HeLa (B), HuH7 (C), and HepG2 (D) cells and fresh primary hepatocytes (E) were transduced once with increasing amounts of eGFP-GLO-expressing lentiviral particles. After 1 week, eGFP protein expression (MFI) and percentage of eGFP⁺ cells were determined in each condition by flow cytometry. TCN was determined by qPCR using genomic DNA and normalizing to albumin gene. The curves represent the “percentage of eGFP⁺ cells:transgene copy number” correlation (▵ curve) or the “eGFP protein:transgene copy number” correlation (● curve). The R² corresponding to eGFP protein:transgene copy number correlation curve is as indicated.

Fig. 3.

Measurement of 3′-UTR-mediated post-transcriptional regulations by FunREG. A, schematic representations of the different transgenes used in this study. The referent eGFP-GLO transgene is shown in the top panel. The transgenes containing selected cisARSs are shown in the bottom panel. LTR, long terminal repeat. B–D, HuH7 cells were transduced once with lentiviral particles expressing the indicated transgene. After 1 week, the transgene copy number and eGFP mRNA amount were determined by qPCR and qRT-PCR using genomic DNA or reverse transcribed total RNA extracted from each transduced cell population, respectively. The eGFP protein amount was determined by flow cytometry on each transduced cell population. B, global post-transcriptional regulation. C, mRNA stability. D, translation efficiency. In this and the following figures, the different ratios (in arbitrary units) are shown on the y axes, error bars represent S.D., and analysis of variance (ANOVA) p values are indicated at the top right of the figure (n = 3). Significant variations using Dunnett post-test are represented by asterisks above the corresponding bar when comparing the referent (or control) condition and the indicated condition or above the line when comparing the two indicated conditions: *, p < 0.05; **, p < 0.01; and ***, p < 0.001.

Fig. 4.

Measurement of siRNA- and miRNA-mediated post-transcriptional regulations by FunREG. A, schematic representation of eGFP-c-MYC transgene. Parts of the eGFP-c-MYC mRNA targeted by the si-eGFP and miRNAs (miR-98 and let-7a) are as shown. B–D, the eGFP-c-MYC-expressing HuH7 cells (from Fig. 3; transgene copy number known) were transfected with the indicated siRNA or miRNA. Three days later, the eGFP protein and eGFP mRNA amounts were determined as described in Fig. 3 except that α-tubulin mRNA was used as internal control. B, global post-transcriptional regulation. C, mRNA stability. D, translation efficiency. ANOVA, analysis of variance.

Fig. 5.

Functional comparisons of selected 3′-UTRs in primary human hepatocytes and liver cancer-derived human cell lines using FunREG. Among genes differentially expressed in normal and pathologic conditions (left and right orange boxes), those susceptible to being post-transcriptionally regulated (red box) by known or putative cisARSs located into their 5′- or 3′-UTR (middle orange box) are selected. The corresponding transgenes (bearing either the 5′- or 3′-UTR of interest) are transferred into normal and pathologic cells used as models by lentiviral transduction (yellow box). Then both types of transduced cells are “injected” in the FunREG pipeline. Two outcomes can be achieved. First there is no functional difference between normal and pathologic cells (top gray box). Therefore the transgene of interest does not contain the cisARS responsible for the differential gene expression, or the deregulation is due to a transcriptional or post-translational mechanism (bottom gray box). On the other hand, there is a functional difference between normal and pathologic cells (top green box) indicative of a post-transcriptional deregulation associated with the pathology (blue box). Following FunREG, the origin of the molecular mechanism (mRNA stability, translation efficiency, or both) responsible for the post-transcriptional deregulation is determined (bottom green boxes).

Fig. 6.

Comparative analyses of post-transcriptional regulations in normal and tumoral hepatic cells. HuH7 cells, HepG2 cells, and hepatocytes (as indicated) were transduced once with lentiviral particles expressing the indicated transgene. After 1 week, eGFP protein and mRNA amounts as well as transgene copy number were determined as described in Fig. 3. A, global post-transcriptional regulation. B, mRNA stability. C, translation efficiency. ANOVA, analysis of variance.