A global RNAi screening approach in Drosophila cells identifies a large group of transcription and splicing factors that modulate RAS/MAPK signaling by altering the expression of MAPK. The small GTPase RAS is among the most prevalent oncogenes. The evolutionarily conserved RAF-MEK-MAPK module that lies downstream of RAS is one of the main conduits through which RAS transmits proliferative signals in normal and cancer cells. Genetic and biochemical studies conducted over the last two decades uncovered a small set of factors regulating RAS/MAPK signaling. Interestingly, most of these were found to control RAF activation, thus suggesting a central regulatory role for this event. Whether additional factors are required at this level or further downstream remains an open question. To obtain a comprehensive view of the elements functionally linked to the RAS/MAPK cascade, we used a quantitative assay in Drosophila S2 cells to conduct a genome-wide RNAi screen for factors impacting RAS-mediated MAPK activation. The screen led to the identification of 101 validated hits, including most of the previously known factors associated to this pathway. Epistasis experiments were then carried out on individual candidates to determine their position relative to core pathway components. While this revealed several new factors acting at different steps along the pathway—including a new protein complex modulating RAF activation—we found that most hits unexpectedly work downstream of MEK and specifically influence MAPK expression. These hits mainly consist of constitutive splicing factors and thereby suggest that splicing plays a specific role in establishing MAPK levels. We further characterized two representative members of this group and surprisingly found that they act by regulating mapk alternative splicing. This study provides an unprecedented assessment of the factors modulating RAS/MAPK signaling in Drosophila. In addition, it suggests that pathway output does not solely rely on classical signaling events, such as those controlling RAF activation, but also on the regulation of MAPK levels. Finally, it indicates that core splicing components can also specifically impact alternative splicing. mapk mRNA expression and processing. Our findings raise the intriguing possibility that regulation of mapk transcript production is a key step for a diverse set of regulatory inputs, and may play an important part in RAS/MAPK signaling dynamics.The RAS/MAPK pathway is a cornerstone of the cell proliferation signaling apparatus. It has a notable involvement in cancer as mutations in the components of the pathway are associated with aberrant proliferation. Previous work has focused predominantly on post-translational regulation of RAS/MAPK signaling such that a large and intricate network of factors is now known to act on core pathway components. However, regulation at the pre-translational level has not been examined nearly as extensively and is comparatively poorly understood. In this study, we used an unbiased and global screening approach to survey the Drosophila genome—using Drosophila cultured cells—for novel regulators of this pathway. Surprisingly, a majority of our hits were associated to either transcription or mRNA splicing. We used a series of secondary screening assays to determine which part of the RAS/MAPK pathway these candidates target. We found that these factors were not equally distributed along the pathway, but rather converged predominantly on  The RAS/MAPK pathway consists of a core module of three kinases  that transmit signals downstream of the small GTPase RAS. Upstream factors such as receptor tyrosine kinases (RTKs), which respond to extracellular signals, lead to RAS activation by a guanine nucleotide exchange factor (GEF). GTP-loaded RAS then triggers the sequential activation of RAF, MEK, and MAPK; active RAF phosphorylates and activates MEK, which in turn phosphorylates and activates MAPK Drosophila and Caenorhabditis elegans have been instrumental in identifying a growing list of key regulators of the RAS/MAPK pathway such as soscswksrCbldosmts/PP2Asur-8/soc-2cnksprysur-6PTP-ERlet-7alph/PP2Chyp/aveOver the last two decades, genetic screens in metazoan models such as The success of the aforementioned genetic screens typically relied on the qualitative modification of a visible phenotype. This consideration, together with the technical limitations associated with genetic screening procedures, usually limit results to a handful of confirmed hits. RNA interference (RNAi) used as a functional genomics tool provides the possibility of a more comprehensive type of analysis providing a systematic means to functionally annotate the genome Drosophila S2 cells that specifically focused on signal regulation between RAS and MAPK. Validated hits were submitted to a series of secondary assays aimed at positioning their regulatory input with respect to the three core kinases. In addition to identifying and correctly positioning most of the components previously known to mediate RAS-induced MAPK activation, the screen led to the discovery of several new factors that act at different steps along the pathway. Notably, we identified five novel components that act upstream of RAF. The homologs of these five proteins are part of a complex named striatin-interacting phosphatase and kinase complex (STRIPAK) mek, mapk, or of the MAPK phosphatase PTP-ER. However, most of the novel factors were associated with mRNA processing and were found to act downstream of MEK and to regulate mapk splicing. Among these were components of the exon junction complex (EJC), which we and others have previously reported to be involved in regulating the splicing of the mapk pre-mRNA mapk's long introns and cause a reduction in the amount of functional protein product. In this study, we focus on the function of a larger group of canonical splicing factors that also regulate mapk splicing. We show that the impact of these factors on alternative splicing (AS) of mapk differs from what we previously described for the EJC, indicating that two different types of regulatory input act on this step in mapk expression.Here, we present the results of a genome-wide RNAi screen in Thus, in addition to providing a comprehensive view of regulatory factors influencing signal transmission between RAS and MAPK, this work suggests that pathway output does not solely rely on post translational regulatory events, such as those controlling RAF activation, but is also tightly governed by the regulation of the expression of core components. In particular, the expression of MAPK emerges as a focal point for multiple different regulatory inputs.To systematically search for and categorize new factors that specifically modulate signaling between RAS and MAPK, we employed a screening strategy that involved three distinct steps: (1) a primary genome-wide RNAi screen, (2) a validation screening step aimed at eliminating false positives, and (3) validated candidates were submitted to a series of a secondary screens to establish the position of their regulatory input relative to known pathway components (epistasis) and assess their specificity to RAS/MAPK signaling .V12 expression (http://www.bioinfo.iric.ca/iricrnai). 309 hit genes, which reproducibly altered pMAPK signal, were identified in the primary screen  were amongst the strongest hits that decreased the pMAPK signal  in Drosophila S2 cells. This assay was used to screen a genome-wide long double-stranded RNA (dsRNA) library for modulation of pMAPK levels induced by RASpression . The resy screen . ImportaK signal . Other kV12pMet-RAS expression system and dsRNA off-target effects. 101 genes of the initial 309 primary hits passed both validation criteria  MAPK activation induced by stimuli upstream of RAS, (2) epistasis screens involving MAPK activation at the level of or downstream of RAS, and (3) JNK activation screens aimed at addressing specificity to the MAPK pathway context , S4, S5.GAP RNAi  were then used as stimuli in two JNK activation assays as a proxy to evaluate specificity to the RAS/MAPK signaling context  based on publicly available protein and genetic interaction data . The canRas85D, raf, mek, mapk, ksr, cnk, and PTP-ER) by quantitative PCR (qPCR)  also seemed to modulate the transcript levels of mapk. Moreover, two factors, gfzf and CG4936, were found to modulate the levels of mek and PTP-ER, respectively   and S8. eIF4AIII  and S6 aectively . CG1603,obtained .mapk are not always strongly affected by the splicing changes induced by EJC depletion. On the other hand, a reverse transcription PCR (RT-PCR) assay spanning the whole mapk transcript is a more sensitive tool allowing for detection of small splicing changes mapk splicing. Consequently, we used the RT-PCR assay that had been used with the EJC to examine mapk splicing. Interestingly, not only did this experiment reveal that nearly all the splicing factors in our set caused shifts in the mapk RT-PCR profile, but these RT-PCR profiles were also clearly different from those produced by EJC depletion  also caused a reduction of MAPK protein levels. Conversely, AKT protein levels, which were used as a control, were not generally sensitive to depletion of these same factors . We alsobona fide RAS/MAPK factors and generally displayed a good specificity to the RAS/MAPK context . FNTA is the alpha farnesyltransferase subunit for mammalian RAS proteins Drosophila  is one of the pivotal regulatory events as it is thought to trigger the release of 14-3-3, which otherwise sequesters RAF in the cytoplasm ksr, cnk, hyp, 14-3-3ε, and 14-3-3ζ) were correctly positioned at the RAS-RAF interval  . Three oV12, suggesting that CKA/Striatin modulates signaling through this pathway and that the other STRIPAK members act in conjunction with Striatin in this context. This is also consistent with the findings that TRAF3-interacting JNK-activating modulator (T3JAM), one of the SLMAP homologs, is linked to JNK signaling Cka as a suppressor of JNK signaling S11) and GAP RNAi, but only marginally affected EGFR signaling . This indicates that GckIII may function upstream or in parallel to RAS and raises the intriguing possibility that the STRIPAK complex regulates multiple aspects of the larger RTK/RAS/MAPK pathway.In addition to PP2A/V12 signaling upon depletion of the catalytic subunit of PP2A (mts), but not of the regulatory B subunit, tws, which support the notion that STRIPAK components are functioning as PP2A regulatory (B) subunits in this context (unpublished data). A similar function for STRIPAK has recently been described in the context of Hippo signaling, where it was found to associate with PP2A and HPO Neurospora has indicated that MAPK regulates STRIPAK function, suggesting the possibility of regulatory feedback phosphorylation Since Striatins are defined as PP2A regulatory (B) subunits Cdc37 was the only bona fide pathway regulator. Among the others, CG8878 was of interest as it is the homolog of the mammalian Vaccinia-Related Kinase (VRK) genes of which two (VRK1 and VRK3) have been recently identified in a recent screen for KRAS synthetic-lethal factors CG8878 appears to act positively on RAS signaling in our experiments.Only 13 of our candidates were found to act downstream of RAF and upstream of MEK. Of these, the RAF chaperone gfzf). GFZF was initially found because of the property of its GST domain to bind to glutathione sepharose beads gfzf may be acting downstream of PDGF to control cell size gfzf clearly stood out from other candidates as it was the only factor that caused a strong reduction in mek transcript levels and had the closest profile to the mek RNAi itself  showed an enhancement of the V12Ras rough eye phenotype . Moreover, lfcsw homozygous females were observed in a background heterozygous for EY10172CG4936, which is indicative of suppression as lfcsw is recessive lethal. Altogether, these data suggest that CG4936 acts on PTP-ER transcription and that this action has bearing on RAS/MAPK dependent developmental processes.mRNA processing and transcription factors formed the largest group of hits in our study and also the largest complex in our network analysis  and S1B,o PTP-ER . One of K levels . This fiR screen . Consistn levels . Furtherhenotype  and suppc allele . CG4936Eus males , as did CG4936, most of the other candidates positioned downstream of MEK (39) caused a decrease in MAPK protein levels (<−0.25 log2-fold and p<1×10−5)  as well as three other candidates: Cdk12, Fip1, and CG1603. Depletion of these latter candidates led to a significant drop in mapk transcript levels as measured by qPCR ; although Fip1 did cause a slight enhancement of lfcsw lethality  . A few omponents . These f by qPCR . However profile , but onlhenotype  and S8A.2KG05512 . Howeverethality . Finallyn levels .Cdk12 is the main RNA polymerase II C-terminal domain kinase. Phosphorylation of the C-terminal domain of RNAP II is required for transcription elongation, RNA processing, and splicing Cdk12 influences either of these steps in the case of mapk expression although it does not seem to induce AS. CDK12 has previously been shown to function in conjunction with Cyclin L in regulating AS Drosophila CycL ortholog, CG16903, was also a hit in our screen that mapped to the MEK-MAPK interval mponents . AccordiCG10754  AS factor mapk RT-PCR profile . Also, Western blot experiments showed a clear drop in MAPK protein levels with no effect on RAS, RAF, MEK, or AKT  suggesting that these tissues may be more sensitive to knockdown of Prp19 than S2 cells. Finally, splicing of mapk was found to be altered in wing disc segments where Caper had been knocked down .servable . To veriisoforms . Prp19 aPrp19 and Caper produced AS changes in mapk that were different from those produced by EJC depletion, we designed RT-PCR assays aimed at detecting specific AS events. Using primer pairs in which the 5′ primer overlapped the exon junction between exons I and III or exons I and IV, we were able to detect an enrichment in exon II–III and II–IV skipping in eIF4AIII depleted samples, consistent with our previous results. However, these exon skipping events were not enriched in Caper and Prp19 RNAi samples. Conversely, using the same strategy, increased skipping of exons IV and VII was observed for Caper and Prp19, but not for eIF4AIII . On the basis of analyses of our secondary screen results and publicly available data, we assessed the specificity of our candidates, grouped them into protein complexes, and positioned their effect relative to the core RAS/MAPK pathway components  that act to control mek and PTP-ER expression adds another layer to the gene expression control network surrounding the RAS/MAPK pathway.In this report, we presented the results of an RNAi screen for factors influencing signaling between RAS and MAPK in mponents . We alsopression . MoreoveDrosophila cells in culture V12 in an assay similar to the one used in our screen. However, very few of the candidates found to modulate RASV12 signaling in their secondary screen were also present in our validated hit set; 14 of our 106 validated genes were found to alter RASV12 induced pMAPK beyond 5% of controls in their study . Second, when selecting which hits to follow up in secondary screening, the authors elected to exclude certain genes linked to large molecular complexes, most likely excluding many of the splicing factors that were retained in our study.When comparing our results to those of three previous RNAi screens examining insulin receptor (InR) and EGFR induced MAPK signaling in ir study . Furtherlet-7 family miRNAs C. elegans MAPK, Mpk-1, as well as ERK2/MAPK1 and p38α/MAPK14 in human embryonic stem (ES) cells, through the binding of specific sites on the 3′ UTR of their respective transcripts C. elegans germ line With the exception of the well described regulation of RAS by Prp19, Prp8, U4-U6-60K, l(3)72Ab, Prp6, SmD2, SF3a120, CG4849, and CG10333—factors included in our set of hits—were found to differentially modulate the splicing of specific sets of transcripts Drosophila using an RNAi-based strategy have demonstrated that knocking down many so-called “core” spliceosome components caused specific changes in specific AS contexts Prp6, l(3)72Ab, crn, Caper, SF3a120, and CG10418, was found to differentially influence AS of Dscam, para, TAF1, and/or dAdarPrp19 knockdown had specific effects on MAPK protein levels and the identification of Prp19 (as well as Prp8) in a separate CNK-based genetic screen supports the notion that these splicing factors are important for mapk expression in vivo. Thus, the fact that we identified specific spliceosome components in our screen and not others may reflect the importance of these particular components in regulating mapk splicing.In this study, we found that multiple splicing factors act downstream of MEK to specifically control MAPK levels. While some of these factors were associated with the regulation of AS, most were components of the spliceosome or factors that co-purify with the spliceosome. Interestingly, many lines of evidence suggest that AS can be modified by spliceosomal factors mapk gene structure that dictate requirement of particular splicing factors have yet to be determined. Likewise, many—if not most—other genes may have structures that preclude AS regulation by components of the spliceosome. Still, AS processes in at least four other Drosophila genes seem to involve some of the same components of the splicing machinery that we have linked to mapk, indicating that a common characteristic may dictate the involvement of these spliceosome components in AS. On the other hand, the fact that some of the spliceosome factors are involved in specific AS contexts and not others, suggests that key differences exist. This observation is important as it implies that specific AS events can be modulated by controlling the activity of these spliceosomal factors. In support of this idea, one of the previously mentioned studies indicates that the control of TAF1 AS by a series of splicing factors—which include Caper and other spliceosome components—is downstream of a Camptothecin-induced ATR pathway The specific elements of the mapk is reminiscent of the AS observed for the E3 ligase MDM2 following camptothecin-induced genotoxic stress One explanation for our results, as well as some of the previous observations, would be that some of the spliceosomal factors in our set may be interchangeable or function as non-essential co-regulators. Thus, removing these components would not greatly disrupt general spliceosome function, but rather lead to an altered spliceosome activity that selectively impacts sensitive AS contexts. An example of this is the interplay between PUF60 and U2AF65, which both function in 3′ splice site recognition; the two factors can either work cooperatively or independently, producing different splicing outcomes on the basis of the presence or absence of either protein mapk splicing may be more important in the context of certain tissues and organs, in development or in disease. Interestingly, the abundance of core spliceosome components has been shown to be regulated and vary both temporally and across different tissues The discovery that MAPK expression is specifically modulated by mRNA processing factors raises multiple questions. Not only will it be vital to define the upstream signals that dictate this activity but it will also be important to assess which other genes are similarly regulated and to identify the common characteristic that renders them sensitive to this type of regulation. Also, the time frame within which these changes occur will have implications as to the role that this type of regulation can play in MAPK signaling. Typically, RAS/MAPK signal modulation has been observed to occur through either rapid post-translational mechanisms or through slightly slower mechanisms involving control of protein stability and transcriptional control. However, the reduction in protein levels we observed as a consequence of AS are only apparent over a period of days, probably because the MAPK protein is relatively stable. This finding indicates that this regulation will not be relevant over the shorter timeframes of previously characterized regulatory events and also implies that a prolonged stimulus will be necessary to produce the effects we observe. Therefore, control of V12pMet-Ras S2 cells diluted in Schneider medium (to a concentration of 1×106 cells/ml) were distributed in 96 well clear plates (Corning) containing 5 µl dsRNA aliquots at a concentration of ∼200 ng/µl. Plates were placed in plastic containers to reduce evaporation and incubated at 27°C for four days. V12Ras expression was induced by adding 0.7 mM CuSO4 to medium 24 h prior to fixation. Cells were resuspended and transferred to concanavalin A coated plates and allowed to settle and adhere for 1 h. Cells were then fixed in 4% paraformaldehyde/PBS, washed, and blocked in 0.2% Triton X-100/0.2% BSA/PBS (PBT/BSA) and incubated overnight with an anti-pMAPK antibody , washed in PBT/BSA, and revealed using an anti-mouse Alexa Fluor 555-conjugated secondary antibody . DAPI (0.04 µg/ml) was used to stain nuclei. Mowiol  was added to wells prior to imaging. An automated fluorescence microscopy system (Zeiss Axiovert) was employed for plate imaging. Autofocus, image acquisition, and analysis were conducted using MetaMorph (Molecular Devices) software. The cell-scoring application in MetaMorph was used for cell segmentation and quantification of fluorescent signal.V12, (2) RAFED, (3) RAFCT, (4) RAFEDCT, (5) MEKEE, (6) MEKEE+PTP-ER dsRNA. The correlations between normalized secondary screen log transformed values and three predetermined epistasis profiles were calculated using a modified uncentered Pearson's correlation:r is the correlation value , x is the secondary screen value , y is the predetermined profile value, and w is the weight applied to a given screening experiment  on the basis of the data from the following secondary screens: (1) RASRAS−RAF = [1 0 0 0 0 0]RAF−MEK = [1 1 1 1 0 0]MEK−MAPK = [1 1 1 1 1 1]r indicates reverse correlation and is observed for positive pathway regulators.A negative V12sev-Ras line has been described previously ElpEgfr was described in Cka alleles lfcswE1171mapk allele was identified in a genetic screen as a dominant suppressor of a dominant negative form of KSR CE162Prp19, CE40Prp19, TE1036Prp19, and CE309Prp8 alleles were recovered in a genetic screen for modifiers of a dominant negative form of CNK .Fly husbandry was conducted according to standard procedures. All crosses were performed at 25°C. The RNAi fly lines were obtained from the VDRC http://www.hadleyweb.pwp.blueyonder.co.uk/CZP/News.htm), was used for focus stacking. Adobe Lightroom and GNU Image Manipulation Program (GIMP) were used for image processing. Wings were mounted in Permount (Fisher) on glass slides and imaged using a Nanozoomer (Hamamatsu).Adult fly eyes were imaged using a stereomicroscope (Leica MZ FL III) and CombineZP, a freely available software package . L1 larvae were heat shocked for 15 minutes at 37°C and later collected for dissection upon reaching late L3 (wandering) stage. Third instar eye-antennal and wing discs were dissected in Schneider medium, fixed, and stained with DAPI and an anti-MAPK antibody  following the same procedure described above for S2 cells.RNAi clones were generated using a line carrying a heat shock inducible flip-out actin promoter driving the expression of GAL4 and GFP in clonal tissues , Fgop2 , Slmap , Strip . Ras85D (refers to the Drosophila gene encoding RAS), ras (gene encoding an IMP dehydrogenase) is referred to by its full name, “raspberry,” to avoid confusion. Following the nomenclature recommended by Flybase, gene symbols are in lower-cased italics and protein symbols are in upper-case without italics. hyp .The following Figure S1Primary screen and screening strategy. (A) Primary screen hits are submitted to validation screening steps to eliminate false positives. Remaining candidates were submitted to secondary screens to assess the position of the regulatory input relative to known pathway components (epistasis) and specificity to the RAS/MAPK signaling context. In addition to this, candidates were screened for their impact on core RAS/MAPK component expression, both at the transcript (qPCR and RT-PCR) and protein level (quantitative immunofluorescence and Western blot). (B) The robustness of the primary screen assay was evaluated by monitoring changes in RASV12-induced pathway activity following knockdown of mek and PTP-ER. The levels of pMAPK are then measured by quantitative microscopy. Results shown for each dsRNA are the mean of 43 sample wells in three separately prepared plates. The calculated Z′-factor was 0.643 for mek depletion and 0.175 for PTP-ER depletion. (C) Distribution of primary screen probe data organized in plate screening order (grey data points). GFP dsRNA (blue) was used as a negative control and reference to normalize screening plate data. mek and PTP-ER dsRNAs  were used as positive controls to verify dsRNA knockdown efficiency.(TIF)Click here for additional data file.Figure S2Promoter validation screens. (A–B) pMet promoter validation screen assays and control experiments. (A) V12Ras expression in V12pMet-HA-Ras stably transfected S2 cells is monitored by quantitative immunofluorescence through the use of anti-HA antibody. Values shown are the average HA signals of duplicate samples normalized to CuSO4 induced controls with no dsRNA treatment. (B) GFP expression in pMet-GFP stably transfected S2 cells is monitored by quantitative immunofluorescence. Values shown are the average GFP signals of duplicate samples normalized to CuS04 induced controls with no dsRNA treatment. (C) Variation of pMAPK, GFP, and HA signal in response to increasing amounts of MTF-1 dsRNA. Values shown are average of triplicate samples normalized to CuSO4 induced controls with no dsRNA treatment. (D–E) Promoter validation screen results (x axis) plotted against pMAPK primary screen values (y axis). The cutoff (dashed line) to identify false positive candidates with effects on promoter activity (data points in red areas) is a function of the pMAPK signal observed in the primary screen  are shown. Factors known to be involved in pMet-driven expression, such as MTF-1, TBP-associated factors (Tafs), and Ctr1B are also shown (grey). (D) V12pMet-HA-Ras validation screen results. The HA signal from the validation screen (x axis) plotted against the pMAPK signal from the primary screen (y axis). The HA and pMAPK values shown are normalized to plate-specific GFP dsRNA controls. (E) pMet-GFP validation screen results. The GFP signal from the validation screen (x axis) plotted against the pMAPK signal from the primary screen (y axis). The GFP values shown are normalized to plate-specific mek dsRNA controls. The pMAPK values shown are normalized to plate-specific GFP dsRNA controls.reen see . MAPK re(TIF)Click here for additional data file.Figure S3Secondary screens. (A) MAPK and JNK pathway models depicting the secondary screen assays used in this study. See GFP dsRNA treated controls unless otherwise indicated. (B–K) are MAPK activation assays with a pMAPK readout. (L–N) are JNK pathway assays with a pJNK readout. (B) RAF-based MAPK activation: Values shown are for EDpMet-raf, CTpMet-raf, and EDCTpMet-raf stably transfected cell lines treated with the indicated dsRNAs and induced with CuSO4 for 24 h. (C) MEK-based MAPK activation: dsRNA treated EEpMet-mek stably transfected cells were induced with CuSO4 for 24 h. (D) RAS GAP RNAi-based MAPK activation: S2 cells were treated with dsRNAs targeting the indicated RAS GAPs or predicted RAS GAPs. Values are normalized to untreated control samples. Combined knockdown of Gap1 and Nf1 produced the highest pMAPK activation and was used in the secondary screen assay. (E and F) Insulin-based MAPK activation. (E) Insulin induction time course: The values shown are baseline normalized signal averages from single well samples of insulin treated S2 cells induced for the indicated times. (F) The signals shown are the average from dsRNA-treated single well samples of insulin induced S2 cells induced for 5′. (G and H) SEVS11-based MAPK activation. (G) Heat shock induction time course of S11pHS-sev stably transfected S2 cells. Samples are induced for 30 minutes at 37°C and incubated at 27°C for the indicated times before sample preparation. Values shown are normalized to a non-induced control. (H) dsRNA-treated S11pHS-sev cells were induced with a 30′ minutes heat shock at 37°C followed by a 2.5 h incubation at 27°C. (I–K) EGFR-based MAPK activation assays. (I) Induction time course was performed on pMet-Egfr stably transfected cells induced with 1∶2 supernatant from pMet-Spi cells. Values are normalized to non-induced controls. (J) Calibration of Spitz concentration: pMet-Egfr cells were induced for 5′ with the indicated dilution of Spitz supernatant and normalized to 1∶1 Spitz induced controls. (K) dsRNA-treated pMet-Egfr cells were induced for 5′ with the indicated concentrations of Spitz supernatant. Values shown are normalized to samples not treated with dsRNA. (L and M) RAC1V12-based JNK activation time courses. (L) Induction time course: V12pMet-Rac1 stably transfected cells were induced with CuSO4 for the indicated times. Values shown are from triplicate samples of two experiments. (M) V12pMet-Rac1 cells were treated with hep dsRNA and induced with CuSO4 for the indicated times. Values shown are from single well samples normalized to samples not treated with dsRNA. (N) Peptidoglycan (PGN) based JNK activation time course: S2 cells were induced with 50 µg/ml LPS for the indicated times. The values shown are averages of four samples from two separate experiments.(TIF)Click here for additional data file.Figure S4qPCR screen. (A) Unsupervised hierarchical clustering of qPCR screen results. SYBR green qPCR assays were used to assess the levels of RAS/MAPK pathway transcripts (top labels) following depletion of the indicated V12Ras screen candidates . The transcript levels are presented as log2 transformed ratios of GFP dsRNA treated controls. RAS/MAPK control dsRNAs are indicated by asterisks. The gfzf dsRNA has the closest profile to the mek control dsRNA, with both reagents causing a reduction in mek levels. PTP-ER and CG4936 also have very similar profiles and cause a reduction in PTP-ER transcript levels. As we previously reported, the EJC components (blue) mago and eIF4AIII cause an observable decrease in mapk transcript levels. However, the knockdown of tsu and RnpS1, which have a weaker impact on pMAPK and MAPK levels, does not cause a readily observable change in mapk. Only CG1603 and Cdk12 had similar profiles to the two stronger EJC components, causing a decrease in mapk transcript levels. The Fip1 dsRNA results in the qPCR screen were not valid, but Fip1 dsRNA was re-tested in the confirmation qPCR experiment presented in mapk or any of the other RAS/MAPK transcripts. (B) Total mRNA FISH performed using an oligo-dT Cy3-labeled probe. The proportion of cells displaying nuclear retention was evaluated by performing segmentation and scoring the overlap of the FISH signal with a DAPI nuclear stain. dsRNA targeting sbr, the homolog of the mammalian nuclear RNA export factor 1 (NXF1), and CG2063, a factor previously linked to mRNA export, both caused an increase in nuclear retention. In comparison mago dsRNA did not visibly alter total mRNA retention. (C) Total mRNA export screen results. A subset of 44 candidates were tested to evaluate their impact on total mRNA export: these included all factors with functions linked to mRNA splicing (red) as well as other factors found to alter MAPK protein or mapk mRNA levels. A nuclear retention index was calculated by normalizing the rate of nuclear retention to the average results of GFP dsRNA treated controls (green). sbr dsRNA was used as a positive control (red arrowhead) for nuclear retention. In addition to sbr, five candidates in our set (orange circles) were also identified in a mRNA nuclear export RNAi screen (TIF)Click here for additional data file.Figure S5RT-PCR screen. An RT-PCR assay encompassing the entire mapk RB, RD, RE, and RF transcripts was used to evaluate the impact on the distribution of mapk transcript isoforms. The same subset of 44 hits selected for the mRNA export screen were also tested here. A majority of factors tested caused a change in the mapk RT-PCR profile when compared to GFP dsRNA treated or untreated controls. The altered profiles were grouped into two categories (A and B) on the basis of similarities in the sizes of the bands observed. Most splicing factors in the set produced an “A” type profile while EJC factors produced a different “B” type profile. Other candidates causing no obvious change in product size, but causing a reduction in band intensity are labeled “R.” Those causing no observable change are labeled “N.” The labels on the right of the bottom panel refer to mapk transcript isoforms that correspond to the bands visible on the gel. Candidates featured in the manuscript are labeled with a green star.(TIF)Click here for additional data file.Figure S6Immunoblot screen. (A–M) Western blot analyses of S2 cells treated with the indicated dsRNAs. Endogenous levels of CNK, AKT, MAPK, and RAS were monitored using specific antibodies. A negative (GFP dsRNA) and two positive (mago and eiF4AIII dsRNAs) controls were included in this experiment.(TIF)Click here for additional data file.Figure S7Co-immunoprecipitation of Drosophila STRIPAK complex components. S2 cells were transfected with expression plasmids carrying cDNAs encoding the fusion proteins indicated at the top of . Cell lysates were immunoprecipitated with the indicated antibodies (bottom of upper panels in (A and B), and left of upper panels in (C)). The immunoprecipitates and equal amounts of cell lysates  were fractionated by SDS-PAGE and immunoblotted with the antibodies indicated at the right (A–C). The tagged STRIPAK complex protein shown in each panel is indicated in parentheses. None of the STag fusion proteins or GFP-FGOP2 are present in α-HA or α-HSV immunoprecipitates when these fusion proteins are expressed alone, and GFP does not co-immunoprecipitate with HA-CKA, HSV-MOB4, HSV-STRIP, or HSV-SLMAP (unpublished data).(TIF)Click here for additional data file.Figure S8Additional genetic interaction data. (A) V12Ras genetic interaction data for additional alleles of Cka, gfzf, and Prp19 as well as alleles of Fip1 and Prp8. (B) Details of molecular lesions present in the three alleles of Prp19 and the CE309Prp8 allele identified in a cnk dominant negative genetic screen  and used in our genetic validation experiments. A protein map of PRP19 and PRP8 showing the amino acid changes found in the Prp19 and Prp8 alleles is presented with “*” indicating residues conserved in humans. The mutation in CE309Prp8, in addition to causing an amino acid change, is located on the second residue of a 3′SS (T4088A) and may impact splicing of Prp8. (C) Additional 1rl rough-eye phenotype genetic interactions for CG4936. Trans-heterozygous DG10305CG4936/EY10172CG4936 suppresses the weak rough eye phenotype of 1rl homozygotes. Eye size is also slightly restored. (H) Representative wings for lfcsw hemizygous males scored in (K). The deletion of the distal end of the L2, L3, and/or L5 vein is a frequently observed phenotype in a lfcsw background. The occurrence, number, and severity of this deletion are more pronounced in 2Cka and CZ811gfzf heterozygous backgrounds. (E) Representative wing images for the ElpEgfr males scored in (J). The additional wing vein near the extremity of the L2 vein (arrow) is characteristic in ElpEgfr flies. Wings displaying a suppressed phenotype  are shown for 2Cka, f04743CG1603, TE1036Prp19, and mapk/1rl (positive control) heterozygotes. (F) Image of a wild-type fly wing with labels indicating the location of the five wing veins (L1–L5) and of the anterior (acv) and posterior (pcv) cross-veins. (G) Proportion of 1rl/1rl males to 1rl/CyO males observed following a 1rl/CyO X 1rl/CyO cross. The wings of 1rl/1rl flies have a “rolled” phenotype; they are slightly curved downwards along the anterior-posterior axis. 1rl/CyO flies display a regular CyO phenotype; pronounced upward curve along a lateral axis. Both 1 Prp19CE162rl/CyO and f07714 rl1Caper/CyO were crossed to 1rl/CyO flies. The frequency of 1 Prp19CE162rl/1rl and f07714 rl1Caper/1rl was lower than that of 1rl/1rl. The 1 Prp19CE162rl/f07714 rl1Caper combination was not viable at 25°C. Trans-heterozygous CG4936 slightly enhanced the proportion of 1rl/1rl flies recovered. (H) 1rl/1rl flies display a deletion of the mid-section of the L4 wing vein that is not fully penetrant. Examples are Shown for wings with weak “+”  and strong “++” (large or multiple deletions) wing vein deletions. Wing deletions were scored for the indicated genotypes. The total amount of individuals scored is indicated below the allele labels. (I) Proportion of lfcsw males to +csw males observed for the indicated genotypes with the total amount of individuals scored indicated below the allele labels. The trans-heterozygous allelic combination of CG4936 behaved similarly to heterozygous EY10172CG4936 in lfcsw genetic interaction experiments (wing vein deletions and viability) and is not shown here. (J) Proportions of wings from ElpEgfr flies with the indicated extra wing vein material phenotype severity for the indicated alleles. Examples of strong and weak additional wing vein phenotypes are shown next to the graph. The total number of wings scored is indicated below the allele labels. The Fip1 and gfzf alleles displayed only slight suppression of the wing vein phenotype and are not shown here. (K) Proportions of wings from lfcsw males with the indicated deletion severity for the indicated alleles. Examples of strong  and weak  wing vein deletions are shown. The total number of wings scored is indicated below the alleles. The Caper and CG1603 alleles only displayed very weak genetic interaction with lfcsw and are not shown here. The Prp19 flies produced too few escapers for accurate scoring of the wing phenotypes.(TIF)Click here for additional data file.Figure S9Cka is important for photoreceptor development. (A) 2Cka mutant clones in pupal eye discs dissected 42 h APF are marked by the absence of GFP (A). The differentiated R7 photoreceptor is marked by the overlapping Elav and Pros stainings . In 2Cka clones, ommatidia lacking the R7 photoreceptor are marked with a white arrow. We also noted some displaced R7 photoreceptors , and rotated ommatidia (white asterisks) in the 2Cka clones.(TIF)Click here for additional data file.Figure S10In vivo evidence for alternative splicing of mapk. (A) Schematic representation of the mapk splice isoforms from mapk transcript was used to detect splicing changes in adult flies. Samples were prepared from five adult flies of the indicated genotypes. Escapers homozygous for 1/rl1rl and also carrying the CE162Prp19 mutant displayed splicing changes compared to a CantonS wild type control strain. The products from this experiment were not verified by sequencing though they were similar to those observed in S2 cells upon Prp19 knockdown . 1rl homozygous flies have a reduced amount of mapk though the size of the RT-PCR product is unchanged (not shown). (C) mapk splicing is altered in larval wing imaginal discs following Caper knockdown. A RNAi construct targeting Caper was expressed in the posterior segment of wing imaginal discs using an engrailed-GAL4 driver. Imaginal discs from 3rd instar larvae were microdissected to separate the posterior (control) and anterior (Caper knockdown) segments. The RT-PCR assay spanning exons III–V was used on extracts from both samples to evaluate inclusion of exon IV. All Prp19 RNAi tested with the Engrailed-Gal4 driver caused a high rate of lethality and escapers that could be recovered were of reduced size and were not found to contain wing discs.(TIF)Click here for additional data file.Figure S11Comparison with previous RTK/MAPK RNAi screens. (A and B) Overlap of validated positive (A) and negative (B) regulators of RASV12 identified in this study with positive and negative regulators reported in three Drosophila RNAi Screening Center (DRSC) RTK/MAPK screens V12 identified in this study with 331 validated hits from the DRSC S2R+ insulin screen that were tested in a RASV12 secondary screen (TIF)Click here for additional data file.Table S1Primary and promoter validation screen data (MS Excel file). Primary screen and validation screen data. The average pMAPK observed in the V12pMet-Ras primary screen (from the primary and confirmation steps) are listed for the 309 initial primary screen hits. The associated values and results for the first of two validation screening steps (elimination of candidates impacting the pMet expression system using pMet-GFP and V12pMet-HA-Ras) are also shown.(XLSX)Click here for additional data file.Table S2GO term enrichment. GO terms found to be enriched in our set of validated hits using the Flymine p-Values are calculated using Benjamini and Hochberg multiple hypothesis test.(DOCX)Click here for additional data file.Table S3Epistasis screen results and calculated Pearson correlations (MS Excel file). Results from the secondary screens used in the epistasis analysis are shown (screens number 1–6). Values shown are log10 transformed normalized pMAPK signals. Calculated Pearson uncentered correlation (r) are shown for the three epistasis profiles . The final epistasis interval is also shown as well as the confidence score for the epistasis result. Candidates that were not positioned are marked with “?” while those with ambiguous positioning (confidence score <0.2) results are marked with “(A).”(XLSX)Click here for additional data file.Table S4Specificity score results (MS Excel file). The data used to calculate the specificity score are presented in the following order: number of times this gene has been identified in previous Drosophila RNAi screens; result observed in the Western blot screen ; and associated specificity score; specificity score derived from the total mRNA nuclear export screen; score associated with a decreased cell count; effect on pMet driven expression derived from the pMet-GFP screen; JNK specificity result derived from the two pJNK secondary assays; combined specificity score derived from all secondary screen results; final specificity score and specificity group . Specificity score calculation is described in the (XLSX)Click here for additional data file.Table S5Summary of secondary screen results (MS Excel file). List of validated screen hits and secondary screen results. Values are expressed as log2 transformed ratios normalized to GFP dsRNA negative controls. Also presented in this table is: the calculated epistasis interval, specificity score, functional group (based on GO annotation), and predicted homologs in Homo sapiens and Saccharomyces cerevisiae. Bona fide RAS/MAPK components are marked with “*.” Genes to which we have associated new gene symbols are marked with an “†.”(XLSX)Click here for additional data file.Table S6Detailed qPCR results (MS Excel file). Values for the confirmation experiment and in vivo qPCR presented in 2 ratios of negative control (GFP dsRNA treated S2 cells or no RNAi flies). The right panel shows the associated p-values (unpaired two-tailed Student's t-test). AGO1 and sprt were not followed-up as the observed increase in mek mRNA levels in the initial qPCR secondary screen was not confirmed in this experiment. The dsRNA probes from our set targeting mapk and ksr overlap with the SYBR green qPCR probes invalidating those results. Both qPCR assays were validated with separate dsRNAs (unpublished data).(XLSX)Click here for additional data file.Table S7Detailed sequencing data from the mapk RT-PCR experiment (MS Excel file). Summary of sequencing data from cloned mapk RT-PCR products. 142 clones were successfully sequenced and aligned in order to investigate the nature of the splicing defects induced by Caper and Prp19 knockdown. mapk exons are labeled according to the numbering scheme we used in this study (I–VIII) as well as Flybase exon IDs. “rl:8/10/13alt” refers to an alternate exon located downstream of exon VIII and has been described previously (XLSX)Click here for additional data file.Table S8qPCR and RT-PCR primers (MS Excel file). Sequence of RT-PCR and qPCR primers used in this study. dsRNA primer sequences are available at the IRIC RNAi database (http://www.bioinfo.iric.ca/iricrnai).(XLSX)Click here for additional data file.Text S1Notes on the exon junction complex and supplemental methods. Document containing additional notes on the exon junction complex as well as an extended description of the experimental methods used in this study.(DOCX)Click here for additional data file.


Immunoglobulin gene enhancers have a conserved function in targeting somatic hypermutation to immunoglobulin genes, thereby supporting the production of high affinity antibodies. immunoglobulin (Ig) genes of activated B cells, providing genetic diversity for the affinity maturation of antibodies. SH requires the activation-induced cytidine deaminase (AID) protein and transcription of the mutation target sequence, but how the Ig gene specificity of mutations is achieved has remained elusive. We show here using a sensitive and carefully controlled assay that the Ig enhancers strongly activate SH in neighboring genes even though their stimulation of transcription is negligible. Mutations in certain E-box, NFκB, MEF2, or Ets family binding sites—known to be important for the transcriptional role of Ig enhancers—impair or abolish the activity. Full activation of SH typically requires a combination of multiple Ig enhancer and enhancer-like elements. The mechanism is evolutionarily conserved, as mammalian Ig lambda and Ig heavy chain intron enhancers efficiently stimulate hypermutation in chicken cells. Our results demonstrate a novel regulatory function for Ig enhancers, indicating that they either recruit AID or alter the accessibility of the nearby transcription units.Somatic hypermutation (SH) generates point mutations within rearranged  immunoglobulin (Ig) genes are subject to a unique mutation process known as somatic hypermutation that allows the immune system to generate high-affinity antibodies. Somatic hypermutation preferentially affects Ig genes, relative to other genes, and this is important in preventing catastrophic levels of general genomic mutations that could lead to B cell cancers. We hypothesized that this preferential targeting of somatic hypermutation is assisted by specific DNA sequences in or near Ig genes that focus the action of the mutation machinery on those genes. In this study, we show that Ig genes across species—from human, mouse, and chicken—do indeed contain such mutation targeting sequences and that they coincide with transcriptional regulatory regions known as enhancers. We show that combinations of Ig enhancers cooperate to achieve strong mutation targeting and that this action depends on well-known transcription factor binding sites in these enhancer elements. Our findings establish an evolutionarily conserved function for enhancers in somatic hypermutation targeting, which operates by a mechanism distinct from the conventional enhancer function of increasing levels of transcription. We propose that combinations of Ig enhancers target somatic mutation to Ig genes by recruiting the mutation machinery and/or by making the Ig genes better substrates for mutation.During the B cell immune response,  Pioneering experiments using chimeric gene constructs in transgenic mice indicated that sequences overlapping with the Ig light chain and Ig heavy chain enhancers distinguish the Ig genes as mutation targets cis-acting regulatory elements was hampered by the laborious transgene experimental system, the relatively low mutation rates of the chimeric genes, and the fluctuation of mutation rates among transgenic lines, perhaps due to integration site effects and copy number variations. A further problem arose from the fact that the putative hypermutation-stimulating sequences included the known enhancers, making it difficult to differentiate between the effects of these sequences on transgene hypermutation versus transgene transcription –associated factors that interact with AID and play roles in transcriptional stalling Ig genes over non-Ig genes. Consequently, this has remained a central unresolved issue in the field.The hypothesis that SH is targeted preferentially to Ig light chain (cIgλ) gene primarily by GCV cIgλ GCV by cis-acting sequences in DT40 has been detected by the analysis of endogenous cIgλ gene diversification green fluorescent protein (GFP) knock-in transgene in DT40 cells depended on the nearby presence of a 10-kb fragment of the cIgλ locus, which was named diversification activator (DIVAC) DIVAC led to the identification of two core regions downstream of the cIgλ C-region that cooperate with each other and with other parts of the 10-kb sequence to stimulate SH of the adjacent GFP transcription unit Ig lambda (Igλ) and Ig kappa (Igκ) enhancer sequences displayed disappointingly low DIVAC activity in DT40 cells The chicken B cell line DT40, whose genome is easily modified by targeted gene integration Ig locus enhancers and enhancer-like elements are core DIVAC sequences that work together to target SH. Regardless of which species they derive from, these elements rely for function on a common set of well-characterized transcription factor binding motifs, highlighting the evolutionary conservation of the SH targeting mechanism. These findings are likely to have implications for the mistargeting of SH to non-Ig genes and the origins of B cell lymphoma.We have now developed a highly sensitive assay that allows analysis of the SH targeting activity of small DNA elements, largely overcoming the shortcomings of previous experimental strategies. Using this new assay, we demonstrate that chicken, mouse, and human GFP2, consisting of a strong viral promoter driving expression of GFP and a drug resistance gene  both endogenous UNG genes were disrupted and the coding sequences of both endogenous AICDA genes were deleted, (ii) AID expression was reconstituted by inserting an AICDA cDNA expression cassette under the influence of the β-actin promoter into one AICDA locus, and (iii) the position of the second AICDA locus was marked by a puromycin resistance gene. When this cell line is transfected by AICDA locus–targeting constructs containing DIVAC-GFP4, targeted integrants into the marked AICDA locus are easily identified by the loss of puromycin resistance.To further increase the frequency at which mutations and stop codons are generated, the GFP4 assay is performed in cIgλ locus with the corresponding sequence of turkey, zebra finch, and ground finch revealed seven evolutionarily conserved sequence contigs downstream of the C-region (cIgλ enhancer (cIgλE) 3′Core. The conserved sequence regions were cloned into the upstream DIVAC insertion site of GFP4  and transfected into UNG−/−AIDR/puro cells. Primary transfectants with targeted integration of a construct were subcloned, and 24 subclones were analyzed for GFP loss by flow cytometry 12 d after subcloning , showed median GFP loss levels of 20%–30%, whereas levels of GFP loss in transfectants of the other conserved sequences (Con1–Con5) were close to the 1.7% median value observed in the no DIVAC control transfectant, UNG−/−AIDR. Interestingly, the Con2 sequence, which displayed activity close to background on its own, substantially increased GFP loss when combined with cIgλE in Con2+cIgλE cells (44.6%). The highest levels of GFP loss were seen when cIgλE and the 3′Core were combined (63.7%) or when they were tested together with their intervening sequence . Importantly, GFP loss in UNG−/−AID−/−cIgλE↔3′Core cells (lacking the AICDA expression cassette) was almost 3,000-fold lower than in cIgλE↔3′Core cells and about 60-fold lower than in UNG−/−AIDR cells.Alignment of the C-region  and S2. bcloning  and 1D. of cIgλE and 3′Core as core DIVAC elements DIVAC, GFP loss from GFP4 in −/−UNG cells is 15- to 20-fold higher than we detect with GFP2 in wild-type cells , MEF2 (myocyte-specific enhancer factor 2), and PU.1-IRF4 (interferon regulatory family-4) binding motifs, all of which are remarkably conserved among avian species , whereas many deletions in the central part of the fragment reduced GFP loss , pC/EBP (CCAAT enhancer binding protein), and pPU.1  in either the upstream or downstream insertion site of GFP4 (cIgλE (27.2%). Removal of the upstream E-box in 5′Δ56 did not decrease DIVAC activity, whereas larger 5′ deletions reduced activity (cIgλE and much higher than the activity of the comparable deletion fragment (5′Δ59) of cIgλE  reduced GFP loss to 20%—roughly the activity of full-length cIgλE—and a larger 3′ deletion removing the composite PU.1-IRF4 site (3′Δ108) strongly reduced GFP loss to 6%  could not be identified in birds, and an enhancer in the intron between the duck Jμ and Cμ segments showed no obvious conservation with mammalian counterparts apart from the presence of multiple E-boxes hIgHEi) and murine (mIgHEi) enhancer fragments contain conserved YY1 (yin yang 1) (μE1), E-box (μE2 and μE4), Ets1 (μA), PU.1 (μB), IRF, and Octamer transcription factor binding sites, and less well conserved regions μE5 and μE3 cIgλE, 3′Core, and hIgλE, we reasoned that the mammalian IgHEi elements might also have SH targeting activity. Strikingly, hIgHEi and mIgHEi yielded high levels of GFP loss  enhancers are also not present in avian species, which contain only a single Igλ light chain locus. The three Igκ enhancers, intron (IgκEi), 3′ (IgκE3′), and Ed (IgκEd) Igκ enhancers were combined (IgκEi+IgκE3′ or IgκE3′+IgκEd), GFP loss markedly increased, and when the three human Igκ enhancers were combined, GFP loss reached 50.9% , we tested various cIgλ DIVAC elements using the GFP2 assay. The full cIgλ DIVAC region  yielded about 10% GFP loss using GFP2 . However, with the cIgλE↔3′Core fragment, GFP loss in the GFP2 assay (6.7%) was only about 10-fold lower than in the GFP4 assay (70.5%), probably because of saturation of the GFP4 assay in the presence of this highly active DIVAC element (see Con2 (which lacks activity on its own) was able to substantially boost the activity of cIgλE (Con2 (Con2 and cIgλE required only the 3′ portion of Con2 and was dependent on one of the two putative IRF binding motifs (pIRF-down) in this region. We conclude that there is good congruence between the results of the GFP4 and GFP2 assays and that the less sensitive GFP2 assay is preferable for analysis of highly active DIVAC elements.To confirm our results in a repair-proficient cellular context (UNG-proficient DT40 cells) and in a different genomic integration site . However, the identity of such putative cooperating elements was unclear because other murine Igλ enhancers were not known.The murine enhancer  [48]. ThIgλE homologues 20–25 kb downstream of mIgλE3-1 and mIgλE2-4 , whereas the 5′Δ84 deletion mutant of hIgλE was less active  increased by the presence of chicken or mammalian DIVAC fragments compared to the no DIVAC control  control, but both exhibited significantly less enhancer activity than their canonical mIgλ enhancer counterparts  requiring multiple transcription factor binding sites, (ii) functioning independent of orientation and when positioned either upstream or downstream of the transcription unit, and (iii) increasing activity through the collaboration of multiple enhancer-like regions, each of which depends on transcription factor binding motifs.Using a highly sensitive, well-controlled assay we provide conclusive evidence that SH is targeted by Ig enhancers as SH targeting sequences yields a conceptual framework within which to reevaluate earlier studies. Most notably, the new results vindicate the early transgenic experiments that showed overlap of SH stimulating sequences with the Igλ, IgH intron, and Igκ enhancers Igκ intron and Igκ 3′ enhancer sequences Igκ intron or 3′ enhancer knockouts in mice to abrogate hypermutation Igκ enhancers to DIVAC function. Similarly, the failure of a previous study to identify SH targeting function associated with the Igκ intron and 3′ enhancers in DT40 cells Igκ distal enhancer. In addition, evidence that E-box Ig enhancers and enhancer-like sequences.The recognition of cis-acting regulatory regions that target SH to a variety of Ig loci. The chicken Igλ locus is best understood and offers several lessons that might be generally applicable. In cIgλ, the enhancer cooperates with an evolutionarily conserved downstream element (3′Core) that itself possesses low levels of transcriptional enhancer activity (Ig enhancers (Con2 and the region between cIgλE and 3′Core) for full DIVAC function  were predictive of off-target SH of non-Ig genes It may not be a coincidence that enhancers, able to exquisitely regulate cell type– and gene-specific expression, have assumed the vital role of targeting SH to the Ig enhancers distinguish themselves from other enhancers that may contain the same or similar transcription factor binding sites. Despite this limitation in our understanding of enhancer function, plausible models for how SH is targeted to Ig genes can be formulated based on what is known about the interaction of enhancers with the transcription initiation complex . The PCR fragments were cloned using the In-Fusion Cloning Kit (Clontech) into the linearized pUC19 provided with the kit and sequenced. Thirty-four sequences covering the first 500 transcribed bases of GFP4 were aligned to the GFP4 sequence to detect sequence variation  to detect sequence contigs conserved during avian evolution. ClustalW2 was also used to create the other sequence alignments shown in Orthologues of the t-tests were used to compare relative GFP transcript and luciferase levels in Two-tailed unpaired 6 cells using the RNeasy Mini kit (Qiagen), and the cDNA was prepared from 1 µg of RNA using the iScript cDNA synthesis kit (Bio-Rad). Quantitative PCR was performed using the DyNAmo HS SYBR Green qPCR kit (Thermo Scientific). GFP transcript levels were normalized to 18S rRNA levels. Samples were denatured for 15 min at 95°C, followed by 40 cycles of 30 s at 94°C, 30 s at 60°C, and 30 s at 72°C. The primers used were as follows: GFPup-F 5′-ggaatatactttgccaagaagcgtt-3′, GFP5up-R 5′-accatcgttgccagaaccatt-3′, GFPcds-F 5′-gagcaaagaccccaacgaga-3′, GFPcds-R 5′-gtccatgccgagagtgatcc-3′, 18S-F 5′-taaaggaattgacggaaggg-3′, and 18S-R 5′-tgtcaatcctgtccgtgtc-3′.Reverse transcription quantitative PCR analysis was carried out on transfectants containing various DIVAC-GFP4 constructs after the cells were treated with 4-OH tamoxifen and subcloned to delete the AID expression cassette. This avoided potential effects on transcript levels due to nonsense-mediated mRNA decay. The resulting AID-negative cells used for analysis were stably GFP-positive (data not shown). RNA was extracted from 5×105′-accatggtgagcaagggcga-3′, GFPp-R 5′-ctaggacttgtacagctcgtccatgc-3′; GAPDHp-F 5′-accagggctgccgtcctctc-3′, GAPDHp-R 5′-ttctccatggtggtgaagac-3′.RNA for Northern blot analysis was prepared from GFP2 or GFP4 cell lines with the RNeasy kit (Qiagen) or TRIzol reagent (Invitogen). 10 µg of total RNA was run on a gel, transferred to a membrane, and hybridized with a GFP probe. The blot was then stripped and reprobed with a GAPDH probe as a loading control. Using Image Lab software (Bio-Rad), bands were quantitated and normalized to the corresponding GAPDH signal, and values were presented relative to the GFP4 no DIVAC control. The probes were PCR-amplified DNA products made with the corresponding primers: GFPp-F Luc2 gene of the minimal promoter containing pGL4.23 vector (Promega). 20 µg of the plasmid was co-transfected into UNG−/−AIDR/puro cells with 2.5–5.0 µg of pGL4.75 Renilla luciferase control vector (Promega) using the Amaxa Nucleofector kit V (Nucleofector program B-023) (Lonza). The relative activity of firefly luciferase to Renilla luciferase was determined using the Dual-Glo Luciferase Assay System (Promega) according to the manufacturer's protocol.Test sequences were cloned between SalI and BamHI sites downstream of the firefly Figure S1GFP4.Introduction of in-frame stop codons by transition mutations within the hypermutation target sequence of  The top line shows the first 500 base pairs downstream of the GFP4 transcription start site. The hypermutation target sequence starts with the underlined ATG start codon and ends with the Linker sequence followed by a XbaI site and the GFP open reading frame. Hypermutation hotspots  are shown in red, with the preferentially mutated base in bold. Mutations in 34 sequences from an UNG-deficient cIgλE↔3′Core subclone after 6 wk of culture are aligned below the GFP4 sequence, with mutations leading to stop codons in bold. When more than six mutations were seen at a given position, the total number is indicated with a subscript. One 3-bp deletion and a single transversion mutation are shown in blue.(PDF)Click here for additional data file.Figure S2Igλ locus sequences from chicken, turkey, zebra finch, and ground finch.Alignment of  Conserved transcription factor binding motifs, identified as described in Con2 sequences containing a conserved E-box and two putative (p) IRF sites, referred to as pIRF-up and pIRF-down to distinguish the upstream and downstream sites. (B) cIgλE sequences containing conserved E-box, NFκB, MEF2, and PU.1-IRF4 binding motifs. (C) 3′Core sequences containing conserved E-box and putative core binding factor (CBF), C/EBP, and PU.1 binding motifs.(PDF)Click here for additional data file.Figure S33′Core, the second autonomous chicken Igλ DIVAC element.Deletion and mutation analysis of  (A) Diagram of the chicken 3′Core fragment with truncations and deletions indicated below and conserved transcription factor binding motifs depicted as rectangles. The sequences of binding motifs and binding motif mutants are shown on the right. (B) GFP loss of subclones in the presence of full-length, truncated, and internally deleted 3′Core sequences. GFP4 assay. (C) Diagram of the chicken 3′Core fragment with binding motif deletions indicated below and conserved transcription factor binding motifs depicted as rectangles. The sequences of binding sites and of binding site mutants are shown on the right. (D) GFP loss of subclones in the presence of binding motif–deleted or binding motif–mutated 3′Core sequences. The first sample in (B) and (D) depict the same data as one another and as the 3′Core data of (PDF)Click here for additional data file.Figure S4Igλ enhancer and mammalian IgHEi enhancer sequences.Alignment of vertebrate  Conserved transcription factor binding motifs, identified as described in Protocol S1, are indicated. Bases fitting the consensus of the binding motifs are in bold. (A) The upstream and downstream E-box, as well as NFκB, MEF2, and PU.1-IRF4 binding sites are highlighted in the alignment of human, murine and chicken Igλ enhancer sequences. (B) Human and murine IgHEi enhancer sequences containing conserved YY1 (μE1), E-box (μE2 and μE4), Ets1 (μA), PU.1 (μB), IRF, and Octamer transcription factor binding sites. Also indicated are the well-studied μE5 and μE3 motifs (PDF)Click here for additional data file.Figure S5Igκ enhancer sequences.Alignment of the human and murine  Conserved transcription factor binding motifs, identified as described in IgκEi sequences containing five conserved E-boxes  and a conserved NFκB binding site. (B) IgκE3′ sequences containing conserved E-box, NFκB, and PU.1-IRF4 binding sites. (C) IgκEd sequences containing conserved E-box, and putative NFκB, PU.1, and IRF binding sites.(PDF)Click here for additional data file.Figure S6cIgλE and Con2.Congruence between the GFP2 and GFP4 assays and analysis of synergy between  (A) Diagram of the Con2-cIgλE region with truncations of Con2 indicated below and conserved transcription factor binding motifs depicted as rectangles. The sequences of binding motifs and binding motif mutants are shown on the right. (B) Flow cytometry profiles of representative subclones of primary transfectants carrying either GFP2 alone (AIDR) or combined with the cIgλ sequence specified above each plot, named according to (A) and W carries the full-length cIgλ DIVAC sequence Con2-cIgλE elements containing full-length, truncated, or mutated Con2 sequences (GFP4 assay). The data for Con2-cIgλE are the same as in (PDF)Click here for additional data file.Figure S7Ig enhancers.Model for the targeting of SH by  Recruitment of lymphoid and general transcription factors  to multiple Ig enhancer and enhancer-like sequences  (top). This leads to the formation of Ig enhancer–bound protein complexes that interact by looping with the transcription initiation complex assembled at the Ig promoter (middle). It is possible that Ig enhancer–bound protein complexes directly or indirectly recruit AID  to the transcription initiation complex (middle) to facilitate SH of the Ig gene. Alternatively, or in addition, the transcription factors recruited by the Ig enhancers might alter parameters of transcription elongation , thereby increasing the amount of single stranded DNA available for deamination by AID (bottom). While looping involving the enhancers is not depicted in this latter case, it could be occurring at the time of Pol II pausing/stalling.(PDF)Click here for additional data file.Protocol S1Protocols used for target vector construction and FACS analysis. Detailed description of the methods used for the construction of the GFP4 and GFP2 targeting vectors and for the flow cytometry analysis of GFP fluorescence and calculation of GFP loss.(DOCX)Click here for additional data file.


At the beginning of the transcription process, the RNA polymerase (RNAP) core enzyme requires a σ-factor to recognize the genomic location at which the process initiates. Although the crucial role of σ-factors has long been appreciated and characterized for many individual promoters, we do not yet have a genome-scale assessment of their function.Escherichia coli. The reconstructed network includes 4,724 σ-factor-specific promoters corresponding to transcription units (TUs), representing an increase of more than 300% over what has been previously reported. The reconstructed network was used to investigate competition between alternative σ-factors (the σ70 and σ38 regulons), confirming the competition model of σ substitution and negative regulation by alternative σ-factors. Comparison with σ-factor binding in Klebsiella pneumoniae showed that transcriptional regulation of conserved genes in closely related species is unexpectedly divergent.Using multiple genome-scale measurements, we elucidated the network of σ-factor and promoter interactions in The reconstructed network reveals the regulatory complexity of the promoter architecture in prokaryotic genomes, and opens a path to the direct determination of the systems biology of their transcriptional regulatory networks. At the beginning of the transcribing process, the RNAP core enzyme requires a σ-factor to recognize the genomic location at which the process initiates [x, x for each σ-factor) and orchestrates initiation of promoter-specific transcription [70 (rpoD) and six alternative σ-factors σ54, σ38, σ32, σ28, σ24, and σ19  have been described in Escherichia coli. Although the importance of σ-factors and their role in the function of the RNAP and bacterial transcription are well known, we do not yet have a genome-wide understanding of the network of regulatory interactions that the σ-factors comprise in any species. With systems biology and genome-scale science emerging and describing the phenotypic functions of bacteria, it is now possible to comprehensively elucidate the structure of the σ-factor network. Here, we present the results from a systems approach that integrates multiple genome-scale measurements to reconstruct the regulatory network of σ-factor-gene interactions in E. coli. This reconstruction is provided here as a resource for the scientific community.The RNA polymerase (RNAP) core enzyme (E) for bacterial transcription is a catalytic multi-subunit complex  was determined by employing chromatin immunoprecipitation and microarray (ChIP-chip) of rifampicin-treated cells  for the leading strand, 755 (35.5%) for the lagging strand, and 647 (30.4%) for both strands   comprising 3,161 binding regions  transporter  profiling at the genome scale with a single nucleotide resolution. A genome-wide TSS map was generated from TSS profiling by rapid amplification of cDNA ends (RACE) followed by deep sequencing after 5′ triphosphate enrichment [We found that 69% of the Eσ-binding regions exhibited strand specificity, with the balance being observed as divergent promoter regions  map , resultiry phase . Gene es [E. coli, and onlcis-acting sequence elements in the promoter region (such as −10 box or −35 box), we analyzed the sequence motifs of the promoter regions ; however, unlike the σ70 sequence motif, σ38 did not have a distinctive −35 box. The similarity in the −10 box sequence motifs of the σ70- and σ38-specific promoters and the degenerate nature of the −35 box sequence of the σ38-specific promoters explains, in part, how a large overlap between σ70 and σ38 regulons is possible.The significant overlap of σ-factor regulons leads to the fundamental questions: what is the molecular basis for the overlap, and what are the consequences of having a complicated σ-factor network? Because each σ-factor has an individual ability to recognize E. coli cell [x binds, and how frequently it does so [With the structure and molecular details of the σ-TUG network in hand, we were able to study its functional states. Because of the limited number of E complexes in a growing oli cell , each σ- does so . We founcis-acting elements in promoters that enable the Eσx to bind. Interestingly, however, σ38 has a negative effect on the expression level of some genes, even though it acts mainly as a positive effector [38, we performed ChIP-chip experiments for RpoB with wild type (WT) E. coli and its isogenic rpoS knock-out strain to obtain differential Eσx binding to the genome. The differential binding intensity of the Eσx to the promoters of 1,139 genes, whose transcription is directly affected by σ38, is shown in Figure 38-specific promoters were bound only by σ38, then the E complex recruited to those promoters would be very scarce. However, the majority of σ38-specific promoters showed significant levels of signaling for Eσx binding in the σ38 deletion strain, indicating recruitment of the Eσx and implying rescue of transcription activity  showed up-regulated expression when rpoS was removed and 291 (26%) showed expression that was down-regulated more than two-fold . The remaining 58% of the genes showed no statistical significance in expression (fold change <2) or were not expressed in either strain. In the absence of rpoS, σ38-specific promoters became active in transcription, leading to expression of the corresponding genes, but at a different level for 469 (41%) of these 1,139 genes.To confirm that the detected binding of the Eσ38-regulated genes was recovered when rpoS was knocked out; however, it is not known which of the other σ-factors is replacing the role of σ38. As σ70 shared the largest portion of promoters with σ38, it is reasonable to assume that σ70 would replace σ38 when σ38 is missing. In E. coli MC4100, it was reported that the amount of σ70 is in abundance during stationary phase [E. coli K-12 MG1655 also showed high protein expression of σ70 during stationary phase in WT and ΔrpoS strain , suggesting that strong σ70 binding resulted in increased transcription. This finding indicates that the presence of σ38 actually contributed to repressing the transcriptional expression of some genes, presumably by competition for shared promoters between σ70 and σ38.Because approximately 89% of these genes were bound by σE. coli, we could now address the issue of the difference between such networks in closely related species. Genome-wide identification of TSSs of two gamma-Proteobacteria, E. coli and Klebsiella pneumoniae, revealed that promoter regions upstream of orthologous genes are differently organized in the two species, resulting in different usage of TSSs [E. coli genome contains seven σ-factors, K. pneumoniae is known to have only five, missing fliA and fecI, which are found in E. coli. The five σ-factors that the two species have in common are highly conserved in terms of amino acid sequence similarity: 95.9% for rpoD, 98.5% for rpoS, 89.8% for rpoN, 95.1% for rpoH, and 96.3% for rpoE. Promoter sequence motifs examined from the TSSs were found to be identical between E. coli and K. pneumoniae, suggesting that the sequence motifs for each orthologous σ-factor are identical [With the detailed reconstruction of the σ-TUG network in  of TSSs . As σ-fadentical . HoweverrpoD and rpoS, we analyzed ChIP-chip datasets for σ70 under exponential phase and σ38 under stationary phase grown in glucose minimal media as described previously [E. coli and K. pneumoniae have 4,513 and 5,305 genes, respectively, and 2,876 coding genes were defined as orthologs by two-way reciprocal alignment. Binding of σ70 and σ38 under specified conditions upstream of those orthologous genes was analyzed and clustered . These two closely related bacteria, E. coli and K. pneumoniae, share the majority of their gene contents, with most of the open reading frames having highly conserved sequences. However, conserved genes showed significantly different σ-factor binding patterns, indicating diverse gene regulation by different transcription initiation  binding and activity, the resource provided here opens up the possibility of developing a comprehensive reconstruction of the entire transcriptional regulatory network in E. coli, which would simultaneously describe the function of σ-factors and TFs that produce the entire expression state of the organism.Genome-scale measurements enabled us to reconstruct the σ-TUG network in E. coli K-12 MG1655 and its isogenic knock-out strains were used in this study. The deletion mutants (ΔrpoS and ΔrpoN) were generated by a λ Red and FLP-mediated site-specific recombination system [E. coli cells were harvested at mid-exponential phase  of approximately 0.5) with the exception of stationary phase experiments (OD600nm approximately 1.5). Glycerol stocks of E. coli strains were inoculated into M9 or W2 minimal media [n system . E. colial media   in accordance with the manufacturer’s instructions. Samples were then quantified using a NanoDrop 1000 spectrophotometer (Thermo Scientific), and the quality of the isolated RNA was checked by visualization on agarose gels and by measuring the ratio of the absorbance at 260 and 280 nm (A260/A280 ratio) of the sample (>1.8).Cell (3 ml) culture was mixed with 6 ml RNAprotect Bacteria Reagent . Samples were mixed immediately by vortexing for 5 seconds, incubated for 5 minutes at room temperature, then centrifuged at 5000 × E. coli K-12 MG1655 grown under four conditions , were taken from a previous study [E. coli deletion mutant ΔrpoS, a previously described protocol [E. coli genome were used (Roche Nimblegen). Hybridization, washing, and scanning were performed in accordance with the manufacturer’s instructions. Three biological replicates were used for stationary phase in glucose minimal media. Probe level data were normalized with a robust multiarray analysis (RMA) algorithm without background correction, as implemented in NimbleScan 2.4 software.Transcriptome datasets with oligonucleotide tiling microarrays for WT us study . In ordeprotocol  was adapE. coli K-12 MG1655. To enrich intact 5′ tri-phosphorylated mRNAs from the total RNA, 5′ mono-phosphorylated rRNA and any degraded mRNA were removed by treatment with a Terminator 5′-Phosphate Dependent Exonuclease (Epicentre) at 30°C for 1 hour. The reaction mixture consisted of 10 μg purified total RNA, 1 μl terminator exonuclease, reaction buffer, and RNase-free water up to total 20 μl. The reaction was terminated by adding 1 μl of 100 mM EDTA (pH 8.0). Intact tri-phosphorylated RNAs were precipitated by adding 1/10 volume of 3 M sodium acetate (pH 5.2), 3 volumes of ethanol, and 2 μl of 20 mg/ml glycogen. RNA was precipitated at −80°C for 20 minutes and pelleted, washed with 70% ethanol, dried in Speed-Vac for 7 minutes without heat, and resuspended in 20 μl nuclease free water. The tri-phosphorylated RNA was then treated with RNA 5′-Polyphosphatase (Epicentre) to generate 5′-end mono-phosphorylated RNA for adaptor ligation. The RNA sample from the previous step was mixed with 2 μl 10× reaction buffer, 0.5 μl SUPERase-In (Ambion), 1 μl RNA 5′-Polyphosphatase, and RNase-free water up to 20 μl. The mixture was incubated at 37°C for 30 minutes and reaction was stopped by phenol-chloroform extraction. Ethanol precipitation was carried out for isolating the RNA as described above. To ligate the 5′ small RNA adaptor , and 1500 U SuperScript II (Invitrogen). After the reaction, RNA was hydrolyzed by adding 20 μl of 1 N NaOH and incubating the mixture at 65°C for 30 minutes. The reaction mixture was neutralized by adding 20 μl of 1 N HCl. The cDNA samples were amplified using a mixture of 1 μl cDNA, 10 μl Phusion HF buffer (NEB), 1 μl dNTPs (10 mM), 1 μl SYBR Green (Qiagen), 0.5 μl HotStart Phusion (NEB), and 5 pM small RNA PCR primer mix. The amplification primers used are shown in Table The raw TSS dataset for exponential phase was taken from a previous study . For theE. coli K-12 MG1655 genome (NC_000913) using Mosaik [The data processing and mapping of the sequencing results to obtain potential TSSs was performed exactly as described previously . In brieg Mosaik  with theE. coli genome [in vivo binding regions of RNAP complex and six σ-factors , we isolated DNA fragments bound to those RNAP subunits from formaldehyde-crosslinked E. coli cells, using ChIP with six different antibodies that specifically recognize each subunit (NeoClone). An E. coli strain harboring RpoH-8myc was constructed as previously described [38 ChIP-chip with anti-c-myc antibody . Cells were grown under appropriate conditions  and enriched-region identification. The log2 ratios of each spot in the microarray were calculated from the raw signals obtained from both Cy5 and Cy3 channels, and then the values were scaled by Tukey bi-weight mean. The log2 ratio of Cy5 (IP DNA) to Cy3 (mock-IP DNA) for each point was calculated from the signals, then, the bi-weight mean of this log2 ratio was subtracted from each point. Each log-ratio dataset (from duplicate or triplicate samples) was used to identify TF-binding regions using the software (width of sliding window = 300 bp). Our approach to identify the TF-binding regions was to first determine the binding locations from each dataset, and then combine the binding locations from at least five of six datasets to define a binding region, using the recently developed MetaScope visualization software and genome browser [The analysis was performed, as previously described ,26. In b browser .E. coli K-12 MG1655 and ΔrpoS deletion mutant cells were grown in M9 minimal media with 0.2% glucose, and were harvested from mid-exponential phase to stationary phase every 2 hours. Cells were pelleted by centrifugation, and were lysed with lysozyme in a lysis buffer (10 mM Tris–HCl (pH 7.5), 100 mM NaCl, and 1 mM EDTA. The supernatant was decanted after centrifugation to remove unlysed cells. The concentration of total protein in the lysate was measured with Qubit Protein Assay Kit (invitrogen), and 5 μg of total protein sample were mixed with 4× SDS-PAGE sample loading buffer (Invitrogen) and 10 mM DTT, then boiled at 90°C for 5 minutes. The boiled samples were separated by electrophoresis with 10% Bis-Tris gel in MOPS buffer, and transferred onto Hybond-ECL membrane (Amersham Biosciences). The membrane was briefly washed in TBS with 0.1% Tween-20 (1× TBS-T) for 5 minutes on a rocker, and then treated with 2% skim milk in TBS-T buffer for 1 hour with gentle shaking. The membrane was washed twice with TBS-T for 5 minutes each on a rocker, and then it was sliced into three pieces. with RpoB, σ70, and σ38 in each slice. Sliced membranes were treated with anti-RpoB, anti-σ70, and anti-σ38 antibodies  for 1 hour on a rocker. The membrane slices were washed once in TBS-T for 15 minutes, followed by three washes of 5 minutes each, and then treated with HRP-conjugated anti-mouse IgG  in dilution for 30 minutes on a rocker, followed by one wash in TBS-T for 15 minutes and three washes of 5 minutes each. Chemiluminescent detection was applied to peroxidase conjugates on membrane to detect the amount of RpoB, σ70, and σ38.All raw and processed data files have been deposited to Gene Expression Omnibus (accession number GSE46740).ABC: ATP-binding cassette; ChIP: Chromatin immunoprecipitation; ChIP-chip: chromatin immunoprecipitation and microarray; E: RNA polymerase core enzyme; IOPR: Intensively overlapped Eσ-binding promoter region; IP: immunoprecipitation; OPR: Overlapped Eσ-binding promoter region; RACE: rapid amplification of cDNA ends; RMA: Robust multiarray analysis; RNAP: RNA polymerase; SPR: Single Eσ-binding promoter region; TBE: Tris-borate-EDTA; TF: transcription factor; TSS: Transcription start site; TU: Transcription unit; σ-TUG: σ-factor-transcription unit gene; WT: wild type.The authors declare that they have no competing interests.BKC, DK, and BOP conceived the idea and designed the research. BKC, DK, and EMK performed the experiments. D, and BKC analyzed the data. DK, BKC, KZ, and BOP wrote the paper, with comments from other authors. All of the authors read and approved the final manuscript.Figure S2. Sequence motifs of σ-factors. Figure S3. The majority of σ38-specific promoters were bound by σ70 when rpoS is missing. Figure S4. Examples of up-regulated and down-regulated genes when rpoS was knocked out. Figure S5. Comparison of transcriptional regulation by two major σ-factors, σ70 and σ38, in two closely related bacteria. Figure S6. Comparison of transcriptional level of σ-factors and their anti-σ-factors. Figure S7. Purine and pyrimidine preferences at transcription start site (TSS) and −1 site. Figure S8. Number of TSSs found in one or multiple conditions. Figure S9. Clusters of Orthologous Groups (COG) clustering analysis of σ-factor regulons.Strand specificity of RNA polymerase (RNAP) binding. Click here for fileEscherichia coli strains and culture conditions for chromatin immunoprecipitation and microarray (ChIP-chip) experiments.Click here for fileEscherichia coli. RRNA polymerase (RNAP) and σ-factor binding regions in Click here for fileBinding intensities of RNA polymerase (RNAP) and σ-factor binding regions.Click here for fileComparison of σ-factor binding regions with known binding regions.Click here for fileEscherichia coli under four different conditions.Identified TSSs of Click here for fileEscherichia coli.Reconstructed σ-factor-transcription unit gene (σ-TUG) network in Click here for fileEscherichia coli genes in the wild type and the ΔrpoS strain.Transcription levels of Click here for file