extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Medicine

text:
RB acute loss induces centrosome amplification and aneuploidy in murine primary fibroblasts

Background Incorrect segregation of whole chromosomes or parts of chromosome leads to aneuploidy commonly observed in cancer. The correct centrosome duplication, assuring assembly of a bipolar mitotic spindle, is essential for chromosome segregation fidelity and preventing aneuploidy. Alteration of p53 and pRb functions by expression of HPV16-E6 and E7 oncoproteins has been associated with centrosome amplification. However, these last findings could be the result of targeting cellular proteins in addition to pRb by HPV16-E7 oncoprotein. To get a more detailed picture on the role of pRb in chromosomal instability and centrosome amplification, we analyzed the effects of the acute loss of retinoblastoma gene function in primary conditional Rb deficient mouse embryonic fibroblasts (MEFs). Moreover, since pRb is a transcriptional repressor, microarray analysis was done on pRb-competent and pRb-deficient MEFs to evaluate changes in expression of genes for centrosome homeostasis and for correct mitosis. Results Acute loss of pRb induces centrosome amplification and aneuploidy in the vast majority of cells analyzed. A time course analysis shows a decrease of cells with amplified centrosomes after 40 days from the adenoviral infection. At this time only 12% of cells still show amplified centrosomes. Interestingly, cells with pRb constitutive loss show a similar percentage of cells with amplified centrosomes. DNA-Chip analyses in MEFs wt (mock infected) and pRb depleted (Ad-Cre infected) cells reveal differential expression of genes controlling both centrosome duplication and mitotic progression. Conclusion Our findings suggest a direct link between pRb status, centrosome amplification and chromosomal instability, and define specific mitotic genes as targets whose gene expression has to be altered to achieve or maintain aneuploidy.

Background
Aneuploidy is considered a common feature of tumors and early stage carcinomas. Aneuploidy and its underlying cause (CIN) could be considered a prerequisite in tumor initiation/progression and like alterations in signaling pathways it is believed essential for tumorigenesis [1,2]. However, the molecular defects underlying aneuploidy and whether it is a cause or a consequence of the tumor phenotype are not completely clear. At least two possible causes of aneuploidy could be suggested: mutation/alteration in genes encoding spindle checkpoint pro-teins and defects in genes controlling centrosome numbers.
Some studies suggest that aneuploidy in cancer cells could arise from defects in the mitotic spindle checkpoint. This mitotic checkpoint monitors that in metaphase, all chromosomes are properly aligned and attached to the mitotic spindle before progressing to anaphase. Although in human and murine cells mutations of some of these mitotic genes such as: hBUB1, hSecurin, hMAD2 and hBUBR1 induced aneuploidy in dividing diploid cells over several generations, they were rarely mutated in human tumors [3][4][5][6][7]). Recently, it was reported a hereditary mutation of the hBUBR1 gene in patients with mosaic variegated aneuploidy (MVA) a rare genetic disease with increased cancer risk [8]. Also, MAD2 overexpression was associated with stable inactivation of the retinoblastoma tumor suppressor and consequently E2F-1 activation [9].
Alternatively, aneuploidy could arise from an initial abnormal chromosome complement that in turn triggers chromosome missegregation and genetic instability [1]. Previously, we showed that transient polyploidization caused by DNA re-replication could be a trigger for aneuploidy in human cells [10]. Moreover, after treatments with anti-mitotic drugs both human and murine pRb deficient cells displayed perpetual aneuploidy and amplified centrosomes [11] suggesting that deregulation of cell cycle progression and alteration in centrosome numbers could be implicated in chromosomal instability.
Loss of centrosome duplication control will often create multipolar spindles that in turn could be responsible for the incorrect segregation of whole chromosomes leading to aneuploidy. Centrosome duplication is coupled to DNA replication and systems of constraints ensure that centrosome duplication occurs during S phase once and only once. In particular, the activation of cyclin E-Cdk2 (cyclin-dependent kinase 2) complex at the G1/S phase transition of the cell cycle allows both DNA replication and centrosome duplication to proceed. [12]. Centrosome amplification was associated with genetic instability in prostate [13] and breast cancer [14] as well as in preinvasive cancer lesions [15]. The observation made in human keratinocytes that centrosome amplification occurs following expression of the HPV16 E6 and E7 oncoproteins [16] suggests a role in this process for the tumor-suppressors p53 and pRb. Also, centrosome amplification induced by a transient G1/S arrest because of the presence of Hydroxyurea, generated aneuploidy both in human and MEFs with pRb dysfunction [11]. However, it is still controversial whether p53 loss/inactivation plays a direct role in centrosome amplification [17] and chromosome instability [18]. Indeed, recently it has been reported that p53 deficient cells need cyclin E over-expres-sion, a target of E2F transcription factor, to induce chromosome instability and centrosome amplification [19]. This suggests that pRb dysfunction, resulting in activation of the E2F family transcription factors, could be crucial in centrosome amplification and chromosome instability.
To get additional clues on the role of pRb in CIN we analyzed the effects of acute loss of retinoblastoma gene function on centrosomes and aneuploidy in primary conditional Rb deficient Mouse Embryonic Fibroblasts (MEFs). This conditional knockout system permits us to assess the effects of acute loss of the retinoblastoma gene by the Cre-lox-mediated excision of Rb's exon 19. In addition by DNA microarray analysis of these pRb deficient cells we found altered expression of mitotic genes that could account for chromosomal instability associated with centrosome alterations. Our results suggest a direct link between pRb status, centrosome amplification and altered expression of genes necessary for the correct mitosis progression in triggering aneuploidy.

Centrosome amplification after acute loss of pRb in primary murine fibroblasts
To evaluate the effects of Rb acute loss on centrosomes we used Mouse Embryonic Fibroblasts (MEFs) carrying conditional Rb alleles (Rb LoxP/LoxP ) in which two LoxP sites are inserted into the introns surrounding exon 19 of the Rb gene [34]. Exponentially growing MEFs (MEF Rb LoxP/LoxP ) were infected with adenoviruses expressing the Cre recombinase (Ad-Cre) in order to generate cells with truncated pRb (Rb -/-), by excision of exon 19, that are functionally equivalent to Rb null cells. As a control Rb LoxP/LoxP MEFs were infected with adenoviruses that did not express the Cre transgene (pRb proficient, indicated as Ad-empty MEFs). Western-blot experiments ( Figure 1A) showed that in Rb LoxP/LoxP MEFs the pRb protein was no longer detectable as soon as 3 days after infection with Ad-Cre in comparison to both uninfected cells and cells infected with adenoviruses that did not express the Cre recombinase. Acute loss of pRb (3 days) in these murine cells resulted in high percentage of cells (55 %) with centrosome amplification as seen by immunocytochemistry against γ-tubulin ( Figure 1B). Actual centrosome reduplication was confirmed by immunostaining of centrin ( Figure 1B). In addition detection of β-tubulin showed the presence of altered mitotic spindles in cells acutely depleted of pRb ( Figure  1B). A time course analysis of the cells acutely depleted of pRb showed ( Figure 1D) that the number of cells with amplified centrosomes decreased over time and after 40 days from the transfection only 12% of cells still showed amplified centrosomes. Cells with pRb constitutive loss showed a similar number (10%) of cells with amplified centrosomes. These results are similar to those previously reported in p53 -/-MEFs that have about 30% of cells with Centrosome amplification in MEFs after pRb acute loss  amplified centrosomes at early passages but after prolonged culture almost of the cells showed a normal centrosome complement (clonal adaptation) [20]. FACScan analyses after Ad-Cre infection showed lack of cells with DNA content less than 2N ( Figure 1E) suggesting that in these cells apoptosis has not taking place. This is consistent with previous findings that in murine cells pRb abrogation doesn't induce apoptosis [21].
These results underline a different effect of acute versus chronic loss of pRb and that the majority of cells with amplified centrosomes are not maintained during growth in culture most likely because of chromosomal instability triggered by amplified centrosomes. We also scored a fraction of pRb depleted cells showing clusters of multiple centrosomes at each spindle pole ( Figure 1C) that could be a safeguard mechanism by which bipolarity of the spindle is maintained and the cells are unlikely to acquire extensive chromosomal alterations.

Acute loss of pRb in primary murine fibroblasts is associated with aneuploidy
Centrosome amplification observed after acute loss of pRb in MEFs might be responsible for chromosomal missegregation and successive aneuploidy. To this aim we examined whether MEFs with acute depletion of pRb had acquired chromosomal instability by carrying out conventional cytogenetic analyses. Alterations in the normal number of chromosomes were frequently observed in MEFs acutely depleted of pRb but not in their wild type parental counterpart. After 7 days from Ad-Cre infection 30% of scored metaphases were aneuploid, both hypodiploid and hyperdiploid ( Figure 2A). The percentage of aneuploid metaphases increased to 45-50% after 30 days from Ad-Cre infection suggesting the occurrence of perpetual aneuploidy in these cells. In addition, microscopic examination at 72 hours revealed that these Rb 'knockout' cells showed a significant increase in the percentage (20%) of cells with micronuclei ( Figure 2B) as well as lobulated nuclei. The presence of micronuclei reveals segregation defects and it may explain the generation of hypodiploid mitoses after pRb acute loss. FACScan analyses of wild type and of pRb acutely depleted MEFs ( Figure 1E) showed a similar distribution of cells in the G1/S and G2/M phases suggesting that loss of pRb did not influence the cell cycle progression. However, specific evaluation of mitotic cells after 72 hours from AdCre infection by MPM2 staining (Figure 2C), an antibody that recognizes mitotic proteins, revealed a higher number of positive (mitotic) cells (15%) in Rb depleted MEFs in comparison to wild type MEFs ( Figure 2D). The increased mitotic index suggests that the duration of mitosis in these cells could be prolonged by activation of the mitotic checkpoint as suggested by MAD2 over-expression in these cells ( Figure 2E).

Presence of significant changes in gene expression in cells acutely depleted of pRb
The results above suggest that pRb acute loss in primary murine fibroblasts can both induce centrosome amplification and chromosomal instability (aneuploidy). In order to better understand whether loss of pRb affects expression levels of specific genes underlying centrosome amplification and aneuploidy, we used microarray analysis to identify differentially expressed genes in cells wherein pRb was acutely depleted in respect to gene expression of control cells. Independent cultures of asynchronously growing age matched MEFs both pRb proficient and deficient were utilized to isolate RNA. This RNA was then subjected to microarray analyses on the Affymetrix GeneChip ® MGU-74A Array platform. The data of the different arrays were compared by means of normalization techniques and clustered to group similar arrays. Differentially expressed genes (DEG) were selected according to a significance level and clustered to group genes with similar expression profiles across the samples. DEGs were then functionally characterized with several databases, and annotation terms were ranked based on their statistical relevance. Finally, a correspondence analysis was performed aimed at highlighting the most relevant genes for each experimental condition. Only the statistically most significant annotation terms were selected (p Value ≤ 1e-3 ) and a minimum statistical value of 2 was used to narrow the list of genes modified by pRb loss. From these analyses we identified 231 individual Affymetrix entries: 186 upregulated and 45 down-regulated [see Additional file 1]. The GO (Gene Ontology) pathway categories showed that the majority of up-regulated transcripts belonged to cell cycle, DNA replication and chromatin assembly. Whereas transcripts with reduced levels were principally associated with cytoskeleton and basal metabolism ( Figure 3). As expected many of the up-regulated genes were known E2F targets [see Additional file 1] in particular in the DNA replication category MCM proteins were strongly upregulated by pRb loss. Additional genes regulating the DNA replication complex were also upregulated, including Cdk2 and cyclin-E that phosphorylate members of the replication complex and are necessary for centrosome duplication. These extensive alterations in the DNA replication pathway reflect a general induction of DNA synthesis. Another interesting observation was the up-regulation of cdc2 and cyclin-B, partners of the kinase complex required for progression into mitosis. In addition pRb loss upregulated other genes involved in mitosis progression, centrosome homeostasis and mitotic checkpoint such as Plk1, Brca1 and Stk6 (Aurka), Nek2, Stathmin 1, Ckap2, Pttg1 (Securin), Cdc20, Mad2l1 ( Table 1). Most of these genes are considered critical regulators of cell cycle progression and centrosome duplication that exhibit aberrant expres-sion in multiple tumors. Our microarray results clearly suggest that the expression differences observed in cells acutely depleted of pRb could account for the phenotype observed. In order to confirm these results we chose a subset of genes from the mitotic group and performed quantitative Real time RT-PCR analysis to assess whether gene expression levels resulting from microarray analysis were an accurate picture of the transcription taking place. Graphic representation of Real time RT-PCR showed that expression levels were very close to those obtained via microarray ( Figure 3B). Although the fold change values between groups may differ, transcripts found to be increased in microarrays were found to be increased in Real time RT-PCR studies.

Discussion
Centrosome amplification and chromosome instability (aneuploidy) occur in virtually all solid tumors analyzed and could be considered early features in tumor initiation and progression [22]. Gain or loss of chromosomes (aneuploidy) could generate in a single step multiple changes required for tumor initiation and progression. At least two possible cause of aneuploidy, not mutually exclusive, could be suggested: defects in genes controlling centrosome homeostasis and mutations in genes encoding mitotic checkpoint proteins.
Stable disruption of cell cycle proteins such as pRb, p53, Brca1, altered expression of Aurora kinases, presence of the viral oncoproteins HPV16-E7 and HPV16-E6 have been associated with centrosome amplification. In addition dysfunction of genes involved in the mitotic checkpoint such as Mad2l1 and BUB family members could lead to chromosomal instability (aneuploidy) facilitating tumorigenesis.
In mammalian cells DNA replication and centrosome duplication must be completed successfully before mitosis and are tightly regulated by a control network in which pRb plays an important role. To this regard we showed that centrosome amplification and perpetual aneuploidy occurred in MEFs cells stably devoid of pRb only following a prolonged G1/S arrest because of exposure to hydroxyurea [11]. Similar results were recently reported in normal human cells lacking of p16 ink4A , a regulator of pRb activity, following hydroxyurea treatment [23]. However, disruption of a checkpoint governed by pRb through inac- tivation of p16 ink4A alone did not lead to centrosome alterations or to chromosomal instability [24].
Here we demonstrate for the first time that also pRb acute loss in conditional deficient Rb MEFs resulted in centrosome amplification and aneuploidy highlighting the importance of pRb in the control of genetic stability. These results are consistent with recent findings showing that p53 deficient cells also need cyclin E over-expression, a target of the E2F transcription factor, to induce CIN and centrosome amplification [19]. Moreover, reduction in hCDC4 protein level by siRNA resulted in a high increase in cyclin E levels and was associated with spindle dysfunction and CIN [25]. Conditions that favor disruption of cell cycle transitions may then contribute to centrosome amplification that in turn results in chromosomal instability through assembly of multipolar spindles. Our DNA microarray analysis showed that several pathways were affected in cells acutely depleted of pRb. Although it is difficult to discuss each of them in detail, it is worthwhile to outline the upregulation of genes involved in centrosome homeostasis and mitosis progression. In particular DNA microarray analysis revealed changes in transcripts of mitotic genes not shown previously, in addition to known genes under E2F control that define a pRb null phenotype [26,27]. We found that critical regulators of mitotic progression and centrosome duplication that harbor oncogenic activity and exhibit aberrant expression in multiple tumors such as Plk1, STK6 (Aurora-A), Stathmin 1 and BRCA1 were upregulated after pRb acute loss. Recently, it has been shown that activation of the RB-pathway resulted in the repression of Plk1 promoter activity and that this action was dependent on the SWI/SNF chromatin remodeling complex [28]. Also the increase of Stathmin 1 expression levels could play a role in chromosomal instability in murine cells. In fact recent studies point to Stathmin 1 as an important mitotic regulator highly expressed in many cancers that is under transcriptional control of E2F [29]. Expression of the HPV16-E7 prevented the down-regulation of Stathmin 1 and led to premature exit from G2 after genotoxic stress [30]. Thus, functional complexes containing E2F and RB appear to be essential for repressing expression of critical mitotic regulators. The increase in the mitotic index of cells acutely depleted of pRb suggested the activation of the mitotic checkpoint triggered by MAD2 levels whose increased gene expression was confirmed by Real time RT-PCR. However, the concomitant over-expression of important mitotic regulators such as Aurora-A and cdc20 could allow some cells to override the activated mitotic checkpoint and then result in aneuploidy [31]. Recently, it was reported that in cells stably depleted of pRb via RNA interference by short hairpin RNAs against Rb transcript increased expression of MAD2 led to chromosomal instability [9]. The explanation for this paradoxical effect of MAD2 was that part of the population adapted to the activated checkpoint, and entered the cell cycle as polyploid cells. This could then cause chromosome misdistribution in subsequent mitoses. However, it could be also possible that additional genetic changes arose during the selection (i.e. Aurora-A/cdc20 over-expression) and overrode the activated mitotic checkpoint.
Altogether, our findings strongly suggest that pRb acute loss resulting in centrosome amplification and likely affecting expression of G2/M genes other than G1/S genes could be crucial in CIN.

Conclusion
Centrosome amplification is a frequent event in almost all types of solid tumors and although it is not completely clear what cellular factors are responsible for chromosome instability associated with initiation of cancer, it is known that the majority of cancers have mutations in components of Rb pathway. The effects of pRb acute loss in primary murine cells seem to link deregulation of cell cycle progression and alteration in centrosome numbers and mitotic genes to induce aneuploidy.
Finally, we hypothesize that the loss of pRb triggered centrosome amplification by altering expression of genes encoding protein for centrosome homeostasis (i.e. cyclin E, Aurora-A, PLK1, Nek2) and induced defects in components of the spindle checkpoint (MAD2) that in turn gave rise to aneuploidy.

Cells and cell culture
Mouse Embryonic Fibroblasts (MEFs) with conditional Rb alleles (Rb LoxP/LoxP MEFs) were infected (MOI: 300, that ensures 90-95% of successful infected fibroblasts) with the empty vector J-pCA13 or with adenoviruses (Ad-Cre) expressing the Cre recombinase under control of the human cytomegalovirus (hCMV) immediate-early promoter to generate MEFs devoid of pRb (Rb -/-). All adenoviruses used in this study were replication-deficient (E1 region deleted). Cells were cultured in DMEM supplemented with 10% FBS, 100 units/ml penicillin and 0.1 mg/ml streptomycin (Euroclone Ltd UK). . DNA chips were scanned with the GeneChip scanner and signals were processed by using the Gene-Chip expression analysis algorithm (GCOS, Affymetrix).

Microarray analysis
Four quality checks were performed to verify the quality of sample preparation and hybridization. First of all for each sample the percentage of probe sets with Detection call "Absent" (A) over the total number of probe sets was determined, as well as for the probe sets with Detection call "Present" (P). Next for each sample the mean value of probe set called A and P respectively was determined. This step of the overall analysis was performed on raw data, before sample normalization, thus differences reflected bias that will be eliminated with the next step of the analysis. Finally the ratios between the expression values for 3' and 5' end of GAPDH and β-actin transcripts was determined. This is useful to control the degree of sample degradation. In order to make different chips comparable, it was necessary to normalize signal intensities, using the Qspline method, a robust non-linear method for normalization using array signal distribution analysis and cubic splines. All probe sets called "Absent" (A) over all conditions and replicates were removed (5012). The 95 th percentile of all the signals of the entire dataset that were flagged with an absent call is determined and used as a threshold to remove all the remaining probe sets whose expression values are always below this value in each sample (3440). Finally 4036 probe sets remain for the next analysis steps. PLGEM was used for the selection of differentially expressed genes [32]. The PAM-clustering method was used to partition the gene expression profiles into k clusters. The optimal number of clusters is automatically determined, based on quality scores obtained from different trials of clustering. Experimental conditions where replicates are available are averaged and the logarithm of the ratio between each exp. cond. and the condition chosen as the baseline are calculated (log Ratios). The similarity of the expression profiles of genes within a cluster suggests a similarity in their regulation. Even for poorly annotated genes (e.g. ESTs) a functional hint can be drawn from the occurrence of known genes within the same cluster. The most representative Gene Ontology (GO) annotations for DEG from each cluster were identified by determining the probability (p Value) of random occurrence of GO terms (functional enrichment). Only the statistically most significant annotation terms are selected (p Value ≤ 1e-3).

Real time RT-PCR
Some differentially expressed genes were selected for Real time RT-PCR. The selected sequences (provided by Affymetrix) associated with the probe sets located on the array were tested against public databases to confirm the identity of the genes. Once confirmed, primer were designed with Primer Express software (Applied Biosystems) choosing amplicons of approximately 70-100 bp. Total RNA used for synthesis of microarray probes (above) was reverse-transcribed in a final volume of 100 μL using the High Capacity c-DNA Archive kit (Applied Biosystems) for 10 minutes at 25°C and 2 hours at 37°C.


extfieldsofstudy: Medicine Biology

text:
Comparison of methods for analysis of selective genotyping survival data

Survival traits and selective genotyping datasets are typically not normally distributed, thus common models used to identify QTL may not be statistically appropriate for their analysis. The objective of the present study was to compare models for identification of QTL associated with survival traits, in particular when combined with selective genotyping. Data were simulated to model the survival distribution of a population of chickens challenged with Marek disease virus. Cox proportional hazards (CPH), linear regression (LR), and Weibull models were compared for their appropriateness to analyze the data, ability to identify associations of marker alleles with survival, and estimation of effects when all individuals were genotyped (full genotyping) and when selective genotyping was used. Little difference in power was found between the CPH and the LR model for low censoring cases for both full and selective genotyping. The simulated data were not transformed to follow a Weibull distribution and, as a result, the Weibull model generally resulted in less power than the other two models and overestimated effects. Effect estimates from LR and CPH were unbiased when all individuals were genotyped, but overestimated when selective genotyping was used. Thus, LR is preferred for analyzing survival data when the amount of censoring is low because of ease of implementation and interpretation. Including phenotypic data of non-genotyped individuals in selective genotyping analysis increased power, but resulted in LR having an inflated false positive rate, and therefore the CPH model is preferred for this scenario, although transformation of the data may also make the Weibull model appropriate for this case. The results from the research presented herein are directly applicable to interval mapping analyses.

INTRODUCTION
Genetic association analyses are becoming a common approach in animal breeding to identify genes or genomic regions that affect quantitative traits (e.g., [17,29,33]). Most analyses utilize statistical models that assume normality of phenotypes [8,9,12,14,32]. Many phenotypic traits of interest in agriculture, however, do not follow a normal distribution (e.g. ordinal traits such as conformation scores, binary traits such as calving/not calving, or time to success/failure traits such as survival time).
Many agriculturally important traits follow a survival distribution (e.g. survival after infection, length of productive life, or days open). To analyze such traits, the Weibull and Cox proportional hazards models are commonly employed [1,2,4,6,7,11,16,23,25,27]. The Weibull model, a generalization of the exponential model, is parametric and is therefore appropriate for only specific distributions; the Cox model is a rank-based semi-parametric method and, therefore, should be appropriate for all distributions for which the hazards between groups are proportional [26]. Both models can appropriately handle datasets that include data from individuals without a recorded time of death, i.e., censored data.
Another distributional difficulty that is often encountered in marker/phenotype association analyses is created by the use of selective genotyping. Selective genotyping is a method to reduce the costs of an experiment by genotyping only individuals from the extremes of the phenotypic distribution [14,15]. Individuals from the phenotypic extremes provide most of the power for a marker-trait analysis, so power can be maximized for a limited number of genotypes by selective genotyping. This, however, also causes the distribution of phenotypes to be non-normal. When the phenotype of all individuals in the experiment is normally distributed, using linear regression to analyze selective genotyping data results in the effects being overestimated [14]. Darvasi and Soller [3] and Ronin et al. [24] derived an approximation to correct for this bias when the complete phenotypic data follows a normal distribution. Maximum likelihood can also be used to appropriately analyze such data [14,20], if the proper distribution can be identified. Henshall and Goddard [10] showed that logistic regression of genotype onto phenotype also resulted in unbiased estimates of effects; however, the original phenotype must be normally distributed for this method as well.
Moreno et al. [19] used simulation to compare Cox, Weibull, and Gaussian models for QTL analysis with a survival phenotype. They found that when the data are censored at a fixed date, the survival models improved power of QTL detection and the accuracy of estimates of QTL location and effect. They did not, however, evaluate the models for analysis of selective genotyping survival data, which was the objective of the current study.
McElroy et al. [18] used the Cox proportional hazards model and linear regression to identify markers associated with survival in selectively genotyped layer chickens infected with Marek disease virus. Using simulation under the null hypothesis of no QTL, they found that both linear regression and the Cox proportional hazards model resulted in valid false positive rates for tests of association. They did not, however, compare the power of these two models, nor did they evaluate the use of a Weibull model. Because the Weibull model is a parametric survival model, it should be more powerful that the Cox model if the data follow a Weibull distribution. Therefore, the objective of this study was to compare the validity of false positive rates and the power of employing the Weibull model, the Cox proportional hazards model, and the linear regression model to analyze QTL associations under full and selective genotyping of survival data.

Data simulation
Survival data were simulated for a backcross under the null hypothesis of no QTL, or the alternative hypothesis that a QTL affecting survival resides at the marker under analysis. To mimic real data, the simulated data were generated to represent actual data from the backcross population of Marek disease viruschallenged layer chickens described by McElroy et al. [18]. Survival times in the real experimental population over the recording time ranged from 33 to 140 d (Fig. 1). Survival times followed a right-skewed distribution with a mean of 65.5 d, a median of 59.0 d, and a standard deviation of 23.9 d. Twentyeight individuals (4.3%) survived to the end of the study and were considered censored in the analyses. A survival function (S (t) KM ) was estimated from the real data using the non-parametric Kaplan-Meier estimator [13]: where j is the rank of a particular day (t) among all chronologically ordered days in which death occurred, k is the rank of the day in which the survival function is being evaluated, n j is the number of animals still alive on day j, and d j is the number of animals dying on day j. This resulted in an estimate of S (t) KM for each time of death (t). Survival data for 700 backcross animals were simulated following methods described by Moreno et al. [19] by generating a survival probability, S 0 (t) i , for where S (t) i is a draw from a random uniform [0,1], β s is the simulated QTL allele substitution effect [5], and x i indicates the QTL allele received from the F1 parent, which was either zero or one and was randomly selected from a Bernoulli distribution with a 50% chance of getting either allele. The value of S 0 (t) i obtained for an animal was then cross-referenced with the estimated Kaplan-Meier function obtained from the real data to get the corresponding time of death. To simulate selective genotyping, only the 20% short (140 animals) and 20% long (140 animals) surviving individuals were considered for analysis and their genotype for the marker (QTL) was assumed known.
The simulation was performed for each of four allele substitution effect levels of the QTL: β s = 0, 0.1, 0.2, or 0.3. The largest effect was chosen to be 0.3 because all analyses had high power for this effect. It should be noted that a larger β s results in an increased risk of death and, therefore, corresponds to a more negative effect on days of survival. Two additional censoring scenarios (0 and 20%) were also considered, in addition to the 4.3% censoring that was present in the real data. For the no censoring scenario, the Kaplan-Meier function from the real data set was extended past day 140 by assuming the same risk of dying for each day past 140 as the average risk from day 101 and 139. For 20% censoring, day 77 was considered as the last day of the study.

Models of analysis
Simulated data were analyzed for associations of marker and phenotype using the Weibull, Cox proportional hazards, and linear regression models. The linear regression model (e.g., [28]) was: where T i is the survival time of animal i, in days; β LR is the increase in the mean survival time associated with inheriting allele 1 versus allele 0; p(Q) i is the probability of inheriting one versus the other QTL allele for animal i; µ i is the average survival time when p(Q) i = 0; and ε i is the residual for animal i.
The Cox model used, following Smith [25], was: where S (t; n) i is the probability that animal i survived at least until time t, where β PH is the allele substitution effect of the QTL on the natural log of the hazard ratio for the Cox model, and p(Q) i is as defined previously. The Weibull model [30] used was: where S (t) i is the probability that animal i survived at least until time t, θ is the shape parameter of the Weibull distribution, α is the scale parameter of the Weibull distribution, β W is the allele substitution effect of the QTL on the natural log of the hazard ratio for the Weibull model, and p(Q) i is as defined previously. For all models, p(Q) i = 0 or 1 for genotyped individuals, depending on which QTL allele they inherited. Selective genotyping data were analyzed in two ways: (i) including only genotypic and phenotypic data from genotyped individuals (SG) in the analysis, and (ii) also including phenotypic data from the ungenotyped individuals (SGI) in the analysis. For the SGI scenario, p(Q) i = 0.5 for ungenotyped individuals because, with unknown genotypes, these individuals were equally likely to have received either allele. These data were then analyzed with the three models of interest. For the 4.3 and 20% censoring scenarios, animals dying on day 140 and 77, respectively, were considered censored for the Weibull and Cox models, and to have died on day 140 and 77 for the linear regression model.
The proper method for including ungenotyped individuals in the survival models, in which the allele inherited is unknown, is to use the average of the hazards for the two possible alleles in the partial likelihood. However, standard statistical packages do not permit modification of the likelihood equations. To allow implementation of standard methods, p(Q) i = 0.5 was used as the explanatory variable for the survival models as an approximation of the average of the hazards for the two possible alleles in the partial likelihood. This method is discussed in detail in the appendix of McElroy et al. [18] for the Cox model. They found that, under the null hypothesis of no QTL effect, the estimated effects are slightly biased away from zero when the approximation is used, but the standard error of the estimated effects is also higher, resulting in an appropriate type I error rate.
False positive rates (assuming no QTL effect) and power of the analyses were calculated based on 1000 replicates for each allelic effect level for each of two significance levels (P < 0.05 and 0.01) for the Full (all individuals genotyped), SG, and SGI genotyping scenarios. A two-tailed binomial test was used to determine if false positive rates differed significantly from expected (i.e. the α level) under the null hypothesis and a two-tailed Fisher exact test was used to determine if the power of the alternate models differed significantly.
The Weibull model for all genotyping scenarios and the linear regression model for the SGI scenario resulted in inflated false positive rates. Although transforming the data to fit a Weibull distribution could have made the Weibull model appropriate for analysis in the Full genotyping scenario and may have improved the performance of the Weibull model in the SGI scenario, no transformations of the data were performed for the current study because transformation would not have addressed the SG scenario, which was the main emphasis of the study. Instead, for the Weibull analyses and the linear regression analyses in the SGI scenario, empirical thresholds with appropriate false positive levels were derived and used to compute power for each level of censoring. Empirical thresholds were obtained from 10 000 replicates of data simulated with no QTL effect.
Average allele substitution effect estimates and their variances across each set of 1000 replicates were also calculated. Estimated effects from linear regression describe the average difference of survival in days between the two QTL alleles, and estimates from the Cox and Weibull models describe the allele substitution effect on the natural log of the hazard ratio. To make the effect estimates from all three models directly comparable, the mean difference of survival in days between groups inheriting one versus the other QTL allele was estimated for the Cox and Weibull models for each replicate. For the Weibull model, estimates in terms of the hazard ratio can be directly converted into estimates in days by: where E(T) is the expected mean days of survival, Γ is the gamma function, and p(Q) i , α, β W , and θ are as described previously. The mean difference in days was calculated by finding the difference between E(T) for p(Q) i = 0 and 1. To obtain estimates in days from the Cox proportional hazards model, the difference in the means of the survival functions for p(Q) i = 0 and 1 was calculated across all times using the Cox proportional hazards estimates for β PH . For this estimation, censored individuals were assumed to have died on the last day of the study. Estimates from the three models were compared based on their correlations, magnitudes, and standard errors. The magnitudes of effect estimates from the three models were compared by using a t-test assuming unequal variances. All simulations and analyses were performed using the R [22] statistical package. Table I shows the false positive rates for the Weibull, Cox, and linear regression models for α = 0.05. False positive rates for the Cox model were not significantly (P > 0.05) inflated for any genotyping scenario. False positive rates for linear regression were not significantly (P > 0.05) inflated in the full and SG cases, but were significantly (P < 0.05) inflated for every censoring level when SGI was used. The Weibull model had a significantly (P < 0.05) higher number of false positives than expected for every censoring and genotyping scenario. Significance of differences from the expectations for the three models with α = 0.01 were similar to those when α = 0.05.

Power between models
Powers of the Cox, Weibull, and linear regression models for different effects and censoring for α = 0.05 are shown in Figure 2. Although not always Linear regression tended to have higher power than the Weibull model with the SGI scenario and 0 and 0.04% censoring. The results for the power between models presented in Figure 2 are for α = 0.05, but trends were similar for α = 0.01. 3 P-value levels determined empirically for these analyses. 4 Bars with different letter superscripts (a, b, or ab) within scenario, censoring, and effect are significantly different at P < 0.05. No letter indicates no significant differences (P > 0.05).

Power among genotyping scenarios
Although not always significant, definite trends in differences of power were apparent when comparing genotyping scenarios (Tab. II). For all three models, across QTL effects and censoring, the Full genotyping scenario had more power than SG and SGI, and SGI had more power than SG. Table II shows results for α = 0.05 but trends were similar for α = 0.01.

Effect estimates
For the Full genotyping scenario, estimates of allele substitution effects on the ln(hazard ratio) scale from the CPH model were never significantly (P > 0.2) different from the simulated values (0.1, 0.2, or 0.3) (data not shown). For the Cox model, the correlation of estimates on the ln(hazard ratio) scale with converted estimates in days (within censoring level) was less than -0.99 for all scenarios, so the Cox estimates in days were adequate to compare estimates in days from all models with the expected estimates that were in terms of the ln(hazard ratio) within censoring level. Effect estimates from the Cox model for the SG and SGI scenarios with non-zero simulated effects were significantly (P < 0.05) larger than expected (i.e. 0.1, 0.2, and 0.3), but effects were less overestimated for the SGI scenario.
Within a censoring and genotyping scenario, mean estimates from all three models were highly correlated (greater than 0.99). For a given model, correlations of mean effect estimates (in days) from different genotyping scenarios were also greater than 0.99. The rest of the results will only consider simulated effects >0. For the Full genotyping scenario, mean estimates from the Cox and linear regression models did not differ significantly (P > 0.05), except for the 0.2 censoring, effect = 0.3, case (Tab. III). For the Full scenario, mean estimates from the Weibull model were significantly (P < 0.05) greater than mean estimates from the Cox model. For the SG and SGI scenarios, mean estimates from the linear regression model were significantly (P < 0.05) larger than mean estimates from the Cox model for all cases. For the Full and SG scenarios, mean Weibull estimates were significantly (P < 0.05) larger than mean estimates from linear regression, with the exception of the SG, no-censoring scenario. For the SGI scenario with low censoring (0 and 0.04), mean estimates from linear regression were significantly (P < 0.05) larger than Weibull estimates. However, for the SGI high-censoring (0.2) scenario, mean estimates from the Weibull model were significantly (P < 0.05) larger than mean estimates from linear regression.
For all three models, standard errors of the means of the effect estimates were less for the Full scenario than for SG and SGI (Tab. III). For the Cox and Weibull models, standard errors were greater for the SG scenario than for the SGI scenario but they did not differ for the linear regression model.   3 Numbers with different letter superscripts (a, b, or c) within a row and genotyping scenario (Full, SG, SGI) are significantly different at P < 0.05. No letter indicates no significant differences (P < 0.05) within a row and genotyping scenario. 4 Standard errors were equal across effects within model and genotyping scenario.

False positive rates
Significance tests under the Cox model were robust to the distribution of phenotypes that was included in the analysis. The Weibull model had significantly (P < 0.05) inflated false positive rates for all scenarios, indicating that the data did not fit a Weibull distribution. This was confirmed by significance (P < 0.01) of a lack of fit test of the survival distribution from the real population to the Weibull distribution using the Cramer-von Mises W test [21]. Tests based on linear regression were robust to the lack of normality in the Full and SG scenarios, but had an inflated false positive rate for the SGI scenario. Further research is needed to understand why the addition of phenotypes of the ungenotyped individuals causes linear regression to have an inflated false positive rate using this particular dataset and to determine if this inflated false positive rate occurs with datasets having different distributions.
In their comparison of models for the analysis of a non-selectively genotyped survival dataset, Moreno et al. [19] first transformed their data to better fit a Weibull or Gaussian distribution. With the transformed data, Moreno et al. [19] found that the Weibull and the Cox models had similar performances. Transformation of our data would likely have resulted in similar results for our Full scenario but for the SG scenario, the transformed dataset would still be statistically inappropriate for the Weibull model, unless all phenotypic records were included in the analysis. The effects of transforming the data on the selective genotyping scenarios are unknown.

Power differences between models
Differences in power between models varied between genotyping scenarios and censoring levels. In the Full scenario, the Cox model had higher power than the linear regression and Weibull models. This is likely because the Cox model is more appropriate for this distribution. The linear regression model had less power than the Cox and Weibull models when censoring was 20%. This was expected because the censored individuals were considered as dying on the last day of the study in the linear regression model, and therefore the extreme long-surviving individuals (which are more likely to have the favorable genotype) were grouped with less extreme long-surviving individuals that would be expected to have a lower frequency of the favorable genotype. This situation reduces the mean phenotypic difference between individuals with the favorable allele and individuals with the unfavorable allele, and thereby reduces power.
In contrast to Moreno et al. [19], we found significant differences in power between the Cox and the Weibull models in the Full scenario. The discrepancy between the two studies is likely due to the fact that the data were transformed to fit a Weibull distribution in the Moreno et al. [19] study, whereas the data were not transformed in the present study.
For the linear regression analyses, Moreno et al. [19] either excluded censored individuals or included them as dying on the last day of the study, as in our study. They found that differences in power between the survival models and both linear regression analyses were small when censoring was low or random, but increased as censoring increased. When censoring was greater than 20% and at a fixed date, linear regression resulted in much lower power than the survival models when censored individuals were excluded. Inclusion of censored individuals as dying on the last day resulted in similar power as the survival models at 20% censoring, but in substantially lower power at 40% censoring. The current study included censored individuals in the linear regression analyses and only examined censoring up to 20%. The differences in power we observed between the linear regression model and the Cox model would likely have been much larger if the censored individuals were excluded from the linear regression analyses or at 40% censoring.

Power differences between genotyping scenarios
For all models and scenarios, power was somewhat (usually less than 10%) lower for the SG and SGI scenarios than for the Full scenario. This is expected because for a normally distributed phenotype, it has been shown that, for a fixed number of genotyped individuals, SG is more powerful than Full [3,14,15], but when genotyping is not constrained, Full is more powerful than SG. This is because the SG scenario excludes some of the information that is used in the Full scenario, as does the SGI scenario. For the simulated survival data, SGI had slightly (usually less than 5%) higher power than SG for all models and scenarios. To determine which individuals are the most extreme for selective genotyping, phenotypes from the entire population must usually be obtained, so phenotypes of ungenotyped individuals should routinely be available for inclusion in a selective genotyping analysis to increase power.

Effect estimation
For the Full scenario, estimates of the hazard ratio from the Cox model were not significantly different from expectations (P > 0.05; data not shown). This is likely because the survival data were appropriately distributed for the Cox model. The linear regression model also estimated effects accurately in the Full scenario when compared to estimates from the Cox model after conversion to days, indicating the robustness of linear regression to deviations from normality. Moreno et al. [19] also found little differences between estimates from the Cox and linear regression models when censored individuals were included in the linear regression analysis as dying on the last day of the study. The Weibull model overestimated the effects in the Full scenario by approximately 50%, likely due to lack of fit of the data to a Weibull distribution.
Even though in the full scenario the estimates of the hazard ratio from the Cox model were not significantly different from the expected, the estimates in days were smaller when censoring was 20% for all models (Tab. III). With censored data, the actual length of survival of the animals is unknown. The effect in days from linear regression is the difference in the means of the two allelic groups, and it is only known that censored individuals survived at least to the date at which they were censored. The difference between the two allelic groups must get smaller since the length of survival time is reduced for the long surviving allelic group as a result of increased censoring from shortening the length of the study.
For the SG scenario, it is well known that linear regression overestimates effects when the phenotypes are normally distributed, and methods have been devised to correct for these biases [3,20,24]. The upward bias of estimates from linear regression is due to a positive correlation between QTL effects and residuals when only extreme phenotypes are included in the analysis [3]. The Cox and Weibull models also overestimated effects in the SG scenario by approximately 100%, although the Cox model did so significantly (P < 0.05) less than the Weibull and linear regression models. For the SGI scenario, linear regression overestimated the true effects slightly more than for the SG scenario, and the Cox and Weibull models overestimated the true effects slightly less. Skewness of the distribution may cause estimated effects from linear regression to increase when including the ungenotyped individuals with a p(Q) i = 0.5, since the mean phenotype of these individuals will not fall on the regression line between the mean phenotypes of the two extremes. With a normally distributed phenotype, the mean of the ungenotyped individuals, when given the mean genotypic probability (0.5), is expected to fall on the regression line between the means of the two phenotypically extreme groups, and therefore should not change the slope of the regression line. Inclusion of non-genotyped individuals in a selective genotyping analysis has been examined by several authors [10,14,31] as a way to reduce biases in effect estimates. However, all of these studies assumed a normal distribution. Further investigation into the proper statistical models for including non-genotyped individuals in analysis of non-normal, selectively genotyped data is needed.

Conclusions
Within the scope of the observed data distribution, the Cox model performed better than the linear regression and Weibull models across the tested genotyping scenarios and censoring levels, without the need to derive empirical significance thresholds. This was likely because the distribution of the data met the assumptions of the Cox model better than that of the other two models. However, survival models are much more computationally demanding than linear regression and the effect estimates from survival models are much more difficult to interpret than the effect estimates from linear regression. This, combined with the fact that the linear regression and Cox models had similar power, suggests that with little censoring and Full or SG genotypic scenarios, linear regression can be the model of choice, since false positive rates for linear regression were valid for these two scenarios. The Weibull model had an inflated false positive rate for all scenarios and linear regression had an inflated false positive rate for the SGI scenario, so significance thresholds had to be determined empirically, which is also computationally demanding.
In cases where selective genotyping is employed, more power can be obtained by including phenotypes from non-genotyped individuals in the analysis. These phenotypes should typically be available since they need to be recorded to determine which individuals are extreme. The Cox model was the only model that did not require derivation of empirical thresholds for SGI, and, therefore, may be the best model to use in this situation. Further research is needed to determine if the Cox model would also be appropriate for normally distributed traits with the SGI scenario.
The current study was based on single marker analysis, rather than employing interval mapping but the genotypic data were entered into the models as probabilities of marker genotypes. The genotypic data from an interval mapping analysis are also used to compute probabilities of genotypes at a specific locus. The methods explored herein should therefore also apply to interval mapping analyses but require further validation of model comparisons because probabilities will be different from 0 and 1 for all individuals.


s2fieldsofstudy: Biology Chemistry

text:
Small C-terminal Domain Phosphatases Dephosphorylate the Regulatory Linker Regions of Smad2 and Smad3 to Enhance Transforming Growth Factor-β Signaling*

Transforming growth factor-β (TGF-β) controls a diverse set of cellular processes, and its canonical signaling is mediated via TGF-β-induced phosphorylation of receptor-activated Smads (2 and 3) at the C-terminal SXS motif. We recently discovered that PPM1A can dephosphorylate Smad2/3 at the C-terminal SXS motif, implicating a critical role for phosphatases in regulating TGF-β signaling. Smad2/3 activity is also regulated by phosphorylation in the linker region (and N terminus) by a variety of intracellular kinases, making it a critical platform for cross-talk between TGF-β and other signaling pathways. Using a functional genomic approach, we identified the small C-terminal domain phosphatase 1 (SCP1) as a specific phosphatase for Smad2/3 dephosphorylation in the linker and N terminus. A catalytically inactive SCP1 mutant (dnSCP1) had no effect on Smad2/3 phosphorylation in vitro or in vivo. Of the other FCP/SCP family members SCP2 and SCP3, but not FCP1, could also dephosphorylate Smad2/3 in the linker/N terminus. Depletion of SCP1/2/3 enhanced Smad2/3 linker phosphorylation. SCP1 increased TGF-β-induced transcriptional activity in agreement with the idea that phosphorylation in the Smad2/3 linker must be removed for a full transcriptional response. SCP1 overexpression also counteracts the inhibitory effect of epidermal growth factor on TGF-β-induced p15 expression. Taken together, this work identifies the first example of a Smad2/3 linker phosphatase(s) and reveals an important new substrate for SCPs.

TGF-␤ 4 signaling, largely mediated via the transcriptional functions of Smad proteins, controls diverse developmental processes and the pathogenesis of many diseases, including cancer, autoimmune, and fibrotic diseases. A key step in TGF-␤ signaling is ligand-induced phosphorylation of R-Smads (Smads 2 and 3 in TGF-␤ signaling; Smads 1, 5, and 8 in BMP signaling) in the distal C-terminal SXS motif, which is mediated by the type I receptor kinase (T␤RI) (1)(2)(3)(4). Smad SXS phosphorylation controls a cascade of downstream events, which include hetero-oligomeric complex formation with Smad4, and the nuclear import of this complex which regulates gene transcription in conjunction with a variety of transcriptional cofactors (3,5). Although C-terminal SXS phosphorylation by T␤RI is key in Smad activation, Smad signaling is fine-tuned for diverse biological responses by further post-translational modifications, including ubiquitination and SUMOylation (6,7), as well as phosphorylation by other intracellular protein kinases (8,9). R-Smads contain two conserved polypeptide segments, the MH1 (N) and MH2 (C) domains, joined by a less conserved linker region. R-Smad linker regions are serine/threonine-rich and contain multiple phosphorylation sites for proline-directed kinases. They are phosphorylated by mitogen-activated protein kinases (MAPKs) and cyclin-dependent kinases (CDKs), which exhibit some preference for specific serine residues in the linker (3,8,9). CDK2/4-mediated phosphorylation occurs mostly at Thr-8, Thr-179, and Ser-213 (10). CDK-dependent phosphorylation of Smad3 inhibits its transcriptional activity, negating the anti-proliferative action of TGF-␤ and serving as a novel means by which CDKs promote aberrant cell cycle progression and confer cancer cell resistance to growth-inhibitory effects of TGF-␤.
Additional kinases, e.g. MEKK-1, CaMKII, protein kinase C, and CK⑀, target R-Smads and regulate Smad-dependent transcriptional responses (18 -21), and TGF-␤ may also induce Smad phosphorylation in the linker region as well as at the SXS motif (15,16). Thus, the Smad linker region is emerging as an important and critical regulatory platform in TGF-␤ signaling.
Phosphorylation state and the subsequent activity of many proteins are controlled by dynamic interplay between kinases and phosphatases. Protein-serine/threonine phosphatases (PS/ TPs) cleave phosphate molecules from serine and threonine residues in target proteins and can largely be grouped into the PPM, PPP, or FCP/SCP families (22).
TFIIF-associating C-terminal domain (CTD) phosphatase (FCP1) and small CTD phosphatases (SCP1, SCP2, and SCP3) are characterized by a conserved DXDX(T/V) motif that is essential for their activity and that they share with a superfamily of phosphotransferases and phosphohydrolases (23)(24)(25)(26). FCP/ SCPs are well known for their ability to negatively regulate RNA polymerase II (RNAPII) by dephosphorylating its CTD at Ser-2 and Ser-5 (25)(26)(27). SCPs have also been shown to silence neuronal genes in non-neuronal tissues (28) and to attenuate androgen-dependent transcription (29). Recently, SCPs were reported to dephosphorylate Smad1 in the BMP pathway (30). It is likely that other as yet unidentified proteins involved in gene regulation are also targeted by SCPs.
We recently discovered that Smad2/3 can be dephosphorylated by the PPM family phosphatase PPM1A at the C-terminal SXS motif, to terminate Smad signaling, implicating a critical role for Smad2/3 dephosphorylation in the regulation of TGF-␤ signaling (31). Using a similar functional genomic approach and taking advantage of phospho-Smad2/3-specific antibodies, we undertook a screen for PS/TPs whose expression reduced Smad2/3 linker/N-terminal phosphorylation. Here we present data identifying SCPs as specific Smad2/3 linker/N-terminal phosphatases, and we show that SCP1 functions to enhance TGF-␤ signaling and counteract the inhibitory effect of EGF on TGF-␤-induced p15 expression. In addition to providing the first example of a Smad2/3 linker phosphatase(s), we reveal an important new SCP substrate and identify the first scenario in which an SCP positively regulates gene transcription.

Construction of Human PS/TP cDNA Expression Library-
Full-length cDNAs were synthesized from HaCaT cell total RNA, by high fidelity PCR, as described previously (31).
Immunoprecipitation of endogenous proteins was performed as described (35) using anti-SCP1 sera (a generous gift from Dr. Soo-Kyung Lee, Baylor College of Medicine). Precipitated proteins were subjected to Western blotting using an anti-Smad2 monoclonal antibody (Cell Signaling).
For growth factor treatment, cells were treated with 2 ng/ml TGF-␤ and/or 50 ng/ml EGF (in Dulbecco's modified Eagle's medium with 0.2% fetal bovine serum), for the indicated times, prior to harvest for immunoprecipitation, Western blotting, and/or in vitro phosphatase assay. EGF activity was confirmed by Western blot analysis of lysates with anti-ERK (M12320; Transduction Laboratories) and anti-P-ERK (9106; Cell Signaling) antibodies. TGF-␤ activity was confirmed by assessing Smad2/3 SXS phosphorylation as described above.
Nucleofection was used to examine the effect of SCP1 expression on endogenous TGF-␤ target gene expression. HaCaT cells were nucleofected with GFP or FLAG-SCP1 DNA using the Amaxa Biosystems Nucleofector TM with solution V and program U-020 (Amaxa Inc.). After 24 h, cells were treated with TGF-␤ and/or EGF for 20 h prior to harvest. Samples were subject to Western blotting using anti-p15 (C-20; Santa Cruz Biotechnology) and antibodies as described above.
Transcription Reporter Assays-Plasmid SBE-luc (containing the luciferase gene under the control of Smad-binding elements (SBE)), p15-luc, and p21-luc were used to assess TGF-␤-induced transcription in HaCaT cells. Transfections were carried out using DEAE-dextran, and TGF-␤ treatment and reporter assays were performed as described (32,34).

SCP1 Dephosphorylates Smad2/3 in the Linkers
-Our laboratory recently discovered that PPM1A can dephosphorylate Smad2/3 at the C-terminal SXS motif (31). Smad2/3 activity is also regulated by phosphorylation in the linker/N terminus at Thr-220 and Ser-245/250/255 in Smad2, the comparable sites Thr-179 and Ser-204/Ser-S208/Ser-213 in Smad3, and at Thr-8 in both Smad2/3 (Fig. 1A). Several kinases responsible for Smad linker phosphorylation have been identified (Fig. 1B), whereas the identity of a phosphatase responsible for dephosphorylating these sites remains elusive. In this study, we sought to determine which phosphatase(s) might dephosphorylate Smad2/3 in the linker region and what effect this would have on TGF-␤ signaling. As Smad2/3 linker phosphorylation occurs on serine/ threonine residues, we screened 40 PS/TPs, including 5 SCPs, 13 PPPs, 18 PPMs, and 4 DUSPs ( Table 1), all of which were assessed for Smad SXS motif phosphatase activity in our previous screen (31). HEK293T cells were co-transfected with FLAG-tagged Smad2/3 and the specified phosphatase for 48 h, and cells were lysed for anti-FLAG immunoprecipitation. Samples were subject to Western blotting with antibodies specific for the linker/N-terminal Smad phosphorylation sites. As shown in Fig. 1C (lane 7), Smad2/3 were phosphorylated in the linker/N terminus in HEK293T cells cultured in regular growth medium alone. SCP1 was clearly able to dephosphorylate Smad2 at Ser-245/250/255 and Thr-8, and Smad3 at Ser-204/ 208/213 and Thr-8 (Fig. 1C, lane 6). Interestingly, SCP1 was unable to dephosphorylate Smad2 or Smad3 at Thr-220 or Thr-179 respectively. GFP as a negative control (lane 7) and other phosphatases such as PPP1CC (lane 2) and PPP2CB (lane 4) were unable to dephosphorylate Smad2/3 at the linker/N terminus ( Fig. 1C; Table 1). All of the phosphatases screened were untagged to ensure no interference with their phosphatase activity, although a tagged set was also made to examine and normalize their expression level in cells (31).
SCP1 Specifically Targets the Linker Regions and Not the SXS Motif of Smad2/3-We next examined the specificity of SCP1 for the Smad2/3 linker by testing its ability to dephosphorylate Smad2/3 at the C-terminal SXS motif. HEK293T cells were cotransfected with FLAG-Smad2/3 and FLAG-SCP1 or FLAG-PPM1A phosphatase. Transfected cells were cultured in the presence or absence of TGF-␤ for 1 h prior to harvest, to stimulate SXS phosphorylation, and lysed for immunoprecipitation and Western blotting as described above. PPM1A failed to dephosphorylate Smad2/3 in the linker/N terminus, in the presence or absence of TGF-␤, as determined by immunoblotting with phospho-specific Smad2/3 antibodies ( Fig. 2A, lanes 4 and 5). The FLAG-PPM1A protein was active as judged by its ability to dephosphorylate Smad2/3 at the SXS motif ( Fig.  2A, lanes 4 and 5). Conversely, SCP1 was unable to dephosphorylate the Smad2/3 SXS motif but was efficient at dephosphorylating the other Smad2/3 Ser(P)/Thr(P) residues, with the exception of Thr(P)-220 (Smad2) and Thr(P)-179 (Smad3) ( Fig. 2A, lanes 6 and 7). The ability of SCP1 to dephosphorylate Smad2/3 at linker/N-terminal Ser/Thr residues was independent of SXS motif phosphorylation ( Fig.  2A, lanes 6 and 7). Equal levels of FLAG-Smad2/3 immunoprecipitated product, and expression levels of FLAG-PPM1A and FLAG-SCP1, were confirmed by immunoblotting with an anti-FLAG antibody.
Smad Linker Dephosphorylation Requires the Catalytic Activity of SCP1 and Is Independent of the Proteasome-SCP1 is a class 2 phosphatase whose activity is dependent on acidic residues in the conserved DXD motif (25). To confirm that SCP1 phosphatase activity is essential for Smad2/3 linker/Nterminal dephosphorylation, we took advantage of a catalytically inactive SCP1 mutant, dnSCP1, which has an Asp-Glu substitution at residue 96 (D96E) and an Asp-Asn at residue 98 (D98N) (25). We found that unlike its wild type counterpart, FLAG-dnSCP1 had no ability to dephosphorylate Smad2 at Ser(P)-245/250/255 and Thr(P)-8, and Smad3 at Ser(P)-204/208/213 and Thr(P)-8 (Fig. 2B, compare lanes 3  and 4). The wild type and phosphatase-inactive SCP1 proteins were similarly expressed, as determined by anti-FLAG immunoblotting of whole cell lysates. We next determined whether the ability of SCP1 to dephosphorylate Smad2/3 is dose-dependent. Almost no linker Smad2/3 phosphorylation was detected on co-transfection with 0.5 g of SCP1, as represented by immunoblots for pS245/250/255 (Smad2) and pS213 (Smad3) in Fig. 2C (lane 3). However, phosphorylation levels increased with decreasing concentrations of SCP1 (Fig. 2C, lanes 4 -6), providing more evidence that SCP1 is a Smad2/3-specific phosphatase. Finally, to confirm that the SCP1-induced decrease in pSmad2/3 is because of phosphatase activity and not because of pSmad2/3 degradation, we examined the effect of SCP1 on Smad2 linker/Nterminal phosphorylation in the presence or absence of the proteasome inhibitor MG132. MG132 treatment did not block the SCP1-induced decrease in linker/N-terminal phosphorylated Smad2 (Fig. 2D, lane 4), suggesting that SCP1 reduces pSmad2/3 levels independently of degradation via the 26 S proteasome. SCP1 Physically Interacts with Smad2/3-The above data suggest that SCP1 is a specific phosphatase for the Smad2/3 linker/N-terminal region. To determine whether Smad2/3 interacts with SCP1, we first generated recombinant GST-SCP1 and GST-dnSCP1 fusion proteins and assessed them for their ability to bind to endogenous Smad2/3 in HEK293T cell lysates. Western blot analysis revealed that GST-SCP1, but not GST alone, could specifically bind to Smad2/3 in HEK293T lysates (Fig. 3A). Use of an even amount of GST, as compared with GST-SCP1 and GST-dnSCP1, was confirmed by using an anti-GST antibody. Equal amounts of total protein and Smad2/3 in each cell lysate were confirmed by immunoblotting with anti-actin and anti-Smad2/3 antibodies, respectively (Fig. 3A). The catalytically inactive mutant dnSCP1 was still able to interact with Smad2/3 (Fig. 3A, lane  3), suggesting that SCP1 phosphatase activity is not essential for Smad2/3 binding. We next wished to determine whether Smad2/3 might interact with SCP1 in vivo. HEK293T cells were co-transfected with FLAG-SCP1 and Myc-Smad3, and co-immunoprecipitation experiments were performed. FLAG-SCP1 co-precipitated Myc-Smad3 (Fig. 3B,  lane 3) as did the catalytically inactive mutant dnSCP1 (lane 5). We next immunoprecipitated endogenous SCP1 from HEK293T cell lysates, using an anti-SCP1 sera, to confirm that these proteins interact at the physiological level. Western blot analysis of products using anti-Smad2 antibody revealed that Smad2 interacts with SCP1 at the endogenous level (Fig. 3C, lane 2). No Smad2 was detected in the IgG control lane (Fig. 3C, lane 1). Taken together, the data in Figs. 2 and 3 suggest that SCP1 is a specific phosphatase for the Smad2/3 linker and N-terminal regions. SCP1 Dephosphorylates Smad2/3 Linkers in Vitro-To rule out the possibility that SCP1 activates another phosphatase in the cell, which in turn dephosphorylates the Smad2/3 linker, we carried out several in vitro cell-free dephosphorylation assays. In the first assay, HEK293T cells were transfected separately with FLAG-SCP1/dnSCP1, FLAG-SnoN (which should not harbor phosphatase activity), or HA-Smad2/3. After 48 h, cells were lysed, and anti-FLAG and anti-HA immunoprecipitations were carried out for SCP1/dnSCP1 or SnoN and Smad2/3, respectively. The ability of SCP1/dnSCP1 and SnoN to dephosphorylate immunopurified Smad2/3 was analyzed. Equal levels of total HA-Smad2/3 were used in each assay reaction, as deter-FIGURE 2. SCP1 requires its catalytic activity to specifically dephosphorylate the linker/N terminus, and not the SXS motif, of Smad2/3. A, SCP1 specifically dephosphorylates Smad2/3 in the linker/N terminus. HEK293T cells were transfected with FLAG-Smad2/3 and the phosphatase FLAG-SCP1 or FLAG-PPM1A. Where indicated, 48 h after transfection cells were treated with TGF-␤ for 1 h prior to harvest, immunoprecipitation (IP), and Western blot analysis as in Fig. 1C. FLAG-phosphatase levels were determined by anti-FLAG immunoblotting of whole cell lysates (WCL). B, Smad2/3 dephosphorylation requires SCP1 catalytic activity. HEK293T cells were co-transfected with FLAG-Smad2/3 and FLAG-SCP1 or catalytically inactive FLAG-dnSCP1. Cells were harvested and subject to immunoprecipitation and Western blot analysis as in Fig. 1C. C, SCP1 dephosphorylates the Smad2/3 linker in a dose-dependent manner. HEK293T cells were transfected with FLAG-Smad2/3 and decreasing concentrations of FLAG-SCP1. Cells were harvested and subject to immunoprecipitation as in Fig. 1C. FLAG-SCP1 levels were determined by anti-FLAG immunoblotting of WCL. D, Smad2 dephosphorylation occurs in the presence of MG132. HEK293T cells were transfected with HA-Smad2 and FLAG-SCP1. Where indicated cells were treated with MG132 for 4 h prior to harvest and analysis as in Fig. 1C. DECEMBER 15, 2006 • VOLUME 281 • NUMBER 50 JOURNAL OF BIOLOGICAL CHEMISTRY 38369 mined by anti-HA Western blotting, and incubation with SCP1 led to a decrease in linker/N-terminal Smad2/3 phosphorylation as compared with SnoN control and the catalytically inactive dnSCP1 (Fig. 4A).

SCPs Dephosphorylate Smad2/3 in the Linker
In a second in vitro assay, immunoprecipitated FLAG-Smad2 or FLAG-Smad3 were incubated with recombinant GST-SCP1 or GST-dnSCP1, and products were analyzed by Western blotting. As shown in Fig. 4B, wild type SCP1, but not dnSCP1, could dephosphorylate Smad2 at Ser-245/250/255 and Thr-8 (compare lanes 1 and 2), and Smad3 at Ser-204/208/213 and Thr-8 (compare lanes 3 and 4). Consistent with our in vivo data and our early screening, GST-SCP1 was unable to dephosphorylate Thr(P)-220/Thr(P)-179 in Smad2/3. Finally, to eliminate that an additional cellular factor was co-precipitated with Smad2/3 from HEK293T cells, which could lead to their dephosphorylation either independently of or in conjunction with SCP1, we purified GST-Smad3 expressed in Escherichia coli. GST-Smad3 was subsequently phosphorylated in vitro utilizing activated ERK2 kinase, which has been shown previously to phosphorylate Smad3 in the linker (12). Recombinant in vitro phosphorylated Smad3 was subsequently incubated with recombinant GST, SCP1, or dnSCP1 in an in vitro phosphatase assay. The ERK2 kinase assay was successful and specific, as shown by the detection of Smad3 with phospho-specific antibodies in samples that had been incubated in the presence of ERK2, but not in the absence of ERK2 (Fig. 4C). Subsequently, recombinant SCP1, but not GST or dnSCP1, led to a significant decrease in recombinant Smad3 phosphorylation at Ser(P)-204/Ser(P)-208/Ser(P)-213 and Thr-8 but not Thr-179 as expected (Fig. 4D). Taken together these data suggest that SCP1 is a true and direct phosphatase of the Smad2/3 linker and N terminus.

Dephosphorylation of Smad2/3 Linkers by SCP2 and SCP3-
We next examined other members of the FCP/SCP family and found that SCP2 and SCP3 could also dephosphorylate Smad2/3 at all non-SXS sites apart from Thr-220/Thr-179 (Table 1; Fig. 5A). A putative additional member of the FCP/ SCP family, CTD small phosphatase like 2 (CTDSPL2), has also been identified recently (36,37). We also tested this protein, which we refer to as SCP4 in this study, in parallel with SCP2 and SCP3 and found that SCP4 expression did not result in dephosphorylation of the linker/N terminus (Fig. 5A, lane 7). FLAG-SCP phosphatase expression was confirmed by immunoblotting with anti-FLAG. The founding member of the FCP/ SCP family, FCP1, was also unable to dephosphorylate Smad2/3 (Fig. 5B, lane 3) when assayed in parallel with FLAG-SCP1 as a positive control.
To further establish the function of SCPs as Smad2/3-specific phosphatases, we examined the effect of SCP overexpression and SCP depletion on endogenous Smad2/3 linker/N-terminal phosphorylation. First, FLAG-SCP1 and FLAG-SCP3 significantly reduced endogenous Smad2 phosphorylation at Ser-245/250/255 and Thr-8 (Fig. 5C, lanes 2 and 4). As expected and consistent with our data presented in Fig. 5, A and B, FLAG-dnSCP1 and FLAG-SCP4 expression had no effect on endogenous Smad2 linker phosphorylation, as is the case for the negative control FLAG-SnoN (Fig. 5C, lanes 1, 3, and 5). Total immunoprecipitated endogenous Smad2 was determined by immunoblotting with an anti-Smad2 antibody, and FLAG-SnoN and FLAG-SCP expression levels were determined by anti-FLAG immunoblotting. Second, as SCP1, SCP2, and SCP3 could all dephosphorylate the Smad2/3 linker, to perform lossof-function analysis we nucleofected HaCaT cells with siRNA oligonucleotides targeting all three SCPs. Combinatorial knockdown of SCP RNA resulted in the reduction of SCP2 and SCP3 RNA levels by ϳ70 and 100%, respectively, although the SCP1 RNA level was only marginally reduced (Fig. 5D, lower  panel). Despite only partial depletion of SCP1/2/3, endogenous Smad2 (Ser-245/250/255) and Smad3 (Ser-208) linker phosphorylation was increased in siSCP1/2/3 nucleofected cells as compared with control cells (Fig. 5D, upper panel). Thus, SCP1, SCP2, and SCP3 appear to be genuine Smad2/3 phosphatases that are specific for the linker region and N terminus.
SCP1 Enhances TGF-␤ Transcriptional Responses-We next sought to determine the effect of SCP1-mediated dephosphorylation of the Smad2/3 linker on TGF-␤ transcriptional responses. We first used SBE-luc, a TGF-␤-responsive synthetic reporter gene dependent on Smad activation. SCP1 was able to increase SBE-luc activity in HaCaT cells (Fig. 6A). Smad2/3 mediates the transcriptional regulation of p15 (38) and p21 (39,40); thus we next compared the effects of SCP1 and dnSCP1 on these natural TGF-␤-responsive promoters. TGF-␤ induced an ϳ35-fold increase in p15-luc activity in HaCaT cells, and expression of SCP1 further increased TGF-␤-induced activity by ϳ2-fold, whereas dnSCP1 was unable to enhance TGF-␤-induced activity, suggesting that the ability of SCP1 to increase the TGF-␤ response is because of its phosphatase activity (Fig. 6B). SCP1 was also able to induce ϳ1.5-fold more p21-luc activity than TGF-␤ alone (Fig. 6C). Taken together, these data suggest that SCP-induced dephosphorylation of Smad2/3 can function to potentiate the Smad-mediated TGF-␤ response. This is in agreement with previous studies demonstrating that mutation of Smad2/3 linker phospho-sites increases the TGF-␤ response in Smad-specific transcriptional assays (10,12).
SCP1 Dephosphorylates the Smad Linker Independently of TGF-␤ and EGF-Smad2/3 linker phosphorylation can be stimulated by EGF via the activation of ERK, which may attenuate the Smad-mediated TGF-␤ response (11). Our data in Fig.  6 further suggest that the absence of linker phosphorylation may enhance the Smad-mediated TGF-␤ response. With this in mind, we sought to determine whether EGF or TGF-␤ growth factors might regulate the phosphatase activity of SCPs toward the Smad2/3 linker, and consequently impact TGF-␤-induced up-regulation of endogenous gene expression.
To determine whether the R-Smad-SCP1 interaction is TGF-␤or EGF-regulated, HEK293T cells were co-transfected with FLAG-SCP1 and Myc-Smad3, treated with TGF-␤ or EGF, and subjected to co-immunoprecipitation. We found that FLAG-SCP1 co-immunoprecipitated Myc-Smad3 regardless of growth factor treatment (Fig. 7A, lanes 3-5), as did the catalytically inactive mutant dnSCP1 (lanes 7-9). The EGF and TGF-␤ ligands were functionally active in this experiment, as judged by the presence of endogenous pERK (Fig. 7A, lanes 5 and 9) and pSmad2-SXS (Fig. 7A, lanes 4 and 8), respectively. We then immunoprecipitated Myc-Smad3 from the same cell lysates and assessed its linker dephosphorylation in the absence or presence of TGF-␤ and EGF. SCP1 was clearly able to dephosphorylate Smad3 at the representative linker/N-terminal sites Ser-208 and Thr-8, regardless of growth factor treatment (Fig. 7B, lanes 3-5). The dnSCP1 mutant was unable to dephosphorylate Smad3 at Ser-208 and Thr-8 under all conditions, as expected (Fig. 7B, lanes 7-9).
To confirm that TGF-␤ and/or EGF do not regulate the phosphatase activity of SCP1, we carried out an in vitro dephosphorylation assay using immunoprecipitated FLAG-SCP1/dnSCP1 and HA-Smad2/3 from separately transfected HEK293T cells. Cells transfected with FLAG-SCP1 were cultured in the absence or presence of TGF-␤ and/or EGF prior to anti-FLAG immunoprecipitations. An in vitro phosphatase assay was then performed as described for Fig. 4A. Equal levels of total HA-Smad2/3 were used in each assay reaction. SCP1 was able to dephosphorylate the Smad2/3 linker in vitro, and this was not affected by TGF-␤ or EGF treatment (Fig. 7C). Taken together the data in Fig. 7, A-C, suggest that TGF-␤ and EGF do not regulate R-Smad-SCP1 interactions and the ability of SCP to dephosphorylate Smad2/3 in the linker/N-terminal region. SCP1 expression level was also unchanged in the absence or presence of TGF-␤ and/or EGF treatment.
Finally, it has been reported that EGF can inhibit TGF-␤induced up-regulation of p15 mRNA and the TGF-␤ anti-proliferative effect (41). The authors found that this was not because of the abrogation of SXS phosphorylation of R-Smads. As EGF is known to stimulate R-Smad linker phosphorylation, we sought to determine whether SCP1 could counteract the inhibitory effect of EGF on TGF-␤-mediated p15 induction. In control cells (GFP-transfected) EGF treatment, both alone or in conjunction with TGF-␤, resulted in a higher degree of Smad2 phosphorylation at Ser-245/250/255 and Thr-8 and Smad3 phosphorylation at Ser-208 and Thr-8, as predicted (Fig. 7D,  compare lanes 1 and 2 with 3 and 4). In addition, EGF cotreatment greatly reduced TGF-␤-induced p15 up-regulation in these cells (Fig. 7D, lane 4). Notably, expression of SCP1 induced higher levels of endogenous p15 than control cells when treated with EGF alone (compare lanes 3 and 7), and p15 levels were also partially restored in TGF-␤/EGF co-treated FIGURE 4. SCP1 can dephosphorylate the Smad2/3 linker/N terminus in vitro. A, HEK293T cells were transfected with FLAG-SnoN, FLAG-SCP1, or FLAG-dnSCP1 and in a separate group with HA-Smad2/3. Proteins were isolated by anti-FLAG or anti-HA immunoprecipitation, respectively. FLAG-proteins were eluted from protein A-Sepharose using FLAG-peptide. Immunopurified HA-Smad2/3 was incubated with eluted SCP1, dnSCP1, or SnoN protein. Products were analyzed by Western blotting using phospho-Smad2/3-specific antibodies. B, HEK293T cells were transfected with FLAG-Smad2/3. After 48 h, FLAG-Smad2/3 was isolated from cells by anti-FLAG immunoprecipitation, and eluted from protein A-Sepharose using FLAG peptide. Eluted FLAG-Smad2/3 was used as the substrate for GST-SCP1 (wt) or GST-dnSCP1 (dn) in an in vitro phosphatase assay. Assay products were analyzed by Western blotting with phospho-Smad2/3-specific antibodies. C, ERK2 phosphorylates GST-Smad3 in vitro. GST-Smad3 was incubated in the presence or absence of recombinant activated ERK2. GST-Smad3 was analyzed for phosphorylation by Western blotting using phospho-Smad2/3-specific antibodies. D, GST-Smad3, in vitro phosphorylated by ERK2 (C), was incubated with recombinant GST, recombinant SCP1, or recombinant dnSCP1 in a phosphatase buffer. Samples were analyzed for GST-Smad3 phosphorylation using phospho-Smad2/3-specific antibodies.
The increase in endogenous p15 in SCP1 overexpressing cells correlated with a decrease in Smad linker/N-terminal phosphorylation (Fig. 7D). Thus, these data suggest that the EGF-mediated inhibition of TGF-␤-induced p15 up-regula-tion may be due, at least in part, to an EGF-mediated increase in Smad2/3 linker phosphorylation.

DISCUSSION
In addition to TGF-␤-induced SXS motif phosphorylation, which is essential for Smad activation, Smad signaling is fine-tuned for diverse biological responses by phosphorylation in the linker region and N terminus. As Smad linker/Nterminal phosphorylation results from interaction with a variety of intracellular protein kinases, the Smad linker/N terminus also serves as a critical platform for modulating cross-talk between TGF-␤ and other signaling pathways. As a consequence, dephosphorylation of the Smad2/3 linker might also serve as an important mechanism for modulating signal cross-talk within cells.
It is surprising, given that Thr-220 (Smad2) and Thr-179 (Smad3) can be phosphorylated by the same kinases as other linker/N-terminal Thr/Ser sites, that Smad2/3 Thr(P)-220/ Thr(P)-179 was not dephosphorylated by SCP1 (or SCP2/3) in vivo or in vitro. Phosphorylation at Thr-220 in Smad2 and Thr-179 in Smad3 could have a unique role, as compared with Ser-245/250/255 (Smad2) and Ser-204/208/213 (Smad3), which excludes it from dephosphorylation by SCPs. In fact, of the 40 phosphatases screened, none had phosphatase activity toward Smad2/3 Thr(P)-220/Thr(P)-179. Thus, it is likely that Smad2/3 can be dephosphorylated at this site by a phosphatase outside the FIGURE 5. SCP1/SCP2/SCP3, but not SCP4 and FCP1, dephosphorylate Smad2/3 in the linker region and at the N terminus. A, SCP1/2/3 dephosphorylate Smad2/3. HEK293T cells were co-transfected with HA-Smad2/3 and the indicated FLAG-SCP. Cells were harvested and subjected to immunoprecipitation (IP) and Western blot analysis as in Fig. 1C. B, FCP1a does not dephosphorylate Smad2/3 in the linker. HEK293T cells were co-transfected with FLAG-Smad2/3 and His-FCP1a, or FLAG-SCP1 as a positive control. Cells were harvested and subjected to immunoprecipitation and Western blot analysis as in Fig. 1C. SCP1 and FCP1a expression was confirmed by immunoblotting of WCL with anti-FLAG and anti-His antibodies, respectively. C, SCPs can dephosphorylate endogenous Smad2. HEK293T cells were transfected with the indicated FLAG-tagged SCP or FLAG-SnoN as a negative control. After 48 h, cells were lysed and subjected to immunoprecipitation (IP) with anti-Smad2 antibody. Products were analyzed by Western blotting with anti-Smad2 and phospho-Smad2-specific antibodies. SCP expression was confirmed by immunoblotting of WCL samples with anti-FLAG. D, SCP reduction enhances linker phosphorylation of endogenous Smad2/3. HaCaT cells were nucleofected with control siRNA or combined siSCP1, siSCP2, and siSCP3 oligonucleotides. Cells were harvested 50 h after nucleofection for Western blot analysis using phospho-Smad2/3-specific antibodies, and antibodies against total Smad2/3 and actin (upper panels). Alternatively, cells were harvested for RNA extraction and RT-PCR analysis of SCPs and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) (lower panels).
scope of our screen. While this manuscript was under review, Knockaert et al. (30) reported that SCP1/2/3 dephosphorylate BMP-induced C-terminal SXS motif phosphorylation of Smad1. It is interesting to note that we did not find SCP1 to be an efficient phosphatase at the SXS motif of Smad2/3 (31) and Smad1 (42), but we found it was specific for Smad2/3 linker dephosphorylation with the exception of Thr(P)-220/Thr(P)-179 ( Fig. 2A). Additionally, we did not find the Smad2/3 C-terminal SXS phosphatase PPM1A to be a Smad2/3 linker phosphatase. We also did not find the SCP1-Smad3 interaction, or the Smad2/3-linker phosphatase activity of SCP1, to be regulated by TGF-␤ (which stimulates SXS motif phosphorylation) or EGF (which can stimulate linker phosphorylation) (Fig. 7, A-C).
In our study we found that SCP1, SCP2, and SCP3 were able to dephosphorylate Smad2/3 in the linker/N terminus, suggesting that all three SCPs can function in a similar manner. We found that unlike SCP1/2/3, the FCP/SCP founding family member FCP1 was unable to dephosphorylate the Smad2/3 linker/N terminus. The SCP1/2/3 proteins are homologous to the FCP1 catalytic domain, but they lack the BRCT binding domain of FCP1 (25).
Although the difference in BRCT domain status does not prevent both SCPs and FCP from dephosphorylating the CTD of RNAPII, it is possible that in the context of Smad2/3 dephosphorylation the FCP1 BRCT domain acts in an inhibitory manner. SCP4/CTD-SPL2, a putative member of the FCP/SCP family, was also unable to dephosphorylate Smad2/3. Our data suggest that SCP1-meditated dephosphorylation of Smad2/3 can function to potentiate the Smadmeditated TGF-␤ response. SCP1 expression increased transcription from a TGF-␤-responsive synthetic reporter gene dependent on Smad activation and also from p15 and p21 gene-specific promoters. The dnSCP1 mutant was unable to enhance TGF-␤-induced activity, suggesting the SCP1-mediated increase in TGF-␤ response is because of its phosphatase activity and, based on our data in this paper, likely a result of Smad2/3 dephosphorylation. This is in agreement with previous studies showing that mutation of CDK2/4 and ERK2 phosphorylation sites in the Smad2/3 linker increases the TGF-␤ response in Smad-specific transcriptional assays (10 -12). However, as others find Smad2/3linker phosphorylation enhances TGF-␤-induced transcription (14 -16), it is possible that the role of Smad2/3 linker phosphorylation is cell type/context-dependent, dependent on the phosphorylating kinase, and/or  HEK293T cells were co-transfected with Myc-Smad3 and FLAG-SCP1 or FLAG-dnSCP1. Transfected cells were untreated or treated with TGF-␤ or EGF for 1.5 h prior to harvest. SCP-bound Smad3 was detected by anti-FLAG immunoprecipitation (IP) and anti-Myc Western blotting. Endogenous Smad2 and pSmad2-SXS, and ERK and pERK, were assessed by immunoblotting of WCL. B, TGF-␤ and EGF do not affect SCP1mediated dephosphorylation of the Smad2/3 linker in vivo. Using cell lysates from A, Myc-Smad3 was immunoprecipitated with anti-Myc antibody. Smad3 phosphorylation was detected by immunoblotting with phospho-Smad3 specific antibodies, and total Smad3 levels were analyzed using anti-Myc antibody. F-SCP1/dnSCP1 expression was confirmed by anti-FLAG immunoblotting of WCL. C, TGF-␤ and EGF do not influence SCP1-mediated dephosphorylation of the Smad2/3 linker in an in vitro assay. HEK293T cells were transfected with FLAG-SCP1, or FLAG-dnSCP1, and in a separate group with HA-Smad2/3. FLAG-SCP1-transfected cells were cultured in the absence or presence of TGF-␤ and/or EGF for 1.5 h prior to harvest, as indicated. FLAG-SCP1 proteins were immunoprecipitated and then eluted from protein A-Sepharose using FLAG-peptide. Separately immunopurified HA-Smad2/3 proteins were incubated with eluted SCP1 or dnSCP1. Products were analyzed by Western blotting using phospho-Smad2/3-specific antibodies. wt, wild type; dn, mutant. D, SCP1 counteracts the inhibitory effect of EGF on TGF-␤-induced p15 expression. HaCaT cells were nucleofected with GFP or FLAG-SCP1 DNA. Cells were untreated or treated with TGF-␤ and/or EGF for 20 h prior to lysis as indicated. WCL were analyzed using antibodies against TGF-␤-induced proteins (anti-p15) and total or phospho-Smad2/3 and ERK. FLAG-SCP1 expression was confirmed by immunoblotting with anti-FLAG. even dependent on the combination of linker Thr/Ser sites phosphorylated. In general, in this study SCP1-mediated Smad2/3 dephosphorylation in vivo cannot be linked to a specific kinase as the phosphorylation is stimulated under physiologically relevant conditions by growth medium, which could contain factors to activate all relevant kinases. However, SCP1 can be linked to in vitro dephosphorylation of ERK2 kinasephosphorylated sites as recombinant ERK2 was used to phosphorylate Smad3 in this experiment. We also noticed that SCP1 could dephosphorylate EGF-induced endogenous Smad2/3 linker phosphorylation in HaCaT cells. This correlated with a higher level of TGF-␤-induced p15 in the presence of EGF (Fig.  7D), suggesting that SCP1, and thus Smad2/3 linker dephosphorylation, results in a more robust TGF-␤ response.
FCP/SCPs are well known for their ability to dephosphorylate the CTD of RNAPII at serines 2 and 5 (25)(26)(27), and until recently this appeared to be the only true target of FCP/SCPs identified. SCPs are transcriptional regulators that negatively regulate RNAPII (25)(26)(27), neuronal gene expression (28), androgen-dependent transcription (29), and BMP signaling (30). It has also been shown that SCP1 inhibits transcription from a variety of promoter-reporter gene constructs, while expression of the dnSCP1 mutant lacking phosphatase enhances transcription (25). Our finding that SCP1, but not the catalytically inactive dnSCP1, led to an enhanced TGF-␤ transcriptional response may be the first example of SCP positively regulating transcription. Thus, there appears to be specificity for SCP1 on TGF-␤ signaling, as we see the opposite effect to what is expected from the negative regulation of global gene transcription through dephosphorylating RNAPII.
SCP2 was initially identified (and called OS4) in a chromosomal region frequently amplified in sarcomas and brain tumors (43) and was subsequently linked to TGF-␤ superfamily signaling in Xenopus (44). Xenopus SCP2/OS4 (XSCP2) was shown to dorsalize the ventral mesoderm, resulting in a phenotype similar to that initiated by activation of Activin (TGF-␤ family) signaling or the inhibition of BMP signaling. Recently, published data suggest that secondary axis formation by XSCP2 is because of inhibition of BMP signaling via its ability to dephosphorylate the Smad1 SXS motif (30). Our identification of SCP2 as a Smad2/3 linker phosphatase that can enhance Smad2/3 signaling is also consistent with the role of XSCP2 in promoting dorsalization of the ventral mesoderm. Thus, it seems possible that on some level SCPs could have a dual function in promoting secondary axis development in Xenopus embryos, simultaneously promoting Activin signaling (via dephosphorylation of the Smad2/3 linker) and inhibiting BMP signaling (via dephosphorylation of the Smad1-SXS motif).
In conclusion, SCP1/2/3 are true phosphatases for the linker/ N-terminal region of Smad2/3 and are likely to be involved in modulating cross-talk between signaling pathways that converge on R-Smads. The combination of linker (de)phosphorylation events is likely to contribute to the final gene responses to Smad signaling, and thus may ultimately be important in cancer and other diseases. To this end a significant increase in Ser-208/ S213 phosphorylation of Smad3 is associated with late stage colorectal tumors (17), suggesting that understanding how (de)phosphorylation of the Smad2/3 linker occurs in normal cells could be clinically relevant. Our identification of Smad2/3 as a critical new substrate for SCPs opens the door to the possibility that these phosphatases may have many, as yet unidentified, key targets in cell signaling.