Data splitting as a countermeasure against hypothesis fishing: with a case study of predictors for low back pain

There is growing concern in the scientific community that many published scientific findings may represent spurious patterns that are not reproducible in independent data sets. A reason for this is that significance levels or confidence intervals are often applied to secondary variables or sub-samples within the trial, in addition to the primary hypotheses (multiple hypotheses). This problem is likely to be extensive for population-based surveys, in which epidemiological hypotheses are derived after seeing the data set (hypothesis fishing). We recommend a data-splitting procedure to counteract this methodological problem, in which one part of the data set is used for identifying hypotheses, and the other is used for hypothesis testing. The procedure is similar to two-stage analysis of microarray data. We illustrate the process using a real data set related to predictors of low back pain at 14-year follow-up in a population initially free of low back pain. “Widespreadness” of pain (pain reported in several other places than the low back) was a statistically significant predictor, while smoking was not, despite its strong association with low back pain in the first half of the data set. We argue that the application of data splitting, in which an independent party handles the data set, will achieve for epidemiological surveys what pre-registration has done for clinical studies.
Introduction
The concept of statistical significance may be the single-most important mathematical invention for applied science. Its use has become so widespread and commonplace that many non-mathematical readers may not be actively aware of its true meaning. To briefly review, the statement “X is correlated with Y at significance level alpha” signifies, “If no true correlation between X and Y exists, the probability of obtaining the observed correlation is less than alpha.” The P-value of a test is therefore a measure of surprise; the smaller the P-value, the greater the surprise. Standard practice has been to set alpha at 0.05, which literally allows for a 5% chance of erroneously reporting a significant finding (Type I error). One cannot interpret the P-value as a probability of having made a Type I error, so 5% significance does not imply that the conclusion is correct with a 95% probability. Such statements are meaningful only in a Bayesian context where one assigns a priori probabilities to hypotheses. The present article addresses non-Bayesian (frequentist) analysis, which is by far the most common in epidemiology.
Ioannidis [1] purports that most scientific findings are likely to be false, despite being reported as statistically significant. One of his arguments, which we support, is that the pressure to publish creates an incentive for researchers to simultaneously address a large number of hypotheses and selectively report only “significant” results. This conduct is labelled hypothesis fishing or data dredging. (In older papers the term data mining has also been used, but the meaning of this has shifted toward discovery of valid patterns in databases.) Also, the investigator may run different statistical tests (e.g. parametric and non-parametric tests) for any given hypothesis, which is a less recognized form of hypothesis fishing.
In an epidemiological and health services setting, complex data sets based on statistical surveys are commonplace, in which hundreds of variables are collected from thousands of people. Data collection for a survey requires enormous effort, with both individual and collective demands on the researchers and respondents. From a purely economic point of view, it appears logical to “turn the data set upside down,” searching for anything of interest buried in it. Therein lies the temptation to launch a large-scale fishing expedition for all potentially interesting hypotheses. Similar to real-life commercial ocean fishing, which requires adjusting the mesh size of the nets upward, researchers investigating multiple hypotheses need to adjust the level of statistical significance down in order to preserve the meaning of statistical significance. The general rule of thumb is to divide alpha by the number of hypotheses, referred to as a Bonferroni correction [2]. However, in accordance with Ioannidis [1], we contend this method is rarely applied. The limited use of Bonferroni correction may result from uncertainty surrounding the exact number of hypotheses “fished for,” but some researchers may also be motivated to keep the “catch.” In epidemiological research, upwards of 100 possible hypotheses are common, and setting the alpha at 0.0005 eliminates much of the fun in a fishing expedition. As a consequence, researchers may be tempted to discuss only a handful of “significant” results, failing to mention the 95 that proved non-significant.
Hypothesis fishing renders P-values almost completely meaningless, and we consider it to be a serious problem for epidemiological survey analysis. The present paper recommends a very simple countermeasure against hypothesis fishing, based on data splitting. The procedure is similar to two-stage analysis of microarray data. We argue that if our method could be applied for survey data in general, it could do for epidemiological studies what pre-experiment registration of RCTs has done for medical experiments.
The rest of the article is laid out as follows: First we review some mathematical methods that might be considered useful for counteracting hypothesis fishing. Then we explain our method in detail, and compare it to two-stage analysis of microarray data, followed by a case study where the method is applied to an analysis of low back pain. The article ends with discussion and conclusion.
Mathematical remedies (that fail to solve the problem)
If one considers hypotheses fishing to be a mathematical problem, it is reasonable to look for mathematical solutions. There is a large mathematical literature that relates to model building and multiple hypotheses, and we will only try to point out the main themes.
The simplest way of handling the problem of multiple hypotheses is to reduce the significance level through Bonferroni correction. It is also possible to use so-called false discovery rate (FDR) [3], which is a way of controlling the expected percentage of rejected null-hypotheses (discoveries) that are falsely rejected. If all the null-hypotheses are true, FDR is equivalent to Bonferroni correction, but otherwise it is less strict. As we mentioned in the introduction, it can be hard to keep track of the number of hypotheses one is really addressing, which makes the use of Bonferroni or FDR cumbersome.
Complex mathematical methods exist that adjust for the effect of model selection on inference (for example, see Madigan and Raftery [4]). In order to utilize these methods, however, one must describe the model-building strategy in a formal mathematical way. Faraway [5] has analysed various data splitting approaches similar to the one we apply, and compares them to mathematical inference adjustment methods. Based on his simulation study, he concluded that data splitting costs more in reduced accuracy than it gains in “honesty”. This conclusion is to be expected, because adjustment methods utilize information that the splitting methods disregard. In our present setting, however, accurate information on modelling procedure is not available, so adjustment methods are not an option.
The task of choosing a set of hypotheses is—at least superficially—related to the problem of choosing the set of predictors to include in a statistical model for the data. An overview of this field is given by Hastie et al.  [6], and we only mention a few key concepts. An obvious goal in statistical modelling of a given set of data is to develop a model that fits the data well, and a model will always fit better when the set of predictors is increased. However, if one includes too many predictors, the model is prone to imitating random properties of the data set, which are not present in the underlying sampling distribution of the data. Therefore, one needs ways to make trade-offs between model fit and model size. This can be done directly through various information criteria, such as Akaike’s Information Criterion (AIC), the Bayesian Information Criterion (BIC), or the Divergence Information Criterion (DIC). A different approach called cross validation is to split the data set in two parts, using one part for estimating model parameters, and the other for evaluating model fit. A problem with this method is that only half of the data is used for each task. A clever remedy is to leave one data point out at the time, estimate the model from the remaining data points, and measure model fit by taking the average fit on the points left out (leave-one-out cross validation).
We mention these methods mainly to point out that they are not very relevant for controlling hypotheses fishing. The use of information criteria or cross validation helps in trading model fit for model size, but does not produce adjustments for the total number of variables. The cross validation procedure of splitting the data set, using one part to estimate model parameters and the other for validation is deceptively similar to the method we apply. But the purpose of validating a model’s predictions is entirely different from our purpose of conducting sound hypothesis testing.
In addition to the inherent difficulties related to mathematical ways of dealing with hypothesis fishing, we do not see the problem as mainly a mathematical one. Some researchers may not be aware of the fact that P-values lose their meaning when the hypotheses are chosen from a large pool of undocumented ones. Others may be vaguely aware of the problem, but choose not to address it unless reviewers demand it. Reviewers, on the other hand, may not feel inclined to insist on purity beyond what they have done in their own scientific work.
The solution: data splitting
We recommend splitting the data set randomly into two sections (Parts 1 and 2). This allows the investigators to identify hypotheses in Part 1 of the data set, while remaining blind to Part 2 until the hypotheses are specified. True hypothesis testing is then performed using only Part 2 of the data. At this point there is no second-guessing. If the alpha-level is set to 0.05, and the P-value in Part 2 is 0.051, the result is by definition not significant, even if it received a P-value of 0.001 in Part 1. In such cases, there will be a temptation to “make a compromise” by computing the average of the P-values from Parts 1 and 2, but this is not allowed. Because the data in Part 1 was used to construct the hypotheses, it is tainted, and cannot take any part in the hypotheses testing.
This procedure is strict with respect to identifying statistical significance. Once a hypothesis is supported, however, the entire data set is used for estimating the effect size. Thus, the purpose is to ensure the proper use of the term statistical significance. Once a significant finding is established, though, it is preferable to obtain the most accurate parameter estimates possible.
Models: hypothesis variables, and confounders
Epidemiological hypotheses are usually formulated within the framework of a model. Assume the hypothesis is that eating mushrooms increases the risk of cancer. To test this hypothesis, one would build a model with predictors like age, gender, smoking status, as well as mushroom habits, in order to control for these confounding factors. (If old people eat more mushrooms, excluding age from the model would give an incorrect positive association between mushroom eating and cancer.)
From a purely computational point of view, no differences exist between predictors associated with hypotheses and confounders, yet the semantics are very different. The confounders are included only as a means of estimating the causal link between the cause (mushrooms) and its hypothesized effect (cancer).
Researchers often use P-values as a road map (in addition to literature reviews and general medical knowledge), when deciding which variables should be included as confounders. A common piece of advice is to include confounders demonstrating an association at a P-value below 0.1 or 0.2. Despite this rationale for including a confounding factor, no similar demands are made for inclusion of the confounder in Part 2. The chosen set of confounders provides the framework within which the hypothesis is defined, and it is not the framework that is being tested. In the mushroom example, the hypothesis is that mushroom eating is associated with cancer when controlling for age, gender, and smoking, not that controlling for each of these factors is necessary.
Size of Part 1 and Part 2
When deciding upon the relative size of Parts 1 and 2, a trade-off exists between the need to identify hypotheses by exploration in Part 1, and the need to achieve statistical significance in Part 2. An even split may be reasonable in cases where the need for exploration is high, particularly if the data set is large, so that half of the data set is sufficient to achieve statistical significance for stronger effects. In cases where greater domain knowledge is available based on the existing literature, a smaller Part 1 is reasonable, especially when the sample size is small.
Multiple hypotheses
It is possible to investigate multiple hypotheses within our splitting regime, using Bonferroni corrections. Assume, in the mushroom-cancer example, that the analysis of Part 1 also provided strong support for the hypothesis that eating bananas protects against cancer. One might then choose to include both mushroom habits and banana habits as hypothesis variables, and consequently divide alpha by 2 (the number of hypotheses). If either mushroom or banana habit fails the significance test in Part 2, it will still be in the model, as a confounder.
It might be the case that both banana and mushroom habits get P-values that fall between alpha/2 and alpha. In this situation, both hypotheses would have passed the significance test individually, but the choice to include two hypotheses resulted in failure of both variables to reach significance. Many non-statisticians would argue that scientific procedures and statistical analysis should be objective, with conclusions based on ‘hard facts’, independent of arbitrary choices of hypotheses. Unfortunately, this is not the case if we wish to claim statistical significance.
The mushroom-banana example is a clear case in which investigators should reduce the alpha-level to account for multiple hypotheses. At the other extreme, if independent research groups investigating different research questions based on independent data sets, their combined effort is obviously not a case of ‘multiple hypothesis testing’. A grey area exists with partially overlapping data sets, hypotheses, and research groups, often making it difficult to decide whether Bonferroni corrections are called for. A pragmatic solution may be to view a published article as a unit, and apply Bonferroni corrections within each one.
Relation to two-stage analysis in genetics
Readers who are familiar with microarray analysis will recognize that our data splitting method is similar to two-stage analysis, as it is routinely performed in genetics [7].
There are a few differences, however. In a microarray context the set of possible hypotheses is given by the number of genes, and the FDR method is normally used to limit the number of incorrect findings. Rather than primarily counteracting hypothesis fishing, microarray two-stage analysis is usually motivated by cost effectiveness: By screening out promising candidates first, and then evaluating them, researchers can make a higher number of valuable discoveries for each monetary unit spent. In a microarray setting the procedure is also likely to be more automatic, as interesting genes are filtered out in two more or less mechanical steps of analysis. In our epidemiological application, on the other hand, there will be a man-in-the-loop, as the researcher builds a model with hypothesis variables and confounders based on a combination of his domain knowledge and Part 1 of the data.
Case study of low back pain in the Ullensaker study
Study sample and setting
Data material consisted of adults enrolled in an epidemiological survey for musculoskeletal pain (MSP) in the Ullensaker municipality, 40 km northeast of Oslo in Norway. In 1990, 4050 inhabitants born in 1918–1920, 1928–1930, 1938–1940, 1948–1950, 1958–1960 and 1968–1970 (age 20–70 years) were sent a postal questionnaire about MSP. Of these people, 67% responded. Individuals who reported low back pain (LBP) during the past year (1990) were excluded from this material (N = 1439), such that the original sample consisted of 1283 participants who were free of LBP in 1990. In 2004, a 14-year follow-up was conducted. A total of 763 participants (59%) responded and formed the present study sample. These 763 participants were randomly divided in two samples with n = 369 (Part 1) and n = 394 (Part 2), respectively.
Outcome measures
To identify respondents with LBP, we used the answer to the question, “During the past year, have you experienced pain or discomfort in your lower back?”. This item was based on the Standardised Nordic Pain Questionnaire [8], which is a self-report questionnaire frequently used in Scandinavian epidemiological studies.
Independent variables (potential risk factors)
In 1990, the survey questionnaire contained a number of socio-demographic and health-related factors, which could be included as risk factors in the present study. Socio-demographic variables were gender, age, marital status, and work status. Health-related variables were body mass index (BMI), smoking status, number of MSP sites other than the low back, duration of previous MSP, use of medication due to MSP, having been examined by a health care provider due to MSP during the last year, comorbidity, family history of musculoskeletal problems, emotional distress, leisure physical activity, participation in competitive sports, sleeping problems, and self-perceived health.
Model and hypotheses
A logistic regression model was developed based on Part 1 of the data set and medical expertise. The number of pain sites and smoking status were included as independent variables. Smoking status was dichotomised as smoking and non-smoking. Number of pain sites was operationalized using participant responses on the Nordic Pain Questionnaire [8]. Specifically, respondents reported whether they had experienced any pain or discomfort from the following 10 areas during the previous year: head, neck, shoulder, elbow, hand/wrist, upper back, low back, hip, knee and ankle/foot (responses were “yes/no”). The total number of pain sites was computed and categorized into the following four categories: no pain sites, 1 or 2 sites, 3 or 4 sites, and 5 or more pain sites.
We also included age, which was categorized into values corresponding to the six birth cohorts: 1918–1920, 1928–1930, 1938–1940, 1948–1950, 1958–1960, and 1968–1970. In addition, gender and marital status (dichotomised into married/partnership versus living alone) were included. Results of the logistic regression model are presented in Table 1.
We hypothesized that smoking would be positively associated with LBP. Therefore, a 1-tailed hypothesis test was conducted. It was also hypothesized that individual pain sites would be positively associated with LBP. To limit the number of hypotheses, though, we hypothesized that the total number of pain sites would affect LBP probability, rather than run analyses for each level of the variable.
Hypotheses testing
The significance level alpha was set to the usual value of 0.05. With two hypotheses, the critical P-value becomes 0.025. Results for Part 2 of the data set are illustrated in Table 2. The P-value of the pain sites variable (0.015) was below the critical value of 0.025, and therefore it is concluded that the number of pain sites was significantly associated with LBP at the 14-year follow-up.
Table 1Parameter estimates from Part 1, controlling for age, gender, and marital statusPredictorOR estimate 95% CI for ORP-valueNumber of pain sitesa0.0121 or 2 pain sites2.292(1.248–4.208)0.0073 or 4 pain sites2.690(1.406–5.147)0.0035 or more pain sites2.944(1.193–7.262)0.019Smoking2.079(1.285–3.363)0.003aThe reference category for number of pain sites was no pain sitesTable 2Parameter estimates from Part 2, controlling for age, gender, and marital statusPredictorOR estimate 95% CI for ORP-valueNumber of pain sitesa0.0151 or 2 pain sites1.328(0.793–2.224)0.2813 or 4 pain sites1.598(0.857–2.979)0.1415 or more pain sites3.941(1.700–9.136)0.001Smoking0.993(0.627–1.571)0.487*aThe reference category for number of pain sites was no pain sites*1-sided P-value
Smoking status received a 1-sided P-value of 0.487, which exceeds the limit of 0.025 by a large margin, and this variable is thus deemed non-significant. This result may seem surprising, given the variable’s strong association with the dependent variable in Part 1 of the data set (2-sided P-value = 0.003). This illustrates the dangers of hypothesis fishing: Our analysis suggests that the smoking status variable may only have been a “lucky winner” in the “P-value lottery” of Part 1.
Parameter estimates
Having concluded that number of pain sites has a significant effect on low back pain at follow up, we estimated the magnitude of the effect from the full data set (Table 3). Because the hypothesis test failed to give significance for smoking status, it is included only as a confounder together with age, gender, and marital status. The 2-sided P-value for smoking status was 0.040, so researchers following a hypothesis fishing procedure would probably have reported it as a statistically significant predictor.
Table 3Parameter estimates of the hypotheses variable number of pain sites, from the complete data set controlling for age, gender, marital status and smoking statusPredictorOR estimate 95% CI for ORP-valueNumber of pain sitesa0.0001 or 2 pain sites1.637(1.116–2.400)0.0123 or 4 pain sites1.983(1.285–3.061)0.0025 or more pain sites3.346(1.846–6.067)0.000aThe reference category for number of pain sites was no pain sites
Discussion
Our study was designed to illustrate a simple and straightforward data splitting method to counteract hypothesis fishing in large-scale epidemiological surveys. This method involves splitting the data set, where the first half is used to identify hypotheses, while the remaining data is used to test the hypotheses. The data splitting procedure was illustrated using data material collected for a population-based health survey administered in Norway in 1990 and 2004. Results demonstrated that the number of pain sites (“widespreadness” of pain) was significantly associated with LBP following a 14-year follow-up. Smoking status was a strong predictor of LBP in Part 1 of the data set (hypothesis identification), but did not achieve significance in Part 2 (hypothesis testing). Therefore, this finding was dismissed as non-significant in our study. For the full data set, the P-value for smoking status was 0.040, so the traditional way of analysing epidemiological data would have given a different conclusion.
In this study, the investigators had free access to the entire data set prior to data splitting and during model development. However, any temptation to “peak” at the material was successfully avoided, as indicated by the discrepant results for smoking status. This indicates that the data splitting procedure can indeed function properly in the absence of strict external control of the data. Nevertheless, we recommend that the data set be handled by an independent party, so that researchers can document claims that only Part 1 of the data set was used for model and hypothesis development.
Ideally, the establishment of an independent international body is recommended to manage splitting of survey data. A fixed date for releasing data for Part 2 would be agreed upon, so that only those hypotheses specified prior to the release date would undergo a true significance test. Although several challenges and practical issues would inevitably need resolution (i.e., data collection, confidentiality, release of data), such an organization should be feasible and acceptable to the scientific community.
Conclusions
Results demonstrated that the number of musculoskeletal pain sites significantly predicts low back pain at a 14-year follow-up, when controlling for age, gender, marital status, and smoking. The application of the data splitting method in our study indicates its potential as an effective and useful method to counteract hypothesis fishing in population surveys. In our opinion, systematic data splitting administered by an independent party would accomplish for statistical surveys what pre-registration has already done for clinical trials.

data splitting
hypothesis fishing
low back pain
two-stage analysis
data dredging


The Role of Presenilin and its Interacting Proteins in the Biogenesis of Alzheimer’s Beta Amyloid

The biogenesis and accumulation of the beta amyloid protein (Aβ) is a key event in the cascade of oxidative and inflammatory processes that characterises Alzheimer’s disease. The presenilins and its interacting proteins play a pivotal role in the generation of Aβ from the amyloid precursor protein (APP). In particular, three proteins (nicastrin, aph-1 and pen-2) interact with presenilins to form a large multi-subunit enzymatic complex (γ-secretase) that cleaves APP to generate Aβ. Reconstitution studies in yeast and insect cells have provided strong evidence that these four proteins are the major components of the γ-secretase enzyme. Current research is directed at elucidating the roles that each of these protein play in the function of this enzyme. In addition, a number of presenilin interacting proteins that are not components of γ-secretase play important roles in modulating Aβ production. This review will discuss the components of the γ-secretase complex and the role of presenilin interacting proteins on γ-secretase activity.
Introduction: the generation of beta amyloid by presenilin and its binding proteins
Alzheimer’s disease (AD) is a complex, progressive neurodegenerative disorder that is neuropathologically characterised by extensive neuronal loss and the presence of neurofibrillary tangles and senile plaques. While the majority of AD cases are sporadic, ∼5% of AD cases are familial (FAD) with mutations in three genes, amyloid precursor protein, presenilin 1 (PS1) and presenilin 2 (PS2) accounting for the majority of cases. A major feature of both sporadic and familial forms of AD, is the accumulation and deposition of a small peptide referred to as beta amyloid (Aβ) within brain tissue of AD sufferers. The mechanisms that underlie the disease processes are poorly understood. However, the accumulation of Aβ is thought to play a pivotal role in neuronal loss or dysfunction through a cascade of events that include the generation of free radicals, mitochondrial oxidative damage and inflammatory processes (reviewed in Refs. [1, 2]). The primary event that results in the abnormal accumulation of Aβ is thought to be the dysregulated proteolytic processing of its parent molecule, the amyloid precursor protein (APP).
The APP molecule is a transmembrane glycoprotein that is proteolytically processed by two competing pathways, the non-amyloidogenic and amyloidogenic (Aβ forming) pathways. How these pathways are regulated remains unclear. However, there are many factors including diet, hormonal status, and genetic mutations that influence the processing of APP to generate Aβ (reviewed in Refs. [1, 3]). Three major secretases are postulated to be involved in the proteolytic cleavage of APP. These include α-secretase (of which the metalloproteases ADAM17/TACE and ADAM 10 are likely candidates), beta APP cleaving enzyme (BACE, formally known as β-secretase) and the γ-secretase. The α-secretase cleaves within the Aβ domain of APP thus precluding the formation Aβ and generating non-amyloidogenic fragments and a secreted form of APP (α-APPs). In the amyloidogenic pathway (Fig. 1), BACE cleaves near the N-terminus of the Aβ domain on the APP molecule, liberating another soluble form of APP, β-APPs and a C-terminal fragment (C99) containing the whole Aβ domain. The final step in the amyloidogenic pathway is the intramembranous cleavage of the C99 fragment by γ-secretase, to liberate the Aβ peptide (reviewed in Ref. [1]). Identification of multiple cleavage sites within the APP molecule has suggested the presence of other secretases, namely ε- and ζ-secretase; that generate the APP intracellular domain (AICD) and a longer fragment of Aβ (Aβ46), respectively [4–7]. Although different enzymes may exists, the same γ-secretase complex that generates Aβ may also generate AICD and Aβ46 as these products are inhibited by γ-secretase inhibitors [4–7] indicating that this enzyme has multiple cleavage sites. However, over-expression and mutagenesis studies have shown that the production of Aβ and AICD may be independent catalytic events [8, 9].
Fig 1Formation of the γ-secretase complex and Aβ generation. A schematic representation of the proposed formation of the γ-secretase complex and the enzymatic activity resulting in Aβ generation. Evidence has been provided to show that PS, nicastrin (NCT), aph-1, and pen-2 are the only components of the γ-secretase complex. Nicastrin and aph-1 are involved in the early formation of the complex, interacting with each other and forming a dimeric sub-complex. The presenilin holoprotein is incorporated into the aph-1: NCT sub-complex, nicastrin undergoes post-translational modifications and the complex is transported to the cell surface (or other Aβ generating compartments, i.e. TGN) as a trimeric complex. Pen-2 is incorporated into the complex and presenilin is cleaved forming the active γ-secretase complex. Following cleavage of APP by BACE to liberate secreted APP, the APP-C99 fragment left embedded within the membrane undergoes regulated intramembranous processing (RIP) by the γ-secretase complex, liberating Aβ1–40 or Aβ1–42 (Modified from Ref. [165])
The γ-secretase enzyme is thought to be an aspartyl protease that has the unusual ability to regulate intramembrane proteolysis (RIP) for a growing list of type 1 integral membrane proteins which include, APP, APP like proteins (APLPs), E-Cadherin, CD44, lipoprotein receptor related protein (LRP), Notch, sterol regulatory element-binding protein (SREBP), interferon response element (IRE1) and activated transcription factor 6 (ATF-6) (for recent review see [10]). The mechanism of RIP and γ-secretase activity is unknown. However a transient hydrophilic environment for catalysis within the lipid membrane must be created. Furthermore, the enzyme (or an enzyme domain) must have the ability to bend and unwind the α-helical substrates, exposing their amide bonds to hydrolysis. It is therefore conceivable that the γ-secretase enzyme is an integral protein of the lipid bi-layer and contains a number of proteins that may have different functions within an enzyme complex. The exact conformation or molecular architecture of the γ-secretase enzyme remains unknown. However, large molecular mass complexes of ∼250 kDa [11, 12], ∼500 kDa [13–17] and ∼2,000 kDa [13, 18] have been identified.
Over the last few years biochemical and genetic approaches have identified four components of the γ-secretase complex, presenilins, nicastrin, anterior pharynx defective (aph-1) and presenilin enhancer 2 (pen-2). Over-expression and expression knockdown studies have provided strong evidence that these proteins are essential for γ-secretase activity [15, 19–24]. Subsequent reconstitution studies in non-mammalian cells have provided evidence that these are the only components responsible for γ-secretase catalytic activity [25–27]. However the active component and the active catalytic site of this enzymatic complex are yet to be identified.
The roles of presenilin and its binding partners within the γ-secretase complex in Aβ generation
Presenilin 1: the catalytic component of the γ-secretase enzyme
Presenilin 1 (PS1) is thought to be the most critical component of the γ-secretase complex as evidence indicates that it is the catalytic component of this proteolytic enzyme. The finding that mutations in PS1 account for the majority of inherited early onset forms of AD and result in the overproduction of the highly amyloidogenic Aβ42 [28–30] provided the initial evidence that PS1 facilitates γ-secretase activity. Subsequent studies showed that PS1 ablation or mutagenesis of two highly conserved aspartate residues within transmembrane domains 6 and 7 of PS1 results in a reduction in Aβ levels [31–34]. Furthermore, aspartyl protease inhibitors and transition state analogue inhibitors which are designed to target the active site of the protease, all reduce Aβ40 and Aβ42 levels and have been shown to affinity label and bind to PS1 [35–37]. In addition, physical interactions between PS1 and the APP-C99 fragment have been identified [38–40]. The studies outlined above provide substantial evidence to indicate that PS1 is the catalytic subunit of γ-secretase. However, this transmembrane protein is not typical of a conventional aspartyl protease, and lacks the typical D(T/S)G motif for an aspartyl protease active site (recently reviewed in [41]. Aspartyl proteases require the presence of two aspartate residues for the hydrolysis of the peptide bond. Although PS1 lacks the typical D(T/S)G aspartyl protease active site it does contain the two aspartyl residues (D257 and D385) which are either critical for the active site on the γ-secretase complex or constitute the active site [42]. Other proteases that contain atypical aspartyl protease motifs and have weak homology with PS1 include the bacterial membrane embedded aspartyl protease, type 4 prepilin protease and the signal peptidases, which are all transmembrane proteins. These proteins share the unusual aspartyl motif GxGD and perform intramembranous cleavage of type I membrane proteins (reviewed in [43]). The topology and atypical characteristics of these proteins make them an unprecedented class of protease and as is the case for PS1, would require interaction with of other proteins within the complex.
The interaction between the components of the γ-secretase complex is not sufficient to create a macromolecular enzyme complex such as γ-secretase (i.e. total mass of all the components is ∼200 kDa). Thus, dimer or trimer formation must occur within the complex. To date the only member of the complex that is known to undergo this process is PS1. The full length PS1 protein is rapidly endoproteolytically cleaved within its characteristic large hydrophilic loop into amino- and carboxy-terminal fragments (NTF/CTF) of ∼27 and ∼17 kDa, respectively [44, 45]. These fragments are thought to interact with each other to form the catalytic component of the γ-secretase enzyme since in the absence of full length PS1, co-expressed NTF and CTF were able to generate γ-secretase activity in presenilin deficient mammalian cells [46, 47]. The stoichiometry and the nature of the interaction between these fragments remain unclear. It has been shown by many studies that the NTF:CTF form a heterodimer in mammalian cells [12, 48–51] leading to suggestions that this heterodimer is the active γ-secretase [52]. However, Cervantes and colleagues [53] provided evidence that the presenilin fragments can form a tetramer by identifying heterodimers as well as NTF and CTF homodimers in yeast. Evidence for heterodimer and NTF homodimer (but not CTF homodimer) formation has been provided by photo-affinity labelled cross-linking studies [54]. This formation provides a core of aspartyl residues required for aspartyl protease activity. However, it has yet to be established whether the hypothetical “core” is formed between fragments from one PS1 molecule or multiple molecules within the complex. Further, if PS1 is the “catalytic core” of the γ-secretase complex the roles of the other interacting proteins need to be determined.
Nicastrin: the substrate docking site and scaffolding for building the γ-secretase complex
Nicastrin was discovered to be part of the γ-secretase complex when it was found to co-immunoprecipitate with PS1 [55]. Nicastrin is a type I transmembrane protein with a 670 amino acid long hydrophilic N-terminal domain, a transmembrane domain, and a relatively short cytoplasmic C-terminus of twenty amino acid residues [55]. The protein undergoes glycosylation and sialylation within the secretory pathway to yield a mature ∼150 kDa protein, the largest component of the γ-secretase complex [14], [56–58]. Nicastrin has been shown to be a critical component of the γ-secretase complex as inhibition of nicastrin function in vitro and in vivo results in a complete loss of APP and Notch cleavage [14, 55, 59–61]. Within this complex, nicastrin appears to be involved in the early formation of the complex, particularly in the formation of the first sub-complex between itself and aph-1 (Fig. 1). Recent evidence suggests that nicastrin is critical for the correct assembly of the γ-secretase complex within the endoplasmic reticulum and the intracellular trafficking of the complex to the cell surface [62, 63]. Furthermore, nicastrin is essential for the interaction between the complex and APP-C99 and thus may act as a receptor for γ-secretase substrates [63, 64]. Therefore, although nicastrin is a critical component, evidence to date suggests that it is unlikely to be a catalytic component of the γ-secretase complex. Instead, nicastrin acts as a scaffold for the building of the active complex, and a possible substrate docking site.
Anterior pharynx defective 1 (aph-1): scaffolding partner for nicastrin and may have possible catalytic activity
The aph-1 protein is a ∼30 kDa, 308 amino acid long 7-transmembrane protein [65] existing in two homologous forms, located on chromosome 1 and chromosome 15 (aph-1a & aph-1b respectively; [19]). In addition, as the result of alternate splicing, a long and short isoform of aph-1a have also been reported, with the short isoform more abundantly expressed in most tissues [21]. Although the exact function of these isoforms remains unknown, multiple γ-secretase complexes containing aph-1 isoforms are thought to exist [66, 67]. The aph-1a isoform appears to be incorporated into active γ-secretase complexes. Deletion of aph-1a causes a reduction in Aβ generation [66] and is the major isoform present in γ-secretase complexes during embryonic development [67]. The γ-secretase complexes incorporating aph-1b are thought to have a redundant function as aph-1b has been found not to be involved in the regulation of other γ-secretase components or γ-secretase activity [66].
The aph-1 protein forms a stable and intermediate ∼140 kDa complex with Nicastrin, before binding PS1. Aph-1 has been shown to interact with immature and mature forms of presenilins and nicastrin [21]. The GXXXG motif located within transmembrane 4 of aph-1 has been shown to be important in this interaction [68], [69]. The aph-1: nicastrin sub-complex appears to play a role in the stabilisation and assembly of PS1 in the γ-secretase complex (Fig 1; [24, 70, 68]). However, the exact role of aph-1 in the γ-secretase complex remains unknown. Expression studies have shown that removal of aph-1 destabilises the presenilin holoprotein and the addition of aph-1 leads to the accumulation of the holoprotein [11, 19–24, 71]. Thus together with nicastrin, aph-1 is thought to contribute the initial formation and trafficking of the γ-secretase complex and thereby provide a scaffold for the proteolytic processing of PS1 and the formation of the active site.
Aph-1 is critical for the activity of the fully constructed γ-secretase complex. Evidence has been provided that as well as binding to immature components of γ-secretase in early stages of complex formation, aph-1 also interacts with the mature forms of PS1, nicastrin and pen-2 [68, 72]. Furthermore, recent evidence has been provided to suggest that this interaction occurs on the cell surface where it also binds the γ-secretase substrate, Notch and facilitates its cleavage [72]. These studies suggest that apart from the scaffolding function it shares with nicastrin, aph-1 has another function within the fully constructed, active γ-secretase complex. Structural and functional similarities between aph-1 and other proteases that possess the ability for intramembranous cleavage (such as rhomboid, [68, 73] suggests that this transmembrane protein may have an enzymatic function within the complex. This postulated novel function for aph-1 needs to be validated by future studies.
Presenilin Enhancer 2 (pen-2): the elusive presenilinase or “molecular clamp” that holds the complex together?
The pen-2 protein is a ∼12 kDa, 101 amino acid long protein containing two transmembrane domains and its N- and C-terminals facing the lumen of the endoplasmic reticulum [74, 75]. Pen-2 has been shown to interact with Nicastrin, aph-1, and also PS1. Without this interaction pen-2 has been shown to be degraded, possibly by the proteasome [76, 77]. Ablation of pen-2 results in significantly reduced PS1 endoproteolysis and Aβ production, suggesting that pen-2 is essential for both presenilinase and γ-secretase activity [15, 19, 22–24]. Likewise, combinational expression studies have shown that when pen-2 is co-expressed with the other components of the γ-secretase complex in a step-wise manner, endoproteolysis of the PS1 holoprotein was enhanced [11, 22, 24–27, 71]. A trimeric sub-complex between presenilin, nicastrin and aph-1 has been identified in cells lacking pen-2 [24, 70] suggesting that pen-2 may be the final protein incorporated into the γ-secretase complex (Fig. 1). Recent evidence has suggested that pen-2 is incorporated into the γ-secretase complex through an interaction with presenilins. The “DYLSF” domain of pen-2 and a “NF” motif on transmembrane 4 of PS1 have been shown to be critical for the interaction between these proteins [78–80]. The cleavage of PS1 occurs within the hydrophilic loop and is distal from the interaction site on transmembrane 4, suggesting that another domain of pen-2 is responsible for the cleavage of PS1 or pen-2 may present PS1 to the elusive “presenilinase” which may be another member of the γ-secretase complex. The C-terminal end of pen-2 has been shown to be important for γ-secretase activity since altering the length of this region by addition or deletion of residues has been shown to reduce Aβ40 and 42 generation without altering the binding of pen-2 to the complex [78] suggesting that pen-2 may have an alternative function within the complex. One suggestion put forward by Hasegawa and colleagues [78] is that the C-terminus of pen-2 may be a linker/space molecule that maintains spatial interactions between proteins within the complex. However, recent evidence using pen-2 C-terminal loss of function mutations suggest that the C-terminus acts as a “molecular clamp” holding together the presenilin fragments and the whole γ-secretase complex [81]. If this is the case then PEN-2 is an integral part of the catalytic process holding the complex together whilst the γ-secretase products are generated.
The newest member of the complex: TMP21, a modulator of γ-secretase activity
Recently another member of the γ-secretase complex was immuno-purified from wild-type PS1 and PS2 blastocyte derived cells [82]. The type 1 transmembrane protein termed TMP21 is a member of the p24 cargo-family which may have a signalling role in the sorting and transport of proteins from the endoplasmic reticulum to the Golgi [83, 84]. It was shown by Chen and colleagues [82] that TMP21 is a member of the complex as it was isolated in a high molecular weight presenilin complex, interact with all of the known components of the γ-secretase complex, co-localised with the complex components in the ER, Golgi and cell surface and destabilised from the complex in the absence of the presenilins and pen-2. Although the over-expression of TMP21 did not alter γ-secretase activity, its suppression resulted in and increase in Aβ40 and Aβ42. However, suppression of TMP21 did not alter the production of AICD, notch cleavage to generate NICD or cleavage of E-cadherin to generate CICD. These results are consistent with the notion that γ- and ε-secretase cleavage activities are independently regulated and indicate a role for TMP21 in modulating γ-secretase activity to generate Aβ. This role for TMP21 appears to be independent of its role in protein transport since the suppression of both TMP21 and p24a (a member of the p24 cargo family that interacts with TMP21) does not result in additional increases in Aβ production to that observed following the suppression of TMP21 only [82]. This finding led the authors to postulate that there are two pools of TMP21, a major pool that is stabilised by, p24a and has no role in Aβ production and a minor pool that modulates Aβ production, independent of p24a. Although, further investigation is required to determine the precise mechanism of action, it appears that TMP21 may function to regulate intramembrane proteolysis controlling γ-secretase activity and thus preventing the over-production of Aβ. This modulator role for TMP21 is important to consider when elucidating mechanisms on how γ-secretase activity is altered (for example by presenilin mutations) resulting in enhanced Aβ40 and Aβ42 production.
Presenilin 2 (PS2): capable of forming a fully functional γ-secretase complex
The presenilin proteins share an overall homology of 67% with the highest similarities in the predicted transmembrane domains. Fewer similarities are found at the N-terminus and in the central region of the hydrophilic loop [85, 86]. Although there are some structural similarities between PS1 and PS2 several lines of evidence suggest that these proteins may have distinct functions. Mice lacking PS1 die before birth and have severe skeletal and brain deformities, whilst those lacking PS2 only develop a mild pulmonary fibrosis and haemorrhage with age [33, 87–90]. Compared to neuronal cultures isolated from PS1 ablated mice, those isolated from PS2 knockout mice exhibit higher Aβ production and are less sensitive to γ-secretase inhibitors [89, 91]. Lai and colleagues [91] also provided evidence to suggest that distinct PS1 containing and PS2 containing complexes exist [91]. If this is the case, evidence to date suggests that PS2 containing complexes have different functions and have less γ-secretase processing power than PS1 containing complexes. However, recent in vitro evidence has suggested that chimeras of PS1 and PS2 fragments retain γ-secretase activity [92] suggesting that active complexes containing both PS1 and PS2 could occur. However, this is unlikely to occur in vivo as PS2 and PS1 transgenic mice have differential effects on γ-secretase activity [93]. In this study mice lacking PS1 were crossbred with transgenic mice containing wild type or mutant PS2/PS1. Compared to mice harbouring wild-type PS1, those harbouring wild-type PS2 had a ∼4-fold reduction in Aβ40 and ∼2-fold reduction Aβ42 brain levels. In addition, the authors reported that although mutations in PS1 lead to higher brain levels of Aβ40 and Aβ42 than PS2 mutations, those mice harbouring PS2 mutations had higher brain levels of Aβ42, whilst Aβ40 brain levels remain unchanged [93]. Taken together these studies suggest that PS1 containing complexes predominate in generating Aβ40 and Aβ42. However, the finding that PS2 is involved in Aβ42 (but not Aβ40) generation suggests that two Aβ generating γ-secretase complexes (γ-40 secretase and γ-42 secretase) exist, with γ-42 secretase complexes containing both PS1 and PS2.
The roles of presenilin binding proteins not incorporated into the active γ-secretase complex
In addition to Aβ generation, the presenilins have been implicated in a variety of intracellular processes including membrane trafficking, Notch signalling, neuronal plasticity, cell adhesion, regulation of calcium homeostasis, the unfolded protein response, and apoptosis. Therefore it is not surprising that the presenilins have a growing list of binding partners (see Table 1). Though the presenilin binding proteins that are incorporated into the γ-secretase complex are critical for the activity of this enzyme, those that are not integral components have equally important roles in modulating Aβ production. A number of non-complex presenilin binding proteins have been shown to alter γ-secretase activity (see Table 1). However, only a few have had supporting evidence that their direct interaction with the presenilins modulate γ-secretase activity. These are described below and shown in Fig. 2.
Table 1Presenilin interacting proteinsInteracting proteinProposed functionInteraction with presenilins shown to modulate Aβ production ReferenceComplex componentsAPH-1Component of the γ-secretase complex; role for initial formation of the complex, or a proteolytic roleYes[19, 65, 67, 123].NicastrinComponent of γ-secretase complex; possible role as a receptor for APP-C99Yes[14, 55–58].PEN-2Component of the γ-secretase complex, role in proteolytic cleavage of the presenilins Yes [19, 24, 70, 76, 77]. TMP21Recently identified component of the γ-secretase complex, role in modulating Aβ productionYes[82]γ-secretase substratesAPP-C99Substrate for the γ-secretase complex: precursor to Aβ generationYes [38–40]E/N cadherinγ-secretase substrate; role in Cell-Cell AdhesionNo[124]IRE1γ-secretase substrate; role in unfolded protein responseNo[125, 126]LRPγ-secretase substrate; Lipid metabolismYes [119]Notch1 γ-secretase substrate; precursor to NICD generationYes [112]Interacting binding proteinsABP-280, Filamin homolog1Actin binding proteinNo[127]Bcl-2 Anti-apoptotic moleculeNo[128]Bcl-XLAnti-apoptotic moleculeNo[129]CalmyrinPossible calcium-myrstol switchNo[130]CALP- calsenilin like proteinNovel member of the calsenilin/KChIp protein familyNo[131]CalsenilinCalcium binding proteinYes[94, 96–99, 101, 102]CLIP-170Linking membrane organelles to microtubules No[132]DRALLIM-domain containing proteinNo[133]FHL2Role in PI3K/Akt activationNo[134]FKBP38Role in mitochondria mediated apoptosis No[135]GoSignalling moleculeNo[136]GSK3βWnt signalling, serine threonine protein kinase No[137]HC5/ZETASubunits of the catalytic 20S proteasomeYes[138]Met1Putative methyltransferaseNo[139]Modifier of cell adhesion protein (MOCA)Regulates proteasomal activity on APPYes[140]Omi/HtrA2Serine protease, proapoptoticNo[141]PAGNeuronal proliferation proteinNo[142]PAMP and PARLMetalloproteases No[143]PKASerine/Theronine protein kinase; β-catenin phosphorylationNo[144]PlakoglobinRole in b-catenin/Tcf-4 activityNo[145]PLD1Phospholipid-modifying enzymeYes [146]PSAPPDZ like proteinNo[147]QM/Jif1Negative regulator of c-JunNo[148]Rab proteinsVesicle mediated protein traffickingYes[149, 150]RyR2Cardiac ryanodine receptor No[151]SEL-10Ubiquitination of proteinsYes[152]SorcinCalcium binding regulator of ryanodine receptorNo[153]Syntaxin 1ASynaptic plasma-membrane proteinNo[154]Syntaxin 5ER-Golgi vesicular transportYes[155, 156]TauMicrotubule binding proteinNo[137]TelencephalinNeuron specific adhesion moleculeNo[40]TPIPTetratricopeptide repeat-containing proteinNo[157]UbiquilinUbiquitin domain-containing proteinNo[158–160]X11 family of proteinsCytoplasmic adaptor proteinsYes[110, 111]β-catenin/δ-catenin (NPARP)Wnt signalling. Cell adhesionNo[161–163] μ-CalpainCalcium-dependent thiol proteaseNo[164]Fig. 2The postulated roles for presenilin interacting proteins not incorporated into the γ-secretase complex in Aβ generation. A schematic representation of the postulated roles of presenilin interacting proteins in (A) promoting or (B) attenuating γ-secretase activity and Aβ generation. (A) Together with the formation of the γ-secretase complex, calsenilin have been shown to interact with the presenilins and promote Aβ generation when over-expressed in mammalian cells. The role of calsenilin in Aβ generation remains unclear but maybe involved in the presentation of the substrate to the γ-secretase complex. (B) The presenilin interacting protein, X11 and other γ-secretase substrates that compete for enzyme activity such as Notch fragments (i.e. NEXT) have all been shown to reduce Aβ generation when over-expressed in mammalian cells. X11 may reduce Aβ production by binding to PS and APP preventing PS mediated cleavage of APP or its presentation to the γ-secretase complex
Calsenilin
Calsenilin (also called DREAM or kChIP3) is a calcium binding protein, that was first identified through yeast two hybrid screening using the PS2 C-terminus as the bait protein [94]. The interaction between calsenilin and the presenilins appear to have a role in APP processing. It was initially demonstrated that calsenilin can form a complex with PS1 and PS2 and regulate presenilin fragment formation in mammalian cells [95]. This study also showed that calsenilin may facilitate presenilin mediated apoptosis, since calsenilin was shown to preferentially interact with the C-terminal fragment of PS2 that results from caspase cleavage of the presenilin holoprotein [96]. Expression studies in vivo (Xenopus) and in vitro (mammalian cells) have shown that the interaction between presenilin and calsenilin may be involved in ER calcium release and apoptosis [97, 98]. Over-expressing calsenilin in neuronal cells resulted in cell death which was enhanced by the co-expression of wild-type or PS1 mutations [99]. These studies have suggested that the interaction between calsenilin and presenilins may function in apoptotic pathways. However, several lines of evidence suggest that calsenilin may also be implicated in APP processing and Aβ generation. Initial in vitro studies showed that over-expression of calsenilin in mouse neuroblastoma cells resulted in a gene dose dependent accumulation of Aβ42 [100]. Using a γ-secretase luciferase based assay and a cell free system established by Li and colleagues [18] to characterise the γ-secretase complex in purified cell membranes, Jo and colleagues [101] recently reported that the over-expression of calsenilin resulted in an increase in γ-secretase activity. This calsenilin/presenilin, mediated activity was markedly reduced using a well-characterised γ-secretase inhibitor (L-685, 458). Furthermore, compared to wild-type mice, brain Aβ40 and Aβ42 levels were reduced in calsenilin ablated mice [102]. Together these studies would suggest that calsenilin has a second function, to facilitate γ-secretase activity.
X11/Mint proteins
A family of cytoplasmic adaptor proteins called X11 (also known as munc-18-interacting proteins—mints) have also shown to have a direct role in APP processing. In particular, several studies have reported that X11α and X11β stabilise APP and inhibit the secretion of Aβ and APP from neuronal cells [103–106]. The molecular mechanisms by which the X11s influence APP processing and Aβ production are not clear. However, this effect appears to be modulated by a direct interaction between X11 and APP. The interaction between the phosphotyrosine binding (PTB) domain within X-11 and the YENPTY motif within APP appears to modulate Aβ generation, since introduction of point mutations within these domains enhanced Aβ generation [103, 106–109]. The binding of X11 appears to stabilise APP, leading to its intracellular accumulation [103, 106–109]. It is conceivable that the X11–APP interaction may inhibit secretase accessibility to APP resulting in impaired Aβ secretion. The X11 proteins have multiple binding domains and have been shown to interact with the PS1 C-terminal fragment through their PDZ domain [110]. The authors also showed an interaction between PS1 and APP in the presence of X11 that was otherwise ablated in the absence of X11. This suggests that an interaction between APP and PS1 is mediated through X11. A subsequent study has reported that X11β and alcadein (and X11β-binding protein) expression prevents the binding of PS1 to APP [111]. The significance of the results from these interaction studies remains unclear. However, they do suggest that X11 proteins may act as inhibitory molecules preventing PS1 mediated proteolytic processing of APP and thus attenuating Aβ production.
Competing substrates
Some presenilin binding proteins reduce the likelihood of Aβ generation by competing for the catalytic activity offered by γ-secretase. One example is Notch1 which undergoes cleavage by γ-secretase to liberate an intracellular domain (NICD) that translocates to the nucleus where it co-activates transcription of genes involved in several developmental pathways [112–115]. The first evidence of substrate competition came from one study that treated neurons with the Noch1 ligand, delta, and found a dose dependent reduction in Aβ levels [116]. In contrast, the over-expression of APP in these neurons resulted in a reduction in Notch signalling [116]. Further evidence for substrate competition was provided by Kimberley and colleagues [117] where APP and Notch based substrates directly competed for γ-secretase activity in a cell free assay. Similar results were achieved in a subsequent study where cells expressing Notch showed a reduction in Aβ production [118]. Additional evidence for substrate competition has been provided recently for another substrate of γ-secretase, the cytoplasmic tail of the low-density lipoprotein receptor—related protein (LRP), which has also been shown to interact with PS1 [119]. The co- expression of the LRP C-terminal fragment and APP in CHO cells has been shown to result in a reduction in secreted levels of Aβ40 and Aβ42 [119].
Taken together, these studies not only suggest substrate competition but also the presence of a single γ-secretase. However, the presence of multiple γ-secretase complexes cannot be ruled out. As discussed above there is evidence in the literature for the formation of γ-secretase complexes with distinct functions. All of the studies described above are undertaken in conditions where there is a large amount of substrate available which could down-regulate the activity of one γ-secretase for another. Whether substrate competition occurs under physiological conditions and if there are certain conditions that promote a higher expression of one substrate over another remains to be determined.
Concluding comments
It is well established that together with presenilin, its binding proteins, nicastrin, aph-1 and pen-2 are the only proteins that constitute the γ-secretase complex responsible for generating Aβ from APP. The function(s) of each γ-secretase component within the complex is gradually becoming apparent. However, there are many facets of γ-secretase activity that are yet to be identified such as the domain responsible for actual catalytic activity; the mechanisms of enzymatic activity within the lipid bi-layer; and the existence of single or multiple γ-secretase complexes. Further investigation using appropriate protein expression models and reconstitution studies such as those used to validate the protein components of γ-secretase complex is required to provide some insight into the processes that underlie the catalytic activity of this enzyme. Indeed, reconstitution of the γ-secretase complex in insect cells and purification of this complex from the cells have provided researchers with the first 3D images of the structure of this intricate enzyme [120, 121]. Identification of the specific catalytic domain(s) within the complex would provide a site that could be a potential target to develop agents that specifically modulate Aβ levels without altering the other known (and unknown) activities of the γ-secretase enzyme. Recent insight into this notion was provided by the finding that certain compounds selectively block the cleavage of APP but not Notch [122]. However, it is important to note that other presenilin interacting proteins not incorporated into the complex and the pathways they are involved in play important roles in APP processing and Aβ generation. These interactions assist in many facets of γ-secretase activity, including modulating substrate/enzyme availability, and thus are important to consider when developing appropriate therapeutic strategies.

presenilin
interacting proteins
beta amyloid
alzheimer’s disease
amyloid precursor protein
gamma secretase


Differential regulation of Krüppel-like factor family transcription factor expression in neonatal rat cardiac myocytes: Effects of endothelin-1, oxidative stress and cytokines

Krüppel-like transcription factors (Klfs) modulate fundamental cell processes. Cardiac myocytes are terminally-differentiated, but hypertrophy in response to stimuli such as endothelin-1. H2O2 or cytokines promote myocyte apoptosis. Microarray studies of neonatal rat myocytes identified several Klfs as endothelin-1-responsive genes. We used quantitative PCR for further analysis of Klf expression in neonatal rat myocytes. In response to endothelin-1, Klf2 mRNA expression was rapidly increased (∼ 9-fold; 15–30 min) with later increases in expression of Klf4 and Klf6 (∼ 5-fold; 30–60 min). All were regulated as immediate early genes (cycloheximide did not inhibit the increases in expression). Klf5 expression was increased at 1–2 h (∼ 13-fold) as a second phase response (cycloheximide inhibited the increase). These increases were transient and attenuated by U0126. H2O2 increased expression of Klf2, Klf4 and Klf6, but interleukin-1β or tumor necrosis factor α downregulated Klf2 expression with no effect on Klf4 or Klf6. Of the Klfs which repress transcription, endothelin-1 rapidly downregulated expression of Klf3, Klf11 and Klf15. The dynamic regulation of expression of multiple Klf family members in cardiac myocytes suggests that, as a family, they are actively involved in regulating phenotypic responses (hypertrophy and apoptosis) to extracellular stimuli.
1
Introduction
C2H2 zinc finger transcription factors possess two cysteine and two histidine residues which co-ordinate Zn2+ within each finger to form a conserved DNA-binding structure. Sp1-like proteins and Krüppel-like factors (Klfs) each have three C2H2 zinc fingers at their C-termini, and Klfs are distinguished by a highly conserved interfinger space sequence [1]. The seventeen mammalian Klfs which have been identified are now designated Klf1-17 [2,3], though many have alternative names. Several were named according to the tissues in which they were originally shown to be enriched (e.g. Klf1 is erythroid Eklf, Klf2 is lung Lklf, Klf4 is gut Gklf, Klf5 is intestinal Iklf and Klf15 is kidney Kklf [2]). Other Klfs are widely expressed including Klf3 (basic Bklf), Klf6 and Klf7 (ubiquitous Uklf). Klf9 was identified as a basal transcription element binding (BTEB) protein, with Klf5 and Klf13 as homologues (BTEB2 and BTEB3, respectively). Klf10 and Klf11 were identified as early genes induced by transforming growth factor β and were named TIEG and TIEG2, respectively.
Klfs bind to consensus GC-rich or CACCC sequences through the three C-terminal zinc fingers [1]. The frequency of such sequences in gene promoters, coupled with the high sequence identity between Klf proteins in their DNA-binding domains, raises questions relating to the specificity of any Klf for a particular promoter and potential functional redundancy. Nevertheless, Klfs can be clustered according to whether they act primarily as transcriptional repressors and/or activators, and the domain structure of the N-terminal regulatory regions. Klf3, Klf8 and Klf12 recruit C-terminal binding proteins (CtBPs) to repress transcription, whereas Klf9, Klf10, Klf11, Klf13 and Klf16 repress transcription through interaction with mSIN3a [1,4]. Klf15 also represses transcription, though the mechanism is not clear. Klf2, Klf4, Klf5, Klf6, and Klf7 are largely transcriptional activators [1], but may suppress gene expression in specific circumstances (e.g. Klf5 negatively regulates expression of Klf4 [5]). In addition to interactions with other proteins, post-translational modifications (e.g. phosphorylation or acetylation) regulate the transactivating activities of different Klfs [1,6]. Globally, Klfs regulate fundamental cellular responses such as growth, apoptosis, angiogenesis and proliferation. For example, Klf5 overexpression is associated with cell proliferation, whereas Klf2, Klf4 and Klf6 are more consistently implicated in cell cycle arrest [7–9]. Klf4 and Klf5 may both promote apoptosis induced by oxidative stress though the mechanism is unclear [9].
Mammalian cardiac myocytes (the contractile cells of the heart) become terminally-differentiated shortly after birth. Subsequent growth of the heart results from an increase in size of individual cardiac myocytes and, in the adult, myocytes may hypertrophy in order to maintain or increase cardiac output. This is associated with physiological and morphological changes (increases in cell size and myofibrillogenesis) and changes in gene expression including increased expression of immediate early genes (IEGs), and re-expression of genes normally expressed in early development [10]. Stimuli such as endothelin-1 (ET-1), which activate Gq protein-coupled receptors are particularly implicated in cardiac myocyte hypertrophy, and promote both the physiological/morphological changes and the changes in gene expression associated with the response [11]. In contrast, oxidative stresses or pro-inflammatory cytokines may induce cardiac myocyte apoptosis [12]. Many studies have examined the intracellular signalling pathways which are activated by various stimuli in cardiac myocytes, and these are presumed to lead to changes in gene and protein expression to promote hypertrophy or to facilitate myocyte death [13].
Many Klfs are expressed in adult hearts to a degree although the cell types in which they are expressed cannot be ascertained. For example, Klf2 is associated with endothelial cells [14,15], and cardiac endothelial cells could account for expression of Klf2 in whole heart extracts [16]. As reviewed by Haldar et al. [17], Klf13 and Klf15 are the only Klfs which have so far been shown to play a role in cardiac myocytes. Klf13 is highly expressed in adult hearts [18] and is required for normal cardiac development in Xenopus
[19]. Klf15 is highly expressed in adult hearts and in cardiac myocytes [20,21]. It appears to be anti-hypertrophic since it is downregulated during hypertrophy, and overexpression of Klf15 in cardiac myocytes suppresses the morphological changes and changes in gene expression induced by hypertrophic stimuli [21]. Our microarray studies highlight the dynamic changes in expression of many genes (including Klfs) induced by H2O2 (an example of oxidative stress) or ET-1 in cardiac myocytes over 4 h [22–25]. Here, we explore further the regulation of Klf family members in cardiac myocytes in response to ET-1, H2O2 and pro-inflammatory cytokines [interleukin 1β (IL-1β) and tumour necrosis factor α (TNFα)]. The dynamic regulation of expression of multiple Klf family members suggests that, as a family, they are actively involved in regulating phenotypic responses of cells to extracellular stimuli.
2
Materials and methods
2.1
Primary culture of neonatal rat cardiac myocytes and preparation of polysomes
Myocytes were dissociated from the ventricles of 2- to 4-day-old Sprague‑Dawley rat hearts by an adaptation of the method of Iwaki et al. [26] as previously described [27]. Cells were plated at 4 × 106 cells/60 mm Primaria culture dish (BD Biosciences) for 18 h in 15% (v/v) foetal calf serum, then serum was withdrawn for 24 h before experimentation. Myocytes were exposed to ET-1 (100 nM), H2O2 (0.2 mM), IL-1β (100 ng/ml) or TNFα (10 ng/ml) with or without pre-treatment (10 min) with inhibitors (10 μM U0126; 50 μM LY294002; 5 μM SB203580). Cardiac myocyte polysomes were prepared by sucrose density centrifugation (0.8–1.6 M sucrose gradients; 5 ml) as previously described [25]. Fractions were collected by upward displacement (fraction 1 from the top of the gradient, 0.8 M sucrose; fraction 12 from the bottom of the gradient, 1.6 M sucrose) whilst monitoring absorbance at 254 nm.
2.2
RNA preparation and quantitative PCR (qPCR)
Total RNA and polysome RNA (fractions 6–11 of the sucrose gradients) were extracted and cDNA synthesized using reverse transcription as previously described [22,25]. qPCR was performed using a Real‑Time PCR System (Model 7500, Applied Biosystems) as described [25] using primers designed to amplify mRNA sequences across an intron (Table 1). qPCR analysis of glyceraldehyde 3-phosphate dehydrogenase (Gapdh) was performed as a control and the relative quantification protocol was used. PCR conditions were 50 °C for 2 min, 95 °C for 10 min (Jump-Start Taq polymerase activation step), followed by 40 cycles of 95 °C for 15 s and 59 °C for 60 s. Following qPCR, dissociation curve analysis was routinely performed to check for aberrant amplification products (e.g. primer‑dimers).
2.3
Western blotting
Cardiac myocyte nuclear extracts were prepared and Western blotting performed essentially as described [28]. Extracts from 1.8 × 106 cells were analysed with separation of proteins on 10% (v/v) polyacrylamide gels. Nitrocellulose blots were probed with Klf6 rabbit polyclonal antibodies (Santa Cruz Biotechnology Inc.; Klf6(R-173), sc-7158, 1/500 dilution). Bands were detected and analysed by scanning densitometry as described [29].
3
Results
3.1
ET-1 regulates expression of multiple klf family members in cardiac myocytes
Our microarray studies (with Affymetrix rat genome 230 2.0 arrays) of the effects of ET-1 on cardiac myocyte gene expression over 4 h [25] identified Klfs as a family of transcription factors which appeared to be dynamically regulated at the mRNA level. Mining the data specifically for Klfs, we identified rapid and transient increases in expression of Klf2, Klf4, Klf5, Klf6, Klf9 and Klf10, with concomitant downregulation of Klf3, Klf11 and Klf15 (Table 2). There was no statistically significant change in expression of Klf13 or Klf16, although Klf13 exhibited a small (non-significant; one-way ANOVA with Tukey post-test) increase in expression over 2– 4 h. Klf1 (probeset 1382033_at), Klf7 (probesets 1380363_at, 1377618_at, 1384497_at) and Klf12 (probeset 1385545_at) were not consistently called “present” with low fluorescence values and are thus expressed at minimal levels. We could not identify Klf8, Klf14 (Sp6) or Klf17 on the arrays.
Using qPCR, we examined more carefully the effects of 100 nM ET-1 on Klf mRNA expression. Klf2 mRNA expression was significantly increased within 15 min, with maximal expression (∼ 9-fold relative to controls) at 30 min (Fig. 1A). Klf4 and Klf6 mRNAs were also upregulated (Fig. 1, B and C), though the relative stimulation was less (∼ 5-fold relative to controls) and the response was delayed relative to Klf2 (maximal expression at 0.5–1 h). Klf5 mRNA was upregulated from 1 h, with maximal expression at 1.5 h (∼ 13-fold relative to controls) (Fig. 1D). For these Klfs, the increase in expression was transient, with expression of Klf2 returning to baseline within 90 min and expression of Klf4, Klf5 and Klf6 declining to basal levels within 4 h. The increases in expression of Klf9 and Klf10 were relatively small (maximal expression < 2-fold at 1.5–2 h) although levels remained elevated over 4–6 h (Fig. 1, E and F). Consistent with our microarray data, Klf3, Klf11 and Klf15 were downregulated in response to ET-1. Whereas the decrease in expression of Klf3 was transient with minimal expression at ∼ 30 min (Fig. 1G), the decrease in expression of Klf11 and Klf15 was sustained over at least 6 h (Fig. 1, H and I). It is notable that the Klfs which were downregulated and those with only a small increase in expression are largely associated with transcriptional repression, whereas the Klfs with the greatest increases in expression (Klf2, Klf4, Klf5 and Klf6) are mostly associated with transcriptional activation [1,4,6]. Further studies focused on this latter group.
3.2
Regulation of Klf2, Klf4, Klf5 and Klf6 by ET-1
To determine whether Klf2, Klf4, Klf5 and Klf6 mRNAs were upregulated as IEGs (i.e. the increase in expression is regulated by pre-existing transcription factors and does not require synthesis of new proteins), we examined the effects of 20 μM cycloheximide (to inhibit protein synthesis). Cycloheximide alone increased the expression of Klf2, Klf4 and Klf6 mRNAs (Fig. 2A; probably due to activation of stress-activated signalling pathways [30,31]), but did not inhibit the increase in expression induced by ET-1. However, the increase in expression of Klf5 by ET-1 was inhibited by cycloheximide (Fig. 2B), indicating that de novo protein synthesis is required for upregulation of Klf5 mRNA. Thus, Klf2, Klf4 and Klf6 are regulated as IEGs in response to ET-1, whereas Klf5 is a second phase gene.
ET-1 potently activates the extracellular signal-regulated kinases 1/2 (ERK1/2) in cardiac myocytes [32]. We used 10 μM U0126 (a selective inhibitor of the ERK1/2/5 cascades [33–35], though ERK5 is not significantly activated by ET-1 [24]) to determine whether the increase in expression of Klf2, Klf4, Klf5 and Klf6 requires ERK1/2 signalling. U0126 minimally affected basal expression of each Klf, but partially inhibited the ET-1-induced increase in expression of Klf2 (56 ± 4% inhibition at 30 min; mean ± SEM, n = 5), Klf4 (43 ± 9% inhibition at 1 h; n = 4), Klf5 (72 ± 8% inhibition at 1 h; n = 4) and Klf6 (45 ± 8% inhibition at 1 h; n = 4) (Fig. 3A). These data suggest that ERK1/2 signalling is required, at least in part, for the upregulation of Klf2, Klf4, Klf5 and Klf6 mRNAs by ET-1.
Signalling through phosphoinositide 3′ kinase (PI3K) may increase expression of Klf2 [36], and p38-MAPK is activated by ET-1 in cardiac myocytes [37], so we examined the effects of 50 μM LY294002 or 5 μM SB203580 (selective inhibitors of PI3K and p38-MAPK, respectively [34]). Surprisingly, LY294002 increased basal expression of Klf2 and Klf4 in cardiac myocytes and promoted further the increase in expression induced by ET-1 (Fig. 3B), indicating that PI3K signalling is not required for the upregulation induced by ET-1 and suggesting that basal PI3K signalling negatively regulates Klf2/Klf4 expression. LY294002 did not affect Klf6 expression, but attenuated the increase in expression of Klf5 by ET-1. Since basal PI3K signalling is required for protein synthesis in cardiac myocytes [38], the effect of LY294002 on the increase in Klf5 induced by ET-1 (which requires de novo protein synthesis; Fig. 2B) may be a reflection of this. SB203580 had a minimal effect on the basal expression of Klf2, Klf4, Klf5 or Klf6, or on the increase in expression induced by ET-1 (Fig. 3B). It is unlikely that p38-MAPK signalling plays a significant role in the response.
3.3
Translation-state analysis of Klf2, Klf4, Klf5 and Klf6 mRNA expression
Given the increasing evidence for translational regulation of specific mRNAs [39,40], we performed translation-state analysis of Klf2, Klf4, Klf5 or Klf6 mRNAs. We used qPCR to compare the relative levels of expression in the total RNA pool and in RNAs associated with cardiac myocyte polysomes (prepared by sucrose density centrifugation; Fig. 4, A and B). Klf2, Klf4 and Klf6 mRNAs were marginally enriched in cardiac myocyte polysomes in unstimulated cells and, following stimulation with ET-1 (1 h), the relative increase in the polysomes was at least as great as in the total RNA pool (Fig. 4, C–E). Klf2 was increased 5.6-fold in the polysomes (4.8-fold in total RNA), Klf4 was increased 6.4-fold (5.0-fold in total RNA) and Klf6 was increased 4.6-fold (4.8-fold in total RNA). Klf5 mRNA was marginally decreased in the polysomes relative to the total pool, but the increase in expression induced by ET-1 in the polysomes (4.3-fold) was slightly higher than in the total pool (3.9-fold) (Fig. 4F). We therefore expect all these Klfs to be efficiently translated into protein. Consistent with this, Klf6 protein (detected as bands of ∼ 38–40 kDa on Western blots) was increased in cardiac myocytes exposed to ET-1, with maximal expression at ∼ 1 h (Fig. 4G). It could be argued that early genes should be efficiently translated and this may be a property of all such genes. However, Irs2 was upregulated as an IEG by ET-1 (no inhibition by cycloheximide, Fig. 2A), but the mRNA was not enriched in the polysomes and the increase in the polysomes was significantly less than that in the total RNA pool (Fig. 4H). Similarly, for interleukin 1 receptor-like 1 (Il1rl1), a second phase gene (upregulation was inhibited by cycloheximide; Fig. 2B), the increase in expression in the polysomes was significantly less than in the total RNA pool (Fig. 4I). It is therefore notable that Klf2, Klf4, Klf5 and Klf6 mRNAs are efficiently recruited to the polysomes for translation into protein.
3.4
Regulation of Klf2, Klf4 and Klf6 by H2O2 and pro-inflammatory cytokines
We considered whether other stimuli also regulate expression of Klf2, Klf4 or Klf6 mRNAs. H2O2 (0.2 mM) promotes cardiac myocyte apoptosis [41], but also stimulates ERK1/2 (and other pathways) [42], and stimulated a transient increase in expression of Klf2 (Fig. 5A). However, the response was less than that of ET-1 (∼ 3.6-fold) and was delayed (maximal increase at 1 h) (Fig. 1A). Consistent with our previous report [23], 0.2 mM H2O2 upregulated Klf4 and Klf6 mRNAs with maximal expression (5.0-fold) from 1 h and sustained expression over at least 6 h (Fig. 5, B and C). We have observed a similar delay in induction of other IEGs by H2O2 compared with ET-1 (e.g. Fos family members, unpublished data), which may reflect the delayed time course for activation of ERK1/2 signalling (maximal activation of ERK1/2 by ET-1 is at ∼ 5 min [43]; maximal activation by H2O2 is at 15–30 min [42]). Consistent with a role for ERK1/2 signalling, U0126 inhibited the increase in expression of Klf2, Klf4 and Klf6 by H2O2 (Fig. 5D). As with ET-1, SB203580 did not affect the response to H2O2 (Fig. 5D), whereas LY294002 enhanced the response (Fig. 5E). In endothelial cells, Klf2 is downregulated by TNFα [15]. IL-1β or TNFα profoundly and rapidly (from ∼ 1 h) downregulated Klf2 mRNA expression, a response which was sustained over at least 6 h (Fig. 6, A and B). Neither cytokine significantly affected the expression of Klf4 or Klf6 (data not shown). The decrease in expression of Klf2 in response to IL-1β was not affected by either SB203580 or U0126, although LY294002 (which itself increased expression of Klf2 mRNA, Fig. 3B) restored the baseline levels of expression (Fig. 6C).
4
Discussion
4.1
Regulation of gene expression by Klf family members
Many studies of individual Klfs relate to specific cells/tissues in which they are highly expressed or for which the phenotype of a transgenic mouse suggests they may play some role. Thus, most studies of Klf2 focus on its regulation in lung tissue, endothelial cells or T cells, whereas many studies of Klf4 are in gut, endothelial cells or smooth muscle cells, and Klf5 is studied largely in gut or smooth muscle cells. Although there are indications that Klfs are transcriptionally regulated in these various cells (e.g. Klf2 expression is increased by shear stress in endothelial cells [14]), and there is an indication of interplay between Klf family members (e.g. Klf4 and Klf5 appear to operate in an antagonistic manner to regulate cell cycle progression [8]), few studies consider the regulation and function of the Klfs as a family. Our data demonstrate substantial and dynamic regulation of nine Klfs by ET-1 in a single cell type (Fig. 1) with at least three regulated as IEGs (Fig. 2A), suggesting that Klfs potentially play a much more significant role in the phenotypic responses of more cell types than has so far been considered.
Klfs are defined by the high degree of homology in their C-terminal DNA-binding domains [1,6]. It is probably not surprising, therefore, that all bind to similar consensus DNA sequences or, with 17 family members, that such elements are relatively common in gene promoters. The Klfs form two principal groups of transcriptional repressors (acting through CtBPs or mSin3a) plus a group which are primarily transcriptional activators, and it could be argued that the different Klf groups all do bind to similar sequences but the effect depends on whether the Klf is an activator or repressor and, if the latter, the mode of action. An additional factor could be that different cells express different Klfs and interactions with other cell-specific transcription factors may result in differential gene expression. In cardiac myocytes, we detected expression of at least 11 Klfs (Table 2), with regulation of several family members from each of the activator/repressor groups by ET-1 (Fig. 1), suggesting that (at least in this system) neither tissue specificity nor classification according to global function is responsible for functional differences between individual Klfs. Since there is evidence for functional specificity in other cells [44,45], minor variations in primary and secondary structure may be sufficient for individual Klfs to have differing affinities for precise sequences in various gene promoters. Expression of any individual gene may therefore reflect the balance of Klfs expressed at any particular time in relation to the precise DNA-binding sites which can be accessed by them.
4.2
Regulation of expression of Klf2, Klf4, Klf5 and Klf6
Of the Klfs which were upregulated by ET-1, Klf2 mRNA expression increased very rapidly and transiently (Fig. 1A), and it was regulated as an IEG (Fig. 2A). Partial inhibition of the response by U0126 (Fig. 3A) suggests that ERK1/2 signalling is required to some degree. ERK1/2 signalling was also required for upregulation of Klf2 by H2O2 (Fig. 5D). In contrast to endothelial cells subjected to shear stress [36], the increase in Klf2 expression by LY294002 and enhanced response to ET-1 in the presence of LY294002 (Fig. 3B) indicated that PI3K signalling negatively regulates Klf2 expression in cardiac myocytes. ERK5 phosphorylation of the transcription factor MEF2 promotes Klf2 expression in other cells [46], but ERK5 is not significantly activated in cardiac myocytes by ET-1 [24], ERK1/2 do not efficiently activate MEF2 [47] and inhibition of p38-MAPKs (which also phosphorylate MEF2 [47]) by SB203580 had no effect on Klf2 expression induced by ET-1 (Fig. 3B), suggesting that MEF2 is not the principal factor involved in this context. Of the transcription factors associated with the Klf2 promoter identified by Ahmed and Lingrel [48], the most probable candidate for regulating Klf2 expression by ET-1 in our study is CREB. CREB is rapidly (within 5–10 min) phosphorylated in cardiac myocytes exposed to ET-1, a response which is inhibited by U0126 [49].
Like Klf2, the increases in expression of Klf4 and Klf6 (Fig. 1, B and C) were inhibited by U0126, (Figs. 3A and 5D), and both were regulated as IEGs (Fig. 2A). Our data are consistent with other systems in which Klf4 is regulated as an IEG by 15-deoxy-Δ12,14 prostaglandin J2
[50] or platelet-derived growth factor [51], and Klf6 is regulated as an IEG by phorbol esters or serum [52,53]. However, for either Klf4 or Klf6, the potential transcription factors or mRNA stabilisation factors which promote the increase in expression are not known. The increase in expression of Klf5 induced by ET-1 was inhibited by cycloheximide (Fig. 2B), so the principal factors promoting its expression must be newly-synthesized proteins, and the response was inhibited by U0126 (Fig. 3A), implicating ERK1/2 signalling in the response. These data are consistent with other studies in which phorbol esters or fibroblast growth factor increase Klf5 expression in smooth muscle cells in an ERK1/2-dependent manner [54] and, in fibroblasts, the increase in expression induced by phorbol esters is inhibited by cycloheximide [55].
Studies of mRNA expression raise the question of whether or not changes in mRNA expression equate to changes in protein expression. In cardiac myocytes treated with ET-1, translation-state analysis indicated that mRNAs for Klf2, Klf4, Klf5 and Klf6 were increased to a similar or greater extent in the polysomes than in the total RNA pool (Fig. 4). Since ET-1 also increases the global rate of protein synthesis in cardiac myocytes [56] (illustrated by the increase in A254 profile of polysome fractions 6–11 with concomitant decrease of monosome fractions 2–4; Fig. 4A), overall, we would predict that the rate of synthesis of each of the Klf proteins should be increased at least in proportion to the mRNA. Consistent with this, we detected a significant, transient increase in expression of Klf6 protein following stimulation with ET-1 (Fig. 4G). Although cellular stresses such as oxidative stress can generally inhibit translation, 0.2 mM H2O2 does not have a significant effect on global protein synthesis over 4 h, and our microarray data indicate that Klf2, Klf4, and Klf6 are increased to a similar extent in the polysomes as in total RNA (data not shown).
4.3
Regulating Klf-dependent gene expression in cardiac myocytes
Downregulation of Klf15 is associated with the hypertrophic response of cardiac myocytes and may be required for hypertrophy to develop [21]. However, our data suggest that the situation regarding Klf-mediated regulation of gene expression in cardiac myocytes is more complex. In cardiac myocytes exposed to ET-1, we detected a rapid decrease in expression of mRNAs for three established repressors of gene expression (Klf3, Klf11 and Klf15; Fig. 1, G, H and I), with simultaneous rapid increases in expression of four Klfs more commonly associated with transcriptional activation (Klf2, Klf4, Klf5 and Klf6; Fig. 1, A–D). In response to ET-1, therefore, there appears to be an immediate overall switch towards increasing Klf-directed gene expression. However, the increases in expression of Klf2, Klf4, Klf5 and Klf6, and decrease in expression of Klf3 were transient, with a delayed increase in expression of Klf9 and Klf10, suggesting that the balance of Klf-regulated gene expression continues to change. A similar effect was detected with H2O2, with an early increase in expression of Klf2, Klf4, Klf5 and Klf6 and simultaneous decrease in expression of Klf3, Klf11 and Klf15 (Fig. 5 and [23]). In contrast, Il-1β or TNFα promoted downregulation of Klf2 expression (Fig. 6) with little effect on Klf4 or Klf6. Clearly, the genes which each of the Klfs regulates in cardiac myocytes remain to be established.

krüppel-like factors
cardiac myocytes
endothelin-1
cytokines
immediate early genes
gene expression