extfieldsofstudy: Biology Medicine

s2fieldsofstudy: Biology Engineering

text:
Noninvasive Metabolic Imaging of Engineered 3D Human Adipose Tissue in a Perfusion Bioreactor

The efficacy and economy of most in vitro human models used in research is limited by the lack of a physiologically-relevant three-dimensional perfused environment and the inability to noninvasively quantify the structural and biochemical characteristics of the tissue. The goal of this project was to develop a perfusion bioreactor system compatible with two-photon imaging to noninvasively assess tissue engineered human adipose tissue structure and function in vitro. Three-dimensional (3D) vascularized human adipose tissues were engineered in vitro, before being introduced to a perfusion environment and tracked over time by automated quantification of endogenous markers of metabolism using two-photon excited fluorescence (TPEF). Depth-resolved image stacks were analyzed for redox ratio metabolic profiling and compared to prior analyses performed on 3D engineered adipose tissue in static culture. Traditional assessments with H&E staining were used to qualitatively measure extracellular matrix generation and cell density with respect to location within the tissue. The distribution of cells within the tissue and average cellular redox ratios were different between static and perfusion cultures, while the trends of decreased redox ratio and increased cellular proliferation with time in both static and perfusion cultures were similar. These results establish a basis for noninvasive optical tracking of tissue structure and function in vitro, which can be applied to future studies to assess tissue development or drug toxicity screening and disease progression.

Introduction
Two-dimensional (2D) cell culture systems and animal models are routinely used to assess toxicity for drug screening and to gain insight into diseases. Although these systems are useful, there is growing recognition of current limitations with 2D cell culture and rodent models, as they have not provided consistency in correlations to human clinical outcomes [1], leaving room for new options to fill this void. For example, animal models are utilized for many studies; however, when assessed for complex disease treatments including but not limited to treatments for viral (HIV), multibacillary (leprosy), and respiratory diseases, they usually fail to adequately represent how clinical treatments affect humans [2,3,4]. This is due to the complex differences in immunological responses that humans and other animals exhibit compared to one another, as well as differences in metabolic and physiological responses. Other complications arise when using 2D cell cultures as human models for clinical applications, which are inherently caused by the absence of multi-dimensional inputs and complex human tissue transport and signaling. Tissue engineering has emerged over the last two decades as an alternative to address the limitations of animal models and 2D cell cultures in modeling the biochemical and structural characteristics of native threedimensional (3D) human tissues. The ability to combine cells and matrices to generate 3D human tissues in bioreactors has advanced rapidly in recent years to make tissue engineered constructs utilizing human cell sources a more advantageous model for studies relevant to clinical applications.
However, significant challenges exist in imaging and characterizing 3D tissues. Current standard approaches, such as histology and quantitative polymerase chain reaction (qPCR) techniques, are inherently destructive and cannot be used to continuously monitor tissue changes over time. Cells cultured in 2D are more easily observed for cellular density and localization over time through noninvasive means because their entire surface is easily observable on a plate. In contrast, analysis of 3D tissue cultures is often limited by the difficulty of visualizing cell features without mechanically sectioning the culture. By sacrificing each 3D culture to assess its biochemical or structural characteristics, dynamic spatiotemporal changes associated with tissue development or drug treatments cannot be measured. Further, the costs and complexity associated with such assessments limit their utility, and substantially more samples are needed to provide conclusive data over the same number of time points when compared to nondestructive analyses.
Different optical imaging methods capable of noninvasively tracking metabolic and morphological changes in 3D tissues over time include confocal microscopy, two-photon excitation fluorescence (TPEF), and fluorescence lifetime imaging, all of which can analyze morphological and biochemical features of tissues [5,6,7]. Recently, significant advances in optical imaging methods capable of probing endogenous cellular components in 3D specimens have emerged as a new approach to the challenges of monitoring tissue characteristics over time, obviating the need for destructive analysis [8,9,10]. Metabolic activity and cellular differentiation status can be assessed by isolating different metabolic fluorophores as endogenous sources of contrast. Specifically, the cofactors NAD(P)H and FAD, which link the tricarboxylic acid cycle to oxidative phosphorylation, can be isolated and quantified to assess osteogenic or adipogenic differentiation through TPEF imaging of human mesenchymal stem cells [9,11]. Quantifying the relative fluroescence of NAD(P)H and FAD, provides a measure of the balance between energy storage (e.g. triglyceride synthesis) and consumption, which changes during processes such as adipogenic differentiation. These methods capable of non-destructive, live cell imaging within 3D human tissues are particularly amenable to drug and toxicology screening applications that require repeated dynamic measurements of cell function.
An aim of this study was to understand metabolic changes in engineered 3D human adipose tissue using a TPEF imaging platform [8]. TPEF imaging provides an ideal approach to localize specific fluorescent biomolecules within a 3D tissue. The use of near infrared light enables deeper penetration into the tissue constructs, and the simultaneous absorption of two photons provides intrinsic depth sectioning without the need for confocal detection using a pinhole. As a result, TPEF imaging can be used to detect the presence of metabolic cofactors such as FAD and NAD(P)H with minimal risk of photo-damage. By measuring the relative fluorescent concentrations of these two cofactors with a high sensitivity, insights have been gained into both anabolic and catabolic behavior of biological systems in vitro [9,11,12]. The expression of these cofactors is known to change during drug treatment, differentiation, and cell division, thus providing a sensitive indicator of cellular metabolic behavior [13,14]. Quantifying cellular differentiation, proliferation, and drug responses is paramount in elucidating clinical treatments for injuries, pathogenesis, and toxicity. Unlike mass spectroscopy or enzyme-linked immunosorbent assays (ELISA), TPEF-based metabolic analysis can detect both temporal and spatial changes in cell responses to injury, disease, or drug treatment.
Modern bioreactor systems allow for more physiologically relevant models of human tissue compared to more traditional static tissue culture plates. Bioreactor systems can be designed to accommodate perfusion flow, media collection, and noninvasive imaging by designing them with imaging windows compatible with fluorescence imaging modalities. These imaging interfaces also have the potential for repeated TPEF imaging as a noninvasive approach to assess the structure of biomaterials, formation and deposition of new extracellular matrix, cellular viability, and metabolic cofactors of in vitro tissue systems. The goal of the present study was to implement a perfusion bioreactor system interfaced with noninvasive optical imaging tools to track the metabolic function of engineered 3D adipose tissue. The development and validation of such a bioreactor system would provide a powerful drug screening platform that enables a nondestructive quantitative measure of lipid metabolism in the study of diseases such as obesity and diabetes. To achieve such goals, metabolic monitoring in the bioreactor system was compared with static 3D cultures, and these outcomes were evaluated and confirmed against more traditional histological assessments of the tissue.

Ethics Statement
The protocols for engineering 3D vascularized human adipose tissue in vitro have been described previously by our lab [15]. hASCs were acquired from subcutaneous adipose tissue which was obtained from abdominoplasties approved under Tufts University IRB (Tufts University IRB Protocol #0906007) from Tufts Medical Center, Department of Plastic Surgery. Since the tissue was taken for an elective surgery and would have been routinely discarded and no identifying information about the patient was obtained, only verbal consent from the patient was required. HUVECs were obtained from Lonza (catalog #CC-2519, HUVEC-Umbil Vein, pooled donor cells, EGM,cryo amp). Verbal consent was approved by the Tufts University, Medford Campus Institutional Review Board (IRB). The patient was asked to review an informational sheet describing what researchers would do with their otherwise discarded tissue. Only if assent was collected, the researchers would be contacted after surgery by the medical staff to pick up the de-identified tissue. Verbal consent was deemed appropriate since no patient identifiers were collected and because the patients had undergone elective surgeries where the tissue of interest would have been discarded. Patients had undergone elective weight loss surgeries, the tissues resulting from these surgeries are routinely discarded. Since we were only using tissue that was procured for other purposes and would be otherwise discarded our IRB committee deemed full written consent unnecessary. Prior to surgery, patients were asked to review a patient informational sheet explaining how researchers would plan to use their discarded tissue. The sheet clearly stated that they may refuse to allow their discarded tissue to be used without any effect to their medical care. If the patient consented, the otherwise discarded tissue would be set aside for research. Additionally, no patient identifiers were collected and therefore all samples were de-identified. Patients were asked to review the informational sheet (as described above). If they consented verbally, a nurse would called the researchers, once the surgery was over and everything went well, to pick up the de-identified tissue. An assent log is kept of dates and initials of the researcher on the IRB protocol who picked up the tissue.

Bioreactor
The 3DKUBE TM system (Kiyatec, Pendleton, South Carolina) was used in the present study with some modifications. The chamber accommodates a segregated culture of two samples, is compatible with unidirectional perfusion flow, and contains an imaging interface for each sample. The 3DKUBE TM chamber accommodates a 6.0 mm diameter68.8 mm height internal volume for tissue placement. An external media reservoir was used to sustain the 3D tissue and provide for perfusion flow supply. The barrel of a 30 mL syringe with the plunger removed was used for this purpose, and the top of the syringe was plugged using a Size 3 VWR Black Rubber Stopper (Radnor, PA). In order to supply oxygen and remove carbon dioxide from the system, an outlet for gas exchange was implemented using a Millex-FG, 0.22 mm, hydrophobic PTFE, 25 mm diameter, EtO sterilized air filter. The gas exchange port was put at the site of the black rubber stopper at the top of the media reservoir. The chamber, peristaltic pump, and media reservoir for gas exchange were connected in series via platinum cured silicone and PharMed tubing (Cole Parmer, Vernon Hills, IL) using luer lock to barb connections. Each component used in the bioreactor system was sterilized by autoclaving with the exception of the modified 3DKUBE TM chambers and media reservoir (syringe barrel/rubber stopper), which were sterilized using a 24 hour EtO sterilization cycle. For each tissue sample introduced to the bioreactor system there was one 3DKUBE TM , one 30 mL syringe barrel, one perfusion pump cassette, and approximately 180 of combined tubing.
The original imaging interface for the 3DKUBE TM was a 1.25 mm thick polystyrene plastic window which was not compatible with the 636 objective (1.2NA, 220 mm working distance) used for TPEF imaging (Fig. 1). To accommodate imaging with this objective, the 3DKUBE TM polystyrene imaging interface was removed and replaced with No. 1.5 thickness (Fischer Scientific, Hampton, NH) cover glass. This modified imaging window increased the internal height of the chamber to 10 mm, and allowed for imaging up to a depth of 200 mm (Fig. 1B).

Perfusion
Optimal flow rates for tissue engineering applications are generally on the order of 400-800 mm/s [16]. These flow rates have been utilized for cardiac and osteogenic tissue engineering, and correlates appropriately to native interstitial lateral flow velocities which have been measured at 500 mm/s [16,17,18,19]. Using 10 rpm for the peristaltic pump and Masterflex PharMed tubing size #13 (0.030 ID) an average flow rate of 0.0056875 mL/ s was measured. Considering the cross-sectional area of the bioreactor chamber inflow and outflow ports, the lateral flow rate perfusing through the adipose tissue samples within the bioreactor chambers was 412.5 mm/s.

Engineered 3D Human Adipose Tissue
The protocols for engineering 3D vascularized human adipose tissue in vitro have been described previously by our lab [15]. Porous silk scaffolds were used to house the cells and fit into the bioreactors. The benefits for the silk biomaterial are the mechanical durability and very slow degradation (months to years), along with biocompatibility and success in 3D adipose tissue formation in our prior studies [9,15]. We have previously described in detail the preparation and characterization of these porous silk scaffolds for the system [20]. Briefly, the Bombyx mori silk cocoons were chopped and boiled in a 0.2M sodium carbonate solution, and rinsed three times in distilled water, and then dissolved in a 9.3M lithium bromide solution [21]. The salt was removed by dialysis against distilled water before being centrifuged to remove any particulates. An 8% w/v silk aqueous concentration was used to form the scaffolds, to which 4 g of sieved granular sodium chloride, particle size 500-600 microns, was added to 2 mL silk solution and allowed to solidify for 72 hrs at room temperature. The scaffolds formed were rinsed in distilled water for 48 hrs to leach out the salt particles. The final scaffolds were punched out into 6610 mm (diameter6height) scaffolds. The scaffolds were autoclaved for sterilization prior to cell seeding.
HASCs were acquired from subcutaneous adipose tissue which was obtained from abdominoplasties approved under Tufts University IRB (Tufts University IRB Protocol #0906007) from Tufts Medical Center, Department of Plastic Surgery. Since the tissue was taken for an elective surgery and would have been routinely discarded and no identifying information about the patient was obtained, only verbal consent from the patient was required. HUVECs were obtained from Lonza (catalog #CC-2519, Walkersville, MD) and cultured according to the manufacturer's protocol in EGM complete media (Lonza, Walkersville, MD). Both human adipose derived stem cells (hASCs) and human umbilical vein endothelial cells (HUVECs) were grown to confluency in 2D cultures before being seeded onto porous silk protein scaffolds at specified cell densities and time points. HASCs were cultured in Dulbecco's Modified Eagle's Medium with Ham's F12 (DMEM/F12) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin streptomycin fungizone (PSF). At 2 days post-confluence, the hASCs were switched to adipogenic induction media comprised of DMEM/F12, 3% FBS, 1% PSF, 500 mM 3isobutyl-1-methylxanthine (IBMX), 5 mM 2,3-Thiazolidinediones (TZD), 1 mM dexamethasone, 17 mM pantothenate, 33 mM biotin and 1 mM insulin. Media supplements for hASC media were purchased from Sigma-Aldrich (St. Louis, MO), media, FBS, and PSF were purchased from Invitrogen (Carlsbad, CA).
The sterile scaffolds were soaked in EGM media to promote cell adhesion and media was aspirated after 1 hr. Trypsinized HUVECs were re-suspended at a concentration of 26 million cells per mL EGM, and seeded in three 20 mL aliquots. The seeded scaffolds were then incubated for 1 hr to allow for cell attachment before adding 6 mL of EGM media to the well. The same seeding protocol was followed for seeding hASCs, except the cell concentration used was 13 million cells per mL hASC maintenance media (same as adipogenic media without IBMX and TZD). Scaffolds seeded with both hASCs and HUVECs were maintained in a 1:1 ratio of EGM media to adipogenic maintenance media.

Timeline
The experimental timeline is shown in Fig. 2, where Day 0 corresponds to when HUVECs are first seeded onto the silk scaffold. At Day 0, adipogenic differentiation of the confluent 2D hASC cultures is induced through the addition of soluble factors [9,15], and on Day 7 the differentiating hASCs are seeded onto the scaffold. Because the tissues in this experiment are introduced to perfusion flow, initial cell adhesion to the silk scaffolds is important. To facilitate this step, the tissues were not introduced to the perfusion bioreactor system until Day 9 (two days after seeding the differentiating hASCs), in order to ensure that the cells remain anchored to the scaffold well after seeding. On Days 9, 16, and 23, the samples were imaged from within the 3DKUBE TM chamber through the custom fitted cover glass. In the data presented in the results, Days 9, 16, and 23 correspond to the time points 0 wk, 1 wk, and 2 wks

Fluorescent Object Classification and Redox Ratio Analysis
Previous analysis of silk as a biomaterial in solution, gel, and scaffold forms found that the fluorescent properties of silk was dominated by tryptophan fluorescent contributions [22]. This factor results in peak single-photon and two-photon excitation wavelengths of roughly 280 and up to 700 nm, respectively [8]. The broad two-photon excitation spectrum of silk results in substantial silk fluorescence even at the longer excitation wavelengths used in this study. However, the relative ratios of silk fluorescence intensity within the specific excitation and emission channels used here are slightly different than the relative ratios of cellular fluorescent intensities that detect NAD(P)H and FAD. By utilizing linear discriminant analysis, the fluorescence intensities of silk and cells can be properly classified and differentiated from one another [9].
Adipose tissue samples were imaged with a confocal microscope (Leica TCS SPC2 confocal microscope, Mannheim, Germany) using a Ti:Sapphire tunable laser (Mai Tai; Spectra Physics; Mountain View, CA). The samples were held flush with the glass coverslip interface allowing a 200 mm penetration depth using a water immersion 636 objective. The fluorescence of NAD(P)H and FAD were isolated by exciting the samples at specific wavelengths and then gathering fluorescence intensities across two different ranges using non-descanned photomultiplier tubes (PMTs). NAD(P)H was excited using a 755 nm excitation wavelength, and the subsequent fluorescence intensity was measured from a PMT detector with a 460(620) nm emission filter. FAD was similarly isolated with an excitation wavelength of 860 nm and PMT detector with a 525(625) nm emission filter [9,11]. A series of depth resolved image stacks were taken from 0-40 mm, 80-120 mm, and 160-200 mm at 2.5 mm increments. Each image volume was then run through a custom image processing program in Matlab that segmented 3D objects and utilized a linear discriminant analysis to distinguish between cells and the silk scaffold structure based on differences in fluorescence intensity at the different emission and excitation wavelengths [9].
After identifying the cells within the 3D depth resolved images, the cell fluorescence intensities, redox state, cell morphology and size were quantified by software [9]. The 12-bit fluorescence intensity image at each excitation and emission combination was normalized by laser power and detector gains [9]. The average NAD(P)H and FAD fluorescence intensities were computed for each cell, and a cell-specific redox ratio of FAD/(NAD(P)H+FAD) was computed and expressed using a false color redox map (Fig. 3). The calculated redox ratio has been used to measure metabolic behavior, and the analysis performed here in a perfusion environment was compared to our previous research conducted in static cultures [9].

Histology
To complement the redox maps and data acquired from the noninvasive imaging analyses, histological H&E stained images were imaged to gain an understanding of cellular proliferation, cellular localization, and ECM formation within the tissues. Tissues were fixed in formalin, and put through a series of dehydration steps and finally paraffin. Paraffin embedded tissues were then cut into 6 mm thick slices, and rehydrated and stained with H&E, according to standard histology protocols [23,24]. Images of the H&E stained samples taken at the edge and the middle sections of the tissues using 106 and 406 objectives. The tissue 'edges' were defined as the surface of the tissue that was imaged through redox ratio analysis from the bottom side of the bioreactor chamber, while 'middle' images lie within the same circular cross sectional area of the perfusion inflow and outflow ports of the bioreactor chambers taken from halfway within the tissue.

Statistical Analysis
A one-way analysis of variance (ANOVA) was performed to determine whether there was a significant difference between the mean redox ratios at the three different time points. Post-hoc two sample t-tests were performed to determine differences between the 0 wk and 1 wk time points, and between the 1 wk and 2 wk time points. Similar t-tests were performed to compare redox ratios between perfusion and previously published static cultures. All t-test statistics were Bonferroni-corrected and significance was defined when p,0.05.

Results
After the redox ratio calculations were performed on the images, the data obtained from the perfusion system was compared with data acquired from static cultures gathered in our previous study [9] (Fig. 3 & 4A). Neither culture showed a significant change in redox ratio between their respective 0 wk and 1 wk time points (Fig. 4a). At the 0 wk time point the sample mean redox ratio was 0.80360.0021 for the static culture, and 0.79260.0137 in the perfusion culture. At 1 wk, the static culture sample mean redox ratio was 0.80860.040, and in perfusion the sample mean was 0.79460.0066. However, both the static and perfusion systems showed a significant decrease in redox ratio from the 1 to 2 wk time points. From 1 to 2 wks, the average redox ratio of cells found within the static culture dropped from 0.80860.040 to 0.71360.0032 (p,0.001). In perfusion, the average cellular redox ratio from 1 to 2 wks significantly decreased (p,0.001) from 0.79460.0066 to 0.7446.0049. In addition, the static and perfusion cultures at the 2 wk time point were significantly different (p,0.001) where the perfusion culture showed a higher redox ratio after 2 wks.
The TPEF images were further analyzed to yield data on cellular density with respect to imaging depth. The relationship between cellularity with respect to depth at 0, 1, and 2 wks in perfusion is shown in Fig. 4B. The observed trend was that cellularity was most dense at the surface layers after 1 wk and then the cells gradually began to distribute themselves more evenly throughout the three imaging depths in perfusion. To supplement the cell count measurements made noninvasively, the cellularity within the silk scaffolds was also assessed as a function of time through histology. H&E images show qualitatively that there was no substantial difference in cellularity and ECM formation with respect to depth within the tissue samples at the end of end of test (2 wks) in perfusion culture (Fig. 5).

Discussion
Over two weeks, the perfusion bioreactor system and imaging platform described here demonstrated the ability to obtain metabolic redox ratio data from 3D tissue engineered human adipose tissue samples in vitro. The average cellular redox ratios gathered through TPEF imaging and subsequent image processing showed similar trends as has been observed in static cultures; consistent redox ratios over the first week were followed by a significant drop at 2 wks. The decrease in redox ratio from 1 to 2 wks is consistent with previous work and indicates hASC differentiation [9]. Upon differentiation, hASCs are not expected to continue to proliferate. This expectation is supported in by the relationship between the average cellular redox ratios (Fig. 4A) and the average cellularity (Fig. 4B). There is no significant change in redox ratio observed within the first week of testing, which would suggest that hASC differentiation is low and proliferation is continuing. This is further supported by the increase in cellularity observed from the 0 to 1 wk time points. Then as the rate of differentiation begins to increase between the 1 and 2 wk time points, the redox ratio decreases and cellular proliferation halts, as observed in the more modest increase in cellularity from 1 to 2 wks compared to the rapid increase from 0 to 1 wks.
Previous analyses demonstrated a decrease in redox ratio coincided with the formation of lipid droplets [9]. The underlying mechanism driving the relationship between redox ratio and lipogenesis has been attributed to an accumulation of mitochondrial NADH during de novo fatty acid synthesis. Energy storage through fatty acid synthesis and lipid droplet formation likely only occurs when the relative energy consumption is low. Decreases in ATP production and NADH oxidation by the electron transport chain combined with an increase in the conversion of NAD+ to NADH by the pyruvate dehydrogenase complex when breaking down pyruvate for fatty acid synthesis collectively cause a decrease in the mitochondrial ratio of NAD+/NADH. Furthermore, FAD bound to lipoamide dehydrogenase in the pyruvate dehydrogenase complex will likely remain in the oxidized form when the less NAD+ is present to accept the electrons. Collectively, these mechanisms are thought to provide a consistent decrease in redox ratio during lipogenesis in both static and perfusion cultures.
The smaller decrease in redox ratio in the perfusion culture relative to static cultures could be a result of a slight delay or reduction in the number of hASCs differentiating and undergoing lipogenesis at 2 wks. Alternatively, the endothelial cells may be more viable or proliferative in the perfusion culture than in static cultures causing a dampening of the expected decrease in redox ratio associated with adipogenic differentation. However, previous comparisons between adipogenic differentiation in hASC monocultures and hASC-endothelial co-cultures yielded no differences between the redox ratio of the two groups beyond Day 1, suggesting endothelial cells likely have little effect on redox ratio Figure 4. Changes in cell redox ratio and density with time. A) Average cellular redox ratio at 0, 1, and 2 wks in perfusion compared to previously published static culture experiments [9]. In both static and perfusion culture, no change in redox was observed between 0 and 1 wks, but the redox ratio decreased (p,0.001) from 1 to 2 wks. Although a significant decrease was observed in both cultures at wk 2, the redox ratio was signficantly higher in the perfusion cultures. B) Cell population measurements per image stack (2386238640 mm volume) at 0, 1, and 2 wks in perfusion flow demonstrate an increase over time, but substantial sample-to-sample variability. doi:10.1371/journal.pone.0055696.g004 changes over time. Nonetheless, CD-31 staining for endothelial cells in histological sections can be performed in future work to gain more insight into the immediate behavior of endothelial cells in the 3D cultures with respect to cellular proliferation and angiogenesis. It is also worth noting that experimental differences between the current perfusion study and previous static culture study [9], including a human endothelial cell source and the different solvents used during silk scaffold processing, may cause a small difference in the temporal pattern of differentiation. Finally, it is worth considering that the mechanical environment produced by perfusion may inhibit adipogenic differentiation or lipogenesis to some degree or selectively detach mature adipocytes with large lipid stores.
Previous studies involving adipose tissue engineering and subsequent characterization for clinical applications have set forth both protocols and analytical benchmarks by which adipose tissue can be analyzed in vitro and compared. Adipose tissue has been engineered using both synthetic and natural polymer scaffolds with both human mesenchymal and pluripotent stem cells [9,15,25,26]. These studies have provided an understanding of how lipid droplet formation, cellular proliferation, lumen formation, and cell metabolism change over time. These observations demonstrate cellular distribution, ECM growth, and vascularity within engineered adipose tissues grown in static culture. Through these previous studies, a standard set of analytical tools used to evaluate the 3D engineered adipose tissue have been developed which include, but are not limited to, histology, immunohistochemistry, and two-photon imaging [7,9,27]. In this study histological analysis with H&E staining was performed and is capable of measuring this cellular distribution and ECM growth, which is helpful in supporting and verifying the data collected from TPEF imaging. In previous work, the relationships have been established between TPEF imaging and Oil Red O staining for lipid droplet formation, as well as CD31 staining for endothelial cell organization [9]. Future work with the bioreactor system used in the current study will further confirm that TPEF relationships with these other traditional analytical benchmarks are maintained in a perfusion environment.
As observed in Fig. 5, there is little, if any, ECM present in the 0 wk samples. This is likely because these samples had only been seeded with differentiating hASCs for two days, which is not sufficient time to accommodate ECM formation. There is, however, ECM present in the 1 and 2 wk cultures (note red arrows on Fig. 5). This is likely a result of increased cellularity within the tissues, accomplished through more allowed time for the cells to adhere to the silk scaffolds and generate an ECM. The ECM of adipose tissue primarily consists of a loose collagenous matrix to provide structural support. It would be expected that if these tissues had remained in culture for longer times, that the cellularity of the tissues would increase while a more robust and expansive ECM developed. We have previously reported such findings using more traditional destructive modes of imaging for engineered 3D human adipose tissue systems. ECM and cellularity have been visually observed through immunohistochemistry, Oil Red O, and image processing cell count algorithms [9,15,27].
Although substantial variability was observed in cell densities, there did appear to be a trend toward higher cell density in the superficial layer of the tissue at 0 wks compared to the deeper sections. This is not surprising because the cells were only just seeded, and most likely did not have much opportunity to migrate throughout the silk scaffolds. Aggregation of the cells was observed within the superficial (0-40 mm) range at the 1 wk time point within the perfusion cultures. However, by 2 wks, cellularity appeared to level out within each of the layers (0-40 mm, 80-120 mm. 160-200 mm) in both the noninvasive imaging analysis (Fig. 3) and H&E stains in the perfusion culture (Fig. 5). Longer experimental time points would be required to confirm this trend, but a more homogenous distribution of cells within a 3D scaffold in perfusion culture environment relative to static has been observed elsewhere [16].
Relative to normal tissue culture plate cell observations, the perfusion bioreactor and imaging system affords distinct advantages, including more physiologically relevant perfusion flow and the ability to perform repeated two-photon imaging analyses with submicron resolution. The impact of this approach is improved efficacy and relevance of future analytics by providing a platform for continuous metabolic and structural analysis of 3D human tissue samples. The approach also implicitly allows for improved cost and labor efficiency by offering an alternative to nondestructive analyses, saving substantial resources. Certainly, optical microscopy evaluation is limited to the outer 0.5-1 mm of the tissue. While no depth-dependent differences in redox ratio were observed in this or previous studies [9], additional evaluation of unstained tissue sections may provide further insight into the necessary scaffold and bioreactor design considerations for highthroughput drug and toxicity screening applications.
In conclusion, the research here demonstrates the ability to interface non-linear optical microscopy with 3D human tissue constructs within a perfusion environment for noninvasive continuous analysis. The perfusion bioreactor system was modified to be compatible with advanced imaging capabilities using high numerical aperture objectives, but was still capable of sustaining continuous flow over weeks to months with media replenishment. The perfusion bioreactor system sustained cultures over the span of weeks, and demonstrated potential to continue for months, as has been previously performed in static cultures. Multiple samples that entered the perfusion system were able to remain in a sterile environment and maintain viability throughout the experiment. TPEF imaging provided metabolic and cellular distribution information which correlated with standard histological analysis. These destructive and non-destructive imaging approaches supported each other, both showing adipose tissue formation as characterized by an increase in cellularity throughout testing, and ECM growth as shown through histology. A constant redox ratio was observed over the first week of testing in both the current perfusion study and previous static study [9], but both demonstrated a significant decrease in redox ratio from the 1 to 2 wk time points, suggesting lipogenesis. This combination of quantitative metabolic imaging and 3D tissue engineering using perfusion bioreactors represents a step toward low-cost, highthroughput methods to study more physiologically relevant engineered human tissue systems in applications such as disease models, drug screening, or developmental biology.


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Bigenomic transcriptional regulation of all thirteen cytochrome c oxidase subunit genes by specificity protein 1

Cytochrome c oxidase (COX) is one of only four known bigenomic proteins, with three mitochondria-encoded subunits and 10 nucleus-encoded ones derived from nine different chromosomes. The mechanism of regulating this multi-subunit, bigenomic enzyme is not fully understood. We hypothesize that specificity protein 1 (Sp1) functionally regulates the 10 nucleus-encoded COX subunit genes directly and the three mitochondrial COX subunit genes indirectly by regulating mitochondrial transcription factors A and B (TFAM, TFB1M and TFB2M) in neurons. By means of in silico analysis, electrophoretic mobility shift and supershift assays, chromatin immunoprecipitation, RNA interference and over-expression experiments, the present study documents that Sp1 is a critical regulator of all 13 COX subunit genes in neurons. This regulation is intimately associated with neuronal activity. Silencing of Sp1 prevented the upregulation of all COX subunits by KCl, and over-expressing Sp1 rescued all COX subunits from being downregulated by tetrodotoxin. Thus, Sp1 and our previously described nuclear respiratory factors 1 and 2 are the three key regulators of all 13 COX subunit genes in neurons. The binding sites for Sp1 on all 10 nucleus-encoded COX subunits, TFAM, TFB1M and TFB2M are highly conserved among mice, rats and humans.

Introduction
Neurons are highly dependent on ATP for their activity and functions [1,2]. Approximately 90 per cent of ATP generated in the brain is synthesized in the mitochondria via oxidative phosphorylation [3]. Eukaryotic cytochrome c oxidase (COX) is the terminal enzyme of the energy-transducing mitochondrial electron transport chain and is embedded in the inner mitochondrial membrane, where it mediates the transfer of electrons from reduced cytochrome c to molecular oxygen. COX also engages in proton pumping in the establishment of an electrochemical gradient for the synthesis of ATP. COX is a complex of 13 subunits; the largest three are encoded in the mitochondrial DNA and the remaining 10 are encoded in the nuclear genome [4]. To form a functional holoenzyme with 1 : 1 stoichiometry of all 13 subunits, exact coordination is essential between the two genomes. The mechanism of regulating this ancient, multi-subunit, bigenomic enzyme critical for cell survival is only beginning to be probed.
Two redox-responsive transcription factors, nuclear respiratory factors 1 and 2 (NRF-1 and NRF-2), have been found to be mediators of such bigenomic coordination in neurons. Specifically of all 10 nucleus-encoded COX subunit genes [5][6][7], and indirectly regulate the three mitochondria-encoded COX subunit genes by activating mitochondrial transcription factors A and B (TFAM, TFB1M and TFB2M) [8,9]. Moreover, both NRF-1 and NRF-2 respond to and are regulated by neuronal activity, and changes in their mRNA and protein levels precede those of COX [6,[10][11][12]. Besides NRF-1 and NRF-2, specificity protein 1 (Sp1) has been postulated to bind to the promoters of a few COX subunit genes [13 -20] and TFAM [21,22]. However, none of these putative sites has been functionally characterized. The goal of the present study was to test our hypothesis that Sp1 is another bigenomic coordinator that regulates all 13 COX subunit genes.
By means of multiple approaches, in silico analysis, electrophoretic mobility shift and supershift assays, chromatin immunoprecipitation (ChIP), RNA interference and overexpression experiments, we document in this study that Sp1 functionally regulates all 13 COX subunit genes in neurons.

In vitro binding of specificity protein 1 to promoters
In vitro electrophoretic mobility shift assays (EMSAs) were carried out using 32 P-labelled probes (table 1) to determine the specificity of Sp1 binding to promoters of murine COX subunit genes (figure 1a-c). GM3 synthase promoter with a known Sp1-binding site at position 234/255 served as a positive control [23] and it formed specific DNA/Sp1 shift and supershift complexes (figure 1a, lanes 1 and 2, respectively). When an excess of unlabelled probe was added as a competitor, no shift band was formed (figure 1a, lane 3). When labelled oligonucleotides were incubated with Sp1 antibody without HeLa nuclear extract, neither shift nor supershift band was observed (figure 1a, lane 7 for Cox4i1-specific oligo), ruling out nonspecific antibody-oligo interactions. As shown in figure 1a-c, all 10 murine COX promoters formed specific DNA/protein shift complexes when incubated with purified HeLa nuclear extract (figure 1a, lanes 4, 9 and 12; figure 1b, lanes 1, 4, 7  . Supershift assays using anti-Sp1 antibodies produced a supershift band of DNA/Sp1/antibody complex for each of the subunits (figure 1a, lanes 8, 11 and 14; figure 1b, lanes 3, 6, 9 and 12 and figure 1c, lanes 3, 6 and 9, respectively). Sp1 mutant showed neither shift nor supershift complexes, and unlabelled oligos also had no effect (figure 1c, lanes 10, 12 and 11, respectively).

3.3.
In vivo occupancy of the promoters by specificity protein 1 ChIP assays were performed to verify possible Sp1 binding to all 10 COX promoters in vivo. b-actin exon 5 (Actb) served as a negative control, whereas GM3 synthase with a known Sp1binding site [23] served as a positive control. As a negative control, another immunoprecipitation from the same stock of cell lysate was done using anti-nerve growth factor receptor (NGFR) p75 antibodies. Polymerase chain reactions (PCRs) targeting regions of 10 COX subunit promoters surrounding putative Sp1-binding sites were carried out in parallel on chromatin immunoprecipitated from N2a cells. A 0.5 per cent dilution of input chromatin (i.e. prior to immunoprecipitation) was used as a standard to indicate the efficiency of the PCRs. The proximal promoters of all 10 nucleus-encoded COX subunits 4i1, 5a, 5b, 6a1, 6b, 6c, 7a2, 7b, 7c and 8a, and mitochondrial transcription factors TFAM, TFB1M and TFB2M were co-immunoprecipitated with Sp1 antibodies and were amplified in semi-quantitative PCRs (figure 2). The amount of DNA precipitated by anti-Sp1 antibodies (Sp1 lanes) was greater than the amount precipitated by anti-NGFR (a negative control for background, NGFR lanes) for each of the 10 COX subunit promoters. GM3 synthase (positive control) showed a clear band, whereas b-actin (Actb, negative control) yielded no band with Sp1 (figure 2).

Effect of Sp1 over-expression
Transfection with vectors over-expressing Sp1 led to an 80-fold increase in Sp1 mRNA ( p , 0.001, figure 4b) and a fivefold increase in Sp1 protein ( p , 0.001, figure 4a). Over-expression of Sp1 caused an increase in the mRNAs of three mitochondrial COX subunits ( p , 0.001 for all, figure 4b), three mitochondrial transcription factors ( p , 0.001 for all, figure 4b) and 10 nucleus-encoded COX subunits ( p , 0.001 for all, figure 4b).
Over-expression of Sp1 also led to a 55 per cent increase in the protein level of Cox1 ( p , 0.001, figure 4a) and a 40 per cent increase in the level of Cox4i1 protein ( p , 0.01, figure 4a).

Effect of depolarization and impulse blockade
To determine if the expression of COX subunit genes was affected by depolarizing stimulation, cells were subjected to 20 mM of KCl for 5 h after incorporating Sp1 shRNA. The KCl regimen was found previously to activate COX gene expression in primary neurons [10,11]. As shown in figure 5, depolarizing stimulation resulted in a significant increase in the mRNA levels of Sp1 ( p , 0.001, figure 5a), three mitochondrial COX subunits ( p , 0.001 for all, figure 5a), three mitochondrial transcription factors ( p , 0.001 for all, figure 5a) and 10 nucleus-encoded COX subunits ( p , 0.001 for all, figure 5a). With Sp1 silencing, both mitochondria-encoded and nucleusencoded COX subunits as well as the three mitochondrial transcription factors failed to respond to KCl. Tetrodotoxin (TTX) at 0.4 mM has been shown to decrease the levels of COX subunit mRNAs as well as COX enzyme activity in vivo and in primary neurons [24][25][26]. To evaluate the effect of impulse blockade on COX subunits, N2a cells were treated with TTX for 3 days. TTX reduced the mRNA levels of Sp1

Discussion and conclusions
Using multiple molecular and biochemical approaches, including in silico analysis, electrophoretic mobility shift and supershift assays, ChIP assays, RNA interference and overexpression assays, we documented that Sp1 transcription factor regulates all 10 nucleus-encoded COX subunit genes directly and three mitochondrial-encoded COX genes indirectly by regulating the three transcription factors important in the transcription of mitochondrial DNA. Knocking down Sp1 resulted in a significant reduction in the expression of all 10 nucleus-encoded and three mitochondria-encoded COX subunit genes, and TFAM, TFB1M and TFB2M genes in neurons. Overexpression of Sp1 resulted in a significant upregulation of Sp1 itself, all 10 nucleus-encoded and three mitochondria-encoded COX subunit genes, and TFAM, TFB1M and TFB2M genes in neurons. The expression of all COX subunits and their transcription factor, Sp1, are tightly coupled to neuronal activity. The Sp1binding sites on promoters of nuclear COX subunit, TFAM, TFB1M and TFB2M genes are conserved among mice, rats and humans. It is noteworthy that COX6b and 7b had an atypical Sp1-binding motif ('GGGCGT' and 'GGGCGA', respectively), whereas other COX promoters as well as those for TFAM, TFB1M and TFB2M all had a typical Sp1-binding sequence. Sp1 is the first C 2 H 2 -type zinc finger transcription factor to be isolated and cloned from mammalian cells. Sp1 recognizes CG-rich sequences in CpG islands and was initially recognized as a constitutive transcription activator of housekeeping genes and other TATA-less genes. Sp1 is ubiquitously expressed, and at least 12 000 Sp1 typical binding sites have been found in the human genome, most of which have been associated with genes involved in many cellular functions [27][28][29][30]. Knockout of Sp1 is embryonically lethal [27,28]. Earlier studies have shown the presence of Sp1-binding sites on the promoter regions of some COX subunit genes. These include COX4i1 in humans, mice and rats [14,31,32], COX5b in mice and humans [16,18,20] and COX6a1 in rats [13]. Putative Sp1-binding sites have also been reported on nucleus-encoded TFAM gene [21,22]. However, functional characterization has not been done on any of these sites. The present study documented that Sp1 bigenomically regulates all 13 COX subunit genes, making it an important regulator of genes involved in the  160  140  120  100  80  60  40  20  0   180  160  140  120  100  80  60  40  20  0   Sp1  COX1 COX2 COX3 TFAM TFB1M TFB2M   4i1  6a1  7a2  5a  5b  6b  6c  7b  7c  8a   4i1  6a1  7a2  5a  5b  6b  6c  7b  7c  rsob.royalsocietypublishing.org Open Biol 3: 120176 production of cellular energy. The expression of Sp1 itself is tightly regulated by neuronal activity. Likewise, the regulation of all COX subunit genes and mitochondrial transcription factors is strongly dictated by neuronal activity, consistent with our earlier studies [10,11,24,26]. Depolarizing stimulation failed to increase their expression in the presence of Sp1 siRNA, and Sp1 over-expression rescued their downregulation induced by impulse blockade. These findings substantiate the important role of Sp1 in regulating COX subunit genes in an activity-dependent manner.
COX is a critical terminal enzyme of the mitochondrial electron transport chain, without which oxidative metabolism cannot be carried to completion and ATP cannot be generated [1,2,4]. The three mitochondria-encoded subunits of COX (COX1, COX2 and COX3) form the catalytic core of the enzyme, whereas the 10 nucleus-encoded subunits reportedly serve modulatory functions [33,34]. COXIV binds ATP directly and regulates COX activity based on the intra-mitochondrial ATP/ADP ratio [35,36]. COX5b and COX6a are also involved in the regulation of COX activity [37,38]. COX5a has a binding site for 3,5-diiodothyronine (T2) and relieves the inhibition of COX enzyme induced by intra-mitochondrial ATP [39]. COX6b is involved in the stabilization of the dimeric COX enzyme, and Cox6c provides a low-affinity binding site for cytochrome c [40]. Three of the nuclear subunit proteins (6a, 7a and 8) have tissue-specific isoforms and presumably regulate COX activity in different tissues based on tissue-specific requirements [33,41]. The functions of subunits Cox7a, 7b, 7c and 8a are not known with certainty.
The mechanism of regulating this multi-subunit, bigenomic and multi-chromosomal protein poses a major challenge. Recently, we demonstrated that all 10 nucleusencoded subunits of COX are regulated by the transcription factors NRF-1 and NRF-2 [5][6][7]. Both factors also regulate indirectly the three mitochondria-encoded COX subunit genes by activating TFAM, TFB1M and TFB2M important for mitochondrial DNA transcription and replication [8,9]. The present study revealed that Sp1 is the third transcription factor known thus far to functionally regulate all 10 nucleusencoded and three mitochondrial-encoded COX subunit genes in neurons in vitro and in vivo. Moreover, our recent study showed that all of the nucleus-encoded COX subunit transcripts and those for TFAM, TFB1M and TFB2M are all transcribed in the same transcription factory [42].
The coordinated regulation of all 13 COX subunit genes is likely to be complex and multifactorial, requiring various transcription factors and coactivators. The mechanism of coordinating multiple transcription factors can be complementary, concurrent and parallel or a combination of complementary and concurrent/parallel. In the case of NRF-1 and NRF-2, our recent study has shown that both NRF-1 and NRF-2 regulate the same set of genes in a parallel and concurrent manner [43]. Moreover, double knock-down of NRF-1 and Sp1 or of NRF-2 and Sp1 in primary neurons affected cell survival (Dhar and Wong-Riley 2012, unpublished data), indicating important roles of these factors in activating key metabolic genes that regulate cell growth, functioning and survival. Sp1-binding sites are in close proximity to those of NRF-1 and NRF-2 (Dhar and Wong-Riley 2012, unpublished data). Thus, Sp1 may physically interact with NRF-1 and/or NRF-2 in regulating some or all of the COX subunit genes. Future studies will be directed at evaluating such possible protein-protein interactions and possible colocalization of these factors in the same or different transcription factories. In addition to transcription factors, a transcriptional coactivator, peroxisome proliferator-activated receptor gamma coactivator 1a (PGC-1a), is also likely to be involved in the regulation of COX in neurons. PGC-1a coactivates NRF-1 and NRF-2 in the control of mitochondrial biogenesis [44,45] and its response to changes in neuronal activity precedes those of NRF-1 and NRF-2 [10,46]. rsob.royalsocietypublishing.org Open Biol 3: 120176 In conclusion, our multi-faceted investigation revealed that Sp1, in addition to NRF-1 and NRF-2, is a critical regulator of all 13 COX subunit genes in neurons ( [5][6][7], the present study).

In silico analysis of promoters of murine COX
subunit genes DNA sequences surrounding the TSPs of all 10 nucleusencoded COX subunit genes, and TFAM, TFB1M and TFB2M were derived from the mouse, rat and human genome database in GenBank. These promoter sequences encompassed 1 kb upstream and up to 500 bps downstream (excluding protein-coding sequence) of the TSP of each subunit gene analysed. These sequences were aligned using MEGALIGN, DNASTAR LASERGENE software. Putative Sp1-binding sequences 'GGGCGG' or 'CCCGCC' were searched using DNASTAR LASERGENE software. Regions of high homology were selected for experimental analyses.

Electrophoretic mobility shift and supershift assays
EMSAs to assay possible binding of Sp1 on all 10 COX subunit promoters were carried out with methods as described previously [7]. Briefly, oligonucleotide probes with putative Sp1-binding site on each promoter (table 1, based on in silico analysis) were synthesized, annealed and labelled by a Klenow fragment (Invitrogen) fill-in reaction with [a-32 P] dATP (50 mCi/200 ng; Perkin-Elmer, Shelton, CT, USA). Each labelled probe was incubated with 2 mg of calf thymus DNA and 5 mg of HeLa nuclear extract (Promega, Madison, WI, USA) and processed for EMSA. Supershift assays were also performed, and in each reaction, 1-1.5 mg of Sp1-specific antibodies ( polyclonal goat antibodies, Santa Cruz Biotechnology, Santa Cruz, CA, USA) were added to the probe/nuclear extract mixture and incubated for 20 min at room temperature. For competition, 100-fold excess of unlabelled oligonucleotides were incubated with nuclear extract before adding labelled oligonucleotides. Shift reactions were loaded onto 4 per cent polyacrylamide gel and run at 200 V for 2.5 h in 0.25Â Tris/borate/EDTA (TBE) buffer. Results were visualized by autoradiography. Mouse GM3 synthase with Sp1-binding site at position 234/255 was designed as previously described [23] and used as a positive control. Sp1 mutants with mutated sequences as shown in table 1 were used as negative controls.

Chromatin immunoprecipitation assays
ChIP assays were performed similar to those described previously [7]. Briefly, approximately 750 000 N2a cells were used for each immunoprecipitation and were fixed with 1 per cent formaldehyde for 10 min at room temperature. ChIP assay kits (Upstate, Charlottesville, VA, USA) were used with minor modifications. Following formaldehyde fixation, cells were resuspended in a swelling buffer (5 mM PIPES, pH 8.0, 85 mM KCl, 1% NP-40 and protease inhibitors added right before use) and homogenized 10 times in a small pestle Dounce tissue homogenizer (7 ml). Nuclei were then isolated by centrifugation before being subjected to sonication. The sonicated lysate was immunoprecipitated with either 0.2 mg of Sp1 polyclonal rabbit antibodies (H-225, sc-14027; Santa Cruz Biotechnology) or 2 mg of NGFR p75 polyclonal goat antibodies (C20, sc-6188; Santa Cruz Biotechnology). Semi-quantitative PCR was performed using 1/20th of precipitated chromatin. Primers targeting promoter sequences near TSP of COX subunit genes were designed (table 2). Mouse GM3 synthase promoter previously found to have Sp1 binding [23] was used as a positive control, and exon 5 of b-actin gene was used as a negative control ( N2a cells were plated in 60-mm dishes at a density of 5 to 8 Â 10 5 cells/dish. They were cotransfected 3 days post-plating with Sp1 shRNA at 1 mg per dish via Lipofectamine 2000. Empty vectors or scrambled shRNA vectors alone were used at the same concentrations as vectors with shRNA against Sp1. Puromycin at a final concentration of 0.5 mg ml 21 was added to the culture medium on the second day after transfection to select for purely transfected cells. Green fluorescence was observed to monitor transfection efficiency. Transfection efficiency for N2a cells ranged from 40 to 75 per cent. Transfected cells were selected by the addition of puromycin. This antibiotic selection yielded essentially 100 per cent transfected cells. After 48 h of transfection, cells were stimulated with 20 mM KCl in the culture media for 5 h as previously described [10,11]. Cells were harvested and processed for the isolation of RNAs and proteins.

Sp1 over-expression and tetrodotoxin treatment
The human Sp1 cDNA clones were obtained from Open Biosystems (Lafayette, CO, USA). The Sp1 cDNA was cloned into pcDNA Dest40 vector using Gateway Multisite Cloning kit (Invitrogen) and according to the manufacturer's instructions. N2a cells were plated in six-well plates at a density of 5 to 8 Â 10 6 cells per well. They were transfected 1 day postplating with 2 mg Sp1-containing plasmids using JetPrime

Real-time quantitative polymerase chain reaction
Real-time quantitative PCRs were carried out in a Cepheid Smart Cycler Detection system (Cepheid, Sunnyvale, CA, USA). SyBr Green (BioWhittaker Molecular Application, Rockland, ME, USA) and EX Taq real-time quantitative PCR hot-start polymerase were used following the manufacturer's protocols and as described previously [7]. Primer sequences are shown in

Statistical analysis
Significance among group means was determined by analysis of variance (ANOVA). Significance between two groups was analysed by Student's t-test. p-values of 0.05 or less were considered significant.

Acknowledgments
Supported by National Institutes of Health grant no. R01 EY018441.


text:
Structural insights into the targeting of mRNA GU-rich elements by the three RRMs of CELF1

The CUG-BP, Elav-like family (CELF) of RNA-binding proteins control gene expression at a number of different levels by regulating pre-mRNA splicing, deadenylation and mRNA stability. We present structural insights into the binding selectivity of CELF member 1 (CELF1) for GU-rich mRNA target sequences of the general form 5′-UGUNxUGUNyUGU and identify a high affinity interaction (Kd ∼ 100 nM for x = 2 and y = 4) with simultaneous binding of all three RNA recognition motifs within a single 15-nt binding element. RNA substrates spin-labelled at either the 3′ or 5′ terminus result in differential nuclear magnetic resonance paramagnetic relaxation enhancement effects, which are consistent with a non-sequential 2-1-3 arrangement of the three RNA recognition motifs on UGU sites in a 5′ to 3′ orientation along the RNA target. We further demonstrate that CELF1 binds to dispersed single-stranded UGU sites at the base of an RNA hairpin providing a structural rationale for recognition of CUG expansion repeats and splice site junctions in the regulation of alternative splicing.

Investigations of CELF1 function in humans have largely focused on its role in regulating alternative splicing and ability to modulate translation of mRNAs. In Xenopus, CELF1 induces deadenylation and appears to play a key role in regulating poly(A) shortening (27)(28)(29). Recent studies, using in vitro assays for assessing mRNA decay in AU-rich mRNAs, have shown that CELF1 binds specifically to mRNAs and stimulates poly(A) shortening in the 3 0 untranslated region (3 0 UTR) by recruiting the poly(A) deadenylase ribonuclease (PARN) (13,30). In this case, binding to target EDEN mRNA GU-rich elements (GREs) is necessary for function (2,12,(31)(32)(33)(34)(35), with EDEN-dependent deadenylation regulated by neighbouring cis-acting AU-rich elements (AREs) (13). In mammalian cells, ARE-binding proteins have been identified that both positively and negatively regulate mRNA decay or translation through interactions with the deadenylation machinery. CELF1 and CELF2 have overlapping but non-identical roles in the regulation of alternative splicing and can promote both exon skipping and exon inclusion (18). CELF2 has been best studied in splicing, and its binding in the vicinity of the branchpoint has been reported to promote skipping of the downstream exon, whereas binding downstream of an exon can promote its inclusion (36,37). These long-range interactions have been reported to be mediated through an interaction between CELF2 and the U2 snRNP, but this interaction appears to be absent for CELF1 (38).
The CELF proteins consist of three 80-100 residue RNA recognition motifs (RRMs) all of which adopt the same structural topology with a four-stranded antiparallel b-sheet packed against two a-helices (39,40). The RNA-binding surface lies across the face of the b-sheet with individual RRMs recognising 3-4 nt of single-stranded RNA via a number of conserved residues that facilitate p-stacking and hydrogen bonding contacts. Structural studies indicate that the third RRM of human CELF1 has an N-terminal extended region of 7 residues, which interacts with the b-sheet and extends the mRNA binding surface. The two N-terminal motifs (RRMs 1 and 2) are connected by a short 8-residue linker, which allows conformational flexibility and co-operative high affinity binding to tandem UGU sites (40,41). In contrast, the third domain in full-length CELF1 proteins is separated by a much longer unstructured linker sequence of variable length (160-230 residues).
Although the individual domains of CELF1 have been studied on multiple RNA substrates and early investigations identified binding to CUG repeats (32,(42)(43)(44)(45), GREs appear to be the most abundant targets for CELF proteins. The 15-mer RNA sequence 5 0 -UGUUUGU UUGUUUGU (EDEN15) represents the consensus EDEN-binding motif identified by systematic evolution of ligands by exponential enrichment (SELEX) methods (12,46). Although the GRE has a high density of UGU sites, there is some apparent redundancy in accommodating multiple RRMs in close proximity (40,41,45). However, many well-characterized natural binding sequences consist of more dispersed UGU repeats (7,13,21,31,46,47) that may be able to engage with all three domains of CELF1 proteins simultaneously.
A number of mammalian mRNAs containing multiple UGU sites for CELF1 have recently been described (13), with the c-fos and TNFa RNA substrates within AREs shown to enhance mRNA decay in HeLa extracts. In these studies, a synthetic GU-rich EDEN motif of 19 nt (EDEN19) derived from the 3 0 UTR of Xenopus c-mos was shown to be a competitive inhibitor in in vitro deadenylation assays (Table 1). The same study reported in vitro assays which show that CELF1 enhances deadenylation of these RNA substrates. The observation that the poly(A) deadenylase PARN is coimmunodepleted with CELF1 strongly suggests that PARN and CELF1 form a protein-mediated complex (30). Glutathione S-transferase (GST) pull-down assays and detection by western blotting confirmed that the two proteins have a direct co-interaction, and this may be regulating deadenylation rates, rather than binding to a common RNA substrate, increasing processivity and poly(A) shortening.
The structural basis for the binding of the full-length CELF1 to its target RNA substrate is still not fully understood. It is unclear whether all three domains of the CELF1 protein are simultaneously recruited to a single RNA-binding element, what the sequence requirements are for this interaction and whether there is selectivity in the arrangement of the three RMMs in this recognition process. Using a stable three-domain CELF1 construct (RRM123), we describe nuclear magnetic resonance (NMR) structural and biophysical studies based on chemical shift perturbation (CSP) analysis, paramagnetic relaxation enhancement (PRE) experiments with spinlabelled RNA substrates and binding affinity studies by isothermal titration calorimetry (ITC). A family of target RNA sequence of the form 5 0 -UGUN x UGUN y UGU, with three variably spaced RRM UGU-binding sites, has enabled us to identify a high affinity RNA motif (K d $ 100 nM) that permits simultaneous interaction of all three RRMs within a single 15-nt substrate. The PRE experiments demonstrate selective signal attenuation in NMR spectra that are consistent with binding selectivity and a non-sequential arrangement of RRMs on consecutive UGU sites along the RNA substrate.
CELF1 is causally associated with DM type 1 where the toxic effects of the RNA 3 0 -UTR (CUG) n expansion in the mutant RNA is mediated primarily by the RNA-binding protein MBNL1 (muscleblind-like 1) (48)(49)(50)(51). The binding of CELF1 to double-stranded CUG repeats was shown to be localized to the single-stranded regions at the base of the RNA hairpin by electron microscopy (52), but the underlying basis of this interaction at the level of the individual RRMs has not been described. We present a structural rationale for the interaction of CELF1 with dispersed UGU sites, across the base of a doublestranded hairpin loop structure which facilitates remodelling of RNA conformation as well as regulating alternative splicing by binding to UGU sites in intronic regions adjacent to and across splice site junctions.

Protein expression and purification
A construct of full-length Xenopus CELF1 was cloned into the pET28a vector. The RRM123 construct was produced by deletion of the codons for residues 215-384 using onestep PCR. The RRM23 construct was produced by the further deletion of the codons for residues 1-107 using the same method. All protein constructs were expressed in Escherichia coli BL21 (DE3) cells as fusions with an Nterminal histidine tag. Growths were carried out in Luria broth for unlabelled material and M9 minimal media for production of isotopically labelled protein. In all, 30 mg/ml kanamycin was used as an antibiotic control in all growths. Protein expression was induced with 1 mM isopropyl b-D-1-thiogalactopyranoside (IPTG) for 16 h at 30 C.
Cells were harvested by centrifugation and lysed by sonication. The lysis buffer for RRM123 was 25 mM potassium phosphate, 500 mM NaCl (pH 7.5). For wild-type CELF1, the lysis buffer was 25 mM potassium phosphate, 50 mM NaCl and 4 M urea. In all cases, a protease inhibitor cocktail (Roche) was added to the lysis buffer immediately before sonication. Cell debris was removed by centrifugation of the lysate, which was then applied to a His-Pur cobalt affinity column and left for 2 h at 4 C.
This column was then washed with 25 ml of a high salt buffer [2 M NaCl, 25 mM potassium phosphate (pH 7.0)] followed by a 10 mM imidazole wash to remove non-specifically binding proteins. RRM123 was eluted into 25 mM potassium phosphate, 200 mM NaCl and 500 mM imidazole (pH 7.0). Wild-type CELF1 was eluted in 25 mM potassium phosphate, 50 mM NaCl, 2 M urea and 500 mM imidazole. RRM123 was purified by gel filtration using a Superdex 200 column and desalted into water. Wild-type CELF1 was immediately desalted into water using a HiTrap desalting column (Amersham Biosciences). The eluted proteins were immediately frozen using liquid nitrogen and then lyophilised and stored at À20 C.

NMR spectroscopy
NMR samples of RRM123 were prepared by dissolving lyophilised protein in 25 mM potassium phosphate, 100 mM NaCl, 10% (v/v) D 2 O (pH 7.0). The RRM123 construct contains seven Cys residues, which could potentially lead to aggregation through disulfide bond formation. NMR sample stability studies showed little evidence of significant degradation over NMR acquisition periods of >96 h even in the absence of DDT. Wild-type CELF1 required the addition of 200 mM urea to the NMR buffer for solubilization of the protein. Experiments were conducted at 25 C on an 800 MHz Bruker Avance III Spectrometer with cryoprobe. Protein concentrations of 200-250 mM were used for all RRM123 experiments. Wild-type CELF1 experiments used protein concentrations of $100 mM. Data were collected using Topspin 2.3 and 3.0 and processed in CCPN Analysis version 2.1.
All RNA molecules were supplied by Dharmacon. Spinlabelled RNA for PRE studies was produced by incorporating 4-thiouridine at a specific site with subsequent derivatization of the nucleobase to form a disulfide using three equivalents of (1-oxyl-2,2,5,5-tetramethylpyrroline-3-methyl)methanethiosulphonate (MTSL) for 2 h in darkness at room temperature, following the procedure of Qin et al. (53). Unreacted MTSL was removed by loading the reaction mixture onto a HiTrap desalting column and eluting into RNAse free water. Spin-labelled RNA samples were used in the same buffered solutions described earlier in the text. The spin label was reduced at the end of the titration by addition of 5 equivalents of sodium ascorbate, after which the sample was left to react for 16 h at room temperature.
ITC ITC was carried out using a high sensitivity microcalorimeter driven by VPViewer2000 software (version 1.4.27) (MicroCal Inc, GE Healthcare). All buffered solutions were degassed for 10 min in a Thermovac (MicroCal) before loading. The cell was filled with 1.4 ml of 12.5 mM RRM123 in 25 mM potassium phosphate, 100 mM NaCl (pH 7.0) in RNase free water. In all, 300 ml of 125 mM RNA in the same buffer solution was loaded into the syringe. All titrations were carried out at 25 C. After an equilibration period of 20 min, 30 injections of 10 ml were added at 600 s intervals. A constant stirring speed of 300 rpm was applied throughout the titration to rapidly mix the contents of the cell. A reference power of 5 mcals À1 was used. Fitting of the resulting data was carried out using the Origin 7.0 fitting program using various binding models to determine ÁH, ÁS, K d and the stoichiometry for the interaction. Baseline correction was necessary for some data sets.

Modelling of RRM123 complexes using MD simulations
Models of the RRM123 construct bound to the EDEN-2U/4U and EDEN-2U/HL RNA sequences were constructed using the programme xLEAP within AMBER6 (54) by splicing together available crystal and NMR structures of the individual RRM complexes with RNA in the Protein Database (accession codes 3NMR, 3NNH and for RRM3, 2RQ4 and 2RQC) (39,40). All hydrogen atoms were introduced, and suitable linkers constructed between the RRMs where these were not present in the available PDB files. TIP3P water was used as an explicit solvent and was added in a truncated octahedron geometry to a distance of 9 Å around the protein. Monovalent (Na + ) counterions were added to ensure overall charge neutrality of the RNA phosphate backbone. Energy minimization was carried out using the SANDER modules of the AMBER program. Molecular dynamics simulations were run without restraints for up to 2 ns at 300 K. The resulting models were viewed using Pymol (55). The flexible N-terminus of the protein (residues 1-13) is absent in the available structures and is also omitted from the model. No evidence from NMR suggested that these played any role in RNA binding. The alignment of the RRMs on the RNA substrate was based on the PRE data where the domains were arranged in the order 2-1-3 from 5 0 to 3 0 on the RNA. A model of the 3-2-1 arrangement was also constructed and did not show any obvious steric problems.

Protein and RNA sample preparation
His-tagged bacterial expression constructs were produced for the individual CELF1 RRMs 1, 2 and 3, for a fragment containing domains 1 and 2 (RRM12) and for the full-length 489 residue CELF1 protein using methodology previously described (33). The isolation of fulllength CELF1 was particularly problematic with the protein readily degrading through proteolysis of the linker region. To address this a three-domain construct RRM123 with the linker sequence truncated to 35 residues Á(215-384) ( Figure 1a) proved to be more stable, water soluble and amenable to biophysical investigation.
On the basis of previously reported potential CELF1binding sites identified in c-fos, TNFa, c-jun and c-mos (Table 1), which contain either multiple UGU sites in close proximity, tracts of GU repeats and/or more widely dispersed UGU sites (13), the consensus sequence UGUN x UGUN y UGU (Figure 1a) was used to investigate the simultaneous binding of all three RRMs to a single RNA element and examine the UGU spacing requirements for this interaction. The analysis of more dispersed UGU sites is more challenging because formation of RNA secondary structure is potentially influential in bringing distant UGU sites into close proximity and may account for CELF1 binding to, and deadenylation of, some known natural mRNAs. The c-jun ARE (Table 1) is predicted to form a stable hairpin with a stem-loop inserted between potential UGU binding sites (bases shown underlined).

Binding affinities for multiple RRM interactions
The affinity and binding stoichiometry of RRM123 with a number of RNA substrates of the general sequence 5 0 -UGUN x UGUN y UGU was examined by ITC in buffered solutions containing 25 mM potassium phosphate and 100 mM NaCl at pH 7.0 ( Figure 1). In all cases aliquots of a 125 mM solution of buffered RNA were titrated into a 12.5 mM buffered solution of protein. The consensus GRE (EDEN11; x = 1, y = 1) produced a binding isotherm with a K d = 2.2 ± 0.3 mM and a binding stoichiometry n = 1.45 ( Table 2). As a control, we repeated the experiment with EDEN-4U (UGUUUUUUGU) in which deletion of the central guanine eliminates the possibility of three UGU binding sites. Both the binding affinity and stoichiometry (K d = 2.08 ± 0.4 mM, n = 1.64) are closely similar to EDEN11. In both cases, a binding stoichiometry mid-way between 1:1 and 1:2 (protein:RNA) is consistent with each RNA substrate carrying only two viable binding sites to accommodate the three binding motifs of RRM123.
We turned to the longer RNA substrates with greater separation between UGU sites. ITC analysis of EDEN19, derived from the natural c-mos sequence, produced a binding stoichiometry closer to 1:1 (n = 0.79) and a 7-fold enhanced binding affinity (K d = 0.33 ± 0.07 mM) compared with the EDEN11 GRE and EDEN-4U (Table 2). Putative UGU-binding sites on EDEN19 are identified in Table 1 (corresponding to site separations with x = 2 and y = 4). There is some potential degeneracy in how the various UGU sites could be occupied, which we attempted to resolve using the sequence EDEN-2U/4U with the same UGU site separation found in EDEN19. EDEN-2U/4U produced a high affinity interaction and binding stoichiometry (K d = 0.11 ± 0.02 mM, n = 0.85) consistent with the simultaneous interaction of all three RRM motifs on the same RNA substrate (Figure 1). Switching the spacer lengths with EDEN-4U/2U, as evident for c-fos (Table 1), showed the same binding characteristics with a similar stoichiometry, but a 9-fold lower affinity (K d = 0.98 ± 0.12 mM, n = 0.67), suggesting some selectivity in how the three domains are arranged on the RNA target.
We examined in more detail the effects of spacer separation between the second and third UGU sites while maintaining the optimum 2 U spacer for the tandem interaction of the two N-terminal RRMs (41). The sequence EDEN-2U/1U, with a single nucleotide spacer analogous to the EDEN11 GRE, gives a binding affinity for RRM123 of 1.50 ± 0.28 mM (n = 1.19), which is 15-fold weaker than for EDEN-2U/4U (Table 2). This is again consistent with steric constraints in accommodating all three motifs in such close proximity. The optimum spacing of UGU sites found in EDEN-2U/4U is also apparent in model structures containing motifs such as double-stranded hairpin loops. The folded secondary structure effectively brings three UGU sites into close proximity at the base of the hairpin. We tested this hypothesis with EDEN-2U/HL (x = 2, y = 14) where a hairpin loop of 14 nt was inserted into the expanded spacer sequence to mimic putative features evident in c-jun. EDEN-2U/HL bound to RRM123 with a 5-fold lower affinity than EDEN-2U/ 4U (K d = 0.55 ± 0.08 mM, n = 0.83). However, the 1:1 stoichiometry similarly suggests that all three RRMs are engaged in binding interactions with available UGU sites.

NMR studies of the RRM123-RNA interaction
We examined the mode of interaction of 15  NOEs for the linker residues indicative of considerable flexibility, although in many cases, sequence specific assignments in the disordered region were not achieved.
We conducted RNA titration experiments with 15 N-labelled RRM123 to further corroborate observed binding stoichiometries identified by ITC. The 11-mer GRE consensus sequence (UGUUUGUUUGU) was used as a control, representing the minimum binding element with three possible UGU binding sites in close proximity. In 1 H-15 N-TROSY spectra, a 1:1 ratio of RNA:protein resulted in extensive line-broadening across all three RRMs and the loss of many resonances, suggesting conformational exchange on an intermediate to slow timescale. However, increasing concentrations up to a 2:1 ratio of RNA:protein produced considerable sharpening, with bound peaks visible for all three domains. Residues G21 and C61 (RRM1), G113 and C150 (RRM2) and G278 (RRM3) are particularly sensitive to RNA binding and are all seen to undergo significant CSPs in the bound state (Figure 2b). NMR titration with EDEN-4U, with only two UGU sites, produced CSP effects at binding saturation and narrow linewidths that were similar to those with EDEN11 (Figure 2b). Both the NMR and ITC data concur that the EDEN11 GRE accommodates RRMs at only two of the three UGU sites with binding saturation of RRM123 achieved by  Table 1). Total heat released is plotted as a function of the molar ratio of RNA to protein, and the non-linear least squares fit to the experimental data are shown as a solid line. Dissociation constants, binding stoichiometries and enthalpies of binding are shown in Table 2. All data were collected at 25 C on 12.5 mM solutions of RRM123 dissolved in RNase free water buffered with 25 mM potassium phosphate and 100 mM NaCl at pH 7.0 and titrated with 300 ml of 125 mM RNA in the same buffer. Binding isotherms were fitted with MicroCal Origin software.
recruiting a second RNA substrate (see schematic inset in Figure 2b). In the context of a natural mRNA target, this could be an intramolecular interaction with a more distant UGU motif with the GRE providing the anchor point for the interaction. In contrast, the NMR titration of RRM123 with the high affinity EDEN-2U/4U RNA substrate resulted in slow-exchange binding across all three domains with binding saturation reached at close to a 1:1 ratio. Bound resonances showed significantly increased line widths consistent with slower tumbling of the complex. CSP effects were readily apparent across all three RRMs with C61 (RRM1), C150 (RRM2) and G278 (RRM3) particularly affected (Figure 3a-c), confirming the simultaneous engagement of all three domains. No further CSP changes were apparent at higher ratios of RNA:protein up to 3:1.
Titration experiments with EDEN-2U/HL (Figure 2d), in which UGU sites are located at the single stranded 5 0 and 3 0 tail ends of a 14 nt hairpin loop, showed similar effects. The 1 H-15 N-TROSY spectra of the complex showed even greater line broadening effects, which precluded an extensive CSP analysis. However, key resolved residues (C61, C150 and G278) within each of the three domains showed similar CSP effects to EDEN-2U/4U, as shown overlayed in Figure 3a. The 1 H NMR spectra of RRM123 bound to EDEN-2U/HL revealed imino proton signals between 12.5 and 13.5 ppm that were consistent with four stable Watson-Crick base pairs within the hairpin double-stranded stem region. In both complexes, a combination of ITC and NMR data strongly support formation of 1:1 complexes in which all three RRMs simultaneously interact with the three UGU sites on a single RNA substrate.
Paramagnetic relaxation spin labelling of the RNA substrate reveals binding selectivity A paramagnetic spin label (MTSL) was covalently attached to the RNA substrate through the incorporation of the nucleobase analogue 4-thiouridine (U*) ( Figure 4). Relaxation enhancement effects in TROSY spectra were used to identify the relative positioning and alignment of the three RRMs of RRM123 bound to the high affinity EDEN-2U/4U RNA substrate (53,56). We first showed that a tetranucleotide RNA fragment 5 0 -U*UGU, with the MTSL at the 5 0 -terminus, was able to bind to the isolated RRM1 and produce specific signal attenuations through PRE effects across the b-sheet binding surface, demonstrating that PREs can be used to map binding interfaces in good agreement with 1 H-15 N CSP methods (57,58).
Subsequently, the high affinity RNA substrate EDEN-2U/4U was MTSL labelled through an additional U* at the 5 0 terminus ( Figure 4) and was isolated by gel filtration. We compared cross-peak intensities for RRM123 in 1 H-15 N TROSY spectra with those of the ascorbatereduced RNA substrate with the spin label chemically quenched. The spin label produced a range of effects from either no attenuation to partial or complete signal broadening for a significant number of residues, suggesting a high degree of binding selectivity (Figure 4a). Mapping the PRE effects across the entire structure of RRM123 (Figure 4a and c) produced an attenuation pattern consistent with the strongest effects (>0.7) in RRM2. Smaller effects (<0.7, but more typically <0.5) were evident for RRM1 (Figure 4a), with the weakest perturbations ( 0.5) observed for RRM3. The PRE effects support a structural model in which RRM2 is primarily bound at the 5 0 -end of the labelled RNA substrate with RRM1 forming a tandem co-operative interaction at the adjacent UGU site ( Figure 5). The flexible 35-residue RRM2-RRM3 linker allows RRM3 to fold back to complete the three-domain interaction by binding at the 3 0 -terminal UGU site. We tested this model by incorporating the U* nucleobase analogue at the 3 0 terminus of EDEN-2U/ 4U. Covalent modification with MTSL resulted in a contrasting pattern of signal attenuation in 1 H-15 N TROSY spectra (Figure 4b and d) with the strongest PRE effects (>0.7) now clearly evident for RRM3 where these were generally weak with the 5 0 -labelled RNA substrate Overlayed spectra of the unbound RRM123 (blue) with RRM123 complexes with EDEN11 (red) and EDEN-4U (green) at an RNA:protein ratio of 3:1. The region shown highlighted in (a) containing cross-peaks of some Gly and Cys residues is expanded in (b). Key residues from all three RRMs that show significant CSP effects on RNA binding are labelled. The proposed binding stoichiometry is shown schematically. (Figure 4a versus b). The overlay of the small region of the TROSY spectrum in Figure 4e shows differential effects, in particular for G176 (RRM2) and E244 (RRM3), in the presence of one or other spin-labelled RNA substrate that illustrate binding selectivity in the organization of the three RRMs in a non-sequential arrangement at UGU sites along the high affinity EDEN-2U/4U substrate.

Modelling the structure and dynamics of the RRM123 interaction with RNA substrates
The trajectory of the RNA sequence across the binding surfaces of all three RRMs was modelled on the basis of NMR and X-ray structural data of complexes of the isolated domains (39,40), using the AMBER modelling suite with an explicit solvent model (see 'Materials and Methods' section). Initial models of the RRM123 complex with the high affinity EDEN-2U/4U substrate were arranged with the 2-1-3 alignment of RRMs from the 5 0 to 3 0 end of the RNA substrate, as supported by the PRE analysis. Various conformations of the flexible RNA spacer sequences between UGU sites were energy minimized and subjected to 1-2 ns of MD simulation to produce an ensemble of sterically and energetically plausible structures. The overall arrangement of the three motifs on EDEN-2U/4U is shown in Figure 5a and b. The conformational flexibility around the 8-residue RRM1-RRM2 linker results in different relative alignments of these two domains. Structural studies of other tandem RRM partners of PABP, Sex-lethal (Sxl) HuD and Hrp1 RNA binding proteins bound to various different single-stranded substrates reveal RNAs with either a linear or kinked trajectory between RRM-binding surfaces (59)(60)(61)(62). All of these are evident in the MD ensemble for the RRM123 complex with EDEN-2U/4U (Figure 5c) and suggest that intrinsic conformational flexibility is essential for optimizing the RNA-binding interface for targeting GREs through multiple interactions with variably spaced UGU sites. We also generated a number of energy minimized structures with the hairpin loop RNA substrate EDEN-2U/ HL, one of which is shown in Figure 6a and b. The models show that the hairpin stem loop, containing four Watson-Crick hydrogen bonded base pairs, brings the three UGU sites within the 5 0 and 3 0 single-stranded tails into an alignment similar to that modelled with EDEN-2U/4U (Figure 5a). The RNA secondary structure and RRM interactions at UGU sites across the base of the hairpin motif remain stable throughout the 2 ns MD simulation.

Targeting of RNA substrates by CELF1 proteins
Recent studies have identified natural mammalian AREs containing GU-rich mRNA candidate-binding sites for CELF1 within HeLa cell extracts. The c-fos and TNFa AREs have been shown to enhance mRNA decay and contain multiple UGUA, UGUU and UGUG sequences in close proximity. The greater abundance of these sites than predicted from a random distribution suggests that these sequences contain high affinity CELF1-binding sites. A 19-nt fragment of c-mos (EDEN19) was shown to be a highly competitive inhibitor of deadenylation of c-fos and TNFa, suggesting that c-mos similarly contains one or more high affinity CELF1 recognition sites within the EDEN19 element (13). The analysis of CELF1-targeted 3 0 -UTRs of mRNAs in Xenopus tropicalis, using immunoprecipitated CELF1/mRNA complexes, identified an enrichment of putative EDEN sequences for which the EDEN11 motif (Table 1) with closely spaced UGU motifs represents the consensus-binding site (46).
We have studied a number of mRNA substrates of the general sequence 5 0 -UGUN x UGUN y UGU and have shown that a stable three-domain CELF1 construct binds with high affinity to the EDEN-2U/4U RNA motif (N = U, x = 2 and y = 4) derived from the c-mos natural target sequence. We have shown from ITC and NMR experiments, including PRE effects using spinlabelled RNAs, that the RRM123 CELF1 construct is able to simultaneously bind to the three UGU sites. The high affinity EDEN-2U/4U RNA substrate leads to binding selectivity in which the three RRMs of CELF1 are aligned in a non-sequential 2-1-3 arrangement from 5 0 to 3 0 along the RNA target sequence.
In contrast, our NMR and ITC studies of the consensus GRE identified by Vlasova et al. (12), which conforms to this general sequence but has single nucleotide spacers (x = 1 and y = 1) between UGU sites, is sterically unable to accommodate all three RRMs in such close proximity (40,41). We show that the GRE only partially binds the CELF1 protein through two of the three possible UGU-binding sites (5 0 -UGUUUGUUUGU). Thus, the GRE with sub-optimal spacing of UGU sites does not capture the full extent of CELF1-RNA recognition but may act as a key anchor point for the initial RNA interaction.
The recognition of UGU(U) motifs by the three RRMs is mediated by a common set of conserved residues on the surface of the b-sheet and adjacent loops that use a combination of base-specific hydrogen bonding and p-stacking interactions (39,40). Further selectivity for binding a GRE appears to be imparted by a syn glycosidic conformation for the guanine base, more typical of left-handed Z-RNA helices (40). RRM3 is, however, distinct in having an N-terminal extension that folds back to interact with the b-sheet and extend the RNA-binding surface (39). These differences may account for the 10-fold higher affinity of RRM3 for a UGU(U) motif (K d $ 2-4 mM) compared with the other isolated RRMs (K d $ 30-60 mM) (39)(40)(41).
However, RRM1 and RRM2 are separated by a short 8-residue linker and the tandem interaction of the two RRMs leads to high affinity co-operative binding (K d $ 0.4-0.7 mM) to closely spaced UGU sites, realizing a >100-fold enhancement in affinity over the isolated domains (41). This tandem co-operative interaction is not evident for an RRM2-RRM3 construct (RRM23) with the 35 residue spacer where interactions between the two motifs are decoupled, resulting in a clear two-step binding process, which reflects the >10-fold difference in affinity between the two RRMs. This is evident from CSP effects with RRM23 in NMR titrations with a number of RNA substrates where perturbations were confined to RRM3 up to a 1:1 binding stoichiometry. ITC measurements indicated a first-step interaction with a K d = 0.52 ± 0.12 mM for RRM3 binding to a UGU(U) site. We conclude that the co-operative binding of RRM1 and RRM2, which leads to a high affinity interaction, together with the strong binding of RRM3 in isolation, ensures that both 'ends' of CELF1 are anchored to an RNA substrate with comparable affinity, of particular relevance in targeting dispersed UGU-binding sites and in the structural remodelling of pre-mRNAs. We tested this model with a designed RNA-binding element EDEN-2U/HL with UGU sites within the 5 0 and 3 0 single stranded tails at the base of a 14-nt hairpin loop. ITC and NMR titrations showed that RRM123 could bind with high affinity (K d = 0.55 ± 0.08 mM) in a 1:1 binding event with NMR CSP effects consistent with all three motifs simultaneously engaging in RNA interactions. Bringing together distant UGU sites through RNA secondary structure formation may account for the observed interaction of CELF1 with mRNAs such as TNFa (13), which have a poor match to the consensus GRE. The known target c-jun was reported by Paillard et al. (47) to bind both Xenopus and human CELF1 and trigger rapid deadenylation when present in the 3 0 UTR of mRNA. Vlasova et al. (12) reported a shorter version of the c-jun sequence, which could also trigger deadenylation. In both cases, the mRNA contains a consensus EDEN11 GRE site, but when considered as a linear sequence, it does not appear to contain a site capable of binding all three RRMs in the manner of a high affinity EDEN-2U/4U sequence. Secondary structure prediction for c-jun, using the Mfold web server (63), provides an explanation for binding of CELF1 to UGU motifs co-localized by the overall fold of the mRNA, as modelled in the complex with EDEN-2U/HL. The c-jun RNA shows two clear double-stranded regions (Figure 6c). One is from bases 17-31, which forms a hairpin held together primarily by Watson-Crick C-G base pairs, similar to the hairpin in the EDEN-2U/HL sequence. The second double stranded region is between bases 1-11 and 44-53, mostly composed of U-A base pairs. The two hairpins are linked by a singlestranded loop region, which contains five of the six possible UGU interaction sites in close proximity. The three highlighted UGU sites result in an arrangement analogous to that shown for EDEN-2U/HL, which is capable of binding all three RRMs of CELF1 with high affinity. There is also a reported dependence of deadenylation efficiency on the presence of AREs by Audic et al. (64), which are found in c-jun and TNFa. We can speculate that these elements facilitate formation of secondary structures that bring UGU sites into more favourable configurations for CELF1 binding, rather than having any direct interaction with CELF1.
This has possible implications both for defining the EDEN-binding motifs and for the role of CELF1 in regulating alternative splicing. Bioinformatic analysis by Masuda et al. (51) reported that partial CELF1 targets tend to cluster in intronic regions flanking alternative exons, with functional analysis consistent with CELF1 binding to the upstream intron facilitating exon skipping. Indeed, CELF1 can promote or inhibit exon inclusion depending on the location of recruitment to the transcript (65,66). It remains a possibility that CELF1 is able to bind across branch points or whole exons to regulate alternative splicing, with RRM1-RRM2 anchored on one side and RRM3 on the other, as illustrated for EDEN-2U/HL, providing a mechanism for exon exclusion.
Although we have worked largely with the truncated RRM123 construct in this study, for reasons of stability, 15 N-labelled full-length wt-CELF1 (489 residues) with an additional 170 residues of flexible linker sequence was isolated in small amounts for RNA-binding studies with both EDEN-2U/4U and EDEN-2U/HL. Although the spectra of the wt-CELF1 in isolation were of high quality, owing to the intrinsic dynamics of the structure, RNA complex formation leads to extensive line broadening and poor resolution. Many of those residues that are expected to be perturbed (C61 in RRM1, C150 in RRM2 and G448 in RRM3), and a variety of residues usually associated with smaller CSPs show characteristic, although not readily quantifiable, binding effects that are consistent with all three RRMs of wt-CELF1 binding to these high affinity RNA substrates.
We also considered reports that differential phosphorylation of CELF1 has been implicated in regulating both protein-protein and protein-RNA interactions, with studies suggesting that phosphorylation of Ser28 in RRM1 of CELF1 increases RNA binding in vitro (67) and that hyperphosphorylation by protein kinase C (PKC) increases the half-life of CELF1 (68). To investigate the possible effects in vitro, we produced an S28D phosphomimetic mutant of the isolated RRM1. However, neither NMR nor ITC measurements could reveal any differences in binding affinity with GU-rich RNA substrates compared with the native RRM1 (data not shown).
The RNA mutation model for DM pathogenesis has shown that CUG expansion repeats in the 3 0 -UTR region lead to the formation of long double-stranded RNA hairpins that sequester RNA-binding proteins (48,49). The subcellular distribution of CELF1 and MBNL1, another developmentally regulated RNAbinding protein that controls alternative splicing, are of particular interest in this context. Although MBNL1 binds to double-stranded hairpins (50) and accumulates in nuclear foci (51), studies suggest that CELF1 is localized to the base of these RNA hairpin structures rather than the stem region (52). Our structural models, shown in Figure 5b, are consistent with this hypothesis and enable us to visualize how this might occur either through binding to an isolated single-stranded CUG repeat or to the single-stranded 'tails' at an RNA junction.
The RRM is a ubiquitous RNA-binding motif that is used in numerous post-transcriptional steps in gene expression and as a regulatory repressor of alternative splicing events. The polypyrimidine tract-binding protein (PTB) is a typical well-characterized example with a similar architecture to CELF1 of multiple flexibly linked RRMs, which bind pyrimidine-rich motifs (e.g. UCUU, C UCUCU) (69-71) within regulatory elements of RNA (72)(73)(74)(75). PTB suppresses splicing by blocking various components of the spliceosome, including the splicing factor U2AF65 (76,77), or by masking splice sites through formation of oligomeric assemblies across intron-exon boundaries (78). Alternative indirect mechanisms suggest that PTB blocks splicing complex formation through its ability to remodel pre-mRNA by bringing together distal regions to form looped structures or by formation of nonproductive interactions with other stem-loop motifs (79)(80)(81)(82)(83). Although full-length PTB has eluded structure determination, the structure, affinity and binding specificity of the individual RRMs have been reported.
It is also apparent that PTB mediates both RNA and protein interactions. The PTB-Raver1 interaction, which modulates splicing of a-tropomyosin (Tpm1), is well characterized structurally and functionally (84,85). Structures derived from Raver1 peptide-RRM complexes reveal the versatility of RRMs as a recognition motif in mediating protein interactions across the distal helical face while engaged in RNA binding across the b-sheet. By analogy, recent studies using in vitro assays for assessing mRNA decay in AU-rich mRNAs have shown that CELF1 binds specifically to mRNAs and stimulates poly(A) shortening in the 3 0 UTR by recruiting the poly(A) deadenylase PARN (13,30). Although the details of this interaction are still ill-defined, it would appear that a Ser-rich motif within the RRM2-RRM3 flexible linker region of CELF1, close to a putative amphipathic helical motif (residues 332-340), facilitates recruitment. Phosphorylation-regulated CELF1 recruitment of PARN to initiate deadenylation and mRNA decay has been proposed (86) and is consistent with functional studies of CELF1 (4) in which a G331D mutation appears to knock-out CELF1 as a deadenylation factor. As a step towards elucidating protein-protein and protein-mRNA interactions of CELF1 linked to mRNA functional regulation, we have presented new structural insights into the selectivity and affinity of the three RRMs of CELF1 for a GU-rich mRNA target sequence containing multiple UGU-binding sites.