For cellular assays, the DNA 19-mers were replaced by siRNAs, and the corresponding three pairs of siRNA-helper oligomer tools were found to target 5’UTR efficiently in a reporter construct in HeLa cells. Addition of the helper oligomer improved silencing capacity of the respective siRNA. We assume that the described procedure will generally be useful for designing of nucleic acid-based tools to silence highly structured RNA targets.RNA target accessibility is one of the most important factors limiting the efficiency of RNA interference-mediated RNA degradation. However, targeting RNA viruses in their poorly accessible, highly structured regions can be advantageous because these regions are often conserved in sequence and thus less prone to viral escape. We developed an experimental strategy to attack highly structured RNA by means of pairs of specifically designed small interfering RNAs and helper antisense oligonucleotides using the 5’ untranslated region (5’UTR) of coxsackievirus B3 as a model target. In the first step, sites accessible to hybridization of complementary oligonucleotides were identified using two mapping methods with random libraries of short DNA oligomers. Subsequently, the accessibility of the mapped regions for hybridization of longer DNA 16-mers was confirmed by an RNase H assay. Using criteria for the design of efficient small interfering RNAs (siRNA) and a secondary structure model of the viral 5’UTR, several DNA 19-mers were designed against partly double-stranded RNA regions. Target sites for DNA 19-mers were located opposite the sites which had been confirmed as accessible for hybridization. Three pairs of DNA 19-mers and the helper 2’-O-methyl-16-mers were able to effectively induce RNase H cleavage  In the last decade, RNA interference (RNAi) has emerged as a new technique that is in the process of developing into a new class of therapeutics –3. ExtenIn dynamically changing viral genomes the best regions to target are highly conserved non-coding RNA stretches, since targeting such regions makes viral escape less likely. However, non-coding RNA regions are frequently involved in the formation of highly structured RNA elements that enables their regulatory function and, at the same time, makes it difficult for oligonucleotide-based tools to access them.locked nucleic acids), which exhibit a high affinity to the RNA target, has been shown to improve silencing efficiency [Probing the accessibility of tightly structured RNA regions for oligomer hybridization can be helpful in siRNA design. In some cases, however, a nucleotide sequence fulfilling thermodynamic criteria for active siRNAs might be impossible to identify in accessible regions. It may then be unavoidable to target partly double-stranded, less accessible regions of the target RNA. The application of modified siRNAs, i.e. LNA-modified siRNAs  of coxsackievirus B3 (CV-B3). CV-B3 is a heart pathogen against which no specific treatment has been approved to date. Characteristic for a member of the genus  regions –13. The  regions . A numbe regions , 15–18,  regions .Renilla reniformis green fluorescent protein (hrGFP). We show that the silencing efficiency of siRNA can be improved substantially by the simultaneous application of 2’-O-methyl-modified helper oligomers. The procedure of designing cooperating combinations of oligonucleotide tools could be generally helpful for targeting highly structured RNAs.Here, we describe a rational design procedure for obtaining active siRNAs targeting highly structured RNA regions. We investigated whether binding of a helper oligomer to the accessible side of a double-stranded stem increases the efficiency of siRNA to binding to the opposite strand of the stem. The 5’UTR of CV-B3 served as a model target in these studies. For cellular assays, this region of the viral RNA was fused with the mRNA encoding humanized 32P)ATP (4600 Ci/mmol) from Hartmann Analytic  and all the chemicals were purchased from Sigma-Aldrich , Serva Electrophoresis  or BioShop . All enzymes were purchased from Fermentas  unless stated otherwise. Unmodified DNA oligomers were purchased from Oligo Service IBB PAS  and they were deprotected after synthesis and purified on 8% polyacrylamide gels.The materials used in this study were obtained from the following sources: (γ-In order to obtain the dsDNA template (nucleotides 1–742 of the CV-B3 Nancy strain under control of the T7 promoter), single-stranded DNA complementary to the 5′ portion of CV-B3 genome was synthesized by reverse transcription as described previously . Subsequ32P at its 5′ end with polynucleotide kinase according to standard procedures.Transcription reactions were performed as described previously . The syn32P-labeled RNA-specific primer (According to a protocol described in  the renac primer . RT-ROL Estimation of the extendible (accessible to hybridization) sites was performed on the basis of RNA sequencing and the length and location of a random sequence in each oligomer library.E. coli RNase H, the 5’-32P-labeled RNA was renatured in an appropriate buffer by heating at 65°C for 5 min and slowly cooling to 37°C. Subsequently, RNase H was added and the cleavage reactions were initiated by adding the DNA 6-mer libraries to separate RNA samples. The mixtures were incubated at 37°C for 10 or 30 min. The reactions were quenched with equal volumes of 20 mM EDTA/7 M urea mixture and frozen on dry ice. The digestion products were analyzed by electrophoresis on 12% polyacrylamide, 8.3 M urea gels. Autoradiography was performed using phosphoimager . Products of RNase H digestion reactions were run on polyacrylamide gels along with the products of alkaline RNA hydrolysis and limited T1 ribonuclease digestion of the same RNA according to protocol described previously [Mapping of RNA accessibility to hybridization with DNA 6-mers libraries and RNase H digestion was performed as described previously –24. Brieeviously –24.The 5’UTR sequence of CV-B3 ranging from nucleotide 1 to 742 (according to GenBank accession No. JX312064.1) was searched for 19-nucleotide-long stretches according to the following principles based on the Reynolds criteria : A at poin vitro transcribed RNA of 5’UTRcvb3 was refolded and hybridized with a five-fold excess of antisense DNA oligonucleotides. Duplexes were incubated in the presence of E.coli RNase H  for 15 min at 37°C. Reactions were quenched with EDTA, mixed with loading dye and run in agarose gel electrophoresis. In case of mixtures composed of two different oligomers, each oligomer was applied at a final concentration of 5 μM.The RNase H assay was performed as described previously , 26. TheRNAstructure 5.4 computer program [OligoWalk application was used to calculate the free energy values of the created DNA-RNA or RNA-RNA duplexes. The binding energies of three DNA 19-mers and three RNA 16-mers with 5’UTRcvb3 RNA sequence were analyzed: DNA_162, DNA_213, DNA_420, as well as and RNA5, RNA8, and RNA9 . A “break local structure” mode was used.The thermodynamic properties of oligomer binding to the RNA target were analyzed by  program . An Olig2. Cells were passaged 24 hours before transfection.For the reporter assay experiment, HeLa cells  were grown in monolayers in an RPMI medium containing 10% heat-inactivated fetal calf serum (FBS), 100 u/ml penicillin and 100 μg/ml streptomycin and with 250 ng amphotericin B per mL (Sigma-Aldrich). Cells were cultivated at 37°C in a humidified atmosphere with 5% COFor experiments involving inoculation with the CV-B3, HeLa cells were grown as described elsewhere .8 PFU/ml was used. 5 hours post-infection, cells were harvested, suspended in PBS and divided into three samples. The in vivo DMS treatment was performed on the basis of protocols published elsewhere [A permissive HeLa cell line  was inoculated with the Nancy strain of CV-B3 (ATCC VR-30) at a multiplicity of infection (moi) of 1. A virus titer of 1.3 x 10lsewhere –31. Brie32P-labeled DNA primers  according to the manufacturer’s instructions in multiple cloning site 2. The insert representing the 5’UTR sequence was synthesized by extension of the dsDNA_5′UTRcvb3 (described above) at both ends with restriction sites EcoRI and KpnI in a standard PCR reaction with specific primers .5 cells/well in a volume of 500 ml without antibiotics. At the same time (reverse transfection protocol), cells were co-transfected with the appropriate amount of siRNAs , 2’-O-methyl-16-mers (0 or 40 nM), and 0.5 μg of hrGFP-5’UTRcvb3 fusion construct. An amount of 1.75 μl of Lipofectamine 2000 (Thermo Fisher Scientific) was applied per well, following the manufacturer’s instructions. Following transfection, cells were incubated at 37°C in humidified atmosphere for 24 hours.The specific siRNA duplexes containing symmetrical 3’-dTdT overhangs and with ON-TARGET modifications  as well 24 hours after transfection, cells were lysed in 24-well plates with laemmli sample buffer. After boiling the lysate at 95°C for 5 min, proteins were separated on a 15% (w/v) polyacrylamide gel. Transfer of proteins to PVDF membranes  was performed with a semi-dry blotter . Subsequently, membranes were incubated with a monoclonal rabbit hrGFP antiserum  overnight in TTBS buffer at 4°C. Secondary antibodies conjugated with alkaline phosphatase (Pierce) were used at a dilution of 1:5000 in 5% skim milk. Detection by chemoluminescence was achieved using ECL Kit (Pierce). To confirm equal loading of samples, membranes were re-probed with a monoclonal mouse antibody specific for GAPDH (Santa Cruz Biotechnology). At least three independent experiments were performed. For the experiment with 0.25 nM concentration of siRNAs the band-density measurements were performed. The values obtained for hrGFP were then normalized to the values obtained for GAPDH in each sample line. The p-values were calculated using Student’s t-test.HeLa cells were co-transfected using the identical protocol as described in the previous section with the only difference being that the cells and transfection mixture were divided into four wells of 96-wells plate. Black plates with transparent bottom were used . At 24 h after transfection, cells were washed with PBS and subjected to fluorescence intensity reading of the expressed hrGFP in a plate reader . The excitation wave length as well as the emission wave length were established experimentally in the plate reader and set at 492 nm and 507 nm, respectively. The fluorescence intensity measurement was performed from the bottom side of the plate. The scanning-mode was applied, which means that 80 different points of each well were measured and the average values of the fluorescence intensity were then calculated for each well. At least three independent experiments were performed, each in duplicate. The p-values were calculated using Student’s t-test.in vitro transcription using a DNA template encoding the sequence of 5’UTRcvb3 corresponding to the 5’UTR region of the CV-B3 virus, Nancy strain. We applied two methods in order to identify the sites accessible to hybridization within the 5’UTR  loaded with the siRNA to hybridize to the other side of the stem .To design helper antisense oligomers which would be able to destabilize the double-stranded stems of the targeted RNA, we tested the binding efficiency of several DNA 16-mers complementary to the 5’UTRcvb3 using the RNase H assay. Nineteen DNA 16-mers were chosen for the test based on our previous mapping results and in several cases the oligomers were shifted in the 3’ direction by 2, 3 or 4 nucleotides . It turnme_5, me_8 and me_9, respectively, of sequences identical to the previously tested DNA 16-mers Nos. 5, 8 and 9  and RNA9 (me_9). The low binding energies exhibited by RNA_8 , RNA_5  and RNA_9  were consistent with their great binding abilities to the RNA target and made its structure more accessible to hybridization of DNA 19-mers  were harvested, suspended in PBS and treated with 0.3% DMS  did not reveal such effects  [An interesting observation was that several predicted siRNA-sites were located on the sides of the double-stranded stems opposite to the sites mapped as accessible. We decided to take advantage of this fact applying the approach of directed, local RNA structure change induced by helper oligomer hybridization, the ODMiR effect ( of RNA) . To estame_5, DNA_213 with me_8, and DNA_420 with me_9 Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.


RNAs are attractive molecules as the biological parts for synthetic biology. In particular, the ability of conformational changes, which can be encoded in designer RNAs, enables us to create multistable molecular switches that function in biological circuits. Although various algorithms for designing such RNA switches have been proposed, the previous algorithms optimize the RNA sequences against the weighted sum of objective functions, where empirical weights among objective functions are used. In addition, an RNA design algorithm for multiple pseudoknot targets is currently not available.We developed a novel computational tool for automatically designing RNA sequences which fold into multiple target secondary structures. Our algorithm designs RNA sequences based on multi-objective genetic algorithm, by which we can explore the RNA sequences having good objective function values without empirical weight parameters among the objective functions. Our algorithm has great flexibility by virtue of this weight-free nature. We benchmarked our multi-target RNA design algorithm with the datasets of two, three, and four target structures and found that our algorithm shows better or comparable design performances compared with the previous algorithms, RNAdesign and Frnakenstein. In addition to the benchmarks with pseudoknot-free datasets, we benchmarked MODENA with two-target pseudoknot datasets and found that MODENA can design the RNAs which have the target pseudoknotted secondary structures whose free energies are close to the lowest free energy. Moreover, we applied our algorithm to a ribozyme-based ON-switch which takes a ribozyme-inactive secondary structure when the theophylline aptamer structure is assumed.Currently, MODENA is the only RNA design software which can be applied to multiple pseudoknot targets. Successful design results for the multiple targets and an RNA device indicate usefulness of our multi-objective RNA design algorithm.The online version of this article (doi:10.1186/s12859-015-0706-x) contains supplementary material, which is available to authorized users. In synthetic biology, biological systems are treated as a circuit composed of biomolecular parts such as nucleic acids and proteins. Since not only natural biomolecules but also artificially-constructed ones can be used as the molecular parts for constructing biological circuits, various efforts have been made to design novel biomolecules which have a desired function. In this context, synthetic RNA devices utilizing a conformational change have intensively been investigated and applied to control biological processes such as gene expression –4. Sincein silico selection pipeline has also been used to design synthetic riboswitches  - [the Hamming distance between the i-th and j-th pseudoknots in bracket notation])/(target structure length) < 0.8, we add a set of the i-th and j-th pseudoknots to the LE80 dataset and mark the i-th and j-th pseudoknots in the Pseudobase dataset for single-target design ; Step 4) we increment i by one; if we reach the last pseudoknot, stop the processing; otherwise go to Step 2. It is noted that, in the procedure from Step 1 to Step 4, we consider the pseudoknots with a length of ≥ 40 and ≤ 80 nucleotides.In addition to the randomly generated datasets, we constructed a two-target pseudoknot dataset based on natural RNA pseudoknots taken from Pseudobase . The twot design , 36; SteAfter removing the target sets having a pseudoknot which cannot be dealt with by NUPACK, we finally obtained 34 target sets of two target pseudoknots as the LE80 dataset. Design results for the LE80 dataset are tabulated in Table f1, f2, and f3 as the objective functions. The results for the three- and four-target designs are tabulated in Table δe1 among the three design algorithms. These results indicate that, compared with the other two design methods, MODENA successfully designed RNA sequences whose lowest energy target structure has the free energy very close to that of the ground state. In the case of the highest energy target structure (δe2) of three-target designs, the RNA sequences designed by MODENA give a mean (0.54) and median (0.30) better than those (mean = 0.96 and median = 0.80) of Frnakenstein and comparable to those (mean = 0.53 and median = 0.30) of RNAdesign. In the case of the δe2 of four-target designs also, MODENA (mean = 1.78 and median = 1.40) outperformed RNAdesign and Frnakenstein in terms of the mean and median. Detailed data of these designs are shown in Additional file To evaluate the design performance for more than two target structures, we performed three- and four-target RNA sequence designs with 100 sets of target structures generated by applying RNAshapes  to the rTo test the design performance with sequence constraints, we re-performed the RNA sequence designs with the sets of the two, three, and four pseudoknot-free targets, where a randomly selected subsequence with a length of 10 % of the original sequence is used as sequence constraints in each design problem. As a result, for the two-target designs, we obtained higher means and medians than those of the designs without the constraints, while results comparable to the designs without the constraints were obtained for the three- and four-target designs. Data of these designs are included in Additional file methods used for each single-target design are described, are available from the MODENA website.Since the multi-target version of MODENA can also perform single-target RNA design, the results for single-target designs without sequence constraints are also included in Additional file F1=E(Θactive)−G, F2=|E(Θinactive)−E(Θactive)−1.0|, F3=−(σinactive+σactive)/2, F4=|r(GC:CONT)−50|, where Θinactive and Θactive indicate the MFE structures of the inactive and active state, respectively; σinactive and σactive are the structure similarities with the inactive- and active-target structures, respectively. RNAstructure 5.3 package was used to evaluate these objective functions. We ran MODENA to design the RNA device mentioned above with option “-conv 0”, which guarantees that the run does not stop until the specified maximum number of generations  is reached.As an example of RNA device design, we re-designed the ribozyme-based RNA device proposed by Win and Smolke . This RNF3 = -1.0). The predicted structures of the designed sequences are shown in Fig. F2 = 0.0). This energy difference is exactly the same as the specified value in objective function F2. This design result indicates that MODENA is useful for designing RNA devices with complex features.As the design result, we obtained one RNA sequence which folds into a ribozyme-active conformation as the ground state; moreover, when the aptamer secondary structure is fixed by the structure constraints, this designed RNA folds into the secondary structure in which the stem II of the ribozyme is missing  predicted for the RNA sequence , which are slightly worse values compared with the designs with the crossover operator . Wilcoxon’s signed rank tests for these comparisons (P = 0.9097 and 0.3296) indicate that the RNA sequences designed with and without the crossover are comparable with respect to δe1 and δe2. In Additional file To examine the effect of the crossover for multiple targets, we performed the two-target designs of the 17 RNAtabupath target sets without the crossover for multiple targets (option “-opCr 0” was used for the purpose). Aa a result, we obtained the means for properties including a predicted energy barrier height and a GC content can be used in the OFs in the multi-target version. In addition, not only sequence property values themselves, but also a function of sequence property values can be utilized in an objective function in the new version. The previous versions of MODENA can use only the two objective functions (a stability score and structure similarity score). (v) The secondary structure constraint, which is useful for modelling the ligand-binding state of an aptamer, has been introduced in the new version. (vi) The new version explores Pareto optimal solutions in the default setting, while the previous versions do weak Pareto optimal solutions. (vii) A simple parallelization of the evaluation part in the GA has been introduced in the new version.Main differences between the new multi-target version and the previous single-target versions are as follows: (i) The previous versions cannot perform the RNA inverse folding with multiple target structures. The previous versions can accept only one target structure as an input. To perform the RNA inverse folding with multiple target structures, it is necessary to take the dependency graph into account in the initialization, mutation, and crossover as implemented in the multi-target version. (ii) The error diagnosis operator of the previous versions has been replaced by negative and positive mutation operators in the multi-target version. (iii) The ‘Mutation of undesired sequence motifs’ operator has been introduced in the new version. (iv) Objective functions more than two can be used in the multi-target version. More sequence f1, f2, and f3, whereas Frnakenstein uses a single OF as the default setting for multi-target RNA design. Due to this algorithmic difference, the stopping criterion (the number of continuous GA generations which have the same number of non-dominated solutions) of MODENA is also different from that of Frnakenstein.Important differences between MODENA and Frnakenstein are as follows. First, MODENA adopts a multi-objective genetic algorithm which can obtain the approximate Pareto optimal solutions at one run, while Frnakenstein designs RNA sequences based on the approach utilizing a weighted sum of objective functions (which includes the case of a single objective function) which explores one of the Pareto optimal solutions as the single optimal solution. MODENA uses multiple OFs such as Secondly, in the default setting of Frnakenstein, RNA sequences are initialized by RNAinverse where a target structure is randomly selected from multiple targets. This initialization does not guarantee the compatibility with multiple target structures. MODENA initializes RNA sequences with the nucleotide assignment algorithm which guarantees the compatibility.Thirdly, MODENA has various functions which are useful for practical RNA device design and are not implemented in Frnakenstein: sequence and structure constraints, pseudoknot design function, target GC content, mutation of undesired sequence motifs, and OpenMP parallelization. Although, in principle, it may be possible to add these functions to Frnakenstein, such a version of Frnakenstein is currently not available.Fourthly, MODENA is written in C and partially in C++ and Frnakenstein is a python program.Finally, MODENA uses originally-developed GA operators different from those of Frnakenstein. As the default setting, the GA operators of Frnakenstein use base pairing probabilities, whereas the GA operators of MODENA do not utilize base paring probabilities. RNA folding with base paring probability computation takes a longer time than that without base paring probabilities, this difference could be one of the reasons for the difference between the computational times of MODENA and Frnakenstein.To know how diverse structures are included in each set of target structures, we calculated the average pairwise structure distance (APSD) for each set of target structures. The calculated APSD is displayed in the filename of each set of target structures, e.g. ‘alpha_operon-apsd13.inp’ indicates a set of target structures with an APSD of 0.13. The target structures (input files) used in the benchmarks can be downloaded from the MODENA website.http://rna.eit.hirosaki-u.ac.jp/modena/multi).Academic users can use MODENA from our website  for various RNA devices, including RNA devices requiring four target structures , isBy virtue of the modular nature of genetic algorithm in which a solution evaluation part and the other optimization parts are clearly separated as different algorithmic parts, MODENA can perform multi-target RNA sequence design based on various widely-used RNA secondary structure prediction methods. It is noteworthy that we can use MODENA to design multistable pseudoknots, whereas the previous inverse folding methods cannot design multistable pseudoknots.methods invoked in MODENA will make MODENA possible to design interacting structured RNA sequences. Such a design tool is useful for automatic design of RNA-RNA interaction circuits [The current version of MODENA has several issues to be improved. For example, addition of RNA-RNA interaction prediction algorithms to the circuits . Since tFrom our experience, we can say that reducing the number of GU base pairs is important to obtain a good design result. For this reason, avoiding a subset of the sequence space having many GU base pairs is desirable for RNA sequence design. Since our sequence sampling approach, the nucleotide assignment algorithm, is rather empirical, development of a more sophisticated GU base pair sampling method is an interesting direction for future research. It is noteworthy that, by introducing more random nature into the nucleotide assignment algorithm, we can give non-zero sampling probabilities to all compatible nucleotide assignments (some weights will be needed to reduce the number of GU pairs).in vitro/in vivo function of the designed RNA sequences. The functions of designed RNAs have to be determined by experiment. We hope that our multi-objective optimization approach gives an effective guide for developing novel RNA devices which work in vitro/in vivo and leads to fruitful collaboration between informatics and experiment in the field of biomolecular device design.It is noted that the present algorithm does not guarantee the desired


Adenosine deaminases acting on RNA (ADARs) hydrolytically deaminate adenosines (A) in a wide variety of duplex RNAs and misregulation of editing is correlated with human disease. However, our understanding of reaction selectivity is limited. ADARs are modular enzymes with multiple double-stranded RNA binding domains (dsRBDs) and a catalytic domain. While dsRBD binding is understood, little is known about ADAR catalytic domain/RNA interactions. Here we use a recently discovered RNA substrate that is rapidly deaminated by the isolated human ADAR2 deaminase domain (hADAR2-D) to probe these interactions. We introduced the nucleoside analog 8-azanebularine (8-azaN) into this RNA (and derived constructs) to mechanistically trap the protein–RNA complex without catalytic turnover for EMSA and ribonuclease footprinting analyses. EMSA showed that hADAR2-D requires duplex RNA and is sensitive to 2′-deoxy substitution at nucleotides opposite the editing site, the local sequence and 8-azaN nucleotide positioning on the duplex. Ribonuclease V1 footprinting shows that hADAR2-D protects ∼23 nt on the edited strand around the editing site in an asymmetric fashion (∼18 nt on the 5′ side and ∼5 nt on the 3′ side). These studies provide a deeper understanding of the ADAR catalytic domain–RNA interaction and new tools for biophysical analysis of ADAR–RNA complexes. This syndrome .adars 1–3) with ADAR1 and ADAR2 proteins having well-characterized adenosine deamination activity  enhances binding of hADAR2-D to the 8-azaN-containing duplex RNA. Finally, trapping hADAR2-D on a 65-bp 8-azaN-containing dsRNA and subsequent cleavage with ribonuclease V1 revealed a footprint on the edited strand that extends 18 nucleotides in the 5′ direction and five nucleotides in the 3′ direction from the site of modification. These studies provide a deeper understanding of the ADAR catalytic domain–RNA interaction and new tools for biophysical analysis of ADAR–RNA complexes.Our laboratory recently discovered RNA substrates for human ADAR2 that are rapidly deaminated by a deletion mutant that lacks both dsRBDs (hADAR2-D) Figure  27). He. He27). Escherichia coli and RNase inhibitor were purchased from New England Biolabs. γ-[32P] ATP was purchased from Perkin-Elmer Life Sciences. The Avian Myeloblastosis Virus (AMV) reverse transcriptase, deoxynucleotide triphosphate (dNTP) mix and RQ1 RNase free DNase were purchased from Promega. Nuclease T1 and nuclease V1 were purchased from Ambion. Pfu Ultra II was purchased from Stratagene. Dpn 1 was purchased from Invitrogen. RNA oligonucleotides were synthesized at the University of Utah DNA/Peptide Core Facility or purchased from GE Healthcare Dharmacon, Inc. or Sigma Aldrich. DNA oligonucleotides were purchased from Integrated DNA Technologies or Sigma Aldrich. Storage phosphor imaging plates from Molecular Dynamics were imaged using Molecular Dynamics 9400 Typhoon phosphorimager. Data were analyzed using Molecular Dynamics ImageQuant 5.2 software. Electrospray Ionization (ESI) mass spectrometry of oligonucleotide samples was carried out at either the Campus Mass Spectrometry Facilities, UC Davis or at Novatia, LLC. Oligonucleotide masses were determined using Mongo Oligo Mass Calculator v2.06.Unless otherwise stated, reagents were purchased from Fisher Scientific, Sigma-Aldrich, or Life Technologies. Brown Centrex columns were purchased from VWR. T4 polynucleotide kinase, T4 DNA ligase, molecular biology grade bovine serum albumin (BSA), chemically competent DH5α a): 5′-UUC CCC ACU UGU CAU UAG ACG UUC AGU UAG UAC CAC CAA UGA CAA UAU UGG GGA A-3′; (b): 5′-UUC CCC ACA UUA GAC GUU CAG UUA GUA CCA CCA AUG UGG GGA A-3′; (b) containing 8-azaN: 5′-UUC CCC ACA UUX GAC GUU CAG UUA GUA CCA CCA AUG UGG GGA A-3′ where X is 8-azaN; (c): 5′-CCC CAC AUU AGA CGU UCA GUU AGU ACC ACC AAU GUG GGG-3′; (c) containing 8-azaN: 5′-CCC CAC AUU XGA CGU UCA GUU AGU ACC ACC AAU GUG GGG-3′; 22-nt native top strand: 5′-UUC CCC ACA UUA GAC GUU CAG U-3′; 22-nt 8-azaN containing top strand: 5′-UUC CCC ACA UUX GAC GUU CAG U-3′; bottom strand of (d): 5′-ACU GAA CCA CCA AUG UGG GGA A-3′; (e) top strand: 5′-CAU UAX GGU GGG UGG AAU AGU AUA ACA-3′; (e) bottom strand: 5′-UGU UAU AGU AUC CCA CCU ACC CUG AUG-3′; (f) bottom strand: 5′-ACU GAA CCA CCA AUG tgg gga a-3′ where lower case letters are deoxynucleotides; (g) bottom strand: 5′-act gaa cCA CCA AUG UGG GGA A-3′; (h) bottom strand: 5′-ACU GAA Cca cca atg UGG GGA A-3′; (i) bottom strand: 5′-ACU GAA CGA CCA AUG UGG GGA A-3′; (j) bottom strand: 5′-ACU GAA CGU CCA AUG UGG GGA A-3′; 65-nt bottom strand: 5′-CAA ACA CCA UUG UCA CAC UCC AAC UGA ACG ACC AAU GUG GGG AAA UAG GAU UCA UAU UAG GAG AU-3′; 65-nt left sequence: 5′-AUC UCC UAA UAU GAA UCC UAU-3′; 65-nt right sequence: 5′-UGG AGU GUG ACA AUG GUG UUU G-3′; primer for (a): 5′-TTG TCA TTG GTG GTA CTA ACT GAA CGT-3′; primer for (b): 5′-CCC ACA TTG GTG GTA CTA ACT GAA CGT-3′; 65-nt DNA splint: 5′-CAA ACA CCA TTG TCA CAC TCC AAC TGA ACG TCT AAT GTG GGG AAA TAG GAT TCA TAT TAG GAG AT-3′.(a) and (b) and DNA oligonucleotides were purified by denaturing polyacrylamide gel electrophoresis and visualized using UV shadowing. Bands were excised from the gel, crushed and soaked overnight at 4°C in 0.5-M NH4OAc, 0.1% sodium dodecyl sulphate (SDS) and 0.1-mM ethylenediaminetetraacetic acid (EDTA). Polyacrylamide fragments were removed using a 0.2-μm filter (Centrex filter or Spin-X column) followed by phenol-chloroform extraction and ethanol precipitation. The RNA solutions were lyophilized to dryness, re-suspended in nuclease-free water, quantified by absorbance at 260 nm and stored at −70°C. The RNAs were later heated at 95°C for 5 min and then slowly cooled to room temperature in 10-mM Tris-HCl, 0.1-mM EDTA pH 7.5, 100-mM NaCl to allow them to refold. All other RNA oligonucleotides were purified in the same manner except for the following modifications: samples were crushed and soaked in 0.5-M NH4OAc and 0.1-mM EDTA and were desalted by either ethanol precipitation and a 70% ethanol wash or Sep-Pak instead of phenol-chloroform extraction and ethanol precipitation. ESI-mass spectrometry (MS) values for RNAs are as follows: (b) containing 8-azaN: calcd. 13698.9, obsd. 13700.5; (c) containing 8-azaN: calcd. 12434.5, obsd. 12434.6; 22-nt 8-azaN containing top strand: calcd. 6881.0, obsd. 6881.9.The 8-azaN phosphoramidite was synthesized as previously described  and RNAsCA GGG GAC GAT TCC AG-3′ and 5′-CTG GAA TCG TCC CCT GAC CAG ACT CTA TTT TG-3′. N-terminal histidine-tagged human ADAR2 deaminase domain (aa 299–701) (hADAR2-D) and hADAR2-D E488Q mutant proteins were expressed in S. cerevisiae strain BCY123 and purified as described previously (The hADAR2-D E488Q mutant was prepared using polymerase chain reaction mutagenesis with template previously described  and the eviously  omittinga) and (b) were radiolabeled with [γ-32P] at the 5′ end as described previously (b), (b) containing 8-azanebularine and (e) top strand were labeled as previously described in (DNA primers for primer extension assays with (eviously . Single-d with γ-P at the d with γ-P at the a) and (b) were carried out under the conditions previously described and products ratios were determined using a primer extension assay (y = A(1-et)(-kobs*), where A is the fitted reaction end point, kobs is the fitted rate constant (min−1) and y is the fraction product at time t. Graphs were generated using KaleidaGraph (Synergy Software).Deamination reactions with RNAs (on assay . DeaminaA * [protein]/([protein] + Kd), where the Kd is the fitted dissociation constant and A is the fitted maximum fraction of RNA bound.Samples containing 2.5-nM RNA and different concentrations of hADAR2-D  were equilibrated in 20-mM Tris-HCl, pH 7, 6% glycerol, 0.5-mM DTT, 60-mM KCl, 20-mM NaCl, 0.1-mM 2-mercaptoethanol, 1.5-mM EDTA, 0.003% NP-40, 160-units/ml RNasin, 100-μg/ml BSA and 1.0-μg/ml yeast tRNA for 30 min at 30°C. Samples were loaded on a 6% 80:1 native polyacrylamide gel, dried at 80°C for 1 h and imaged. Bands were quantified by volume integration using ImageQuant software such that all radioactive signal not found in the free RNA band was quantified as bound RNA. The data were fitted to the following equation: Fraction bound = d), (f), (g) and (h) were hybridized at a concentration of 0.5 μM in 10-mM Tris-HCl, 0.1-mM EDTA pH 7.5 and 100-mM NaCl by heating at 95°C for 5 min and allowing them to slowly cool to room temperature. The melting temperature (TM) was determined for each as previously described  was added and the reaction was allowed to proceed overnight at 16°C. The reaction was then phenol-chloroform extracted, ethanol precipitated, 70% ethanol washed and dried. The sample was re-dissolved in 1X RQ1 Reaction Buffer and treated with 50 U of RQ1 RNase-Free DNase for 1 h at 37°C, loaded onto a 12% denaturing gel polyacrylamide gel and visualized using phosphor imaging. RNA bands were excised from the gel, crushed and soaked overnight at 4°C in 0.5-M NH4OAc, 0.1% SDS and 0.1-mM EDTA. Samples were filtered through a Spin-X column, phenol-chloroform extracted, ethanol precipitated, 70% ethanol washed and dried. The ligated 65-nt RNA was redissolved in 10-mM Tris-HCl, 0.1-mM EDTA pH 7.5 and 100-mM NaCl and mixed with the 65-nt bottom strand to a final concentration no greater than 20 nM for the ligated strand and 200 nM for the complementary strand.The 65-nt RNA strand containing 8-azaN used in RNase V1 footprinting experiments was prepared using a three-piece-splinted ligation ,35. The 2 and 1.0-μg/ml yeast tRNA for 30 min at 30°C. Samples were then incubated with 0.0007 units of RNase V1 for 7.5 min. RNase V1 Inactivation/Precipitation Buffer was added to each sample followed by ethanol precipitation. Nuclease T1 digestion was also carried out on the single-stranded-labeled 65-nt RNA using the RNA Structure-Function Protocol from Life Technologies with 0.7 units for 15 min. Samples were then treated with 20 units of PNK in 1X PNK buffer for 30 min at 37°C. Samples were heated at 98°C for 10 min in denaturing loading buffer and fractionated on a 12% denaturing gel, dried at 80°C for 1 h and imaged.Samples containing no greater than 3-nM RNA with various concentrations of hADAR2-D  were equilibrated in 10-mM Tris-HCl, pH 7.1, 0.2% glycerol, 60-mM KCl, 25-mM NaCl, 10-μM 2-mercaptoethanol, 15-μM EDTA, 100-μg/ml BSA, 0.3-mM MgCla) containing the reactive site previously identified in the S. cerevisiae bdf2 mRNA as a substrate for hADAR2-D  under our standard assay conditions . Reducing the size of the stem by four base pairs and removing the 1 × 3 asymmetrical internal loop led to RNA (b)  compared to that observed for (a) under the same conditions. Nevertheless, this reaction rate is comparable (or superior) to those observed for other model substrates used to study the reaction of full length ADARs .b) with the analog at the reactive site  (Kd = 27 ± 16 nM), whereas (b) with A at the reactive site binds over 100-fold weaker (Kd > 3200 nM)  lacking two base pairs on the end of the stem binds hADAR2-D less tightly that (b) (Kd = 310 ± 60) suggesting the catalytic domain requires the longer duplex . Th. Th28). e Figure . Using Eker Kd > 200 nM (Fker Kd > 200 nM (Fb) is accessible by chemical synthesis, scale-up of the synthesis of this RNA proved challenging. We then asked whether an intermolecular duplex that retains the sequence and secondary structural elements found near the editing site in (b) would also bind tightly to hADAR2-D. Thus, intermolecular duplex RNA (d), with the hairpin loop of (b) replaced with additional base paired structure, was prepared bearing either A or 8-azaN at the reactive site  (Kd = 27 ± 16 nM) , when A is placed at the reactive site, the hADAR2-D affinity measured by quantitative EMSA is substantially lower (Kd > 3200 nM)  Figure ,37,40,41d) bearing 8-azaN at the reactive site and hADAR2-D, we evaluated the effects of changing the sequence and structure of the complementary strand. This was carried out with duplexes (f–j) , near the 5′ end (g) and middle (h). In addition, sequence changes were made to pair mismatched bases in the internal loop (duplexes (i) and (j)). EMSA was then performed with hADAR2-D and each of these duplexes along with the 8-azaN-containing single strand and a duplex with a fully 2′-deoxy complementary strand as controls. No stable complex was observed with either the 8-azaN containing single strand or the duplex with an entirely DNA complement  further establishing the strict requirement for duplex RNA in the substrate for ADAR2's catalytic domain. Duplexes with DNA/RNA chimeric complementary strands  form complexes of varying stability (Table d). However, (h) with the 2′-deoxy block in the middle of the complementary strand binds particularly poorly with a measured Kd = 342 ± 88 nM. Thermal denaturation (TM) studies were carried out with these three duplexes along with parent duplex (d) to allow a comparison of the effects of these changes on duplex stability and hADAR2-D binding (Table d) and (h) have the same thermal stability but bind hADAR2-D with >10-fold difference in dissociation constant. Thus the ribose requirement is not simply due to an effect on duplex stability and is more likely a result of interactions between the catalytic domain and this strand that require ribose(s). Sequence changes that eliminate mismatches adjacent to the editing site (RNAs (i) and (j)) improve binding to hADAR2-D. This provides additional evidence for the importance of duplex RNA structure surrounding the editing site for ADAR2 catalytic domain interactions.Since a high affinity complex could be observed by EMSA with  Figure  that inti) . Interestingly, the hADAR2-D E488Q mutant showed substantially less smearing in the gel than did the wild-type hADAR2-D suggesting differences in dissociation rates for the two complexes  with enhanced catalytic activity (i.e. higher single turnover rate constant for deamination of model substrates compared to the wild-type enzyme). Given t) Figure . As predomplexes . Furtheret al. observed a faint 16-nt protection, with 8-nt protection on the 5′ side and 7-nt protection on the 3′ side at low concentrations of ADAR2, but results were obscured at higher concentrations because the entire RNA stem loop used in the study was protected from cleavage. This also correlated to a second shift in EMSA, leading to the conclusion that the entire RNA is coated with ADAR2 at the higher concentrations. We reasoned that if the bdf2-derived sequence bearing 8-azaN at the editing site were present in a longer otherwise unmodified RNA duplex, differential reactivity to a duplex RNA-specific nuclease should be observed in the presence of hADAR2-D and new information about ADAR catalytic domain binding would be obtained. To generate such a structure, we used a splinted ligation strategy to insert RNA (i) into a long (65 bp) duplex  that binds tightly to hADAR2-D and the GluR B R/G RNA (e) that does not, one is tempted to conclude that a duplex longer than five base pairs on the 5′ side of the editing site is required for full binding to the catalytic domain. This idea is consistent with the observation that the 8-azaN-containing 39 mer (c) binds more weakly than does 43 mer (b) that has two more base pairs 5′ to the editing site . It. It28). e Figure . Analysie Figure ,50. The e Figure ,51. Furte Figure ,51. Togei) and (j) suggests an ancestral ADAR target may have been similar in structure to these (Interestingly, the modular nature of the ADAR genes has led to the suggestion that ADARs acquired dsRBDs late in evolution . Our resto these .et al. who showed that an editing site complementary strand could be extensively modified with 2′-O-methyl groups at nucleotides distal to the editing site but not at proximal nucleotides (hADAR2-D is clearly duplex RNA-dependent as indicated from our studies of the effects of changes to the editing site complementary strand Figure  and 2. Hleotides .The insight and new RNAs reported here will continue to be useful for defining characteristics of the interaction between hADAR2's catalytic domain and RNA. For instance, 2-aminopurine-modified catalytic domain substrates can now be used to define the role of ADAR domains in the base-flipping step of the reaction ,40,49. ISupplementary Data are available at NAR Online.


The specific interactions between RNA-binding proteins and their target RNAs are an essential level to control gene expression. By combining ultra-violet cross-linking and immunoprecipitation (CLIP) and massive SoliD sequencing we identified the RNAs bound by the RNA-binding protein CELF1, in human HeLa cells. The CELF1 binding sites deduced from the sequence data allow characterizing specific features of CELF1-RNA association. We present therefore the first map of CELF1 binding sites in human cells. •CLIPSeq identifies CELF1 binding sites in human cells.•CELF1 binds to protein coding, lincRNA and antisense RNA. This includes but is not limited to nuclear splicing, cleavage and polyadenylation, nuclear export and cytoplasmic localization of the mRNA, cytoplasmic deadenylation, RNA degradation and translational control of messenger RNA.CELF1  is a conserved RNA binding protein that controls alternative splicing in the nucleus and cytoplasmic deadenylation, mRNA stability and translation in the cytoplasm in-vitro selection experiments identified UGU-rich motifs as required for CELF1 binding in-vivo relies on UV cross-linking and immunoprecipitation (CLIP) of RNA-binding proteins These findings highlight the need for the systematic identification of the repertoire of RNAs associated with CELF1 in human cells. Analysis of known CELF1 RNA ligands and of 33.1CELF2  is a founding member of the CELF family of RNA-binding proteins CELF2 A, indicain-vivo. Cells were rapidly collected and lysed. The cell extracts were treated with RNAse T1 to generate small ribonucleic complexes (32P-ATP and T4 Polynucleotide Kinase (The CLIP-seq protocol is presented in 3.2Two different libraries of CELF1 CLIP-seq (libA and libB) were sequenced in 3 different runs. As a reference, an mRNA-seq library was generated following a protocol as similar as possible to the CLIP library protocol. Briefly, oligo(dT)-selected RNAs were fragmented with RNAse T1, phosphorylated on the 5′end with T4 PNK and a library prepared with the SREK (SoliD). This mRNA-seq library was sequenced in one run.The sequential processing steps are depicted in ,Respectively, 10,067 and 4331 clusters were considered significantly higher in libA and libB . These nBecause CELF1 is known to preferentially associate with UGU repeats on RNAs, we next looked whether specific hexamers were overrepresented in our data sets using RSAT oligo-analysis CELF1 is an evolutionary conserved RNA Binding protein. We therefore compared the overall overlap between its RNA targets identified by RIP-chip or CLIPseq in C2C12 cells  complemented with 10% FCS, 100 U/ml penicillin and 100 μg/ml streptomycin (Invitrogen). Proteins were detected in western blots using anti-CELF1-2 , anti-CELF2  and anti-ACTIN .4.22 +, no Mg2 +), UV irradiated them 3 times at 4000 μJ/cm2 and 254 nm on ice, recovered them by scrapping and stored them at − 80 °C. We immunoprecipitated CELF1/RNA complexes from resuspended cell pellets as described 32P to visualize RNA-protein complexes, or 1 mM unlabelled ATP to prepare the libraries. We eluted the RNA/protein complexes in NuPAGE 1 × loading buffer and fractionated them by PAGE in a neutral NuPAGE 4–12% bis-tris gel run in 1 × MOPS (Invitrogen). The complexes were transferred onto nitrocellulose membrane (protran BA-85) and the CELF1 complexes were cut out. RNAs were recovered following proteinase K digestion in PK buffer  2 mg/ml) for 30 min at 37 °C. The digestion was pursued in PK buffer with urea (7 M) for 30 min at 60 °C. Proteins were then extracted by phenol/CHCl3 and RNA precipitated with ammonium acetate and isopropanol using glycogen as carrier. RNAs were washed twice with 80% ethanol, dried and resuspended in water. For cloning and sequencing, radioactive experiments were run in parallel with nonradiolabeled experiments and nonradioactive samples were further used for cloning and sequencing. The RNA fragments were ligated to adapters, reverse transcribed and amplified by PCR following manufacturer instruction using the Small RNA expression kit (Ambion # 4397682).We made CLIP experiments from untreated cells essentially as described For mRNA-seq, total RNAs were extracted from growing Hela-Kyoto cells using RNAeasy columns (Qiagen). Poly(A) + RNAs were selected on oligo-dT (Promega) and partially fragmented using RNAse T1. The size of the fragments was controlled using a bioanalyzer. We constructed a cDNA library for deep sequencing following manufacturer instructions using the Small RNA expression kit as described above. One RNASeq library was prepared and sequenced.SOLiD sequencing was performed at the Genoscope on a SoliD 3 system. Two different CLIP-seq libraries and one mRNA-seq library were sequenced for 50 cycles following manufacturer recommendations.4.3The detailed procedure and the R script are available as supplementary materials.4.3.1Raw sequencing data were collected as colorfasta (.csfasta) and quality files . Sequence data were trimmed with cutadapt 4.3.2The resulting sequence files were mapped with SHRIMP 2 4.3.3Read cluster identification was conducted in parallel on the two libraries using FindPeaks 4.0 4.3.4Cluster strand was defined based on mapped read strand. The annotation of the cluster based on their genomic location was conducted using R script and the GENCODE annotation (Release 19 (GRCh37.p13)).4.3.5Binding data for CELF1 targets were obtained from CLIPSeq and RIP-Chip experiments published in CELF1-binding clusters library A.Supplementary Table 1CELF1-binding clusters library B.Supplementary Table 2CELF1 conserved targets.Supplementary Table 3Detailed_procedure.Image 1Solid reads quality analysis script.Image 2FindPeaks analysis script.Image 3The following are the supplementary data related to this article.


Human erythrocytes are terminally differentiated, anucleate cells long thought to lack RNAs. However, previous studies have shown the persistence of many small-sized RNAs in erythrocytes. To comprehensively define the erythrocyte transcriptome, we used high-throughput sequencing to identify both short (18–24 nt) and long (>200 nt) RNAs in mature erythrocytes.Analysis of the short RNA transcriptome with miRDeep identified 287 known and 72 putative novel microRNAs. Unexpectedly, we also uncover an extensive repertoire of long erythrocyte RNAs that encode many proteins critical for erythrocyte differentiation and function. Additionally, the erythrocyte long RNA transcriptome is significantly enriched in the erythroid progenitor transcriptome. Joint analysis of both short and long RNAs identified several loci with co-expression of both microRNAs and long RNAs spanning microRNA precursor regions. Within the miR-144/451 locus previously implicated in erythroid development, we observed unique co-expression of several primate-specific noncoding RNAs, including a lncRNA, and miR-4732-5p/-3p. We show that miR-4732-3p targets both SMAD2 and SMAD4, two critical components of the TGF-β pathway implicated in erythropoiesis. Furthermore, miR-4732-3p represses SMAD2/4-dependent TGF-β signaling, thereby promoting cell proliferation during erythroid differentiation.Our study presents the most extensive profiling of erythrocyte RNAs to date, and describes primate-specific interactions between the key modulator miR-4732-3p and TGF-β signaling during human erythropoiesis.The online version of this article (doi:10.1186/s12864-015-2156-2) contains supplementary material, which is available to authorized users. Human erythrocytes provide gas transport throughout the body and comprise the majority of cells in whole blood. Human diseases related to red blood cells (RBCs) or erythrocytes, such as anemia or malaria, affect hundreds of millions of people worldwide and present huge health concerns. Although we have gained a significant understanding of how these diseases occur and many treatment options are available, we still cannot explain many aspects of these erythrocyte diseases. Since the precise regulation of both coding and noncoding RNAs is essential for erythrocyte development, these erythrocyte diseases are often accompanied by significant transcriptome changes. During erythropoiesis, microRNAs actively regulate proliferation and/or differentiation of erythroid cells during physiological and pathological adaptions . DysreguDuring terminal maturation of erythrocytes, the nucleus is extruded from progenitors, leading to anucleate cells with no further RNA production. Therefore, mature erythrocytes were once thought to lack RNAs and have significantly lower signals from RNA-binding dyes such as methylene blue. However, many studies have shown erythrocytes do contain diverse and abundant small RNA species , includiHowever, previous transcriptomic analyses were limited to known erythrocyte microRNAs using microarrays , or knowin vitro erythroid differentiation. Our study is the first to comprehensively profile the erythrocyte transcriptome, and reflects the utility of high-throughput sequencing to identify critical modulators of human development.Here, we employed high-throughput sequencing to characterize both short  and long  RNAs in human erythrocytes. For long RNA profiling, we prepared RNA-Seq libraries using a protocol that allows for identification of both polyadenylated and non-polyadenylated RNAs. A total of 6843 transcripts were expressed in all three analyzed erythrocyte samples. While this number is far less than that of typical nucleated cells, these analyses established a surprisingly diverse RBC transcriptome. In parallel, short RNA sequencing libraries were prepared and the miRDeep pipeline was utilized to identify both known and putative microRNAs. From these analyses, we identified in mature erythrocytes an abundant, diverse set of microRNAs that include both known and putative microRNAs. The joint analysis of transcriptomes identified several loci with expression of both long and short RNAs, suggesting their coordinated regulation of expression or processing. Furthermore, we performed a functional investigation of the uncharacterized, primate-specific miR-4732-3p within the miR-144/451 locus. MiR-4732-3p was predicted to target both SMAD2 and SMAD4 , componein vitro differentiating CD34+ erythroid progenitors (Day 8 of differentiation) of two individuals. RNA from these nucleated erythroid and peripheral blood mononuclear cells was isolated and used to prepare strand-specific long RNA-seq libraries  to determine the relative enrichment of the top 500 erythrocyte RNA transcripts in the day 8 erythroid progenitor (D8) vs. PBMC samples. We observed a highly significant enrichment of the top 500 erythrocyte transcripts in the erythroid progenitor transcriptome Fig. . TogetheRecent studies have suggested that intron retention and nonsense mediated decay may contribute to degradation of most transcripts during terminal differentiation of granulocytes  and erytWe also did not identify enrichment for inflammatory-related gene signatures typically found in leukocytes samples. None of the transcripts that encode leukocyte marker such as CD20 (MS4A1) and different CD3 mRNAs, were expressed in any erythrocyte sample. However, these transcripts were identified in the PBMC transcriptomes. Together, this sequencing data validates the successful depletion of leukocytes in the purified erythrocyte samples.Next, we examined the most abundant transcripts to obtain insights into erythroid biology. Several of the most highly expressed RNAs are Y RNAs or different components of ribonucleoprotein complexes that represent persistent expression after terminal differentiation. Many of the highest expressed genes in the erythrocyte transcriptome also encode proteins highly relevant for erythroid cell differentiation. For example, BNIP3L mediates mitochondrial clearance during reticulocyte terminal differentiation . AbundanIn addition, several highly expressed erythrocyte transcripts encode proteins with known functions previously not associated with erythrocytes. For example, ADIPOR1 encodes the cellular receptor for adiponectin, a hormone secreted by adipocytes that regulates fatty acid catabolism and glucose levels. Interestingly, Japanese individuals with anemia present significantly higher serum levels of adiponectin that that of unaffected individuals, yet the function of such an association remains unknown . Highly In parallel with long RNAs, we also sequenced short (18–24 nt) RNA species from five erythrocyte samples. The short RNA libraries were constructed, pooled, and then applied to the Illumina HiSeq 2000 to generate a total of 97,144,661 reads. To discover small-sized RNAs with microRNA-like characteristics, we employed the miRDeep2 pipeline  Fig. 2a2a, a widWe employed a cutoff for microRNAs based on a miRDeep score of ≥1, and ≥ 20 reads in at least one sample, like that previously used . In all,Several of the most abundant microRNAs in our dataset were previously shown to be enriched in human erythrocytes, thus validating our findings. For example, the most abundant microRNA, miR-486-5p, accelerates erythroid differentiation , and itsThe putative microRNAs identified by miRDeep were further manually annotated and curated using both the UCSC genome browser (hg38) and miRBase v.21). These analyses lead to 72 putative unannotated microRNAs, and selected microRNA sequences that met criteria for putative microRNAs are shown  . Overexp and bax , 43. OveGiven the ability to suppress TGF-β signaling activity, we hypothesized that miR-4732-3p may promote proliferation by suppressing SMAD2 and SMAD4. Consistent with this hypothesis, overexpression of miR-4732-3p in differentiating CD34+ erythroid progenitors resulted in a significant increase in total cell number Fig. . FurtherThe discovery of abundant and diverse erythrocyte RNAs opens the possibility of using these readily accessible genetic materials as biomarkers to monitor various human diseases and treatments that involve erythroid cells. Sequencing allows for characterization and quantification of not only annotated RNAs, but also novel mRNA isoforms and noncoding RNAs. To the best of our knowledge, this is the first study that has employed high-throughput sequencing to extensively profile both the long and short RNA transcriptomes of human erythrocytes.Here we focus on the primate-specific miR-4732-3p. While this microRNA has been annotated in miRBase (v.21), it has few reads (<10 per sample) in other datasets and is uncharacterized. We found that miR-4732-3p is an abundant erythrocyte microRNA within the erythroid-enriched miR-144/451 locus. The identification of multiple elements within the miR-144/451 cluster is not surprising, as miR-144 and miR-451 are both highly expressed and upregulated by GATA1 during erythrocyte development . ConsideThe human erythrocyte is one of the most commonly studied cell types, often used as a model system to understand the general principles of molecular genetics, biochemistry, membrane biology, and cell physiology. As new RNA synthesis ends with nuclear extrusion, the expression level of any RNA species is determined mainly by its turnover and decay . The selAs many microRNAs are highly conserved between species , few priIn this study, we demonstrated that miR-4732-3p inhibits TGF-β signaling to promote erythroid cell expansion during differentiation. As inhibition of SMAD2 and SMAD4 via RNAi-mediated mechanisms reflects phenotypes previously documented , 43, thiHere we describe the first comprehensive, as well as joint analysis of the human erythrocyte transcriptome. We have made several important observations. First, in addition to microRNAs, mature erythrocytes also contain long transcripts. Although smaller in number, erythrocyte long transcripts are highly enriched in annotated exons and largely encode proteins critical to erythrocyte differentiation and functions. Therefore, quantitative analysis of the erythrocyte transcriptome may reflect erythrocyte development history and pathological adaptations. Second, miRDeep identified 287 known and 72 putative novel microRNAs that include many microRNAs that are either uncharacterized, or not previously associated with erythrocytes. Third, the joint analysis of long and short transcripts identified a long noncoding RNA and previously uncharacterized microRNAs in the miR-144/451 locus. Finally, functional studies demonstrate the primate-specific microRNA miR-4732-3p regulates the TGF-β pathway during human erythropoiesis. This study presents a novel post-transcriptional modulator of erythroid development, and features a new transcriptomic data set that provides new insights into erythroid biology.Blood samples were obtained from healthy adults via venipuncture into vacutainer tubes (BD) using a protocol (Pro00050295) approved by the Institutional Review Board at Duke University Medical Center. Written, informed consent was obtained from each donor.We have obtained consent to publish from the blood donors to report individual donor data.Blood was kept on ice after a single draw and was processed within 30 min of sample acquisition. For isolation of erythrocytes from whole blood, blood samples were processed as previously described . BrieflyTo prepare long RNA‐Seq libraries, 1 μg of purified total input RNA was used with the TruSeq Stranded Total RNA Sample Prep Kit with Ribo‐Zero Globin (Illumina). A total of 100 ng of erythrocyte RNA was used to prepare short RNA-seq libraries with the TruSeq Small RNA Sample Prep Kit (Illumina). Total RNA-seq and small RNA libraries were separately pooled and analyzed using the Illumina HiSeq 2000 sequencing system. All reads for total RNA-seq and small RNA-seq were sorted based on quality, and raw sequences with removed 3’ linker sequences were obtained in FASTQ format. All primary sequencing data are publicly available through the Gene Expression Omnibus (GEO dataset GSE63703). For long RNA-seq, the RNA-seq data was processed using the TrimGalore toolkit to trim low quality bases and Illumina sequencing adapters from the 3’ end of the reads. Only pairs where both reads were 20 nt or longer were kept for further analysis. Reads were mapped to the GRCh37r73 version of the human genome and transcriptome  using thMobilized Peripheral Blood CD34+ Cells (AllCells) were grown in StemLine II media containing Glutamine (Sigma) with 20 % BIT (StemCell Technologies) and 1 % Penicillin/Streptomycin (Gibco). On Days 0–7, cells were supplemented with growth factors IL-3 at 20 ng/mL (Invitrogen), SCF at 50 ng/mL (Invitrogen), and Erythropoietin (Epo) at 3U/mL . On days 8–12 cells were only supplemented with IL-3 and Epo, and on days 13–16 cells were only supplemented with Epo. K562 cells (ATCC) were maintained in RPMI containing glutamine (Gibco), with 10 % Fetal Bovine Serum (Hyclone) and 1 % Penicillin/Streptomycin (Gibco). All cell counts were performed using a standard hemocytometer.K562 and CD34 erythroid progenitor cells were transfected with indicated plasmids and/or oligos using Lipofectamine LTX (Invitrogen), HiPerfect (Qiagen), or Attractene (Qiagen) reagents. For luciferase values, all assays were performed in biological quadruplicate, and Firefly and Renilla luciferase activities were measured using the Dual-Glo Luciferase assay (Promega) and luminometer (Tecan Infinate F200).http://rsb.info.nih.gov/ij/).Cells were lysed with RIPA buffer supplemented with protease inhibitor cocktail (Sigma). Denaturing sample buffer was added to protein samples, and samples were boiled and resolved on Tris–HCl polyacrylamide gels. Resolved proteins were then transferred onto polyvinylidene fluoride (PVDF) membranes  and probed with the following primary antibodies: anti-rabbit Smad2 and anti-rabbit Smad4 , and anti-mouse α-Tubulin (Sigma). The following secondary antibodies were used: goat anti-rabbit IgG-HRP (Santa Cruz) and anti-mouse IgG-HRP (R&D Systems). The western blot was visualized by enhanced chemiluminescence  and exposed to film. Densitometric measurements were performed with ImageJ software . The resulting cDNA was used to assess RNA expression by qPCR with TaqMan microRNA real-time assays and Taqman universal master mix (ABI). Results were calculated using the comparative CT method. We compared relative levels of microRNAs with probes specific for the indicated mature microRNA and normalized by U6 snRNA expression. To quantify relative mRNA expression, RNA was reverse-transcribed with SuperScript II following the manufacturer’s protocol (Invitrogen). The resulting cDNA was used to assess RNA expression by qPCR with Power SYBRGreen PCR mix (ABI) and primers specific for targets, with GAPDH for normalization. All qPCR reactions were performed in technical triplicate using the StepOnePlus system (ABI). Primers for qPCR were previously described , 43.MiR-4732-3p microRNA mimic or non-targeting (NT) microRNA mimic #1 (Dharmacon) were used for microRNA overexpression. For microRNA inhibition, miR-4732-3p antisense 2’-O-Methyl oligonucleotide (AMO) or non-targeting (NT) antisense 2’-O-Methyl oligonucleotide (Dharmacon) were used. For mRNA knockdown, SMAD2 and SMAD4 SMARTpool siGENOME siRNAs (Dharmacon) or Allstar negative control siRNA (Qiagen) were used.For luciferase reporter constructs, a portion of the 3' untranslated region (UTR) of SMAD2 and SMAD4 flanking the predicted miR-4732-3p binding site was amplified and cloned into the XhoI and NotI sites downstream of Renilla luciferase in the psiCheck-2 vector (Promega). The 3’UTR mutated reporters were constructed using the QuikChange II Site-Directed Mutagenesis kit  to mutate the predicted targets of the miR-4732-3p seed sequence. Reporter construct PCR and mutagenesis primers are listed  data repository with the GEO series accession number GSE63703.


The non-coding RNA 7SK is the scaffold for a small nuclear ribonucleoprotein (7SKsnRNP) which regulates the function of the positive transcription elongation factor P-TEFb in the control of RNA polymerase II elongation in metazoans. The La-related protein LARP7 is a component of the 7SKsnRNP required for stability and function of the RNA. To address the function of LARP7 we determined the crystal structure of its La module, which binds a stretch of uridines at the 3′-end of 7SK. The structure shows that the penultimate uridine is tethered by the two domains, the La-motif and the RNA-recognition motif (RRM1), and reveals that the RRM1 is significantly smaller and more exposed than in the La protein. Sequence analysis suggests that this impacts interaction with 7SK. Binding assays, footprinting and small-angle scattering experiments show that a second RRM domain located at the C-terminus binds the apical loop of the 3′ hairpin of 7SK, while the N-terminal domains bind at its foot. Our results suggest that LARP7 uses both its N- and C-terminal domains to stabilize 7SK in a closed structure, which forms by joining conserved sequences at the 5′-end with the foot of the 3′ hairpin and has thus functional implications. OH, LARP7 binds almost exclusively to the non-coding RNA 7SK  are involved in various important functions in RNA metabolism and are found in nearly all eukaryotes . Besides RNA 7SK –11. In D RNA 7SK ,13. Othe the RNP .et al.  to bind the apical loop of the 3′-hairpin. Taken together, our data support a model where both structural domains of LARP7 are combined to bind 7SK. LARP7 wrapping around the 3′ region includes the sequences closing the lariat form of 7SK. This constitutes a first evidence for the closed conformation of 7SK predicted by computational and phylogenetic analyses . LARP7 fin vitro transcription. LARP7 full-length and the truncated versions were expressed in Escherichia coli. Mutagenesis was performed by the Quikchange approach (Stratagene).Several RNAs and protein constructs were designed, as detailed in the Supplementary Material section. All RNAs  were obtained by Plate-shaped crystals were obtained with 30% PEG 3350 and 0.1-M succinic acid, pH 7.0 at 4°C with several RNAs  but very few crystals diffracted well. All RNAs leading to crystal formation comprised the 7SK 3′-end oligonucleotide 325–332, but not systematically the HP4 hairpin. The structure was solved from a crystal obtained in a drop initially set up with RNA 300–332 comprising the HP4 hairpin but gel analysis of the drop showed that the RNA was degraded. After structural analysis, it appeared that the largest piece of RNA bound to the protein was a 5-mer corresponding to the 3′-end, which was probably protected from degradation by binding to the protein.a = 163.452 Å, b = 33.50 Å, c = 119.08 Å, α = 90.0°, β = 128.99° and γ = 90.0°. The structure solution was obtained by molecular replacement, using Phaser  was incubated 20 min at 4°C with increasing concentrations of proteins in a buffer containing 250-mM NaCl. Native gel analysis was performed as detailed in the Supplementary Material section.The ϒ-The 5′-labeled 262-HP4 RNAs (50 nM) were mixed with protein in a similar buffer as in electrophoretic mobility shift assay (EMSA) and incubated at 4°C. The concentrations of proteins were chosen to ensure 100% binding . The RNases concentrations were chosen to produce significant cleavages in 5 min, as detailed in the Supplementary Material section.Two multiple sequence alignments were obtained independently. The first, for LARP7, resulted from a BLAST search starting with the human sequences Q4G0J3. Sequences were examined in the C-terminal region to distinguish LARP7 from other LARP sequences. The process led to about 50 sequences, treated with ClustalOmega, before visualization with Pymol Figure . The secThe starting model of the extended M1-HP4 RNA bound to the C-terminal domain was obtained as follows. First, a model for the RNA M1-HP4 was created from a linear sequence comprising the 5′-nucleotides (residues 1–8 of 7SK) linked with sequence AGA to the sequence 289–328 . This AGA, adjacent to G8, was expected to favor a GAGA tetraloop, thus folding the M1 extension into an independent hairpin of seven base-pairs. This linear sequence was submitted to MC-fold , which pλ = 1.033 Å). The Aviex charge-coupled device detector was positioned to collect data in the Q-range 0.008–0.33 4 Å−1 . All solutions were mixed in a fixed-temperature (15°C) quartz capillary with a diameter of 1.5 mm and a wall thickness of 10 μm, positioned within a vacuum chamber. Fifty microliter of a monodisperse sample of RNA–protein complex (70–130 μM) was injected onto a size-exclusion column , using an Agilent HPLC system, and eluted directly into the SAXS flow-through capillary cell at a flow rate of 0.2 ml min−1. The elution buffer consisted of 20-mM Na HEPES, pH 7.2, NaCl 200 mM and 2-mM DTT. SAXS data were collected continuously, with a frame duration of 1.0 s and a dead time between frames of 0.5 s. Selected frames corresponding to the main elution peak were averaged using FOXTROT, a dedicated home-made application. A large number of frames were collected during the first minutes of the elution, and these were averaged to account for buffer scattering, which was subsequently subtracted from the signal during elution of the protein. Data reduction to absolute units, frame averaging and subtraction were done using FOXTROT. All subsequent data processing, analysis and modeling steps were carried out with PRIMUS and other programs of the ATSAS suite (http://www.embl-hamburg.de/biosaxs/atsas-online/).SAXS experiments were conducted on the SWING beamline at the SOLEIL synchrotron  with P65 manually docked on the apical loop was sorted out by fitting with CRYSOL to the SAXS experimental data. The position of the P65 on HP4 was then further refined with program SASREF by rigid body molecular modeling against the shapes of the complex calculated from the SAXS data . In thisTetrahymena  comprises three regions Figure . Followirahymena . The xRRrahymena . Betweenrahymena . In addiE. coli the full-length LARP7, the La module comprising the LAM and RRM1 regions with an N-terminal extension (1–208) and the C-terminal domain comprising the RRM2 (433–582). Several boundaries were tried for the La module, but only 1–208 was considered for a structural analysis . Elucidation of the role of the C-terminal RRM2 was approached by biochemical experiments with the construct (433–582). Crystallization assays were focused on complexes of the La module with RNA.In the prospect of elucidating how these modules are combined in LARP7 to bind specifically the 7SK RNA, we expressed in in vitro by EMSA experiments . However, a check of the drop content after crystal mounting showed that the RNA was degraded. The formation of crystals with similar unit cells in drops initially containing RNAs which all comprised the 325–332 sequence immediately suggested that this short UUUCUUUU-3′ stretch of 7SK sequence was the longest oligonucleotide possibly present in the crystal. Indeed, difference Fourier showed densities for only three and five nucleotides in monomers A and B, respectively . The electronic densities corresponded to pyrimidines and were interpreted as 5′-CUUUU-3′.The crystallization assays included single-stranded oligonucleotides  as well as RNAs comprising the HP4 hairpin . Thermofluor experiments  showed aThe asymmetric unit of the monoclinic crystals contains two protein molecules. The best defined monomer B will be described in the following text Figure . AlthougRRM1 shows a variant form of the RNA recognition fold found in many RNA-binding proteins , an anciIn LARP7, the linker connecting RRM1 to LAM follows a similar path as in HsLa, and has a similar size of nine residues (10 in HsLa) between the RR sequence from the ‘wing 2’ motif (Arg110-Arg111), which marks the C-terminal boundary of the LAM domain . This reThe larger stretches of residues absent from the sequence and structure of LARP7 are highlighted in blue in the 3D structure of La Figure . The larTaken together, these observations support the 3-stranded RRM as a genuine characteristic of LARP7 proteins. The La module comprising this shortened RRM1 binds the 3′ domain of 7SK Figure . The exiIn the electronic map, three uridines in one monomer and five nucleotides in the second monomer are visible in the cleft formed between LAM and RRM domains Figure  and B. AThe base of the terminal uridine U-1 stacks on Phe56 from the LAM-α3 helix. The stacking of the base of U-3 further restricts the binding pocket, which is limited at the bottom by Phe77, lying on the sugar ring. The terminal ribose binds Asn50 and Asp54 Figure . Both 2′Specific binding of the penultimate residue U-2 involves residues from both LAM and RRM1 domains Figure . The basThe upstream nucleotides, U-4 and C-5, are only visible in one monomer of the asymmetric unit. They lie approximately in the same planes as U-3 and U-1, respectively, as shown in Supplementary Figure S4A. The ribose-phosphate chain is driven apart from helicity, with the bases U-4 and C-5 unstacked. This arrangement could be linked to the proximity of the long loop 3 connecting β2 and β3, which was previously involved in RNA binding . The RNA is facing the aromatic residues from the signature sequences RNP1 (Phe168 and Phe170) and RNP2 (Tyr128). This suggests that the β-sheet in LARP7 RRM1 may bind RNA. Analysis of the packing contact does not reveal direct interactions with the RNA, apart from Tyr128 stacking on U-4. Interestingly, the mutation of Tyr128, hallmark of the RNP2, was reported in an earlier work to result in a loss of binding specificity to 7SK . ResidueMost RRM-containing proteins have at least two RRMs, which often combine, leading to an expanding wealth of RNA- and protein-binding catalog. We therefore set out to investigate the function of the second, RRM2 domain at the C-terminus of LARP7.in vivo  was assayed in binding experiments. It showed strong binding to several RNAs derived from 7SK, including those restricted to the 3′-hairpin, HP4 Figure  and B. Ain vivo . In agren Figure  and B, tn Figure  and D, rKnowing the tendency of RRM domains to pack together, supported by packing contacts observed in the crystals of LARP7, we wondered whether an interaction could be established between RRM1 and RRM2. However, binding the La module to the 3′-end of a hairpin mutated at G312 did not recruit the RRM2 domain Figure . SimultaTo position the N- and C-terminal domains on the 7SK 3′-hairpin, we used footprinting experiments  and compAt the foot of the HP4 hairpin, the adenines A296–A297 show strong V1 cleavages, indicative of base-pair formation. They might possibly pair with the terminal poly-U, but the V1 signal is unchanged upon complexation with the N-terminal domain, which captures the 3′-end. This suggests that A296–A297 connect elsewhere in 7SK. Binding of full-length LARP7 induces the disappearance of this V1 cleavage, which suggests a protection induced by the central linker region. An alternative explanation is that the structure probed by the V1 cleavage melts upon LARP7 binding, suggesting a conformational response of the RNA upon LARP7 binding, a situation reminiscent of the telomerase case . This hyin vivo study  is well conserved . It has in vitro transcription. Modeling with MC-fold (2 2.1) as shown in Supplementary Figure S6A.An RNA named M1-HP4 was designed by linking the 5′-sequence of 7SK (GGAUGUG) to the 3′ region at C299 by a GAAA sequence, and produced by  MC-fold  indicate MC-fold . This wa MC-fold ,54. It w MC-fold . Finally MC-fold . During The SAXS study thus confirmed that LARP7 C-terminal domain binds to the apical loop of HP4. Moreover, the atomic models generated with the SAXS study allowed a mutational analysis. Based on the alignment of P65 with LARP7 , we chose two residues close to the RNA in the model and conserved in LARP7, but different in P65 and not from the RNP sequences. Residues Tyr513 and Lys517 of P65 align with Lys535 and Asp539 of LARP7, respectively . In an EMSA experiment with M1-HP4, we observed that while D539A had no visible effect, the mutation K535A clearly decreased the binding to RNA .The N-terminal domain was docked manually on the M1-HP4 model, by anchoring the U-4 nucleotide observed in the crystal on C328 from the model. The terminal U triplet was from the structure. This still leaves the N-terminal domain quite free to rotate around the connection. A more precise all-atoms modeling was not attempted, as it will require more data to orient the structural elements with confidence. Interestingly, the residue Glu130 which was suggested to be involved in 7SK-binding  is positioned toward the M1 region of the RNA in the working model shown in Figure The crystal structure of the LARP7 N-terminal domain, described here, is the first 3D structure of the La module of a member of the LARP family different from La showing the linked domains of the La module in a complex with RNA. Until the recent publication of the individual structures of the two domains of the La module from LARP6 .The triplet of uridines at the 3′-end of 7SK binds into the cleft between the LAM and RRM1 domains. It is constrained in a characteristic hooked conformation, allowing strict recognition of the penultimate uridine, with a contribution of the RRM1 revealing LARP7 specificity. The relative orientation of the two domains of the La module seems to be, as in HsLa essentially driven by the 3′-terminal uridines binding in the cleft between the domains, with U-2 located exactly at the same position. In the course of the La structural analysis, it was hypothesized that the two domains move freely in the absence of RNA , a hypotComparing the present structure with HsLa shows that while the LAM domains are similar, the RRM1 are different. In LARP7, a smaller RRM1 is formed, with its β-sheet composed of three instead of four strands. The absence of the C-terminal α3 helix of RRM1 combines with the reduction of the length of the N-terminal helix of the RRM to increase the accessibility to the RNA-binding residues of the central β-sheet of RRM1. In RRMs, the fourth β-strand often contributes H-bonds for the specific recognition of the RNA substrate. In LARP7, while it cannot be excluded that a fourth β-strand and α3 helix are recruited from downstream sequences, possibly via the RNA, the RRM1 shows several specific residues that could play a role in binding RNA. Residues including His138 and Ile154 were seen to participate in the penultimate uridine recognition, in a different way compared to La, thus showing a LARP7-specific response to a common task. Another residue, Glu130, also identified as LARP7-specific in the sequence alignment was shown by mutation to be involved in binding the 3′-terminal domain of 7SK. Glu130 is on the other side of the β-sheet, too far to be involved in the recognition of the terminal uridines. It may be participating in the specific function of LARP7, which is the recognition of 7SK. Further work will be necessary to identify the eventual binding site on 7SK. Interestingly, the RRM1 is very different in LARP6, where the RNA-binding surface is blocked by additional helices (in vivo (in vivo (The 3′-hairpin of 7SK, HP4, was recently elegantly demonstrated to be the specific target of LARP7 in vivo . We now (in vivo , but witin vivo (The N-terminal domain binds not only the terminal uridines but also the 5′ region at the foot of the 7SK 3′-hairpin. This sequence, which is highly conserved, has been previously hypothesized to form seven base pairs with the 5′-end of 7SK, thus forming a small stem named M1 . We showin vivo .The 7SK RNA is 5′-capped by the methyl-transferase MePCE , which hWe show that two domains of LARP7 bind 7SK, in a head-to-tail arrangement schematized in Figure Several recent reports of major disfunction in humans suggested that LARP7 and 7SK work as a pair to regulate the transcription factor P-TEFb. The present work showing how LARP7 is entwined with the 7SK RNA suggests functional correlations. Firstly, LARP7 binding could narrow the range of 7SK conformations, thus facilitating recognition by stabilizing a functional RNA structure. Secondly, LARP7 binding may help compact the RNA, by minimizing the phosphate–phosphate repulsions with its basic stretches of residues working as polyamines in the packaging of nucleic acids in viral capsids. Such a chaperoning mechanism could be aided by MePCE binding to the 5′-end. Thirdly, complex formation leads to reduction of the RNA surface accessible to other partners, such as HEXIM, PTEFb or hnRNPs. A heterologous RNA–protein surface, as observed in the Tat-TAR system, was shown to operate in P-TEFb recognition . LARP7 bPDB: 4WKR.Supplementary Data are available at NAR Online.


Moreover, a C186S mutation at the 4th transmembrane region of EAAT1, found in episodic ataxia patients, significantly decreased its process tip localization. The transmembrane transporter domain fragments of glutamate transporters also localized to astrocyte process tips in cultured hippocampal slice. These results indicate that the transmembrane transporter domain of glutamate transporters have an additional function as a sorting signal to process tips.Glutamate transporters in the central nervous system remove glutamate released from neurons to terminate the signal. These transporters localize to astrocyte process tips approaching neuronal synapses. The mechanisms underlying the localization of glutamate transporters to these processes, however, are not known. In this study, we demonstrate that the trimeric transmembrane transporter domain fragment of glutamate transporters, lacking both N- and C-terminal cytoplasmic regions, localized to filopodia tips. This is a common property of trimeric transporters including a neutral amino acid transporter ASCT1. Astrocyte specific proteins are not required for the filopodia tip localization. An extracellular loop at the centre of the 4 Two major glutamate transporter subtypes in brain are primarily expressed in astrocytesThese glutamate transporters belong to solute carrier family 1 (SLC1) of transporters. SLC1 transporters are secondary active transporters which use electrochemical gradient of sodium to transport substrates. SLC1 family transporters including glutamate transporters have 2 helical hairpins for substrate binding, and form a trimer with a concave surface at the centre of the extracellular faceAs their name implies, astrocytes have several stem branches, each containing thousands of finely branched processes that extend from the stems to the surface of neurons8911121314In this study, we report that the transmembrane SLC1 transporter domain of glutamate transporters mediates filopodia tip localization in astrocytes in primary culture. Their cytoplasmic tails known as binding sites of cytosolic scaffolding proteins are not necessary. The transmembrane transporter domain fragments also localized to filopodia tips of COS-7 cells, indicating astrocyte specific protein is not required for the localization. Our studies indicate that the function of SLC1 transporter domain is not limited to transporting substrates, and it acts as a sorting signal to process tips.First, we studied whether the localization of two major glutamate transporters in astrocytes, EAAT1 and EAAT2, is differentially regulated. We expressed EAAT1 or EAAT2 with mEGFP fused to the N-terminus, mEGFP-EAAT1 or mEGFP-EAAT2, in astrocytes in primary culture in combination with the plasma membrane marker Lck-mCherry17Next, to figure out which part of EAAT2 mediates the filopodial localization, we studied effects of removing its N- and C-terminal cytoplasmic tails. We truncated the C terminal cytoplasmic tail according to a previous study20Contrary to our expectations, after the removal of both cytoplasmic C- and N-termini (EAAT2ΔNC), localization of EAAT2 to filopodia tips became stronger, 3.3 times higher density at the tip, with some transporters retained within the ER . SurprisEAAT1 and EAAT2 are solute carrier family (SLC1) transporters, which form bowl-shaped trimers. In mammals, there are two groups of proteins classified as SLC1 family transporters: glutamate transporters EAAT1-5, and neutral amino acid  transporters ASCT1-2. To determine if filopodial localization is a property specific for glutamate transporters or it also applies to ASCTs, we studied localization of ASCT1 in astrocytes in primary cultures. Full-length ASCT1 showed 2.2 fold stronger localization to the entire length of filopodia than the soma, while its localization to filopodia tips are not stronger (1.1 fold) compared to the shaft of filopodia . In contTo figure out if filopodial localization of the SLC1 transporters requires astrocyte- specific proteins, we expressed the above constructs in COS-7 cells. The transmembrane transporter domain fragments of EAAT1, EAAT2 and ASCT1 localized to filopodia tips, reproducing the results obtained with astrocytes . These rIt has been reported that endogenous EAAT1 localizes to cell surface and not to ER in astrocytes in primary cultureTo figure out if transporter activity is required for filopodia tip localization of glutamate transporters, we introduced mutations known to completely abolish the transporter activity of EAAT1Pyrococcus horikoshii GluTPh, remained in ER and failed to localize to filopodia . The deletion mutants still localized to plasma membrane, but its localization to filopodia or to their tips were lost, to 1.1 fold signal intensity . These rTo figure out if the intra-bowl insertions are sufficient for filopodia tip localization, we inserted the encoding sequences of the intra-bowl insertions of EAAT2 into mEGFP-GluTPh. However, EAAT2 intra-bowl insertion failed to confer plasma membrane localization or filopodia localization to GluTPh. The chimeric GluTPh construct was exclusively localized to ER, similar to the wild type GluTPh . These rth transmembrane region, near the N terminal of the intra-bowl insertion required for process tip localization. We speculated that the mutation might also affect the process tip localization of EAAT1. Since EAAT1 mostly localize to ER and not to filopodia tips in cultured cells, we used the transmembrane transporter domain fragment, EAAT1ΔNC, to evaluate the effect of the C186S mutation. The C186S mutation significantly reduced the extent of filopodia tip localization of EAAT1ΔNC from 2.5 fold to 1.5 fold  EAAT1 ΔNC: 45–506, ii) EAAT1 ΔN: 45-C-term, iii) EAAT2 ΔNC: 41–504, iv) EAAT2 ΔI: N term-187 and 240-C term, v) ASCT1 ΔNC: 39–486, vi) GluTPh: 7-C terminus, vii) GluTPh-IBI EAAT2: GluTPh 7–142, EAAT2 179–243, GluTPh 153-C term were inserted into pmEGFP-C1 or pmCherry-C1, which are derived from pEGFP-C1 (Clontech) by introducing A206K mutation to monomerise the EGFPMouse EAAT1 and EAAT2 were cloned using PCR from a mouse brain cDNA library, a human ASCT1 cDNA clone was purchased from the Kazusa DNA Research Institute Collection, and GluTPh was cloned using PCR from 8–109 pfu/μl of adenovirus against HEK293A cells.Recombinant adenoviruses were made using the Gateway system (Invitrogen). Constructs to be expressed were inserted into pENTR11-dual vector and then transferred to pAv/CMV/DEST-V5 adenoviral vectors. The adenoviral vectors were introduced to HEK293A cells (Invitrogen) using Lipofectamine LTX and Lipofectamine plus (Invitrogen). Recombinant viruses were amplified by transfection of HEK293A cells grown in 16 cm plates, and harvested 3 days after transfection. The cells were harvested by centrifugation and re-suspended in 2 ml hippocampal slice culture medium, and lysed by 3 cycles of freezing and thawing, followed by centrifuge at 600 × g. The resulting virus suspensions obtained as the supernatant contained 104 cells/well in DMEM/10% FBS, and the indicated plasmids were transfected using Lipofectamine LTX and Lipofectamine Plus (Invitrogen) or linear polyethyleneimine (Polysciences) on the day of plating. The transfected cells were imaged following 2 days of incubation.COS-7 cells were plated in 24-well plates containing uncoated glass coverslips at 5 × 105 cells/well. Recombinant adenoviruses are added 4–7 days after plating at ~1000 m.o.i/well, and the astrocytes were imaged on the next day of infection.Primary cultures of mouse astrocytes were prepared from C57BL/6JJ P0 mice as described previouslyHippocampal organotypic slice cultures were prepared from P7 SD rats as previously described, using “donut” plates containing Millipore membranes2 4; MgCl2 4; NaHCO3 26.2; NaH2PO4 1 and glucose 11, aerated with 95% O2 and 5% CO2. Images were taken with an FV BX61W1 laser scanning confocal microscope system (Olympus) with LUMPlanFL IR 0.90w 60 × objective lens (Olympus), Fluoview software, with excitation at 488 nm for monomeric EGFP (mEGFP)  and 559 nm for mCherry. Signal intensities were analysed using Metamorph (Molecular Devices) or ImageJThe coverslips with the COS-7 cells or the primary cultured astrocytes were transferred to a glass-bottomed culture plates filled with HBSSM.K.H. and M.Y. designed the research and wrote the manuscript, M.K.H. performed experiments and analysed the data.