MHC class I-related molecule MR1 presents riboflavin- and folate-related metabolites to mucosal-associated invariant T cells, but it is unknown whether MR1 can present alternative antigens to other T cell lineages. In healthy individuals we identified MR1-restricted T cells (named MR1T cells) displaying diverse TCRs and reacting to MR1-expressing cells in the absence of microbial ligands. Analysis of MR1T cell clones revealed specificity for distinct cell-derived antigens and alternative transcriptional strategies for metabolic programming, cell cycle control and functional polarization following antigen stimulation. Phenotypic and functional characterization of MR1T cell clones showed multiple chemokine receptor expression profiles and secretion of diverse effector molecules, suggesting functional heterogeneity. Accordingly, MR1T cells exhibited distinct T helper-like capacities upon MR1-dependent recognition of target cells expressing physiological levels of surface MR1. These data extend the role of MR1 beyond microbial antigen presentation and indicate MR1T cells are a normal part of the human T cell repertoire.DOI:http://dx.doi.org/10.7554/eLife.24476.001 White blood cells called T cells recognize germs and infected cells, and get rid of other cells in the body that look different to healthy cells – for example, tumor cells. These activities all depend on a molecule called the T cell receptor (or TCR for short), which is found on the surface of the T cells. Each TCR interacts with a specific complex on the surface of the target cell. One of the molecules recognized by the TCR is known as MHC class I-related (shortened to MR1). This molecule attracts TCRs to infected cells, but it was not know if the MR1 molecule could attract TCRs to cancer cells too.Lepore et al. now show that there are indeed T cells in humans that recognize cancer cells through interaction with the MR1 molecules produced by the cancer cells. This new group of T cells has been named MR1T, and the cells can be easily detected in the blood of healthy individuals. The cells can be classified as a new cell population based on their capacity to recognize MR1 and how they react with different types of cancer cells.Importantly, the MR1 that attracts these TCRs is the same in all people, and so the same TCR may recognize MR1-expressing cancer cells from different patients. The next challenge is to identify MR1T cells that recognize and kill cancer cells from different tissues. These studies will hopefully pave the way for new and broader strategies to combat cancer.DOI:http://dx.doi.org/10.7554/eLife.24476.002 T lymphocytes can detect a diverse range of microbial and self-antigens, including lipids presented by non-polymorphic CD1 molecules , and phoMR1 is a non-polymorphic MHC I-like protein that is expressed at low to undetectable levels on the surface of many cell types . FollowiHere we report a novel population of MR1-restricted T cells (hereafter termed MR1T cells) that is readily detectable in blood from healthy individuals. MR1T cells express diverse TCRα and β genes and were not able to recognize previously identified microbial or folate-derived ligands of MR1. Instead, MR1T cells recognized self-antigens presented by MR1 and expressed a broad selection of cytokines and chemokine receptors. Functionally, MR1T cells were capable of inducing dendritic cell (DC) maturation and promoted innate defense in intestinal epithelial cells. These findings demonstrate that MR1 can present non-microbial antigens to a novel population of functionally diverse human T cells with potentially wide-ranging roles in human immunity.+ target cells was fully inhibited by blocking with anti-MR1 monoclonal antibodies (mAbs)  , and thuparallel . Importaparallel   and stimulated with irradiated A375-MR1 cells in the absence of exogenous antigens. The choice of A375-MR1 cells relied on their potent capacity of inducing sterile DGB129 T cell activation  and theihighCD137+ T cells detected was consistently higher after stimulation with A375-MR1 cells (range 0.034–0.072% of T cells) than after co-culture with A375-WT cells (range 0.015–0.032%) (+ T cells after antigen exposure (highCD137+ T cells (0.065%) were sorted from one of these donors  . As the exposure . We alsoDonor C, , which wDonor C, . AccordiDonor C, .10.7554/Collectively, these findings suggested that identified MR1-responsive T cells are a novel yet readily detectable polyclonal population of lymphocytes in the blood of healthy human individuals (hereafter termed MR1T cells).E. coli lysate, activation of different MR1T cell clones was not enhanced by the E. coli lysate . The RNA-seq datasets revealed that MR1+ APC stimulated DGB129 cells upregulated 403 genes and downregulated 413 genes, whereas DGB70 cells up-regulated 432 genes and down-regulated 285 genes . MR1T cells were first rested for three weeks before being stimulated with A375-MR1 cells for 20 hr. The transcriptional profiles of the sorted activated cells (expressing similar levels of both CD25 and CD137 activation markers) were subsequently compared with their unstimulated control counterparts (negative for the two markers) by RNA-sequencing . Biologi85 genes .TBX21, FOXP3, FOS, RXRA, FOSL2, IRF4, BATF and TRIB1. However, while DGB129 cells were further influenced by expression of MYC, HSP90AB1 and CREM, DGB70 cells were instead regulated by EGR1, JUNB, and SREBF1 , which was prevented by anti-MR1 blocking antibodies  outside of the MAIT cell compartment. Human MR1T cell clones displayed differential recognition of a variety of target cells expressing constitutive levels of MR1. The different recognition of target cells might reflect different antigen presentation capacity, although they equally presented microbial antigens to MAIT cells, or the presence of different antigens. Indeed, preliminary antigen purification from a representative target cell line identified two diverse fractions that each stimulated only one of two MR1T cell clones, both derived from the same donor. Although the nature of these molecules remains to be determined, they do not derive from RPMI 1640 culture medium and are present in tumor cells grown in vivo. Furthermore, initial characterization of the molecules showed that they formed stable complexes with plastic-bound MR1 without forming a Schiff base and activated specific MR1T cells without need of APC processing.Using as APCs A375 cells expressing high levels of MR1, we estimated that MR1T cells represent between 1:2500 and 1:5000 of total circulating T cells. These frequency estimates could not be determined by direct ex vivo identification of MR1T cells due to lack of specific markers, and were obtained using two approaches. In the first, frequencies were calculated after overnight stimulation and subtracting the frequency of T cells responding to MR1-negative APC to the frequency of cells responding to MR1-positive APC. In another set of experiments the overnight-stimulated T cells were immediately cloned without previous in vitro expansion and individual MR1T cell clones were identified with functional assays. These observations indicated that MR1T cell are readily detectable in the blood of healthy individuals with a size that resembles that of MHC-restricted memory T cells recognizing pathogen-derived peptides in pathogen-exposed individuals . The calIn some respects, MR1T cells resemble another population of autoreactive T cells restricted to non-polymorphic antigen presenting molecules and present at high frequency in human blood. These T cells react to self-lipids presented by CD1 molecules . In analIt is currently unclear whether MR1T cells can be classified into distinct subgroups based on phenotype, gene expression and function. Since MR1T cells exhibit non-invariant TCRs with diverse antigen specificities, express a wide range of different surface markers and release multiple soluble effector molecules, it will be challenging to identify these cells using MR1 tetramer staining combined with conventional phenotypic analyses. Interestingly, some activated MR1T cell clones also displayed atypical functions for T cells, including the release of growth factors VEGF and PDGF, which support mesenchymal cell proliferation, vasculogenesis, and angiogenesis , suggestEGR-1 , human rIL-2  and irradiated PBMC (5 × 105 cells /ml). In other experiments, MR1 T cells clones were generated using the same protocol from sorted CD3+CD69+CD137high upon a single overnight stimulation with A375-MR1 cells (ratio 2:1). T cell clones were periodically re-stimulated following the same protocol (+monocytes by culture in the presence of GM-CSF and IL-4 as previously described (The following cell lines were obtained from American Type Culture Collection: A375 (human melanoma), THP-1 (myelomonocytic leukemia), J.RT3-T3.5 (TCRβ-deficient T cell leukemia), HEK 293 (human embryonic kidney), CCRF-SB (acute B cell lymphoblastic leukemia), Huh7 (human hepatoma), HCT116 , LS 174T (goblet-like cells from colon adenocarcinoma), and EMT6 (mouse breast carcinoma). SKW-3 cells (TCRα- and β-deficient human T cell leukemia) were obtained from the Leibniz-Institute DSMZ-German Collection of Microorganisms and Cell Cultures. All the used cells were routinely tested for mycoplasma contamination and were negative. None of the cell lines used in this study is present in the database of commonly misidentified cell lines. Cells lines were not authenticated. Two representative MAIT clones (MRC25 and SMC3) were used in this study generated from blood of two different healthy donors and maintained in culture as previously described . MR1T ceprotocol . Monocytescribed .A human MR1A cDNA construct linked to β2m via a flexible Gly-Ser linker was generated by PCR as previously described . The K43β2m-MR1-Fc fusion construct was obtained using human MR1A-β2m construct described above as template. DNA complementary to β2m-MR1A gene was amplified by PCR using primers: β2mXhoI_f 5’- CTCGAGATGTCTCGCTCCGTGGCCTTA and MR1-IgG1_r 5’-GTGTGAGTTTTGTCGCTAGCCTGGGGGACCTG, thus excluding MR1 trans-membrane and intracellular domains. The DNA complementary to the hinge region and CH2-CH3 domains of human IgG1 heavy chain was generated using the following primers: NheI-hinge-f 5’-CAGGTCCCCCAGGCTAGCGACAAAACTCACAC and IgG1NotI_r 5’-GCGGCCGCTCATTTACCCGGAGACAGGGAGA from pFUSE-hIgG1-Fc1 (InvivoGen). The β2m-MR1A and IgG1 PCR products were joined together using two-step splicing with overlap extension PCR and the resulting construct subcloned into the XhoI/NotI sites of the BCMGSNeo expression vector. CHO-K1 cells were transfected with the final construct using Metafectene Pro (Biontex), cloned by limiting dilutions and screened by ELISA for the production of β2m-MR1-Fc fusion protein. Selected clones, adapted to EX-CELL ACF CHO serum-free medium (Sigma), were used for protein production and β2m-MR1-Fc was purified using Protein-A-Sepharose (Thermo Fisher Scientific) according to the manufacturer instructions. Protein purity was verified by SDS-PAGE and Western Blot. Protein integrity was assessed by ELISA, using anti-β2M mAb (HB28) as capture and the conformation-dependent anti-MR1 mAb 25.6 as reveling .Cell surface labeling was performed using standard protocols. Intracellular labeling was performed using the True-Nuclear Transcription Factor Buffer Set according to the manufacturers’ instructions. The following anti-human mAbs were obtained from Biolegend: CD4-APC (OKT4), CD8α-PE (TuGh4), CD161-Alexa Fluor 647 (HP-3G10), CD69-PE (FN50), CD3-PE/Cy7, Brilliant Violet-711, or Alexa-700 (UCHT1), CD137-biotin (n4b4-1), CXCR3-Brilliant Violet 421 (G025H7), CD83-biotin (HB15e) and TRAV1-2- PE (10C3). CD86-FITC (2331), CCR4-PECy7 (1G1) and CCR6-PE (11A9) mAbs were from BD Pharmingen. All these mAbs were used at 5 µg/ml. Biotinylated mAbs were revealed with streptavidin-PE, -Alexa Fluor 488, or -Brilliant violet 421 . The MR1-specific mAb clone 26.5 (mouse IgG2a) was provided by Ted Hansen, Marina Cella and Marco Colonna, Washington University School of Medicine, St. Louis (MO) . Unlabelhttp://www.imgt.org). The cDNA-sequencing data set has been deposited in the GenBank repository under accession numbers MF085360-MF085372.TCRα and β gene expression by MR1T cell clones was assessed either by RT-PCR using total cDNA and specific primers, or by flow cytometry using the IOTest Beta Mark TCR Vβ Repertoire Kit (Beckman Coulter) according to the manufacturers’ instructions. For RT-PCR, RNA was prepared using the NucleoSpin RNA II Kit (Macherey Nagel) and cDNA was synthesized using Superscript III reverse transcriptase (Invitrogen). TCRα and β cDNAs were amplified using sets of Vα and Vβ primers as directed by the manufacturer . Functional transcripts were identified by sequencing and then analyzed using the ImMunoGeneTics information system  and the supernatant collected (S1). Next, the pellet was re-suspended in methanol, sonicated, centrifuged , and the supernatant obtained (S2) was pooled with the S1 supernatant. The final concentration of methanol was 10%. The total cell extract (pool of S1 and S2) was then loaded onto a C18 Sep-Pak cartridge (Waters Corporation) and the unbound material was collected and dried (fraction E-FT). Bound material was eluted in batch with 75% (fraction E1) and 100% methanol (fraction E2), collected and dried. The E-FT material was re-suspended in acetonitrile/water (9:1 vol/vol) and loaded onto a NH2 Sep-Pak cartridge (Waters Corporation). Unbound material (fraction N-FT) and 4 additional fractions (N1-N4) were eluted with increasing quantities of water. Fraction N1 was eluted with 35% H2O, fraction N2 with 60% H2O, fraction N3 with 100% H2O, and fraction N4 with 100% H2O and 50 mM ammonium acetate (pH 7.0). Fractions N1-N4 were then dried and stored at −70°C until use. All collected dry-frozen fractions were then thawed and re-suspended in H2O 20% methanol  or 100% H2O  prior to being tested in T cell activation assays.Total cell lysates were generated from a single pellet of 2.5 × 102 Sep-Pak cartridges as above.Mouse EMT6 breast tumors were provided by A. Zippelius and were prepared as described . Freshly4/well unless otherwise indicated) were co-cultured with indicated target cells (5 × 104/well) in 200 µl total volume in duplicates or triplicates. T cells were cultured with indicated APCs for 24 hr. In some experiments, anti-MR1 mAbs (clone 26.5) or mouse IgG2a isotype control mAbs (both at 30 µg/ml) were added and incubated for 30 min prior to the addition of T cells. In some experiments A375-MR1 and THP1-MR1 cells were cultured four days in PBS supplemented with 5% human AB serum. Cells were washed every day and then used to stimulate T cell clones upon assessment of target cell viability by trypan blue staining. In such type of experiments, T cell activation assays were also performed in PBS supplemented with 5% human AB serum. E. coli lysate was prepared from the DH5α strain (Invitrogen) grown in LB medium and collected during exponential growth. Bacterial cells were washed twice in PBS and then lysed by sonication. After centrifugation , the supernatant was collected, dried, and stored at −70°C. APCs were pulsed for 4 hr with E. coli lysate equivalent to 108 CFU/ml (unless otherwise indicated) before addition of T cells. In some experiments, APCs were pre-incubated with 6-FP or Ac-6-FP (Schircks Laboratories) or pulsed with 6,7-dimethyl-8-D-ribityllumazine  for 4 hr before co-culture with T cells. In control experiments with TCR γδ cells, the APCs were first treated for 6 hr with zoledronate  prior to T cell addition. Activation experiments with plate-bound recombinant human β2m-MR1-Fc were performed by coating β2m-MR1-Fc onto 96 well plates (4 µg/ml) and loading with cartridge-purified cell lysates for 4 hr at 37°C before washing twice and adding T cells. Supernatants were collected after 24 hr and IFN-γ or GM-CSF were assessed by ELISA. Multiple cytokines and chemokines in cell culture supernatants were analyzed using the Milliplex MAP human cytokine/chemokine magnetic bead panel – Premixed 41-plex  according to the manufacturer’s instructions. Samples were acquired on a Flexmap 3D system (Merck Millipore) and Milliplex analyst software was used to determine mean fluorescence intensity and analyte concentration.MR1-restricted T cells (5 × 10MUC2 gene expression by LS 174 T cells was performed in a 20 µl reaction volume containing 0.5 µM of each primer and 2.5 µl of cDNA in Power SYBRgreen MasterMix (Applied Biosystems). β-actin was used as reference gene. All the reactions were carried out in triplicate. The following method was run using an ABI 7500 Fast Real-Time PCR System (Applied Biosystem): initial incubation for 20 s at 50°C, incubation for 10 min at 95°C, 40 cycles of 15 s at 95°C and 1 min at 60°C. The PCR primers used were as follows: β-actin_f 5′-GCCACCCGCGAGAAGATGA, β-actin_r 5′-CATCACGATGCCAGTGGTA; MUC2_f 5′-ACCCGCACTATGTCACCTTC, MUC2_r 5′-GGGATCGCAGTGGTAGTTGT. Changes in gene expression were quantified using the ΔΔCt method.Mo-DCs were co-cultured with MR1T cell clones at 1:1 ratio. When indicated Mo-DCs were pre-incubated with anti-MR1 mAbs  for 30 min prior addition of T cells. Supernatants were harvested after 48 hr and tested for IFN-γ release by ELISA to assess T cell activation. Cells were collected at the same time and analyzed by FACS for CD83 and CD86 expression. T cells were excluded by the analysis using anti-CD3 mAbs. LS 174 T cells were co-cultured with MR1T cell clones at 1:1 ratio in the presence or absence of anti-MR1 (clone 26.5) or mouse IgG2a isotype control mAbs . MR1T cell activation was evaluated by measuring IFN-γ, IL-8 (R&D) and IL-13 (Pharmingen) in the supernatants by ELISA. Q-PCR for XhoI/NotI sites of the BCMGSNeo expression vectors and the resulting constructs were used to co-transfect J.RT3-T3.5 cells by electroporation. Transfectants expressing TRAV1-2 and CD3 were FACS sorted. The TCRα and β functional cDNA from MR1T clones were cloned into the XmaI/SalI sites of a modified version of the p118 lentiviral expression vector  according to the manufacturer's protocol. RNA quality, assessed using the Agilent Bioanalyzer, exhibited RNA integrity number (RIN) ≥6.9. Next, cDNA libraries were prepared using 200 ng total RNA and a 2 µl volume of a 1:500 dilution of ERCC RNA Spike in Controls (Ambion). Samples were subjected to cDNA library synthesis using the TruSeq Stranded mRNA Library Preparation Kit (Illumina) according to the manufacturer's protocol with minor modifications; 13 PCR cycles used, and two additional rounds of purification with Agencourt Ampure XP SPRI beads (Beckman Courter) to remove double-stranded cDNA >600 bp in size. The length distribution of the cDNA libraries was monitored using DNA 1,000 kits with the Agilent Bioanalyzer. The samples were then subjected to an indexed PE sequencing run of 2 × 51 cycles on an Illumina HiSeq 2000.Comparative transcriptome analysis between resting and A375-MR1 stimulated MR1T cell clones was performed by RNA-sequencing. T cells were stimulated with A375-MR1 cells and after 20 hr DAPI2 fold-change >2), a subnetwork of the global transcription factors-gene network was created and centrality scores for nodes were generated by both PageRank and betweeness centrality algorithms using NetworkX library in Python. The network was visualized using Cytoscape software and betweeness centrality score were used to define the radius of the nodes (circles in the network). The RNA-sequencing data set has been deposited in the Gene Expression Omnibus repository under accession code GSE81063.Raw RNA-seq reads in fastq format were aligned to the hg19 genome assembly using STAR aligner . Gene ant-test .For cytokine secretion assays and Q-PCR data were analyzed using Unpaired Student’s  In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been favorably evaluated by Tadatsugu Taniguchi (Senior Editor) and three reviewers, one of whom is a member of our Board of Reviewing Editors. The reviewers have opted to remain anonymous.Thank you for submitting your article "Functionally diverse human T cells recognize non-microbial antigens presented by MR1" for consideration by Summary:The paper by Lepore et al. identifies and determines the specificity of MR1-restricted T cells in human peripheral blood. In contrast to current concepts of MR1-restricted T cells, termed mucosal associated invariant T cells that are also MR1-restricted, the MR1-reactive T cells studied here appear to be specific for non-microbial ligands. While the ligands have not been identified at the molecular level, the data presented appear to be quite convincing for this conclusion. As such, the paper provides an advance for understanding of MR1-restricted T cells because they now appear to be specific for non-microbial antigens.The manuscript was evaluated by a member of the Board of Reviewing Editors and external referees. After a thorough online discussion, they came to the consensus that the paper is interesting but the authors need to address several major points as outlined below. As is often the case, specific critiques become more apparent with discussion. In any event, the full reviews of the referees are shown for completeness but the authors should be encouraged to submit a revised manuscript that addresses all of the major concerns in the consensus review.Essential revisions:1) Address the level of MR1 on the APCs that you used. It seems that the A375-MR1 cell expresses very high levels that may explain why others have not seen MR1-restricted stimulation previously. What happens when you use an APC with lower MR1 expression?2) Address why CD137 was used as an activation marker. This seems to be atypical as with CD69 expression only, the MR1 specific response was undetectable .3) Describe the epitope recognized by the anti-MR1 blocking antibody. Verify its monospecificity.4) Address the limitations of the transcriptional and cytokine profiles. They appear to have been done on clones in culture for some time.5) There are concerns about the accuracy of the precursor frequency estimates. Caveats should be more fully discussed. Also discuss more fully the significance of the estimates.6) We expect that the TCR sequences will be submitted if the manuscript is published.eLife. Your revised article has been favorably evaluated by Tadatsugu Taniguchi (Senior Eeditor), a Reviewing Eeditor, and one reviewer.Thank you for resubmitting your work entitled "Functionally diverse human T cells recognize non-microbial antigens presented by MR1" for further consideration at eLifeWe apologize for the delay in reaching a decision, which was largely due to a tardy reviewer.The manuscript has been improved but there is one quite significant issue that must be addressed before acceptance, as outlined below:Although the authors have responded to most of the reviewers' comments, the reviewers and Editors are particularly concerned that the sequences of the TCR are being withheld because of a patent application. The authors' identification of the TCRs is based on the sequences, and those data are the primary data on which this conclusion is drawn. Also, the statement that they will make sequences available "for collaboration" is unacceptable. The sequences must be available on publication and deposited in a public database. These conditions are non-negotiable. Essential revisions:1) Address the level of MR1 on the APCs that you used. It seems that the A375-MR1 cell expresses very high levels that may explain why others have not seen MR1-restricted stimulation previously. What happens when you use an APC with lower MR1 expression?We thank the reviewers for raising this important issue that we realized was not sufficiently clear in the submitted manuscript. We have changed the revised manuscript to clarify this issue .It is very important to note that one of the major findings of our study is the reactivity of a large fraction of MR1T cell clones to APCs expressing constitutive, and indeed low, MR1 surface levels, including monocyte-derived dendritic cells, and some tumor cell lines . These rad hoc APC that do not express classical MHC class I and CD1 molecules and instead express MR1. This approach was chosen, as there was no information in the literature about the existence of MR1-autoreactive T cells and there are no reagents allowing their direct identification, i.e.antigen-loaded tetramers.To test the hypothesis whether MR1-autoreactive non-MAIT T cells exist in peripheral blood, we used as stimulatory cells in vitrostimulation of T cells with APC expressing high levels of antigen-presenting molecules or high doses of self-antigens is instrumental to reveal the existence of self- reactive T cells and this aspect has been recently discussed (1). Furthermore, the use of APC expressing large amounts of CD1 molecules was necessary to reveal self-reactive T cells restricted to CD1a and CD1c molecules (2-5).In addition, several studies investigating MR1-restricted T cells utilized MR1- overexpressing APCs, which have been also important for the characterization of MAIT cell reactivity and the identification of the recognized antigens (6-8).2) Address why CD137 was used as an activation marker. This seems to be atypical as with CD69 expression only, the MR1 specific response was undetectable .The activation marker CD137 (4-1BB) is widely used to selectively identify antigen- specific T cells (9). CD69 also represents a very sensitive surface marker to detect early T cell activation, but if not analyzed in combination with other markers its high sensitivity may have some limitations (9). Firstly, some T cell subsets repetitively stimulated in vivo, i.e.NKT, MAIT cells and some TCR γδ cells, display constitutive high level of CD69 (10-12), which may mask specific stimulation of other less abundant T cell populations. Secondly, CD69 up-regulation by T cells can also occur in response to environmental stimuli, independently of T cell receptor-mediated antigen recognition , and therefore it might give false positive results if used to study T cell antigen specificity (9). Thirdly, CD69 expression remains high on T cells repetitively stimulated in vitro. We selected the combination of CD69 and CD137 markers because of their complementary properties, the first being more sensitive the second more restrictive.By contrast, CD137 expression is highly dependent on recent activation driven by T cell receptor engagement (15) and therefore is more restrictive than CD69 up-regulation to identify antigen-specific T cells . In addition, CD137 expression allows selective detection of antigen-specific T cells belonging to CD83) Describe the epitope recognized by the anti-MR1 blocking antibody. Verify its monospecificity.The specificity of 26.5 anti-MR1 monoclonal antibodies utilized in this study was extensively characterized in two studies , although the epitope recognized was not entirely mapped. This antibody cross-reacts with mouse, rat and bovine MR1, because of the high degree of MR1 conservation across these species, but not with any other known antigen-presenting molecules . The 26.5 mAbs recognizes a conformation-depend epitope present only on properly loaded, and thus T cell stimulatory, MR1 molecules . Importantly, specific mutations of residues present on the α-helices forming the MR1 groove prevent or impair the antibody binding to MR1 . The authors interpreted the results as evidence that the binding occurs on the MR1 surface nearby the region interacting with MAIT TCR. This interpretation is in agreement with the mAb capacity of blocking MR1-mediated T cell activation . The conformation-dependent specificity of 26.5 mAb has also been confirmed in ELISA using recombinant MR1 proteins by us  and others (7). The 26.5 mAbs have been largely used to study specific MR1-dependent T cell stimulation. In some of our experiments, not included in the manuscript, we also utilized the 12.2 anti-MR1 mAbs , which recognize a different conformation-dependent epitope of MR1. Also this mAb blocked both MAIT and MR1T cell activation similarly to 26.5 mAbs (our unpublished data).4) Address the limitations of the transcriptional and cytokine profiles. They appear to have been done on clones in culture for some time.The T cell clones used for the transcriptome studies are derived from the same donor, were kept in culture in identical conditions and stimulation was performed with the same antigen presenting cells on the same day. Despite these identical conditions, important differences were observed, indicating true differences among analyzed cells.Great diversity in cytokine release following antigen stimulation was observed, confirming the divergent functions of the tested MR1T cell clones. A concern of the reviewers might be that in vitroexpansion could induce biases in the cytokines released by the clones. According to other studies (20), these biases affect the secretion of IFN-γ and not of other soluble factors. Indeed, T cell clones derived from ex vivosorted Th1, Th2 or Th17 cell populations, retained cytokine secretion patterns typical of the sorted functional subset and acquired the capacity of releasing IFN-γ upon in vitroexpansion (20). Accordingly, our MR1T clones, which were cultivated using conditions similar to those used in other studies (20), maintained differential patterns of cytokine release, and also secreted IFN-γ upon antigen stimulation.The transcriptome analysis was performed by comparing T cell clones both in resting state and sorted following stimulation with the same APCs, according to equivalent expression levels of CD25 and CD137 see . In addi2 fold change >2) great differences were observed.The transcriptome analysis was performed using three biological replicates using three MR1T cell clones. We reasoned that with this approach it would have been possible to identify a common transcriptional signature unique to MR1T cells. Unfortunately, the data relative to the third clone did not pass the quality control and were excluded from the analysis. Importantly, despite highly stringent cut-off criteria  There are concerns about the accuracy of the precursor frequency estimates. Caveats should be more fully discussed. Also discuss more fully the significance of the estimates.highCD137+ activated T cells after stimulation with APCs expressing or lacking MR1 approximate the frequency of MR1T cells. We were aware that some of the T cells included in this gate might not be true MR1T cells. For this reason, we performed a second type of frequency evaluation in which single cell cloning was made after sorting of CD69highCD137+ T cells stimulated overnight with MR1-expressing A375 cells. The analysis of the clones showed that the sorted CD69highCD137+ T cells were enriched in MR1T cells. This second approach revealed similar frequency estimates.We agree with the reviewers that the method used to estimate MR1T cell precursor frequency has intrinsic limitations, being based on the assumption that the differences in the percentage of CD69Other methods are needed to exactly define MR1T cell frequencies. The use of MR1- tetramers is hampered by current lack of knowledge of nature and repertoire size of MR1T stimulatory antigens.In conclusion, we are confident that the frequency estimates we obtained, although being approximations, indicated that MR1T cells are readily detectable ex vivoin all the donors analyzed without the need of in vitroexpansion, and therefore suggested that they are not a rare T cell population in the blood of healthy individuals. The revised manuscript has been modified  to thoroughly discuss these issues.6) We expect that the TCR sequences will be submitted if the manuscript is published.The MR1T TCR sequences are subject of a pending patent application. We will make them available for collaborative studies.The manuscript has been improved but there is one quite significant issue that must be addressed before acceptance, as outlined below:Although the authors have responded to most of the reviewers' comments, the reviewers and Editors are particularly concerned that the sequences of the TCR are being withheld because of a patent application. The authors' identification of the TCRs is based on the sequences, and those data are the primary data on which this conclusion is drawn. Also, the statement that they will make sequences available "for collaboration" is unacceptable. The sequences must be available on publication and deposited in a public database. These conditions are non-negotiable.As requested we have added the sequences information. In the subsection “TCR gene analysis of MR1T cell clones”, we provide the GenBank accession numbers where all the TCR sequences reported in References1) Richards DM, Kyewski B, Feuerer M. 2016. 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Synonymous single nucleotide polymorphisms (sSNPs) are considered neutral for protein function, as by definition they exchange only codons, not amino acids. We identified an sSNP that modifies the local translation speed of the cystic fibrosis transmembrane conductance regulator (CFTR), leading to detrimental changes to protein stability and function. This sSNP introduces a codon pairing to a low-abundance tRNA that is particularly rare in human bronchial epithelia, but not in other human tissues, suggesting tissue-specific effects of this sSNP. Up-regulation of the tRNA cognate to the mutated codon counteracts the effects of the sSNP and rescues protein conformation and function. Our results highlight the wide-ranging impact of sSNPs, which invert the programmed local speed of mRNA translation and provide direct evidence for the central role of cellular tRNA levels in mediating the actions of sSNPs in a tissue-specific manner. Synonymous single nucleotide polymorphisms (sSNPs) occur at high frequency in the human genome and are associated with ~50 diseases in humans; the responsible molecular mechanisms remain enigmatic. Here, we investigate the impact of the common sSNP, T2562G, on cystic fibrosis transmembrane conductance regulator (CFTR). Although this sSNP, by itself, does not cause cystic fibrosis (CF), it is prevalent in patients with CFTR-related disorders. T2562G sSNP modifies the local translation speed at the Thr854 codon, leading to changes in CFTR stability and channel function. This sSNP introduces a codon pairing to a low-abundance tRNA, which is particularly rare in human bronchial epithelia, but not in other human tissues, suggesting a tissue-specific effect of this sSNP. Enhancement of the cellular concentration of the tRNA cognate to the mutant ACG codon rescues the stability and conduction defects of T2562G-CFTR. These findings reveal an unanticipated mechanism—inverting the programmed local speed of mRNA translation in a tRNA-dependent manner—for sSNP-associated diseases. Synonymous single nucleotide polymorphisms (sSNPs) in protein-coding regions occur at much higher frequency in the human genome  than iniGenome-wide association studies link sSNPs with ~50 diseases in humans, highlighting the wide-ranging impact of SNPs that exchange synonymous codons; however, their association with alterations in protein conformation and function remains elusive . ConceptCFTR gene, which vary in disease severity and penetrance  no. CRM-CCL-2), HEK 293 cells (ATCC no. CRL-1573), SH-SY5Y (German cell collection (DSMZ no. ACC209) and immortalized CFBE41oThe cDNAs of wild-type CFTR, ΔF508-CFTR and CFTR sSNP variants were subcloned into the pcDNA3 vector (Life Technologies) and transfected by using either Lipofectamine LTX, Lipofectamine Plus (Life Technologies), or by using polyethylenimine . Constructs were verified by sequencing. In patch-clamp experiments, CFTR cDNAs were cotransfected with those of GFP and then 36–60 h later, GFP-expressing cells were selected for study .http://gtrnadb.ucsc.edu). Single-stranded DNA oligonucleotides resembling full-length tRNAs were annealed to obtain double stranded, full-length tDNA templates flanked at the 5′-end with T7 promoter sequence and at the 3′-terminus with CCA methyl-2-aminoethanesulfonic acid (TES), adjusted to pH 7.3 with Tris . The bath (intracellular) solution contained: 140 mM NMDG, 3 mM MgCl2, 1 mM CsEGTA, and 10 mM TES, adjusted to pH 7.3 with HCl  and was maintained at 37°C. A large Cl- concentration gradient was imposed across the membrane patch  and voltage was clamped at –50 mV to enhance the amplitude of CFTR channel openings.CFTR Cleviously , 66. The- channels were activated promptly following membrane patch excision by the addition of the catalytic subunit of protein kinase A  and ATP  to the intracellular solution. To prevent channel rundown, PKA and ATP were added to all intracellular solutions. In this study, membrane patches contained ≤5 active channels, determined by using the maximum number of simultaneous channel openings as described previously [CFTR Cleviously .i), Gaussian distributions were fit to current amplitude histograms. For open probability (Po) and burst analyses, lists of open and closed times were created by using a half-amplitude crossing criterion for event detection. For wild-type CFTR, T2562C, and wtl openings of T2562G-CFTR, transitions <1 ms in duration were excluded from the analysis, whereas for sc openings of T2562G-CFTR, transitions <4 ms in duration were excluded. Dwell time histograms were plotted with logarithmic x axes with 10 bins decade-1, and the maximum likelihood method was used to fit 1- or 2-component exponential functions to the data. Burst analysis was performed as described by Cai et al. [tc (the time that separates interburst closures from intraburst closures), which was determined from analyses of closed time histograms. The mean interburst interval (TIBI) was calculated by using the following equation [Tb = (mean burst duration) x (open probability within a burst). Mean burst duration (TMBD) and open probability within a burst (Po(burst)) were determined directly from experimental data by using pCLAMP software. Burst analysis was not performed on small-conductance T2562G-CFTR openings because of their small size. Only membrane patches that contained a single active CFTR Cl- channel were used for burst analyses.We recorded, filtered, and digitized data as described previously , but addi et al.  by usingequation :Po=Tb(TN channels (each with an open probability of p), the probability of k channels being simultaneously open (Pk) follows the binomial distribution:To investigate whether wtl and sc T2562G-CFTR channels are distinct channels that do not interact with each other, we performed a binomial analysis of single-channel data. Assuming that wtl and sc channels behave independently, in a membrane patch with Because of the small single-channel conductance of sc channels, we selected for analysis excised inside-out membrane patches with 1 active wtl channel and 1 active sc channel.Po, we adopted the approach of Manivannan et al. [P0)( and P2)( are the probability of channels residing in the closed state (L0), open level 1 (L1), and open level 2 (L2), respectively , P(1), and P(2) were determined by using Eqs P0) or these channels are nonidentical .To perform binomial analysis on wtl channels and sc channels that exhibit different n et al.  for coopectively . p1 is tp1)(1−p2)P(1)=p1(P(2)=p1p2where P. When N = 2, if CR = 1, the channels are independent and identical; if CR > 1, the channels are nonidentical  and if CR < 1, the channels exhibit cooperativity [To distinguish between dependent and nonidentical channels, we calculated the cooperativity ratio (CR) :CR= cultured at an air–liquid interface were infected with recombinant adenovirus serotype 5 at a multiplicity of infection of 100 for 1 h. The vectors encoded the cDNAs of CFTR constructs driven by a cytomegalovirus promotor. Four days after gene transfer, epithelia were examined by immunocytochemistry. Epithelia were fixed with 4% paraformaldehyde (Electron Microscopy Sciences), permeabilized with 0.3% Triton X-100 (Thermo Fisher Scientific) and blocked with 10% normal goat serum  in SuperBlock (Thermo Fisher Scientific). The epithelia were incubated with the anti-CFTR antibodies 769 and 596 (1:100) and anti–β-catenin primary antibody (1:100), followed by Alexa Fluor–conjugated secondary antibodies (Molecular Probes).R2 of 0.9418 and 0.9475, respectively. Data analysis was performed by using Prism 5 (GraphPad Software Inc.) software.Cells were lysed for 15 min on ice in MNT buffer  and centrifuged for 5 min at 14,000 x g (4°C) to remove cell debris. Equal amounts of lysates were mixed with SDS-loading buffer, incubated for 10 min at 37°C, separated by SDS-PAGE, and blotted onto a PVDF membrane (Millipore). Western blots were probed with the corresponding primary antibodies at 4°C overnight, followed by detection with HRP-labeled secondary antibodies and visualized by using ECL and the FujiFilm Las-4000 system . CFTR protein band intensities were normalized to the expression level of (i) NPT, which is also expressed as a selection marker on the same pcDNA3 plasmid, and (ii) ACTB, to account for differences in transfection efficiency and sample loading on the gel, respectively. The linear range for CFTR and NPT western blot assays ranged from 6.25 μg to 100 μg total protein lysate with - cells transfected with either wild-type or T2562G-CFTR were seeded into 6-well dishes . At 70% confluency, 100 μg/ml cycloheximide (CHX) was added to all wells to inhibit de novo CFTR synthesis and cells were further incubated at 37°C to evaluate the plasma membrane stability of CFTR. At each time point, a well of cells was labeled at 4°C for 1 h with noncleavable biotin reagent , which does not penetrate the cell and attaches to amino groups located at the membrane surface. Cells were lysed in MNT buffer, and the biotin-labeled entities were immunoprecipitated with Streptavidin-sepharose bead conjugate  and washed twice with 10 mM Tris-HCl pH 8.5, containing 300 mM NaCl, 0.05% Triton X-100, and 0.1% SDS. The beads were boiled in SDS-loading buffer, and spotted onto PDFV membrane (Millipore) through a slot–blot manifold. CFTR-positive biotinylated conjugates were detected by using mouse anti-CFTR NBD1 antibodies. As a control, an aliquot of the beads was immunostained with β-actin antibodies; it was empty, showing the complete lysis of the cells and no retention of whole cells. The change in plasma membrane stability was determined by the time-dependent decrease of membrane localized CFTR-positive signal in the time course of incubation at 37°C and was compared to the zero time point at which de novo CFTR synthesis was inhibited.The plasma membrane stability of T2562G-CFTR was compared to that of wild-type CFTR by using a procedure conceptually similar to the protocol described in . CFBE41oTa) was defined as the temperature at which 50% of the protein remained soluble [Thermoaggregation assays were performed as described previously . Briefly soluble .Total RNA was extracted by using TRI Reagent (Sigma-Aldrich) according to the manufacturer's protocol. RNA concentration was measured by using the NanoDrop photometer (PEQLAB Biotechnology), and RNA integrity was checked with the 2100 Bioanalyzer and RNA6000Nano Chips (both Agilent) or by denaturing agarose gel electrophoresis. RNA was stored at –80°C for further use.18 primers and Revert Aid H Minus M-MuLV Reverse Transcriptase (Fermentas). Amplification was performed in clear 96-well plates (Sarstedt) sealed with adhesive tape (Sarstedt) in a Mx3005P qPCR cycler (Agilent) by using QuantiFast SYBR Green PCR master mix (Qiagen) containing 6-carboxyl-X-rhodamine (ROX) as reference dye. CFTR and NPT were amplified with the following primer pairs (5′ to 3′): CFTR forward, CCTATGTCAACCCTCAACACG and CFTR reverse, ACTATCACTGGCACTGTTGC; NPT forward, TGCTCCTGCCGAGAAAGTAT and NPT reverse, GCTCTTCGTCCAGATCATCC. Primers were used at a final concentration of 300 nM. Relative expression levels were calculated by using the ΔΔCT-method and normalized to NPT signals. Each reaction was performed in duplicate with the following controls included in each run: a no-template control (NTC) and a not-reverse-transcribed sample. qRT-PCR assays for CFTR and NPT displayed a linear range over 6  and 5  orders of magnitude, respectively. qPCR efficiencies were 99%  and 108%  for CFTR and NPT, respectively. Data analysis was performed by using MxPro QPCR (Agilent) and Prism 5 software.Steady-state mRNA levels were determined by qRT-PCR. Total RNA was pretreated with DNase I (Fermentas) and reverse transcribed by using oligo-(dT)Thr(CGU) levels were analyzed by using the QuantiFast SYBR Green RT-PCR kit (Qiagen) containing ROX reference dye. tRNAThr(CGU) and 5S rRNA were amplified by using the following primer pair (5′ to 3′) at a final concentration of 1 μM: tRNAThr(CGU) forward, GGCCAAGTGGTAAGGC and tRNAThr(CGU) reverse, AGGCACGGACGGG; 5S rRNA forward, CCATACCACCCTGAACGC and 5S rRNA reverse, GTATTCCCAGGCGGTCTC. tRNA signals were normalized to 5S rRNA values.tRNAThr(CGU) was detected by overnight hybridization at 60°C with Cy3-labeled probes complementary to the tRNA anticodon loop (5′-Cy3-CTTCGGTTTACGAGACCGACGCCTTA-3′). Blots were subsequently washed at 35°C 3 times with 6x SSC (supplemented with 0.1% SDS), followed by 1 wash with 6x SSC, 1 wash with 2x SSC, and a final wash with 0.2x SSC and imaged on the FujiFilm Las-4000 system . Intensities were normalized to 5S rRNA probed also with 5′-Cy3-labeled oligonucleotide (5′-AAGTACTAACCGCGCCCGAC-3′).For northern blotting, equal amounts of total RNA were separated by denaturing PAGE, in some cases stained with SYBR gold (Sigma-Aldrich) for visualization and subsequently transferred onto HyBond-N+ membrane . tRNAEscherichia coli tRNALys(UUU), E. coli tRNATyr(AUA), and Saccharomyces cerevisiae tRNAPhe(GAA)) were added to each total RNA sample prior to deacetylation. Quantification was performed by normalizing the median of the Cy3-tRNA signal of each tRNA species to the corresponding Atto647-labeled HEK tRNA signal. Note that the human genome lacks a gene for tRNAThr(GGU) reading the Thr codon ACC (http://gtrnadb.ucsc.edu), which is likely to be decoded by tRNAThr(IGU) via deamination of adenine (A) to inosine (I) in the tRNA wobble position [tRNA microarrays  were perposition .https://doi.org/10.17504/protocols.io.hfcb3iw).Absolute tRNA levels of HeLa cells were determined by using the tRNA microarrays. Gel-purified tRNA from the total RNA was ligated to increasing concentrations of the Cy3-labeled fluorescent stem-loop RNA/DNA oligonucleotide. Only values in the linear range  were considered from several microarrays. A detailed protocol is available at protocols.io (Thr(CGU) [Thr(CGU) were used (5′ to 3′):tRNA silencing was performed by using shRNAs targeting the anticodon loop of tRNAThr(CGU) . In brieGGCGTCGGTCTCGTAAACCGAAGTTCAAGAGACTTCGGTTTACGAGACCGACGCC and shThr2, GTGGTAAGGCGTCGGTCTCGTAATTCAAGAGATTACGAGACCGACGCCTTACCAC (underlined nucleotides denote the sense tRNAThr(CGU) target sequence). Cells were analyzed for CFTR protein level or tRNAThr(CGU) level 48 h after transfection. A detailed protocol is available at protocols.io (https://doi.org/10.17504/protocols.io.hgfb3tn).shThr1, - cells, in 3 independent biological replicates, were used to isolate mRNA-bound ribosome complexes, followed by extraction of RNase I digestion-derived, ribosome-protected fragments (RPFs) as described in [fastx-toolkit , and sequencing adapters were cut by using cutadapt . Processed reads were uniquely mapped to the human genome (GRCh37) by using Bowtie (0.12.9), allowing a maximum of 2 mismatches (parameter settings: -l 16 -n 1 -e 50 -m 1—strata—best y). RPFs were binned in groups of equal read length, and each group was aligned to the start or stop codons as described in [i in a gene were normalized to the average number of footprints across this gene. These ratios were then averaged across all genes to give the ribosome dwelling occupancy of a given codon in the transcriptome. The Met codon was excluded from these calculations because N-terminal Met is influenced by the presence of harringtonine used in the processing buffers. The first 51 nt were excluded from the calculations to avoid depletion of ribosomes at the beginning of genes by runoff elongation during cell harvesting [Approximately 5 million CFBE41oribed in . Cells wribed in , 71. Takribed in , 71. Theribed in . The rearvesting .n observations. Sample sizes of cell analyses were selected to demonstrate reproducibility among independent biological replicates and with adequate power to resolve significant differences among conditions. Patient samples were blindly allocated during experiments without prior knowledge of genotype; each separate patient-derived primary HBE cell sample was considered a single biological replicate. Differences between groups were evaluated by using 2-tailed Student t test implemented in Prism 5 or SigmaPlot 12 (Systat Software Inc.) software. Differences were considered statistically significant when P < 0.05.If not stated otherwise, results are expressed as means ± SEM of tRNA microarray data have been deposited with the Gene Expression Omnibus (GEO) under the accession number GSE53991. The sequencing data were also submitted to GEO under the accession number GSE74365.S1 FigA) Location of the sSNPs studied on a CFTR structural model [B) Representative immunoblots of CFTR variants transiently expressed for 24 h and used for the quantification shown in C, D) Representative analysis of the effects of the T2562G sSNP on exon 15 splicing using Bioanalyzer and DNA1000 Chip (Agilent), presented as capillary electrophoregram (C) and band intensity profile (D). The chip runs with 15 bp and 1500 bp standards marked as lower (LM) and upper marker (UM), respectively. pET01 denotes vector with the splicing minigene cassette [E) Quantification of the full-length and skipped exon from the electrophoregrams in C and D. Data are means ± SEM (n = 3). The underlying data of panel E can be found in Click here for additional data file.S2 FigA) Ubiquitination of T2562G-CFTR compared to wild-type CFTR. Immunoprecipitated CFTR was probed with anti-CFTR antibodies, while CFTR-ubiquitin-HA conjugates were detected with an anti-HA antibody. Input denotes the cell lysates prior to immunoprecipitation probed with an anti-CFTR antibody. Representative blots of three biological replicates are shown. (B) Quantification of the ubiquitinated CFTR species. The ubiquitination of wild-type was set as 100%. Data are means ± SEM (n = 4); *, P < 0.05 versus wild-type CFTR. (C) Proteasome inhibition results in increased protein expression of T2562G-CFTR. HeLa cells expressing wild-type and T2562G-CFTR were incubated with and without the proteasomal inhibitor MG132 and analyzed by immunoblotting with the anti-CFTR NBD2 (596) antibody. (D) Quantification of CFTR protein expression in MG132-treated cells from C normalized to NPT expression to account for differences in transfection. The expression of each variant without MG132 was set as 100%. Data are means ± SEM (n = 3–4); * P < 0.05 versus wild-type CFTR. (E) Co-immunoprecipitation of CHIP and RMA1 with CFTR variants expressed in HeLa cells using an anti-CFTR antibody and probed with anti-CHIP and anti-RMA1 antibodies. Input denotes the cell lysates prior to co-immunoprecipitation probed with anti-CFTR, anti-ACTB, anti-CHIP or anti-RMA1 antibodies. Note that to avoid overloading the gel only 5% of the input amount was loaded for visualization. Equal amounts of cell lysates were used for each CFTR variant as demonstrated by the ACTB immunostaining.  Quantification of CHIP and RMA1 bound to CFTR variants from the immunoblots in E. The amount of co-immunoprecipitated CHIP and RMA1 were normalized to the amount of immunoprecipitated CFTR variant (i.e. the sum of bands B and C), which was arbitrarily set to 1. Data are means ± SEM (n = 4–5); * P < 0.05 versus wild-type CFTR. (H) Representative autoradiogram of pulse-chase analysis of CFTR variants. (I) Quantification of the pulse-chase autoradiograms represented as average disappearance of band B (maturation and/or degradation) and formation of band C. Results are plotted as percent of band B density at the 0 min time point and are means ± SEM (n = 5–7); P = 0.7123 for time point 240 min. (J) Quantification of the amount of membrane-localized CFTR upon inhibition of de novo synthesis at 37°C using surface biotinylation of non-permeabilized cells. Data are means ± SEM (n = 3); * P < 0.05 versus wild-type CFTR. The underlying data of panels B, D, F, G and I can be found in ((TIF)Click here for additional data file.S3 FigA) Representative immunoblots of limited trypsin digestion of wild-type and T2562G-CFTR in semi-intact HeLa cells probed with anti-CFTR NBD2 (596) antibody. The positions of the band B and C forms of CFTR protein are indicated; NBD2 denotes characteristic nucleotide-binding domain 2 (NBD2)-containing fragments. (B) Quantification of full-length CFTR (sum of bands B and C) from panel A relative to untreated samples (set to 100%). Data are means ± SD (n = 2). The underlying data of panel B can be found in ((TIF)Click here for additional data file.S4 FigA-C) Representative single-channel recordings of T2562G-CFTR in excised inside-out membrane patches from HeLa cells transiently expressing T2562G-CFTR. In A, the recordings were made in the absence and presence of ATP (1 mM) and PKA (75 nM) in the intracellular solution. Neither sc nor wtl channel openings of T2562G-CFTR were observed in the absence of ATP and PKA . However, two T2562G-CFTR channels, one sc and one wtl, were activated following phosphorylation with PKA. In panels B and C, the recordings were made in the presence of ATP (1 mM) and PKA (75 nM) in the intracellular solution. In B, the membrane patch contained only one active wtl channel, whereas in panel C the membrane patch contained only one active sc channel. Dashed lines indicate the closed channel state and downward deflections correspond to channel openings. For presentation purposes, all single-channel recordings were digitally filtered at 50 Hz.((TIF)Click here for additional data file.S5 FigA) Representative single-channel current amplitude histograms of wild-type, T2562G- and T2562C-CFTR. The histograms were made from 10-s long recordings filtered at 50 Hz using the experimental conditions shown in B) Single-channel current-voltage (i-V) relationships of wild-type, T2562G- and T2562C-CFTR. Data are means ± SEM (n = 6–10). Continuous lines are the fit of first order linear regression functions to mean data. Note that the i-V relationship of T2562C is obscured by that of wild-type CFTR. (C) Single-channel conductance (γ) of wild-type, T2562G- and T2562C-CFTR determined from the slope of the i-V relationships in B; * P < 0.05 versus wild-type CFTR. The underlying data of panels B and C can be found in ((TIF)Click here for additional data file.S6 FigA) A representative 3-minute single-channel recording of T2562G-CFTR in an excised inside-out membrane patch from a CHO cell commenced following full channel activation. This membrane patch contained two channels, one with sc and the other with wtl openings. ATP (1 mM) and PKA (75 nM) were continuously present in the intracellular solution. The 2-s long segments labelled 1–4 indicated by bars are displayed on an expanded time scale beneath the 3-minute recording and the segment labelled 5 indicates the high resolution recording displayed in B. Arrows and dashed lines indicate the closed channel state and downward deflections correspond to channel openings. For presentation purposes, single-channel records were filtered digitally at 50 Hz. (B-D) Binomial analysis of the T2562G channel recording displayed in A. (B) High resolution T2562G-CFTR channel recording. Letters and arrows indicate openings to different channel levels. Abbreviations: L0, both channels closed; L1-sc, one sc channel open; L1-wtl, one wtl channel open; L2-sc+wtl, one sc channel and one wtl channel open. (C) The single-channel current amplitude histogram for the T2562G-CFTR recording shown in B. The continuous line is the fit of a Gaussian distribution to the data (R = 0.94) and the dashed lines show the individual components of the Gaussian function. The vertical dashed lines indicate the positions of the different channel levels with the closed channel level (L0) shown on the left. (D) Probability (P(k)) of the wtl and sc T2562G-CFTR channels residing in either the closed level (L0), open level 1, (L1) or open level 2 (L2). Columns represent predicted P(k) values calculated using mean Po values from membrane patches containing either one wtl or one sc channel . Symbols and error bars show experimental P(k) values calculated using Po values from three membrane patches containing one wtl channel and one sc channel. The underlying data of panel D can be found in ((TIF)Click here for additional data file.S7 FigA) Representative immunoblot analysis of CFTR T2562 sSNP variants in HeLa cells. NPT served as a transfection control. (B) Quantification of steady-state protein expression levels from the immunoblots in A. CFTR expression  was normalized to that of NPT to correct for differences in transfection efficiencies. The level of wild-type CFTR (ACT codon) was set to 100%. Data are means ± SEM (n = 8); * P < 0.05 versus wild-type CFTR. (C) qRT-PCR quantification of mRNA expression levels for CFTR T2562 codon variants at 24 h after transfection. The mRNA level of wild-type CFTR (ACT codon) was set to 100%. Data are means ± SEM (n = 3–7).  Thermal aggregation propensity analysis for CFTR sSNP variants. Thermal aggregation temperatures (aT) are expressed as means ± SEM (n = 2–5); * P < 0.05 versus wild-type CFTR. (F) Quantification of the expression of CFTR codon variants in CHO cells as described in panel B. The level of wild-type CFTR was set to 100%. Data are means ± SEM (n = 4); ** P < 0.01. The underlying data of panels B-C and E-F can be found in ((TIF)Click here for additional data file.S8 FigA) Microarray analysis of relative tRNA abundance of all tRNAs in CFBE41o- and four CF patient-derived HBE cells were measured relative to the values for HeLa tRNAs. tRNA probes are depicted with their cognate codon and the corresponding amino acid; tRNAsThr highlighted by the box are presented in n = 3–5).  Correlation between codon usage and absolute tRNA concentration for HeLa (B) and CFBE41o- cells (C). (D) Differences in tRNA abundance between CFBE41o- and HeLa cells. Negative sign denotes that the corresponding tRNAs are more abundant in CFBE41o- cells and vice versa. Inset, box-plot with designated outliers. The underlying data of panels A-D can be found in ((TIF)Click here for additional data file.S9 FigA) Northern blot of HeLa cells transfected with increasing amounts (ng/well) of in vitro transcribed tRNAThr(CGU). 5S rRNA served as a loading control. Lane 1 (0 ng tRNA) represents nontransfected HeLa. (B) Transfection of tRNAThr(CGU) does not alter the cellular tRNA pool. (C) Addition of tRNAThr(CGU) (150 ng) does not change polysomal profiles. Mock denotes control HeLa cells. M:P denotes the ratio between monosomal (M) and polysomal (P) peaks. (D) tRNAThr(CGU) is increased in both M and P fractions. The ratio of tRNAThr(CGU) in M and P to which 150 ng tRNAThr(CGU) were added was quantified by qRT-PCR, normalized to 5S rRNA and expressed as fold-change ± SD compared to tRNAThr(CGU) levels in polysomal and monosomal fractions of untreated control HeLa (mock) cells. Values are means ± SEM (n = 3). (E) Reduction of tRNAThr(CGU) levels (reduced to ~75 or ~60%) using two shRNAs (shThr1 and shThr2), measured with qRT-PCR and normalized to 5S rRNA. Values are means ± SEM (n = 3). (F) Reduction of tRNAThr(CGU) decreases the protein levels of both wild-type and T2562G-CFTR. Representative immunoblot of four biological replicates. The positions of the band B and C forms of CFTR protein are indicated. NPT served as an internal transfection control and ACTB as a loading control. (G) Transfection with shRNAs (shThr1 and shThr2) to reduce tRNAThr(CGU) does not alter polysomal profiles. Note that tRNAThr(CGU) can only be partially downregulated as complete knockdown would perturb global translation and cell viability. The underlying data of panels D and E can be found in ((TIF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S1 Text(DOCX)Click here for additional data file.S1 Data(XLSX)Click here for additional data file.


Interleukins are a group of immunomodulatory proteins that mediate a variety of immune reactions in the human body. To investigate the association between interleukin gene polymorphisms and recurrent pregnancy loss (RPL), we reviewed 21 studies from MEDLINE, EMBASE, OVID SP and PubMed to evaluate RPL-related interleukin gene polymorphisms. Meta-analysis was performed on 12 of the polymorphisms, and a review included the others. Our integrated results indicated that IL-1β (-511C/T) , IL-6 (-634C/G) , IL-10  , and IL-18   consistently associated with RPL after meta-analysis. IL-17A rs2275913 and IL-17F rs763780, IL-21 rs2055979 and rs13143866, IL-1β (-31C/T), IL-6 (-2954G/C), and IL-10 (-536A/G) were reported only once as having a significant association with RPL. The potential mechanism underlying miscarriage and these polymorphisms and future research directions are also discussed. Recurrent pregnancy loss (RPL), defined as three or more consecutive pregnancy losses before 20 weeks of gestation, affects approximately 1–5% of pregnant women. An incrInterleukins are a group of immunomodulatory proteins leading a variety of immune reactions in human body. These cytokines also include many molecules that play a part in human conception. IL-1 isSingle-nucleotide polymorphisms (SNPs) are variations at a single nucleotide position in DNA sequence among individuals. If more than one percent of the population carries the different nucleotide at a specific position in the DNA sequence, this variation is defined as a SNP. They can occur in non-coding regions, such as the promoters, and coding regions, such as the gene body at a total frequency of approximately every 200–1000 bases. SNPs in promoters are suspected to affect transcription factor binding, which mWe searched the following electronic databases: EMBASE (1980 to March 2015), MEDLINE (1950 to March 2015), OVID SP and PubMed (last update March 2015). We used the terms “recurrent pregnancy loss” ; “interleukin” ; “polymorphism” . Various combinations of the terms  were searched in the databases. Seven genes  were selected in the first stage, and then we restricted the meta-analysis to the articles on IL-1β, IL-6, IL-10 and IL-18, to the meta-analysis. Relevant articles were obtained by manual search and retrieval. All the authors compared these papers carefully to ensure none of the papers were duplicated in the analysis.The inclusion criteria were as follows: (i) Case-control study that genotyped patients with RPL and normal controls; (ii) RPL was defined as three or more losses before 20 weeks of pregnancy; (iii) Genotypes were identified by polymerase chain reaction (PCR) or SNP array; and (iv) Polymorphisms information were observed in candidate genes. When faced with overlapping study populations, we generally selected only the study with the most extensive data. If the entire study did not meet the inclusion criteria but one subset did, only the subset was eligible. Studies that did not provide reliable explanations of the selection criteria and the distribution of the genotypes were excluded. Reviews and meta-analyses were excluded.10.1371/journal.pmed1000097) and Meta-analysis on Genetic Association Studies Checklist were used for this meta-analysis  and 95% confidence intervals (CIs) from individual studies were calculated for both dominant (AA+AB vs. BB) and recessive (AA vs. AB+BB) genetic models with theI2. After eThe search strategy yielded 83 citations, and 33 citations were excluded after title and abstract screening. Fifty full manuscripts were obtained for detailed evaluation and scrutiny. Sixteen studies were excluded for the following reasons: five studies were excluded because they were reviews; four studies were excluded for different study objectives; seven studies were excluded because they focused on protein levels; and three studies were excluded because they lacked genotyping data . UltimatThe quality assessment of the included articles is presented in The most common studied variant in IL-1β was the -511C/T polymorphism. The five included studies had a total of 1052 patients and 915 controls for analysis, 20–23. Twelve studies with a total 1000 patients and 1192 controls investigating the IL-6 (-174G/C) polymorphism were included in the analysis, 31, 32.Nine studies with 1318 patients and 1338 controls investigating the relevance of the IL-10 (-1082G/A) polymorphism were included in the analysis. No assoFive studies with 1228 patients with RPL and 1231 controls were included in the meta-analysis, 38–41. Only one study reported that the IL-4 (-590C/T) polymorphism was associated with RPL in northern Indian women under the dominant model. One stuAfter meta-analysis, we found that five polymorphisms were related to RPL. To investigate their location and corresponding histone markers and sequence functions, we found their reference SNP ID Numbers (rs#) and located them with the UCSC Genome Browser Human Feb. 2009 (GRCh37/hg19). Based on seven H3K27Ac Mark Chip-seq results provided by UCSC, we found that almost all of the positive SNPs located in the H3K27Ac binding area, which indicated they are near regulatory elements . HoweverIn our systematic review, we analyzed seven interleukin genes and 21 polymorphisms in a total of 9401 patients. Twelve of the polymorphisms were included in the meta-analysis, and the others were only included in the review due to a limited number of reports. Our integrated results indicate that IL-1β (-511C/T), IL-6 (-634C/G), IL-10 , IL-18 (-137G/C) and IL-18 (-105G/A) consistently associated with RPL. IL-17A rs2275913, IL-17F rs763780, IL-21rs2055979 and rs13143866, IL-1β (-31C/T), IL-4 (-590C/T), IL-6 (-2954G/C), and IL-10 (-536A/G) were reported on only once with respect to their association with RPL.An increasing number of studies have demonstrated that a successful pregnancy depends on immune balance, including immunotolerance, the immune response and relative cytokines levels. The corDifferent interleukins are secreted by different immune cells and play distinct roles at different tissues. IL-1β is a pro-inflammatory cytokine that assists in B cell proliferation and maturation, natural killer (NK) cell activation and T cell stimulation, 23. TheAll of these pregnancy-assisting functions of interleukins are based on normal interleukin expression and sequence and appropriate regulation, such as with DNA methylation and SNPs. In some situations, SNPs are located in the gene promoter; sometimes, they also appear within gene bodies and other non-coding regions. In these sensitive areas, even one base alteration will change relative protein binding and transcription levels. These cAnother issue in polymorphisms study is about inaccurate locus description. IL-1β (-5887C/T) and IL-1β (+3593C/T), IL-1β (-1903C/T) and IL-1β (-31C/T) share the same SNP ID, and after checking the Human Feb. 2009 (GRCh37/hg19) Assembly, we believe considered that IL-1β (+3593C/T) and IL-1β (-31C/T) seems to be more accurate descriptions for their location. And when we attempted to find the location of IL-6 (-634), we found that it shares the same accession number (rs1800796) with IL-6 (-572). Based on the variation in the versions of the human genome assembly, we recommend that clinicians use the SNP ID to detect polymorphisms in clinical cases and have provided a corresponding list of these locations in Ethnicity is also an important factor during SNP analysis. Different populations have different ancestors and migration histories. Linkage disequilibrium caused these SNPs to capture most genetic variations. DNA variation at promoters contributes to complex human traits by altering the spatial or temporal pattern of gene expression. To include more patients in the meta-analysis, we checked the allele frequency of each SNP in different populations in the NCBI dbSNP database . We founAnother source of potential bias arose from the definition of “normal controls”. Most studies defined controls of RPL as having no history of miscarriage or two previous live births. However, there was no clear definition of the control groups. Some papers defined controls as women with at least one successful pregnancy and without abortion; some used ethnically matched healthy controls; one article even used male blood samples. AlthougEven though RPL is strictly defined as three or more consecutive miscarriages before 20 weeks, the standards used in the studies were different. Some studies included only uncertain miscarriages. Others defined RPL as more than two spontaneous abortions. After a discussion with all the authors, we included studies with this definition of RPL for the polymorphism analysis. One article selected patients attending a miscarriage clinic, and theOur review was the first attempt to calculate interleukin gene polymorphisms and to provide an estimate of the interleukin genes in women with RPL. Rigorous selection criteria were applied, and only those articles with reliable genetic detection methods and absolute definitions of RPL were taken into consideration. Two authors performed these analyses independently. To identify higher quality reports, we used the Newcastle-Ottawa Quality Assessment Scale to score each included paper . Every sInterleukin genes play important roles in the expression of these proteins and are further involved in the immune response to pregnancy. The present meta-analysis demonstrated consistent and remarkable associations between the IL-1β (-511C/T), IL-6 (-634C/G), IL-10 , IL-18 (-137G/C) and IL-18 (-105G/A) polymorphisms. This review also showed that IL-17A rs2275913, IL-17F rs763780, IL-21 rs2055979 and rs13143866, IL-1β (-31C/T), IL-4 (-590C/T), IL-6 (-2954G/C), and IL-10 (-536A/G) correlate with RPL. Although these polymorphisms contain only one base change, our study added further evidence indicating that RPL is a polygenic disease, implying that these polymorphisms are potential markers for RPL.S1 Checklist(TIFF)Click here for additional data file.S2 Checklist(TIFF)Click here for additional data file.S1 Fig(TIF)Click here for additional data file.S2 Fig(TIF)Click here for additional data file.S3 Fig(TIF)Click here for additional data file.S4 Fig(TIF)Click here for additional data file.S5 Fig(TIF)Click here for additional data file.S1 Table(TIF)Click here for additional data file.