Adhesion between medullary thymic epithelial cells and thymocytes is regulated by miR-181b-5p and miR-30b*

Molecular Immunology 114 (2019) 600–611

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Molecular Immunology journal homepage: www.elsevier.com/locate/molimm

Adhesion between medullary thymic epithelial cells and thymocytes is regulated by miR-181b-5p and miR-30b*

T

Larissa Cotrim-Sousaa, Amanda Freire-Assisa,b, Nicole Pezzic, Pedro Paranhos Tanakaa, ⁎ Ernna Hérida Oliveiraa, Geraldo Aleixo Passosa,c,d, a

Molecular Immunogenetics Group, Department of Genetics, Ribeirão Preto Medical School, University of São Paulo (USP), Ribeirão Preto, SP, Brazil State University of Minas Gerais, Passos, MG, Brazil c Graduate Program in Basic and Applied Immunology, Ribeirão Preto Medical School, University of São Paulo (USP), Ribeirão Preto, SP, Brazil d Laboratory of Genetics and Molecular Biology, Department of Basic and Oral Biology, School of Dentistry of Ribeirão Preto, USP, Ribeirão Preto, SP, Brazil b

A R T I C LE I N FO

A B S T R A C T

Keywords: Cell adhesion Thymus mTECs Single-positive thymocytes miRNA transfection miR-181b-5p miR-30b*

In this work, we demonstrate that adhesion between medullary thymic epithelial cells (mTECs) and thymocytes is controlled by miRNAs. Adhesion between mTECs and developing thymocytes is essential for triggering negative selection (NS) of autoreactive thymocytes that occurs in the thymus. Immune recognition is mediated by the MHC / TCR receptor, whereas adhesion molecules hold cell-cell interaction stability. Indeed, these processes must be finely controlled, if it is not, it may lead to aggressive autoimmunity. Conversely, the precise molecular genetic control of mTEC-thymocyte adhesion is largely unclear. Here, we asked whether miRNAs would be controlling this process through the posttranscriptional regulation of mRNAs that encode adhesion molecules. For this, we used small interfering RNA to knockdown (KD) Dicer mRNA in vitro in a murine mTEC line. A functional assay with fresh murine thymocytes co-cultured with mTECs showed that single-positive (SP) CD4 and CD8 thymocyte adhesion was increased after Dicer KD and most adherent subtype was CD8 SP cells. Analysis of broad mTEC transcriptional expression showed that Dicer KD led to the modulation of 114 miRNAs and 422 mRNAs, including those encoding cell adhesion or extracellular matrix proteins, such as Lgals9, Lgals3pb, Tnc and Cd47. Analysis of miRNA-mRNA networks followed by miRNA mimic transfection showed that these mRNAs are under the control of miR-181b-5p and miR-30b*, which may ultimately control mTEC-thymocyte adhesion. The expression of CD80 surface marker in mTECs was increased after Dicer KD following thymocyte adhesion. This indicates the existence of new mechanisms in mTECs that involve the synergistic action of thymocyte adhesion and regulatory miRNAs.

1. Introduction The thymus appears in evolution from the jawed vertebrates and is absent in the Agnatha as lampreys and mixinoids. Thymus is a primary lymphoid organ whose main function is associated with the development and maturation of T cells and the induction of central immune tolerance (Geenen and Savino, 2019). The stroma of this organ is what guarantees its functional identity, but its development is intrinsically dependent on interactions with other cell types, mainly the precursors of T cells (thymocytes). This process is termed “thymic crosstalk”, which is characterized by both migration and cell-cell adhesion. During this process, thymocytes interact sequentially with cortical thymic epithelial cells (cTECs) and then with medullary TECs (mTECs) and

receive signals to advance their differentiation (Mendes-da-Cruz et al., 2019; Irla, 2019). Thymus-seeding progenitors (TSPs) from the bone marrow reach the thymus through the cortical-medullary junction and interact with the cTECs that form the cortex of this organ. Within the cortex, the TSPs develop into double-negative (DN) CD4−/CD8− and then into doublepositive (DP) CD4+/CD8+ thymocytes. The DP thymocytes reach the thymic medulla and develop into single-positive (SP) CD4+ or CD8+ that interact with mTECs (Yoganathan et al., 2019). Sequential interaction with the cortex and then the thymic medulla has important immunological implications for the thymocyte population, as they are respectively submitted to positive (PS) and then to negative selection (NS) (Petrie and Zúñiga-Pflücker, 2007; Love and Brandoola, 2001; Hu

⁎ Corresponding author at: Molecular Immunogenetics Group, Department of Genetics, Ribeirão Preto Medical School, University of São Paulo (USP), Via Bandeirantes 3900, 14049-900 Ribeirão Preto, SP, Brazil. E-mail address: [email protected] (G.A. Passos).

https://doi.org/10.1016/j.molimm.2019.09.010 Received 12 February 2019; Received in revised form 3 September 2019; Accepted 5 September 2019 Available online 26 September 2019 0161-5890/ © 2019 Elsevier Ltd. All rights reserved.

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Brazil, in temperature-controlled (22 °C) under specific-pathogen-free conditions in sterile ventilated racks, 12-h dark/light cycles and received sterile water and food ad libitum. Mice were killed by CO2 inhalation. Experimental procedures followed ethical guidelines under strict guidance and approval from the University of São Paulo Ethics Committee for Animal Experimental Research (Approval # 008/20161). Thymocytes were isolated from the thymus according to a previously described protocol (Donate et al., 2013). Briefly, thymi were dissected in RPMI 1640 medium, thymocytes were obtained by 2–3 passages of the thymic fragments through a 10-μm mesh nylon membrane (Sefar Inc., Depew, NY, USA), and pelleted thymocytes were resuspended in phosphate-buffered saline (PBS) pH 7.4. Fluorescence activated cell sorting (FACS) analysis using BD FACSCalibur (BD Biosciences) flow cytometer with a phycoerythrin (PE)-labeled anti-CD3 antibody (Biolegend CNS, Inc. San Diego, CA, USA) indicated that this procedure yielded a thymocyte population with a purity of ≥ 90%. These cells were used for further cell adhesion assays.

et al., 2015; Abramson and Anderson, 2017; Passos et al., 2018, 2019; Ribeiro et al., 2019). From the thymic stroma side, differentiation of progenitor of thymic epithelial cells (TECs) into cTECs and mTECs with the concomitant organization of histologically distinct areas of the thymus (cortex and medulla) occurs simultaneously with the migration of T cell precursors to the thymus. The reciprocal crosstalk between the TECs and the thymocytes allows the three-dimensional thymic arrangement and maturation of both cell populations. Unlike cTECs, knowledge concerning the differentiation of progenitor TECs into mTECs is greater, and its function is more well-characterized. Crosstalk between developing thymocytes and mTECs contributes to the formation of the thymic medulla, and signaling from thymocytes involving the CD40 ligand and NF-kB leads to the induction of primordial pathways for mTEC maturation (Nitta and Suzuki, 2016; Abramson and Anderson, 2017; 2019; Ribeiro et al., 2019). Mature mTECs can be identified by their CD45−, Epcam+, Uea1+, Ly51−, MHC-II+ and CD80+ phenotype. A subpopulation named mTEClow shows low expression of MHC-II, CD80 and Autoimmune regulator (Aire), whereas mTEChigh cells show increased expression of these molecules (Manley and Condie, 2010; Lopes et al., 2015; Matsumoto, 2019). The mTECs are unique in that they express the majority of their functional genome and yet maintain their morpho-functional characteristics. Most of the genes expressed by these cells encode peripheral tissue antigens (PTAs), a set of which is regulated by Aire and another set of which is regulated by FEZ family zinc finger 2 (Fezf2), and this ectopic expression is called promiscuous gene expression (PGE) due to the vast range of PTA that it encodes. The functional significance of PGE is immunological, since ectopic proteins (i.e. peptides) in this process are presented via MHC-I or MHCII to developing thymocytes as PTAs. This ensures the representation of the self in the thymus and the elimination of self-reactive thymocyte clones before they reach the periphery (Derbinski et al., 2001; Kyewski et al., 2002; Ucar and Rattay, 2015; Passos et al., 2015, 2018; 2019). In addition, the various costimulatory molecules expressed by the TECs, such as CD40 and CD80, which bind to the CD40 L and CD28 molecules, respectively, and also the adhesion molecules and extracellular matrix ligands, such as integrins, claudins, selectins, collagens, fibronectins and laminin, mediate mTEC-thymocyte interactions (Lopes et al., 2015; Passos et al., 2015; Savino et al., 2015; Mendes-da-Cruz et al., 2019). Alteration in any of these molecules changes their interaction ability and impairs the mTEC-thymocyte adhesion (LinharesLacerda et al., 2010). In a study by our group, we demonstrated the participation of Aire in the mTEC-thymocyte adhesion process through controlling the expression of mRNAs that encode adhesion molecules, such as Vcam1, Lama1 and Icam4 (Pezzi et al., 2016). In addition, Aire may also affect the expression of miRNAs in mTECs, which can posttranscriptionally regulate downstream genes involved in thymic crosstalk (Macedo et al., 2013; Ucar et al., 2013). The role of miRNAs in thymus biology and in the mechanisms of central tolerance has been demonstrated in recent years (Khan et al., 2014, 2015; Guo et al., 2016a, 2016b). However, nothing is yet known regarding the possible participation of miRNAs in mTEC-thymocyte adhesion (Passos et al., 2018). In this study, we used a strategy of reducing Dicer expression by means of small interfering RNA (siRNA), followed by transfections with miRNA mimics in mTECs to identify Dicer-dependent miRNAs that could regulate mTEC-thymocyte adhesion.

2.2. Medullary thymic epithelial cell line (mTEC) The mTEC 3.10 line was established from a C57BL/6 mouse thymus, and medullary phenotype was initially determined by immunostaining with Th-3 and Th-4 antibodies, which recognize the cortical and medullary phenotypes, respectively (Hirokawa et al., 1986). These cells were further evaluated using a panel of anti-cytokeratin monoclonal antibodies, which confirmed the original distinct medullary phenotype (Nihei et al., 2003). We reassessed the medullary phenotype of this cell line by labeling with anti-mouse CD45-PerCP, anti-mouse CD326 (EpCam) and anti-mouse Ly51-PE antibodies in addition to labeling with lectin agglutinin (Ulex europaeus UEA-1)-FITC. These labeling confirmed the medullary epithelial phenotype of the mTEC 3.10 line as CD45−, EpCam+, Ly51−, UEA+ (Pezzi et al., 2016). With the purpose of evaluating the percentage of mTEC 3.10 line (control vs Dicer silenced) expressing the surface molecules CD80 and MHC-II we used flow cytometry in a FACSCanto II apparatus (BD Biosciences – San Jose, CA, USA) and labeling cells with a 1:250 dilution of anti-mouse CD80-APC or anti-mouse MHC-PE monoclonal antibodies (BD Biosciences). 2.3. mTEC-thymocyte cell adhesion assay In order to evaluate the effect of partial Dicer knockdown (KD) on mTEC-thymocyte adhesion, we used a previously established cell adhesion assay protocol (Ribeiro-Carvalho et al., 2002; Savino et al., 2004, 2007; Ocampo et al., 2008; Oliveira et al., 2016) with some modifications (Pezzi et al., 2016). Briefly, mTEC 3.10 line were cultured in RPMI 1640 medium containing 10% fetal bovine serum and antibiotics at 37 °C in a 5% CO2 atmosphere. Cells in semi-confluence were detached from their culture flasks by conventional trypsin/EDTA treatment, washed once with PBS at room temperature, resuspended in RPMI 1640 medium and seeded in new culture flasks (2 × 106 cells per 75 cm2 Corning ® cell culture flask). mTEC 3.10 line subjected to siRNAinduced Dicer KD were co-cultured with thymocytes as described below. Co-cultures with mTEC 3.10 line treated only with Hiperfect reagent served as control. Thymocytes were added to control or Dicer KD mTEC 3.10 cultures in a ratio of 50:1 (thymocyte:mTEC) and co-cultured for 1 h in RPMI medium containing 10% fetal bovine serum and antibiotics at 37 °C in a 5% CO2 atmosphere. After 1 h, non-adherent thymocytes were carefully removed from cultures by washing with PBS at 37 °C and discarded. The culture flasks were then washed more vigorously with PBS at 4 °C to remove the adherent thymocytes, which were kept for counting and phenotyping. Single-positive (SP) CD4+ or CD8+ cells were stained with a APC-Cy7-labeled anti-CD4 and with a FITC-labeled anti-CD8 antibody (Biolegend), respectively, and quantified using a BD

2. Materials and methods 2.1. Mice and separation of thymocytes Female C57BL/6 mice aged of four- to five-week and weighting 18–22 g were used for surgical removal of the thymus and further thymocyte preparation. The animals were housed in the Central Animal Facility of the University of São Paulo, Campus of Ribeirão Preto, SP, 601

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LRSFortessa flow cytometer (BD Biosciences). Next, mTECs 3.10 were detached from their culture flasks by conventional trypsin / EDTA treatment and resuspended in PBS for counting. Cell counts for either thymocytes or mTECs were performed on a Cellometer Auto T4 Cell Viability Counter (Nexcelon Bioscience, Lawrence, MA, USA). Experiments were performed in triplicate (three co-cultures with control mTECs and three co-cultures with Dicer KD mTECs) and repeated at least three times with similar results. Then, the adhesion index (AI) was calculated as follows: AI = number of adhered thymocytes / number of mTEC cells. Statistical analysis was performed by Student´s t-test using GraphPadPrism 6.0 platform (Pezzi et al., 2016). P < 0.05 was considered statistically significant.

manufacturer´s instructions. The expression levels of miRNAs were normalized to snoRNA202 (Applied Biosystems), which is often used as a housekeeping miRNA and the respective relative expression were calculated by the comparative threshold cycle (2-ΔCt). 2.5. Dicer knockdown via siRNA The in vitro liposome-mediated transfection of mTEC 3.10 line with anti-Dicer TriFecta® (IDT, Integrated DNA Technologies, Coralville, IA, USA) small interfering RNA (Dicer siRNA) was used to knockdown (KD) Dicer according to a previously standardized protocol from our laboratory (Macedo et al., 2013). Briefly, confluent cultures of mTEC 3.10 line were transfected with 10 nM each of the three siRNA duplex sequences (Table 2) using HiperFect ® Transfection Reagent (Qiagen, GmbH, Hilden, Germany) according to the manufacturer´s instructions. After transfections, cells were cultured during 24 to 42 h in RPMI as described above, to reach the inhibition of Dicer mRNA. Dicer gene KD efficiency in mTEC 3.10 line ranged from 60 to 75% among samples. However, to test the hypothesis proposed in this study, only samples that presented a silencing efficiency of 75% were used i.e. 30 h posttransfection of siRNA sequences.

2.4. Total RNA preparation and reverse transcription real-time quantitative PCR (RT-qPCR) Total RNA was prepared using the mirVana kit ® (Ambion, Austin, TX, USA) according to the manufacturer´s instructions. RNA preparations were confirmed to be free of proteins and phenol by UV spectrophotometry. RNA degradation was assessed by microfluidic electrophoresis using Agilent RNA 6000 nano chips and an Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Only RNA samples that were free of proteins and phenol and had an RNA integrity number (RIN) ≥ 9.0 were used for cDNA synthesis with SuperScript reverse transcriptase enzyme according to the manufacturer´s instructions (Invitrogen Corporation, Carlsbad, CA, USA) (Pezzi et al., 2016). The confirmation of Dicer gene knockdown, as well as the transcriptional levels of adhesion molecules genes (Tnc, Lgals3bp, Col4a2, Lgals9, Cd47, Vcam1, Lama1, MHC II and Cd80) was assayed RT-qPCR. The expression level of each gene was normalized to housekeeping gene Hprt, which is commonly used as a reference. The Primer3 web tool (http://frodo.wi.mit.edu/primer3) was used to select pairs of oligonucleotide primers spanning an intron/exon junction with an optimal melting temperature of 60 °C. The cDNA sequences of these genes were retrieved from the NCBI GenBank database (http://ncbi.nlm.nih.gov/nuccore?itool=toolbar). The forward (F) and reverse (R) sequences (presented in the 5´to 3´orientation) were as follow: Dicer (NM_148948.2) CCCAAATGTAGA ACCCGAGA and CAACCGTACACTGTCCATCG, Hprt (NM_013556.2) CCCCAAAATGGTTAAGGTT and CAAGGGCATATCCAACAACA, Tnc (NM_011607.3) CTTGGGAACTCGTTGTGGCT and CTGGTGTCCAGAC GACCTTC, Lgals3bp (NM_011150.3) GACATGCGCTTGGTTAACGG and AACCCAGGGATCTGCAACTG, Col4a2 (NM_009932.4) CATTGAAGGCC CCACAGGAT and GGATGAGAGGAGGTGGTCCT, Lgals9 (NM_010708) AGTTCTGTCGTCCACCATCG and CTCAAACCGGGGGTTGAAGT, Cd47 (AB012693) ACTGTGGTCATCCCTTGCAT and TGGCATCGCGCTTATC CATT, Vcam1 (NM_011693) GTCACGGTCAAGTGTTTGGC and AGATC CGGGGGAGATGTCAA, Lama 1 (NM_008480) GGTCATGCAGAGGCTG ACTT and CAGCTGGTCACGGTCAATCT, Mhc-II (C57Bl/6 H2-Mb2) (U35334.1) TCCCTTCCCACAGCAACAAG and AGGAATGAGACTTGCG CCAT, Cd80 (NM_001359898) TGCCTAAGCTCCATTGGCTC and AGA GTTGTAACGGCAAGGCA. Gene expression was quantified using a StepOne Real-Time PCR System (Applied Biosystems, USA). The analyses were performed using the cycle threshold (Ct) method, which allows for quantitative analysis of the expression of a factor using the formula 2−DΔC^t, in which ΔCt = Ct target gene - Ct of the housekeeping gene Hprt, and ΔΔCt = ΔCt sample - ΔCt. For miRNA RT-qPCR amplifications, we used the specific TaqMan probes (TaqMan MicroRNA Assays, Applied Biosystems) in whose kit are included the primers for cDNA synthesis and the miRNA probes mmu-miR-30b-3p MIMAT0004524 (CUGGGAUGUGGAUGUUUAC GUC) and mmu-miR-181b-5p MIMAT0000673 (AACAUUCAUUGCUG UCGGUGGGU). We used the TaqMan Gene Expression Master Mix (Applied Biosystems) and PCR conditions were made according the

2.6. Western blotting of DICER protein Western blotting of DICER protein was performed according to a conventional protocol as previously standardized in our laboratory (Pezzi et al., 2016). Briefly, electrotransfer of the SDS-PAGE of total proteins extracted from control or Dicer KD mTEC 3.10 line was performed to a polyvinylidene fluoride (PVDF) membrane (BioRad, Hercules, CA, USA). Membrane blocking was made with 5% reconstituted nonfat-dried bovine milk in 1 x PBS (pH 7.4) with 0.1% Tween 20 for 2 h at room temperature. The membrane was then incubated overnight at 4 °C with 1:500 dilution of primary anti-Dicer mouse monoclonal antibody (D-11 antibody, kappa light chain of IgG2a, Santa Cruz Biotechnology Inc., Dallas, TX, USA) followed by 3 x washing in PBS with 0.1% Tween 20, and incubation with 1:10,000 dilution of horseradish peroxidase (HRP)conjugated donkey anti-mouse secondary antibody (IgG-HRP sc-2318, Santa Cruz Biotechnology) for 50 min at room temperature. The membrane was developed using a peroxidase substrate for chemiluminescence (LuminataForte ™, Merck Millipore) in an ImageQuant ™ LAS 500 apparatus (GE Life Sciences, Piscataway, NJ, USA), which allowed visualization of protein bands that were quantitatively analyzed to glyceraldehyde 3-phosphate dehydrogenase (GAPDH) protein. The same membrane that was used to detect DICER protein was washed and reused in an incubation with an anti-GAPDH rabbit polyclonal primary antibody (Cell Signaling Technology, Beverly, MA, USA) following incubation with peroxidase conjugated anti-rabbit antibody. The membrane was developed as described above. 2.7. Microarray hybridizations The microarray hybridizations used in this study followed a protocol established in our laboratory and previously described (Oliveira et al., 2016). For miRNA profiling, we used total RNA for direct labeling with Cy3 using the Agilent miRNA Complete Labeling and Hybridization Kit (Agilent Technologies, Mississauga, ON, Canada). The Cy3-labeled RNA samples were hybridized to Agilent mouse 8 x 15 K-format oligonucleotide miRNA microarrays, and slides were washed according to the manufacturer´s instructions (Agilent Technologies) and scanned using an Agilent DNA microarray scanner. For mRNA profiling, we used total RNA to synthesize dscDNA and cyanine 3 (Cy3)-CTP-labeled complementary amplified RNA (cRNA) using the Agilent Linear Amplification Kit (Agilent Technologies) according to the manufacturer´s instructions. The (Cy3)-cRNA samples 602

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2.11. Validation of miRNA-mRNA interactions

were hybridized to Agilent mouse 8 x 60 K-format oligonucleotide microarrays (Agilent Technologies) and slides were washed according to the manufacturer´s instructions (Agilent Technologies) and scanned using an Agilent DNA microarray scanner.

2.11.1. Determination of the hybridization minimum free energy (MFE) Some pairs of interactions generated by the GenMir ++ algorithm were selected based on the biological function of the target mRNA. Then we used the RNA-Hybrid algorithm (Rehmsmeier et al., 2004) (https://bibiserv2.cebitec.uni-bielefeld.de/rnahybrid) to validate the miRNA-mRNA pairing. This method involves a dynamic programming algorithm that calculates the most favorable hybridization between a given miRNA and its target, calculating the minimum free energy (MFE) based on the thermodynamic state. It is postulated that an RNA duplex is more stable and thermodynamically stronger when the free energy is low (Lewis et al., 2005). In this study, we used a threshold of -20 (kcal/ mol) to select the interactions.

2.8. Microarray data analysis The hybridization signals from the scanned miRNA or mRNA oligonucleotide microarrays were extracted using the Agilent Feature Extraction software, version 10.7.1.1. The expression profiles from independent preparations of control or Dicer-silenced mTEC 3.10 line were analyzed by comparing the microarray hybridizations of the respective samples. The numerical, quantitative microarray data were normalized to the 75th percentile and analyzed in the R statistical environment (version 2.13.1) (https://www.r-project.org/). In the data preprocessing phase, we used the arrayQualityMetrics (Kauffmann, 2009) and Agi4 × 44PreProcess (Lopéz-Romero, 2010) tools, which have algorithms that allow the qualitative evaluation of arrays besides correcting the background and normalizing of the data. For the analysis of differentially expressed mRNAs, we used the functions of the Limma package (Ritchie, 2015), which applies a linear model in the gene-wise statistical analysis. For an analysis of multiple tests, Empirical Bayes and the Benjamini-Hochberg correction were applied. For the analysis of the differentially expressed miRNAs, we used algorithms that allowed both the pre-processing of the data and the statistical analysis, which is based on the linear model used in the Limma package (AgiMicroRNA package) (Lopéz-Romero, 2010). In this work, we considered as differentially expressed those mRNAs or miRNAs that presented p-value ≥ 0.05 with correction by FDR (Benjamini-Hochberg) and fold-change ≥ 1.5. The differentially expressed transcripts were then submitted to hierarchical clustering and heat-maps were constructed to evaluate the expression pattern of mRNAs or miRNAs. The Euclidean distance and the complete linkage method were used to group the samples and the RNAs.

2.11.2. miRNA mimic transfections We used miRNA mimic transfections as a direct way to access the function of a given miRNA throughout its overexpression within the cell milieu. This strategy is also referred as gain-of-function approach. We used miRIDIAN miRNA mimics (Dharmacon, Inc., Horizon Discovery, Waterbeach, UK) whose sequences were based on the miRBase databank (http://mirbase.org) as follow mmu-miR-30b-3p (CUGGGAUGU GGAUGUUUACGUC) and mmu-miR-181b-5p (AACAUUCAUUGCUGU CGGUGGGU). The mTECs 3.10 were transfected with Hiperfect Transfection Reagent ® (Qiagen, Valencia, CA, USA). The preparation of the miRNA mimic-Hiperfect liposome complex was made by mixing 40 nM miRNA mimic in 500 μl serum-free MEM medium plus 20 μl Hiperfect Transfection Reagent. The mixture was vigorously agitated by successive pipetting during 3 min and then left at room temperature during 10 min. The entire volume of the liposome complex was then added mTECs 3.10 in a proportion of 40 nM miRNA mimic / 1 × 105 cells. These were cultured in 25 cm2 Corning® culture flasks for 48 h at 37 °C in an atmosphere with 5% CO2. The control mTECs 3.10 were transfected with miRIDIAN microRNA negative control (Dharmacon). After incubation time, the total RNA was extracted whose samples served for cDNA synthesis and RT-qPCR of the target mRNAs as mentioned above. Finally, to provide a functional implication for these results we evaluated the effect of miRNA mimic transfection on mTEC-thymocyte interaction through the cell adhesion assay (see above) 48 h after transfections.

2.9. Functional enrichment of differentially expressed mRNAs The list of differentially expressed mRNAs was subjected to functional enrichment analysis from the DAVID annotation tool (Database for Annotation, Visualization and Integrated Discovery) version 6.7 (http://david.abcc.ncifcrf.gov). The aim was to identify Gene Ontology (GO) categories represented by differentially expressed mRNAs. A given functional category was considered significantly represented if at least three mRNAs had been included with a score ≤ 0.05 and BenjaminiHochberg correction.

3. Results 3.1. Silencing of Dicer in mTEC 3.10 line with Dicer siRNA Because of their critical role during the miRNA maturation of most mammalian cells, Dicer-knockout (KO) cells are often unable to be generated. To overcome this inherent difficulty, we used Dicer siRNA in mTEC 3.10 line, which, although it did not completely reduce the total amount of Dicer mRNA target transcripts, the observed reduction did allow us to evaluate the main hypothesis of the study. The most statistically significant time elapsed after transfection of mTEC 3.10 line with Dicer siRNA, hereafter referred to as the silencing time, was equal to 30 h, as evaluated by RT-qPCR (Fig. 1a). This silencing time allowed an approximately 60% reduction of DICER protein expression in mTEC 3.10 line, as evaluated by Western blot analysis (Fig. 1b).

2.10. Reconstruction of miRNA-mRNA interaction networks To establish the miRNA-mRNA interactions we used the algorithm GenMir++ (Huang et al., 2007a,b) available at (http://psi.toronto. edu/genmir). This algorithm uses the microarray miRNA and mRNA expression profiles from the same RNA sample to identify the miRNAmRNA candidate pairs that are best supported by the expression data. We focused on those expression profiles of miRNAs compatible with mRNA repression. The candidate pairs were checked in the Targetscan database (http://www.targetscan.org) for a set of potential target mRNAs for each of the identified miRNAs. The GenMir ++ algorithm calculates the scores by attempting to reproduce the mRNA profile by a weighted combination of the averaged normalized expression profile of the entire genome and the negatively weighted profiles of a set of regulatory miRNAs. Scores are plotted and a threshold is determined to select no more than 20% of predicted interactions. Finally, the graphical program Cytoscape v2.1 (www.cytoscape.org) was used to design the interaction networks.

3.2. mTEC-thymocyte adhesion assay To evaluate the effects of Dicer silencing, we performed adhesion assays. With these, we were able to quantify the number of thymocytes adhered by each mTEC in co-culture (adhesion index, AI). The results demonstrated that thymocyte adhesion to Dicer-silenced mTEC 3.10 line was significantly higher in comparison to thymocyte adhesion with control mTEC cells (Fig. 2a-c). Flow cytometry of CD4+ and CD8+ 603

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Fig. 1. Decreasing Dicer mRNA or DICER protein expression by means of anti-Dicer siRNA in medullary thymic epithelial cells (mTEC 3.10 line). Relative transcriptional Dicer gene expression as evaluated by RT-qPCR. The mTEC cells exhibited significant decrease of Dicer mRNA 30 h post-transfection of siRNA (A). Western-blotting (WB) (B) confirmed DICER protein decrease 30 h post-transfection. Western-blot membrane was developed in a LAS 500 apparatus that runs the ImageQuant ™ program (GE Life Sciences, Piscataway, NJ, USA), which allowed visualization of protein bands. The image of the WB membrane was not cut out nor coupled with another image. The actual image shows the results of three independent determinations of control or Dicer-silenced samples on the same WB membrane.

3.3. Large-scale mRNA expression profiling in mTEC 3.10 line

single-positive (SP) cells showed that both phenotypes adhered to mTECs. From the total population of adhered thymocytes to control mTECs, 4.3% were CD4+ and 0.5% CD8+ SP cells (Fig. 2d). When Dicer is reduced in mTECs, 4.8% of adhered thymocytes were CD4+ (11.6% increase over control) and 0.8% were CD8+ SP cells (42.8% increase over control) (Fig. 2e). The percentage of double-positive CD4/CD8 cells did not change significantly in the groups tested (Fig. 2d and 2e).

Analysis of mRNA expression on a genomic scale was performed using Agilent platform microarrays (8 × 60 K oligo arrays), and quantitative expression data were processed using the R platform statistical functions available from Bioconductor (https://www.bioconductor. org/). Pearson's correlation analysis of normalized data showed that the transcriptome of mRNAs is different when control mTECs 3.10 are Fig. 2. Dicer knockdown in mTEC 3.10 line increase thymocyte adhesion. Co-culture of control (mock transfected) mTECs 3.10 with fresh thymocytes (A) or mTECs 3.10 subjected to Dicer siRNA-induced knockdown. Conventional light microscopy, Giemsa staining, 20 x magnification (B). Arrows for reference indicate some thymocytes. The numbers of adhered thymocytes to mTECs were quantified and data are shown as a bar graph. Bars correspond to adhesion index (thymocytes/mTECs), in which values correspond to mean ± standard deviation (s.d.). Data from three independent experiments, which were significantly different between control versus Dicer knockdown. (*** P ≤ 0.0008, Student t test) (C). CD4 and CD8 cell surface markers phenotyping of adhered thymocytes through flow-cytometry analysis. CD4 single-positive (SP) thymocytes (left upper quadrant) and CD8 SP thymocytes (lower right quadrant) that were adhered to control mTECs 3.10 (D). CD4 SP thymocytes (left upper quadrant) and CD8 SP thymocytes (lower right quadrant) that were adhered to Dicer KD mTECs 3.10 (E). CD4/CD8 double-positive thymocytes that were adhered to control (D) or to Dicer KD mTECs 3.10 are depicted in the right upper quadrants (D and E respectively).

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Fig. 3. Large-scale mRNA expression profiles of mTEC 3.10 line; control or subjected to Dicer siRNA-induced knockdown (Dicer KD). Total RNA samples were extracted from control or Dicer KD mTEC cells 30 h post-transfection of siRNA, labeled and hybridized to Agilent microarrays. Unsupervised dendrograms and heat-maps were constructed using R platform. Red = upregulated mRNAs, green = downregulated mRNAs and black = unmodulated mRNAs (Fold-change FC ≥ 1.5, false discovery rate FDR ≤ 0.05, Pearson correlation metrics, 75 percentile) (A). Confirmation of microarray results for those upregulated mRNAs that encode cell adhesion proteins, after anti-Dicer siRNA transfection, was made though RTqPCR. Expression levels of each mRNA were normalized using Hprt mRNA (n = 3). Data are presented as mean ± s.d. (n = 3). The difference between gene expression in control and Dicer knockdown was evaluated using Student t test (* P ≤ 0.05, ** P = 0.01, *** P ≤ 0.001) (B).

However, when these cells adhere to thymocytes, the percentage of cells expressing CD80 is even higher in the Dicer-silenced group. The percentage of cells expressing MHC-II did not change significantly in the groups tested.

compared to Dicer-silenced mTECs 3.10. We then analyzed transcriptome profiles comparing these two types of samples by defining the differential gene expression using linear modeling features of the Limma package. A contrast matrix coupled to unsupervised dendrograms was then designed to allow a comparison between control and Dicer-silenced mTECs 3.10 (Fig. 3a). We found that 422 mRNAs were differentially expressed, with 246 induced and 176 repressed. The full list of data generated in these experiments are freely available online at ArrayExpress EMBL-EBI repository (https://www.ebi.ac.uk/ arrayexpress/) under the accession number MTAB-4628. We then analyzed the set of modulated (induced or repressed) mRNAs comparing control and Dicer-silenced mTECs 3.10 in terms of Gene Ontology (GO) functional categories through the DAVID gene functional classification tool. Since genes can appear in more than one category, we used the clustering tool provided by DAVID to group terms that had similar annotations. The gene groups were then arranged in order of importance according to the enrichment score that was calculated according to the EASE Score (p value) of each gene member of the group. A category was considered significant when it encompassed at least three genes with a score ≤ 0.05 with Benjamini-Hochberg correction.

3.6. miRNA expression profiling in mTECs 3.10 The DICER enzyme is an endoribonuclease involved in pre-miR processing that produces the miR/miR duplex, thus generating the majority of miRNAs in a mammalian cell. Therefore, we determined large-scale miRNA expression profiles of Dicer-silenced and control mTEC 3.10 line. Expression analysis of the miRNAs at the genomic scale was performed using Agilent platform microarrays (8 x 15 K oligo arrays), and the data were processed using statistical functions from the AgiMicroRna package available from Bioconductor. Pearson's correlation analysis of normalized data showed that the transcriptome of miRNAs is different when control mTEC 3.10 line were compared to Dicer-silenced mTECs 3.10 (data not shown). Fig. 5a shows a comparison of the pattern of differentially expressed miRNAs between Dicersilenced mTEC 3.10 vs. mTEC 3.10 control cells. We identified 114 differentially expressed miRNAs, 55 induced and 59 repressed, (adjusted p value ≤ 0.05, Benjamini-Hochberg FDR correction and foldchange ≥ 1.5). The full list of data generated in these experiments are freely available online at the ArrayExpress EMBL-EBI repository (https://www.ebi.ac.uk/arrayexpress/) under the accession number MTAB-4627.

3.4. Confirmation of mRNA expression modulation through RT-qPCR To confirm the type of modulation of selected genes (mRNAs) that were included in the cell adhesion category and whose modulation is consistent with the increase of mTEC-thymocyte adhesion, we used RTqPCR analysis. The results shown in Fig. 3b indicate that the respective Tnc, Lgals3bp, Col4a2, Lgals9 and Cd47 mRNAs were induced in the Dicer-silenced mTECs 3.10 in comparison to control mTECs 3.10. In addition, we also evaluated the modulation of the following mRNAs, although they were not identified by microarray analysis: Vcam1, Lama1, Cd80 and Mhc-II. These mRNAs are categorized into functions of cell adhesion, adhesion to extracellular matrix components and communication between mTECs and thymocytes. These results show that the Vcam1, Lama1 and Cd80 mRNAs were induced, but the Mhc-II mRNA showed no differential modulation when comparing Dicer-silenced mTECs 3.10 with control mTECs 3.10.

3.7. Confirmation of miRNA expression modulation through RT-qPCR We use RT-qPCR to confirm the type of modulation of selected miRNAs as obtained by microarray method and whose modulation was consistent with Dicer-knockdown (Fig. 5b). The results confirm that miR-181b-5p was repressed in the Dicer-silenced mTECs 3.10 relative to control mTECs 3.10. The miR-30b * was also repressed, although it showed no statistical difference. Functional experiments with miRNA mimics have validated the effect of these two miRNAs on mTECs. 3.8. miRNA-mRNA posttranscriptional interaction networks

3.5. Phenotyping CD80+ / MHC-II+ mTECs 3.10 To evaluate whether the differences in the transcriptional profiles between Dicer-silenced mTECs 3.10 and control mTECs 3.10 were related to the posttranscriptional control that the differentially expressed miRNAs exert on target mRNAs in these cells, we used the GenMir ++ algorithm (Huang et al., 2007). This algorithm calculates the probability of an interaction between a given miRNA and a given target mRNA based on quantitative gene expression data. To determine the

To evaluate the expression of the cell surface markers CD80 and/or MHC-II in mTECs, we next used flow cytometry. Assays were performed comparing control mTECs 3.10 with Dicer-silenced mTECs 3.10 (before thymocyte adhesion) and control mTECs 3.10 with Dicer-silenced TECs 3.10 (after thymocyte adhesion). Fig. 4 shows that the percentage of cells expressing CD80 is higher in the Dicer-silenced mTECs 3.10. 605

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Fig. 4. CD80 and MHC-II cell surface protein phenotyping of mTEC 3.10 line through flow-cytometry analysis. Control mTECs 3.10 (left upper quadrant), mTECs 3.10 after adhesion with thymocytes (right upper quadrant), Dicer knockdown mTECs 3.10 (lower left quadrant) and Dicer knockdown mTECs 3.10 after adhesion with thymocytes (lower right quadrant). This figure shows that the percentage of CD80+ mTECs 3.10 is higher when Dicer is reduced with synergistic effect of thymocyte adhesion.

miRNA-mRNA interactions, the 246 induced mRNAs and the 59 repressed miRNAs were considered. The algorithm returned a complex network with 1,141 possible interactions, from which those that involved mRNAs encoding proteins related to cell adhesion were selected. Fig. 6a shows the interactions of four repressed miRNAs in Dicer-silenced mTECs 3.10 (miR-181b-5p, miR-30b*, miR-328-3p, and miR294-3p) with their respective mRNA targets (Lgals9, Tnc, Cd47, Col4a2, Lgals3bp, Cd274, and Rsad2). 3.9. Validation of miRNA-mRNA interactions To validate the interaction network data, we utilized different approaches. In the first one, we used the RNA-Hybrid program (Rehmsmeier et al., 2004) (https://bibiserv.cebitec.uni-bielefeld.de/ rnahybrid/), which analyzes the secondary structure of miRNAs and determines the most favorable hybridization site between a given miRNA and its target mRNA. This methodology is based on the principles of interaction thermodynamics and is determined by the minimum free energy (MFE). miRNA-mRNA interactions with MFE values ≤ -20 were considered stable. Table 1 shows several selected stable interactions, as calculated by the GenMir++ algorithm and having a MFE ≤ -20. Moreover, we made use of miRNA (miRNA mimics) transfections to increase the intracellular levels of miR-181b-5p or miR-30b* in mTECs

Fig. 6. Bayesian miRNA-mRNA interaction network reconstructed on the basis of microarray expression profiling and involving mRNA targets that encode cell adhesion proteins. Networks were obtained through use of GenMir++ algorithm (A). Validation of target mRNAs within mTEC 3.10 cell milieu was done through miRNA mimic transfection followed by RT-qPCR of mRNAs (B).

Fig. 5. Large-scale miRNA expression profiles of control mTEC 3.10 line or mTEC 3.10 line subjected to Dicer siRNA-induced knockdown (Dicer KD). Total RNA samples were extracted from control or Dicer KD mTECs 3.10 30 h post-transfection of siRNA, labeled and hybridized to Agilent miRNA microarrays. Unsupervised dendrograms and heat-maps were constructed using R platform. Red = upregulated miRNAs, green = downregulated miRNAs and black = unmodulated miRNAs (Fold-change FC ≥ 1.5, false discovery rate FDR ≤ 0.05, Pearson correlation metrics, 75 percentile) (A). Confirmation of microarray results for those downregulated miRNAs (miR181b-5p and miR-30b*) after anti-Dicer siRNA transfection was made though RT-qPCR. Expression levels of each miRNA were normalized using SNOR202 that was taken as constitutive miRNA. Data are presented as mean ± s.d. (n = 3). The difference between miRNA expression in control and Dicer knockdown was evaluated using Student t test (*** P ≤ 0.001) (B).

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Table 1 Pairs of miRNA-mRNA, their 3´UTR hybridization and thermodynamic minimal free energy (MFE). mRNA-miRNA Pair Hybridization of miRNA with mRNA 3´UTR Tnc miR-181b-5p

MFE target 5' U G ACACC U 3'CCUA UGACAGCA AUGAAU GGGU GCUGUCGU UACUUA3' U G CAA 5' target 5' U CCC ACGACU U 3'GAUGUAGGC UCUGCA UCCU CUGCAUUUG AGGUGU AGGG3' U UC 5' target 5' C UCACU G 3'CACACGGA GAGGCACU GUGUGUUU CUUCGUGA3' U UCC AA 5' target 5' G C U UGU C 3'GACG AGACA UCC GUCCC CUGC UUUGU AGG UAGGG3' A UG UC 5' target 5' U C 3'GCCUACUG CAGUAG UGGGUGGC GUCGUU3' U ACUUACAA 5' 5' U CCAC GUAACUU C 3'GAC AAUGUCCACA UCCC CUG UUGUAGGUGU AGGG3' CAU UC 5 5' U C A 3'UCCGCUG UAGC UGAAU GGGUGGC GUCG ACUUA3' U U UU CAA 5' 5' U UGU A 3'GCCCAC GGCAGC GUGGG UGGGUG CUGUCG UACUU3' G U ACAA 5' 5' C GCUCAGCUCCCAU U 3'AAGCA UCC CAUCCCA UUUGU AGG GUAGGGU3' CUGCA U C 5' 5' A AG G G 3'GGA C CUACGGGCAA UUU CUU G GGUGCUUGUU AAG3' G AG U G 5' 5' U G CCUG A UG G 3'ACG GGA UUUCUUC U GG UGC CCU AAAGGAG A CU3' A A UG A 5'

Tnc miR-30b* Tnc miR-294 Lgals3bp miR-30b* Lgals3bp miR-181b-5p Lgals9 miR-30b* Cd47 miR-181b-5p Col2a4 miR-181b-5p Lama1 miR-30b* Cd80 miR-382 Vcam1 miR-376a

siRNA duplex sequences

Duplex 2 Duplex 3

−21.8 −25.7 −24.2 −23.5 −22.1 −23.1 −28.8 −22.1 −22.9 −21.2

transfected only with a miR-30b* mimic. However, when these cells were transfected with the two miRNA mimics together (miR-181b5p + miR-30b*), the adhesion index significantly decreased further.

Table 2 Anti-Dicer siRNA duplexes.

Duplex 1

−27.2

5’-AGAACGAAAUGCAAGGAAUGGACTC-3’ 3’-CUUCUUGCUUUACGUUCCUUACCUGAG-5’ 5’ -ACAAGAAACGGAAUCACAUCACACU-3’ 3’-GCUGUUCUUUGCCUUAGUGUAGUGUGA-5’ 5’- GCAGUUGUCCUAAACAGAUUGAUAA-3’ 3’ - GUCGUCAACAGGAUUUGUCUAACUAUU-5’

4. Discussion In this study, we asked if miRNAs could play a role in mTEC-thymocyte adhesion. miRNAs have been associated with several thymic processes including the control of the maintenance and function of the epithelium (Papadopoulou et al., 2012; Zuklys et al., 2012; Khan et al., 2014), development and differentiation of T cells within the thymus (Cobb et al., 2005; Kohlhaas et al., 2009; Koralov et al., 2008; Muljo et al., 2005; Thai et al., 2007), except miR-21, which is apparently dispensable for physiologic T cell development or endogenous thymic regeneration (Kunze-Schumacher et al., 2018), control of promiscuous gene expression (PGE) (Macedo et al., 2015, 2013; Oliveira et al., 2016; Ucar et al., 2013; Ucar and Rattay, 2015; Passos et al., 2015, 2018, 2019), in pancreas-infiltrating T lymphocytes (Fornari et al., 2015; reviewed in Mendes-da-Cruz et al., 2018), in aging thymus (Virts and Thoman, 2010; Ye et al., 2014), in thymic involution caused by T. cruzi infection (Linhares-Lacerda et al., 2010), sex differences in thymic tissue during human minipuberty (Moreira-Filho et al., 2018) and molecular differences between thoracic and cervical thymus in mice (Assis et al., 2018). Nevertheless, nothing has been reported so far about the eventual participation of miRNAs in mTEC-thymocyte interaction. Considering that more than half of mRNAs in humans and mice are under the regulation of miRNAs (Fabian, 2010; Vasudevan, 2012;

3.10. These miRNAs were chosen based on their predicted mRNA targets, which encode cell-adhesion proteins and on their MFE (Table 1). RT-qPCR was used to validate the inhibitory effects of these miRNAs upon the target mRNAs. Fig. 6b shows that Tnc, Lgals3bp, Lgals9 and Cd47 mRNA levels were significantly repressed in mTECs 3.10 transfected with miRNA-181b-5p or miRNA-30b* mimics. 3.10. The effect of miR-181b-5p and/or miR-30b* on mTEC-thymocyte adhesion Finally, we were interested in evaluating whether the validated miRNAs could have an effect on the biological system of this study, i.e., mTEC-thymocyte adhesion. The mTECs 3.10 were transfected with either miR-181b-5p or miR-30b* mimics, or with a mixture of both, and co-cultured with fresh thymocytes as described above for the adhesion assay. Fig. 7 shows that mTECs 3.10 transfected with only miR-181b-5p showed significantly lower adhesion with thymocytes compared to cells

Fig. 7. Decrease of mTEC-thymocyte adhesion following miRNA mimic transfection. The numbers of adhered thymocytes to mTECs were quantified and data are shown as a bar graph. Bars correspond to adhesion index (thymocytes/mTECs), in which values correspond to mean ± standard deviation (s.d.). Data from three independent experiments, which were significantly different between control versus Dicer knockdown. Student t test (** P ≤ 0.001).

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transport. TAP1 and TAP2 form a heterodimeric protein involved in the processing and transport of peptides within the cytoplasm (Fisette et al., 2016). Peptide presentation via MHC-I by mTECs to CD8+ thymocytes is crucial for negative selection and the interactions between the peptide-MHC complex and TCR during thymic crosstalk. The induction of these mRNAs after Dicer silencing strongly suggests the presence of posttranscriptional repression by miRNAs in mTECs. The mRNAs related to regulation of cell migration were also represented, such as Ccl4, Ccl5 and Cxcl10. It has been previously observed that the CCL4 chemokine exerts in vitro thymocyte chemoattraction (Mendes-da-Cruz et al., 2006). Biological processes related to cell adhesion, adhesion junctions and proteins of the extracellular matrix were also represented among the set of induced mRNAs after Dicer silencing, including Col4a2, Tnc, Lgals3bp, Lgals9 and Cd47. It is well-known that developing thymocytes interact with the thymic microenvironment while they migrate and differentiate. TECs bind to developing thymocytes via extracellular matrix ligands and their receptors, and galectins (GAL) represents one of the main components of this process. GAL-9, encoded by the Lgals9 mRNA, is a poorly-studied lectin, and its role in the thymus is not fully elucidated. There has been a suggestion that its action may be related to potentiation of cell adhesion by the presence of GAL-9 ligands in T cells (Hirashima et al., 2004). The protein encoded by Lgals3bp mRNA is a component of the extracellular matrix and a GAL-3 ligand that interacts with collagens, fibronectins, and integrins, which characterizes its function in promoting cell adhesion (Hughes, 2001). GAL-3 is expressed in TECs in a controlled manner through physical contact with thymocytes (Savino et al., 2004). This suggests that binding promoted by the protein encoded by Lgals3bp mRNA is associated with mTEC-thymocyte adhesion. Other induced mRNAs are those that encode TNC proteins, which modulate cell adhesion and lymphocyte migration (Clark et al., 1997), those that encode CD47, which promotes cell-cell interactions by interacting with specific integrins (Brown and Frazier, 2001), as CD47deficient thymiglioblas have lower cellularity (Guimont-Desrochers et al., 2009) and Col4a2, which encodes collagen type IV, an extracellular matrix component involved in cell adhesion. The validation of the expression of the Lgals3bp, Lgals9, Tnc, Cd47 and Col4a2 mRNAs by RT-qPCR confirmed the predictive value of their evaluation by microarray hybridizations. These mRNAs were chosen for validation based on their role as mediators of cell adhesion and/or cell-extracellular matrix interactions. We analyzed by RT-qPCR the expression of the following mRNAs that encode proteins involved with mTEC-thymocyte adhesion: Lama1, Vcam1, Cd80 and H2ab1 (MHC-II). Lama1 mRNA encodes the LM-111 isoform, and its expression is induced in Dicer-silenced mTEC cells. In addition to its role in the migration of thymocytes, LM-111 is able to stimulate the proliferation of TECs in humans and mice (Savino et al., 2015). It appears that VCAM-1 would be restricted to the thymic cortex (Paessens et al., 2008), but previous experiments in our group demonstrated its expression in mTECs and its relation to thymocyte adhesion (Pezzi et al., 2016). The Cd80 costimulatory molecule is essential for thymic crosstalk, and its absence may cause defects in mTEC differentiation and maturation of thymocytes (Williams et al., 2014; Lopes et al., 2015). In addition to the transcriptional expression, we evaluated CD80 at the level of protein expression by flow cytometry, and this allowed us to observe its increase under Dicer-silencing with synergistic effect of thymocyte interaction. This result shows the importance of cellular interaction in the expression of a functional marker in mTECs, i.e. CD80, and that miRNAs exert negative control in the expression of this molecule. When mTEC-thymocyte contact occurs, that is, a physiologically occurring situation in the thymus, reduction of the negative effect of miRNAs might occur, which results in increased expression of this marker. Although we tested MHC-II by the same strategy, we found

Zhang et al., 2019) and that miRNAs exert effects on thymic architecture (Ucar et al., 2015), it is quite likely that mRNAs encoding medullary thymic epithelial cell (mTEC)-thymocyte adhesion molecules are also under miRNA posttranscriptional control. To evaluate this hypothesis, we utilized small interfering RNA (siRNA) to silence Dicer in vitro in a murine mTEC line (the mTEC 3.10 line). This approach is based on the assumption that, after silencing Dicer, a set of Dicer-dependent miRNAs would be perturbed with consequences for the posttranscriptional regulation of their target mRNAs. Among the target mRNAs would be those encoding adhesion molecules in mTECs. This led us to trace the comparative transcriptional profiles of mRNAs of control and Dicer-silenced mTECs 3.10. In addition to evaluating Dicer mRNA levels by means of RT-qPCR, we also evaluated the DICER protein expression by Western blot analysis. We found that the siRNA duplexes used efficiently silenced Dicer. As DICER is a key enzyme in the miRNA maturation process, its reduction, although partial and transient, is an adequate strategy to investigate whether miRNAs participate in a given biological process. This was guaranteed by the considerable siRNA-induced reduction levels of 75% in mRNA and 60% in DICER protein. The mode of action of siRNAs in mammalian cells result in partial and transitory reduction of the targeted gene expression. Indeed, it represents a useful and widely used tool for identifying the effects of loss of function of a given gene by reducing its expression (Fakhr et al., 2016). The Dicer mRNA and protein reduction obtained in this study, although it did not suppress all miRNAs, which would be lethal to the cells, was satisfactory to disrupt the expression of a set of DICER-dependent miRNAs, including those most directly involved with the mTEC-thymocyte adhesion process. To determine the functional significance of the results and to evaluate the participation of miRNAs in mTEC-thymocyte adhesion, we utilized cell adhesion assay. This assay represents a useful and practical experimental model for assessing the functional relationship of miRNAs in thymic crosstalk. This approach allowed us to observe that Dicer silencing in mTECs 3.10 significantly increased their ability to undergo CD4+/CD8+ single-positive (SP) thymocyte adhesion. This finding is consistent with previous observations that mTECs establish crosstalk with SP thymocytes (reviewed in Mendes-da-Cruz et al., 2019; Irla, 2019). After reduction in Dicer expression, CD8+ SP thymocytes showed a greater relative increase in adhesion with mTECs compared to CD4+ SP thymocytes. As we did not observe significant morphological changes in mTECs 3.10 using conventional light microscopy and after treatment with antiDicer siRNA, we hypothesized that the change in adhesive properties of mTECs were due to dysregulation in either the mRNA and/or the miRNA set and/or the posttranscriptional interaction between these two RNA species. To identify which mRNAs and miRNAs were dysregulated upon Dicer silencing, particularly those encoding adhesion molecules, we investigated the respective transcriptional profiles over a large scale and in a non-biased manner using microarray hybridizations. Dicer silencing promoted differences in the mRNA transcriptomes of mTECs 3.10 compared to control mTECs 3.10. Statistical analysis revealed a set of 422 modulated mRNAs, of which 246 were induced and 176 were repressed. The set of 422 mRNAs was subsequently analyzed by the Gene Ontology (GO) tool included in the DAVID platform to identify the most represented functional categories. The most represented processes among the repressed mRNAs were mainly related to gene transcriptional regulation, DNA binding sites and nuclear protein binding. Any process related to cell adhesion or cell communication was shown in the repressed mRNA set. Among the induced mRNAs, the most represented biological processes were immune responses, processing and antigen presentation via MHC-I, highlighting H2d1, H2q7, H2t23, H2q10, which encode classical and non-classical MHC-I molecules. The Tap1, Tap2 and Tapbp mRNAs were also represented and are associated with processing and peptide 608

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a key event for the occurrence of negative selection, i.e. the interaction of mTECs with thymocytes (Passos et al., 2015). That is why we decided in this study to use the mTEC-thymocyte adhesion model system as an appropriate method to answer this type of question. Nevertheless, there are still no reports of the participation of these two miRNAs in thymus function and/or cell adhesion. Aiming to better understand the role of the set of differentially expressed miRNAs, including miRNA-181b-5p and miRNA-30b, and to confirm whether the differential expression of mRNAs is associated with the posttranscriptional control of miRNAs, we searched for interactions between the 246 induced mRNAs and 59 repressed miRNAs, considering that the main mechanism of action of miRNAs is negative regulation of gene expression. The identification of miRNA targets is often still performed by prediction in databases via algorithms based on the interaction of the seed sequence and the conservation of the mRNAs. This type of tool, although important in the study of miRNAs, can still generate false positive results. The GenMir ++ algorithm is a Bayesian statistics model that identifies miRNA-mRNA interactions not only based on the respective miRNA and target mRNA sequences but also on the expression data obtained by microarray hybridizations (Huang et al., 2007). This algorithm considers the occurrence of repression of the target mRNA after its interaction with the miRNA. In this work, we used the GenMir ++ algorithm together with the Cytoscape graphical program (http:// www.cytoscape.org) to investigate miRNA-mRNA interactions and to design interaction networks. The miRNA-mRNA interactions were then reanalyzed using the target prediction database TargetScan (http://www.targetscan.org) and confirmed in silico using the RNA-Hybrid algorithm (Table 1). With this procedure, we have been able to show that modulation of the expression of the mRNAs Tnc, Lgals9, Lgals3bp, Cd47, Col4a2, Vcam1, Lama1 and Cd80 is under the control of miRNAs in Dicer-silenced mTECs 3.10. To validate several of these interactions considered essential within the cellular milieu and confirm the function of miR-181b-5p and miR30b* in mTEC-thymocyte adhesion, we transfected the respective miRNA mimics into mTECs 3.10 and proceeded with the adhesion assay. The transcriptional levels, as evaluated by RT-qPCR, allowed validation of the posttranscriptional regulation of miR-181b-5p on the target mRNAs Tnc, Lgals9, Lgals3bp and Cd47. We also validated the regulation of miR-30b* on the target mRNAs Lgals9, Lgals3bp and Cd47. The adhesion assay performed after miRNA transfection confirmed its role in modulating cell adhesion. The increase of miR-181b-5p in the cell milieu caused a decrease in the cell adhesion index. The increase of miR-30b* had no significant effect on adhesion, but the simultaneous increase (co-transfection) of miR-181b-5p plus miR-30b* caused a significantly greater reduction in mTEC-thymocyte adhesion. This represent an evidence that these miRNAs play their role synergistically. In conclusion and on the functional point of view, these results represent an extended view on the mechanism of fine-tuning of mTECthymocyte adhesion in which miRNAs play their role. We identified a set of miRNAs involved in such control, highlighting miR-181b-5p and miR-30b*, which act on the mRNAs that encode extracellular matrix or adhesion molecules, as discussed above. To the best of our knowledge, no other work to date has measured, in a direct manner, the function of miRNAs in thymic crosstalk. Overall, these results indicate the existence of two new mechanisms, one of them involves the participation of miRNAs expressed in mTECs that control its adhesion with SP thymocytes. The other involves the synergistic action between miRNAs and thymocyte adhesion in controlling the expression of CD80 surface marker in mTECs. These mechanisms deserve further investigation because they are associated with mTEC-thymocyte adhesion, a crucial process for negative selection and induction of central immune tolerance.

no difference in expression of this marker comparing control vs Dicersilenced mTECs. The induction of mRNAs after Dicer silencing in mTECs suggests that they might be subjected to posttranscriptional modulation by miRNAs. This led us to trace the comparative transcriptional profile of miRNAs of control and Dicer-silenced mTECs 3.10. As expected, Dicer silencing provoked alterations in the global expression profiling of miRNAs. Statistical analysis identified 114 differentially expressed miRNAs, 55 of which were induced and 59 were repressed. Reduction of DICER protein levels due to silencing most likely affected the maturation of a group of miRNAs. As the microarray hybridization protocol used in this study only allows detection of the mature miRNAs, it was then possible to detect those miRNAs affected by Dicer silencing. However, as the analysis of microarray expression data is essentially comparative, it was to be expected that we would find these induced miRNAs. Nevertheless, other possibilities could still explain the set of induced miRNAs: 1) Dicer silencing, as expected, was partial, and the maintenance of the residual DICER protein could support the expression of mature miRNAs, or 2) the maturation of certain miRNAs is independent of Dicer, and consequently is refractory to its silencing. Since it was not part of the objectives of this work to investigate which exact mechanism supported the presence of the induced miRNAs, we focused on the set of repressed miRNAs after Dicer silencing and the consequences of this on the posttranscriptional regulation of mRNAs that encode cell adhesion molecules. miRNA-181b-5p (repressed in Dicer-silenced mTECs 3.10) presented modulation with a -2.934-fold change. This miRNA belongs to the miRNA-181a-d family and was studied in breast cancer cells and validated as a NFκB modulator by demonstrating its interaction with the 3′UTR region of this transcription factor, as well as inhibiting induced cell proliferation and migration by the cytokine CCL18 (Whang et al., 2016). Although the reduction in miR-30b*, as assessed by RT-qPCR, was not statistically significant, this technique confirmed the downregulation profile obtained with the microarray technique (-1.52 foldchange reduction). Considering that this miRNA is an element that featured stable miRNA-mRNA interaction, we insisted on this and functional experiments using the corresponding miRNA mimic showed that miR-30b* synergistically with miRNA-181b-5p, has an effect on mTECs. This miRNA is associated with different types of cancer, such as melanoma or glioblastoma, and is expressed in several additional tissues, including thymic tissue (Chiang et al., 2010). Two other members of this miRNA family (miR-181a1 and miR-181b1) with nucleotide sequences other than miR-181-5p studied herein are expressed in TECs, but apparently have no role in thymus development (Stefanski et al., 2018). The suspicion that miRNAs are somehow involved with T cell differentiation in the thymus has been discussed for years, raising questions about their possible involvement with dysregulation of T cell proliferation (Liston et al., 2010). Evidence began to emerge from Dicer deletion in the thymus of mice whose experiments showed that such deletion reduced the number of invariant natural killer T (iNKT) cells (Seo et al., 2010). Shortly thereafter, evidence emerged regarding the involvement of miRNAs with thymus stress response (Belkaya, 2011, 2014) or thymic involution (Papadopoulou et al., 2011; Guo et al., 2013). The work of Khan et al (2014) focused on the effect of canonical miRNAs on TECs themselves. These authors annulled the DGCR8 enzyme gene in mice, which is involved in miRNA processing. They observed that DGCR8-deficient TECs are unable to maintain correct thymic architecture and cellularity provoking breakdown of negative T cell selection. Our group was interested in the subject and we were able to give functional sense to miRNA dysregulation in the thymus of nonobese diabetic autoimmune (NOD) mice during the period when the loss of immune tolerance in these animals occurs (Macedo et al., 2015). Our suspicion was that miRNAs could be involved in some way with 609

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Author contributions

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LC-S: discussed and designed the study design, conceived and performed all experiments, transfected mTEC cells with anti-Dicer siRNA, performed cell adhesion assays, performed microarray hybridizations, analyzed and interpreted all data. AF-A: analyzed and interpreted the mTEC transcriptome (mRNAs and miRNAs), designed interaction networks by bioinformatics. NP: prepared cell lysates and performed western-blot experiments. EHO and PPT: transfected mTEC cells with anti-Dicer siRNA, performed cell adhesion assays, performed flow cytometry and data analysis, GAP: conceived the study, raised the hypothesis, discussed and designed the study design, interpreted and organized all transcriptome data (mRNA and miRNA) and wrote the manuscript. Declaration of Competing Interest All authors declare no competing interests. Acknowledgments This work was funded by the following agencies, Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, São Paulo, Brazil, Grant Nos. 13/17481-1 and 17/10780-4), Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq, Brasília, Brazil, Grant No. 306315/2013-0 and 305787/2017-9). This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) – Brazil – Financial code 001. We thank Dr Wilson Savino (Laboratory on Thymus Research, Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, RJ, Brazil) that gently ceded the mTEC 3.10 line. We thank Dr Daniella A. Mendes-da-Cruz (Oswaldo Cruz Institute, Fiocruz, Rio de Janeiro, RJ, Brazil), Dr Eduardo A. Donadi (Ribeirão Preto Medical School, USP, Ribeirão Preto, Brazil) for help and discussions, MSc Denise B. Ferraz (Ribeirão Preto Medical School), Mr Roger R. Fernandes and Mrs Aline F. Tiballi (School of Dentistry of Ribeirão Preto, USP, Ribeirão Preto, Brazil) for technical assistance. References Abramson, J., Anderson, G., 2017. Thymic epithelial cells. Annu. Rev. Immunol. 35, 85–118. https://doi.org/10.1146/annurev-immunol-051116-052320. Assis, A.F., et al., 2018. Predicted miRNA-mRNA-mediated posttranscriptional control associated with differences in cervical and thoracic thymus function. Mol. Immunol. 99, 39–52. https://doi.org/10.1016/j.molimm.2018.04.003. Belkaya, S., 2011. Dynamic modulation of thymic microRNAs in response to stress. PLoS One 6 (11), e27580. https://doi.org/10.1371/journal.pone.0027580. Belkaya, S., van Oers, N.S., 2014. Transgenic expression of microRNA-181d augments the stress-sensitivity of CD4(+)CD8(+) thymocytes. PLoS One 9 (1), e85274. https:// doi.org/10.1371/journal.pone.0085274. Brown, E.J., Frazier, W.A., 2001. Integrin-associated protein (CD47) and its ligands. Trends Cell Biol. 11 (3), 130–135. https://doi.org/10.1016/S0962-8924(00)01906-1. Chiang, H.R., et al., 2010. Mammalian microRNAs: experimental evaluation of novel and previously annotated genes. Genes Dev. 24, 992–1009. https://doi.org/10.1101/gad. 1884710. Clark, R.A., et al., 1997. Tenascin supports lymphocyte rolling. J. Cell Biol. 137 (3), 755–765. https://doi.org/10.1083/jcb.137.3.755. Cobb, B.S., et al., 2005. T cell lineage choice and differentiation in the absence of the RNase III enzyme dicer. J. Exp. Med. 201 (9), 1367–1373 https://doi.org/1084/ jem.20050572. Derbinski, J., et al., 2001. Promiscuous gene expression in medullary thymic epithelial cells mirrors the peripheral self. Nat. Immunol. 2 (11), 1032–1039. https://doi.org/ 10.1038/ni723. Donate, P.B., et al., 2013. T cell post-transcriptional miRNA-mRNA interaction networks identify targets associated with susceptibility/resistance to collagen-induced arthritis. PLoS One 8 (1), e54803. https://doi.org/10.1371/journal.pone.0054803. Fabian, M.R., 2010. Regulation of mRNA translation and stability by microRNAs. Annu. Rev. Biochem. 79, 351–379. https://doi.org/10.1146/annurevbiochem060308103103. Fakhr, E., et al., 2016. Precise and efficient siRNA design: a key point in competent gene silencing. Cancer Gene Ther. 23, 73–82. https://doi.org/10.1038/cgt.2016.4. Fisette, O., et al., 2016. Molecular mechanism of peptide editing in the tapasin-MHC I complex. Sci. Rep. 6, 19085. https://doi.org/10.1038/srep19085. Fornari, T.A., et al., 2015. Comprehensive survey of miRNA-mRNA interactions reveals that Ccr7 and Cd247 (CD3 zeta) are posttranscriptionally controlled in pancreas

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