Ferrets were experimentally inoculated with SARS-CoV-2 (severe acute respiratory syndrome (SARS)-related coronavirus 2) to assess infection dynamics and host response. During the resulting subclinical infection, viral RNA was monitored between 2 and 21 days post-inoculation (dpi), and reached a peak in the upper respiratory cavity between 4 and 6 dpi. Viral genomic sequence analysis in samples from three animals identified the Y453F nucleotide substitution relative to the inoculum. Viral RNA was also detected in environmental samples, specifically in swabs of ferret fur. Microscopy analysis revealed viral protein and RNA in upper respiratory tract tissues, notably in cells of the respiratory and olfactory mucosae of the nasal turbinates, including olfactory neuronal cells. Antibody responses to the spike and nucleoprotein were detected from 21 dpi, but virus-neutralizing activity was low. A second intranasal inoculation (re-exposure) of two ferrets after a 17-day interval did not produce re-initiation of viral RNA shedding, but did amplify the humoral response in one animal. Therefore, ferrets can be experimentally infected with SARS-CoV-2 to model human asymptomatic infection. In December 2019, clinical cases of pneumonia of unknown aetiology were first reported in Wuhan, Hubei province in Central China. Metagenomic sequencing revealed the causative agent to be a novel, severe acute respiratory syndrome (SARS)-related coronavirus, designated SARS-CoV-2 , presentCoronaviruses have been identified in diverse mammalian species, with two zoonotic strains being of substantial public health concern this century; SARS-CoV in 2002 and Middle Eastern respiratory syndrome coronavirus (MERS) in 2012 . SARS-CoUnderstanding the pathogenesis of SARS-CoV-2 in an animal model for human disease  is impor50) was calculated according to the method of Spearman–Karber [Vero E6 cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% heat-inactivated fetal calf serum (FCS), HEPES, sodium bicarbonate, 100 units/mL penicillin and 1000 µg/mL streptomycin . The SARS-CoV-2 inoculum of strain SARS-CoV-2/human/Australia/VIC01/2020 (GISAID accession number EPI_ISL_406844) was propagated in Vero/hSLAM cells (ECACC 04091501) and supplied by Public Health England . The phyn–Karber ,29,30.6 TCID50/mL of SARS-CoV-2/Australia/VIC01/2020 [6 TCID50/mL of the same inoculum. Clinical monitoring and sampling were done as described  Act 1986 (ASPA) with study PP3405816/1/001 approved on 30 April, 2020 by the Animal Welfare and Ethical Review Body (AWERB) of the Animal and Plant Health Agency and was reported according to the ARRIVE guidelines . Twelve C01/2020  delivereescribed .®, Destron Fearing, Dallas, TX, USA) and clinical signs were monitored daily. Clinical samples of nasal washes in Dulbecco’s PBS  as well as oro-pharyngeal (throat) and rectal swabs  were obtained prior to infection and on 2, 4, 6, 8, 10, 14, 19 and 21 dpi. At the same time, environmental samples of food, water and swabs  of the metal cage surface and ferret fur coat, along the dorsal midline were collected. Samples were stored at 4 °C until processing and archived at –80 °C. Blood samples (clotted and EDTA anticoagulated) were taken from the jugular vein or cranial vena cava prior to infection and on 4, 8, 14 and 21 dpi. Necropsies of two ferrets were performed on each of 3, 5, 7, 14 and 21 dpi as well as, for two ferrets, 7 days after re-challenge (24 dpi). Bronchoalveolar lavage (BAL) was performed on the left lung lobe after euthanasia. A comprehensive panel of respiratory, gastrointestinal and lymphoid tissues were taken for virological and pathological analyses. Tissue samples for virological analysis were stored at –80 °C in L-15 Leibovitz medium containing 1% (v/v) FCS, 100 units/mL penicillin and 1000 µg/mL streptomycin . Samples for pathological examination were fixed in 10% neutral buffered formalin at room temperature.Weight, temperature  FCS, 100 units/mL penicillin and 1000 µg/mL streptomycin . Total RNA was extracted from all samples using a QIAmp Viral RNA Biorobot Kit . Viral RNA was detected using the SARS-CoV-2 E gene real-time RT-qPCR [50 titre, used for the inoculation. A virus stock diluted in standard tissue culture medium was used to minimize potential confounding effects caused by the sample matrix, should it contain substances inhibitory to either PCR or virus titration. Viral genome copies were quantified using a tenfold dilution series of an Ultramer DNA oligonucleotide equivalent to 120 bp of the SARS-CoV-2 E-gene (nCoV-E-Sarbeco control plasmid) with sequence 5′-GAGACAGGTACGTTAATAGTTAATAGCGTACTTCTTTTTCTTGCTTTCGTGGTATTCTTGCTAGTTACACTAGCCATCCTTACTGCGCTTCGATTGTGTGCGTACTGCTGCAATATTGTT. There was direct correlation between REU and genome copy standard lines (Swabs and tissue samples were suspended in L-15 Leibovitz medium containing 1% ( RT-qPCR . The reprd lines  and a lohttps://github.com/AMPByrne/WGS/blob/master/RefGuidedAlignment.sh) using the inoculum reference sequence. Sequence outputs were aligned using MAFFT version 7.427 [https://github.com/flu-crew/flutile).Viral RNA was extracted manually using the QIAamp Viral RNA Mini Kit (Qiagen) from clinical samples according to the manufacturer’s instructions but without carrier RNA and eluted in 25 µL nuclease-free water. For clinical samples, viral RNA was then used to generate double-stranded cDNA using sequence-independent single-primer amplification (SISPA)  and purion 7.427 , visualion 7.427  and sequ2SO4 and evaluated at OD 450 nm. NP ELISA data were analysed by subtracting the no-antigen control well from the NP antigen well for each sample, to account for the higher nonspecific binding in the NP ELISA as a result of the higher concentrations of serum used.SARS-CoV-2 antibody levels were evaluated in heat-inactivated serum samples. Antibody titres to His-tag recombinant viral proteins spike subunit 1 (S1) (REC31828-100) and nucleoprotein (NP) (REC31812-100)  were determined by direct ELISA. Antigen-coated ELISA plate wells , or control wells with no antigen, were blocked with 20% soya milk in PBS and washed with PBS/0.1% Tween-20 before adding serum samples diluted in 20% soya milk/PBS/0.05% Tween-20 . Plates were then incubated with Protein–AG–HRP . Antibody binding was detected with TMB , and the reaction was then stopped with 0.5 M H50 units of SARS-CoV-2/human/Italy/LAZ-INMI1-isl/2020 and incubated at 37 °C for one hour. Following incubation, 50 µL of each dilution was transferred per well into 96-well plates containing 90% confluent Vero E6 monolayers. Following a second 1 h incubation at 37 °C and 5% CO2, plates were overlaid with DMEM (Gibco) without serum (WOS) and incubated for five days. The cells were examined for cytopathic effect (CPE) using an inverted light microscope, and the neutralising antibody titre was calculated according to the method of Spearman–Karber [50).To determine virus-neutralizing antibody titre, doubling dilutions of serum were mixed with an equal volume of 100 TCIDn–Karber ,29,30 anin situ hybridisation (ISH). For IHC, tissue sections were quenched for endogenous peroxidase with 3% methanol/hydrogen peroxide, blocked with goat serum and epitope unmasking was accomplished using pH 9 retrieval buffer  or Protease XXIV (Sigma-Aldrich). This was followed by incubation with rabbit monoclonal anti-S or rabbit polyclonal anti-NP antibody  and anti-rabbit ENVISIONTM polymer (Dako). The signal was developed using 3,3-diaminobenzidine (DAB) (Sigma-Aldrich), and tissues were counterstained in Mayer’s haematoxylin  as per the manufacturer’s instructions. Tissues were dewaxed and hydrated through xylene and alcohol, respectively, and treated with RNAscope® hydrogen peroxide with heat-mediated retrieval used the Target Retrieval Solution and Protease Plus . Tissue sections were then hybridised with RNA probes, followed by amplification with Hybridise Amp (ACD), and the signal was then developed with DAB and counter-stained with Mayer’s haematoxylin (Leica). Sections were washed with 1× wash buffer (ACD) between incubations. De-hydrated sections were then mounted with glass coverslips using dibutyl phthalate xylene or Cytoseal (ACD). Formalin-fixed, paraffin-embedded SARS-CoV-2/human/Italy/LAZ-INMI1-isl/2020-infected cell pellet sections were used as a positive control. Separate serial sections of tissue were stained with haematoxylin and eosin for histopathological evaluation.ISH used twenty pairs of double Z RNA probes targeting the S gene  with the RNAScopeGraph and statistical analysis was performed with Graphpad Prism 7. ELISA statistical analysis used a Wilcoxon matched pairs, 2-tailed test.Daily monitoring of clinical parameters, including weight, temperature and respiratory signs, showed that ferrets exhibited mild or no clinical signs for the duration of the study. No weight loss was recorded outside of the normal range (5%), and temperatures remained within the normal range (maximum +1.2 °C relative to baseline body temperature), except in the case of two different ferrets that showed temperatures of +1.5 °C for single days  were detected in four of the twelve ferrets (33%), while for two ferrets (17%), the shedding over time remained near LLoQ for the assay. Viral RNA detected in throat swabs showed the same trend as the nasal wash samples, but amounts were approximately 10-fold lower  analysis of viral RNA in upper respiratory tract samples from three animals identified four nonsynonymous nucleotide substitutions relative to the inoculum. The amino acid changes in the ORF1ab-encoded protein, A1670E and F1925C, were both found in samples from two of three animals, whilst the L3606F polymorphism was identified in all three animals. A single polymorphism, Y453F, was identified in the spike protein in samples from all three animals.Viral RNA was quantified by real-time RT-qPCR in all tissues from animals necropsied at 3, 5 and 7 dpi C and in Lesions were not detected on gross necropsy examination, and there was no significant increase in lung weight (not shown). Although viral RNA and antigen were not detected in the lungs by histological methods, the lungs appeared to be variably congested with occasional bronchiolitis. Systematic examination of tissues by histopathology revealed no marked changes. In the nasal turbinates, mucosal epithelial cells appeared to be uniform in shape and cilia were present, with rare presence of intra-epithelial neutrophils and apoptotic bodies. Moderate periportal lymphoplasmacytic hepatitis was observed in the liver of all ferrets, but metagenomic analysis did not reveal the presence any coronavirus sequences.®) also confirmed this observation  detection of viral S or NP antigen distribution in tissues revealed presence of viral antigens only within the respiratory and olfactory epithelium of the nasal cavity. At the cellular level, S antigens were localized to the apical aspect of the respiratory and olfactory epithelium a,d. In cervation c,f. No vervation C or viraPresence of viral RNA in the environment was evaluated by analysing samples taken from food and water as well as swabs of metal cage surfaces and the fur of ferrets swabbed along the dorsal midline, a common mutual grooming location A. Viral p = 0.0156 for both S1 and NP). There was no increase in antibody levels in samples from 7 or 14 dpi. Due to higher nonspecific binding in the NP ELISA, the data were analysed over a smaller OD range by subtracting the no-antigen control well from the NP antigen well for each sample. Mean background OD values for the NP ELISA were as follows: pre-infection: 0.1708 (range 0.1325–0.2035), and post-infection: 0.1884 (range 0.1085–0.2645). Of the four ferrets with detectable S1 and NP antibody by ELISA, only two of four had low neutralizing antibody titres  and NP ELISA as well as virus neutralization assays were conducted using serum samples collected prior to infection and on 4, 8, and 14 dpi as well as at the end of the study on 21 or 24 dpi . Both S1- and NP-specific antibody levels increased significantly A,B compay titres C.This study demonstrates that ferrets can be experimentally infected with SARS-CoV-2 via the intranasal route, similar to other reports ,11,17,27Currently, there is limited evidence of lower respiratory tract infection of ferrets with SARS-CoV-2. In this study, only low levels of viral RNA were detected in the cells isolated from the bronchoalveolar lavage of one ferret. While viral RNA or antigens were detected in the lungs in some studies ,17,27, rImmunohistochemical analysis revealed, for the first time, SARS-CoV-2 infection in cells of the ferret olfactory mucosae and associated neuronal tissue. Similarly, infection of olfactory neuronal cells in the golden Syrian hamster  was receThe challenge strain employed in this and another study  was isolSARS-CoV-2-infected ferrets were able to mount a humoral immune response such that virus-specific antibodies could be detected after 21 dpi. Antibody levels directed against the S1 antigen were higher in comparison with those of the NP antigen, a finding that could reflect the greater immunogenicity of the viral S envelope protein. A low neutralizing antibody response was only detected in two of four ferrets. Other studies reported neutralizing titres ranging from 1:8 to 1:1024 ,17,27. SThe ferret model has also been used to assess SARS-CoV-2 dissemination, and both the airborne and contact transmission routes have been proposed to play a role. ,11,17,57The asymptomatic transmission of SARS-CoV-2 represents a serious challenge for the control of COVID-19 . Our stu


Background: NL101 has shown activities against multiple myeloma and acute myeloid leukemia, but its anti-lymphoma activity remains unknown. The transcription factor c-Myc is frequently dysregulated in aggressive B cell lymphomas such as double-hit lymphoma, for which the standard of care is still lacking. A novel approach to target c-Myc needs to be explored. Although the role of oncogenic microRNA-21 (miR-21) was well established in an inducible mice model of B cell lymphoma, whether targeting miR-21 could inhibit the growth of B cell lymphoma and its underlying mechanisms is unclear.Methods: We used MTT assay and flow cytometry to determine the inhibitory effect of NL101 on the cell proliferation of B cell lymphoma in vitro. The lymphoma xenograft mice models were generated to evaluate the anti-lymphoma function in vivo. Western blot and qPCR were applied to measure the expression levels of protein and microRNA, respectively. To investigate the mechanisms of action in NL101, we used genechip to profile differentially-expressed genes upon NL101 induction. Luciferase reporter system and chromatin immunoprecipitation were used for the validation of target gene or miRNA.Results: Nl101 significantly inhibited B cell lymphoma proliferation through induction of cell cycle arrest and apoptosis. NL101 suppressed the growth of B cell lymphoma in vivo and prolonged the survival of lymphoma xenograft models. Gene expression profiling revealed that miR-21 was significantly decreased upon the induction of NL101 in B cell lymphoma. The miR-21 level was associated with the sensitivity of NL101. miR-21 inhibited Mxd1 expression via directly combining to Mxd1 3'-UTR; c-Myc activated miR-21 expression by directly binding to the miR-21 promoter.Conclusion: NL101 significantly inhibited the growth of B cell lymphoma in vitro and in vivo. The novel c-Myc/miR-21/Mxd1 positive-feedback loop is critical for the maintenance of B cell lymphoma survival. Targeting miR-21 to block c-Myc/miR-21/Mxd1 loop represents a novel potential strategy of c-Myc-directed therapy. The widespread dysregulation of miRNA expression is closely related to the pathogenesis of B cell lymphomas The transcription factor c-Myc is frequently dysregulated in hematological malignancies, especially in a large proportion of aggressive B cell lymphomas such as Burkitt lymphoma (BL) and diffuse large B cell lymphoma (DLBCL). The transgenic and knock-in mouse models in c-Myc driven by immunoglobulin heavy chain (IgH) locus enhancer highlights the critical role of c-Myc dysregulation in the development of B cell lymphoma c-Myc functions as a transcription factor that regulates the expression of a plethora of target genes and miRNAs, which promote or suppress the development of B cell lymphoma. To activate transcription, c-Myc heterodimerizes with Max to bind to a consensus sequence DNA element, enhancer box (E-box) of downstream target genes In this study, we found that c-Myc, miR-21 and Mxd1 form a positive-feedback loop, which is pro-survival for B cell lymphoma; NL101, a DNA/HDAC dual-targeting small compound NL101 injection was supplied by Hangzhou Minsheng Institute of Pharmaceutical Research . For mice model, NL101 injection was diluted with phosphate buffered solution (PBS) to 15 mg/mL.OCI-LY3 and Ramos were purchased from Cobioer Biotechnology Co., Ltd ; OCI-LY10 was from Guandao Bioengineering Co., Ltd ; U2932, Raji, Rec-1, Jeko-1, Maver-1 and JM1 were purchased from the American Type Culture Collection (ATCC). OCI-LY3 and Raji cells were cultured in IMDM , HEK293T cells were cultured in DMEM , the rest of cell lines were cultured in RPMI 1640 . All mediums were supplemented with 10% fetal bovine serum (FBS) and 1% penicillin/streptomycin solution.Peripheral blood, pleural fluid and ascites were obtained from patients with lymphoma, and mononuclear cells were isolated using Ficoll solution  and cultured in IMDM with 20% FBS. The inclusion criteria for the patients were: chronic lymphocytic leukemia or B cell lymphomas with pleural and/or abdominal dissemination, which were confirmed by pathology. All patients provided written informed consent. The study conformed to the Declaration of Helsinki, and was approved by the Institutional Review Board of the Second Affiliated Hospital, Zhejiang University School of Medicine.6 Ramos cells were injected into the right axillary region, tumor size was measured daily, and tumor volume was calculated using the equation: Volume (mm3) = [maximal diameter (mm) × minimum diameter (mm2)]/2. The mice were randomly divided into vehicle- and NL101-treated groups, and 15 mice were initially included in each group. When the volume of xenograft tumor reached 100 mm3, NL101 was given 15 mg/kg intravenously every other day for 2 weeks. For the control group, an equivalent volume of PBS was given. Two weeks after treatment, 5 mice were euthanized to remove the tumors. The remaining mice were continued to be followed up. Mice were sacrificed when tumor sizes reached 2 cm3, and tumors were excised and weighed. The body weight was also measured every day. The overall survival rate was measured from the date of transplantation until death.NOD-SCID mice at the age of 4-6 weeks were purchased from Shanghai SLAC Laboratory Animal Co. Ltd, Shanghai, China, and housed at the Experimental Animal Research Center of Zhejiang Chinese Medical University. To establish the lymphoma xenograft model, 5×10Cell proliferation was determined by the MTT  assay kit  according to the manufacturer's instructions. Cells were briefly seeded in 96-well plates and treated with gradient concentration of NL101 for 48 h. MTT was added at a final concentration of 0.5 mg/mL, and the cells were incubated for another 4 h. The supernatant was removed, DMSO was added, and the absorbance at 570 nm was read by a microplate reader.105 per well were treated with NL101 of the indicated concentration for 48 hours, and then collected and fixed with 100% ethanol for 30 min. The fixed cells were centrifuged and re-suspended in 0.5 mL of phosphate-buffered saline solution containing 50 µg/mL each of RNase A and propidium iodide . The stained cells were analyzed in a fluorescence-activated cell sorter within 4 h. The percentages of cells in the G0/G1, S, and G2 -M phases were determined using the CellQuest program (BD Biosciences).Cells seeded at 2After NL101 treatment for 48 h, the cells were collected and stained with fluorescein isothiocyanate-annexin V and propidium iodide using AV-FITC/PI apoptosis detection kit , according to the manufacturer's instructions. Annexin V and propidium iodide positive cells were determined by flow cytometry.2AX and Parp1 were purchased from Santa Cruz Biotechnology, US; antibodies against Caspase 3, cleaved caspase 3 were from Cell Signaling Technology, US; anti-c-Myc and anti-Mxd1 were purchased from Abcam, US.Cells were lysed with RIPA lysis buffer containing protease inhibitors  at 4 °C for at least 30 min. Protein concentration was measured using BCA protein assay kit . Then, equal amount of proteins was separated by 8-12% SDS-PAGE, transferred to a polyvinylidene fluoride (PVDF) membrane , and probed with the indicated antibodies, followed by ECL detection. Antibodies against pATR, pCHK1, pATM, pCHK2, γHCells were treated with 10 μM NL101 for 0, 3 and 6 h, and then subjected to total RNAs isolation using Trizol reagent (Invitrogen). Affymetrix Human Genome U133 plus 2.0 array, which contained 54,675 probesets, was used for genechip analysis performed in Shanghai Biotechnology Corporation, China.-ΔΔCt method. Each experiment was performed in triplicate.miRNAs were extracted using the miRNA Kit  and reverse transcribed by TaqMan MicroRNA Reverse Transcription kit . Total RNA was extracted by Trizol  and reverse transcribed to cDNA. qPCR was performed using the SYBR Green PCR kit . miRNA and mRNA expressions were respectively normalized to U6 and GAPDH as internal controls. The primer sequences  were as follows: miR-21, forward, 5'-GCAGTAGCTTATCAGACTGATG-3' and reverse, 5'-GGTCCAGTTTTTTTTTTTTTTTCAAC-3'; U6, forward, 5'-CTCGCTTCGGCAGCACA-3' and reverse, 5'-AACGCTTCACGAATTTGCGT-3'; GAPDH, forward, 5'-GGAGCGAGATCCCTCCAAAAT-3' and reverse, 5'-GGCTGTTGTCATACTTCTCATGG-3'. The relative expression level of each transcript was calculated by the 2The c-Myc cDNAs were generated by RT-PCR using total RNAs purified from Ramos cells. The primers were as follows: forward, 5'-TGATCTAGAGCTAGCGAATTCGCCACCCTGGATTTTTTTCGGGTAGTG-3', and reverse 5'-GTAGTCAGCGGCCGCGGATCCTTACGCACAAGAGTTCCGTAG-3'. The cDNA was cloned into pCDH-EF1-MCS-(PGK-Puro) vector . The genomic DNA of Ramos was used as a template to amplify pri-miR-21 with the following primers: forward, 5'-AACGCGCGGTGACCCTCGAGTTCGATCTTAACA-3', and reverse 5'-TAAATAAAAAGCGGCCGCACAAAAGACTCTAAGTGCCAC-3'. The resulting pri-miR-21 fragment was inserted into pLL3.7 plasmid . To generate a construct that expresses short harpin RNA (shRNA), shRNA duplexes for c-Myc (TRCN0000039640) were purchased from Sigma and inserted into pLKO.1 vector . The lentiviral constructs and packaging vectors (pMD2.G and psPAX2) were co-transfected into HEK293T cells with LipoFliter . The packaged recombinant lentiviruses were harvested from the supernatant at 48h post-transfection and concentrated by ultrafiltration . Ramos and OCI-LY10 were infected with lentivirus at a mutiplicity of infection (MOI) of 50 using 10 μg/mL polybrene .Ramos and OCI-LY10 cells were seeded in 24-well plates and transfected with antago-miR-21 or antago control   according to the manufacturer's instructions. The efficiency of the transfection was determined by qPCR.The 3'-UTRs containing predicted miR-21 binding sites were amplified by PCR using the following primers: Mxd1-3'UTR forward, 5'-CAGTAATTCTAGGCGATCGTTTGAAGCATCCAAGAATTC-3', and reverse 5'-TTTTATTGCGGCCAGCGGCCTCAAGACCTCAAAAATATGAATG-3'; PDCD4-3'UTR forward, 5'-AATTCTAGGCGATCGCTCGAGTTTTTTTTTGTTTTTCGAGGGGG-3', and reverse 5'-ATTTTATTGCGGCCAGCGGCCGCCCAGTTTTTGTAGAAAAAT-3'. The 3'UTRs were then cloned into a psiCHECK2 vector . The mutant Mxd1 3'UTRs at miR-21 binding sites were generated using Multi Site-Directed Mutagenesis Kit , the wild type psiCHECK2-Mxd1-WT was used as a template, and the primers were as follow: psiCHECK2-Mxd1-MUT1, forward 5'-CCAAAGCATGAGTTGAAAGCAGCATTTAAGAGCACTTAA-3', and reverse 5'-TGCTTTCAACTCATGCTTTGGAGTTCTGAGATCAGGA-3'; psiCHECK2-Mxd1-MUT2, forward, 5'-CGACTTTGTTGCCGTTTCAGTCATGACAAAACAGAGCACATGTATATGTA-3', and reverse, 5'-TCTGTTTTCTCATGAGTCAAAGCGCAACAAAGTCGGAGACCACTTAAGGG-3'. To generate psiCHECK2-Mxd1-MUT3, psiCHECK2-Mxd1-MUT 1 was used as a template, and site-directed mutagenesis was done using mutant 2 primers.The miR-21 core promoter (-1000 to +1) was amplified by PCR using forward primer 5'-GGTACCGAGCTCTTACGCGTGCTTTTTAGGCTTGTCACAA-3' and reverse primer 5'-GATCGCAGATCTCGAGAGGTGGTACAGCCATGGAGA-3' and cloned into a pGL3.0 luciferase vector , and the resulting construct was designated as pGL3.0-miR-21-WT. The mutant constructs were generated using Multi Site-Directed Mutagenesis Kit with the following primers: pGL3.0-miR-21-MUT1, forward, 5'-GTGTCTTACTACTCACCTGACACCATTTTTTA-3', and reverse 5'-GATCTCTAGTTAAGACACCAACCAGATTTTCCTT-3'; pGL3.0-miR-21-MUT2, forward, 5'-TCTGGTCGAATAAGCTAAAAGTAAAAAATCAAAACGC-3', and reverse, 5'-CCGTTCTGTTACTCTATTATTCTTAGTGTGATTTTT-3'; pGL3.0-miR-21-MUT3, forward, 5'-CCGTTCTGTTACTCTATTATTCTTAGTGTGATTTTT-3', and reverse, 5'-ATAGAGTAACAGAACGGCAAGAAAACTGGGTTA-3'. All constructs were confirmed by sequencing.For the target sites of miR-21 on Mxd1 3'-UTR, 200 ng of miR-21 mimic, 500 ng wild-type or mutant psiCHECK2-Mxd1 was co-transfected into HEK293T using LipoFiter. PDCD4 3'UTR was used as a positive control. For the miR-21 promoter assay, 500 ng pCDH-c-Myc, 150 ng wild type or mutated pGL3.0-miR-21, was co-transfected into HEK293T using LipoFiter. pGL3.0 and pCDH mock vectors were transfected as controls for luciferase assay and transfection, respectively. miR-21 mimic and mimic control were purchased from Ribobio . Forty eight hours after HEK293T transfection, a luciferase reporter assay was performed using Dual-Luciferase Reporter Assay System  according to the manufacturer's protocol.With the EZ-Magna ChIP A/G Assay Kit , chromatin immunoprecipitation assays were performed following the manufacturer's protocol. The antibody against c-Myc was purchased from Abcam (US). Primers flanking the c-Myc binding sites on the miR-21 promoter were used for qPCR, and the sequences were as follows: forward, 5'-CATTGCACACCCTCTGGGGAAATT-3, and reverse 5'-CAAGACTTCATTCTAAATACACAG-3'.http://www.targetscan.org/vert_71/) and miRbase (http://www.mirbase.org/) databases were searched to identify the potential miR-21 target genes and their binding sites. The predicted target genes and differentially-expressed genes derived from the genechip analysis were imported into the online Draw Venn Diagram tool (http://bioinformatics.psb.ugent.be/webtools/Venn/) to identify the overlapping genes. The single cell sequencing dataset, CancerSEA (http://biocc.hrbmu.edu.cn/CancerSEA/), was used to evaluate functions of miR-21 target genes. miR-21 promoter sequence was extracted from the USCS database . A LASAGNA-Search 2.0 dataset (https://biogrid-lasagna.engr.uconn.edu/lasagna_search/) was obtained to predicate the binding sites of c-Myc on miR-21 promoter.The TargetScan . The comparisons of the expression levels, luciferase activities, tumor volumes and outcome among different groups was evaluated by one-way analysis of variance (ANOVA) or the Student's test. The Spearman's rank correlation coefficient was used as a statistical measure of association and |R| ≥ 0.3 was considered significantly correlated. All the statistical tests were 2-sided, and a value of in vivo. NOD-SCID mice were injected with Ramos cells subcutaneously and were subject to NL101 or vehicle treatment once the lymphoma xenografts developed. As a result, NL101 significantly inhibited lymphoma growth starting from day 4 after the first dose of NL101, P < 0.05  were differentially expressed, including 361 upregulated and 192 downregulated genes, and miR-21 was the most downregulated gene with more than 1000-fold change, P = 2.73E-19 ; thus, there is DNA/histone deacetylase (HDAC) dual-targeting activity in NL101 et al. analyzed the function of miR-21 in noncancer cells of the tumor microenvironment and found that miR-21 expression in tumor associated macrophages (TAMs) is responsible for promoting tumor growth, and miR-21 inhibition in TAMs may improve cytotoxic T cell activity and reduce angiogenesis, leading to tumor suppression To characterize other NL101 functions besides DNA damage and HADC inhibition, we conducted an analysis of gene expression profiles following NL101 exposure and found that microRNA, MAPK signaling and cell cycle pathways are mostly affected. The essential role of microRNAs has been extensively studied in B cell lymphomas, subtypes of which share common and distinct miRNA signatures with diagnostic and prognostic implications et al. found that in head and neck squamous cell carcinoma, vitamin D receptor (VDR) suppresses c-Myc but enhances Mxd1 expression, and such opposing effect on c-Myc/Mxd1 axis leads to a significant transcriptional inhibition of c-Myc target genes Mxd1 serves as a transcription repressor that antagonizes the transcriptional activation of c-Myc. Dual targeting c-Myc/Mxd1 axis has become an important regulatory mechanism in cancer pathogenesis. Salehi-Tabar et al. demonstrated that Sophocarpine, a tetracyclic quinolizidine alkaloid, inhibits miR-21 expression by blocking Dicer-mediated miR-21 maturation The human miR-21 gene is mapped to chromosome 17q23.2. The genomic locus encoding miR-21 is not amplified in most cancers. The regulation of miR-21 is a complex process that involves transcriptional and posttranscriptional steps along the biogenesis pathway Direct therapeutic targeting of c-Myc has been challenging because of its intrinsically disordered structure, which lacks an enzymatic active site for small molecule inhibitors in vitro and in vivo, partly through the induction of apoptosis following DNA damage response. With gene expression profiling, miR-21 was identified as a determinant of NL101 sensitivity in B cell lymphoma. Furthermore, miR-21 represses Mxd1 to enhance c-Myc transcriptional activity, and then c-Myc directly transactivates miR-21 expression. Thus, c-Myc/miR-21/Mxd1 represents a novel positive feedback loop that plays an essential regulatory role in the survival of B cell lymphoma. By targeting miR-21 to block c-Myc/miR-21/Mxd1 axis, NL101 shows its promise as an anticancer agent for B cell lymphoma treatment in the future.In summary, we demonstrated that NL101 efficiently suppresses the growth of B cell lymphoma


NKG2C and HLA-E on clinical outcomes of aggressive B-cell non-Hodgkin lymphoma patients (B-NHL) has not been clarified. In this study, we analyzed the distribution of NKG2C deletion status and HLA-E variants in 441 patients and 192 healthy individuals. Homozygous deletion of NKG2C (NKG2C−/−) was more often found in high-risk patients compared to patients with a lower risk and consequently was associated with reduced 2-year progression-free survival. The HLA-E*01:01 allele frequency was increased in B-NHL patients and was strongly related with complete remission. Our results show that absence of NKG2C and HLA-E allelic variations is predictive for B-NHL outcome; while carriers of HLA-E*01:01 are characterized by high, complete remission rates, NKG2C−/− was rare, but associated with poorer outcome. Prospective validation of our results identifies patients that may benefit from risk-adapted therapy.NKG2C and its ligand HLA-E represent key molecules for NK cell-mediated immune responsiveness. However, the impact of genetic variants in NKG2C and HLA-E on clinical outcomes of 441 B-NHL patients. Homozygous deletion of NKG2C (NKG2C−/−) was three-fold increased in patients compared to 192 healthy controls. Among studied patients, NKG2C−/− was more abundant in International Prognostic Index (IPI) high-risk patients compared to patients with a lower IPI (p = 0.013). Strikingly, NKG2C−/− was associated with a significantly reduced 2-year PFS  (p = 0.0062) and represented an independent risk factor for 2-year PFS in multivariate analysis (p = 0.005). For HLA-E, the cognate ligand of NKG2C, the HLA-E*01:01 allele frequency was increased in B-NHL patients compared to controls (p = 0.033) and was associated with complete remission in univariate (p = 0.034) and multivariate (p = 0.018) analysis. Our data suggest that NKG2C and HLA-E genotyping is a promising tool for both defining risk groups of aggressive B-NHL and predicting response to immune therapeutic approaches.Aggressive B-cell lymphomas account for the majority of non-Hodgkin lymphomas (B-NHL). NK cells govern the responses to anti-CD20 monoclonal antibodies and have emerged as attractive targets for immunotherapy in subtypes of B-NHL. NKG2C and its cognate ligand HLA-E represent key molecules for fine-tuning of NK cell-mediated immune responses. Here, we investigated the impact of genetic variants of  Aggressive B-cell non-Hodgkin lymphoma (B-NHL) represents malignant tumors of the immune system . First-lIn recent years, increasing evidence suggests that NK cells as effectors of the innate immune system are critically involved in surveillance of B-NHL . ParticuNKG2C and HLA-E have been identified and functionally characterized. It has been described that heterozygous or homozygous deletion of a 16-kb section within the NKG2C gene correlates with decrease or absence of NKG2C surface expression, respectively [HLA-E*01:01 and HLA-E*01:03, are equally distributed in populations of different origins [HLA-E*01:03 encoded protein shows a higher peptide-binding affinity and higher surface expression compared to the HLA-E*01:01 encoded protein [NKG2C and HLA-E in balancing anti-tumor immune response or disease outcome in hematological malignancies has not been investigated.Genetic variants in ectively ,20. Thusectively ,21,22. F origins ,24. Howe protein ,26. CurrNKG2C deletion and HLA-E variants in CD20 positive B-NHL patients (n = 441) and healthy controls (n = 192) and related the results of the patients with clinical characteristics as well as disease outcome.As the NKG2C/HLA-E axis is a central element for fine-tuning the immune responsiveness of NK cells, we here addressed the question if genetic variations in the encoding genes were relevant for clinical outcome and therapy response of B-NHL. We determined allele and genotype frequencies of the NKG2A and NKG2C are both located on chromosome 12, only the NKG2C locus is deleted in 4–14% of individuals depending on the population analyzed. Here, NKG2C genotype distribution was determined in B-NHL patients (n = 441) and controls (n = 189), respectively. Neither NKG2C deletion nor genotype frequencies of copy number variations were different between B-NHL patients and healthy controls (NKG2C deletion (NKG2C−/−) frequency was two-fold higher in B-NHL patients (4.5%) than in healthy controls (1.6%), this finding, however, did not reach statistical significance.While controls . AlthougNKG2C−/− status . Here, NKG2C−/− status was 3-fold increased in the IPI high-risk group compared to patients with a lower IPI score  (19.1 ± 7.9 months) compared to NKG2C positive patients with NKG2C+/− or NKG2C+/+  showed that besides B symptoms  and iPET , the NKG2C−/− status was an independent predictive factor for 2-year PFS in B-NHL patients . No association of NKG2C absence with the overall survival was found in univariate analysis (data not shown).As shown in HLA-E genotyping was performed in patients (n = 441) and controls (n = 192), respectively , whereas the HLA-E genotypes were not found to be different between patients and controls. Furthermore, HLA-E allele or HLA-E genotype frequencies were not associated with clinical or prognostic parameters (data not shown).To analyze the impact of HLA-E as cognate ligand of NKG2C, ectively . HLA-E*0n = 441) were stratified into two groups according to their HLA-E*01:01 allele carrier status (HLA-E*01:01/*01:01 or HLA-E*01:01/01:03 vs. HLA-E*01:03/01:03). Univariate analysis  in patients with complete remission (CR) compared to patients with limited therapy response .Regarding therapy response at the end of treatment, patients  showed that the HLA-E*01:01 allele carrier status was an independent prognostic factor correlating with complete remission in B-NHL patients at the end of treatment . Both homozygous and heterozygous HLA-E*01:01 allele carriers had the same clinical correlation. By contrast, B symptoms , higher IPI score  and unfavorable iPET status  were significantly associated with less favorable treatment outcome.Multivariate analysis  includinThe interplay of NKG2C/A receptors with their ligand HLA-E is thought to play a crucial role in the fine-tuning of immune responses ,10. GeneNKG2C gene deletion and HLA-E allelic variations in patients with B-NHL. We here demonstrate for the first time that the homozygous NKG2C deletion status (i) is increased in high risk B-NHL patients; (ii) is associated with a reduced 2-year PFS in univariate analysis, and (iii) represents an independent prognostic factor for 2-year PFS in multivariate analysis. For HLA-E, the corresponding ligand of NKG2C, frequencies of the HLA-E*01:01 allele are increased in B-NHL patients, and the HLA-E*01:01 status is related with complete remission in both univariate and multivariate analysis.In our study, we focused on the diagnostic and prognostic potential of NKG2C deletion (4.5%) in B-NHL patients and healthy controls (1.6%) in this study are comparable to other population studies including Japanese, Dutch [NKG2C gene deletion has been shown to be associated with the susceptibility to psoriasis [NKG2C deletion was found at a higher frequency in B-NHL patients compared to healthy controls without reaching statistical significance. By contrast, NKG2C deletion was detected more often in high-risk than low-risk patients (11.11% vs. 3.59%). Here, the absence of NKG2C may favor the risk of progression in B-NHL patients as HLA-E bearing tumor cells are not being recognized by NK cells, while the probability of an inhibition by the NKG2A receptor may rise. Noticeably, parameters being incorporated in the IPI score, including age, performance status (ECOG), Ann Arbor stage, extra nodal manifestation, and elevated LDH are independent from the homozygous NKG2C deletion status (data not shown).The low frequency of homozygous e, Dutch , Mexicane, Dutch , German e, Dutch  and Spane, Dutch . So far,soriasis  and virasoriasis ,29,30, bNKG2C deletion could serve as a molecular marker, which may be considered for risk stratification of aggressive B-NHL, although this is restricted to a minor fraction of patients. However, patients with a homozygous NKG2C deletion also suffer from a reduced 2-year PFS at end of treatment, supporting the notion that the NKG2C genotype could be clinically relevant. Multivariate analysis revealed that homozygous NKG2C deletion was an independent prognostic risk factor for the prediction of 2-year PFS, whereas IPI was not. A recent report also emphasized that distinct molecular alterations, structural variants, and recurrent mutations, were predictive of disease outcome independent of the clinical IPI score in DLBCL subtypes [The successful implementation of the IPI score as a model to identify high-risk subgroups of B-NHL patients with unfavorable PFS or OS  in clinical trials enrolling patients from different health care centers is controversially discussed ,31,32,33subtypes  and therNKG2C were associated with impaired NK cell activation or proliferation [Accumulating evidence suggests that NK cells contribute to successful elimination of malignant B-cells during rituximab therapy ,36. Of nferation ,13. In tferation .HLA-E*01:01 is linked to comparatively low HLA-E surface expression, whereas substantially higher HLA-E expression is found in the presence of at least one HLA-E*01:03 allele in healthy individuals [HLA-E*01:03 allele or the HLA-E*01:03/01:03 genotype in infectious diseases [HLA-E*01:01 allele seems to be underrepresented in classical Hodgkin lymphomas, especially among Epstein Barr virus (EBV)-positive cases compared to healthy controls [HLA-E*01:01 genotype in aggressive B-NHL. Importantly, increased frequency of HLA-E*01:01 allele carriers was related with complete remission after the end of treatment in univariate and multivariate analysis. Here it is likely that the low surface expression of the HLA-E*01:01 allele might attenuate the inhibition of NK cells via NKG2A/HLA-E interactions, which in turn supports the antibody-dependent cellular cytotoxicity of NK cells during rituximab treatment favoring a complete remission. Nevertheless, this observation has to be confirmed in independent investigations. Notably, genome wide analyses show that classical HLA is associated with lymphoma and is suggested to be an independent factor for disease risk [For the cognate ligand of NKG2C, HLA-E, two main alleles exist that are equally present in all populations analyzed . HLA-E*0ividuals ,24,25 asividuals  or chronividuals . Severaldiseases  and sevediseases ,39,40. Icontrols . We obsease risk .ClinicalTrials.gov NCT00554164) enrolled patients with newly diagnosed aggressive B-cell or T-cell lymphomas (age 18–80 years) and an Eastern Co-operative Oncology Group (ECOG) performance status ≤ 3 [The multicenter study PETAL (Positron Emission Tomography-Guided Therapy of Aggressive Non-Hodgkin Lymphomas) randomized controlled trial  receiving two cycles of R-CHOP. After the pretreatment, patients were randomized and allocated according to the interim PET (iPET) scan result in groups, with favorable iPET (n = 396) being treated either with four cycles of R-CHOP or with the same the number of cycles but with two additional doses of rituximab, and with unfavorable iPET (n = 42) receiving either six additional cycles of R-CHOP or six blocks of an intensive protocol originally designed for Burkitt’s lymphoma [n = 192) with a mean age of 51.31 ± 9.95 years were used as controls.Here, we focused on a subgroup of this study cohort including patients with CD20 positive aggressive B-cell lymphoma (B-NHL) and a positive baseline positron emission tomography PET, n = 1 receivilymphoma ,45. The NKG2C deletion or HLA-E was performed as previously described [NKG2C and HLA-E genotype distributions in patients and controls did not significantly deviate from the Hardy-Weinberg equilibrium.For patients and controls, genotyping of the escribed ,46. NKG2https://CRAN.R-project.or/package=survminer). Multivariate Cox regression according to proportional hazards assumption was used to identify prognostic factors for PFS or OS. p values < 0.05 were considered statistically significant. Remaining statistical analyses were performed using either SPSS 23.0 , GraphPad Prism 8.3.1  or GENEPOP 4.7 (https://genepop.curtin.edu.au/).Metric data are given as mean ± standard deviation. Chi-square tests were used to analyze categorical data. Binomial logistic regression was used to identify clinical parameters influencing the therapy response after ending of treatment. Progression-free survival (PFS) and overall survival (OS), respectively, were defined as the time from the date of iPET to disease progression or death from any cause. Probabilities of OS and PFS were analyzed using the Kaplan–Meier method in combination with the log-rank test implemented in the R package survminer (version 0.4.0; NKG2C and HLA-E allelic variations are predictive of outcomes in B-NHL; while allele carriers of HLA-E*01:01 are characterized by high complete remission rates, NKG2C absence is rare but significantly associated with poorer outcomes. Prospective validation of our results is warranted to identify patient subgroups, which may provide benefits from risk-adapted therapy.In conclusion, our results provide substantial evidence that absence of


Dactylis glomerata), an important perennial forage grass. Although some genes affecting tiller initiation have been identified, the tillering regulatory network is still largely unknown, especially in perennial forage grasses. Thus, unraveling the regulatory mechanisms of tillering in orchardgrass could be helpful in developing selective strategies for high-yield perennial grasses. In this study, we generated high-throughput RNA-sequencing data from multiple tissues of tillering stage plants to identify differentially expressed genes (DEGs) between high- and low-tillering orchardgrass genotypes. Gene Ontology and pathway enrichment analyses connecting the DEGs to tillering number diversity were conducted.Tillering is an important agronomic trait underlying the yields and reproduction of orchardgrass  and D20170203 (a low-tillering genotype), which significantly differed in tiller number. Pathway enrichment analysis indicated that DEGs related to the biosynthesis of three classes of phytohormones, i.e., strigolactones (SLs), abscisic acid (ABA), and gibberellic acid (GA), as well as nitrogen metabolism dominated such differences between the high- and low-tillering genotypes. We also confirmed that under phosphorus deficiency, the expression level of the major SL biosynthesis genes encoding DWARF27 (D27), 9-cis-beta-carotene 9′,10′-cleaving dioxygenase (CCD7), carlactone synthase (CCD8), and more axillary branching1 (MAX1) proteins in the high-tillering orchardgrass genotype increased more slowly relative to the low-tillering genotype.Here, we used transcriptomic data to study the tillering mechanism of perennial forage grasses. We demonstrated that differential expression patterns of genes involved in SL, ABA, and GA biosynthesis may differentiate high- and low-tillering orchardgrass genotypes at the tillering stage. Furthermore, the core SL biosynthesis-associated genes in high-tillering orchardgrass were more insensitive than the low-tillering genotype to phosphorus deficiency which can lead to increases in SL biosynthesis, raising the possibility that there may be distinct SL biosynthesis way in tillering regulation in orchardgrass. Our research has revealed some candidate genes involved in the regulation of tillering in perennial grasses that is available for establishment of new breeding resources for high-yield perennial grasses and will serve as a new resource for future studies into molecular mechanism of tillering regulation in orchardgrass. Dactylis glomerata L.) is a winter perennial grass native to northern Africa, western and central Europe, and temperate Asia [Tillering is a specialized branching in grasses that occurs from the basal node and grows independently of the mother stem by means of its own adventitious roots , 2. Moreate Asia . As an iate Asia . In Chinate Asia . Howeverate Asia , 8. Therate Asia , unravelMONOCULM 1 (MOC1), TILLERS ABSENT 1/STERILE AND REDUCED TILLERING 1 (MOC3/TAB1/SRT1), LAX PANICLE 1 (LAX1), and LAX2, which control the formation of tiller buds [FLORAL ORGAN NUMBER1 (FON1) in rice [gibberellin 2beta-dioxygenase (GA2ox), DWARF27 (D27), and DWARF53 (D53), which control the outgrowth of tiller buds [D27 is involved in the biosynthesis of strigolactones, which regulate the outgrowth of tiller buds in rice [Tillering is a very complex process influenced by many factors, including nutrients, hormones, and genetic factors. Among these factors, nitrogen has a particularly substantial regulatory effect on tillering in rice, both promoting the initiation and outgrowth of tiller buds . Moreoveler buds –24. More in rice . The othler buds , 26, 27. in rice . However in rice . UndoubtMAX1 and ABA2. Furthermore, the core SL biosynthesis-associated genes in high-tillering orchardgrass were more insensitive than the low-tillering genotype to phosphorus deficiency which can lead to increases in SL biosynthesis, raising the possibility that there may be distinct SL biosynthesis way in tillering regulation in orchardgrass. Thus, our research has identified candidate genes that may regulate tillering in perennial grasses, which can be used to develop selective breeding strategies for high-yield perennial grasses and provide ideas for future research.However, unraveling the regulatory mechanisms of tillering is vital for breeding high-yield perennial grasses. Thus, we used transcriptomic data for the first time to study the mechanisms underlying tillering in perennial orchardgrass. We demonstrate that the high-tillering genotype may be differentiated by their low expression patterns of genes involved in SL, ABA, and GA biosynthesis at the tillering stage, such as Dactylis glomerata L.) genotypes were first observed and evaluated in the field , and 70% relative humidity . Samples− 05. Read counts per gene were expressed as the expected number of Fragments Per Kilobase of transcript sequence per Million base pairs sequenced (FPKM).Total RNA extracted from samples was used for RNA-seq library construction and sequencing. According to the manufacturer’s instructions, approximately 3 μg of RNA from each sample was used to create 23 sequencing libraries with the NEBNext® Ultra™ Directional RNA Library Prep Kit for Illumina®  . LibraryP < 0.05 and |log2 (FC)| ≥ 1, as identified by DESeq2, were assigned as differentially expressed [P < 0.05 were considered significantly enriched among DEGs [Differential expression analysis between the two groups was performed using the DESeq2 R package (1.16.1), and genes with an adjusted xpressed . The cluong DEGs . FinallyA weighted gene co-expression network analysis (WGCNA) was conducted using the WGCNA package in R (v3.3.0) . We seleDWARF27 (D27), 9-cis-beta-carotene 9′,10′-cleaving dioxygenase (CCD7), carlactone synthase (CCD8), and more axillary branching1 (MAX1) from the SL biosynthesis pathway for further verification. First, we evaluated the expression of these four genes in shoot base and root tissues by quantitative real-time PCR (qRT-PCR), which aimed to verify the validity of RNA-seq results. Then, we conducted a phosphorus starvation experiment to further verify the differential expression of these genes in the SL biosynthesis pathway between the two genotypes, because phosphorus deficiency in plants can lead to in vivo increases in SL synthesis [Based on current research into tillering mechanisms and our experimental results, we chose four DEGs ynthesis , 37. In 2O, and 1 μl of cDNA samples at a 4-fold dilution. GAPDH was used as the internal reference gene [-△△Ct method [Total RNA was extracted from samples using the HiPure plant RNA Mini Kit . A total of 1 μg of RNA was used as the template for reverse transcription using the MonScript™ RTIII All-in-One Mix with dsDNase . Then, qRT-PCR was conducted on the Bio-Rad CFX Connect PCR Detection System  using MonAmp™ SYBR Green qPCR Mix (Monad); qRT-PCR reactions were 10 μl in volume, containing 5.0 μl of SYBR Green qPCR Mix, 0.2 μl of each primer, 3.6 μl of Hnce gene . The relt method . Three rP < 0.01). The flow cytometry analysis showed that both AKZ-NRGR667 and D20170203 are diploid genotypes  and principal component analysis (PCA), as shown in Figure In total, 23 qualified cDNA libraries were separately constructed and subjected to RNA-seq. The quality of RNA-seq analysis depends on the quality of sequencing and the correlations among biological replicates. In our study, Q30 values exceeded 93%, the percentage of GC was greater than 50%, and error rate was only 0.03 Table . Moreove2 (FC)| ≥ 1 and P ≤ 0.05. Under these criteria, 16,646 , 15,957 , 13,609 (5134 upregulated and 8475 downregulated), and 14,560 (5041 upregulated and 9519 downregulated) genes were significantly differentially expressed in the A(h)_B vs D(l)_B, A(h)_S vs D(l)_S, A(h)_R vs D(l)_R, and A(h)_L vs D(l)_L comparisons, respectively _B vs D(l)_B (tiller buds), A(h)_S vs D(l)_S (shoot bases), A(h)_R vs D(l)_R (roots), and A(h)_L vs D(l)_L (leaves), with thresholds of |logTo examine the function of the identified DEGs, GO and KEGG pathway analyses were conducted. GO subcategory analysis revealed that “DNA integration,” “DNA metabolic process,” “ADP binding,” and “oxidoreductase activity” were significantly enriched in A(h)_B vs D(l)_B, A(h)_S vs D(l)_S, A(h)_R vs D(l)_R, and A(h)_L vs D(l)_L comparisons Figure . These fIn general, the combination of the KEGG pathway and GO subcategory analyses indicated that AKZ-NRGR667 and D20170203 differed in the expression of genes related to DNA replication and photosynthesis. Furthermore, although genes associated with “Diterpenoid biosynthesis,” “Cyanoamino acid metabolism,” “Carotenoid biosynthesis,” and “Biosynthesis of amino acids” were not significantly enriched, they could be associated with the differences in tillering ability between AKZ-NRGR667 and D20170203.Further, WGCNA was used to investigate the co-expression network of the identified candidate genes with differential expression. According to pairwise correlations and gene expression trends among all samples, co-expression networks were constructed using normalized microarray expression data for 13,345 genes from all samples using the WCGNA R package. Among these genes shown in Fig. VDE was higher in the shoot base of AKZ-NRGR667 relative to D20170203, indicating less violaxanthin in AKZ-NRGR667. Finally, violaxanthin is catalyzed into xanthoxin through the 9-cis-epoxycarotenoid dioxygenase (NCED). Xanthoxin is transformed into ABA through the xanthoxin dehydrogenase (ABA2) and further hydroxylated into 8′-hydroxyabscisate by the enzyme (+)-abscisic acid 8′-hydroxylase (ABAH)  by ferredoxin-nitrite reductase (NirA), and nitrile is transformed into NH3 by nitrilase (NIT). NH3 is converted into l-glutamine by glutamine synthetase (GS), which is catalyzed to form l-glutamate by glutamate synthase (GOGAT). NRT, NR, NIT, GS, and GOGAT were upregulated in tiller buds of AKZ-NRGR667 relative to those of D20170203, while only NR and NIT were upregulated in the shoot base of AKZ-NRGR667. NR, NirA, NIT, GS, and GOGAT were upregulated in AKZ-NRGR667 roots relative to D20170203 roots. Nrt, NIT, and GS were upregulated in AKZ-NRGR667 leaves relative to D20170203 leaves. Overall, the nitrogen metabolism of AKZ-NRGR667 was more active than that of D20170203, suggesting that there might be a higher utilization rate of nitrogen in AKZ-NRGR667.Additionally, nitrogen has a substantial regulative effect on tillering formation . Thus, tD27, CCD7, CCD8, MAX1) from the SL biosynthesis pathway were consistent between the transcriptome results and qRT-PCR results . Moreover, the relative expression levels of D27 and CCD8 in AKZ-NRGR667 were mostly lower than those in D20170203 _B vs D(l)_B and A(h)_S vs D(l)_S comparisons Fig. a. This sGO subcategory analysis revealed that AKZ-NRGR667 and D20170203 differed in their ability to synthesize DNA and bind ADP Figure . KEGG paD27, CCD7, CCD8, and MAX1) involved in SL biosynthesis were downregulated in the shoot base of AKZ-NRGR667  involved in the SL biosynthesis pathway from transcriptome results were consistent with qRT-PCR results  according to the above results, indicating that CCD7 and MAX1 are two key genes that in turn regulate the process of tillering in orchardgrass via the SL pathway. However, there is little information about the key regulatory genes underlying SL biosynthesis [In general, four pathways were identified and examined in detail, revealing that tillering in orchardgrass could be regulated by complex mechanisms mediated by plant hormones and/or nitrogen metabolism. Among these factors, we chose the SL biosynthesis pathway for further study, as tiller number can be increased by decreases in SL biosynthesis. Phosphates are a negative regulator of SL biosynthesis that can alter the expression of SL biosynthetic genes , and regs Figure . Then, wand CCD8 . Combineynthesis . Hence, MAX1 and ABA2, and vice versa. Furthermore, the core SL biosynthesis-associated genes in high-tillering orchardgrass were more insensitive than the low-tillering genotype to phosphorus deficiency which can lead to increases in SL biosynthesis, raising the possibility that there may be distinct SL biosynthesis way in tillering regulation in orchardgrass. Our research has revealed some candidate genes involved in the regulation of tillering in perennial grasses that is available for establishment of new breeding resources for high-yield perennial grasses and will serve as a new resource for future studies into molecular mechanism of tillering regulation in orchardgrass.In conclusion, we used transcriptomic data to study the tillering mechanism of perennial orchardgrass. We demonstrate that high-tillering genotypes may be differentiated by their low expression patterns of genes involved in SL, ABA, and GA biosynthesis at the tillering stage, such as Additional file 1 : Supplemental Figure S1 The morphological photographs of D20170203 and AKZ-NRGR667. A and B, photographs of D20170203 and AKZ-NRGR667 in field experiment. C, the tiller number of D20170203 and AKZ-NRGR667 in field experiment. D and E, photographs of D20170203 and AKZ-NRGR667 in pot experiment. F, the tiller number of D20170203 and AKZ-NRGR667 in pot experiment. “*” indicates that P-value < 0.01.Additional file 2 : Supplemental Figure S2 The nuclei DNA content of D20170203 and AKZ-NRGR667 by flow cytometry analysis.Additional file 3 : Supplemental Figure S3 The phenotype characteristics of D20170203 and AKZ-NRGR667. A, the plant height of D20170203 and AKZ-NRGR667. B, the stem of D20170203 and AKZ-NRGR667. C, the leaves of D20170203 and AKZ-NRGR667. Sample labels are as follows: D represents the D20170203, and A represents the AKZ-NRGR667.Additional file 4 : Supplemental Figure S4 The Pearson correlation and principal component analysis (PCA) based on all expressed genes. A, Pearson correlation. B, principal component analysis (PCA). Sample labels are as follows: A(h)_B, the tiller bud of AKZ-NRGR667; A(h)_S, the shoot base of AKZ-NRGR667; A(h)_R, the root of AKZ-NRGR667; A(h)_L, the leaf of AKZ-NRGR667; D(l)_B, the bud of D20170203; D(l)_S, the shoot base of D20170203; D(l)_R, the root of D20170203; D(l)_L, the leaf of D20170203.Additional file 5 : Supplemental Figure S5 GO functional classification of DEGs in four pairwise groups. Including A, A(h)_B vs D(l)_B. B, A(h)_S vs D(l)_S. C, A(h)_R vs D(l)_R. D, A(h)_L vs D(l)_L. The greater dot represents the more DEGs. The red color indicates the smaller padj value, and the purple color indicates the bigger padj value. The coloration scale and annotation are presented to the right of this figure.Additional file 6 : Supplemental Figure S6 KEGG functional classification of DEGs in four pairwise groups. Including A, A(h)_B vs D(l)_B. B, A(h)_S vs D(l)_S. C, A(h)_R vs D(l)_R. D, A(h)_L vs D(l)_L.. The greater dot represents the more DEGs. The red color indicates the smaller padj value, and the purple color indicates the bigger padj value. The coloration scale and annotation are presented to the right of this figure.Additional file 7 : Supplemental Figure S7 KEGG functional classification of genes from different modules of WGCNA. Including brown, cyan, greenyellow, magenta and yellow modules. The greater dot represents the more DEGs. The red color indicates the smaller padj value, and the purple color indicates the bigger padj value. The coloration scale and annotation are presented to the right of this figure.Additional file 8 : Supplemental Figure S8 KEGG functional classification of genes from different modules of WGCNA. A, pink module. B, green module. C, red module. D, turquoise. E, greenyellow module. The greater dot represents the more DEGs. The red color indicates the smaller padj value, and the purple color indicates the bigger padj value. The coloration scale and annotation are presented to the right of this figure.Additional file 9 : Supplemental Figure S9 The results of protein sequence alignment for genes involved in SL biosynthesis in orchardgrass. D27, DWARF27; CCD7, 9-cis-beta-carotene 9′,10′-cleaving dioxygenase; CCD8, carlactone synthase; MAX1, more axillary branching1.Additional file 10 : Supplemental Figure S10 qRT-PCR validation. The expression of genes encoding enzymes catalyzing corresponding biochemical reactions in different tissues are shown from green to red, and the coloration scale and annotation are presented to the upright corner of this Fig. S and R represent the expressions of shoot base and root respectively. D27, DWARF27; CCD7, 9-cis-beta-carotene 9′,10′-cleaving dioxygenase; CCD8, carlactone synthase; MAX1, more axillary branching1.Additional file 11 : Supplemental Table 1 The data of initial field selectionAdditional file 12 : Supplemental Table 2 The primers information for qRT-PCRAdditional file 13 : Supplemental Table 3 RNA-seq statisticsAdditional file 14 : Supplemental Table 4 Statistics of annotation analysis of unigenesAdditional file 15 : Supplemental Table 5 Percent of genome regionsAdditional file 16 : Supplemental Table 6 The detail information of KEGG in A(h)_S vs D(l)_SAdditional file 17 : Supplemental Table 7 The detail information of KEGG in A(h)_L vs D(l)_LAdditional file 18 : Supplemental Table 8 The expression of key genes regulating tillering


The detailed process verification of a low-defect GAA nanowire and electrical characteristics were investigated in this article. The GAA Si NW FETs, which were intentionally fabricated within the controlled Si grain, exhibit a steeper subthreshold swing (S.S.) of about 65 mV/dec., higher driving currents of 327 µA/µm (n-type) and 297 µA/µm (p-type) @ Vth ± 0.8 V, and higher Ion/Ioff (>105 @|Vd| = 1 V) and have a narrower electrical property distribution. In addition, the proposed Si NW FETs with a GAA structure were found to be less sensitive to Vth roll-off and S.S. degradation compared to the omega(Ω)-gate Si FETs. It enables ultrahigh-density sequentially stackable integrated circuits with superior performance and low power consumption for future mobile and neuromorphic applications.We introduce a single-grain gate-all-around (GAA) Si nanowire (NW) FET using the location-controlled-grain technique and several innovative low-thermal budget processes, including green nanosecond laser crystallization, far-infrared laser annealing, and hybrid laser-assisted salicidation, that keep the substrate temperature (T To catch up to Moore’s law, and to benefit from a faster computing speed and economical chip, there are two approaches ,2 followTM with semiconductor-on-insulator wafers, e.g., SOI, GOI, or III-V-OI [Low-temperature polycrystalline-silicon (LTPS) thin-film transistors (TFTs) fabricated by excimer laser crystallization (ELC) drew tremendous attention in the 1980s ,8 for thIII-V-OI ,12. AlthIII-V-OI ,14,15. H2 for a nano-scale transistor, which is much smaller than the grain size of the Si channel, integrated with several innovative low-thermal budget processes was demonstrated. The single-grain GAA Si NW FETs and processes earn not only high performance and low variability but the extremely scaled NW dimension, which, in turn, enables the application for advanced monolithic 3D-ICs.In this article, we adopted a μ-Czochralski process using pulse laser crystallization followed by a chemical mechanical polish (CMP) and surface modification steps to fabricate a thin and high-crystallinity location-controlled grain (LCG) Si nanowire channel for its high positioning accuracy, predictability, and compatibility with our present process. A gate-all-around configuration to detach the Si and buried SiOThe process flow of the monolithic 3D single-grain gate-all-around Si nanowire FET is depicted in 4OH:H2O2:H2O = 1:4:20 and HCl:H2O2:H2O = 1:1:6) at 75 °C for 10 min to remove polymer residues and particles caused by the photoresist stripping process  for 5 min  for its excellent step coverage and self-limiting nature around the NW as high-K gate dielectric and metal electrode (HK/MG), respectively (31+ or BF249+ at a dosage of 5 × 1015 cm−2 (10 KeV) for N-type or P-type MOSFETs through self-aligned ion implantation and was activated by a CO2 FIR-LA to form a highly-activated, abrupt, and ultra-shallow junction source and drain regions  system to form a high-resistivity Ni2Si phase. After removing the unreacted Ni by soaking into a 1:10 HNO3 solution at 50 °C for 600 s, the second-step annealing was followed by a CO2 FIR-LA with 100W laser power at room temperature to transform the Ni2Si phase into a low-resistivity NiSi phase  was also adopted to replace the conventional high-temperature Salicide process ,19. In tSi phase h.2 deposition as an interlayer dielectric layer (ILD) followed by a standard metallization process using tungsten (W) metal were then proceeded to accomplish the first stacked device tier (sub < 400 °C)  a,b.Moreover, a quantitative grain size distribution analysis was conducted by the ImageJ software e. Compar−1 representing a nanocrystalline Si signal vanished after the CMP process, revealing the poor crystallinity on the surface due to the slower cooling rate in the interfacial region [In addition to the μ-Czochralski process, the CMP process was also adopted for fabricating a thin and uniform Si channel after the short-pulse GNS-LA. As a result, the CMP not only eliminates the surface roughness but also removes defect and nanocrystalline Si phase layer on the uneven channel surface. l region . Ultra-thin-body (UTB) silicon in an insulator (SOI) configuration has been widely studied and adopted in our previous works ,27,28,29By precisely controlling the soaking time of dilute HF that consumes sacrificial oxide and buried oxide, we can simply obtain a single-grain GAA Si NW FET. fin/Hfin/Lgate = 15 nm/15 nm/30 nm was achieved by 5 min soaking time as presented in 2 and a single-grain Si lattice. The selected area diffraction pattern (SADP) for the laser-crystallized channel in A progressive evolution of device configuration fabricated by increasing HF soaking time is exhibited in sh) source/drain extensions with abrupt, ultra-shallow junctions (USJ) [One of the bottle-necks in monolithic 3D GAA Si NW FET devices is to form highly activated (low sheet resistance Rns (USJ) ,35. How The introduced FIR-LA is an intraband excitation process ,37. It c31+ dopant implantation at a heavy dosage of 5 × 1015 cm−2 and 5 KeV ion energy. With an FIR-LA condition of 125 W laser power for 100 µs at 350 °C annealing temperature, the amorphous layer shall be epitaxial regrown. 18 dopant concentration was observed in conventional RTA at 800 °C for 10 s annealing time which increases the risk of current leakage between source and drain due to lateral diffusion in the nanodevices. In contrast, the FIR-LA contributes only a 7.3 nm diffusion depth which reflects the fact that a short-pulse and long-wavelength laser annealing effectively suppresses the SCEs. The series resistance in the inset table, which is another index of the annealing process and dominates the final performance of a scaling device, was further reduced from 213 to 93 Ohm/γ by adopting the FIR-LA to replace conventional RTA. The nearly diffusionless dopant profiles and low resistivity represent the advantage of such a low-thermal budget FIR-LA in monolithic 3D-ICs. A high-resolution transmission electron microscope (HRTEM) image of a Si after the dopant implantation process is shown in 2 facet (111) formed during conventional two-step RTA, which will lead to Fermi-level pining and a junction spiking leakage current [sub = 25 °C), to form a thin and uniform Si-rich NiSix film as well as to suppress the formation of NiSi2 facet (111).Nickel salicidation is a crucial process for advanced MOS transistors for its low Si consumption, low formation temperature, and low line width sheet resistance dependence as compared to cobalt or titanium silicide ,19. Prev current ,39. Addi current . Here, w2 facet (1 1 1) was found, which promises a high-quality and low-resistivity NiSi film. The morphology of the NiSi film prepared by the hybrid laser-assisted salicidation process was analyzed by high-resolution transmission electron microscopy (HRTEM). The second-step FIR-LA melts the high-resistivity Ni2Si phase near the surface and the melt front diffuses downward to the Si and Ni2Si interface. The mixing of Ni2Si and Si occurs via liquid phase diffusion [2Si diffused from the surface to the bottom.The X-ray diffraction (XRD) patterns of nickel silicide formed by the hybrid laser-assisted salicidation process were displayed in iffusion , which ld| = 1 V of the Ω-gate Si FETs and the GAA Si NW FETs with Wfin/Hfin/Lgate = 15 nm/15 nm/30 nm simultaneously using location-controlled-grain technique. The Ω-gate Si FETs exhibits subthreshold swing (S.S.~71 mV/dec.), driving current 291 µA/µm (n-type) and 274 µA/µm (p-type) at Vth ± 0.8 V. The single-grain GAA Si NW FETs have better gate controllability and therefor exhibits superior electrical properties with steeper subthreshold swing (S.S.) of about 65 mV/dec., higher driving currents (Ion) of 327 µA/µm (n-type) and 297 µA/µm (p-type) at Vth ± 0.8 V, and higher on/off current ratio (Ion/Ioff) of >105. The off currents of the GAA Si NW FETs, extracted from the curves at Vg = 0 V, reach about 1nA/µm which was much lower than the Ω-gate Si FETs. th) and S.S. dependencies of these two types of single-grain transistor on the gate length (Lg), the device structures having a similar size were chosen for comparison. The data were collected and plotted in th decrease while the Lg becomes shorter. Further, the S.S. is found to be increased with the shrinkage in Lg. It indicates that the SCEs dominate the output characteristics at sub-100 nm of both transistors. The slower Vth roll-off and S.S. ramp up rate in the line graph of the GAA Si NW FET elucidate that the gate-all-around configuration has better ability in suppressing SCEs. This evidence can be found in Fin increases from 10 to 50 nm for the Ω-gate Si FETs. Comparatively, the GAA Si NW FETs reveals a lower S.S. in the same WFin and a smaller ΔS.S. (43 mV/dec.).To extract the threshold voltage  and Vth deviations (from 0.51 to 0.44 V at FWHM). By introducing the LCG technique for monolithic 3D-ICs, we can prevent the electrical property variation caused by the random grain size and the unpredictable boundaries. d-Vg behaviors before and after the FIR-LA in To validate the feasibility of the monolithic 3D-IC sequential integration process, the transfer characteristics of a GAA Si NW FET with a two-layer metal interconnect (M1-M2) were prepared and tested before and after FIR-LA. Evidently, the lack of obvious changes in the IIn summary, novel methodologies to fabricate GAA Si NW FETs with high performance and low power consumption were reported. By integrating the location-controlled-grain technique, laser crystallization, far-infrared laser dopant activation, and hybrid laser-assisted salicidation, sub-50 nm GAA Si NW FETs with lower sensitivity to Vth roll-off and subthreshold swing degradation as the gate length scaled down were presented. Furthermore, electrical validation results provide convincing evidence for the feasibility of monolithic three-dimensional integrated circuits. The advanced 3D architecture demonstrated in this article enables high-bandwidth sequentially stackable circuits to achieve superior performance and low power consumption for future mobile and neuromorphic applications.


Functional enrichment analysis identified significant enrichment in immune, growth, and apoptotic pathways among the methylation-regulated genes. Regardless of clinical phenotype, all A-T participants exhibited downregulation of critical genes involved in B cell function  and upregulation of several genes associated with senescence and malignancy, including SERPINE1. These findings indicate that gene expression differences may be associated with phenotypic variability and suggest that DNA methylation regulates expression of critical immune response genes in people with A-T.People with ataxia-telangiectasia (A-T) display phenotypic variability with regard to progression of immunodeficiency, sino-pulmonary disease, and neurologic decline. To determine the association between differential gene expression, epigenetic state, and phenotypic variation among people with A-T, we performed transcriptional and genome-wide DNA methylation profiling in patients with mild and classic A-T progression as well as healthy controls. RNA and genomic DNA were isolated from peripheral blood mononuclear cells for transcriptional and DNA methylation profiling with RNA-sequencing and modified reduced representation bisulfite sequencing, respectively. We identified 555 genes that were differentially expressed among the control, mild A-T, and classic A-T groups. Genome-wide DNA methylation profiling revealed differential promoter methylation in  The underlying defect in A-T is absence or reduced production of the ATM protein, a phosphoinositide 3-kinase-like enzyme involved in multiple cellular functions including double stranded DNA repair, cell cycle control, tumor surveillance, telomere length monitoring, and apoptosis2. Cells with ATM mutations have higher sensitivity to ionizing radiation that results in increased chromosomal breakage, decreased cell survival3, and increased malignancy risk4.Ataxia-telangiectasia (A-T) is a rare autosomal recessive disease with a prevalence of approximately 1 in 40,000–100,000 live births6. Many people with classic A-T also have immune defects. These immunodeficiencies most commonly manifest as low or absent IgA levels, low circulating B and T cell numbers, and suboptimal antibody responses to vaccines9. Nevertheless, a subset of individuals with A-T exhibit functionally normal immune function with milder and slower neurological progression10. Despite this contrast in immune profile, individuals with this mild  A-T phenotype have a higher risk for cancer, similar to the risk for individuals with classic A-T. Indeed, up to 30% of people with A-T will develop cancer during their lifetime11. Lymphoid malignancies are most frequent during the first two decades of life, while solid tumors are more common in adults. Adults with A-T also exhibit symptoms of premature aging, including cardiovascular disease, metabolic syndrome, and glucose intolerance12.Infants with A-T are born phenotypically normal. However, most individuals with A-T develop ataxic symptoms as toddlers, often followed by ocular apraxia, telangiectasias, and loss of ambulatory skills in later childhoodATM gene, likely contributing in part to the variability in disease presentation and survival12. Although some genotype-phenotype correlations have been reported in founder populations, mutational hotspots in the ATM gene are not universally found16. In addition, there are no clinical biomarkers or molecular signatures that predict disease progression, malignancy risk, survival, or response to therapy. Having these tools could help with disease surveillance and improve survival for people with A-T.The majority of people with A-T have compound heterozygous mutations of the 17. Accordingly, we sought to determine the association between phenotypic variability10 and the transcriptional and epigenetic landscapes in people with A-T. We hypothesized that people with mild  disease progression and people with classic (more severe) disease progression would have different transcriptional and DNA methylation signatures from each other and from non-A-T healthy control study participants. As there are common phenotypic similarities found among all people with A-T, we also hypothesized that participants with A-T regardless of phenotype would share some common gene signatures. Finally, we hypothesized that genes differentially expressed in people with A-T would be regulated in part by DNA methylation.Identifying molecular pathways associated with specific A-T phenotypes could also be useful in validating biomarkers. This in turn might help identify subsets of people with A-T who may be at higher or lower risk for developing certain disease-specific conditions such as severe infection and malignancy. Identifying genes and gene processes regulated by DNA methylation could also provide insights into regulatory mechanisms that represent therapeutic targets to modulate gene expression and attenuate disease phenotype in people with A-TPBMCs from mild and classic A-T study participants display different transcriptomes from one another and from non-A-T controls. Eight participants with A-T were assigned to mild or classic phenotypes based on a phenotypic classification described by Fievet et al.10. Participants characterized as mild A-T had ≤ 50% of the standard criteria, whereas participants characterized as classic A-T had > 50% of the standard criteria (p < 0.003) . No participants were acutely ill at the time of evaluation. Following grouping of study participants, isolated peripheral blood mononuclear cells (PBMCs) enriched for lymphocytes were used for genome-wide transcriptional and DNA methylation profiling studies with RNA-sequencing (RNA-seq) and modified reduced representation bisulfite sequencing (mRRBS), respectively. We examined complete blood counts obtained from 7/8 A-T participants within less than 1 year of the molecular studies. In participants with A-T, the average lymphocyte percentage was 27.7%  with an average white blood cell count of 9.4 ×103 . These results are consistent with normal or near-normal peripheral lymphocyte counts. To estimate the relative proportion of B and T cells, we applied an in silico cellular deconvolution procedure to the bulk RNA-seq data. This approach revealed decreased frequencies of B and T marker gene profiles among study participants with A-T compared with control participants  Table . Controlq-value <0.05 revealed 555 DEGs among the three groups, which separated into four k-means clusters  processes in cluster 1 included those involved with apoptosis and regulation of phosphate and nitrogen reactions  of differentially expressed genes (DEGs) revealed clustering by group assignment Fig. . PC1 expers Fig. . K-meansons Fig. . Gene onpes Fig. .Figure 1A core set of transcriptional responses is common to both mild and classic A-T participants compared with control participants. We recognized similarities among the mild and classic A-T signatures and identified a core set of 20 DEGs expressed in both mild and classic A-T participants when compared with control participants ; however, the classic A-T group exhibited lower ATM expression compared with non-A-T controls . Thus, when the A-T groups were merged together, ATM expression was not significantly different from non-A-T controls , FCER2 (involved in IgE synthesis), CD22 (inhibitor of B cell responses), and CD79A   Fig. 18–23.FigSERPINE1 was upregulated in all A-T participants regardless of phenotype , a protein elevated in conditions of premature aging, insulin resistance, and coronary heart disease24. Other genes upregulated among A-T participants included HSF4, NPIPA5, RBPMS2, SLC1A3, and TMEM178B. Functions of these genes include regulation of heat shock proteins, stimulation of visceral smooth muscle proliferation, and episodic ataxia with gain-of-function mutations27. The shared DEGs identified among participants with A-T included genes linked to conditions well described in patients with A-T, including early senescence, the presence of ataxia, the development of malignancy, B cell immune dysregulation, and metabolic syndrome28.Of additional interest was the finding that pe Figs.  and 2c. DNA methylation is statistically likely to regulate gene expression differences in A-T. To determine whether DNA methylation is associated with differential gene expression among these groups, we performed genome-wide DNA methylation profiling using modified reduced representation bisulfite sequencing (mRRBS)29. PCA revealed differentially methylated cytosine-phospho-guanine residues (CpGs) between mild A-T, classic A-T, and non-A-T controls , we found increased enrichment in apoptotic processes in mild and classic A-T participants compared with non-A-T controls. The participants with classic A-T phenotypes also displayed enrichment of immune processes compared with mild A-T and non-A-T controls. DEGs upregulated in classic A-T participants included the immune response genes TLR5, TLR8, STAT2, and STAT6 as well as genes encoding complement components . CISH, a gene encoding protein that negatively regulates cytokine signaling34, was also upregulated among classic A-T participants. These findings suggest that people with a classic A-T phenotype exhibit an upregulated immune response at baseline whereas those with mild A-T phenotypes do not. This dysregulated immune program may be compensatory for the ineffective host response observed among people with classic A-T.ATM is part of the DNA damage response and maintains genomic integrity by responding to DNA damage through p53-induced cell cycle arrest, repair, and apoptosis. It has previously been shown that apoptosis is activated through ATM via miR-34a/HDAC1ATM gene expression was higher in mild A-T participants compared with those with classic A-T. The participants with mild A-T also had higher expression of genes involved in inflammation, including CXCL3 and IRAK2, and GO processes associated with upregulation of cell migration and cell growth. These inflammatory and growth signatures found in participants with mild A-T but not classic A-T could signify an adaptive response to DNA injury unique to people with mild A-T36. This inflammatory transcriptional signature in the mild A-T participants could also contribute to the higher malignancy risk reported among people with A-T and carriers of ATM mutations40.Although ATM protein levels were not measured in this study, PAX5, CD79A, CD22, and FCRL1 are members of a core set of genes that are critical for B cell development and function41. These genes were downregulated among all participants with A-T compared with non-A-T controls. Furthermore, in addition to the role of PAX5 in B cell development, this gene also acts as a tumor suppressor42. Indeed, downregulation and dysregulation of PAX5 may contribute to the higher risk of malignancy, particularly B cell lymphomas, observed among people with A-T44. We also found that SERPINE1 was upregulated among all participants with A-T compared with non-A-T controls. SERPINE1 has been shown to mediate cellular senescence, and a null mutation in SERPINE1 has been associated with longer telomere length, lower prevalence of diabetes, and longer life span in an extended Amish kindred with a null mutation24. Supporting a possible link between elevated SERPINE1 and A-T phenotype are the observations that people with A-T have accelerated telomere shortening45, and that premature aging is increasingly recognized in A-T as overall life expectancies have increased with effective supportive care12.We identified a core set of differential transcriptional responses present in both mild and classic A-T participants. 30, we found that cis DNA methylation was associated with gene expression at the loci of 146 genes that are predominantly involved with immune responses. This finding indicates that DNA methylation differs between participants with mild and classic A-T phenotypes and non-A-T controls and provides insights into the mechanisms that regulate gene expression in A-T. Recognition of DNA methylation as a regulator of gene expression may offer opportunities to modulate abnormal gene expression among people with A-T. These findings also raise mechanistic questions regarding the link between DNA damage and differential methylation patterning that are beyond the scope of our study.Using a previously published computational toolAs A-T is a rare disease, limitations to our study include the small number of participants in each group. Nevertheless, the differences in transcriptional signatures between the two A-T groups and non-A-T controls were striking. While some DEGs were similar between A-T phenotypes, many of the DEGs and cellular processes diverged between the two A-T phenotypes, consistent with the observed variability in clinical phenotypes present in people with A-T. The non-A-T controls in this study were older than the classic A-T participants. Although the difference in age was not statistically significant, age could represent a potential confounder in this study. We used an isolation approach to enrich for lymphocytes. Nevertheless, this approach would not necessarily normalize frequencies of B and T cell subsets between groups; indeed, our deconvolution procedure revealed a decreased proportion of B and T cell marker gene profiles comparing the A-T groups with the control group. These results can be interpreted to mean a decrease in the representation of these cell types in the cells subjected to bulk RNA-seq. Alternatively, as the analysis relies on cell type-specifying marker genes, these results may also represent a decrease in expression of these genes in otherwise balanced cell populations. Regardless, in this study each sample consisted of a composite of lymphocyte populations, representative of the group. Additionally, the use of bulk RNA-seq on mixed cell populations allowed for aggregate measures to reflect the distinct milieus of mild and classic A-T and to include rare populations of cells. Future studies examining molecular signatures in specific lymphocyte subsets may provide a more granular perspective into the role of ATM in immune regulation.In summary our findings indicate that gene expression differences are associated with phenotypic variability and that DNA methylation may regulate the transcriptional expression of several critical immune response genes among people with A-T.ATM mutations, and/or increased chromosomal breakage after in vitro exposure to x-rays, as previously established46. Demographic information was obtained from chart review. Participants were recruited sequentially from the outpatient Johns Hopkins A-T Clinic and assigned to either a mild or classic A-T phenotype based on a phenotypic classification previously described by Fievet et al.10. Healthy non-A-T young adults were recruited as controls.The work described has been carried out in accordance with The Code of Ethics of the World Medical Association (Declaration of Helsinki) for studies involving humans. The institutional review board of the Johns Hopkins Medical Institutions approved the study (IRB NA_00051764), and written informed consent was obtained from every participant and/or his/her guardian. All participants with A-T met the diagnostic criteria for A-T based on clinical symptoms and laboratory findings of either elevated alpha-fetoprotein, diminished ATM protein, pathogenic 47. Briefly, a 7-mL venous blood sample was transferred into a 50-mL conical tube and PBS was added to achieve a 20-mL volume. 15 mL of Ficoll-Paque Plus was transferred into a separate 50-mL conical tube using a syringe. The 20 mL of diluted blood was gently layered onto the Ficoll. The sample was spun at 400 ×g for 30 minutes at room temperature with no brake. The PBMC layer was then collected at the diluted plasma/Ficoll interface. PBS was added to the PBMCs to bring the volume up to 20 mL. The sample was then spun at 200 ×g for 10 minutes at room temperature. The supernatant was discarded and the cells were resuspended in 1 mL of PBS and counted using a TC20 automatic cell counter (Bio-Rad). 4 mL of PBS was added to the sample and spun at 200 ×g for 10 minutes to remove any contaminating Ficoll, platelets, and plasma proteins. For transcriptional and DNA methylation profiling, the sample was resuspended in 350 µL of RLT plus (Qiagen) containing 1% beta-mercaptoethanol, vortexed for 30 seconds, and then transferred to a −80 °C freezer.PBMCs were isolated using the Ficoll-Paque Plus Method  using a process that maintains the viability of B and T lymphocytes to account for relative lymphopenia among A-T versus control participants as previously described49 with the following modifications. Libraries for RNA sequencing were prepared with the NEBNext Ultra I RNA Library Prep Kit for Illumina with poly(A) mRNA selection (12 ng input) and sequenced using 1 ×75 single-end reads on an Illumina NextSeq 500 instrument using a NextSeq 500/550 V2 High Output reagent kit. Fastq files were trimmed using Trimmomatic v0.38 to remove end nucleotides with a phred score less than 30 while requiring a minimum length of 20 bp and then aligned to the hg38 reference genome using Tophat v.2.1.0 with default parameters. Code used for RNA-seq data processing is available from https://github.com/ebartom/NGSbartom. The RNA-seq quantitation pipeline in SeqMonk v1.44.0 was used to generate gene and transcript expression levels for protein-coding genes (counts data for uniquely mapped reads over exons). Lowly expressed genes were excluded by including only those genes with more than 1 count per million in at least 3 of the samples. Library sizes were re-computed after filtering these genes, and sample normalization factors were calculated and applied using the trimmed mean of M values (TMM) procedure as implemented in edgeR v3.24.3 (R v3.5.1 with RStudio v1.2.1578). ANOVA-like testing and pairwise comparisons  biologic processes was performed using Molecular Signatures Database (MSigDB) (The Broad Institute).RNA was isolated using the Qiagen AllPrep DNA/RNA Micro kit. Transcriptional profiling was performed as previously describedlection 1 ng inputhttps://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k). The PBMC reference had been previously processed and clustered into seven distinct cell-types using Seurat51. Reference counts were library-size corrected and log-transformed with SCANPY52, and 2,000 highly variable genes were identified and used for downstream analysis. AutoGeneS selected 600 marker genes through 5,000 generations of its optimizer that minimized correlation and maximized Euclidean distance amongst the gene-set53. These marker genes were then used to estimate cell type proportions within the bulk RNA-seq data set. A Mann-Whitney U test was used to test significance between the merged A-T and control groups.Bulk RNA-seq deconvolution was performed using the 10X Genomics PBMC 3 K data set as a single-cell RNA-seq reference  for detailed explanation29. 250–300 ng of genomic DNA was used as input. Code for mRRBS data processing can be found in the supplement to Singer (2019)29. Bisulfite conversion efficiency averaged 99.6%  as estimated by the measured percent of unmethylated CpGs in λ-bacteriophage DNA (New England BioLabs N3013S) added at a 1:200 mass ratio to each sample. In total, we determined the methylation status of 6,697,115 unique CpGs (nearly 25% of the CpG methylome). Beta scores at individual CpG positions were quantified using the SeqMonk platform with the bisulphite feature methylation pipeline. Bismark coverage (counts) files for cytosines in CpG context were analyzed with respect to differential methylation with the DSS v2.34.0 R/Bioconductor package. To detect differentially methylated CpGs, we used DSS to generate a beta-binomial regression model with an arcsine link function fitted using the generalized least square method and then applied a Wald-test with FDR q-value <0.05. Putative differentially methylated genes were identified as those containing a differentially methylated cytosine in their promoter, defined as the transcriptional start site ± 1 kb. Our previously described methylation difference-filtering algorithm then selected for promoter CpGs with a methylation directionality (hypermethylated or hypomethylated) inverse to the corresponding gene expression directionality (down- or up-regulated) between groups30. We used a threshold of 25% for the difference-filtering procedure to establish the list of 146 methylation-regulated genes. Using the gene body coordinates of the 146 methylation-regulated genes, we performed network analysis via the GREAT tool56 with the basal+extension association rule  against the whole genome as background.Genome-wide DNA methylation profiling was performed using modified reduced representation bisulfite sequencing (mRRBS) as previously describedK-means clustering and heat maps were generated using the Morpheus web interface (https://software.broadinstitute.org/morpheus/). K was selected using the elbow method. Venn diagrams were created with the VennDiagram v1.6.20 R package. All indicated tests were two tailed unless otherwise stated. Computational analysis was performed using “Genomics Nodes” and “Analytics Nodes” on Quest, Northwestern University’s High-Performance Computing Cluster.Principal component analysis was performed with the prcomp base R statistical function. Supplementary Information.