To date, GWASs have identified more than 100 loci linked to general cognitive function and 108 loci linked to schizophrenia. These genetic variants are mostly intronic or intergenic. Genes identified around these genetic variants are densely expressed in brain tissues. Schizophrenia-related genetic risks are consistently correlated with lower general cognitive function (gr = −0.20) and higher educational attainment (gr = 0.08). Cognitive functions are associated with many of the socioeconomic and health-related outcomes. Current treatment strategies largely fail to improve cognitive impairments of schizophrenia. Therefore, further study is needed to understand the molecular mechanisms underlying both cognition and schizophrenia.General cognitive (intelligence) function is substantially heritable, and is a major determinant of economic and health-related life outcomes. Cognitive impairments and intelligence decline are core features of schizophrenia which are evident before the onset of the illness. Genetic overlaps between cognitive impairments and the vulnerability for the illness have been suggested. Here, we review the literature on recent large-scale genome-wide association studies (GWASs) of general cognitive function and correlations between cognitive function and genetic susceptibility to schizophrenia. In the last decade, large-scale GWASs ( Cognitive functions play important roles in mental and physical well-beings. This is supported by observations that people with higher intelligence tend to have greater educational attainment, more professional jobs, higher incomes, and increased longevity ,2. AccorCognitive disturbances are a core feature of schizophrenia—a psychiatric disorder with clinical and genetic heterogeneity ,10. Comph2 = 0.33–0.85) . . h2 = 0.g), Intelligence Quotient (IQ), fluid intelligence, etc., which could have been subject to phenotypic heterogeneity. By contrast, GWASs using samples from nearly 300,000 individuals successfully detected more than 100 genome-wide significant loci related to cognitive function [Genome-wide association studies (GWASs) that examine millions of genetic variants are a powerful tool to identify common variants responsible for susceptibility to common and complex diseases. The largest GWAS to date is the Psychiatric Genomics Consortium (PGC) using 36,989 patients with schizophrenia and 113,075 controls, which has identified 108 loci including genes and genetic variants related to schizophrenia . Severalfunction ,43. In afunction ,40,41,42function ,47,48 toIn this article, we review the literature on recent large-scale GWASs of general cognitive function and genetic correlations between cognitive function and schizophrenia.g) is defined as a latent trait underlying shared variance across multiple subdomains of cognition [g, principal component analysis (PCA) is required on at least one cognitive measure across at least three domains, e.g., logical memory for verbal declarative memory, digit span for working memory, and digit symbol coding for processing speed. In other words, the first unrotated principal component of several distinct neuropsychological tests is obtained from the PCA. For example, an average of eight neuropsychological tests across COGENT cohorts were selected: digit span, digit symbol coding, verbal memory for words, visual memory, semantic fluency, word reading, verbal memory for stories, phonemic fluency, vocabulary, and the trail-making test [g factors extracted from different cognitive tests were strongly correlated (>0.98) [g.A number of tests have been used to measure various domains of cognitive functions. It is difficult to perform GWASs of cognitive functions uniformly because these cognitive tests vary among study cohorts. Twin and family studies show strong genetic correlations across diverse cognitive domains . Under tognition ,39,44,45 (>0.98) , supportg approach [RP4-665J23.1 on 1p22.2 and CENPO on 2p23.3) and three  genome-wide significant loci, respectively , and subtests from the Wechsler Adult Intelligence Scale (WAIS)-III [n = 36,035) detected three genome-wide significant loci, including several genes, e.g., CYP2D6 and NAGA at 22q13.2, FUT8 at 14q23.3 and PDE1C at 7p14.3 [To assess crystallized intelligence, either the National Adult Reading Test or the WAIS vocabulary subtest is used. Fluid intelligence, which may be equivalent to AIS)-III . Fluid iAIS)-III . This teAIS)-III ,40. Scort 7p14.3 .g [g and fluid intelligence [r2 = 0.92, p = 1.18 × 10−5). Several of these loci overlapped with those associated with schizophrenia, such as 1p21, 1p34, 2q24, 2q33, 3p21, 3q22, 4q24, 5q21, 6p22, 7q22, 8q24, 11q25, 12q24, 14q12, 14q32, 16q22, and 22q13 [Because performance on the VNR is correlated with g ,53, the lligence ,41,42,43lligence ,38,39, and 22q13 ,41,43.gr > 0.70) [Educational attainment, represented by the number of years of education, is strongly influenced by genetic and environmental factors ,55. At l > 0.70) ,40,41,43The genetic variants related to general cognitive function were mostly intronic or intergenic. The genes identified around these genetic variants were densely expressed in the brain ,56, specTCF20, CYP2D6, and NAGA. In addition, this locus was shown to have quantitative trait locus (eQTL). NAGA encodes lysosomal enzymes that modify glycoconjugates, and CYP2D6 encodes cytochrome P450 enzymes that metabolize a broad range of drugs [KCNJ3, GNL3 and STRC, were also identified as eQTLs. Although these genes shared by two phenotypes are not localized in specific pathways, they may provide potential drug targets for improving cognitive impairments in patients with schizophrenia.Smeland et al. (2017) extensively investigated shared genetic loci of the GWAS by conditional false discovery rate analysis and identified 21 genomic loci jointly influencing cognitive functions and vulnerability to schizophrenia . Of the of drugs . Other lPolygenic overlaps between alleles of general cognitive function and schizophrenia risk have been examined ,57. On tgr) from GWASs and is a powerful tool for investigating genetic architectures of common traits and diseases [gr of approximately −0.2 (Linkage disequilibrium score regression (LDSC) analysis estimates genetic variant correlations  fCognitive function has been shown to be correlated with socioeconomic and health-related outcomes as well as neuropsychiatric disorders, as evidenced by LDSC analysis  39,40,4,42,43. EBetter cognitive function was associated with lower levels of neuroticism, depressive symptoms, and insomnia . PhysicaIntelligence decline is conceptualized as intra-individual difference in intellectual performance between different time points ,47,48,69(a)Deteriorated group: patients with a difference of 10 points or more between premorbid IQ and present IQ;(b)Preserved group: patients with a difference of less than 10 points between premorbid IQ and present IQ (premorbid IQ above 90);(c)Compromised group: patients with a difference of less than 10 points between premorbid IQ and present IQ (premorbid IQ below 90).The degree of intelligence decline in patients with schizophrenia is typically classified into three intellectual levels ,75,76,77The compromised IQ subgroup includes patients who have intellectual disability. Although cognitive impairments are a core feature of schizophrenia, approximately 30% of patients are classified into the preserved IQ subgroup .So far, GWAS, PRS, or LDSC analysis has not been performed based on the above classification  in patients with schizophrenia. As the current diagnostic criteria for schizophrenia is independent of cognitive traits and genetic architectures, GWASs based on intelligence decline subgroups may reveal novel genetic variants specific to cognitive impairments. Caution is needed in interpreting data from IQ measures, as they are subject to non-specific consequences of schizophrenia, effects of medication, and cognitive decline preceding the onset of illness. Additionally, IQ scores by themselves cannot describe specific cognitive domains that are relatively more affected than others in individual patients.In this paper, we reviewed the literature of recent large-scale GWASs targeting general cognitive function, a phenotype that captures shared variations in performance on tests of several cognitive domains. Studies on polygenic correlations between cognitive function and schizophrenia were also addressed. In the last decade, large-scale GWASs have identified more than 100 loci linked to general cognitive function and schizophrenia. Genetic variants identified are mostly intronic or intergenic, and genes around them are densely expressed in brain tissues. Substantial proportions of the heritability of these phenotypes are explained by polygenic architectures consisting of many genetic variants with small effects. General cognitive function has been reported to be genetically correlated with socioeconomic and health-related outcomes, as well as neuropsychiatric disorders. In particular, lower general cognitive function has been consistently correlated with schizophrenia risks. Current treatment strategies largely fail to improve cognitive impairments of schizophrenia. In order to progress, further study is needed to understand the shared pathogenesis for general cognitive function in relation to the illness.


Supplemental Digital Content is available in the text BMPR2) gene and exaggerated activation of transforming growth factor (TGF)-β signaling play a critical role in this process.Idiopathic pulmonary arterial hypertension (IPAH) is characterized by intense remodeling of small pulmonary arteries. Loss-of-function mutation of bone morphogenetic protein receptor II  signaling target genes, but not TGF-β signaling target genes. Moreover, we observed an increased expression of other BMPRs, that is, anti-Mullerian hormone type-2 receptor and the activin receptor-like kinases (ALK) 1, ALK3, and ALK6.We report a novel frameshift mutation  of the The patient was prescribed a combination of macitentan, sildenafil, and nifedipine, which successfully controlled her symptom of syncope and normalized N-terminal pro-brain natriuretic peptide level after 3 months of medication.In light of these results, we propose a new pathogenetic mechanism for IPAH, based on enhanced BMP signaling via the functional replacement of mutated BMPR2 by other BMP receptors. Among the involved TGF-β receptors, the bone morphogenetic protein receptor type II (BMPR2) and its mutations are particularly relevant to IPAH. BMPR2 mutations have been identified in about 10% to 25% of sporadic IPAH and more than 70% of familial IPAH patients.–5 Currently, the most accredited hypothesis is that loss-of-function BMPR2 mutations result in aberrantly increased TGF-β signaling, which is responsible for the pulmonary arterial remodeling. Here, we report a novel frameshift mutation  of the BMPR2 gene, identified in a 19-year-old IPAH patient. We focused on the changes in bone morphogenetic protein (BMP) and TGF-β signaling associated with this BMPR2 mutation and, in particular, on the aberrant expression of type I and II BMP receptors.Idiopathic pulmonary arterial hypertension (IPAH) is a progressive disease characterized by a sustained elevation of pulmonary arterial pressure associated with the obliteration of distal pulmonary arteries.2BMPR2 gene coding region from peripheral blood mononuclear cells (PBMCs) and analyzed BMP/TGF-β signaling in the PBMCs of the patient and in those of 2 age- and sex-matched controls with no underlying disease. The detailed experimental methods are described in the Supplementary Methods . All participants gave written informed consent for molecular genetic research studies and publication of clinical data. All exons of the BMPR2 gene were amplified by polymerase chain reaction (PCR) using primer pairs corresponding to each exon . This study was approved by the institutional review board of Kyungpook National University Hospital (KNUCH2019-08-016).For the genetic mutation analysis, we sequenced the 33.1A 19-year-old Korean female was admitted with recurrent episodes of syncope. She had experienced exertional dyspnea, 3 years before, and syncope, 2 months before admission. The frequency and duration of syncope had been gradually increasing and she lost her consciousness for 15 minutes at admission. On physical examination, there was evidence of right heart failure with elevated jugular venous pressure and mild lower extremity edema. Abnormal laboratory findings included an elevated concentration of plasma N-terminal pro-brain natriuretic peptide (NT-proBNP), that is, 1682 pg/mL , and serum uric acid, 11.9 mg/dL . Other parameters, including complete blood count, liver function tests, urinalysis, and cardiac enzymes, were within normal limits. Initial electrocardiogram showed a sinus rhythm, increased amplitude of R wave in V1 to V3, and S wave in V4 to V6, and inverted T waves in leads III, aVF, and V1 to V6, with 1st-degree atrioventricular block. The chest radiograph revealed mild enlargement of the central pulmonary arteries with relative paucity of peripheral vessels. Computed tomography angiogram also showed a dilatation of pulmonary arteries, with right ventricular and atrial enlargement. Two-dimensional echocardiogram revealed a hypertrophied and enlarged right ventricle, as well as enlarged right atrium, with a displacement of interventricular septum toward left ventricle. Doppler tracing of peak tricuspid regurgitation velocity (TRV) was 4.22 m/s and the estimated right ventricular systolic pressure was 86 mm Hg, suggesting IPAH. The patient was prescribed a combination of macitentan , sildenafil , and nifedipine , and reported no additional episodes of syncope. Follow-up NT-proBNP level normalized to 52 pg/mL and peak TRV to 2.54 m/s after 3 months of medication.3.2BMPR2 gene identified a novel heterozygous frameshift mutation consisting in the insertion of T at the coding nucleotide position 119 (c.119InsT in NM_001204.6) in exon 2 , BMPR1A (ALK3), and BMPR1B (ALK6) was increased in the patient's PBMCs compared to controls , BMPR1A ALK3), and BMPR1B (ALK6) was significantly increased in the patient's PBMCs compared to controls. Among type II BMP receptors, the expression of anti-Mullerian hormone receptor (AMHR) 2 was increased in the patient's PBMCs. This gene exhibited the closest evolutionary relationship with BMPR2 as shown by phylogenetic tree analysis , in a young female patient with IPAH. More than 560 variants have been described for BMPR2 in ClinVar site (https://www.ncbi.nlm.nih.gov/clinvar), and most of the BMPR2 mutations known to be associated with IPAH cause a loss-of-receptor function.,10 The c.119InsT mutation generates a premature stop codon at a position corresponding to amino acid 48 in exon 2 and; therefore, a truncated peptide containing only the extracellular ligand-binding domain  with a mutation in the kinase domain of BMPR2 showed defective Smad1/5 activation. However, in the PBMCs of our IPAH patient, we found increased phosphorylation of both Smad1/5/8 and Smad2/3, which are the intracellular signaling molecules of BMP and TGF-β pathways, respectively. This conflict can be derived from the difference in the experimental cell source of PBMCs and PASMCs due to a context-dependent TGF-β-antagonizing function of BMPR2. However, it is worth noting the difference in the BMPR2 mutation site. As mentioned above, the mutation in our patient was located in BMPR2 exon 2, encoding the extracellular ligand-binding domain and; therefore, resulted in a truncated BMPR2 protein, unable to reach the cell surface. On the other hand, BMPR2 with mutations in the intracellular signaling domain may maintain cell surface expression, albeit in the absence of protein function. Recent evidence revealed that, based on the type of mutation, BMPR2 can either display a “wild-type-like” subcellular localization, on the plasma membrane and in the perinuclear area, or be abnormally distributed in the cytoplasm. This mutation site-related difference in cell surface expression may affect the expression of complementary BMP receptors such as AMHR2, ALK1, and ALK6. Our result implies the presence of at least 2 different molecular mechanisms of BMPR2-associated IPAH.An additional novel finding of this study is the apparently paradoxical activation of Smad1/5/8, despite attenuated BMPR2 expression  of the Conceptualization: Sun Ha Choi, Seungwoo Han.Data curation: Sun Ha Choi, Youn-Kwan Jung, Ji-Ae Jang, Seungwoo Han.Formal analysis: Sun Ha Choi, Youn-Kwan Jung, Ji-Ae Jang, Seungwoo Han.Funding acquisition: Seungwoo Han.Investigation: Youn-Kwan Jung, Seungwoo Han.Methodology: Sun Ha Choi, Youn-Kwan Jung, Ji-Ae Jang, Seungwoo Han.Resources: Sun Ha Choi, Seungwoo Han.Supervision: Seungwoo Han.Validation: Youn-Kwan Jung, Ji-Ae Jang, Seungwoo Han.Visualization: Youn-Kwan Jung, Ji-Ae Jang, Seungwoo Han.Writing – original draft: Sun Ha Choi, Seungwoo Han.Writing – review and editing: Sun Ha Choi, Youn-Kwan Jung, Seungwoo Han.


PTGS2, a TNBC signature gene involved in the conversion of arachidonic acid to prostaglandins. Our study reveals the existence of rich TNBC-specific signatures, and many can be drug target and biomarker candidates for clinical applications.Triple-negative breast cancer (TNBC) has poor clinical prognosis. Lack of TNBC-specific biomarkers prevents active clinical intervention. We reasoned that TNBC must have its specific signature due to the lack of three key receptors to distinguish TNBC from other types of breast cancer. We also reasoned that coupling methylation and gene expression as a single unit may increase the specificity for the detected TNBC signatures. We further reasoned that choosing the proper controls may be critical to increasing the sensitivity to identify TNBC-specific signatures. Furthermore, we also considered that specific drugs could target the detected TNBC-specific signatures. We developed a system to identify potential TNBC signatures. It consisted of (1) coupling methylation and expression changes in TNBC to identify the methylation-regulated signature genes for TNBC; (2) using TPBC (triple-positive breast cancer) as the control to detect TNBC-specific signature genes; (3) searching in the drug database to identify those targeting TNBC signature genes. Using this system, we identified 114 genes with both altered methylation and expression, and 356 existing drugs targeting 10 of the 114 genes. Through docking and molecular dynamics simulation, we determined the structural basis between sapropterin, a drug used in the treatment of tetrahydrobiopterin deficiency, and  BRCA1, was determined as a contributing factor for TNBC [Triple-negative breast cancer (TNBC) is a subtype of breast cancer, accounting for 15–20% of all breast cancer cases. It is characterized by the absence of estrogen receptor (ER), progesterone receptor (PR), and epidermal growth factor receptor 2 (HER2/neu) [for TNBC . Despitefor TNBC ,5,6.TET1 can affect the PI3K/mTOR pathway in TNBC [DNMT1 is associated with higher histologic grade, ER negativity, PR negativity, and higher Ki-67 in TNBC [DUSP1 is linked with increased risk of TNBC [BRCA1 promoter has poor survival in TNBC patients [SPHK1 may increase spontaneous metastasis to lungs in TNBC patients [EN1 is associated with poor survival and increased risk of developing brain metastasis in TNBC patients [WWOX may suppress TNBC cell metastasis and proliferation by downregulating the JAK2/STAT3 pathway [DNA methylation and gene expression are promising sources to identify TNBC-specific biomarkers. For example, studies observed that altered DNA demethylation in  in TNBC , alterna in TNBC , hyperme of TNBC , and metpatients . Multiplpatients ,12. For patients , high expatients , and WWO pathway . HoweverPTGS2 , and an existing drug, sapropterin, targeting PTGS2.We reasoned that (1) the lack of three key receptors in TNBC should lead to its unique signature distinguishable from other types of breast cancer; for example, lack of ER may cause substantial changes in gene expression in TNBC ; (2) as https://xenabrowser.net/hub/) [We used 116 TNBC cases and 100 TPBC cases from The Cancer Genome Atlas (TCGA) data. We downloaded both methylation (Illumina Infinium HumanMethylation450 BeadChip) and expression data (RNA-SeqV2) generated by TCGA from UCSC xena (et/hub/)  (Table Set/hub/) .n = 21,231) of RefSeq genes. The raw methylation intensities for each probe were represented as β-values (Equation (1)), which were converted into M-values with the beta2m function in R package lumi for statistics analysis [p-value was adjusted as false discovery rate (FDR) using the Benjamini and Hochberg (BH) method. The threshold for DMS identification was an adjusted p-value < 0.05 and an absolute delta Β-value >0.2 ).The Illumina HumanMethylation450 BeadChip array contains 485,577 probes covering 99%  were calculated according to all CpG sites at the corresponding region (Equation (3)), and the average β-value was converted to an M-value with the beta2m function. Average regional methylation data between TNBC and TPBC were compared with R package limma to identify differentially methylated regions (DMRs). We identified hypermethylated DMRs with a threshold of adjusted p-value < 0.05 and a delta β-value >0.2, and hypomethylated DMRs with a threshold of adjusted p-value < 0.05 and a delta β-value < −0.2 ). Genes harboring DMRs were termed as differentially methylated genes (DMGs). The equations described above are listed below.M is the methylated allele intensity, U is the unmethylated allele intensity, i = 1, 2, 3, …, k, and k is the number of CpG cites in a region.CpG site and gene mapping files were downloaded from N = 84) and TPBC (N = 64) samples was analyzed with the R limma package. We adjusted each p-value as false discovery rate (FDR) using the Benjamini and Hochberg (BH) method. We used the log-transformed expression value to identify differentially expressed genes (DEGs), including upregulated genes with an adjusted p-value < 0.05 and logFC (fold change) > 2, and downregulated genes with an adjusted p-value < 0.05 and logFC < −2 in TNBC compared with TPBC.Differential expression between TNBC , and classified them into four distinct groups: HypoUp, HypoDown, HyperUp, and HyperDown . We detehttp://david.abcc.ncifcrf.gov/, version 6.8) [p-value < 0.05 considered as statistically significant in both clusterProfiler and DAVID analyses.We applied enrichGO and enrichKEGG functions in Bioconductor package clusterProfiler for DMGs and DEGs to explore important processes or pathways involved in TNBC genesis and development . We perfion 6.8) , with a We used principal component analysis for all DMSs in DMEGs to distinguish between TNBC and TPBC. Two random forest classifiers were trained based on the methylation level of all DMSs and expression level of all DMEGs using the R randomForest package. The leave-one-out cross-validation (LOOCV) method was used to evaluate the performance of the classifier. Plots of the receiver operating characteristic (ROC) curve of the classifier and the calculation of the area under the curve (AUC) were fulfilled using the R verification package. We performed stratification analysis to avoid the interferences from other clinical factors including age, menopause status, race, and stage.version 5.1.2, released 20 December, 2018) contains 12,112 drug entries including 2556 Food and Drug Administration (FDA)-approved small-molecule drugs, 1280 FDA-approved biotech (protein/peptide) drugs, 130 nutraceuticals, and 5842 experimental drugs [We screened DrugBank to identify drugs potentially targeting the upregulated DMEGs. DrugBank  from the Protein Data Bank (PDB) (www.rcsb.org) and optimized its structure for binding calculation using UCSF chimera [PTGS2 were screened for their ability to bind to 5IKV using molecular docking and molecular dynamics simulations (MDS). Binding pattern determination for the prostaglandin-endoperoxide synthase 2 (PTGS2)–inhibitor complex was performed using Autodock 4.0 [To explore the determination of structural basis between protein targets and drug-binding sites, we downloaded protein structure 5IKV for gene  chimera . FDA-appdock 4.0 ; MDS of dock 4.0   different DMRs between TNBC and TPBC, including 396 located in TSS1500, 394 in TSS200, 270 in gene body, and 2063 in intergenic regions  larger than that of hypermethylated ones, indicating that TNBC is less methylated than TPBC. Of all the DMGs harboring DMRs, only 1.65% (15/911) had methylation alterations in all three regions, suggesting that methylation is region-specific in TNBC  DEGs including 332 upregulated and 378 downregulated genes , cellular component (CC), and molecular function (MF) terms, and KEGG pathways. The significantly enriched categories included cell proliferation, immune response, cell differentiation, extracellular space, sequence-specific DNA binding, and transcriptional mis-regulation in cancer . The GO  targets ,32,33,34 targets . p = 3.12 × 10−16, AUC = 0.992 for age >50 group; p = 7.37 × 10−10, AUC = 0.979 for age ≤50 group), menopause , race , and tumor stage   .The resuThe DrugBank database contains available FDA-approved drugs and experimental compounds, and provides detailed drug and drug target information. To explore if there could be any drugs targeting the DMEGs, we allocated the 114 DMEGs into several categories, including receptor, protein, and other structural components . We searPTGS2, GSTP1, GABRP, and TF  in its TSS500, TSS200, and gene body regions, targeted by 111 drugs. PTGS2 is overexpressed in TNBC, and promotes metastasis by regulating crucial molecules  involved in multiple tumor-promoting signaling pathways [GSTP1, harboring six CpG sites (four DMSs) in the gene body region was targeted by 37 drugs. GSTP1 is a driver gene and potential prognostic and therapeutic marker for TNBC [GABRP harboring six CpG sites (three DMSs) in its TSS1500 and TSS200 regions is closely related with basal-like TNBC and is a prognostic marker for TNBC [GABRP was targeted by 16 drugs. TF harbors seven CpG sites (three DMSs) in its TSS1500 and TSS200 regions, and is known to play key roles in drug delivery in TNBC tumor cells [TF was targeted by 32 drugs. In addition, S100B can suppress the migratory capacity of ER-negative breast cancer cells, and the expression of S100B is a predictive marker for metastasis in breast cancer [Among the 10 genes in DMEGs with targeted drugs, several are known to be associated with TNBC, such as , and TF . PTGS2, pathways ,37,38. Gfor TNBC ,40. GABRfor TNBC ,41. GABRor cells ,43. TF wt cancer ,45. The t cancer ,47,48.The 111 drugs targeting PTGS2 included 82 FDA-approved drugs, three anti-neoplastic drugs, and 19 potential drugs A. We selBased on the docking results, the 5IKV–sapropterin conformation was used for MDS analysis . In MDS,ER, PR, and HER2/neu are not expressed [Abnormal gene expression contributes to cancer development. This is well reflected in TNBC, in which xpressed ,51,52. Fxpressed ,54,55. Two improvements were used in our study in order to improve the situation. One is that we used TPBC instead of other types of breast cancer as the control in order to provide the highest contrast to TNBC. We reasoned that the difficulty in identifying TNBC-specific signals could be caused by the marginal differences in abnormal signatures between TNBC and non-TNBC. Hence, increasing the signal-to-noise ratio between TNBC and non-TNBC would be critical to identify the TNBC-specific signatures. The other is that we coupled methylation sites and their located genes as a single unit. We reasoned that this would significantly decrease the noise from unrelated methylation and gene expression, leading to increased specificity to identify TNBC-specific signatures. Indeed, our study following these steps led to identifying 114 TNBC-specific DMEGs, and 356 potential therapeutic drugs targeting 10 of the 114 DMEGs, of which 288 were existing drugs used for the treatment of various diseases including cancer, indicating that our approach indeed provides a powerful means to identify TNBC-specific signatures.PTGS2 in mammary glands can induce tumorigenesis, and that PTGS2 is often over-expressed in human breast cancer [The structural relationship between PTGS2 and sapropterin is interesting. PTGS2 is an enzyme that converts arachidonic acid to prostaglandins. Animal studies showed that overexpression of the t cancer ,57. SaprIn summary, data from our study demonstrate the presence of rich biomarkers in TNBC and their potential as drug targets and biomarkers for clinical TNBC application.We highlighted the crucial roles of DMGs and DEGs in the development of TNBC. Our study demonstrated 114 dysregulated DMEGs in TNBC, which could serve as molecular biomarkers for the early detection of TNBC. Additionally, we identified 356 potential drugs targeting 10 of the 114 genes, of which 288 are FDA-approved drugs, and some are applied to clinical cancer treatment.


UVSSA, UBP12, and RDO2 exhibit increased UV sensitivity in both seedlings and adults. UV sensitivity in atcsa-1, uvssa, and ubp12 mutants is specific to dark conditions, consistent with a role in NER. Interestingly, chr8 mutants exhibit UV sensitivity in both light and dark conditions, suggesting that the Arabidopsis CSB homolog may play a role in both NER and light repair. Overall our results indicate a conserved role for UVSSA, USP7 (UBP12), and TFIIS (RDO2) in TCR.Plant DNA is damaged by exposure to solar radiation, which includes ultraviolet (UV) rays. UV damaged DNA is repaired either by photolyases, using visible light energy, or by nucleotide excision repair (NER), also known as dark repair. NER consists of two subpathways: global genomic repair (GGR), which repairs untranscribed DNA throughout the genome, and transcription-coupled repair (TCR), which repairs transcribed DNA. In mammals, CSA, CSB, UVSSA, USP7, and TFIIS have been implicated in TCR. Arabidopsis homologs of CSA (AtCSA-1/2) and CSB (CHR8) have previously been shown to contribute to UV tolerance. Here we examine the role of Arabidopsis homologs of UVSSA, USP7 (UBP12/13), and TFIIS (RDO2) in UV tolerance. We find that loss of function alleles of  Unable to move, plants must adapt to their surroundings. An important and unavoidable component of a plant’s environment is solar radiation, which includes both beneficial visible light and damaging ultraviolet (UV) rays. UV radiation harms a variety of cellular components including DNA. UV damaged DNA, primarily pyrimidine photodimers, is repaired by photolyases, using the energy from visible light (light repair), and by nucleotide excision repair (NER) (dark repair) .Nucleotide excision repair is a conserved multistep pathway involving damage recognition, strand unwinding, excision, repair synthesis, and ligation. Damage recognition is via one of two NER sub-pathways. Global genomic repair (GGR) identifies UV damage in DNA throughout the genome, while transcription coupled repair (TCR) initiates repair of transcribed strands. TCR has been well studied in humans, where deficiencies in this process can result in Cockayne Syndrome and UV-sensitive syndrome . UV damaArabidopsis thaliana has two CSA homologs, AtCSA-1/ CSAat1A (At1g27840) and AtCSA-2/ CSAat1B (At1g19750)   . Despite1g19750) . The Ara1g19750) . CHR8 RNe in NER . UBP12 (e in NER . The Ara1 (DOG1) . RDO2/TF1 (DOG1) . In this1 (2). UVSSA amino acid sequences were aligned in CLUSTAL Omega  (CHR8), SAIL_58_C12 and SALK_061538 (UVSSA), GABI_742C10 (UBP12) (RDO2) (-2 s-1) and grown under long day conditions (16 h light/8 h dark) at 20°C and 50% relative humidity. For adult growth, 14 day old plants were transplanted into soil  and grown under the same conditions.The following T-DNA alleles were used in this study: SALK_030558 (AtCSA-1) , SALK_00 (UBP12) , and SAL) (RDO2) . Col-0 w) (RDO2) , while C) (RDO2) . All plaCHR8, UVSSA, AtCSA-1, and UBP12 were amplified for 30 cycles and RDO2 for 26 cycles using the primers indicated , a CFX Connect Real time PCR detection system (Bio-Rad), and the primers listed in EF1α (At5g60390)  according to the manufacturer’s instructions including a DNase treatment. RNA was quantified with a Nanodrop spectrophotometer (Thermo Scientific) and 1 μg used to synthesize cDNA, using the Maxima First Strand cDNA synthesis kit . For semi-quantitative RT-PCR, ndicated  and the 5g60390)  was usedThe following data was collected from plants transplanted to soil: flowering time (day the first bud is detected), rosette diameter at 4 weeks, number of shoots and silique length at 6 weeks.-2 UV-C . Plates were rotated 90° and incubated in either long day or dark conditions for the indicated number of days, then scanned. Image J was used to measure root and hypocotyl length.Seeds were plated, stratified, and grown vertically in the conditions above for 3 days, then seedlings irradiated with 1000 J m-2 UV-C, incubated in the dark for 3 days, then returned to long day conditions. Three days later, individual leaves were scored as either undamaged (green) or damaged (yellow or brown), and % damaged leaves  was calculated for all plants.For adult UV assays, 21 day old plants in soil were irradiated with 500 J mt-tests (p ≤ 0.05) were used to assess statistical significance.All experiments were performed at least twice and representative experiments shown. Two-tailed student’s In this study we identify the Arabidopsis UVSSA homolog. Arabidopsis UVSSA (encoded by At3g61800) is 39% and 28% identical to rice and human UVSSA, respectively. Clear UVSSA homologs are found throughout the animal and plant kingdoms including angiosperms, gymnosperms, ferns, and moss. One million maximum parsimony phylogenetic search replicates for UVSSA homolog amino acid sequences recovered a single most parsimonious tree (score 2561)  that is UVSSA and the other TCR gene homologs: AtCSA-1, CHR8 (CSB homolog), UBP12 and UBP13 (USP7 homologs), and RDO2 (TFIIS homolog). With respect to absolute levels of expression  (RDO2 (SALK_027259)  has previously been shown to result in reduced levels of both UBP12 and UBP13, thus acts as a weak double mutant  and chr8-2 (SAIL_273_G11) , located 38 bp upstream of the start codon, and uvssa-2 (SALK_061538), located in the first intron past the start codon.In order to examine the role of these genes in Arabidopsis UV tolerance, T-DNA insertion mutants were obtained. Previously described alleles of _030558)  and RDO2_027259)  were utie mutant . In prevsitivity . Here we273_G11) . For UVSchr8-1 and chr8-2 insertion sites detected no CHR8 transcript, indicating these are null alleles ]. Real-time qPCR with an intron-specific primer was used to quantify the effect of the uvssa-1 allele on splicing, and large amounts of the unspliced product were detected , and decreased apical dominance (increased number of shoots)  , consist shoots) . The othlowering , howeverlowering , consistAtCSA-1 and CHR8 (CSB) loss of function resulted in increased UV sensitivity in the dark  after 2 days of incubation, but not after 3 days  following UV treatment. he light , consistcubation , exhibitatcsa-1 mutants exhibit increased dark specific UV sensitivity ] and yeast Rad26 is CHR8 . In thisAi lines . Also, un to TCR , so one UVSSA results in defective TCR and UV sensitive syndrome , however, it was milder than observed for the other TCR mutants and not detectable 3 days after seedling UV treatment. Interestingly, the UV sensitive phenotype of both rdo2 and atcsa-1 was stronger in hypocotyls than in roots, at 2 days than at 3 days, and in adults than in seedlings, suggesting the role of these genes in UV tolerance may vary with tissue, time, and phenotype assessed (growth versus tissue death).In mammals, in addition to acting during transcript elongation, TFIIS has been shown to facilitate transcription re-initiation following RNAP arrest, and is recruited to the stalled polymerase in a CSB and CSA dependent manner . In yeasIn this study, we have identified the Arabidopsis UVSSA homolog and shown that Arabidopsis UVSSA, USP7 (UBP12/13), and TFIIS (RDO2) homologs contribute to UV tolerance, along with CSA and CSB (CHR8) homologs, suggesting conservation in the mechanisms of TCR.All datasets for this study are included in the manuscript and the WAK, AS, and DS performed the experiments. JM conducted the phylogenetic analysis. DS wrote the first draft of the manuscript. All authors contributed to revised manuscript and approved the final version, designed the experiments and analyzed the data.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


BACKGROUND. Understanding the integrated immunogenomic landscape of advanced prostate cancer (APC) could impact stratified treatment selection.METHODS. Defective mismatch repair (dMMR) status was determined by either loss of mismatch repair protein expression on IHC or microsatellite instability (MSI) by PCR in 127 APC biopsies from 124 patients ; MSI by targeted panel next-generation sequencing (MSINGS) was then evaluated in the same cohort and in 254 APC samples from the Stand Up To Cancer/Prostate Cancer Foundation (SU2C/PCF). Whole exome sequencing (WES) data from this latter cohort were analyzed for pathogenic MMR gene variants, mutational load, and mutational signatures. Transcriptomic data, available for 168 samples, was also performed.RESULTS. Overall, 8.1% of patients in the RMH cohort had some evidence of dMMR, which associated with decreased overall survival. Higher MSINGS scores associated with dMMR, and these APCs were enriched for higher T cell infiltration and PD-L1 protein expression. Exome MSINGS scores strongly correlated with targeted panel MSINGS scores , and higher MSINGS scores associated with dMMR mutational signatures in APC exomes. dMMR mutational signatures also associated with MMR gene mutations and increased immune cell, immune checkpoint, and T cell–associated transcripts. APC with dMMR mutational signatures overexpressed a variety of immune transcripts, including CD200R1, BTLA, PD-L1, PD-L2, ADORA2A, PIK3CG, and TIGIT.CONCLUSION. These data could impact immune target selection, combination therapeutic strategy selection, and selection of predictive biomarkers for immunotherapy in APC.FUNDING. We acknowledge funding support from Movember, Prostate Cancer UK, The Prostate Cancer Foundation, SU2C, and Cancer Research UK. Understanding the relationship between tumor genomics and the immune response in advanced prostate cancer (APC) has acquired major therapeutic relevance with the advent of immunotherapy, especially after the failure of anti-CTLA4 (ipilimumab) in 2 large phase III trials in unselected patients , 2. Of pThe mismatch repair (MMR) system is a post-replicative, high-fidelity, single-strand repair mechanism that recognizes and reverses DNA base mismatches and insertion/deletion (indel) loops; compromised MMR results in microsatellite instability and a hypermutator phenotype that has been associated with chemotherapy resistance but immunotherapy sensitivity . ImmunotMultiple approaches have been developed to leverage molecular tumor profiling data toward that end, including mutational signatures associated with dMMR  and evalGiven the clinical need to identify and characterize dMMR tumors in APCs, we analyzed 127 mCRPC biopsies from a cohort of 124 mCRPC patients. For 85 patients, we had matched HNPC and CRPC samples . We firsIn view of concerns regarding discordance between dMMR_IHC and dMMR_MSI, we then evaluated 698 unstable of 3,214) microsatellites present in our previously published targeted gene sequencing panel consisting of the coding regions of 113 genes (0.6-Mb panel) and estimated MSINGS  microsat. These aP = 0.005) as shown in The median overall survival (OS) for the dMMR_IHC/dMMR_MSI group was shorter than in the MMR-proficient (pMMR) group in univariate and multivariate analysis , 4.09; 95% CI, 1.52–10.94; https://doi.org/10.1172/JCI121924DS1). In contrast, a single CRPC sample had MMR protein IHC heterogeneity, these biopsies having been acquired from a large pelvic mass arising from a previously irradiated prostate. These data indicate that dMMR can be focal in primary disease, but that having dMMR in primary disease strongly associates with developing dMMR CRPC.Overall, for 85 patients we had both  HNPC and CRPC samples available for analyses. Of these 85, 5 patients (5.88%) had evidence of IHC-negative foci within their primary disease samples acquired at diagnosis; 4 of these 5 (80%) had diffusely negative dMMR_IHC in mCRPC biopsies, with 1 patient progressing with MMR IHC–normal disease at mCRPC biopsy. Two of the 5 hormone treatment–naive prostate cancer (HSPC) samples with IHC-negative foci demonstrated the coexistence of IHC-positive prostate cancer, i.e., heterogeneous staining (P = 0.005), providing further evidence for dMMR as a potential predictive biomarker for immune checkpoint inhibition in lethal prostate cancer.We next evaluated whether dMMR mCRPC is enriched for programmed death ligand 1 (PD-L1) (CD274) protein expression, given the key role of this protein in regulating anticancer immune responses. We performed PD-L1 IHC with a validated antibody to the PD-L1 carboxy terminal domain  on 51 mCRPC biopsies, with a pathologist blinded to dMMR status scoring membranous staining in tumor cells . Five of+ cells, with and without FOXP3, and CD8+ lymphocytes per mm2 of tumor determined through 180 multispectral, multicolor immunofluorescence (IF) image cubes . Tissue sites included lymph node biopsies (n = 35), bone (n = 12), liver (n = 2), soft tissue metastases (n = 1), and 1 sample from transurethral resection of the prostate . T cell infiltration was strikingly heterogeneous, ranging from 0 to 828 lymphocytes/mm2  in biopsies from patients in this cohort for whom we had sufficient tumor tissue. D-TILs was here defined as numbers of CD4m2 e.g., . Rankingm2 e.g., . Of the r = 0.73, P < 0.0001)  . Surpris 0.0001) .n = 1), nonsynonymous somatic mutations (n = 6), or biallelic events (n = 7) in canonical MMR genes  had higher dMMR-associated DNA mutational signature activity . Interestingly, inferred immune infiltrate also correlated with PD-L1  and PD-L2  mRNA expression . The overall geometric mean of expression of these transcripts strongly correlated with CD8A expression (P < 0.05) when stratified by biopsy site. This approach filtered out genes associated only in specific metastatic settings. Only 2 genes met these stringent criteria: the immune checkpoint molecule BTLA and the cytolytic molecule PRF1  was strongly negatively associated  , ADORA2A (A2A receptor), PDCD1LG2 (PD-L2), CD8A, IL10, CD80 (B7-H1), HAVCR2 (TIM-3), and CD274 (PD-L1).We next examined the expression levels of a curated set of genes associated with immune checkpoints   . InteresWe then applied this approach to an exploratory analysis of 762 immune-related genes  Figure  in orderLinking genomics to the immunologic features of tumors is of major interest in the field of immunooncology. In May 2017, the FDA granted accelerated approval for the anti-PD1 monoclonal antibody pembrolizumab — the first tissue-agnostic antitumor agent to be approved for solid malignancies underpinned by dMMR , 23. HowUsing NGS data, hundreds of microsatellites can be queried at a time, potentially improving the chances of MSI detection. Using MSINGS analysis of all satellites in exomes, we show that a high MSINGS score associates with a high proportion of dMMR signatures. Next, by delineating satellites for MSINGS analysis in our targeted sequencing panel, we show a strong correlation between MSINGS score from exomes and MSINGS score from targeted sequencing, indicating that MSINGS scores can be reliably obtained from these and can provide a robust tool for identifying prostate cancer underpinned by dMMR. A major challenge, however, remains determining the cutoff point in the MSINGS score that determines the presence of a dMMR tumor. Our data indicate that a cutoff that clearly discriminates dMMR cancers may be difficult to define.Finally, using transcriptomic analysis, we conducted a comprehensive study of the immunological consequences of dMMR in mCRPC. Our data showed that prostate cancers with prominent dMMR mutational signatures have higher inferred immune cell infiltration. Qualitatively, dMMR mutational signatures associated with increased T cell–related transcripts, as well as immune checkpoint–related transcripts including PD-L1 and PD-L2. Interestingly, dMMR tumors had higher expression of factors involved in T (and NK) cell recruitment (CCRs and CXCRs) and function , with multiple genes related to immune checkpoints, including CD200R1 and the metabolic immune checkpoint adenosine receptor 2A (ADORA2A), also being upregulated . Our detIn conclusion, our data show that a subset of lethal prostate cancers is underpinned by dMMR defects. We show that dMMR is usually present at diagnosis, and our data indicate that these tumors constitute a discrete subtype with decreased survival time, with only a proportion of cases having high mutation load and PD-L1 IHC staining. dMMR exome mutational signatures and high MSINGS scores are associated with complex immune mRNA profiles that may require further sub-classification based, for example, on the degree of myeloid cell infiltration, which can affect clinical behavior and responses to immune checkpoint therapies.We analyzed data from 2 cohorts of men with CRPC: (i) an updated combined cohort of men with mCRPC from multiple institutions comprising the SU2C/PCF Prostate Cancer Dream Team and (ii) a cohort of men with CRPC referred to the Royal Marsden, whose diagnostic samples and/or mCRPC biopsies were molecularly characterized between January 2015 and June 2016 at the Institute of Cancer Research . Patients were included in this study if they had available formalin-fixed, paraffin-embedded (FFPE) tissue samples from metastatic sites or primary tumors for MMR panel testing by IHC. Diagnostic tissue was obtained from prostate needle biopsy, TURP, or prostatectomy procedures. CRPC tissue was obtained from metastases within bone, lymph node, soft tissues, or visceral organs. All tissue blocks were sectioned and only considered for IHC analyses if adequate material was present . Patients with histologic features supporting a diagnosis of pure neuroendocrine or small cell cancer were not included. Demographics and clinical data were retrospectively collected from hospital electronic patient records.2O2, and nonspecific staining was blocked using protein block serum-free solution . Detection by diaminobenzidine reaction was performed using a Dako REAL EnVision Detection System .FFPE samples were cut in 3-μm sections onto charged glass slides. Immunostaining was performed with antibodies against MSH2, MSH6, MLH1, PMS2 , and PD-L1 . Heat-induced antigen retrieval was performed using Tris-EDTA buffer, pH 8.1, in a MenaPath Antigen Access Unit at 125°C for 2 minutes to detect MSH2, MSH6, MLH1, and PMS2 or microwave oven for 18 minutes for the detection of PD-L1. Endogenous peroxidase was inactivated using 3% HScoring of MSH2, MSH6, MLH1, and PMS2 was achieved by segregating cases in a binary fashion between positives and negatives using College of American Pathologists criteria for biomarker reporting in colorectal carcinomas . In brie2O2 for 10 minutes. Tissue sections were incubated for 60 minutes at room temperature with antibodies against CD4  and CD8 . A second layer of antibodies using Alexa Fluor 555–conjugated IgG (H+L) goat anti-rabbit  and Alexa Fluor 488–conjugated IgG (H+L) goat anti-mouse  was used to detect CD4 and CD8, respectively. Tissue sections were treated with an avidin/biotin blocking kit according to the manufacturer’s protocol  and rabbit/mouse normal serum at 5% for 30 minutes. Next, tissue sections were incubated for 60 minutes with antibodies against FOXP3 conjugated to biotin  and EpCAM conjugated to Alexa Fluor 647 . Tissue sections were incubated with streptavidin peroxidase (HRP)  for 15 minutes, followed by a TSA Coumarin detection system  for 10 minutes. Nuclei were counterstained with DRAQ7 , and tissue sections were mounted with ProLong Gold antifade reagent . After staining, slides were scanned using a multispectral camera provided by the Vectra (PerkinElmer) system  at 98°C for 36 minutes in a water bath. Endogenous peroxidase was inactivated in 3% H) system . Wheneve+FOXP3– cells, CD4+FOXP3+ cells, CD8+ cells, and EpCAM+ tumor cells. All tissue segmentation, cell segmentation, and phenotype determination maps were reviewed by a pathologist in our group. For each image, the tumor area (in mm2) and the number of CD4+FOXP3–, CD4+FOXP3+, and CD8+ cells were determined to calculate the lymphocytic D-TILs (LD-TIL) determined as: /.Linear unmixing of multispectral images was done using inForm Cell Analysis software version 2.1.1. A tissue segmentation algorithm was developed using EpCAM positivity as a tumor mask to separate neoplastic cells from adjacent stroma. A cell segmentation algorithm was developed using DRAQ7 as nuclear marker and phenotype determination was based on staining for EpCAM, CD4, FOXP3, and CD8. Cells in tumor areas selected by the algorithm were separated into bins as follows: CD4DNA (1 ng) was amplified using the MSI Analysis System (Promega) according to the manufacturer’s protocol. The MSI Analysis System is composed of 7 fluorescently labeled microsatellites including 5 mononucleotide repeat markers  for detecting MSI and 2 pentanucleotide repeat markers (Penta C and Penta D) for sample identification. The PCR products were run in an ABI 3730 DNA Analyzer and subsequently analyzed using GeneMapper 4.0 software (Thermo Fisher Scientific). Samples with microsatellite instability in 2 or more loci were defined as MSI-high, whereas samples with a single locus were defined as MSI-low; samples were microsatellite-stable (MSS) if no instability at any of the loci tested was detected. For the purpose of statistical analysis, cases were dichotomized between MSI-high and MSS/MSI-low.n = 127) or in 2 or more public databases ; and (ii) a non-Bayesian approach using multiplicative updates (http://software.broadinstitute.org/gsea/) with the default parameters.Mutational signatures were derived by assessing types of somatic mutation and the nucleotides immediate upstream and downstream of the mutations (96 base substitutions in trinucleotide sequence contexts) using 2 methods analyzed independently: (i) Mutation Signature Profiling (a Bayesian non-negative matrix factorization [NMF] method) using default parameters   set to absolute quantification output. Gene-level transcripts per million (TPM) was used as input, and LM22  whose gene expressions we examined were CD28, ICOSLG, ICOS, TNFRSF9, TNFSF9, TNFSF4, TNFRSF4, CD70, CD27, CTLA4, PDCD1, CD274, PDCD1LG2, CD47, HAVCR2, LGALS9, ADORA2A, CD200, CD200R1, CD276, VTCN1, TNFSF14, BTLA, TIGIT, CD8A, PRF1, IL10, CD80, GZMA, CD86, IFNG, and LAG3 , Gleason score, age, PSA and nodal status, stage, and presence of metastatic disease at diagnosis. One or more factors were missing in approximately 16% of patients, and these were considered to be missing at random. Multiple imputation by chained equations with the above coefficients was used to generate 20 imputations; per-imputation estimates were combined using Rubin’s rules. Youden’s statistic  was usedAll SU2C/PCF study individuals provided written informed consent for collection of fresh tumor biopsies and for comprehensive molecular profiling of tumor and germline samples. All Royal Marsden Hospital patients gave written informed consent and were enrolled in institutional protocols approved by the Royal Marsden NHS Foundation Trust Hospital  ethics review committee (reference no. 04/Q0801/60).DNR, PR, DL, JSdB, and EMVA designed the research; DL, WY, SC, JM, GS, and M. Clarke coordinated overall sequencing and bioinformatics analysis; RR, S. Miranda, AF, IF, and LSTM were involved in pathology review; M. Crespo carried out IF analysis; MBL carried out microsatellite instability analysis; DD, CB, GB, AS, DK, SA, KPB, KM, S. Salipante, CL, and ADA analyzed data and provided analytical tools; S. Sumanasuriya, MSF, DB, ZZ, CM, DM, and S. Mullane acquired data and were clinical contributors; DNR, PR, and DL wrote the manuscript, which all the authors reviewed; JSdB and EMVA provided funding; WY, AA, CGD, JSdB, and EMVA supervised the work; MTS, CCP, MET, HB, MAR, M. Cieslik, DR, EH, NS, JA, WA, HS, and PSN are SU2C-PCF Dream Team principals, and CLS and AMC are Dream Team co-leaders.


Whether treatment with these drugs carries the same risk for developing MOH is currently unknown.The treatment of migraine is impeded by several difficulties, among which insufficient headache relief, side effects, and risk for developing medication overuse headache (MOH). Thus, new acutely acting antimigraine drugs are currently being developed, among which the small molecule CGRP receptor antagonists, gepants, and the 5-HT1B/D and higher 5-HT2A expression) may be involved in MOH. The decreased 5-HT may increase cortical spreading depression frequency and induce central sensitization in the cerebral cortex and caudal nucleus of the trigeminal tract. Additionally, low concentrations of 5-HT, a feature often observed in MOH patients, could increase CGRP expression. This provides a possible link between the pathways of 5-HT and CGRP, targets of lasmiditan and gepants, respectively. Since lasmiditan is a 5-HT1F receptor agonist and gepants are CGRP receptor antagonists, they could have different risks for developing MOH because of the different (over) compensation mechanisms following prolonged agonist versus antagonist treatment.Pathophysiological studies on MOH in animal models have suggested that decreased 5-hydroxytryptamine  levels, increased calcitonin-gene related peptide (CGRP) expression and changes in 5-HT receptor expression (lower 5-HT1F receptor agonists (lasmiditan) and the small molecule CGRP receptor antagonists (gepants). Data on the effects of 5-HT1F receptor agonism in relation to MOH, as well as the effects of chronic CGRP receptor blockade, are awaited with interest.The acute treatment of migraine will certainly improve with the advent of two novel classes of drugs, i.e., the 5-HT The neurovascular disorder migraine is one of the most common diseases worldwide , 2. Whil1F receptor agonists such as lasmiditan, and small molecule CGRP receptor antagonists (gepants) [This unmet need for adequate and safe treatment of migraine has resulted in the development of new drugs, among which 5-HTgepants) –13. Evengepants) –17 and tgepants) –20 was tgepants) , 5, 21. gepants) . This maFrom epidemiological, clinical, and fundamental animal studies, a substantial amount of evidence regarding the pathophysiology of MOH is available , 22–26, 1B/1D agonists  are mostriptans –44. Addi1B/D receptor expression decreases [2A expression [2A expression is also observed in animal models of MOH [2A receptor level and possibly increased CGRP expression seem to be involved in the pathophysiology of MOH, based upon animal research models.As described above, MOH is a disorder with headache for ≥15 days per month in a patient with pre-existing headache, while taking acutely acting medication for ≥3 months according to certain requirements , 45 withecreases , 61 uponecreases , 26, 62.pression . Such ans of MOH . Additios of MOH –66, mimis of MOH , providis of MOH . This iss of MOH , althougs of MOH , 69. In 1F receptor agonists [1F receptor agonist) [1F receptor agonist, whereas triptans have a higher affinity for the 5-HT1B/1D receptors [1B receptor [in vivo [in vitro or in vivo, lasmiditan does not appear to carry the same cardiovascular risk as triptans, which makes it potentially applicable to a wider population. Although it has a lower risk for cardiovascular side effects, lasmiditan may induce central side effects such as dizziness, fatigue, and paresthesia [1F receptor agonist lasmiditan, multiple gepants  are currently being developed for the treatment of migraine [The development of new acutely acting drugs has mainly been driven by growing understanding of the pathophysiology of migraine, together with the above-mentioned shortcomings of the currently available drugs. For example, small-molecule CGRP receptor antagonists (gepants) , specifiagonists , TRPV1 ragonists –73, EP4 agonists , and gluagonists  have allagonists . Some ofagonist) , 76, 77 agonist) , 78, 79.eceptors . This dieceptors , coronareceptors , 81, andeceptors  through receptor , for whi[in vivo  and in v[in vivo , lasmidiesthesia , 76. Simmigraine , 84. Themigraine . They semigraine  as CGRP migraine . Additiomigraine , 88. Pre1F receptor agonists, such as lasmiditan, carry the same risk. Theoretically, this could be possible because the 5-HT1B, 5-HT1D and 5-HT1F receptors all bind to a Gi/o–coupled receptor and negatively couple to adenylyl cyclase and, thus, share the same effect: decreased production of cyclic AMP [1F (as well as 5-HT1D) receptor, which has been described to be present in blood vessels [1B receptor, underlining that not all characteristics of stimulation of certain receptors can be predicted based on their shared intracellular signaling pathways. Clearly, 5-HT1B/1D receptor agonists with a poor potency at the 5-HT1F receptor, such as ergotamine, are also capable of inducing MOH [1F receptor is not required for this phenomenon. There are, to the best of our knowledge, currently no data suggesting that the 5-HT1F receptor would or would not be involved in the generation of MOH, so clinical data on the frequent use of 5-HT1F receptor agonists such as lasmiditan are awaited with interest.A question that is of great interest, is whether novel drugs like lasmiditan and the gepants will have the capability to induce MOH. While, as outlined above, the exact mechanisms behind MOH are currently unknown, it makes sense to hypothesize that MOH may have to do with desensitization and / or downregulation of the receptors involved in the drug response. It is likely that treatment with agonists will lead to a receptor desensitization and / or downregulation, while treatment with receptor antagonists will lead to receptor upregulation  Fig. 1)1), as prclic AMP , 93. On  vessels , does nocing MOH , so the Regarding CGRP receptor blockade, chronic and frequent administration of gepants has been attempted in clinical trials investigating prophylactic treatment of migraine , 96, 97,As described above, CGRP is a central component of migraine. Levels of CGRP are increased in animal models of MOH, which is probably reflecting CGRP levels in MOH patients –69, and Opposed to current acutely acting antimigraine drugs and drugs acting on the CGRP pathway, the relationship with MOH has not extensively been discussed or investigated for novel acutely acting antimigraine drugs. For example, although lasmiditan has been extensively investigated with regard to risk for cardiovascular side effects and efficacy of migraine treatment as described above, currently no data are available regarding its relation to MOH . To esti1F receptor agonists and the small molecule CGRP receptor antagonists (gepants). Data on the effects of 5-HT1F receptor agonism in relation to MOH, as well as the effects of chronic CGRP receptor blockade, are awaited with interest.In conclusion, the acute treatment of migraine will certainly improve with the advent of two novel classes of drugs, i.e., the 5-HT


Gene expression regulatory mechanisms in models of middle cerebral artery occlusion (MCAO) have been assessed in some studies, but questions remain. The discovery of differentially expressed genes (DEGs) between MCAO and control rats analyzed by next-generation RNA sequencing is of particular interest. These DEGs may help guide the clinical diagnosis of stroke and facilitate selection of the optimal treatment strategy. Twenty rats were equally divided into control and MCAO groups. Three rats from each group were randomly selected for RNA sequencing analysis. Sequence reads were obtained from an Illumina HiSeq2500 platform and mapped onto the rat reference genome RN6 using Hisat2. We identified 1,007 significantly DEGs with p<0.05, including 785 upregulated (fold change [FC]>2) and 222 downregulated (FC<0.5) DEGs, in brain tissue from MCAO rats compared with that from control rats, and numerous immune response genes were identified. Gene ontology (GO) analysis revealed that the majority of the most enriched and meaningful biological process terms were mainly involved in immune response, inflammatory response, cell activation, leukocyte migration, cell adhesion, response to external stimulus, cell migration, and response to wounding. Also enriched were immune-related pathways and neural-related pathways. Similar to GO molecular function terms, the enriched terms were mainly related to cytokine receptor activity. Six DEGs were verified by quantitative real-time polymerase chain reaction (qRT-PCR). Protein-protein interaction analysis of these hits revealed that MCAO affects complement and coagulation cascades and chemokine signaling pathway. Our study identified novel biomarkers that could help further elucidate MCAO mechanisms and processes. Ischemic stroke is a leading cause of global mortality and morbidity. Immediately following the event, neurovascular reperfusion to the infarcted area can injure the tissue, resulting in more serious disability , 2. MiddIn recent decades, mRNA expression profiling performed by microarray or high-throughput RNA sequencing (RNA-seq) has been used to uncover molecular mechanisms and explore diagnostic and predictive biomarkers, particularly in complicated diseases such as diabetes, cancer, and stroke –12. RNA-The main aim of this study was to identify protein-coding genes regulation networks 4 days after MCAO. RNA-seq was used to investigate the mRNA profiles in the brain tissue of rats subjected to MCAO. By a series of bioinformatics analyses, many immune-related pathways  and neural-related pathways  were identified, and six key genes were verified by quantitative real-time polymerase chain reaction (qRT-PCR).Male Sprague Dawley rats  were obtained from the Experimental Animal Center of Anhui Medical University by Animal Experiments of Anhui university of Chinese medicine. All rats were anesthetized with sodium pentobarbital during all surgical procedures to minimize suffering, and the MCAO and sham surgeries were performed as previously described . Four daTotal RNA was extracted using mirVana™ miRNA Isolation kit  following the manufacturer's instructions. RNA sample quantity and quality were determined using a NanoDrop 2000  and an Agilent 2100 bioanalyzer . TruSeq® Stranded Total RNA Sample Preparation kits  were used to prepare libraries following the manufacturer's instructions. Purified libraries were quantified by a Qubit® 2.0 Fluorometer  and Agilent 2100 bioanalyzer. Clusters were generated by cBot with the library and sequenced on an Illumina HiSeq 2500 platform . All sequencing was performed at Origin-Biotech Inc. .http://www.bioinformatics.babraham.ac.uk/projects/fastqc). Trimming was performed by seqtk for known Illumina TruSeq adapter sequences, poor reads, and ribosome RNA reads (https://github.com/lh3/seqtk). The trimmed reads were then mapped to the Rattus norvegicus reference genome (rno6) by the Hisat2 (version: 2.0.4) [FastQC was conducted for Quality control (QC) of RNA-seq reads (v. 0.11.3) (: 2.0.4) , 17. Str: 2.0.4) . Gene co: 2.0.4) , and fra: 2.0.4) . edgeR w: 2.0.4)  and thre: 2.0.4) .GO and KEGG pathways were enriched by R package (v 3.5.1) to better understand the functions of the DEGs . In our STRING is a database that provides comprehensive information about interactions between proteins, including prediction and experimental interaction data . In our qRT-PCR is the gold standard of mRNA detection and was performed to verify the RNA-seq results. Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) served as the internal control. Relative mRNA expression was determined using the 2-ΔΔCT method. Six genes were analyzed: Top2a, Cdk1, Ccna2, Ccnb1, Th, and Esr1. Brain tissue was sequenced from the MCAO and control groups (n=3/group), and each experiment was performed in triplicate.2FC|>1. We identified 1,007 DEGs: 785 upregulated and 222 downregulated. Differential mRNA expression between the MCAO and control groups was represented in volcano and scatter plots , osteoclast differentiation (rno04380), complement and coagulation cascades (rno04610), malaria (rno05144), leishmaniasis (rno05140), chemokine signaling pathway (rno04062), Chagas disease (American trypanosomiasis) (rno05142), hematopoietic cell lineage (rno04640), amoebiasis (rno05146), and phagosome (rno04145). The top 30 enrichment pathways are presented in Pathway enrichment analysis of DEGs could provide further insight into the function of genes and their interactions. The DEGs between the MCAO and control groups were enriched to 31 subclasses of pathways in 5 broad categories  after these were analyzed using the KEGG database . We perfSignificantly altered DEGs were used to construct a PPI network based on the STRING database. The network comprises 744 nodes and 5,266 interactions . More thA total of 25 modules were obtained using default criteria in MCODE. Modules were listed in descending order by MCODE score. Four modules with MCODE score ≥4 and nodes ≥10 were named modules 1, 2, 3, and 4. These were selected for module network visualization, GO enrichment analysis, and KEGG pathway enrichment analysis Figures 5. Most DETop2a, Cdk1, Ccna2, and Ccnb1 were overexpressed in the MCAO group . Th and We analyzed the protein-coding mRNA expression profiles in cerebral hemispheres from experimental and control rats by high-throughput RNA-seq. This identified 1,007 DEGs, which were subjected to GO, KEGG pathway, PPI, and PPI module analysis to provide a better understanding of pathological mechanisms that occur after MCAO.Pathway enrichment analysis identified 21 DEGs enriched for the cell cycle , namely, Mcm6, Ttk, Bub1b, Cdkn2c, Ccnb1, Bub1, Plk1, Orc1, Tgfb1, Mcm3, Cdc6, Mcm5, Pttg1, Cdc20, Chek2, Ccnd1, Mcm2, Espl1, Rbl1, Cdk1, and Ccna2. Most were upregulated in MCAO. Following Cyclin A  or B  binding and activating cyclin-dependent kinase 1 , Cdk1 phosphorylates key substrates leading the cell to G2-phase arrest, M-phase, and cytokinesis promotion. Some studies reported that Cdk1 activation is involved in multiple neuronal death by activating phosphorylation of Bad27 (a proapoptotic protein) or inhibitory phosphorylation of Bcl-xL, Bcl-2, and Mcl-1, which are antiapoptotic . Others GO enrichment analysis revealed that more than 78 DEGs are involved in the inflammatory response (p=0.0078), including 75 that were upregulated. Several inflammatory response pathways were activated like complement and coagulation cascades, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and leukocyte transendothelial migration. Dozens of cytokines were dysregulated. The macrophage recruitment factor CCL2 –34 was uPathway enrichment analysis showed that 27 DEGs were involved in the neuroactive ligand-receptor interaction (p=0.00832); 10 and 17 were up- and downregulated, respectively. Upregulated receptors in MCAO were Htr2b, Gabrr1, C5ar1, Cnr2, P2ry6, Htr2a, Galr1, C3ar1, Glra1, and Mc3r. Downregulated receptors in MCAO were Gabrr3, Gabrq, Brs3, Gabra6, Cckar, Trhr, Ptafr, Glra2, Chrnb4, Gpr50, Gabre, Tspo, P2rx2, Ntsr1, Chrna6, Calcr, and Drd3. Lan et al. found decreased serotonin receptor expression in MCAO rats, which was improved with treadmill exercise . We founBy high-throughput RNA-seq, we analyzed the protein-coding mRNA expression profile in control and MCAO groups in brain tissue. And some GOs, pathways, and genes that were identified could play key roles with MCAO. These findings may help us to understand the underlying mechanism of protein-coding mRNAs with MCAO.


Trm cells constitute a first line of defense against local infections in barrier tissues, but they are also found in non-barrier tissues and play a role in antitumor immunity. Their differentiation in tissues and their phenotypical, transcriptional, and functional characteristics are the object of active research. Herein, we will discuss the potential existence of committed CD8+ Trm precursors and the genealogy of memory CD8+ T cell subsets. In addition to the priming of naive T cells, there is some plasticity of antigen-experienced effector and memory T cell subsets to generate Trm precursors. Local inflammation, antigen presentation, and cytokines drive Trm differentiation. It is of prime interest how specific dendritic cell subsets modulate priming and differentiation of Trm cells, as well as their reactivation within tissues. Research on how we can manipulate generation of memory T cells subsets is key for improved vaccination strategies.Tissue-resident memory CD8 Memory T cells are further subdivided based on their distribution and trafficking properties. Circulating memory T cells can be further subdivided as central memory T (Tcm) cells that re-circulate between secondary lymphoid organs, blood and lymph, and effector memory T (Tem) cells that can also access the tissues , 2. Convpulation , 14, whipulation –17. Thisadherin) , Itgae ( (CD103) , 19, and (CD49a) , 20–22, 2, S1pr1 , and Ccrr1  (+ T cells (Trm cells can be generated from KLRG1ecursors . These KExKLRG1) . These cExKLRG1) . T-bet aExKLRG1)  and regu T cells . In addi T cells .+ T cells upon viral infection impairs the formation of Trm cells by blocking migration into the tissue, despite increasing the number of circulating memory T cells  were sorted 6 days after infection with HSV based on KLRG1 expression. The authors showed that, upon adoptive transfer, only the KLRG1− population generated matured CD103+ Trm cells in the skin of HSV-infected recipient mice. Moreover, Trm differentiation requires a distinct program that combines effector and memory cell transcriptional programs, sharing some features with early effector CD8+ T cells or Tem cells but also some of the Tcm cell properties  show a similar abundance of particular TCR clones tracked by CDR3 sequences, suggesting that a common naive T cell precursor is able to give rise to both Trm and Tcm cells after skin immunization  in the brain , co-stimulation , and cytokines that modulate T cell differentiation . According to the “Signal strength model”  T cells differentiate into Trm cells in multiple scenarios: skin infection with VACV  , 69–71. n Figure D. These n Figure   followed by epicutaneous VACV-OVA infection also induced Trm cells in the skin (via blood or lymph (bona fide Trm cells without recirculating capacity (+ CD103+) within the tumor mass. Furthermore, when mice transferred with Tcm cells were challenged with MC38-OVA colon adenocarcinoma, CD69+CD103+ OVA-specific CD8+ T cells were found in the skin proximal to rejected MC38-OVA tumors 45 days after inoculation  but not CD11bhi CD8α− (cDC2s) induce generation of Trm cells, in a DNGR-1-dependent fashion. The blockade of specific priming signals provided by cDC1s such as CD24, IL-12, and IL-15 reduces T-bet induction and generation of Trm precursors; however, cDC1 priming blockade does not affect the generation of circulating memory T cells , co-stimulation , or cytokines signal 3) derived from different DC subsets are required for differentiation and for reactivation upon rechallenge. The requirement of antigen for Trm cell differentiation in tissues has been described  derived , 66, 67.C Figure . HoweverC Figure , 9, 89, C Figure . Inflammhallenge . Moreoves Figure .+ cDC1 seem to be necessary to promote recall of circulating memory CD8+ T cells upon secondary infections with pathogens such as L. monocytogenes or certain viruses  acquire antigens from live-attenuated influenza virus in vivo and expanded specific cytotoxic CD8+ T cells in vitro  cells represent the newest layer of complexity in memory subsets. By virtue of their location, they act as sensor and effector cells, triggering both innate and adaptive responses, therefore providing a superior immunity against reinfection in the tissue . Several factors may condition the relative efficiency of Trm generation from difference sources, including the type of challenge , the presence of specific antigen driving reactivation and tissue-specific signals that can promote Trm generation in an antigen-independent fashion (Naive T cells differentiate into Trm in many scenarios: infectious and even non-infectious, such as chemical hapten inflammation , 66, 67. fashion , 71, 73.Different subsets of DCs may affect differentially the priming of Trm precursors. cDC1s drive priming of Trm precursors in the LN, but not Trm tissue differentiation, in a VACV skin infection model, and targeting malaria antigen to cDC1s generates antigen-specific Trm in the liver, requiring both models antigen presence in the target tissue , 84. AntME, SK, SI, and DS conceived and wrote the manuscript. ME and SI did the figures.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Various miRNAs have been reported to regulate the chondrogenic differentiation of bone marrow mesenchymal stem cells (BMSCs); however, whether miR-134 plays a role in this biological process remains undetermined. In the present study, we first evaluated the chondrogenic differentiation of BMSCs by Alcian blue staining, and examined the miR-134 expression by quantitative real-time PCR (qRT-PCR) during this process. And miR-134 inhibitor was used to investigate the functions of miR-134 in chondrogenic differentiation of BMSCs by Alcian blue staining, qRT-PCR, and Western blot. Subsequently, the correlation between miR-134 and SMAD6 was assessed via bioinformatics analysis and dual-luciferase reporter assay. Finally, the role of SMAD6 in chondrogenic differentiation of BMSCs was also determined through Alcian blue staining, qRT-PCR, and Western blot. As results showed that miR-134 expression was significantly down-regulated during chondrogenic differentiation, and inhibition of miR-134 obviously promoted chondrogenic differentiation. Dual-luciferase reporter assay indicated that miR-134 could directly target the 3′-UTRs of SMAD6, inhibit miR-134 expression in BMSCs, and up-regulate SMAD6 expression. Moreover, we found that overexpression of SMAD6 significantly promoted chondrogenic differentiation, and that SMAD6-induced promotion of chondrogenic differentiation could be reversed by miR-134 mimics. In conclusion, our findings suggest that miR-134 may act as a negative regulator during chondrogenic differentiation of BMSCs by interacting with SMAD6. The damage to articular cartilage, primarily caused by trauma and excessive mechanical conditions, is the most common clinical disease ,2. Articin vitro, such as osteoblasts, adipocytes, and chondrocytes [Bone marrow mesenchymal stem cells (BMSCs) are one of the mesenchymal stem cells that can be isolated and differentiated into multiple cell types drocytes ,13. Due drocytes . Therefodrocytes ,18. A vadrocytes ,20. MiRNdrocytes ,22. Eviddrocytes ,24. Sincdrocytes ,26. Recedrocytes .In the present study, we aimed to investigate the miR-134 roles during chondrogenic differentiation of BMSCs, and attempted to reveal the underlying mechanisms. Due to the bioinformatics analysis results showing that SMAD6 is a targetted gene of miR-134, we hypothesized that the effects of miR-134 on chondrogenic differentiation of BMSCs may be by targetting SMAD6. Therefore, we tested this hypothesis in the present study and demonstrated that miR-134 may act as a negative regulator during chondrogenic differentiation of BMSCs by targetting SMAD6.2 humidified incubator. For the chondrogenic differentiation of BMSCs, a Chondrogenic Differentiation Kit  was applied under the instruction provided by manufacturers. Briefly, BMSCs were cultured in modified DMEM medium, which supplemented with dexamethasone, ascorbate, sodium pyruvate, and transforming growth factor-β 3 (TGF-β), and the chondrogenic differentiation medium was refreshed every 2 days.BMSCs of Sprague–Dawley rat  were provided by Cyagen Biosciences, Inc.  and cultured at 37°C with the Dulbecco’s modified Eagle’s medium , supplemented with 10% FBS, 10 mg/ml of streptomycin, and 10 U/ml of penicillin, in a 5% COAfter being cultured in normal DMEM or chondrogenic medium for 0, 14, and 21 days, BMSCs were fixed using 4% formalin for 30 min at 4°C. After being washed with PBS for 5 min, three times, BMSCs were then incubated with 1% Alcian blue 8GX (Sigma) at room temperature for 1 h. Subsequently, stained BMSCs were washed with deionized water for three times, and signals were detected by a flatbed scanner . Image-Pro Plus software  was applied to quantitate the staining intensity of cells.Total RNAs of culture BMSCs at 0, 14, and 21 days were extracted by an RNAiso Plus reagent  according to the protocols obtained from the manufacturer and the RNA concentration was determined using a spectrophotometer at 260 nm, and 2 μg of total RNAs were reverse transcribed into cDNA using BestarTM qPCR RT kit . The qPCR reaction was performed using a BestarTM qPCR MasterMix kit  with the Applied Biosystems 7500 PCR system . The sequence of primers used in the present study is shown in Both miR-134 mimics and inhibitors were purchased from GenePharma . Before transfection, cells were seeded in 24-well plates and cultured at 37°C for 24 h, then 50 nM miR-134 mimics or inhibitors were added into the medium with lipofectamine 2000 (Invitrogen) and cultured for another 24 h. Nonsense control (NC) of miR-134 was used as control and transfected under the same condition.g for 15 min at a low temperature. After collecting the total protein extracts, a BCA Protein Assay Kit  was used to quantitate the protein concentration. Each sample (40 μg) of protein was loaded and separated by 10% SDS-PAGE, and then transferred onto PVDF membranes . Subsequently, membranes were incubated with 5% non-fat milk in TBST at room temperature for 2 h to block non-specific sites. The membranes were then incubated with corresponding primary antibodies against collage II , SOX9 , aggrecan , and SMAD6  overnight at 4°C. After being washed with TBST for three times, the membranes were subjected to incubation with HRP-conjugated donkey-anti-rabbit IgG  for 2 h. Finally, the target blots were visualized by an enhanced chemiluminescence reagent, and protein expressions of collage II, SOX9, aggrecan, and SMAD6 were normalized to GAPDH .For protein extraction, the cultured BMSCs were lysed with RIPA buffer containing PMSF, and then cell lysis was centrifuged at 10000 5 cells/well) were cultured in 24-well plates for 24 h, then 100 ng of pcDNA3-SMAD6-WT or pcDNA3-SMAD6-Mut plasmids were co-transfected into BMSCs cells with 100 pmol of miR-134 mimics or NC using Lipofectamine 2000 (Invitrogen) following the protocols obtained from manufacturers. The luciferase activity was determined by a Dual Luciferase Reporter Assay System (Promega), and firefly luciferase activity was normalized to Renilla luciferase activity.The 3′-UTRs of SMAD6 including the target binding sites for miR-134 were chemically synthesized and cloned into pcDNA3.0 vector (Invitrogen) to form pcDNA3-SMAD6-WT plasmid, and the mutant SMAD6 binding sites were also cloned into pcDNA3.0 to produce pcDNA3-SMAD6-Mut plasmid. For the luciferase reporter assay, BMSCs . One-way analysis of variance was applied to assess the difference between means, and the difference between means was considered significant if Since glycosaminoglycan deposition is a critical indicator of cartilage extracellular matrix accumulation, Alcian blue staining was performed to assess chondrogenic differentiation of BMSCs by examining glycosaminoglycan accumulation at 0, 14, and 21 days. As results indicated that staining intensity of Alcian blue was significantly increased at 14 and 21 days under chondrogenic medium (induction) A. To detInhibitor of miR-134 was transfected into BMSCs to evaluate whether miR-134 exhibits effects on chondrogenic differentiation. After treatment, BMSCs at 0, 14, and 21 days were stained with Alcian blue, and expressions of three chondrogenic differentiation markers: collage II, SOX9, and aggrecan were measured by qRT-PCR and Western blot assay. Results from Alcian blue staining indicated that staining intensity of BMSCs with miR-134 inhibitor transfection was obviously enhanced compared with control group and NC-treated group at 14 and 21 days A. AdditiTo explore the underlying mechanisms of miR-134 involved in the chondrogenic differentiation of BMSCs, bioinformatics analysis was applied to screen the targets of miR-134. According to the prediction results from Targetscan software, miR-134 was shown to have binding sites in the 3′-UTRs of SMAD6 A,B.In order to further investigate the underlying mechanisms of interaction between miR-134 and SMAD6 during chondrogenic differentiation, BMSCs transfected with pcDNA 3.0 vector, SMAD6, and SMAD6 plus miR-134 mimics were subjected to Alcian blue staining analysis of chondrogenic differentiation, and qRT-PCR/Western blot analysis of collage II, SOX9, and aggrecan expressions. Alcian staining of BMSCs indicated that SMAD6 transfection remarkably increased the staining intensity compared with pcDNA3.0-transfected cells and blank control cells, and the application of miR-134 mimics could reverse the SMAD6-induced enhancement of intensity A. Moreovin vivo measures that aim to produce new tissues in situ, while the latter is mainly based on in vitro methods that attempt to engineer neo-cartilage tissues, which can be subsequently implanted into the patients [Cartilage injuries may be caused by excessive loading on joint, inflammation, and foreign body intrusion ,31. Virtpatients . Due to patients ,38. BMSCpatients ,40.in vitro can be directed by multiple stimulus, such as growth factors, cell–matrix interaction, and mechanical loads [The chondrogenesis process of BMSCs al loads . Recentlal loads ,43. An ial loads  in MSCs al loads . In addial loads ,47. In tSMADs, the critical mediator of canonical TGF-β signaling pathway, are proteins that are responsible for transducing the TGF-β signal into nucleus. Previous studies revealed that SMADs could regulate the corresponding gene expressions by interacting with transcription factors in the nucleus when TGF-β signaling is activated . DysfuncIn conclusion, we revealed that miR-134 may serve as an important negative regulator during chondrogenic differentiation of BMSCs by targetting SMAD6. Further studies are also needed to validate and explore the deeper mechanisms and functions of miR-134 during chondrogenic differentiation of BMSCs by SMAD6. For example, inhibition of miR-134 followed by knockdown of SMAD6 will be performed to explore whether miR-134 can inhibit chondrogenic differentiation of BMSCs via SMAD6.