N6-methyladenosine (m6A) RNA methylation has been shown to have prognostic value in cancer. Nonetheless, its potential role regarding immunity, metabolism, and stemness in soft tissue sarcoma (STS) remains unknown. We comprehensively estimated the m6A modification patterns and corresponding immunity, metabolism, and stemness characteristics based on 568 STS samples and 21 m6A regulators. The m6Ascore was constructed to quantify m6A modification patterns in individuals using machine learning algorithms. Two distinct m6A modification patterns among the STS patients were identified, which exhibited differences in prognosis, immune cell infiltration, metabolic pathways, stemness, somatic mutation, and copy number variation. Thereafter, immunity-, metabolism-, and stemness phenotype-related genes associated with m6A modification were identified. Furthermore, patients with lower m6Ascores had increased antitumor immune responses, survival benefit under immunotherapy, tumor mutation burden, immunogenicity, and response to anti-PD-1/L1 immunotherapy. Immunotherapy sensitivity was validated using the IMvigor210 dataset. STS patients with lower m6Ascore might be more sensitive to docetaxel and gemcitabine. Finally, pan-cancer analysis illustrated the significant correlations of m6Ascore with clinical outcomes, immune cell infiltration, metabolism, and stemness. This study revealed that m6A modification plays an important role in immunity, metabolism, and stemness in STS. Evaluating the m6A modification pattern and development of m6Ascore may help to guide more effective immunotherapy and chemotherapy strategies. Soft tissue sarcoma (STS) is a type of malignant tumor that originates from mesenchymal tissues. Compared to other cancers (such as tumors of the respiratory or digestive system), STS has a lower incidence rate and higher heterogeneity . Despite6-methyladenosine (m6A) has a key role in current tumor research  were assessed. In addition, the m6Ascore was developed using machine learning algorithms to quantify individual differences among different STS subtypes. m6Ascore was shown to predict responses to immunotherapy and chemotherapy. Finally, a pan-cancer analysis illustrated significant correlations of m6Ascore with prognosis, immune cell infiltration, metabolism, and stemness in other cancers, which indicated that it may help to guide the use of immunotherapy and chemotherapy in other cancers.The method details are described in the The detailed workflow for m6A patterns and subsequent analyses are shown in Next, we investigated the difference in the expression of the 21 m6A regulators between the normal and tumor tissues. Fat and muscle tissue samples from the The Genotype-Tissue Expression (GTEx) database were used as adjacent normal tissue samples. Of the 21 regulators, 20 showed significant differences between STS and normal tissues, while ZC3H13 did not , especially regarding anti-tumor immune cells, in the TCGA and GEO cohorts  and the “limma” package were used, which led to the identification of 84 differential pathways , five metabolic pathways , and the six stemness indices.2FC|>0.5 and FDR<0.05) were identified to be used in WGCNA    Table S6Considering the individual heterogeneity and complexity of m6A modification, we quantified the m6A modification pattern of individual STS patients using principal component analysis based on the 141 abovementioned genes. Thus, m6Ascore was defined for each STS patient. We visualized the changes in the attributes of individual patients in different clusters using an alluvial diagram , dedifferentiated liposarcoma , desmoid tumor , leiomyosarcoma , myxofibrosarcoma , malignant peripheral nerve sheath tumors , and synovial sarcoma . K-M survival analysis of these seven subtypes showed that UPS (with a lower m6Ascore) had improved survival, while LMS (with a higher m6Ascore) had poorer survival  and pro-tumor immune cells (Th2 cells)   and loss (p=0.02) burden and a higher arm-level gain burden (p=0.04) compared to the high-m6Ascore group S7F. TheWe further assessed the ability of m6Ascore to predict the clinical benefit of immunotherapy. The low-m6Ascore group had higher expression of immune checkpoint-related genes  than the high-m6Ascore group (We then used the Tumor Immune Dysfunction and Exclusion (TIDE) algorithm to predict the likelihood of response to immunotherapy, and it demonstrated that the low-m6Ascore group had a lower TIDE score and may therefore be more likely to respond to immunotherapy than the high-m6Ascore group (p<0.01)   and gemcitabine (P <0.01)  , and expression levels of immune checkpoint-related genes can be used to predict the response to immune checkpoint blockade immunotherapy. Of the 32 cancer types, 18 exhibited a significant correlation between m6Ascore and TMB, as shown in radar charts  with distinct prognoses, distinct characteristics of immunity, metabolism, and stemness in STS. m6Acluster-A had better survival and enrichment of immune-stimulating cells, promoting type I immunity-mediated anti-tumor effects , 18. InvTo further investigate the functions of these m6A regulators, we performed GSVA involving typical biological pathways, and we found that the cell cycle, mismatch repair, VEGF signaling, immune-related pathways, metabolic-related pathways, and EMT  differedSeveral studies have reported that m6A modification may regulate glycolysis , 26, butTo further investigate genes regulated by m6A modification, the immune/metabolism/stemness phenotype-related genes associated with m6A modification were explored using WGCNA. The three subsequent unsupervised consensus clustering analyses demonstrated that the immune phenotype (ImmuneScore)-, metabolism phenotype (retinoic acid metabolism)-, and stemness phenotype (mRNAsi)-related genes clustered into four, three, and three phenotype clusters, respectively. Each phenotype cluster had unique immunity/metabolism/stemness features and different expression of m6A regulators, contributing to different prognoses. In addition, the immune, metabolism and stemness phenotype-related genes could provide reference for subsequent studies on m6A modification involved in immune, metabolism and stemness in STS. On the other hand, DNA methylation, as a form of epigenetic regulation, can lead to abnormal gene expression, thereby driving oncogenesis . Our prem6Acluster-A subtype had higher expression of immune checkpoint-related genes than m6Acluster-B, and therefore might be sensitive to immune checkpoint inhibitors. However, the individual-level heterogeneity and complexity of m6A modification cannot be ignored; quantification of m6A modification patterns to distinguish individual differences could guide immunotherapy use in STS. Therefore, we constructed an m6A scoring system designated m6Ascore to quantify the m6A modification patterns in individuals. As expected, m6Ascore had many profound clinical implications. First, it was an outstanding indicator of m6A modification patterns. Second, it overcame the shortcoming of STS histological type, which is a high-performing prognostic factor as indicated by an analysis of 10000 cases  but it iThis study has several limitations. First, the heterogeneity of samples from the TCGA database combined with the GEO database was disregarded, although the batch-effect correction algorithm was used. Second, there were few STS samples due to the low incidence of STS, and the samples tended to be UPS or DDLPS. Third, there was a lack of clinical cohorts to validate the findings regarding the correlations between m6A modification and the tumor immune landscape and the prognostic value of m6Ascore in STS.The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/The patient data in this work were acquired from the publicly available datasets whose informed consent of patients were complete.Z-DH, H-YG, CH and R-XW conceived and designed this study. Z-DH, H-YG and R-XW carried out the analysis procedure. R-XW, Z-DH and L-LL analyzed the results. Z-DH, L-LL, R-XW and Z-ZL contributed analysis tools. Z-DH, R-XW, CH and H-YG participated in the manuscript writing. All the authors reviewed the manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


TUBB3, NEUROD 1 and 6, and MBP, were downregulated in luteolin-treated hNSCs. Furthermore, immunostaining showed that percentages of GFAP+ cells were significantly higher in luteolin- and AICAR-treated hNSCs compared to control hNSCs. Additionally, RT-qPCR results showed that luteolin upregulated the expressions of GFAP, BMP2, and STAT3, whereas the expression of TUBB3 remained unchanged. Next, we evaluated the effects of luteolin in LPS-induced mice model of depression that represents defects in astrocytogenesis. We found that oral administration of luteolin (10 mg/Kg) for eight consecutive days could decrease the immobility time on tail suspension test, a mouse behavioral test measuring depression-like behavior, and attenuate LPS-induced inflammatory responses by significantly decreasing IL-6 production in mice brain-derived astrocytes and serum, and TNFα and corticosterone levels in serum. Luteolin treatment also significantly increased mature BDNF, dopamine, and noradrenaline levels in the hypothalamus of LPS-induced depression mice. Though the behavioral effects of luteolin did not reach statistical significance, global gene expression analyses of mice hippocampus and brain-derived NSCs highlighted the modulatory effects of luteolin on different signaling pathways involved in the pathophysiology of depression. Altogether, our findings suggest an astrocytogenic potential of luteolin and its possible therapeutic benefits in neuroinflammatory and neurodegenerative diseases. However, further studies are required to identify the specific mechanism of action of luteolin.Luteolin is a natural flavone with neurotrophic effects observed on different neuronal cell lines. In the present study, we aimed to assess the effect of luteolin on hNSCs fate determination and the LPS-induced neuroinflammation in a mouse model of depression with astrocytogenesis defect. hNSCs were cultured in basal cell culture medium (control) or medium supplemented with luteolin or AICAR, a known inducer of astrogenesis. A whole-genome transcriptomic analysis showed that luteolin upregulated the expressions of genes related to neurotrophin, dopaminergic, hippo, and Wnt signaling pathways, and downregulated the genes involved in p53, TNF, FOXO, and Notch signaling pathways. We also found that astrocyte-specific gene GFAP, as well as other genes of the key signaling pathways involved in astrogenesis such as Wnt, BMP, and JAK-STAT pathways were upregulated in luteolin-treated hNSCs. On the other hand, neurogenesis and oligodendrogenesis-related genes,  Neural stem cells (NSCs) are self-renewal cells that can be differentiated into neurons or glial cells following neurogenesis and gliogenesis processes, respectively . For mosThe natural flavone luteolin , present in several edible plants, in fruits and tea including rosemary tea , has beeRecently, neuro-inflammation was shown to be involved in the pathogenesis of different CNS diseases such as depression . In addiThe present study aimed to assess the effect of luteolin on hNSCs fate determination and to explore the effect of luteolin on a pathological animal model with astrocytogenesis defect, the LPS-induced depression mice.15H10O6 , is the major component of the outer membrane of Gram-negative bacteria. It was used in the present study to induce depression in mice. This molecule was purchased from Wako (Japan). To prepare the mice treatment solution, LPS was dissolved in PBS.The human neurotypic SH-SY5Y cells were purchased from the American Type Culture Collection (ATCC). The cells were cultured in 100 mm petri dish or 96-well plates depending on the purpose, with a 1:1 (v/v) mixture of Dulbecco’s minimum essential medium  and Ham’s F-12 nutrient mixture  supplemented with 15% fetal bovine serum , and 1% penicillin -streptomycin  solution  and 1% non-essential amino-acid at 37°C in a 95% humidified air-5% CO2 incubator.TM Neural Stem Cells) are cryopreserved human fetal brain-derived neural stem cells derived from cortex of a male fetus donor aged 16 weeks that were purchased from Gibco, United States (Cat. no. A15654). It was cultured in T25 flasks , or in 6-well plates  with KnockOutTM DMEM/F-12  supplemented with 2% StemProTM Neural Supplement , 20 ng/mL of fibroblast growth factor (FGF) basic recombinant human, 20 ng/mL epidermal growth factor (EGF) recombinant human and 2 mM GlutaMAXTM-I Supplement (Cat. no. 35050), 6 U/mL heparin , and 200 μM ascorbic acid . For adhesion, Geltrex®  was used to coat the 6-well plate. The hNSCs were cultured at 37°C in a 95% air/5% CO2 humidified incubator. The medium was changed every 2 days.The hNSCs . After treatment, 5 mg/ml of MTT was added, and the cells were incubated for further 12 h. The MTT formazan was dissolved in 100 μl of 10% SDS (w/v). Absorbance was measured at 570 nm using a microtiter plate reader after overnight incubation .Cell viability was determined using a tetrazolium salt reduction assay: the MTT  assay on the human neurotypic SH-SY5Y cells. Cells were seeded at the density of 2 × 10® at a density of 2.5 × 104 cells/cm2. After 48 h, growth medium was replaced by differentiation medium: Knockout DMEM/F12 supplemented with 2% StemPro® neural supplement, 2 mM GlutaMAX-I supplement, 6 units/mL heparin and 200 μM ascorbic acid, supplemented or not with 1 μM of Luteolin or 1 μM of AICAR. Throughout the study, differenciated hNSCs were used at P0.Differentiation of hNSCs was induced following the previously described protocol . Briefly® at the density of 2.5 × 104 cells/cm2 and were incubated at 37°C, 5% CO2 for 48 h. After incubation, cells were treated with a differentiation medium supplemented or not 1 μM luteolin or 1 μM AICAR at 37°C for 24 h. After incubation, the cells were washed twice with PBS and fixed using 4% (w/v) cold formaldehyde  diluted in cold PBS for 30 min at room temperature. The fixed cells were later washed three times with PBS and permeabilized using 0.2% Triton-X surfactant  diluted in PBS for 5 min at room temperature. After removing the Triton-X solution, cells were washed twice again using PBS, then blocked for 1 h at room temperature by 5% Fetal Goat Serum  diluted in PBS. After incubation, the blocking buffer was removed and replaced by the diluted primary antibody solution. We used the primary antibodies of the rabbit polyclonal anti-Glial fibrillary acidic protein (GFAP) primary antibody (Cat. no. ab7260), the mouse monoclonal anti-beta III Tubulin (β3-tubulin) primary antibody (Cat. no. ab78078) and the rabbit polyclonal anti-Myelin Basic Protein (MBP) primary antibody (Cat. no. ab124493) to stain astrocytes, neurons, and oligodendrocytes, respectively. The antibodies were purchased from Abcam (Japan) and were diluted in 1% Fetal Goat Serum in PBS before use. After overnight incubation with primary antibodies at 4°C, cells were washed with PBS and incubated with the secondary antibodies solution Goat-anti mouse Alexa Fluor® 594  and Goat-anti-rabbit IgG H&L Alexa Fluor® 488  appropriately diluted in 1% Fetal Goat Serum in PBS for 2 h at room temperature avoiding light exposure. After incubation, cells were washed with PBS, and nuclei were counterstained with DAPI using drops of ProLong Diamond Antifade Mountant . Fluorescence was detected with the IXplore Pro microscope system  monitored by OLYMPUS CellSens Dimension 1.18 software version XV3.17 . Automated multichannel Cell count and cell imaging were performed by CellSens Dimension 1.18 software at magnification ×10.For immunocytochemistry, cells were seeded in the Nunc Lab-Tek Chamber Slide System  coated with Geltrexad libitum. After acclimatization, the mice were randomly assigned into four experimental groups (n = 6/group): Control group (PBS), the depression model group: lipopolysaccharide group (LPS), control group treated with Luteolin (PBS + L) and lipopolysaccharide group treated with Luteolin (LPS + L). All experimental animals were maintained in accordance with the Guide for the Care and Use of Laboratory Animals, and the protocol was approved by the Animal Ethics Committee of the University of Tsukuba, Japan. At day 1 and prior to depression induction by LPS, the behavioral test: Tail Suspension Test (TST) was performed to all mice to screen their initial stress status. The TST methodology used in our study was as described by Steru  were used for animal experiments. All animals were purchased from Charles River Laboratories JAPAN Inc., Kanagawa, Japan and were housed individually at controlled temperature (25°C), with a 12/12 h light/dark cycle, and had access to food and water by Steru . BrieflyAfter performing the second TST test on day 9, all mice were sacrificed by cervical dislocation. Two mice brains from each group were used to separate NSCs, two mice brains were used to separate astrocytes and the remaining two brains were washed twice with cold PBS and immersed in liquid nitrogen prior to their storage at −80°C.TM Dissociators  were used for the mechanical dissociation steps. After dissociation, the Debris Removal Solution was used for the removal of debris followed by a subsequent removal of erythrocytes using the Red Blood Cell Removal Solution. Cells were processed immediately for separation.Prior to NSCs and astrocytes isolation, the whole mice brain tissues were dissociated using the Adult Brain Dissociation Kit  in accordance to the manufacturer’s instructions. This kit permitted to obtain single cell suspensions by combining mechanical dissociation with enzymatic degradation of the extracellular matrix. Briefly, the neural tissue was enzymatically digested using the kit components and the gentleMACS® Column, which was placed in the magnetic field of a MACS Separator. The magnetically labeled prominin-1 + cells were retained within the column. The unlabeled cells run through; this cell fraction is thus depleted of prominin-1 + cells. After removing the column from the magnetic field, the magnetically retained prominin-1 + cells were eluted as the positively selected cell fraction. To separate the astrocytes, first Fc receptors were blocked with the mouse FcR Blocking Reagent. Then, the ACSA-2 + cells were magnetically labeled with Anti-ACSA-2 MicroBeads. The cell suspension was loaded onto a MACS® Column, which was placed in the magnetic field of a MACS Separator. The magnetically labeled ACSA-2 + cells were retained within the column. The unlabeled cells run through; this cell fraction was thus depleted of ACSA-2 + cells. After removing the column from the magnetic field, the magnetically retained ACSA-2 + cells were eluted as the positively selected cell fraction.Immediately after the brains tissues dissociations, the NSCs were separated using the Anti-Prominin-1 MicroBeads  and the astrocytes were isolated using the Anti-ACSA-2 (astrocyte cell surface antigen-2) MicroBeads kit  according the manufacturer’s instructions. Briefly, for the separation of NSCs, first the prominin-1 + cells were magnetically labeled with AntiProminin-1 MicroBeads. Then, the cell suspension was loaded onto a MACSAfter separation with the Anti-ACSA-2 MicroBeads kit , the negative and positive ACSA-2 cells fractions were collected separately and counted using the Guava ViaCount Software (Version Number – 2.5.2), then the astrocyte fraction was cultured in 30 mm petri dish in DMEM, high glucose cell culture medium supplemented with 10% inactivated fetal bovine serum , and 1% penicillin -streptomycin  solution  at 37°C in a 95% humidified air-5% CO2 incubator for 7 days.ELISA tests were performed on astrocytes culture media, on mice sera and on mice hypothalamus and frontal cortex tissues to confirm the installation of depression in LPS treated mice, and to confirm the antidepressant effect of luteolin in this mice model.® ELISA kit . The Tumor Necrosis Factor TNFα, and the corticosterone levels were quantified in all mice sera using the Quantikine® ELISA kit cat. number MTA00B purchased from R&D Systems, United States. The Corticosterone ELISA kit (cat. number ADI-900-097) purchased from Enzo Life Sciences, NY, United States was used to quantify corticosterone levels in mice sera.The pro-inflammatory cytokine IL-6 was quantified in the mice primary astrocyte culture medium at day 1, day 3, and day 7 and in mice sera using the mouse Quantikineg, 4°C. The supernatant was collected and stored at −80°C. The dopamine, Sert and NA were quantified using ELISA kits . Pro and mature BDNF were measured by colorimetric sandwich ELISA kit . The experiments were conducted following the manufacturer’s instructions. The results of each treatment group were corrected by their respective total protein content determined using PierceTM BCA Protein Assay Kit .The serotonin (Sert), noradrenaline (NA), dopamine, pro-and mature-BDNF levels were quantified in mice hypothalamus and frontal cortex (2 mice from each group were used). First, we homogenized 20 mg of tissue in 1 mL of RIPA buffer. The homogenate was centrifuged for 5 min at 10,000 × To extract hNSCs RNA, cells were seeded in 6 well plates at the density of 2.5 × 104 cells/cm2 and were incubated at 37°C for 48 h. After that, they were treated with differentiation medium with or without 1 μM of luteolin or 1 μM of AICAR and were incubated at 37°C for 8 and 24 h. After the treatment, cells were washed twice with ice-cold PBS (−). To extract RNA from mice hippocampus, two mice brains of each animal group were dissected and hippocampus tissues were used. The isolated NSCs from two mice brains were immediately used to extract total RNA.The ISOGEN kit  was use for all total RNA extraction experiments following the manufacturer’s instructions, as reported previously by To elucidate the molecular mechanism underlying the effect of luteolin treatment on hNSCs fate determination, we compared the global gene expression of untreated and luteolin-treated hNSCs and to elucidate the molecular mechanism underlying the effect of luteolin treatment on LPS-induced depression model NSCs and hippocampus, we performed three global gene expression comparison studies. First, we compared the global gene expression of LPS group and control group (LPS vs. PBS), then we compared the global gene expression of control group treated with luteolin and untreated control group (PBS + L vs. PBS) and finally we compared the LPS group treated with luteolin and LPS group (LPS + L vs. LPS).These genomic analyses were made by performing microarray analysis using Affymetrix GeneChip 3′ IVT PLUS reagent kit  according to the kit user’s guide. Data normalization and transformation was done by Expression Console software. Subsequent analysis of the differentially expressed genes was performed using Transcriptome Analysis Console (version 4) and DAVID online tool . Heatmap20/dNTP were incubated for 5 min at 5°C, then placed in ice for 1 min. The Superscript® IV reverse transcriptase solution was added. The mixture was vortexed and centrifuged, then TaqMan probes specific to, Glial Fibrillary Acidic Protein (GFAP) (Hs 00909233_m1), Bone Morphogenetic Protein 2 (BMP2) (Hs00154192_m1), Signal Transducer and Activator of Transcription (STAT3) (Hs00374280_m1), Tubulin, beta 3 class III (TUBB3) (Hs00801390_s1), NOTCH 1 (Hs01062014_m1), NOTCH 3 (Hs00166432_m1), Myelin Basic Protein (MBP) (Hs 00921945_m1) genes and master mix TaqMan were added to 100 ng/9 μL of obtained cDNA, and the mixture was introduced to 7500 Fast Real-time PCR  and the following conditions were applied: 50°C for 2 min, followed by 95°C for 10 min, and 40 cycles of 95°C for 15 s followed by 60°C for 1 min. Glyceraldehyde-3-Phosphate Dehydrogenase (GAPDH) gene (Hs02786624_g1 GAPDH) was used as an endogenous control. All primers were purchased from , and all reactions were run in triplicates.RNA extracts obtained from hNSCs treated or not with 1 μM luteolin for 8 and 24 h were used to validate the microarray data. Reverse transcription was performed using the Superscript IV reverse transcriptase kit  following the manufacturer’s guidelines. A mixture of RNA samples (30 ng/μl) and Oligo d (T)t-test was used when two groups were compared in MTT assay, astrocytes quantification and all ELISA tests. Statistical analysis of the results obtained in the immunocytochemistry and the real-time polymerase chain reaction (RT-qPCR) validation results were carried out using one-way analysis of variance (ANOVA) followed by Dunnett’s post hoc test. The difference in signal intensities between control and luteolin-treated hNSCs was tested using student’s t-test. Statistical analysis of TST results were carried out using paired t-test to compare the same groups results obtained between Day 1 and Day 8, and using One-way ANOVA test to compare different groups results at Day 1 and at day 8. Results are expressed as mean ± standard deviation unless otherwise indicated. The criterion of statistical significance was p < 0.05.Statistical analysis was performed using SPSS software . A Student’s p < 0.001), concentrations ranged from 5 to 10 μM did not affect cell viability while higher concentrations induced cell toxicity in a dose dependent-manner after 24 and 48 h treatment (p < 0.01). Concentrations ranged between 0.5 and 5 μM did not affect cell viability, while higher concentration significantly reduced the viable SH-SY5Y cell number to 70% (Data not shown).Results from MTT assay indicate that 1 μM luteolin slightly increased cell viability of SH-SY5Y cells after 24 h and 48 h treatment (reatment . Treatmep < 0.05), with upregulation of 2638 genes and downregulation of 3232 genes. Top 10 significantly up and downregulated genes and their functions are presented in We evaluated the effect of luteolin on the transcriptomic changes in hNSCs during differentiation at 24 h using DNA microarray. We found that treatment with 1 μM luteolin could significantly regulate the expressions of 5870 genes in hNSCs , 23 genes involved in Wnt signaling pathway (p = 0.091), 23 genes in the dopaminergic synapse (p = 0.025) and 23 genes involved in the Neurotrophin pathway (p = 0.024). Also, luteolin treatment downregulated 30 genes in FOXO signaling (p = 0.026), 29 genes in dopaminergic synapse (p = 0.046), 26 genes in TNF signaling (p = 0.015), 19 genes involved in p53 signaling pathway (p = 0.009), and 13 genes in notch signaling (p = 0.049)  .p < 0.05; NEUROG2), DNA (cytosine-5-)-methyl transferase 1 (DNMT1), DNA (cytosine-5-)-methyl transferase 3 beta (DNMT3B), neuronal differentiation 1 (NEUROD1), neuronal differentiation 6 (NEUROD6), NOTCH3, bone morphogenic protein (BMP7), and TUBB3 that are known to regulate neuron differentiation. The expression of methyl-CpG binding protein 2 (MECP2) that is known to regulate neuron maturation, was also downregulated (WNT7B), GFAP, and Janus kinase (JAK) 2 expression  differentiation process . We founegulated . Besidespression , whereasvia upregulation of astrogenic genes such as GFAP, WNT, and JAK, while it suppressed the neuronal and oligodendrocytes differentiation by downregulation of NEUROG2, DNMT1, DNMT3B, NEUROD1, NEUROD6, NOTCH3, BMP7 and TUBB3, and MBP.Taken together, results from microarray analysis showed that 1 μM luteolin treatment for 24 h induced differentiation of hNSCs and directed its fate toward astrocytes. Besides, these results also suggest that luteolin modulated the differentiation of hNSCs toward astrocytes + and GFAP+ cells were detected after 24 h of incubation (+ cells were not detected within 24 h of differentiation induction with both control and treatment solutions (Data not shown). When treated with differentiation medium, 59.97% of hNSCs were differentiated into neurons (TUBB3+ cells) and 40.03% cells were differentiated into astrocytes (GFAP+ cells); however, 1 μM AICAR and 1 μM Luteolin treatments significantly decreased the percentages of TUBB3+ cells to respectively 36.66 and 33.2% (p < 0.05) and increased the percentages of astrocytes (GFAP+ cells) to 64.44 and 67.8%, respectively (p < 0.05). These data indicate that as AICAR treatment, luteolin treatment enhanced the differentiation of hNSCs into astrocytes  and luteolin treated  cells compared to differentiation medium-treated control cells. This finding confirms that luteolin stimulated the differentiation of hNSCs into astrocytes.We validated the expressions of astrogliogenic genes using RT-qPCR. As shown in GFAP gene, which, in turn, induces astrocytogenesis  and with the positive astrogenic inducer AICAR   and it was significantly decreased after 24 h treatment  , and it increased after 24 h treatment  (= 0.002) . STAT3 w 0.0001) . On the  = 0.03) .NOTCH1 and NOTCH3 were not expressed in any treatment condition after 8 h . When treated with 1 μM AICAR for the same duration (24 h), the expression of NOTCH1 gene was unchanged and that of NOTCH3 significantly increased to 1.48 (p = 0.001). This result shows that unlike AICAR, luteolin inhibited Notch signaling and therefore inhibited the self-renewal of hNSC    . On the ctively)  suggestip < 0.05). The validation analysis of this gene expression using RT-qPCR also showed that hNSCs did not express the MBP gene after 24 h of luteolin treatment (Data not shown), confirming, therefore, that luteolin did not induce oligodendrocyte genesis of hNSCs.Our immunostaining results using the oligodendrocytes marker anti-MBP protein antibody showed that 1 μM luteolin treated hNSCs did not differentiate into oligodendrocytes within 24 h of differentiation induction (data not shown). In its turn, the global gene expression analysis shows that this treatment downregulates the MBP gene  slightly decreased the immobility time of LPS-induced depression mice on the 8th day of TST to 77.5 ± 52.5 s compared to the initial test performed on the 1st day with a value of 107.17 ± 41.1 s. It also decreased the immobility time of mice treated with PBS + L to 77.17 ± 35.5 s compared to the initial test at day 1 (86.83 ± 55.4 s). These decreases were statistically non-significant .p = 0.008), however, luteolin treatment didn’t restore the loss of astrocytes number , while LPS treatment significantly increase the production of IL-6 by astrocytes (LPS vs. PBS group), and luteolin treatment significantly decreased this pro-inflammatory cytokine secretion by astrocytes in LPS-induced depression mice (LPS + L vs. LPS group)  .p > 0.05), the TNF-α level showed a decrease of 41.1% in control mice treated with luteolin (PBS + L) compared to untreated mice (PBS). LPS treatment significantly increased this pro-inflammatory cytokine level of 2.31% in mice sera compared to control mice (p = 0.01), and a statistically significant decrease of this cytokine level (73.15%) was observed when LPS-induced depression mice were treated with luteolin (p = 0.04) (p = 0.007). LPS treatment significantly increased this stress hormone level of 0.06% compared to control mice (p = 0.1), and luteolin treatment significantly decreased its level in LPS-induced depression mice .We evaluated the level of serum and central nervous system depression’s biological markers in mice.  = 0.04) . The corp > 0.05) the levels of pro-BDNF, serotonin and noradrenaline in the cerebral cortex of LPS-induced depression mice model  also significantly changed the levels of the mature BDNF, the noradrenaline and the dopamine in the hypothalamus of LPS-induced depression mice, and it didn’t significantly affect the level of pro-BDNF. Likewise, it didn’t change (ce model .p < 0.05). It downregulated 325 genes and upregulated 57 genes compared to untreated mice (PBS group) (data not shown). Five neuronal biological processes, namely neuron migration (GO:001764), negative regulation of neurogenesis (GO:0050768), generation of neurons (GO:0048699), regulation of neuron projection development (GO:0010975), and neurogenesis (GO:0022008); two glial biological processes particularly the gliogenesis (GO:0042063) and glial cell proliferation (GO:0014009); and four development biological processes related to brain development (GO:0007420), central nervous system development (GO:0007417), cell differentiation (GO:0030154) and nervous system development (GO:0007399), were enriched by downregulation  and to response to polysaccharide (GO:0032496) were enriched by upregulation (p = 9.87E-05). The downregulated genes include the Fos, Gnas, Creb3, Gnai2, Hspa1b, the Hspa8, Hsp90ab1, Hsp90aa1, Hsp90b1 and the Pik3cd  by evaluating the transcriptomic changes in NSCs. Results show that LPS injection (850 μg/kg) significantly regulated 382 genes  compared to untreated depression model mice (LPS + L group vs. LPS group), with upregulation of 642 genes and downregulation of 45 genes (data not shown). Top 10 significantly up and downregulated genes and their functions are presented in Arf1, Ddx6, Ndrg1, Rab3a, Actb, Apcdd1, App, Apoe, B2m, Ctnna1, Ctnnb1, Cav1, Cdc42, Clu, Dbi, Dynlt1f, Eef1a1, Eef2, Eif4g2, Fn1, Gja1, Gstp1, Gpm6a, Hey1, Hspb1, Hsp90aa1, Hexa, H2-D1, H2-K1, Id3, Id4, Itm2c, Il2, Mif, Mt2, Mt), Mgll, Ntrk2, Ppia, Pcnt, Pmp22, Plpp3, Ptn, Prom1, Plp1, Rpl4, Serpine2, Slc1a3, Slc9a3r1, Snx3, Sod1, Ywhae, and Vim, while those modulating the gliogenesis biological process are: Ndrg1, Apcdd1, Ctnnb1, Clu, Dbi, Eef2, Fn1, Gstp1, Id4, Mt2, Mt3, Ntrk2, Plpp3, Ptn, Plp1, Serpine2, Sod1, Vim. The regulated genes also significantly modulated different KEGG signaling pathways (p = 3.53E-09). Twenty-four genes of this signaling pathway were enriched by upregulation. It includes 5 genes modulating the ATP synthase, H + transporting mitochondrial complexes , 8 genes of NADH dehydrogenase , 6 genes of cytochrome c oxidase subunits , as well as guanine Gnai2, Slc25a4, Slc25a5, and Uqcrb. Also, twenty-six genes in Alzheimer’s disease KEGG signaling pathway , 26 genes in Huntington’s disease KEGG signaling pathway , 17 genes in Non-alcoholic fatty liver disease (NAFLD) KEGG signaling pathway , 10 in Estrogen signaling pathway  KEGG signaling pathway, 10 genes in thyroid hormone signaling pathway , 9 genes in HIF-1 signaling pathway , 7 genes glycolysis/gluconeogenesis , and 7 genes in adherens junction  were also enriched by upregulation. Systemic lupus erythermatosus , was the only KEGG signaling pathway enriched by downregulation. Details about regulated genes are mentioned in We also evaluated the effect of oral administration of luteolin (10 mg/kg body weight) on the transcriptomic changes in NSCs of LPS-induced depression mice. The obtained results show that treatment with luteolin could significantly regulate the expression of 687 genes in NSCs of this animal model  compared to untreated normal mice, with upregulation of 314 genes and downregulation of 428 genes (data not shown). Moreover, we found that luteolin treatment significantly downregulated 4 glial biological processes, namely the glial cell migration (GO: 008347), negative regulation of gliogenesis (GO:0014014), glial cell differentiation (GO:0010001), and negative regulation of oligodendrocyte differentiation (GO:0048715) in NSCs of normal mice (PBS + L group), while it downregulated 8 neuronal biological processes, up regulated 9 others , the downregulation of 9 genes involved in the glial cell differentiation , the downregulation of 4 genes related to the negative regulation of gliogenesis  and the downregulation of the 4 genes involved in the glial cell migration . The luteolin treatment also enriched 8 KEGG signaling pathway by downregulation and only one KEGG signaling by upregulation (p = 0.04) with 6 genes being upregulated . The KEGG signaling pathways enriched by downregulation were Hippo signaling pathway , tight junction , thyroid hormone signaling pathway , gap junction , HIF-1 signaling , dopaminergic synapse , Alzheimer’s disease , and Parkinson’s disease   on the transcriptomic changes in NSCs of normal mice  . Detailsp < 0.05). It downregulated 348 genes and upregulated 317 genes in this animal model of depression (LPS group mice) compared to untreated mice (PBS group) (data not shown). Nine neuronal biological processes including neuron differentiation (GO:0030182), neurogenesis (GO:0022008) were significantly downregulated. Ten genes related to gliogenesis (GO:0042063), 6 genes related to oligodendrocyte differentiation (GO:0048709), and 9 genes related to glial cell differentiation (GO:0010001) were also significantly downregulated. However, ten immune biological processes were significantly upregulated . LPS injection also significantly enriched KEEG signaling pathways by upregulation , cell adhesion molecules (CAMs) , tuberculosis , and antigen processing and presentation . Details about regulated genes are presented in We used DNA microarray to study the transcriptomic changes in the hippocampus of the depression mice model. Results show that LPS injection (850 μg/kg) significantly regulated 665 genes  compared to untreated depression model mice (LPS + L group vs. LPS group), with upregulation of 324 genes and downregulation of 1047 genes (Data not shown). Top 10 significantly up and downregulated genes and their functions are presented in p = 0.04) namely the Col2a1, Egfr, Fgf18, Il4ra, Lama5, Pik3cg, Prlr, Rxra, and the Rps6; 7 genes in cytokine-cytokine receptor interaction  including the Relt, Ccr2, Eda, Inhbb, Il17ra, Il4ra, and the Prlr; 5 genes in serotonergic synapse  namely the Htr5b, Gnas, Alox12, Alox15, Alox8; 5 genes in Jak-STAT signaling  including the Crebbp Il4ra, Pik3cg, Prlr, and the Stat5b; and 4 genes in Estrogen signaling pathway  including the Elovl5, Acaa1a, Acaa2, Acsl3, Acadl, Elovl2, and the Fads2. Besides, luteolin treatment downregulated 7 genes in fatty acid metabolism KEEG signaling  particularly the Elovl5, Acaa1a, Acaa2, Acsl3, Acadl, Elovl2, Fads2; 5 genes in biosynthesis of unsaturated fatty acids  including the Elovl5, Acaa1a, Acot1, Elovl2, Fads2, and 8 genes in GABAergic synapse  namely the Gabrb3, Gphn, Gad1, Glul, Gnai3, Gnb1, Gng2, and the Prkacb . Results show that treatment with luteolin significantly regulated the expression of 1371 genes in hippocampus of this animal model  compared to untreated normal mice, with upregulation of 381 genes and downregulation of 978 genes (Data not shown). Luteolin treatment significantly downregulated 5 immune biological processes, including inflammatory response , 6 glial processes including gliogenesis , glial cell differentiation (GO:0010001), oligodendrocyte differentiation (GO:0048709), and 4 neuronal processes namely neuron projection development , neuron differentiation , neurogenesis  and myelination  , as well as different neuronal processes, particularly, neurogenesis , neuron differentiation , positive regulation of neurogenesis , and positive regulation of neuron differentiation (GO:0045666)  on the transcriptomic changes in hippocampus of normal mice (PBS + L group vs. PBS group) using DNA microarray . The obt8 genes) . Besides0045666) .p = 0.08, 9 genes), GABAergique synapse , neurotrophin signaling pathway , hippo signaling pathway , PI3K Akt signaling pathway  and signaling pathways regulating pluripotency of stem cells , were significantly upregulated . Numerous studies reported that a modification of astrocytes in the frontolimbic regions like the hippocampus, amygdala and ventral striatum is associated with depression. Likewise, most major depressed patients’ post-mortem brain analyses reported a decreased number of astrocytes in frontolimbic structures . The cauGene ontology enrichment analysis of differentially expressed genes between luteolin-treated and untreated control hNSCs showed that the most significantly enriched biological process was cell differentiation, followed by, but not limited to, neurogenesis, neuron development, neuron projection development, brain development, and regulation of astrocyte differentiation. When we averaged the signal intensities of genes related to astrogenesis-specific biological processes, we found that astrocyte differentiation- and positive regulation of astrocyte differentiation-related genes had higher signal intensity in luteolin-treated hNSCs compared to control cells, whereas negative regulation of astrocyte differentiation-related genes had lower signal intensity. Several KEGG signaling pathways, namely NOTCH, FOXO, TNF, p53, Hippo, dopaminergic synapse, and neurotrophin pathways, were also significantly regulated.NOTCH1 and NOTCH3 , JAK2, STAT4, STAT6, and Brain-derived neurotrophic factor (BDNF). Besides, we also found that luteolin treatment could upregulate the expression of NEUROG1, whereas downregulate the expression of NEUROG2. NEUROGs are the proneural genes, which encode basic-helix-loop-helix (bHLH) transcription factors. Although both NEUROG1 and NEUROG2 individually promote neurogenesis, NEUROG1 exerts a non-canonical role through inhibiting the proneural activity of NEUROG2, thus, in turn, induces the expression of negative regulators of neurogenesis, and represses the expression of positive regulators of neurogenesis, such as NEURODs , an important zinc finger protein transcription factor implicated in neurogenesis and in a pathological pathway of negative regulation of BDNF , is crucial for NSC self-renewal and neurogenesis , the transcription factor of GFAP. Therefore, we opted to evaluate the expression of STAT3, its upstream regulator BMP2, and GFAP after 8 h of hNSCs differentiation induction by luteolin treatment. There was an increased expression of the STAT3 in luteolin-treated hNSCs after 8 h, while BMP2 and GFAP were not expressed. Since it was reported that GFAP expression depends on both BMP2 signals and STAT3 expression  may highlight the inhibitory effect of luteolin treatment on hNSCs self-renewal process , however, luteolin treatment didn’t restore this cell loss. On the other hand, results from isolated mice astrocytes showed that secretion of IL-6 by astrocytes isolated from both the control group (PBS group mice) and the control group treated with luteolin (PBS group + L mice) was unchanged. Besides, this cytokine level was significantly increased in the cell culture media of astrocytes isolated from LPS treated mice, and it was significantly decreased in the cell culture media of astrocytes isolated from luteolin treated LPS-induced depression mice. These results highlighted the installation of the neuroinflammatory process in LPS treated mice that was neutralized by the luteolin treatment. In this context different studies, reported the secretion of IL-6 by astrocytes in different pathological conditions including major depression disease  and gliogenesis (GO:0042063) that were mostly upregulated with luteolin treatment in mice NSCs and not in hippocampi. In NSCs, except the pression . In theipression .in vivo transcriptomic study showed also that LPS injection enriched different KEGG signaling pathways by downregulation in mice NSCs with the estrogen signaling (mmu04915) being the most enriched signaling pathway (p = 9.87E-05). Luteolin treatment enriched by upregulation, the estrogen signaling , the HIF-1 (mmu04066) and the thyroid hormone (mmu1919). In the NSCs of normal mice group, it enriched the Wnt signaling by upregulation.Our Recently, the role of estrogen signaling has been highlighted with studies reporting its neuroprotective effects on the brain by sheltering it from inflammation and stress , and invOn the other hand, thyroid hormone signaling, a key modulator of neuropsychiatric disorders including depression, is known as a regulator of the developmental program in the brain. The thyroid hormone action targets diverse cellular processes such as progenitor turnover, cell survival and differentiation, cellular homeostasis, and metabolic regulation. In mice embryonic neural stem cells, it promotes neuronal differentiation by inhibiting STAT3 signaling through the thyroid receptor α1, and inhibits astrocytic differentiation, in rodents Thyroid Hormone signaling activates mitochondrial metabolism during NSC commitment to neuronal precursor cells, and in humans, it regulates hippocampal neurogenesis dependent behaviors such as mood-related behaviors .As previously mentioned, the Wnt signaling plays a curcial role for NSC self-renewal and neurogenesis ; and it via the modulation of the estrogen, HIF-1 and thyroid hormone signaling pathways in the NSCs of mice. Equally these findings highlight the modulatory effect exerted by luteolin on signaling pathways involved in NSCs fate determination namely the Wnt signaling.Considering these findings, our results point out an anti-depressant effect of luteolin treatment exerted especially In the hippocampus of LPS induced depression model mice, the luteolin treatment enriched the GABAergic synapse by downregulation, and the PI3K-AKT (mmu04151), the JAK-STAT (mmu04630), and the estrogen signaling (mmu04915) by upregulation.Recently, PI3K/Akt/GSK-3β/mTOR signaling pathway has been associated to neurobiology of depression and seems to be modulated by some pharmacological antidepressant strategies. In addition to its aforementioned role in the modulation of GFAP gene expression, JAK/STAT pathway is involved in mediating several functions of the central nervous system, including neurogenesis, synaptic plasticity, gliogenesis, and microglial activation, and all of which have been implicated in the pathophysiology of mood disorders. Moreover, there is also direct evidence from studies in populations with depressive disorders, suggesting that JAK/STAT pathways may be involved in the pathophysiology of depression and the antidepressant actions of current treatments have been shown to be mediated by JAK/STAT-dependent mechanisms . Figure in vitro study using hNSCs, the in vivo study confirms the potential astrogenic effect of luteolin in the defected astrogenesis model and highlights its modulatory effect on WNT-JAK-STAT signaling. This in vivo study highlighted also the modulatory effect of luteolin on different signaling pathways involved in the pathophysiology of depression. So far, the anti-depressant effect of luteolin treatment via Suppressing Endoplasmic Reticulum Stress was reported  limited the study of the protein expression of genes evaluated by RT-qPCR as well as the study of the composition of the NSC population including the percentage of SOX2 and PAX6 expression that would be informative and the percentage of mature neurons and TUBB3 percentage within the NSC population. The results from this in vitro study, therefore, require a future replication with other type of hNSCs.Given the non-static nature of NSCs, the effects of its source and its derivation method that may significantly affect its differentiation potential into specific cell types, the disrupt of commercialization of the hNSCs used in our study , GSE181285 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181285), and GSE181522 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE181522).All data generated or analyzed during this study are included in this published article and its supplementary information files. Microarray data are deposited in the Gene Expression Omnibus (GEO) under Accession Numbers: The animal study was reviewed and approved by the Animal Ethics Committee of the University of Tsukuba, Japan.MA contributed to conceptualization, methodology, investigation, formal analysis, visualization, software, and writing – original draft. FF contributed to formal analysis, visualization, software, validation, and writing – review and editing. KS contributed to methodology, investigation, and supervision. HI contributed to conceptualization, resources, supervision, project administration, and funding acquisition. All authors read and approved the final manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Pancreatic ductal adenocarcinoma (PDAC) is a biologically aggressive malignancy showing a remarkable resistance to existing therapies and is often diagnosed at an advanced stage, leaving only about 15–20% of patients with an option for surgical resection [KRAS, TP53, SMAD4, and CDKN2A, which occur alone or in combination in >50% of the cases, whereas recurrent mutations in a number of other genes, including ARID1A, KDM6A, MLL3, TGFBR2, RBM10, and BCORL1, are found in <10% of tumors [ERBB2 amplification; BRAF gene fusions/mutations; and mutations in the DNA damage repair (DDR) genes BRCA1, BRCA2, or PALB2 are found in a small percentage of PDAC patients [BRCA1, BRCA2, PALB2, ATM, ATR, CHEK2, RAD51, and FANC) that cause homologous recombination deficiency (HRD) lies between 14.5 and 16.5% of PDAC cases [BRCA mutational signatures (BRCAness phenotype) can also act as surrogate biomarkers of HRD [The mutational landscape of PDAC is dominated by recurrent driver mutations in patients . NeverthAC cases . Moreoves of HRD . Thus tas of HRD . Since Hs of HRD . Additionally, “bulk” transcriptomic profiling has identified two broad PDAC subtypes with distinct biology, namely Classical and Basal-like, with Basal-like tumors associated with significantly poorer outcome ,12,13,14+ cytotoxic T cells and high numbers of immunosuppressive immune cell populations, rendering most PDAC patients poor candidates for immunotherapy [+ T cell tumor infiltration is inversely correlated with myeloid cell enrichment [+ T cells can exhibit exhausted phenotypes which can increase with disease progression [+ T cell signatures were associated with increased expression of the immune checkpoint TIGIT  [In addition, the tumor microenvironment (TME) of PDAC has been established as an important player affecting disease progression and response to therapy ,17,18. Totherapy ,20. Indeotherapy ,22. Howeotherapy . Moreoveotherapy . Thus, Botherapy   . The lig domain) . Recent  domain) ,24,28. T domain) ,17. It iKRAS and MYC, can also modulate the PDAC TME and enhance its immunosuppressive nature [BRCA1- and BRCA2-deficient tumors are associated with increased immune infiltrates, the rates of response to ICI are low. Recent evidence in mouse models of breast and colorectal cancer suggest that BRCA2-deficient tumors are more susceptible to ICIs than BRCA1-deficient tumors [CDKN2A, which is a feature of PDAC, has been identified as a biomarker of immune checkpoint therapy resistance in solid tumors [Many studies have demonstrated that genetic changes, such as e nature ,30,31. Ce nature . While Bt tumors ,33. In ad tumors . These sIn conclusion, for the implementation of precision oncology in the management of PDAC patients, the use of appropriate biomarkers in routine clinical care is necessary. Cancer biomarkers detected in tumor tissue, blood, or other fluids can aid in the early detection of PDAC or its recurrence and may have prognostic as well as predictive roles. These biomarkers are still being discovered.


YABBY genes in Cucurbitaceae. Here, we identified 59 YABBY genes from eight cucurbit species, including cucumber (C. sativus L.), melon (C. melon L.), watermelon (C. lanatus), wax gourd (B. hispida), pumpkin (C. maxima), zucchini (C. pepo L.), silver-seed gourd (C. argyrosperma), and bottle gourd (L. siceraria). The 59 YABBY genes were clustered into five subfamilies wherein the gene structures and motifs are conserved, suggesting similar functions within each subfamily. Different YABBY gene numbers in eight cucurbit species indicated that gene loss or duplication events exist in an evolutionary process across Cucurbitaceae. The cis-acting elements analysis implied that the YABBYs may be involved in plant development, and phytohormone, stress, and light responses. Importantly, YABBY genes exhibited organ-specific patterns in expression in cucumber. Furthermore, a gene CsaV3_6G038650 was constitutively expressed at higher levels at different fruit development stages and might play a crucial role in cucumber fruit development. Collectively, our work will provide a better understanding for further function identifications of YABBY genes in Cucurbitaceae.YABBY transcription factors play important roles in plant growth and development. However, little is known about  YABBY gene family, which belongs to the zinc finger protein superfamily, is plant-specific transcription factors (TFs). All YABBY members share two highly conserved domains that are characterized by a N-terminal C2C2 zinc finger domain and a helix–loop–helix motif  at their C-terminus [YABBY gene family was described and six members were identified in Arabidopsis, including YABBY1 (YAB1)/FILAMENTOUS FLOWER (FIL), CRABS CLAW (CRC), INNER NO OUTER (INO), YABBY2 (YAB2), YABBY3 (YAB3), and YABBY5 (YAB5) [YABBY genes are unique to seed plants [YABBY gene family have been performed in various plant species, such as tomato (9) [The terminus ,2. The fd plants ,5 and gemato 9) , Chinesemato (9) , rice , OsYAB4 (TOB2), and OsYAB5 (TOB1) are enriched in lateral organ primordia and play a crucial role in rice spikelet development [YABBY genes have diverse roles in plant growth and development; meanwhile, the functions of each subgroup are both differentiated and conserved.Based on evolutionary relationships, the angiosperm and YAB5 ,11. Subsiosperms ,13. In Aelopment ,15. FIL,eristems ,16. FIL and YAB5 ,17, and elopment ,19. Whilelopment ,21, INO tegument . Furtherd leaves ,24,25. Dattering ,27. OsYA carpels . OsYAB3 elopment ,30. ThesC. sativus L.), melon (C. melo L.), watermelon (C. lanatus), and squash/pumpkin (C. maxima) [YABBY gene family has been characterized in many plant species, little is known of YABBY gene characteristics in Cucurbitaceae. There are only two reports on the YABBY gene functions in cucumber. Liu et al. (2018) found that CsYAB1, CsYAB3, and CsINO are involved in the integument of ovules by interacting with CsSPL [CsYAB5 regulates leaf morphology, and vascular and fruit development [The Cucurbitaceae are widely distributed in the tropics and subtropics . The maj maxima) . These c maxima) ,33,34 an maxima) . Many cu maxima) , and vit maxima) . Importa maxima) ,39, the  maxima) ,41, the  maxima) ,43. Althth CsSPL . The othelopment .YABBY genes in Cucurbitaceae, including cucumber (C. sativus L.), melon (C. melo L.), watermelon (C. lanatus), wax gourd (B. hispida), pumpkin (C. maxima), zucchini (C. pepo L.), silver-seed gourd (C. argyrosperma), and bottle gourd (L. siceraria). We systematically performed gene structure, conserved motifs, chromosomal location, cis-acting elements, and phylogenetic analysis of 59 YABBY genes in the eight cucurbit species. The evolutionary relationship of YABBY genes between cucumber and other cucurbit species was explored. Finally, the expression patterns of YABBY genes in cucumber were investigated in various organs/tissues and different development stages of ovary/fruit. Our results will provide valuable clues for the function identifications of YABBY genes in Cucurbitaceae.Here, we identified and characterized the http://pfam.xfam.org/ (accessed on 6 October 2021)) and was used to identify YABBY genes in eight cucurbit species by HMMER 3.0 software . All predicted YABBY sequences were further manually examined to confirm the conserved C2C2 zinc finger domain at N-terminus and YABBY domain at C-terminus using CDD [https://www.ncbi.nlm.nih.gov/cdd (accessed on 6 October 2021)) and SMART [http://smart.embl-heidelberg.de (accessed on 6 October 2021)). The YABBY genes of each species were mapped on chromosomes by the online software MapGene2Chrom [http://mg2c.iask.in/mg2c_v2.0/ (accessed on 6 October 2021)). The physicochemical properties, including molecular weights (MW) and isoelectric points (pI), were predicted with the ProtParam tool on the ExPASy server [https://web.expasy.org/protparam/ (accessed on 6 October 2021)). The genome data for eight species in Cucurbitaceae were downloaded from Cucurbit Genomics Database (http://cucurbitgenomics.org/ (accessed on 6 October 2021)) and Arabidopsis protein data (Araport11 protein lists) was downloaded from TAIR (https://www.arabidopsis.org/ (accessed on 6 October 2021)).The YABBY domain PF04690) was downloaded from the Pfam protein families database [90 was dosing CDD  (https:/nd SMART  (http://ne2Chrom  (http://y server  (https:/https://itol.embl.de/ (accessed on 7 October 2021)). To explore the gene duplication events, the collinearity analysis was performed with Multiple Collinearity Scan toolkit (MCScanX) [Multiple sequence alignment of all identified YABBYs in Cucurbitaceae was carried out using ClustalW, and a phylogenetic tree was generated by neighbor-joining (NJ) method with default parameters: bootstrap method setting to 1000, Poisson model, and complete deletion in MEGA 11. The tree was visualized and optimized via Interactive Tree Of Life (iTOL) (MCScanX) .YABBY gene structures were analyzed by Gene Structure Display Server (GSDS) [http://gsds.gao-lab.org/ (accessed on 7 October 2021)). The MEME online program [https://meme-suite.org/meme/tools/meme (accessed on 7 October 2021)) was employed to predict the motifs within the 59 Cucurbitaceae YABBY protein sequences. All of the identified r (GSDS)  ).The promoter sequences (2000 bp upstream of ATG) of 59 software . The cisdatabase  (http://The cucumber cultivar (“Xintaimici”) was grown in a greenhouse in Beijing, China. Roots and tender stems of two-week-old seedlings, the third true leaves, tender tendrils, male and female buds at 8 DBF (days before flowering), and ovaries/fruits at different growth stages were selected as samples, frozen in liquid nitrogen, and stored at −80 °C.UBIQUITIN EXTENSION PROTEIN (UBI-EP) gene [t-tests (p ≤ 0.05). The primers were listed in The sample RNA was extracted using the Quick RNA Isolation Kit . The FastKing gDNA Dispelling RT SuperMix  was applied to synthesize the first-strand cDNA with the extractive RNA template. qRT-PCR was performed using the UltraSYBR Mixture (Low ROX)  on an Applied Biosystems 7500 real-time PCR system . The EP) gene  was usedCsYABBY genes at 4 DBF, ovaries from two near isogenic lines with different fruit lengths were obtained from publicly available transcriptomic data, which were downloaded from Gene Expression Omnibus (GEO) and analyzed to reveal the genes and gene networks that regulate fruit length in cucumber (GSE60346) [http://cucurbitgenomics.org (accessed on 20 October 2021)) by Hisat2, and the TPM values were recalculated. Every line had two biological replicates. The p-value ≤ 0.05 and fold-change ≥ 1.5 were used to define differential expression genes. The expression pattern of the YABBY genes was shown on a heatmap using TBtools software [For the expression patterns of SE60346) . Clean tsoftware .YABBYs were finally obtained in Cucurbitaceae, including 8 from cucumber (C. sativus L.), 4 from melon (C. melon L.), 9 from watermelon (C. lanatus), 5 from wax gourd (B. hispida), 11 from pumpkin (C. maxima), 9 from zucchini (C. pepo L.), 10 from silver-seed gourd (C. argyrosperma), and 3 from bottle gourd (L. siceraria)  search using YABBY domain (PF04690) across eight cucurbit species. Based on a further confirmation of conserved C2C2 domain and YABBY domain by CDD and SMART analysis, 59 ceraria) . These gceraria) .YABBY genes on chromosomes of seven cucurbit species , respectively. The YABBY genes of each cucurbit species were randomly distributed on their chromosomes and distribution results were shown in YABBY genes in cucumber, the nine YABBYs were distributed on chromosome 1, 2, 5, 6, 8, 10, and 11 other than chromosome 3, 4, 7, and 9 in watermelon  by MCScanX. The collinear gene pairs between cucumber and melon, wax gourd, watermelon, bottle gourd, pumpkin, and zucchini were 10, 9, 14, 14, 19, and 21, respectively (CsYABBYs showed a syntenic relationship (at least two syntenic gene pairs of each CsYABBY gene) with zucchini YABBYs, 75% of CsYABBYs  shared two, or more than two, syntenic pairs with the YABBYs in watermelon, pumpkin, and bottle gourd   is less than that obtained by synteny analysis  , suggestanalysis . YABBYs open reading fame (ORF) from eight cucurbit species to explore the YABBYs evolution in Cucurbitaceae. An unrooted evolutionary tree was conducted by MEGA 11 and used for distinguishing different YAB subfamilies. As shown in YABBYs contained exon numbers ranging from 5 to 12 and intron number varying from 4 to 11. Among them, all YAB2 subfamily members had six exons and five introns, most members of the CRC subfamily (except Carg17317 with nine exons and eight introns in silver-seed gourd) shared seven exons and six introns  and methyl jasmonate  were identified, while fewer response elements associated with auxin (TGA-element), cytokinin (O2-site), gibberellin (P-box), and salicylic acid (TCA-element) were discovered. Moreover, the stress response elements—such as TC-rich repeats for defense and stress, MBS for drought stress, and LTR for low temperature stress—were also found. Notably, we detected some development-related elements but with fewer numbers, including CAT-box (meristem), circadian (circadian control), GCN4_motif (endosperm), MSA-like (cell cycle), and RY-element (seed-specific)  . Taken tYABBYs in Cucurbitaceae, we selected cucumber as a representative species to examine YABBY gene expression patterns in different organs including root, stem, leaf, tendril, male bud, female bud, and different development stages of ovary/fruit. As shown in CsYABBYs exhibited various expression patterns in test materials. The four genes CsaV3_1G030340, CsaV3_2G002960, CsaV3_3G003040, and CsaV3_5G003950 had high expression levels in leaf. We noticed that CsaV3_2G002960 also expressed higher in tendrils and CsaV3_2G024750 shows high expression level in stems. Moreover, we found that CsaV3_5G003950 and CsaV3_5G033400 are highly expressed in floral organs. In addition, CsaV3_2G002960, CsaV3_3G003040, and CsaV3_6G038650 also had higher expression levels in male and female buds. However, the expression of CsaV3_5G031440 was mostly undetectable in all organs  but not fruit development stages, whereas CsaV3_6G038650 shows high expression at all fruit development stages  other than ovary development stages  were higher expressed in line 408 which is characterized by long fruit compared to the line 409 which has a short fruit [YABBYs may play various and important roles during plant growth and development in cucumber, such as leaf, tendril, stem, male flower, and ovary/fruit development.To explore the detailed expression profiles of l organs . Given tt stages A. The sit stages . Four YArt fruit   being lower than that obtained by synteny analysis and one and/or two YABBY subfamilies are lacking in melon, wax gourd, zucchini, and bottle gourd. Possibly due to the low quality of silver-seed gourd genome draft, we could not perform the synteny analysis between cucumber and silver-seed gourd. Hence, further studying is recommended to explore whether lower quality of genome draft or gene loss events during evolutionary process are the cause of fewer YABBY genes and the lacking of some subfamilies in these cucurbit species.Recently, the whole-genome sequences of eight cucurbit species—including cucumber, melon, watermelon, wax gourd, pumpkin, zucchini, silver-seed gourd, and bottle gourd—were released or updated ,64,65,66ectively . Like Arand YAB5 . Notably species . We spece family . The synBY genes , we notiax gourd , and theax gourd . Furtherax gourd , bottle families . Since tCarg17317 with nine exons and eight introns in silver-seed gourd) members contained seven exons and six introns. YAB2 and CRC subfamilies commonly contained motif 1, motif 2, motif 4, and motif 6; meanwhile, the YAB2 subfamily had motif 8 but lost motif 5, whereas the CRC subfamily had motif 5 but lacked motif 8 /YAB3, YAB2, and YAB5 are associated with leaf and floral organ development in Arabidopsis and rice [YAB2 homolog) CsaV3_5G003950, CsaV3_3G003040, CsaV3_2G002960, and CsaV3_1G030340 in leaf and floral organs suggested the functional conservation of these genes between cucumber, Arabidopsis, and rice  was reported in involved with integument of ovules but not with functional verification [Importantly, the expression patterns of and rice ,29,30,67and rice . Meanwhibidopsis ,21 and rbidopsis , in fema in stem , althougCsaV3_2G002960, CsaV3_3G003040, CsaV3_5G003950, and CsaV3_5G033400—showed higher expression levels at all ovary development stages but not in fruits (CsaV3_5G003950—the homolog of YAB1—had very high transcriptional level and thus might mainly regulate ovary development of cucumber, this is in line with the function of YAB1 which is involved in abaxial cell type specification in leaves and fruits in Arabidopsis [TOB1, TOB2, and TOB3—are required to maintain proper function of the spikelet and branch meristems [CRC homolog in cucumber, CsaV3_5G033400, was significantly expressed in the early development stage of ovary at 12DBF and 9DBF (YAB2 homolog CsaV3_6G038650 is more highly expressed in fruit than ovary (YAB2 is involved in polarity development of leaf and flower [CsaV3_6G038650 in fruit implied its prominent function in cucumber fruit development. Coincidentally, fas—a YABBY-like transcription factor homologous to Arabidopsis YAB2—regulated fruit development by controlling carpel number in tomato, resulting in an extreme fruit size [SH1 or ObSH3—which are closely related to Arabidopsis YAB2—is required for seed shattering during domestication [YAB2 gene between cucumber, tomato, cereals, and Arabidopsis, and CsaV3_6G038650 is a possibly major regulatory factor of fruit development in cucumber. Taken together, the results implied that the YABBY genes possibly play vital roles in many aspects of plant growth and development in cucumber—such as leaf, tendril, and ovary/fruit—which will contribute to the applications of YABBY genes in breeding of cucumber, even Cucurbitaceae.Furthermore, four genes—n fruits A, suggesbidopsis ,16. In rand 9DBF A, indicaan ovary A. Althouuit size . Additiotication ,27. ThesYABBY genes were identified from eight cucurbit species. A systematic characterization study was performed for chromosomal location, gene structure, conserved motifs, cis-acting elements, evolutional relationship, and gene duplication. The evolutionary relationship showed that the YABBY genes from Cucurbitaceae are classified into five subfamilies. Gene duplication events occurred between cucumber and watermelon, bottle gourd, pumpkin, and zucchini, which contributes to YABBY gene family expansions in these four cucurbit species. However, we have not confirmed whether or not low quality of genome draft or gene loss events are the cause of fewer YABBY genes in bottle gourd, melon, and wax gourd—further verification is required. The expression patterns of most YABBY genes in cucumber were similar with that in Arabidopsis and rice, indicating the function conservation of these YABBY genes among Cucurbitaceae. However, the different expression patterns of several CsYABBY genes are needed to illuminate by their function verifications. Importantly, we identified CsaV3_6G038650 as a potential regulatory factor of fruit development in cucumber. In conclusion, our study provided a foundation for further research on YABBY gene functions which will facilitate breeding in Cucurbitaceae.In this study, 59


Prostate cancer (PCa) is the commonly generated noncutaneous neoplasm among men worldwide. Glycolysis had been validated to promote cancer progression. However, the clinical significance of glycolytic regulators in PCa was not well understood. Here, we discovered that glycolytic regulators were dysregulated in PCa samples using GSE8511, GSE6919, and GEPIA. By detecting the expression of these regulators in PCa samples, we found that SLC2A1, SLC2A3, HK2, PFKFB2, TPI1, PKM2, and LDHA had higher expression in PCa compared with normal tissues. Moreover, both higher expression of TPI1, ALDOA, ENO1, LDHA, and PKM and lower expression of LDHB and HK2 were significantly related to shorter progression-free survival time in PCa. Of note, an 8 gene-based risk score was further constructed and confirmed to have a good performance in predicting progression-free survival (PFS) time in PCa. The signature risk score significantly correlated with NK cell, neutrophil cell, macrophage M2 cell, and myeloid dendritic cell infiltration levels in PCa. After bioinformatics analysis, our data suggested glycolytic regulators participated in the regulation of multiple nonmetabolic biological processes, such as RNA transport, biosynthesis of antibiotics, and cell cycle. We recapitulate that the glycolytic regulator signature was a prospective indicator for prognosis and immune cell infiltration levels in PCa. Prostate cancer (PCa) was one of the widely occurring malignant neoplasms amid males worldwide . The letAs a multifactorial pathema, PCa has the characteristics of abnormal activities of diverse regulatory pathways. Among the pathways, Warburg effect was regarded as one pivotal indicator of cancer cell . Warburg+, CD4+, or CD8+ cells were found to be protumorigenic. In addition, CD20+ B cells were enriched in PCa samples compared to normal tissue. High infiltration levels of IL-23-positive cells correlate with abiraterone effectiveness in PCa. Several factors had been reported to correlate to immune infiltration. For example, GPR30 knockdown reduced macrophage infiltration and M2 polarization in PCa. HCG18 and MCM3AP-AS1 increased abundance of M2 macrophage infiltration in PCa. However, the effect of glycolytic enzymes on immune infiltration in PCa remained to be unclear.The immune therapy is becoming the promising method to treat tumors, such as lung cancer, gastric cancer, and colon cancer. Despite the fact that PD-1/L1 has not been confirmed to be efficacious in PCa, several novel immune therapies had been approved. For example, Sipuleucel-T was approved for the treatment of castrate-resistant PCa. Besides this, several other immunotherapies such as ipilimumab and CAR-T cell therapy are in clinical development of PCa. Thus, there was still an urgent need to identify novel biomarkers for immune therapy response prediction in PCa. Immune infiltration had been reported as a crucial factor affecting the efficiency of immune therapy in cancers. In PCa, immune infiltration had been confirmed to influence PCa development. For example, high levels of CD3Here, we for the first time validated glycolytic enzyme expression pattern in PCa utilizing public datasets, such as GSE6919, GSE8511, and GEPIA datasets. Then, we determined the expression of glycolytic enzymes in the tissues of PCa. Meanwhile, we also explored the association existing in survival time and gene expression. Finally, we conducted bioinformatics analysis to identify the potential impacts of the dysregulation of glycolytic enzymes on PCa. Collectively, our literature could offer new biomarkers for PCa prognosis.2-transformed values by RMA indicated normalized gene expression level. A total of 75 metastatic prostate tumor samples, 52 normal samples, and 192 PCa samples were included in the GSE6919 database. A total of 16 benign prostate samples, 13 metastatic prostate tumor samples, and 12 local PCa samples were included in the GSE8511 database. The validation of glycolytic enzyme expression pattern in PCa was performed using the GEPIA database (http://gepia.cancer-pku.cn/).Normalized GSE6919 and GSE8511 datasets were completely analyzed by robust multiarray average (with RMA) method under R 2.6.2 statistical software with affy package from BioConductor, and then, they were applied to ensure the differently expressed mRNAs . Here, wP < 0.05 meant there was obvious difference.PPI information was validated by the STRING database search tool. We defined that the comprehensive score > 0.4 was significant. PPI network construction was completed by Cytoscape software. Plugin molecular complex detection (MCODE) toolset was applied to screen the cytokeratin (PPI) network module according to MCODE scores and node number. The study was approved by the Research Ethics Committee of the Central Hospital of Wuhan, and verbal consent was obtained from all patients. All samples were collected from the Central Hospital of Wuhan, Tongji Medical College.β-actin as an internal control. Relative mRNA expression was calculated using the 2− ΔΔCt method.qRT-PCR for mRNAs was performed as described previously. The Ct values were normalized using To establish a prognostic signature, the relationship between glycolytic enzymes and OS was evaluated by LASSO and multivariate Cox regression analyses using glmnet packages in R. The Kaplan–Meier (K-M) survival analysis was performed based on the risk scores. The receiver operating characteristic (ROC) curve was analyzed based on the survival ROC package in R.T-test or Mann–Whitney U test was utilized to compare the difference existing in different groups. P < 0.05 (∗∗) meant there was significant difference with a 95% confidence level. Prism software (GraphPad) was applied to perform statistical analyses.All derived data of three independent experiments was represented as the mean ± SD. As mentioned above, some glycolytic gene expression had been reported to be overexpressed in human cancer samples, but the most glycolytic genes had not been reported in PCa so far. Thus, we applied GSE691930 and GSE8511 two GEO databases to determine whether glycolytic gene levels were differently expressed in PCa tissues, after comparison with those in normal prostate tissues.GSE8511 database analysis result showed that compared to normal tissues, HK2, PFKFB3, SLC2A1, PGK1, PKM2, and LDHA were upregulated, and PGAM1, SLC2A3, and LDHB were downregulated in PCa . GSE6919Next, the GEPIA dataset was applied to validate these analyses based on RNA-sequence data. We observed a similar result that HK2, SLC2A1, LDHA, PFKFB2, TPI1, and ENO1 were upregulated, while PFKFB3, ALDOA, PGAM1, SLC2A3, and LDHB were downregulated in PCa compared to those in normal prostate tissues .RT-PCR assay was then conducted to examine glycolytic regulators' expression levels in 80 normal prostate tissues and 20 PCa tissues. The data indicated that HK2, SLC2A1, PKM2, LDHA, SLC2A3, PFKFB2, and TPI1 levels were higher in the samples of PCa than those in the samples of normal prostate Figures . In contλmin = 0.025)-derived optimal lambda value ∗EXP (HK2) + (0.1896)∗EXP (PGK1) + (0.1718)∗EXP (PKM) + (0.0105)∗EXP (LDHA) + (0.3124)∗EXP (ALDOA) + (−0.1481)∗EXP (LDHB) + (0.0121)∗EXP (TPI1) + (0.1128)∗EXP (ENO1). Then, we calculated each patient's risk score, and then, we utilized “survminer” R package to acquire the median cutoff point and classified those patients into the high- and low-risk groups . Our datThen, we evaluate the correlation of glycolysis signature risk score with neoplasm immune infiltration levels in PCa. Our results showed higher risk score was significantly negatively related to lower NK cell infiltration levels , neutropDespite the function of these regulators in modulating glycolysis had been elucidated clearly, the nonmetabolic function of glycolytic regulators in PCa was not yet clear. Herein, we conducted coexpression analysis for glycolytic regulators to identify their downstream targets. Bioinformatics analysis results showed that positively-related glycolytic regulators took part in regulating MAPK cascade, mitochondrial electron transport, NIK/NF-kappaB signaling, cell adhesion, mitochondrial respiratory chain complex I assembly, extracellular matrix organization, Wnt signaling pathway, mitochondrial translational elongation, inflammatory response, and angiogenesis . GlycolyPCa is the commonly generated noncutaneous neoplasm in men worldwide . DespiteEmerging studies showed that cancer cells were prone to sustaining energy supply via Warburg effect even under ambient oxygen supply. Multiple enzymes comprising SLC2A1, SLC2A3, HK2, PFKFB2, PFKFB3, ALDOA, TPI1, PGK1, PGAM1, ENO1, PKM2, LDHA, and LDHB were reported to be involved in regulating this biological process. Previous studies reported these glycolytic regulators were dysregulated in multiple types of cancers. For instance, PKM2 was upregulated in cervical cancer , and knoIn our context, we discovered HK2, SLC2A1, PKM2, ALDOA, SLC2A1, SLC2A3, HK2, PFKFB2, PFKFB3, ALDOA, TPI1, PGK1, and PGAM1 expression was increased. HK2, PFKFB2, PFKFB3, ALDOA, TPI1, PGK1, and PGAM1 expression was decreased in PCa. Besides, our data suggested that upregulated SLC2A3, LDHB, and PFKFB3 and downregulated SLC2A1 had association with longer PFS in PCa. Moreover, we revealed dysregulation of TPI1, ALDOA, ENO1, LDHA, PKM, LDHB, and HK2 exhibited a correlation with PFS time in PCa. Furthermore, we constructed a new 8-gene glycolytic regulator signature, which could predict the PFS time and tumor immune infiltration levels of PCa. These results showed glycolytic regulators could be thought as newly produced biomarker for PCa prognosis.Although glycolytic regulators' roles in Warburg effect had been demonstrated clearly, the nonmetabolic function in human cancers needed to be deeply explored. For example, PKM2 promotes ovarian cancer growth though regulating CCND1 and CDKN1A expression . PGK1 coTo sum up, the present study showed glycolytic regulators were dysregulated and associated with the PFS time in PCa. Nonetheless, we still need more validation of these glycolytic regulators' molecular mechanism in PCa, but our study could also provide a new hint of unearthing novel biomarkers for PCa prognosis.


Critical limb ischemia (CLI), the most severe form of peripheral artery disease, results from the blockade of peripheral vessels, usually correlated to atherosclerosis. Currently, endovascular and surgical revascularization strategies cannot be applied to all patients due to related comorbidities, and even so, most patients require re-intervention or amputation within a year. Circulating angiogenic cells (CACs) constitute a good alternative as CLI cell therapy due to their vascular regenerative potential, although the mechanisms of action of these cells, as well as their response to pathological conditions, remain unclear. Previously, we have shown that CACs enhance angiogenesis/arteriogenesis from the first days of administration in CLI mice. Also, the incubation ex vivo of these cells with factors secreted by atherosclerotic plaques promotes their activation and mobilization. Herein, we have evaluated the long-term effect of CACs administration in CLI mice, whether pre-stimulated or not with atherosclerotic factors. Remarkably, mice receiving CACs and moreover, pre-stimulated CACs, presented the highest blood flow recovery, lower progression of ischemic symptoms, and decrease of immune cells recruitment. In addition, many proteins potentially involved, like CD44 or matrix metalloproteinase 9 (MMP9), up-regulated in response to ischemia and decreased after CACs administration, were identified by a quantitative proteomics approach. Overall, our data suggest that pre-stimulation of CACs with atherosclerotic factors might potentiate the regenerative properties of these cells in vivo. Peripheral artery disease (PAD) is an under-estimated and under-diagnosed cardiovascular disease (CVD) that is asymptomatic at initial stages  and affeDespite encouraging results in pre-clinical and clinical trials, the variability seen between these studies, together with the fact that EPCs become dysfunctional under pathological situations, has somehow slowed down their potential use in the clinic. Nobody seems to doubt their angiogenic properties ,26. HoweIn addition, we have also described that the incubation of healthy CACs with the factors secreted by atherosclerotic plaques (AP) affects their protein expression pattern, reflecting an initial response to an inflammatory environment, which enhanced their activation and mobilization ex vivo . MoreoveAn overview of the experimental workflow and follow-up of ischemic changes is shown in CACs were isolated from peripheral blood of healthy donors and cultured as described ,22. Non-Atherosclerotic femoral arteries were obtained from patients undergoing surgical endarterectomies at the Hospital Universitario Puerta del Mar, Cadiz, Spain, and cultured as described ,22. FinaBalb-C Nude (CAnN.Cg-Foxn1nu/Crl) female mice (n:32), age 9 weeks, were anesthetized with ketamine (100 mg/kg) and xylazine (10 mg/kg), both subcutaneously, before surgery. Ischemia was induced in left limbs by double femoral artery ligation (FAL), occluding the distal and proximal ends with double knots (non-absorbable 6/0), as described ,28,29.5 human CACs  or 5·105 hCACs pre-incubated with AP secretome  . Mice receiving stimulated CACs  were distributed in 3 groups depending on the AP secretome source: SF1, from patient-1 (n:5); SF2, patient-2 (n:5); SF3, patient-3 (n:5). Additionally, healthy mice  were used as baseline controls of blood flow and ischemic symptoms. Finally, sham surgical controls , were used for proteomic assays, vascular density changes, immune cell detection, and cytokine analysis.Twenty-four hours after surgery, ischemic (undergoing FAL) mice received an intramuscular injection of either 50 μL physiological serum without cells , or 50 μL physiological serum containing 5·10F, n:15) A. The inescribed , and wasAnimal experimentation was approved by the Ethical Committee from both the University of Cadiz and the Andalusian Committed of animal experimentation (registration number ES110120000210). The current study followed the standard guidelines for animal research included in the Spanish laws (RD 53/2013) as well as the European Regulations (2012/707/UE).Blood flow was measured at baseline  and after surgery (post-) for both paws, and also at days 1, 7, 14, and 21 using a Laser Doppler system . Perfusion was expressed as the ratio of left (ischemic) vs. right (non-ischemic) limbs. In parallel, ischemic symptoms  were registered during the entire experiment using the Tarlov score (motility changes) , togethe2 chamber. Left limbs muscles were harvested: low frontal muscles  were fixed in 10% formaldehyde for 15 days and then cryoprotected in 30% sucrose in PBS 1X during 24 h. Tissue was then embedded in OCT using tissue molds before congelation for appropriate preservation until sectioning and processing for IHC; low back muscles (gastrocnemius and soleus) and middle muscles  were snap-frozen in liquid N2 and stored at −80 °C for further proteomic analysis and Alu-based quantification respectively.Mice (n:32) were euthanized on day 21 after surgery in a CO2, the number of blood vessels per mm2, or blood vessels diameter (μm).IHC was performed with transversal tissue sections, in order to detect cells and vessels in all groups , as described . PrimaryQuantification of human DNA (hDNA) was carried out with amplification of specific Alu sequences by qPCR, as described ,29,33. BIn order to identify proteins differentially expressed between ischemic vs. non-ischemic conditions, and moreover, in response to CACs treatment (with/without atherosclerotic pre-conditioning), proteins in all groups  were analyzed by mass spectrometry (MS), using a based label-free quantitative approach, as described . Thus, lm/z; Orbitrap full-scan maximum injection time 100 ms; MS2 scan AGC target 1e5 at 15,000 FWHM resolution; maximum injection 50 ms; normalized collision energy 30; dynamic exclusion time 20 s; isolation window 1.2 m/z; 20 MS2 scans per full scan. Duplicates for each biological sample were run by MS.Peptide mixtures were analyzed by nano-LC-MS/MS on an Orbitrap Q-Exactive HF-X  coupled to an EASY-LC 1000 system . Peptides were loaded onto a two-column setup in A-buffer (0.1% FA) using a Luna Omega (1.6 μm) column, and eluted with a 250 nL/min flow rate with an increasing gradient of B-buffer (95% ACN and 0.1% FA), starting at 2% B, going to 45% B in 80 min and to 98% B in 90 min. The Q-Exactive was operated in data-dependent acquisition mode using the following settings: full-scan automatic gain control (AGC) target 3e6 at 120,000 FWHM resolution; scan range 350–1400 p-value < 0.05 and fold-change rates > 1.5 (up-regulated) or <0.6 (down-regulated). Data processing and graphs were done with Proteome Discoverer . The Ingenuity Pathway Analysis  platform was used for functional classification analysis.Data analysis was done with Proteome Discoverer v.2.4 . The database search was performed with Mascot v.2.6 against the Swiss-Prot database . The following criteria were used: fixed modification: carbamidomethylation (C); dynamic modifications: oxidation (M); precursor mass tolerance 10 ppm; fragment mass tolerance 0.06 Da; enzyme: trypsin; and 2 missed cleavages were allowed. Data filtering was performed using a percolator, resulting in a 1% false discovery rate. Additional filters were also used: search engine rank 1 peptides and Mascot ion score >20. Differentially expressed proteins were defined as follows: Mouse cytokines (62) were measured in the ischemic tissues on day 21 using a Cytokine antibody array , following the manufacturer’s instructions. Briefly, the protein fractions from back muscle tissues (left limbs) were used for cytokine detection, pooled into five groups  with equal amounts of protein per sample . Additionally, Transforming Growth Factor β1 (TGFβ1) levels were evaluated by ELISA  following the manufacturer´s guidelines. Again, pooled samples from all groups were used , including duplicates per group. Cytokine levels were compared between all groups vs. SH, and changes were defined with fold-change rates >1.5 (up-regulated) or <0.6 (down-regulated). Hierarchical clustering was done using Perseus software (v1.6.0.2) .p-value < 0.05.Protein-related statistics were obtained with Proteome Discoverer 2.4 and IPA software, while functional-related statistics were performed with GraphPad Prism 8 software. The normal distribution of the data was analyzed by Shapiro–Wilk test, applying then either Kruskal–Wallis test and Dunn’s test as post hoc analysis, or the two-way ANOVA test completed with Tukey’s multiple comparisons test for post hoc analyses. Data were presented as mean ± SEM (parametric) or mean ± SD (non-parametric tests), accordingly. Differences were considered statistically significant when p-value: 0.0052)  in all animals  compared to baseline , and blood flow remained lower until day 7 when perfusion ratios started to increase. A slight recovery of blood flow was registered in all ischemic limbs by day 14 B. At thi 0.0052) B, while p-value: 0.013), and showed less motility (Tarlov score) and worse ischemic symptoms than CACs-treated mice  were reported from day 1 after FAL compared to pre-surgery , althougted mice C, with 7ted mice D. On theted mice C. Finallted mice .2) was similar in all groups  compared to shams, including 107 proteins not detected in these controls A, therefAdditionally, 41 of the 62 angiogenic cytokines analyzed  were altOverall, according to IPA functional analysis, a bioinformatics tool based on specific algorithms and integrated databases mainly focused on biomedical literature, the molecular changes related to ischemia  represented a potential activation of vasculogenesis and angiogenesis , together with an increase of cell movement and migration, and inhibition of apoptosis/necrosis B. IPA alRegarding cell treatment, a differential protein expression profile was seen on day 21 in CACs treated mice  vs. untreated SC, including many proteins overexpressed in SE/SC but not in SF/SC (with any patient donor) .Overall, processes like vasculogenesis, angiogenesis, or cell movement, appeared more pronounced in response to CACs, according to IPA B, althouSimilarly, although cell death was down-regulated in response to ischemia in the long term, the apoptotic/necrotic process was slightly lower in SE, with several related proteins up-regulated only in these mice . Other functions significantly up-regulated in SE vs. SC were endothelial cell activation or Nitric Oxide/Reactive Oxygen Species (NO/ROS) production. Again, some of the proteins involved were Matrix metalloproteinase 9 (MMP9), CD44, or CRAMP.Atherosclerotic pre-conditioned CACs also promoted protein expression changes in SF vs. SE mice G. For inOverall, according to IPA, protein changes correlated with a significant decrease of cell viability, cell movement, and inflammatory response in SF mice compared to SE , togetheFinally, several differences were also identified within the SF groups, including many proteins up-regulated in SF1 compared to SF2 and SF3 groups, related to lipid and carbon metabolism, diabetes or insulin resistance, among others: Acetoacetyl-CoA synthetase (AACS), Acetyl-CoA carboxylase (ACACA), Carnitine O-palmitoyl-transferase 1 (CPT1), 1-acylglycerol-3-phosphate O-acyltransferase (AGPAT2) .p: 0.019)   B and mac: 0.021) D were fo: 0.021)  in ische: 0.021) E, were bp-value:0.022), as well as a negative correlation between blood flow ratios and neutrophils  and macrophages numbers .Finally, positive correlations were reported between the number of neutrophils and macrophages found on day 21 . These results suggest that, after the initial vessel sprouting seen in CACs-treated mice at early stages , vessel According to our early day’s results, CACs migrate to the ischemic tissues right after administration , are detFAL-induced ischemia triggered a considerable number of molecular changes that were further evaluated with the functional bioinformatics tool IPA. Thus, considering the changes found, many proteins related to artery occlusion, vascular lesion, or muscle damage were identified in all ischemic mice at early stages, together with others related to increased angiogenesis and vasculogenesis . SimilarAfter 21 days of FAL, IPA software reported an increased cell mobilization, mainly of immune cells, in all ischemic mice, together with up-regulation of vasculature development, angiogenesis, and vasculogenesis. Remarkably, 34 proteins over-expressed 2–4 days after FAL   were alsIn addition, many proteins were only found overexpressed in the long term, such as MMP9, MMP2, caspase 3 and 6 (CASP3/6), or mast cell protease 4 precursor (MCPT4), in all mice that underwent FAL. Among these, MMPs have been associated with atherogenesis and PAD . Indeed,CACs administration promoted, at the first days, protein changes related to oxidative stress and NO bioavailability , while in SC mice (untreated) this effect was not detected (at least not from the proteins identified) . NO partIn the long term, many proteins up-regulated in response to ischemia (at early and long-term), were lower in CACs-treated mice, although still higher than in shams . On the contrary, proteins like CRAMP, APAF1, or S100A8, were up-regulated only in SE mice (not in SC). Thus, CACs somehow affected several processes within the ischemic tissues, promoting lower apoptosis/necrosis , up-regulated endothelial cell activation, or NO/ROS production in SE mice compared to untreated ones . Again, processes related to vasculature development were slightly more up-regulated in CACs-treated mice (SE and SF), corroborating the initial sprouting and further vessel reorganization described . AltogetRemarkably, IPA highlighted TGFβ as a major upstream regulator in ischemic mice, after identifying many proteins correlated to TGFβ1, including MMP9, MMP2, or CD44 E. IndeedLikewise, CRAMP was also upregulated in SE while down-regulated in SC mice (vs. SH). CRAMP, known as an antimicrobial peptide, has additional activities such as chemo-attraction, immune cell activation, or angiogenesis . Up-reguFinally, up-regulation of granulocyte colony-stimulating factor (G-CSF) in CACs-treated mice , supportNext, we tested how the pre-stimulation of CACs with the factors released by atherosclerotic plaques, termed here as the AP secretome, could influence their effect over CLI mice, based on previous results suggesting the activation of CACs in response to an atherosclerotic environment ex vivo ,22. We aInterestingly, SF mice had on average higher perfusion ratios than SE (mice with unstimulated CACs) or untreated SC. Also, many proteins down-regulated in SF vs. SE mice correlated with down-regulation of cell movement and related inflammatory response, and a decrease in cell viability and atherosclerosis-related processes. Elevated levels of FasL in SF F might cBesides, despite the overall improvement seen in SF mice, several differences were found between the sub-groups SF1, SF2, and SF3, treated with CACs pre-stimulated with atherosclerotic factors from different donors. Indeed, SF1 mice had the lowest perfusion recovery and more necrotic fingers (compared to other SF groups) but a still higher percentage than SC, while SF2 and more significantly SF3 had the best recovery (compared to SC and even SE mice). In addition, the protein profile of SF1 was closer to SC mice than to other SF ones. As an example, TGFβ1 levels were similar in SF1, even lower, than in SC D.Remarkably, we found out that patient-1 (SF1) had the worst clinical prognosis among all three donors, and moreover, it was the only diabetic patient. Interestingly, several key enzymes involved in the metabolism of lipids/fatty acids and carbohydrates, among others, were overexpressed only in SF1 mice . Many ofProteomic and IHC results highlighted the potential of CACs to modulate immune cell recruitment. Indeed, two days after cell administration, neutrophils were recruited in both SC and SE mice, however, from day 4 onwards, CACs started to regulate such mobilization, reducing the levels of the inflammatory cells in the ischemic limbs . In the The capacity of CACs to modulate neutrophils and macrophages mobilization has already been described . This cohigh and IL-10low) express inducible nitric oxide synthase (iNOS) and CD40, and produce TNFα, IL6, or IL1β, while M2 macrophages (IL12low and IL10high) express arginase-1 (ARG1) and CD206 and produce, among others, TGFβ or IL10 to facilitate tissue repair [On the other hand, neutrophils and macrophages are also thought to play an important role in the angiogenic/arteriogenesis processes, promoting revascularization and tissue remodeling . Both cee repair ,94,95,96e repair ,98, so wAccording to our results, SE mice  showed high levels of CD40 and IL1β compared to untreated SC, but also ARG1 and CD68 (M2 associated markers), and therefore, we could not indicate any clear polarization tendency in SE mice. On the contrary, SF mice showed high levels of IL10 and TGFβ and low levels of IL1β, suggesting a possible polarization towards an M2 profile in this group. IL6 was also up-regulated in SF mice, although this cytokine can induce polarization of both, M1 or M2 macrophages in a context-dependent manner . Thus, aOverall, CACs promoted revascularization in CLI mice, by enhancing from early stages the maturation of a functional vascular network and, for instance, a higher blood flow recovery in the long term. Of note, CACs did not induce a significant increase of vascular density, compared to similar studies with these and other cells , althougBased on the changes seen in the long term in CACs-treated mice, their effect on the first days seems crucial, enhancing reperfusion while modulating the participation of immune cells to assist in tissue restoration without producing an exacerbated inflammatory response, reducing cell death and, for instance, the progression of the ischemic damage.We have provided, to our knowledge, the widest overview of the protein changes taking place in a mouse model of CLI and in response to CACs treatment. While some of the proteins identified might be taken as molecular markers of CLI, our results bring the opportunity to further explore the biological meaning of the molecular changes found here. Indeed, the role of proteins such as MMP9, CD44, or TGFβ in the ischemic process and in response to CACs should be further investigated. Finally, in the current study, we have also shown, for the first time, that pre-stimulation of CACs with atherosclerotic factors not only does not seem to exert a negative impact on these cells but on the contrary, atherosclerotic pre-conditioning also appears to potentiate the regenerative properties of CACs in vivo.