Comparative transcriptome analysis of different concentrations of MeJA showed that a total of 7836, 6797 and 8310 genes were differentially expressed in the comparisons of CKvsM10, CKvsM50, CKvsM100, respectively. The analysis of differentially expressed genes (DEGs) showed phenylpropanoid biosynthesis, vitamin B6, ascorbate and aldarate metabolism-related genes were significantly enriched. The transcripts of flavonoid and terpenoid metabolism pathways and plant hormone signal transduction, especially the jasmonic acid (JA) signal-related genes, were differentially expressed in CKvsM50 and CKvsM100 comparisons. In addition, the transcription factors (TFs), e.g., MYC2, DELLA, MYB111 played a key role in rosemary suspension cells under MeJA treatments. qRT-PCR of eleven DEGs showed a high correlation between the RNA-seq and the qRT-PCR result. Taken together, MeJA alleviated peroxidative damage of the rosemary suspension cells in a wide concentration range via concentration-dependent differential expression patterns. This study provided a transcriptome sequence resource responding to MeJA and a valuable resource for the genetic and genomic studies of the active compounds engineering in rosemary.To study the effects of Methyl jasmonates (MeJA) on rosemary suspension cells, the antioxidant enzymes’ change of activities under different concentrations of MeJA, including 0 (CK), 10 (M10), 50 (M50) and 100 μM MeJA (M100). The results demonstrated that MeJA treatments increased the activities of phenylalanine ammonla-lyase (PAL), superoxide dismutase (SOD), peroxidase (POD), catalase (CAT) and polyphenol oxidase (PPO) and reduced the contents of hydrogen peroxide (H Rosmarinus officinalis Lour.) is a famous ornamental and medicinal homologous plant. Rosemary, as an excellent natural antioxidant and preservative, has been used in various industries widely [2O2 induced hepatocyte damage through the SIRT1 pathway and rosemary extract, and significantly up regulate the expression of Nrf2 in colon cells and inhibit an HCT116 xenograft tumor formation in mice. Moreover, rosemary extract shows a higher antioxidant potential and increases the oxidative stability of oil by more than 30% compared to conventional synthetic antioxidants [Rosemary , and lignin biosynthesis , and 112 putative AP2/ERF TFs in Isatis indigotica [SgHMGR, SgSQS, SgCS and SgCYP450 were up-regulated and their responses in the presence of MeJA were related to the concentration and timing of MeJA treatment in Siraitia grosvenorii [HMGCR, FPPS, IDI, GGPPS, REF/SRPP and transcription factors  increased with MeJA treatment in TKS [Taxus, there were 18 genes showing increased transcript abundance following elicitation of MeJA, which was involved in the biosynthesis of terpenoid backbone, and then multiple candidates for the unknown steps in paclitaxel biosynthesis were identified [Peucedanum praeruptorum [MeJA treatment can induce the biosynthesis of many secondary metabolites  and acts as an elicitor of secondary metabolite production across the plant kingdom . SlMYC1 reatment . There wviscapus . Transcret Basil . MeJA trdigotica . Based odigotica . The ressvenorii . MeJA-ret in TKS . Based oentified . A totalruptorum . The secAt present, no study has shown the transcriptome of rosemary responding to MeJA. In our study, we investigated the transcriptome of rosemary suspension cells responding to different concentrations of MeJA using high-throughput sequencing technologies. Putative gene expression profiles of rosemary suspension cells were investigated and DEGs were classified under different concentrations of MeJA. By comparing and analyzing the sequencing data of control and illuminated groups, the genes involved in the regulation of primary and secondary metabolism and their regulatory networks were established. These experiments reveal dynamic gene expression changes in responding to different concentrations of MeJA and provide new insights into the genetic and genomic regulation of plant functional metabolites.Rosemary callus was obtained by the following methods: treating rosemary leaves with 0.1% mercury bichloride solution, then cutting the leaves into small pieces about 0.5 cm × 0.5 cm, using the inoculation method that the back of leaves contacts with the medium, on Murashige and Skoog (MS) medium  with 0.5 mg/L 1-Naphthaleneacetic acid (NAA) and 4.0 mg/L N-(Phenylmethyl)-9H-purin-6-amine (6-BA). After the suspension culture for several generations in the MS liquid medium supplemented with 1.0 mg/L 2,4-dichlorophenoxyacetic acid , which lasted for 8 days at 25 ± 0.5 °C with shaker speed 120 rpm in the dark, it could derive rosemary suspension cell lines with high cell viability and stable growth. The culture was performed by transferring 4 g·FW/20 mL of 6-day-old culture (cells plus medium) to 80 mL of the fresh growth medium, which lasted for 8 days. MeJA were sterilized and added to the medium on day 6 of the culture process. Baes on the pre-experiment, 10, 50 and 100 μM MeJA treatments could promote the accumulation of rosmarinic acid, carnosic acid and flavonoids in rosemary suspension cells, so we chose the concentration of MeJA solution was 0, 10, 50 and 100 μM. Each test was repeated three times. After 48 h treatment, all materials were stored at −80 °C for later use.Total RNA was isolated from rosemary suspension cells (three replicates) with the RNAprep Pure Plant Kit . The integrity and concentration of the RNA samples were further measured using an Agilent 2100 Bioanalyzer  and the purity of the RNA samples was assessed using the NanoPhotometer® spectrophotometer . RNA libraries were prepared using the True-seq RNA sample preparation kit according to the manufacturer’s instructions. The constructed library was tested on the Agilent 2100 Bioanalyzer and the ABI StepOnePlus Real-Time PCR System. Finally, mRNA libraries were sequenced on an Illumina HiSeq 4000 platform .The cDNA libraries of four samples were sequenced by 2 × 100 bp paired-end sequencing on an Illumine HiSeq platform according to the manufacture’s instructions. We used Trinity to assemble clean reads by de novo, while removing PCR duplication to improve assembly efficiency. Then the assembled transcripts were clustered by tgicl to remove redundancy and obtain UniGene. Trinity consists of three independent modules: inchworm, chrysalis and butterfly, which process a large number of reads in turn. The assembled UniGene will be annotated with seven functional databases (KEGG (Kyoto Encyclopedia of Genes and Genomes), GO (Gene Ontology), NR, NT, Swissprot, Pfam and KOG).http://deweylab.biostat.wisc.edu/rsem/rsem-calculate-expression.html, Access date: 30 January 2019, RESM Version: v1.2.8; RESM Parameter: default;) to calculate the gene expression level of each sample. RESM is a software package for RNA-seq reads to calculate the expression of genes and transcript subtypes.We used bowtie2 to align clean reads to the genome sequence and RESM  of 0.001 and a fold change of 2 were set as the default threshold for defining significant differential expression.Degseq method is based on Poisson distribution. In this project, DEG detection is carried out according to the method described by Wang et al. 2010 . Differehttp://www.geneontology.org/, Access date: 30 January 2019) and KEGG  annotation results and the official classification, DEGs were classified, and the enrichment factors were analyzed using the phyper function in R software .According to the GO  in a 20 µL final volume containing 10 µL of 2 × SYBR Premix Ex TaqTM , 1 µL of 10 × diluted cDNA, and 0.8 µL specific primer pairs, and 7.4 µL of ddH2O2, proline (pro) were assayed using ELISA Kit  and a micro-plate reader (Rayto RT-6100) according to the manufacturer’s instructions. The principle of the assay: the kit assay plant PAL, SOD, POD, CAT, PPO, MDA, H2O2 and proline level in the sample, use purified plant PAL, SOD, POD, CAT, PPO, MDA, H2O2 and proline antibody to coat microtiter plate wells, make solid-phase antibody, then add PAL, SOD, POD, CAT, PPO, MDA, H2O2 and proline to the wells. Combined antibody labeled with HRP became an antibody-antigen-enzyme-antibody complex. After washing completely, TMB substrate solution was added. TMB substrate became blue. At HRP catalyzed enzyme, the reaction is terminated by the addition of a sulphuric acid solution and the color change is measured spectrophotometrically at a wavelength of 450 nm. The concentration of PAL, SOD, POD, CAT, PPO, MDA, H2O2 and proline in the samples is then determined by comparing the OD of the samples to the standard curve. Assay procedure: add standard, set standard wells, test sample wells, add standard 50 μL to standard well; add sample, set blank wells separately, test sample well, add sample dilution 40 μL to test sample well, then add testing sample 10 μL , add sample to wells, do not touch the well wall as far as possible, and gently mix; add enzyme, add HRP-conjugate reagent 100 μL to each well, except blank well; incubate, after closing the plate with closure plate membrane, incubate for 60 min at 37 °C; configurate liquid, 20-fold wash solution diluted 20-fold with distilled water and reserve; wash, uncover the closure plate membrane, discard liquid, dry by swinging, add washing buffer to every well, still for 30 s then drain, repeat 5 times, dry by patting; color, add chromogen solution A 50 uL and B to each well, evade the light preservation for 15 min at 37 °C; stop the reaction, add stop solution 50 μL to each well, stop the reaction (blue color changes to yellow); assay, take blank well as zero, read absorbance at 450 nm after adding stopping solution within 15 min.Briefly, 0.1 g of rosemary suspension cells (fresh weight) were taken from each of the treatment groups and rapidly frozen with liquid nitrogen. The samples were maintained at 2–8 °C after melting. Then, 1 mL PBS (pH 7.4) was added, followed by homogenization by hand or grinders and centrifugation for 20 min at the speed of 2000–3000 rpm. Supernatant was removed. The activities of PAL, SOD, POD, CAT, PPO and the contents of MDA, HQuantitative results for rosemary metabolite content, enzyme activity and gene expression analyses are presented as the means ± standard deviations (SDs) of at least three biological replicates. The effects of MeJA conditions on metabolite contents and gene expression were analyzed by one-way analysis of variance (ANOVA) followed by Duncan’s test using SPSS version 19.0. Figures were prepared using GraphPad Prism 8.0 and Excel 2016 software.2O2 were the lowest in the 100 μM MeJA treatment group, the highest of the concentration was the CK treatment , M10_, M50_ and M100_). After removing the linker sequence, the RNA-Seq data of the twelve rose-mary suspension cells libraries produced 66,314,608 to 71,191,500 reads, respectively, due to differences in concentrations of MeJA. The values of Q20 were higher than 97% in all the samples, indicating the high reliability of the rosemary suspension cells transcriptome sequencing data .Sesamum indicum L. (65.02%), Erythranthe guttata L. (17.59%), Dorcoceras hygrometricum L. (2.08%), Salvia miltiorrhiza L. (2.08%) and Ipomoea nil L. (0.75%). The 17.59% and 2.08% of the total number of Nr annotations accounted for, respectively, 2.08% and 0.75%  . The unind 0.75% . The disnd 0.75% . Similarnd 0.75% . At lastnd 0.75% .defensin-like cystein-rich peptide, extracellular ribonuclease LE, PREDICTED: titin-like, pathogen-related protein STH-2, hypothetical protein SELMODRAFT_431225, aquaporin-like protein, partial, major pollen allergen Lol p 11-like and extensin-3—highly expressed in CK, M10, M50 or M100 sample libraries  were compared and analyzed . GalactoMapman analysis of the DEGs was distributed in the cell wall, lipids, ascorbate and glutathione, sucrose and amino acid pathways A–C. MoreGO and KEGG enrichment and Mapman analyses of the DEGs showed that MeJA affected the synthesis of rosemary suspension cells’ metabolites via multiple pathways, including plant hormone signal transduction, reactive oxygen species (ROS) clearance, osmotic balance, phenylpropanoid biosynthesis and many other metabolism pathways that may play important regulatory roles.LOX2S, 2 AOS, 1 AOC, 2 OPR, 1 OPCL1, 7 ACX, 1 MEP2 and 1 J-O-MT. LOX2S, OPCL1 and ACX  were up-regulated from 0 to 100 μM MeJA treatments, OPR (Unigene16468_All) and ACX  were down-regulated from 0 to 100 μM MeJA treatments. AOS was down-regulated under 10 μM MeJA treatment but up-regulated under 100 μM MeJA treatment. Other DEGs showed different expression patterns in the three combinations  was up-regulated under 10 μM MeJA treatment, others were down-regulated. COI1 was drastically up-regulated from 0 to 100 μM MeJA treatment. JAZ (CL9927.Contig1_All) was down-regulated under 10 μM MeJA treatment, others were up-regulated. 1 MYC2 (CL4564.Contig5_All) was down-regulated from 0 to 100 μM MeJA treatment, 6 MYC2  were down-regulated from 0 to 100 μM MeJA treatments, the number of up-regulated DEGs under 100 μM MeJA treatment were the most, secondly was 50 μM MeJA treatment, the least was 10 μM MeJA treatment , cytokinine (56 DEGs), gibberellin , abscisic acid , ethylene , brassinosteroid , and salicylic acid  C–I. The  pathway C. There  pathway D,E, but  pathway F–I.PAL, 4 4CL, 8 CCR, 6 HCT, 8 C3’H (CYP98A), 8 F6H1, 3 CSE, 1 caffeoyl-CoA-O-methyltransferase, 21 β-glucosidase, 27 cinnamyl-alcohol dehydrogenase, 66 peroxidase. There were more numbers of up-regulated DEGs than down-regulated DEGs under 10 μM MeJA treatments, but there were more numbers of down-regulated DEGs under 50 and μM MeJA treatment. Among these DEGs, PAL (CL3764.Contig6_All), 4CL (CL5102.Contig4_All), CCR (Unigene974_All), HCT (CL10076.Contig1_All), F6H1 (CL352.Contig1_All), peroxidase (Unigene16028_All), β-glucosidase (CL41.Contig21_All), caffeoyl-CoA-O-methyltransferase (CL11417.Contig1_All), cinnamyl-alcohol dehydrogenase (CL5952.Contig4_All) were drastically up-regulated from 0 to 100 μM MeJA treatment , which was down-regulated more significant under 100 than 50 μM MeJA treatment  and HMGCR  were drastically up-regulated under different concentrations of MeJA treatments. There were 3 significant DEGs that were assigned to the MEP pathway, including 2 DXS and 1 ispH. DXS were down-regulated under 10 μM MeJA treatment, but up-regulated under 50 and 100 μM MeJA treatments, ispH was drastically down-regulated under different concentrations of MeJA treatments. The result showed that 100 μM MeJA treatment could significantly induce the DEGs expression of MVA biosynthesis compared to 50 and 100 μM MeJA, 10 μM MeJA treatment could significantly inhibit the DEGs expression of MVA biosynthesis in rosemary suspension cells. In addition, the DEGs FPS (FDPS) and GGPS, which directly act on terpenoids to synthesize precursors geranyl diphosphate (GPP), farnesyl pyrophosphate (FPP) and geranylgeranyl pyrophosphate (GGPP) and other DEGs in the terpenoid biosynthesis pathway showed differential expression patterns under different concentrations of MeJA, for example FDPS , GGPS, FACE2, ICMT, PCME (Unigene7748_All) and FOLK were up-regulated from 0 to 100 μM MeJA treatment. We also found a large number of DEGs were assigned to ubiquinone and other terpenoid-quinone biosynthesis, carotenoid biosynthesis, sesquiterpenoid and triterpenoid biosynthesis, zeatin biosynthesis, steroid biosynthesis, monoterpenoid biosynthesis, diterpenoid biosynthesis and N-Glycan biosynthesis pathways  treatment, while the expression of 4CL was the highest in the 10 μM treatment. The expression pattern of DFR was the highest in 0 μM MeJA, followed by 50, 10 and 100 μM MeJA treatments. In summary, the qPCR verification results showed that the relative expression of the genes was similar to the transcriptome, affirming the consistency of RNA-seq data and our results.In order to further verify the reliability of gene expression profiles obtained by RNA-seq, the expression pattern of 11 DEGs was estimated by qRT-PCR in rosemary suspension cells under different concentrations of MeJA . The expPAL in Arabidopsis [FDPS and GGPS of precursors as GPP were up-regulated in rosemary suspension cells under 100 μM MeJA, which was similar to the results of Taxus and Chrysanthemum indicum [In our study, phenylpropanoid and terpenoid backbone biosynthesis pathways were significantly enriched. Phenylpropanoid biosynthesis was closely related to the biosynthesis of flavonoids and rosmarinic acid. Exogenous MeJA regulated phytoalexin and polyphenol biosynthesis by up-regulating bidopsis . PAL wasbidopsis . In planbidopsis . During  indicum ,40. TherArabidopsis, 177 PLP enzymes might regulate the expression of genes of hormone synthesis and signal transduction, AtPDX2 could be induced under light, drought and low-temperature stress [Arabidopsis vtc1 mutant, the content of Asc was 70% lower than the wild type, while the content of ABA and the expression of NCED were significantly increased [2O2 treatments, the antioxidant enzyme system and secondary metabolites synergistically eliminated excess ROS in S.baicalensis [BY-2 cells by different mechanisms [2O2 and MDA were reduced in rosemary suspension cells under different concentrations of MeJA. These phenomena improved the repair capacity of oxidative damage and ROS scavenging ability in plant cells and thereby enhancing the ability to cope with external factors in plant [After the external environment changed, the balance was destroyed and ROS was overproduced to damage the plants, which could induce the synthesis of phytoalexin ,42. Plane stress ,47,48. Increased . Asc plancreased ,51,52. Acalensis ,54. Prolchanisms . Differechanisms ,57. In oin plant . The chaArabidopsis arf6 and arf8 single mutants and sesquimutants, ARF6 and ARF8 gene dosage affected the accumulation of JA and the expression of MYB [miR393 could stabilize ARF1 and ARF9 to increase glucosinolate and decrease camalexin in Arabidopsis [Salvia miltiorrhiza hairy roots [Tripterygium wilfordii suspension cells, the contents of triptolide and triptolide increased significantly with exogenous ABA after 10 days [AACT, MCT, CMK and CYP450 in terpenoids biosynthesis increased significantly by exogenous MeJA [Mentha canadensis [ODC, ADC, PMT, QPRT and bHLH in nicotine biosynthesis were up-regulated, which affected the accumulation of nicotine and other pyridine organisms in Nicotiana tabacum [JAZ8 participated in the biosynthesis of phenolic acids in Salvia miltiorrhiza under MeJA treatment [ORCA3 enhanced the expression of Tdc, Str, and D4h in terpenoid biosynthetic genes increased the accumulation of terpenoid indole alkaloids in Catharanthus roseus [MYC2 was released from JAZ to transcribe and activate the down-stream response genes in Arabidopsis [Hevea brasiliensis [Nicotiana tabacum [Catharanthus roseus [Chrysanthemum indicum [Vitis vinifera [ARF, DELLA, EIN3, JAZ, MYC2, further study of these genes is required. Thus, 100 μM MeJA could significantly induce the genes of JA biosynthesis and signal transduction and inhibit certain hormone signal transduction, especially SA signal transduction. MeJA could regulate the biosynthesis of secondary metabolites via the complex network of plant hormone signal transduction in rosemary suspension cells, such as terpenoids, flavonoids.Plant hormones play an important role in the synthesis of secondary metabolites, and the regulatory process involves a variety of signal transduction and interaction factors . In Arabn of MYB . Over-exbidopsis . Exogenobidopsis . Exogenory roots . DELLA wry roots ,65,66. I 10 days , and theous MeJA . The connadensis . Under M tabacum . JAZ8 pareatment . Over-exs roseus ,73. MeJAbidopsis ,75,76, Hiliensis , Nicotia tabacum , Catharas roseus , Chrysan indicum  and Vitivinifera . In our Salvia miltiorrhiza, PAP1 could induce the expression of PAL, C4H and CHS to promote the accumulation of anthocyanins [MYB39 and MYB4 could negatively regulate the synthesis of rosmarinic acid [ERF115 had different regulatory effects on salvianolic acid and tanshinone in different tissues and organs [MYC2 mediated plant responding to stress, regulated the genes in the biosynthesis of many secondary metabolites [Arabidopsis, several R2R3-MYB were involved in the regulation of flavonoid biosynthesis [MYB4 negative controlled sinapate ester biosynthesis through down-regulated C4H in a UV-dependent manner [MYB11, -12, -111 regulated flavonol biosynthesis by up-regulated CHS, CHI, F3H, F3’H and FLS [MYB75, -90, -113, -114 controlled anthocyanin biosynthesis in vegetative [MYB123 controlled the biosynthesis of proanthocyanidins in the seed coat [MYB5, -14 played a key role in seed coat polymer biosynthesis in Medicago truncatula [OsWRKY13 was an important TF to resist the infection of rice blast fungus by directly or indirectly regulating the genes of SA and JA signaling pathway [li049, the expressions of genes related to lignan biosynthesis were significantly up-regulated and the content was increased in Isatis indigotica hairy root [TcDRREB regulated the key gene of the paclitaxel synthesis pathway to increase the content of paclitaxel increased with JA treatment in Taxus suspension cells [JAZ1 to MYC2 in Arabidopsis [RIM1 was a negative regulator for JA signaling in Rice [MYB111 was significantly up-regulated under 50 and 100 μM MeJA, and down-regulated under 10 μM MeJA. Therefore, MeJA might play a regulatory role in the synthesis of rosmarinic acid, terpenoids, flavonoids and other metabolites by including the MYB, AP2-EREBP, MYC2 and DELLA in rosemary suspension cells.TFs played a key role in plant growth, development, secondary metabolism and resistance to stress . In Salvocyanins , MYB39 anic acid ,85, ERF1d organs . MYC2 meabolites . AP2/ERFabolites ,89. In Aynthesis ,91,92, Mt manner , MYB11,  and FLS ,94, MYB7getative , MYB123 eed coat . MYB5, -uncatula . OsWRKY1 pathway . Over-exiry root . TcDRREBon cells . DELLA cbidopsis . RIM1 wa in Rice . In our 2O2 and MDA, thus accelerated the ROS scavenging. A comparative analysis of global gene expression patterns provided subsets of DEGs in rosemary suspension cells under MeJA treatment. Our study revealed the expression profiles of genes involved in plant hormones signaling pathway, flavonoids, terpenoid backbone and phenylpropanoid biosynthesis pathway, TFs, indicating their regulatory role in the synthesis of the active compounds in rosemary suspension cells under MeJA treatment. We suggested a feasible working model based on the result (In conclusion, MeJA increased the activities of PAL, SOD, POD, CAT and PPO, and reduced the contents of He result . These t


Acid-sensing ion channels (ASICs) are proton-gated cation channels and key mediators of responses to neuronal injury. ASICs exhibit unique patterns of distribution in the brain, with high expression in neurons and low expression in glial cells. While there has been a lot of focus on ASIC in neurons, less is known about the roles of ASICs in glial cells. ASIC1a is expressed in astrocytes and might contribute to synaptic transmission and long-term potentiation. In oligodendrocytes, constitutive activation of ASIC1a participates in demyelinating diseases. ASIC1a, ASIC2a, and ASIC3, found in microglial cells, could mediate the inflammatory response. Under pathological conditions, ASIC dysregulation in glial cells can contribute to disease states. For example, activation of astrocytic ASIC1a may worsen neurodegeneration and glioma staging, activation of microglial ASIC1a and ASIC2a may perpetuate ischemia and inflammation, while oligodendrocytic ASIC1a might be involved in multiple sclerosis. This review concentrates on the unique ASIC components in each of the glial cells and integrates these glial-specific ASICs with their physiological and pathological conditions. Such knowledge provides promising evidence for targeting of ASICs in individual glial cells as a therapeutic strategy for a diverse range of conditions. ACCN 1 to 4) have been cloned so far and are categorized as ASIC1-4, with two splice variants identified in ASIC1 and ASIC2 [+ ions mostly, thus the ASICs belong to the degenerin/epithelial sodium channel (DEG/ENaC) family [50), with functional homomeric ASIC1a, 1b, 2a, and 3 activated at pHs of 6.5, 5.9, 4.4, and 6.6, respectively [Acid-sensing ion channels (ASICs) are proton-gated cation channels found extensively in the nervous system ,2. At lend ASIC2 ,4. ASICsnd ASIC2 ,6. The cnd ASIC2 ,8,9. Pro) family ,11. Homo) family ,13. ASICectively .Prior reviews have not gathered ASIC information for each of the glial cell types nor discussed how the glial ASICs relate to specific physiology and pathology. Furthermore, most reviews concentrate on ASICs in neurons, while very few have covered their role in glial cells. ASICs are widely expressed in neurons and are less expressed in glial cells throughout the nervous system ,16. ASICIn light of the pivotal role that ASICs play in several physiological and pathological processes ,27, seleThere are three types of glial cells in the central nervous system (CNS): astrocytes, microglia, and oligodendrocytes . Each of+ and alterations of blood flow [Astrocytes critically regulate neurotransmission and neuronal function throughout the CNS ,43,58. Tood flow ,62.Microglia are essential in maintaining proper brain function through environment surveillance and clearance ,45,46,47Oligodendrocytes play an important role in promoting effective myelin performance ,49,50. MUnder abnormal conditions, glial cells exhibit different behaviors which contribute to the appearance of pathology ,54,55. AIn microglial cells exposed to pathological conditions, contrary effects are observed. In an experiment with over 90% CD11b-HSV-TK microglia ablation during EAE conditions, inflammatory effects were repressed . This inDeletion of oligodendrocytes under specific pathological conditions has not been experimentally performed yet. As discussed earlier, however, many studies have ablated oligodendrocytes under healthy physiological conditions. All these studies report similar results of demyelination and disintegrated axonal support ,69,70,71ASICs are expressed in glial cells , indicat+ and Ca2+ ions when pH levels drop below 7.0 [ASIC1, ASIC2, and ASIC3 have been detected in the astrocytes . ASIC1 celow 7.0 . In astrelow 7.0 ,59,80, Relow 7.0 ,81, and elow 7.0 ,82. Howe2+, in addition to Na+ [2+ plays an important role in long-term potentiation (LTP), which is critical for synaptic plasticity that contributes to learning and memory by activation of ASIC1a in neurons [One feature that distinguishes functional ASIC1a from other ASIC components is its unique permeability to Can to Na+ ,13. Calcn to Na+ . Intrace neurons ,84,85. A neurons .ASICs are involved in neurodegenerative diseases ,86,87. TIn a mouse MCAO model, ASIC1a demonstrated a role in ischemic brain injury due to its calcium-dependent or calcium-independent mechanisms ,92. ActiFinally, a role for ASICs in brain tumors has been established. ASIC1a is highly expressed in malignant gliomas ,98 and sV) subtype 1, is prominently expressed in microglial cells and plays a role in free radical generation that contributes to ischemic brain injury [V1 is also involved in electrical signaling by activating ASIC1a in microglial cells, leading to further proton influx of Na+ and Ca2+ [ASIC1a, 2a and 3 are expressed in microglial cells . Anothern injury . Recent and Ca2+ .ASIC1a and ASIC2a contribute to the physiological function of microglial cells ,103. As β plaque accumulation is characteristic of Alzheimer’s disease (AD) [The primary function of microglia is their role in environment surveillance and phagocytosis ,115. As ase (AD) . In addiase (AD) . In partase (AD) ,103. Undase (AD) .Our recent study focusing on ASIC2 pathogenesis in ischemic brain injury found that in hippocampal, cortical, and striatal neurons, deletion of ASIC2 reduced surface ASIC1a levels, acid-activated current density, and acidosis/ischemia-induced neuronal injury . We alsoExpression of ASIC3 in response to stimuli induces primary and secondary hyperalgesia . Thus, pUsing imaging and immunofluorescence techniques, it has been determined that ASIC1a, ASIC2a, and ASIC4 mRNAs are expressed in oligodendrocyte lineage cells (OLC) . When olOligodendrocytes serve primarily to form myelin sheaths which enhance electrical conduction of synaptic transmissions in the brain . NG2-gli2+ in the EAE model [Oligodendrocyte dysfunction is implicated in several disease processes, most notably multiple sclerosis (MS) . MS is cAE model . ModulatAE model , but thiOPC plays an important role in chronic conditions such as obesity and cancer ,126. OPCASICs are expressed in glial cells. In this review, we discussed the integral roles that ASICs play in glial cell physiology and pathology, and how each unique ASIC component contributes to the specified function of each glial cell type. While lots of information exists on the topic, there are still many knowledge gaps within the literature. This section aims to summarize the major findings of the review and provide guidance for future research in the field.2+ influx [Astrocytes play major roles in support and regulation of physiological and pathological states ,53,58. W+ influx ,85,106. + influx ,90,126.Despite these general findings, no studies directly focus on this relationship between ASICs in astrocytes and specific physiological or pathological outcomes. For example, there has been a lot of evidence that inhibition of ASIC1a or deletion of ASIC1a gene in neurons leads to protection during pathological conditions ,27, but Microglia execute the primary role of phagocytosis in the brain . This isThere have been many studies that focus on the relationship between microglial malfunction and ensuing inflammation ,117. How2+ signaling, making it an important component in the myelin formation process [Oligodendrocytes are myelin-producing cells which insulate axons to allow for enhanced neuronal transmission . ASIC1a  process . From a  process . Under t process . Additio process , whereas process .Oligodendrocytic ASIC1 might directly contribute to disease conditions, specifically MS . MS is tThis review focuses on the theme of ASICs in glial cells from limited literature, summarizing their expression; detailing how ASICs in astrocytes, microglia, and oligodendrocytes contribute to physiological function and how pathological environments perpetuate ASIC dysregulation to result in disease presentations; and describing research gaps between ASICs and glial cells . This isThese findings help organize information for future research to concentrate on channel-specific components in individual glial cells. The gaps outlined in this review provide guidance for areas of concentration in glial ASIC research to promote targeted therapeutic options for various disease conditions.


SPP1) is involved in immune regulation, cell survival, and tumor progression. Studies have demonstrated that SPP1 plays an important role in certain individual tumors. However, the expression profile and oncogenic features of SPP1 in diverse cancers are remaining unknown. Therefore, we performed a comprehensive analysis using The Cancer Genome Atlas (TCGA) database. Raw data of 33 cancer types were download from the University of California Santa Cruz (UCSC) Xena website. The expression of SPP1 and its relationship with tumor prognosis, immune invasion, tumor microenvironment, and immunotherapy were analyzed using the R language. The function analysis was conducted using Gene Set Enrichment Analysis (GSEA). The oncogenic features of SPP1 was validated by wound-healing assay and EdU staining assay. SPP1 highly expressed in most cancers. The expression of SPP1 was significant related to prognosis, tumor mutation burden (TMB), microsatellite instability (MSI), and immune checkpoint genes, suggested that SPP1 plays an essential role in the tumor immune microenvironment and immune cell infiltration. The immune/stromal scores correlated positively with the SPP1 expression, and the relationship was affected by tumor heterogeneity and immunotherapy. In addition, SPP1 could predict the response of tumor immunotherapy. Functional analysis revealed the SPP1-associated terms and pathways. Finally, SPP1 significantly elevated cell proliferation and migration in A549, Huh7, HT-29, A2780 tumor cell lines. In conclusion, this study indicated that SPP1 involved in tumorigenesis, tumor progression, and regulated tumor immune microenvironment, revealing SPP1 might be a potential target for evaluating prognosis and immunotherapy in multiple cancers.Secreted phosphoprotein 1 ( Cancer is a primary cause of high morbidity and high mortality worldwide. According to the World Health Organization, More than 18 million cancer new cases and 9.6 million cancer deaths occurred in 2018 [The common and differential features existing in various types of cancers have been contributing to researches focusing on potential mechanisms of cancer, as well as estimating the clinical prognosis. The Cancer Genome Atlas (TCGA) is based on multiple levels of tumor data from different human cancer cell lines and tissues, focusing on molecular mutations associated with the occurrence and progression of cancer. The TCGA database contains data of 33 cancer types, which can be used for survival analysis and prognosis evaluation, such as overall survival (OS), disease-specific survival (DSS), disease-free interval (DFI), and progression-free interval (PFI). The TCGA contributed to establishing the significance of cancer genomics, helping us understanding cancer in multiple levels, and profoundly impacting the treatment concept of cancer.SPP1) is found to express in different tissues and cell types. The biological functions of SPP1 are variable, specifically in some certain physiological and pathological conditions, including drug resistance, cell proliferation, invasion, survival, stem-like behavior and tumor metastasis [SPP1 plays an important role in several tumor-associated processes, such as proliferation, invasion, migration, angiogenesis, and metastasis [SPP1 promotes epithelial-mesenchymal transition during metastasis and regulates the tumor microenvironment in favor of metastasis [SPP1 has been shown to correlated with the progression and prognosis of different tumors [SPP1 could be used as an indicator of poor prognosis in numerous human cancers, for instance, lung cancer, gastric carcinoma, colorectal cancer, and prostate cancers [SPP1 regulates tumorigenesis by interacting with integrins or the CD44 receptor, subsequently activating Wnt signaling [As a multifunctional and secreted integrin-binding glycoprotein, secreted phosphoprotein 1  [As a landmark cancer genomics program, TCGA had characterized over 20,000 primary cancer and matched normal samples from 33 cancer types. Transcriptome RNA-seq data of 33 cancers were download from the University of California Santa Cruz (UCSC) Xena website (sc.edu/) . 33 canchttps://www.rdocumentation.org/packages/ggpubr) in R software . In addition, the gene expression was also identified via the Oncomine database (http://www.oncomine.org/resource/login.html) [The Ensembl gene ID of transcriptome data was converted into Symbol ID to extract the expression of target genes in each tumor patient. Differential gene expression was analyzed based on ggpubr R package (in.html) . SettingThe signature score reflected the status of the studies biological process in a tumor and were calculated according to the method of Rocha et al . Cutoff-https://CRAN.R-project.org/package=survival) [The survival database of 33 cancer types  were download from UCSC Xena database. To assess the correlation between gene expression and prognosis of cancers, Kaplan-Meier (KM) curves and Cox proportional hazard regression survival analyses were performed using the survival package  . The surTMB is the total number of mutations identified in per tumor sample . We downhttps://R-Forge.R-project.org/projects/estimate/) to calculate the tumor microenvironment indicators for each tumor sample and obtain the immune cell score, stromal cell content, and tumor purity [We used estimate package  algorithm was conducted to estimate the relationship between gene expression and 22 infiltrated immune cells based on expression file . We selehttps://www.ncbi.nlm.nih.gov/geo/) including GSE111636, GSE67501, GSE26383, GSE79691, and GSE100797. These cohorts were used for prediction of patient response to immunotherapy [Immunotherapy datasets were downloaded from the GEO database (otherapy . The GSEotherapy . The t-thttp://www.gsea-msigdb.org/gsea/index.jsp). R software was conducted to gene set enrichment analysis (P < 0.05) and exhibited the top five terms.Functional analysis of gene was proceeded with GSEA . We downSPP1 in the liver cancer tissue were detected by commercial ELISA (Enzyme-linked immunosorbent assay) kits (MultiSciences) as specified by the manufacturer. The mRNA expression of SPP1 and immune-related molecules were measured by quantitative real-time PCR (qRT-PCR). Total RNA was isolated using Trizol reagent (Takara). cDNA was synthesized using a PrimeScriptTM RT Msater Mis (Takara). qRT-PCR analyses were conducted with SYBR® Premix Ex TaqTM II (Takara) with specific primers as follows:The concentration of SPP1: 5ʹ-CTCCATTGACTCGAACGACTC-3ʹ (F),5ʹ-CAGGTCTGCGAAACTTCTTAGAT-3ʹ (R);CD44: 5ʹ-CTGCCGCTTTGCAGGTGTA-3ʹ (F),5ʹ-CATTGTGGGCAAGGTGCTATT-3ʹ (R);CD80: 5ʹ-AAACTCGCATCTACTGGCAAA-3ʹ (F),5ʹ-GGTTCTTGTACTCGGGCCATA-3ʹ (R);LAIR1: 5ʹ-ATCGGGGTCTCAGTGGTCTTC-3ʹ (F),5ʹ-TGCTTTATCTGATTCTGGCGATG-3ʹ (R);NRP1: 5ʹ-GGCGCTTTTCGCAACGATAAA-3ʹ (F),5ʹ-TCGCATTTTTCACTTGGGTGAT-3ʹ (R);HAVCR2: 5ʹ-CTGCTGCTACTACTTACAAGGTC-3ʹ (F),5ʹ-GCAGGGCAGATAGGCATTCT-3ʹ (R).Immunohistochemistry was performed as reported elsewhere . Paraffi3 cells/well. EdU kit  was used for detecting cell proliferation according to the manufacturer’s instruction.The human cell lines A549 (lung carcinoma), HuH-7 (hepatoma carcinoma) and HT-29  were purchased from the Cell Bank of Chinese Academy of Sciences. The human cell lines A2780 (ovarian carcinoma) was obtained from the Tumor Cell Bank of the Chinese Academy of Medical Sciences . Cell migration was determined by performing a wound healing assay. Briefly, cells were inoculated into 6-well plates. Cells reached 90% confluence and were scratched using a 100 μl pipette tip to form a wound-like gap. The cells were maintained in medium, and images were captured at 0 hour and 24 hours. ImageJ software was used to quantify the area of the wound to calculate the cell migration rate. EdU staining was used for the detection of cell proliferation. Briefly, cells were seeded in 24-well plates at a density of 2 × 10SPP1 expression between different tumor stages were compared suing the Kruskal-Wallis test. R software (version 3.6.3) was performed all analyses and chart visualize. Measurement data were expressed as the mean ± standard deviation (SD) from three independent experiments. The tumor and normal samples were compared using the unpaired t-tests. P-value <0.05 was considered statistically significant.Gene differential expression from TCGA database was analyzed by Wilcoxon rank sum test. Survival data was analyzed using the Kaplan-Meier and Cox statistical methods. Spearman correlation analysis was used for the correlation analysis in this study. The difference in SPP1, as an important gene in certain cancer types, involved in the occurrence and development of tumors. We speculated that SPP1 might be used as a potential prognostic and immune-related biomarker in human cancers. In this study, we conducted the correlation analysis between SPP1 expression and clinical characterize, survival, TMB, MSI, the tumor immune microenvironment, immunotherapy, and GSEA. Furthermore, we also performed in vivo experiments to evaluate the oncogenic effect of SPP1. These results identified that high expression of SPP1 could serve as a biomarker for prognostic and tumor immunotherapy. This study will contribute to a deeper understanding of the critical role of SPP1 in human cancers.SPP1 expression differences in tumor and normal samples of cancers. Initially, we used the Oncomine database to analyze the expression level of SPP1 in different tumor and normal tissues. The result showed that SPP1 was overexpressed in multiple types of cancer, including bladder, brain and central nervous system, breast, cervical, colorectal, esophageal, gastric, head and neck, kidney, liver, lung, ovarian, pancreatic, and prostate cancers, and in lymphoma, melanoma, and sarcoma in most data sets . In som tissues ). Except tissues ). In addnd liver , Table SSPP1 in human cancers, we studied some well-known biological processes including differentiation, proliferation, retinoblastoma (RB) pathways, TP53, and centrosome amplification. The most fundamental trait of tumor cells is differentiation and proliferation. We studied differentiation using nine genes , which overexpressed in poorly differentiated bladder, glioblastoma, and breast tumors [SPP1 . In most cancers, high expression of SPP1 means more dedifferentiation, more proliferation, more RB and TP53 mutations, and higher centrosome amplification.In order to validate the important role of t tumors . 110 gent tumors . Centrost tumors . We explt tumors  and TP53t tumors . These sSPP1 expression in the prognosis of different types of cancer, we divided all patients with cancer into high-expression and low-expression groups according to the median value of their SPP1 expression and conducted survival analysis  using the Kaplan-Meier and COX methods. The Kaplan-Meier analysis indicated that the high SPP1 expression significantly decreased OS in six cancers: CESC (p = 0.022), GBM (p = 0.050), LGG (P < 0.001), LIHC (P < 0.001), LUAD (p = 0.035), and PAAD (p = 0.042) , SPP1 expression was associated with OS. The hazard ratio (HR) value was greater than 1, indicating that SPP1 is a high-risk gene in tumors. In other words, the higher the expression of SPP1, the worse the prognosis . In the DFI and PFI analysis, we observed the same phenomenon in certain types of cancer . These results confirmed that SPP1 might act as a powerful biomarker for predicting prognosis in most cancers. Increased and decreased SPP1 expression had different prognostic values depending on the type of cancer. In addition, we investigated the relationship between SPP1 expression and clinical characteristics  of 33 types of cancer. Increased expression of SPP1 was only positively correlated with certain tumor stages, such as COAD, ESCA, LIHC, and READ . SPP1 expression was correlated to pathological stages including BLCA, LGG, LGG, and LIHC. The difference was statistically significant (P < 0.05) with a higher grade being associated with a higher expression level . Gender difference existed in most tumors, but they were not associated with the expression level of SPP1 (Table S4). However, ESCA was more common in males, in which the expression level of SPP1 was higher, and SARC occurred more frequently in women, in which SPP1 expression was lower . Age is associated closely with the occurrence of various tumors. Patients aged ≥ 60 years old formed a higher proportion in BLCA, COAD, LUAD, LUSC, and STAD. Patients aged < 60 years old formed a higher proportion in ACC, CESC, LGG, PCPG, TGCT, and THCA (Table S4). SPP1 expression levels were markedly higher in older (≥ 60 years old) than in younger (< 60 years old) patients in BLCA, CESC, HNSC, LIHC, PRAD, SARC, and THYM. Only in SKCM was SPP1 expression higher in younger patients .To assess the predictive value of the = 0.042) . High SPy in UVM ). In addrognosis ). In theSPP1 genes in patients with cancer using the cBioPortal database. The SPP1 mutation frequency was the primary alteration in uterine tumors, melanoma, stomach tumors, cervical tumors, lung squamous cell carcinoma, colorectal tumors, lung adenocarcinoma, GBM, HNSC, ccRCC, and LGG. In bladder tumors, sarcoma, breast tumors, prostate tumors, PCPG, esophagus tumors, and pancreas tumors, gene amplification was the most important genetic alteration (SPP1 expression and the TMB and MSI (Table S5). SPP1 expression correlated positively with the TMB in ACC, CESC, COAD, KIRC, LGG, OV, PRAD, SARC, STAD, and THYM, and correlated negatively in ESCA . In add in ESCA ). SPP1 eand PAAD ).FigureSPP1 expression in cancer, we evaluated the relationship between immune and stromal scores and SPP1 expression using the CIBERSORT analysis. SPP1 expression correlated positively with the tumor immune and stromal scores at P < 0.001 (Table S6). The top three tumors that correlated most significantly with SPP1 were LGG, OV, and THCA (immune score) , SPP1 also showed the same trend. This result indicated that tumor grades and stages did not affect the relationship between SPP1 and immune/stromal score. However, in READ and THCA, SPP1 was not correlated with stage І of READ and stage Ⅳ of THCA, only correlated with other stages (Table S7). This result indicated that tumor grades and stages affected the relationship between SPP1 and immune/stromal score.The tumor microenvironment is a complex milieu of stromal cells, tumor cells, and immune cells. Stromal cells support tumor growth , and imme score) , and COAe score) . We furtSPP1 and 47 genes associated with immune infiltrating cell types in these tumors, including those encoding the tumor necrosis factor (TNF) superfamily (TNFSF), the TNF receptor superfamily (TNFRSF), multiple CD molecules , and markers of exhausted T cells, such as PDCD1 (programmed cell death 1) and PDCD1LG2 (programmed cell death 1 ligand 2), CTLA4 (cytotoxic T-lymphocyte associated protein 4), LAG3 (lymphocyte activating 3), and HAVCR2 (Hepatitis A virus cellular receptor 2). In most tumors, SPP1 was co-expressed with markers of immune cells and showed significant positive correlations with them. For example, except for KIR3DL1 , HHLA2 (encoding HERV-HLTR-associating 2), TNFSF9, TNFRSF25, and TNFRSF18, SPP1 expression correlated significantly and positively with other immune cells in LGG , LAIR1 (leukocyte associated immunoglobulin like receptor 1), CTLA4, HAVCR2, and CD80. Similarly, we further performed correlation analysis of SPP1 expression with these immune checkpoint genes based on different stages and grades of the tumor. Heatmap showing most genes positively correlated with SPP1, especially HAVCR2, NRP1 (neuropilin 1), LAIR1 (leukocyte associated immunoglobulin like receptor 1), CD80, PDLD1LG2, and CD86 . In LIHand CD86 ). SPP1 end LAIR1 ). TogethSPP1 expression with immune and stromal scores in COAD, LGG, and OV patients treated with or without immunotherapy. In LGG, SPP1 was significantly positive correlated with immune and stromal scores regardless of whether patients received immunotherapy or not . The repy group . In OV, py group . The heapy group . These rSPP1 expression. The results indicated that SPP1 expression had significant correlations with resting mast cells in 7 types of cancer, with macrophages in 21 types of cancer, with neutrophils in 10 types of cancer, with B cell in 6 types of cancer, with DC in 5 types of cancer, with CD8 in 5 types of cancer, and with monocytes in 5 types of cancer (Table S8). The results also revealed that at the level of immune cell infiltration, BRCA, COAD, GBM, LUAD, SARC and STAD correlated most strongly with SPP1 expression.Tumor-infiltrating immune cells (TIICs) are a part of the complex microenvironment that regulates tumor development and progression . TherefoSPP1 in tumor immunotherapy, we downloaded and analyzed immunotherapy datasets from the GEO database including GSE111636, GSE67501, GSE26383, GSE79691, and GSE100797. The RCC cohort was derived from data which was given in the literature with receiving immune checkpoint therapy [SPP1 expression and the effect of immunotherapy, we divided patients into two groups according immunotherapeutic effect. With the except of GSE111363 and GSE79691 cohorts, SPP1 expression showed a high level in progressing group and a low level in regressing group including RCC, GSE67501, GSE26383, and GSE100797 cohorts  compared the immunotherapy response rate. We found that the lower SPP1 was related to a better response rate to immunotherapy than the high-expression group  , SPP1 has significant prediction power for tumor immunotherapy   , i, k, lE100797) , j). In E100797) . CollectSPP1 contribute to tumorigenesis, GSEA was performed, including functional analysis using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) (Table S9 and Table S10). We identified the top five signaling pathways in GO analysis and found that SPP1 had a major impact on sensory perception of smell and olfactory receptor activity in most tumors. The higher the expression of SPP1, the more active these two pathways were in BRCA, COAD, ESCA, SRAC, STAD, and UCEC . The KEGG analysis showed that SPP1 primary affects the olfactory transduction pathway in most tumors.To determine which signaling pathways associated with and UCEC . The lowSPP1, we evaluate the ability of SPP1 in cell proliferation and migration. We used siRNA to knockdown SPP1 expression in tumor cell lines. Following transfection, cell migration and proliferation were evaluated by EdU staining assay and wounding healing assay. The results showed that si-SPP1 also decreased cell proliferation of these tumor cell lines . We furll lines ).FigureSPP1 is an oncogene in many cancers [SPP1 in different aspects of tumor biology. In the present study, we analyzed the expression of SPP1 in multiple tumors using the Oncomine and TCGA databases, revealing significantly differences of SPP1 expression in various types of cancer. Compared with that in normal tissues, the SPP1 mRNA level was significantly higher in most tumor tissues, including bladder, brain and central nervous system, cervical, colorectal, esophageal, gastric, head and neck, liver, lung, ovarian, pancreatic, and prostate cancer, and melanoma, while the expression trend of SPP1 was inconsistent kidney cancer and sarcoma. In the TCGA database, SPP1 expression was significant higher in most tumor tissues, but showed lower expression in KICH, PAAD, SKCM, and THYM, however, without statistical significance. The analysis results in the two databases were broadly similar. However, there were also some differences that might have been caused by the data collection and analysis methods. In addition, according to the Kaplan-Meier and COX survival analyses, we found that high expression of SPP1 in most cancers is indicative of a poor prognosis, such as in CESC, ESCA, GBM, LGG, LIHC, LUAD, MESO, PAAD, PRAD, and STAD. Studies have also found that SPP1 could promote the proliferation, migration, and invasion of malignant tumor cells and inhibit cell apoptosis, leading to a poor prognosis in certain tumors [SPP1 is an important prognostic biomarker for many cancers [Increasing evidence indicates that  cancers . Therefon tumors . Increas cancers ,37.SPP1 has been observed in various immune cells and is involved in initiating immune responses [SPP1 expression was related to cancer immunity. SPP1 expression was significantly associated with the degree of infiltration in macrophages , resting mast cells, B cells, and neutrophils in at least six types of cancer according to CIBERSORT analysis.The immune system normally recognizes and clears tumor cells in the tumor microenvironment. However, the growth of tumor cells also inhibits the immune system. When tumor cells are killed, the immune system is also suppressed. The biological function of esponses . NotablySPP1 promoted glioma progression by upregulating GBM-infiltrating neutrophils and macrophages, and was associated with infiltration of these cells within tumor specimens [SPP1 was also involved in neutrophil activation [SPP1 attenuated neutrophil migration [SPP1 expression was observed to correlated positively with immune and stromal scores. Immune checkpoint inhibitors have revolutionized cancer treatment [SPP1 and 47 common immune checkpoint genes. SPP1 expression was observed to correlated positively with immune checkpoint gene expression in multiple types of cancer. Studies have demonstrated that SPP1 could interact with CD44 in prostate cancer, GBM, and breast cancer [SPP1 might trigger pro-inflammatory stimuli, such as that mediated by TNF [SPP1 tumor immunity and its related cancer biomarkers. In recent years, immunotherapies have shown gradually increasing efficacy in treating tumors. Studies have demonstrated that immune checkpoint genes have a critical influence on immune cell infiltration and immunotherapy [SPP1 was likely correlated with immune cell infiltration and might contribute to the immunotherapy in hepatocellular carcinoma [SPP1 and immune checkpoint genes, which provided a theoretical basis for combined molecular targeting immunotherapy in the future. We also found that SPP1 was associated with immunotherapy response in BLCA, RCC, and SKCM. Therefore, SPP1 might be a potential indicator in immunotherapy.Previous studies suggested that pecimens ,40. SPP1tivation , and neuigration . Currentigration ,44. In treatment ,46. Thert cancer . Moreoved by TNF . Almost d by TNF . As a chd by TNF . This stotherapy . SPP1 waarcinoma .This worSPP1, especially in uterine cancer and melanoma. The TMB and MSI are emerging biomarkers that have predictive value in cancers [SPP1 had a major impact on olfactory transduction. However, the underlying mechanisms requires further investigation.Genetic mutation is the major cause of tumorigenesis. Mutation and amplification were observed to be the main alteration frequency with mutation types of  cancers ,54,55. T cancers . For exa cancers . The hig cancers . Previou cancers ; however cancers . Thus, t cancers ,61. Ther cancers . The olf cancers . Our stuSPP1 could regulate tumorigenesis, tumor progression, and prognosis. The expression of SPP1 was significantly different in diverse human cancers. Therefore, SPP1 expression could be a valuable prognostic biomarker, because SPP1 upregulation resulted in a significant decrease in patient survival in certain cancer types. In addition, we revealed that the SPP1 expression was related to cancer immunity. Immunotherapy did not affect the immune/stromal score in LGG, andIn summary, our findings suggested that SPP1 may serve as an attractive target in cancer mechanistic research and in treatment.affected immune/stromal score in COAD and OV. Therefore, Click here for additional data file.


Oryza sativa), wheat (Triticum aestivum), and barley (Hordeum vulgare); however, these families have not been fully identified and comprehensively analyzed in maize (Zea mays L.).Nicotianamine (NA), 2′-deoxymugineic acid (DMA), and mugineic acid (MA) are chelators required for iron uptake and transport in plants. Nicotianamine aminotransferase (NAAT), 2′-deoxymugineic acid synthase (DMAS), transporter of MAs (TOM), and efflux transporter of NA (ENA) are involved in iron uptake and transport in rice (ZmNAAT, 9 ZmDMAS, 11 ZmTOM, and 2 ZmENA genes by genome mining. RNA-sequencing and quantitative real-time PCR analysis revealed that these genes are expressed in various tissues and respond differently to high and low iron conditions. In particular, iron deficiency stimulated the expression of ZmDMAS1, ZmTOM1, ZmTOM3, and ZmENA1. Furthermore, we determined protein subcellular localization by transient expression of green fluorescent protein fusions in maize mesophyll protoplasts. ZmNAAT1, ZmNAAT-L4, ZmDMAS1, and ZmDMAS-L1 localized in the cytoplasm, whereas ZmTOMs and ZmENAs targeted to plasma and tonoplast membranes, endomembranes, and vesicles.Here, we identified 5 Our results suggest that the different gene expression profiles and subcellular localizations of ZmNAAT, ZmDMAS, ZmTOM, and ZmENA family members may enable specific regulation of phytosiderophore metabolism in different tissues and under different external conditions, shedding light on iron homeostasis in maize and providing candidate genes for breeding iron-rich maize varieties.The online version contains supplementary material available at 10.1186/s12870-021-03422-7. Iron (Fe), an essential micronutrient for all organisms, acts as a cofactor for many enzymes and plays an important role in numerous cellular functions in plants, including respiration, photosynthesis, and chlorophyll biosynthesis. Iron deficiency in the human diet leads to health problems such as anemia, which affects more than one billion people. In plants, iron deficiency causes leaf senescence, which limits plant growth and reduces quality and yield . However3+) to ferrous (Fe2+) iron on the root surface, and transport of Fe2+ into the root by the plasma membrane protein IRON-REGULATED TRANSPORTER1 (IRT1) [3+–MA complexes form in the rhizosphere and are transported into plant root cells by YELLOW STRIPE1/YELLOW STRIPE1–LIKE (YS1/YSL) iron transporters [Oryza sativa), it can be further converted into MA by a hydroxylation reaction in barley (Hordeum vulgare) and other graminaceous plants [2+ and assist in iron trafficking [Although most soils have relatively high levels of iron, iron has low solubility under aerobic conditions and therefore is often unavailable for plant uptake . Plants 1 (IRT1) , 7. Stra1 (IRT1) . Fe3+–MAsporters . The MA sporters , 11. Alts plants . Althougerrous Fe+ iron onfficking .3+ and NA–Fe2+ have been detected in the phloem sap, they are proposed to be chelators for phloem iron transport [2+ or DMA–Fe3+, indicating possible switching of chelators between NA and DMA in phloem [3+ and citrate–Fe2+ were detected in xylem sap of Arabidopsis thaliana and rice. Moreover, knockout of a citrate efflux transporter, RICE FERRIC REDUCTASE DEFECTIVE3-LIKE1 (OSFRDL1), leads to decreased citrate and iron contents in the xylem sap [OsTOM1 and OsTOM2 was induced in the root stele, indicating that DMA serves as a compensatory chelator in xylem iron transport in response to fluctuating iron status [In addition to acting in iron uptake, phytosiderophores also participate in iron transport within the plant. Iron is transported in a chelated form in plants due to its low solubility and high reactivity. The iron chelators DMA, NA, and citrate assist in long-distance trafficking of iron in various species –16. Sincransport , 18. OsYn phloem –21. Citrylem sap , 22. Sinn status , 24.Zea mays L.) chlorosis mutants, ys1 and ys3, are characterized by a yellow stripe phenotype, resulting from impaired MA–Fe3+ uptake and disrupted phytosiderophore secretion, respectively [HvNAAT in transgenic tobacco (Nicotiana tabacum) plants leads to overconsumption of NA, causing interveinal chlorosis in young leaves [Interfering with phytosiderophore biosynthesis and release generates strong iron-deficient phenotypes and growth defects. Two classical maize , a homolog of OsTOM1, was induced in roots under iron deficiency [In addition to providing precursors for DMA biosynthesis, cytosolic NA is also consumed by EFFLUX TRANSPORTER OF NA (ENA), which may export NA out of cells and transport NA between intracellular compartments . Therefonditions , 30. The tissues . The expficiency . These oNAS family genes in maize, which suggests that the regulation of NA biosynthesis is finely tuned by multiple genes that have diverged in expression pattern or protein subcellular localizations [Maize is a major crop worldwide. Although the iron content of corn kernels is higher than that of brown rice , it is sizations . This raZmNAS genes are encoded by a multi-gene family in maize, we found 5, 9, 11, and 2 genes encoding putative ZmNAAT, ZmDMAS, ZmTOM, and ZmENA proteins, respectively. Compared to genes of these families identified in wheat (Triticum aestivum), barley, and rice, maize tends to have a larger number of DMAS and TOM proteins  with previously identified NAAT, DMAS, TOM, and ENA proteins as queries Table S. ConsistZmNAAT-like1–4, ZmDMAS-like1–8, ZmTOM-like1–8, and ZmENA1–2 according to their chromosomal locations. The amino acid lengths of the ZmNAATs and ZmDMASs varied modestly, ranging between 434–474 aa and 310–360 aa, respectively. TOM and ENA proteins belong to the major facilitator superfamily (MFS), but they show large variation in protein lengths, ranging between 317–589 aa and 188–452 aa, respectively, suggesting that TOMs and ENAs may vary in substrate affinity or subcellular localization. Most NAATs and DMASs were predicted to localize in the cytosol, but most TOMs and ENAs were predicted to localize in the plasma membrane or vacuolar membrane.Previously identified genes were annotated, and newly identified genes were named ZmNAAT, ZmDMAS, ZmTOM, and ZmENA genes were mapped on the maize genome . The expression of ZmNAAT genes, encoding enzymes regulating the first step of DMA biosynthesis, exhibited two types of patterns. ZmNAAT1 and ZmNAAT-L4 expression was significantly reduced in roots under iron-excess conditions. ZmNAAT-L2 and ZmNAAT-L3 expression trended downward in iron-deficient and iron-excess conditions in shoots  database [The E-box (CANNTG) and G-box (CACGTG) are nditions , 34. Botnditions . Thus, wdatabase . We founZmNAAT, ZmDMAS, ZmTOM, and ZmENA genes in different tissues using RNA-seq data , embryo-preferred (ZmTOM-L7 and ZmDMAS-L7), root-preferred , endosperm-specific (ZmTOM-L1), and anther-specific (ZmTOM-L8). Previously identified ZmNAAT1, ZmDMAS1, and ZmTOM1 exhibited root-preferential expression patterns, suggesting they may work together in phytosiderophore biosynthesis and secretion in root cells. Newly identified genes were expressed in almost all tissues with different specificities, implying essential roles of phytosiderophores in iron trafficking and storage.We analyzed the expression of ata Fig.  and qRT-ata Fig. . The heaZmNAAT1/L2/L4, ZmDMAS1/L2/L3/L4/L7, and ZmTOM1/3/L5/L7 . We found some genes exhibited abundant expression in the ear and/or tassel, including /L7 Fig. . This suZmTOM-L4 was preferentially expressed in the leaf and root, while ZmTOM-L5 expression was mainly detected in the embryo at 12 days after pollination (DAP). ZmENA1 was expressed in almost all tissues with relatively high expression in the ear and embryo at 12 DAP  and transiently expressed them in maize mesophyll protoplasts. All the selected ZmNAAT and ZmDMAS proteins  localized in the cytoplasm and nucleus, as did the GFP control Fig. .Fig. 7SuZmTOM and ZmENA proteins localized at membrane systems. Since plasma membrane and complex endomembrane localization was observed for ZmTOM1, ZmENA1, and ZmENA2, we co-transformed an endoplasmic reticulum (ER) marker to specify the inner membrane localization. Because the GFP signals are not fully overlapped with the ER marker in both single and z-stacked images, we speculate that the inner membrane localizations are not the ER. We found spot-like signals for ZmTOM1 and ZmENA2, suggesting they are targeted to small vesicles. ZmTOM2 localized to both the tonoplast and spot-like vesicles, indicating it may mediate import-flux of phytosiderophores into the vacuole.3+, while NA and DMA are essential for intercellular, phloem, and possibly xylem iron transport. Since NA also serves as the intermediate for DMA/MA biosynthesis, the export of NA (mediated by ENA) and conversion of DMA (mediated by NAAT and DMAS) are key steps affecting iron homeostasis in plants. Over-consumption of NA induced by constitutively expressing HvNAAT in transgenic tobacco plants leads to interveinal chlorosis in young leaves [Solanum lycopersicum) mutant chloronerva [ZmTOM1 mutant (ys3) [Graminaceous plants use a chelation strategy to take up sufficient iron and prevent its overaccumulation, and the balance of NA-MA metabolism plays a crucial role in regulating these processes. DMA and MAs are secreted into the rhizosphere to acquire Feg leaves . Similaroronerva . These rnt (ys3) , 25. TheNAS genes are encoded by multi-gene families in a broad range of graminaceous plants, including maize, rice, wheat, and barley [NAS genes are grouped into class I and class II [NAS genes are preferentially expressed in root and stem tissues, and their expression is up-regulated in response to iron deficiency. Conversely, class II genes are mainly expressed in the leaf and are up-regulated in response to excess-iron conditions [OsNAS3 suggests that NA biosynthesized by OsNAS3 under excess-iron conditions is associated with iron detoxification, redistribution, and storage, while NA produced by OsNAS1 and OsNAS2 under normal iron conditions may play roles in iron transport [NAS genes may be differentially regulated under fluctuating iron levels, and they may associate with different transporters or enzymes and thereby participate in NA secretion or DMA biosynthesis to balance iron uptake, transport, and storage. This raises the question of whether enzymes associated with NA-DMA metabolism (NAAT and DMAS) and transport for NA/DMA secretion (ENA and TOM) are also encoded by multi-gene families, which could allow for fine regulation of these pathways.d barley , 42–44. class II , 44. Clanditions , 45. In ransport . These rNAAT and DMAS genes were duplicated in wheat. Six NAATs were identified in rice, but only OsNAAT1 responded to fluctuations in iron levels [ZmNAAT1, ZmDMAS1, and ZmTOM1/2/3, we identified four ZmNAATs, eight ZmDMASs, eight ZmTOMs, and two ZmENAs in maize. The expression of ZmNAAT and ZmDMAS genes was regulated differently in response to environmental iron conditions, though the expression patterns were consistent with their phylogenetic classifications. ZmNAAT1 and ZmNAAT-L4 belonged to the same sub-class and were repressed in roots under iron-excess conditions, whereas ZmNAAT-L2 and ZmNAAT-L3 were grouped into another sub-class and exhibited reduced expression in iron-deficient and iron-excess conditions in shoots. Likewise, three different expression patterns were observed for ZmDMAS genes. In response to iron deficiency, ZmDMAS1 showed a similar expression trend in roots with that of its orthologs OsDMAS1, HvDMAS1, and TaDMAS1 [ZmDMAS1 under iron deficiency, the expression of ZmDMAS-L2, ZmDMAS-L3, ZmDMAS-L7, and ZmDMAS-L8 was stimulated in shoots in iron-excess conditions. In addition, ZmDMAS-L4 and ZmDMAS-L6 expression was repressed in roots under iron deficiency. The expression trends of these DMAS family genes were associated with their classifications, indicating their potential roles in balancing iron uptake and homeostasis.n levels . Here, i TaDMAS1 . The clo TaDMAS1 . The phy TaDMAS1 . In contOsENA1 expression was strongly up-regulated under iron-deficient conditions [ZmENA1. As a member of another sub-class of the MFS family, TOM was first identified as a MA efflux transporter in rice and barley, and OsTOM1/2 and HvTOM1 expression is induced in iron-deficient roots [TOM and two ENA genes were identified in rice, they had a broad spectrum of expression patterns. OsTOM1 was expressed in shoots and roots, while OsTOM2 was expressed in the epithelium, scutellum, and dorsal vascular bundles of seeds [OsENA1 expression was stimulated under iron deficiency [ZmENA1 expression was induced and repressed in shoots under iron deficiency and excess, respectively. We found that the TOM gene family had expanded in maize, and the expression of these ZmTOM genes exhibited diverse tissue specificities and different responses to environmental iron conditions. Even though most ZmTOM genes were leaf- and root-preferentially expressed, embryo- (ZmTOM-L5/L7), endosperm- (ZmTOM-L1), and anther-preferred (ZmTOM-L8) expression patterns were also identified. ZmTOM1 and ZmTOM3 expression was induced by iron deficiency, while that of ZmTOM2 was stimulated under iron-excess and -deficient conditions. Therefore, we speculated that duplication of ZmTOM genes may result from the need for fine regulation of DMA/MA secretion in maize.Although TOMs and ENAs belong to the MFS family, their amino acid sequences were less conserved compared with DMAS proteins, which may suggest that these TOMs and ENAs have different substrate specificities. ENA is an NA efflux transporter, and nditions , 29. Simnt roots , 24. Altof seeds , 24. OsEThe subcellular compartmentalization of enzymes and transporters provides another layer of functional regulation. NA and DMA are biosynthesized in specific vesicles, which may be derived from the ER , 50. FurOne way to improve essential micronutrients in the human diet is by enhancing the iron content in cereal grains. Several approaches have been used to enhance the iron content in seeds, including upregulation of genes associated with iron uptake and transport, as well as modification of the expression of endosperm-specific genes , 56. It ZmNAAT, 9 ZmDMA, 11 ZmTOM, and 2 ZmENA genes in maize and determined their phylogenetic relationships, subcellular localization, and gene expression patterns in different tissues and under different iron conditions. We found abundant expression of these genes in the tassel, ear, and developing seeds, indicating essential roles of NA and phytosiderophores in iron transport during reproductive development. In addition, the expression of these genes was regulated by iron status, suggesting that the balance between NA and phytosiderophores within plant cells is essential for iron homeostasis. All the examined ZmNAATs and ZmDMASs localized in the cytoplasm, whereas ZmTOMs and ZmENAs localized in the plasma and tonoplast membrane, endomembrane, and vesicle. This indicates that ZmTOMs and ZmENAs may contribute to intercellular export and intracellular sequestration of NA and phytosiderophores. Furthermore, the expansion of the ZmNAAT, ZmDMA, ZmTOM, and ZmENA gene families suggests that fine regulation of NA-phytosiderophore metabolism contributes to iron homeostasis in maize. These results provide valuable insight into the potential functions of ZmNAAT, ZmDMA, ZmTOM, and ZmENA genes for breeding iron-rich maize varieties.In this study, we identified 5 Zea mays L.) inbred line Z58 was obtained from the Center for Crop Germplasm Resources, Chinese Academy of Agricultural Sciences. All seeds were planted in a greenhouse. For expression analysis in different tissues, we collected root, crown root, stem, leaf, ear, and tassel tissues before pollination. We also collected embryo and endosperm at 12, 21, and 28 days after pollination (DAP) to determine expression patterns in developing seeds. For the different iron treatments, Z58 seedlings were cultured at 28 °C with a 16-h-light/8-h-dark cycle. The seeds were germinated, and the seedlings were incubated in normal Hoagland nutrient solution with Fe3+-EDTA (Fe3+ concentration = 100 μM) until the trefoil stage. Then, the seedlings were transferred to Hoagland nutrient solution without Fe3+-EDTA or with excessive Fe3+-EDTA (Fe3+ concentration = 500 μM) for deficient and excess iron treatments, respectively. Shoots and roots were harvested at 24, 48, and 96 h after the different Fe3+-EDTA treatments. Seedlings sampled before the iron treatments were used as controls (0 h). All samples were immediately frozen in liquid nitrogen and quickly stored at − 80 °C.The maize  and maize as queries (Table Shttp://ensembl.gramene.org/Zea_mays/Tools/Blast) was used to identify putative homologous proteins. Candidates were obtained with a set of relatively sensitive cutoffs  and used as queries against the maize annotated protein database using HMMER. Table Shttps://web.expasy.org/protparam/) was used to calculate the physicochemical properties of each protein, including the number of amino acids (aa), isoelectric point (pI), and molecular weight (MW). WoLF POSRT II (https://wolfpsort.hgc.jp/) and Plant-mPLoc (http://www.csbio.sjtu.edu.cn/bioinf/plant-multi/) were used to predict the subcellular locations of each protein. All genes were mapped to maize chromosomes and named according to their locations. The gene structures were analyzed in GSDS v.2.0. The MEME online software (https://meme-suite.org/meme/) was used to predict the conserved domain of proteins. For cis-element analysis, 2000-bp upstream promoter sequences from the translation start site were analyzed in the PLACE database (https://www.dna.affrc.go.jp/PLACE/?action=newplace), and the G-box and E-box distribution was visualized by TBtools software [Next, we used HMMER to test the putative proteins. The HMM for the canonical domains of NAAT (PF00155), DMAS (PF00248), and TOM/ENA (PF07690) were obtained from the Pfam database . qRT-PCR was performed in a 20-μL volume containing 10 μL SYBR Green , 0.4 μL ROX II , 0.2 μM gene-specific primers, and 5 μL 5× diluted cDNA. The reaction was performed using the ABI 7500 Real Time Thermal Cycler. The expression of ZmActin1 was used as an internal control  following the manufacturer’s instructions. The first-strand cDNA was obtained by reverse transcription using cDNA TranScript One-Step gDNA Removal and cDNA Synthesis SuperMix . Primers for qRT-PCR assays Table S were desArabidopsis thaliana WALL-ASSOCIATED KINASE 2) at the N terminus of mCherry and ER retention signal His-Asp-Glu-Leu at its C terminus [To construct plant transient expression vectors, the amplified open reading frames (ORFs) were cloned into the XhoI-XbaI site of the pRTL-2NGFP plasmid  to expreterminus . For mesterminus . After cAdditional file 1: Figure S1. The multiple sequence alignments of NAAT, DMAS, TOM, and ENA proteins.Additional file 2: Figure S2. E-boxes and G-boxes in the ZmNAAT, ZmDMAS, ZmTOM, and ZmENA promoter regions.Additional file 3: Table S1. Genes used for the identification and phylogenetic analysis of the NAAT, DMAS, TOM, and ENA families in maize.Additional file 4: Table S2. Identified NAAT, DMAS, TOM, and ENA proteins in maize, wheat, barley, and rice.Additional file 5: Table S3. The cutoffs for the identification of NAAT, DMAS, TOM, and ENA proteins in maize using BLAST and HMMER.Additional file 6: Table S4. Primers used in this study.


Zingiber officinale Roscoe), an important affinal drug and diet vegetable. Recent completion of the ginger genome sequencing provides an opportunity to investigate the expression profiles of AP2/ERF genes in ginger on a genome-wide basis.AP2/ERF transcription factors (TFs) constitute one of the largest TF families in plants, which play crucial roles in plant metabolism, growth, and development as well as biotic and abiotic stresses responses. Although the AP2/ERF family has been thoroughly identified in many plant species and several AP2/ERF TFs have been functionally characterized, little is known about this family in ginger  analysis of ZoAP2/ERFs during development and responses to abiotic stresses were investigated in ginger.A total of 163 AP2/ERF A comprehensive analysis of the AP2/ERF gene expression patterns in various tissues by RNA-seq and qRT-PCR showed that they played an important role in the growth and development of ginger, and genes that might regulate rhizome and flower development were preliminary identified. In additionally, the ZoAP2/ERF family genes that responded to abiotic stresses were also identified. This study is the first time to identify the ZoAP2/ERF family, which contributes to research on evolutionary characteristics and better understanding the molecular basis for development and abiotic stress response, as well as further functional characterization of ZoAP2/ERF genes with an aim of ginger crop improvement.The online version contains supplementary material available at 10.1186/s12870-021-03329-3. AP2-1) was found to determines the identity of perianth organs in flowers of Arabidopsis [The APETALA 2/Ethylene-Responsive element binding Factor (AP2/ERF) family composes one of the largest plant-specific transcription factors (TFs) and plays essential roles in various biological processes , 2. Its bidopsis , a largebidopsis –9, leaf bidopsis , and seebidopsis . Ethylenbidopsis –14, respbidopsis –18 and pbidopsis . ERFs habidopsis , Howeverbidopsis , 22.Zingiber officinale Roscoe) is an important medicinal material containing abundant gingerols and exhibiting many biological properties, including antioxidant, antimicrobial, and anti-inflammatory properties, which have various effects on the central nervous system [Ginger  has been reported. Because of the importance of the AP2/ERF family genes in various physiological processes, it is of significance to systematically study the AP2/ERF family in ginger. The evolutionary characteristics and tissue-specific expression of the AP2/ERF gene family in ginger could be characterized taking the advantage of recently sequenced ginger genome [Arabidopsis thaliana, Solanum tuberosum, Oryza sativa and Musa acuminata and investigated the expression profiles of AP2/ERF genes in various ginger tissues, particularly in the stage of rhizome expansion and floral organ formation. This study provides valuable clues for future investigations aiming at the functional characterization of the AP2/ERF family genes and can be utilized in the genetic improvement of ginger.At present, investigations and analyses of the AP2/ERF gene family have been performed in many plants at the whole genome scale, including Arabidopsis , rice 55, 26, por genome . In thisZoAP2/ERF genes could be mapped on the chromosomes and were renamed from ZoAP2#01 to ZoAP2#35, ZoERF#001 to ZoERF#114, ZoRAV#1 to ZoRAV#3, and ZoAP2Solo#1 to ZoAP2Solo# 5 based on chromosomal locations  that could not be conclusively mapped to any linkage group were renamed ZoERF#115-ZoERF#119, respectively. The validated ZoAP2/ERF gene sequences are available in Additional file A total of 163 AP2/ERF family candidate genes were obtained using the hidden Markov model (HMM) method with the the AP2/ERF family (PF00847) as the query. The annotation of these genes was further checked using available ginger transcriptome data. Fifteen erroneously predicted AP2/ERF gene models were manually curated. According to the classification system reported by Nakano et al. (2006), we divided the AP2/ERF family into four subfamilies, AP2, ERF, RAV, and Soloist, in ginger. A total of 157 Gene characteristics, including the coding sequence (CDS) length, protein molecular weight (MW), isoelectric point (pI), and conserved domain, were analyzed. Among the 163 ZoAP2/ERF proteins, ZoERF#003 and ZoERF#023 were identified to be the smallest proteins with 117 amino acids (aa), whereas the largest one was ZoAP2#02 (671 aa). The MW of the proteins ranged from 12.92 (ZoERF#23) to 73.07 kDa (ZoERF#02), and the pIs ranged from 4.58 (ZoERF#106) to 11.17 (ZoERF#005) , LG (52nd amino acid and 53rd aa), AA (62nd and 63rd aa) and YD (65th and 66th aa) elements were highly conserved. The WLG element (51st to 53rd aa) was more conserved in the ERF and RAV subfamilies than in the AP2 subfamily. In the AP2 subfamily, WLG elements (51st to 53rd aa) were converted into YLG elements(51st to 53rd aa) method based on the multiple sequence alignment of 166 ZoAP2#32 gene having 5 exons, the other members of the AP2 subfamily contained more than 6 exons. Overall, the number of exons was conserved in the AP2 subfamily, although the exon positions varied. Most members of the ERF subfamily and RAV subfamily contained only one exon with the AP2 domain located in the exonic region , Motif-10 in three genes , Motif-7 in four ZoERF genes , and Motif-3 only in ZoERF#009. In the AP2 subfamily, 17 genes contained six motifs including Motif-1, Motif-3, Motif-4, Motif-5, Motif-6, and Motif-9. Eleven ZoAP2 genes with Motif-10 and 15 ZoAP2 genes with Motif-7 were detected, respectively.By comparing the genomic DNA sequences, we obtained the intron and exon structure of ZoAP2/ERF genes ; GARE-motif, P-box, and TATC-box (gibberellin responsiveness); TCA-element and SARE ; CGTCA-motif and TGACG-motif (MeJA-responsiveness); and TGA-element, TGA-box, AuxRR-core, AuxRE and TGA-element (auxin responsiveness). Abiotic stress response elements contained MBS (drought-inducibility), LTR(low-temperature responsiveness), WUN-motif(wound-responsive element), TC-rich repeats(defense and stress responsiveness), DRE , and GC-motif (anoxic specific inducibility). Light-responsive elements, included TCCC-motif, GATA-motif, chs-CMA1a, G-box, MRE, Box 4, Gap-box, TCT-motif, GA-motif, AE-box, LAMP-element, Box II, ATCT-motif, chs-CMA2a, I-box, AT1-motif, ATC-motif, ACE, chs-Unit 1 m1, ACA-motif, chs-CMA2b, L-box, CAG-motif, GTGGC-motif, GT1-motif, 3-AF1 binding site, Sp1, AAAC-motif, GATT-motif, TGGCA, sbp-CMA1c, 4 cl-CMA2b, Pc-CMA2a, and LS7. Other response elements, included GCN4-motif (endosperm expression), ARE (anaerobic induction), circadian (circadian control), AT-rich sequence , MSA-like (cell cycle), MBSI (flavonoid biosynthetic genes regulation), CAT-box and NON-box (meristem expression), motif I (root specific regulation), and RY-element (seed-specific regulation).The cis-regulatory elements, TC-rich repeats, TGA-element, ARE, ABRE, G-box, Box 4, LTR, CGTCA-motif and CGTCA-motif, were more abundant in most ZoAP2/ERF genes. WUN-motif that is related to wound responsiveness was uniquely found in ZoAP2#29, ZoERF#086, ZoERF#069, ZoERF#038, ZoERF#109 and ZoAP2#18. Seed-specific regulation cis-element, RY-element, was only found in ZoERF#087, ZoERF#059, ZoAP2#23, ZoERF#050, ZoRAV#2, ZoAP2#02, ZoAP2#25, ZoAP2#05, ZoAP2#09, ZoAP2#12, ZoAP2#28, ZoRAV#3 and ZoERF#070. Motif I, a cis-element of root specific regulation, was found in ZoERF#071, ZoERF#051, ZoERF#066, ZoERF#098, ZoERF#010, ZoERF#046 and ZoERF#107. MBSI, a cis-element of flavonoid biosynthetic gene regulation, was found in ZoERF#100, ZoERF#077, ZoERF#026, ZoAP2#11, and ZoERF#046. MSA like element that is related to cell cycle was unique to ZoERF#079, ZoERF#050, ZoERF#091, ZoERF#046, ZoERF#107 and ZoERF#056.To further study the regulatory mechanism of the ZoAP2/ERF family genes during the abiotic stress responses, the upstream 2-kb sequences of 157 The gene ontology (GO) analysis was performed using rice protein sequences as the reference. The result showed the putative participation of ZoAP2/ERF proteins in diverse biological, cellular and molecular processes  located on LG4 and the other three  located on LG7, which indicates a hot spot of the ZoAP2/ERF gene distribution in LG7. In addition to tandem duplication events, 15 pairs of segmentally duplicated genes were found in the ginger genome. Analyses of homologous protein families are of great significance in establishing the kinship of species and predicting the function of new protein sequences. Many homologous genes were present on different chromosomes in ginger, indicating high conservation of the AP2/ERF gene family  and 4.66 Ma, respectively.Based on chromosome mapping analysis, a total of 157 AP2/ERF TFs were found unevenly distributed on 11 ginger chromosomes  and three monocotyledonous plants , was constructed. The AP2/ERF family of ginger contained three subfamilies: AP2, ERF, and RAV. To explore the evolutionary relationship of each gene, a phylogenetic tree was constructed among ginger and other plant members within each subfamily. Simultaneously, the motifs of the corresponding proteins were determined. As indicated in Fig. M. acuminata (33 members), followed by A. thaliana (two members). A total of 10 conserved motifs were detected in the protein sequences of the AP2 subfamily members in all the five plants , O.sativa , and S. tuberosum (2 orthologous gene pairs distributed on LG6 and LG9)  and two monocots (banana and rice). A total of 113 ZoAP2/ERF genes showed syntenic relationships with those in banana, followed by rice (8), potato (2), and Arabidopsis (0)  and four ZoAP2 genes showed constitutive expression . Some genes exhibited preferential expression across the detected tissues. Three genes in roots , two genes in mature florescences (ZoAP2#16 and ZoAP2#33), one gene in anthocauli (ZoAP2#19), two genes in leaves (ZoAP2#21/33), and one genes in the meristem of stems (ZoAP2#08) showed the highest transcript expression levels. The expression of some genes exhibited significant trends in different developmental stages. For example, the expression levels of ZoAP2#16/19/30/33/35 were gradually increased, whereas that of ZoAP2#27 was gradually reduced along with the flower development , severn genes in mature flowers (ZoERF#001/008/013/015/029/112/113), three genes in top stems (ZoERF#002/003/056), 15 genes in roots  and 22 showed constitutive expression . Some genes displayed preferential expression across the detected tissues. Three genes in young flower buds (113), one gene in the second internode of rhizomes, eight genes in basal stems (ZoERF#010/011/ 019/044/076/082/089) and four genes in the first bud of rhizomes (ZoERF#006/035/052/061) showed the highest transcript abundances. The expression of some genes exhibited significant trends in different developmental stages. For example, the expression levels of ZoERF#15/30/31/60/085/106 were gradually increased, whereas those of ZoERF#014/054/ 089/095 were gradually reduced during different developmental stages of the flower. The expression of ZoERF#001/006/013/019/046/051/055/061/066/083/096 was reduced in flower emerging stage and then increased in flower elongation stage. The expression of ZoERF#004/035/048 /049/082/098 was increased in inflorescence emerging stage and then reduced in inflorescence elongation stage. Among the three ZoRAV subfamily genes, ZoRAV#1 and ZoRAV#2 showed no significant fluctuation with developmental stages, while ZoRAV#3 was highly expressed in mature inflorescences  developmental stages were also investigated and the results showed that the expression of some ZoAP2/ERF genes was associated with the rhizome development in ginger. The transcripts of ZoAP2#17/19/27/30 were gradually reduced across different developmental stages of rhizome. The expression of ZoERF#001/006/013/ 082/096/103/114 was increased between the rhizome bud and first rhizome inter-node stages, while it was reduced in the following developmental stages.The transcriptional levels of all the163 ZoRAV#3 and ZoAP2#16 were highly expressed in mature inflorescences than in other tissues, while ZoERF#13 was more abundant in mature inflorescences, roots, and the second internode of rhizome. ZoERF#096, ZoERF#103, ZoERF#114, ZoERF#119, and ZoAP2#30 were more abundant in roots than in other tissuses , Arabidopsis (125 Mb), and ginger (1.7Gb), indicating that the number of AP2/ERF family members is relatively stable and that there is no absolute correlation with genome size.The AP2/ERF genes comprise a large family of TFs that are ubiquitous to all plant species and play vital roles in various physiological processes. They have been intensively studied in many plants, such as thaliana , popularthaliana . Howeverthaliana , 26–35. AP2/ERF genes in ginger was similar to that in tartary buckwheat [The gene structure of ZoAP2/ERF genes was assessed in this study. As shown in Fig. uckwheat . The difcis-acting GCC-box or DRE motifs with the assistance of the AP2 domain. In previous studies, variations in the YRGVR motif of the AP2 domain might influence normal interactions of AP2/ERF genes with downstream target genes, and therefore these 16 AP2/ERF proteins might be worthy to further characterize their functions and binding specificities [The domains and motifs of TFs are often related to DNA binding,protein interaction and transcriptional activities . Motif aficities , 38.ThesZoERF#074 and ZoERF#075 genes. ZoERF#074 was highly expressed in mature inflorescences and basal stems, but ZoERF#075 was not expressed in any organ/tissues. The composition and position of their motifs  were identical  contained Motif-3, Motif-5, and Motif-9, but ZoERF#062  did not  that were highly expressed in mature inflorescences were clustered together   and AIL6(At5g10510) are key regulons in floral organ positioning, identity, and growth [ANT and AIL6 in ginger (ZoAP2#02/05/09/14 and ZoAP2#32) were not expressed in flower initiation and growth phases, suggesting the functional divergence of AP2 genes during evolution. Motif composition analysis indicates that ZoAP2#02/05/09/14 gained an extra motif element (Motif-10) in the N -terminus compared with its homolog ANT, while ZoAP2#32 has lost Motif-6/3/9 compared with its homolog AIL6 in Arabidopsis  promotes internode elongation by promoting both GA biosynthesis and signaling pathways [ZoERF#064 was an ortholog of AtERF11. ZoERF#006 was also highly expressed in the internodes of rhizome, which is probably related to the elongation of rhizome. ERF139 is a key factor within a negative regulatory cascade that controls vessel expansion in poplar [ZoERF#70 is an ortholog of ERF139; according to the expression pattern, it showed the highest expression in leaf buds, and its expression gradually reduced from the tender to old internodes during the rhizome growth and development. Therefore, we need to further verify whether ZoERF#006 possesses a function similar to ERF139 and plays a vital role in regulating the growth and development of rhizome.Rhizome is the economic organ of ginger, and the process of rhizome enlargement is the focus of attention in cultivation. Zhou et al. (2016) reported that pathways . Based on poplar . In gingCBF1, CBF2, and CBF3, belonging to the A1 group of the DREB subfamily, are cold-inducible at low temperatures [CBF1, CBF2, and CBF3 genes of Brassica. napus [ZoERF#010, ZoERF#046, ZoERF#077, ZoERF#078, and ZoERF#083 were classified to the DREB-A1 group. ZoERF#010, ZoERF#083, and ZoERF#046 were dramatically induced by cold, whereas ZoERF#077 and ZoERF#078 did not significantly respond to cold treatment. However, the expression patterns of ZoERF#010, ZoERF#083, and ZoERF#046 were different in response to cold treatment. ZoERF#046 quickly responded to low temperatures at 1 h, peaked at 12 h, and then reduced gradually within 24-48 h  corresponding to the AP2 domain (PF00847) and the B3 domain (PF02362) were downloaded. AP2/ERF genes were retrieved from the ginger genome database by HMMER v3.0. The default parameters were applied and the cutoff was set to 0.01. The Pfam and SMART databases were used to determine the existence of AP2 core sequences. Finally, 163 AP2/ERF gene models were identified in the ginger genome for further analyses. The basic information of the identified AP2/ERF proteins was obtained using the tools at the ExPasy website (http:// web.expasy.org/protparam/).The largest number of AP2/ERF genes were found in ginger genome (data from our ginger genome research project) by two BLASTP methods. The candidate genes were searched by BLASTP using a score value of ≥100 and e-value of ≤ e-10. Then the HMM files from the Pfam protein family database  genes were investigated by studying the conserved motifs of the AP2/ERF proteins. The ZoAP2/ERF protein sequences were aligned with ClustalW with default parameters. The exon-intron structure of the ZoAP2/ERF genes was determined by Gene Structure Display Server (GSDS: http://gsds.cbi.pku.edu.cn/) and the conserved motifs of AP2/ERF proteins wereevaluated by the MEME online program (http:/meme.nbcr.- net/meme/intro.html) [The structural differences among ro.html) .The method of mapping ZoAP2/ERF genes to the chromosomes of ginger was according to Xing et al. . The anaA. thaliana, S. tuberosum, O. sativa and M. acuminata were downloaded from the UniProt database (https://www.uniprot.org/). Multiple sequence alignments of AP2/ERF protein sequences from ginger and Arabidopsis were generated using MUSCLE with default parameters. The obtained multiple sequence alignments were used to construct a ML phylogenetic tree using MEGA X program with partial deletion, 80% cutoff, the JTT + G + I + F amino acid substitution model, and 1000 bootstrap replicates. The obtained ML tree was uploaded to iTOL (https://itol.embl.de/) [According to the number of the AP2 conserved domain and the existence of the B3 domain, ZoAP2/ERF genes were divided into different groups. The AP2/ERF protein sequences of mbl.de/)  to draw Z. officinale accessions ‘LAIWU No.2’ was used in this study. For the expression analyses of ZoAP2/ERF genes, six-month-old seedlings including ginger flowers, flower buds, anthocauli, stems, rhizome buds, 1st, 2nd and 3rd rhizome internodes, functional leaves , leaf buds, and roots were collected. To investigate the function of ZoAP2/ERF genes in response to different abiotic stresses, two-month-old seedlings with five leaves were used. For drought and salinity treatments, the plant was watered with 15% PEG6000 and 200 mM NaCl solution, respectively. The ginger plantlets were subjected to 40 °C and 4 °C for heat and cold stress treatments, respectively. The leaf samples were collected at 0, 1, 3, 6, 12, 24, and 48 h after cold, drought, and salt treatments, respectively. The leaves were collected at 0, 1, 3, 6, 12, and 24 h after heat treatment. The collected samples were quickly placed in liquid nitrogen and stored at − 80 °C for subsequent analyses.The P value threshold for multiple comparison tests and analyses by manipulating the FDR value. P < 0.001 and the absolute value of log2 ratio > 1 were used as thresholds to judge the significance of differences in the gene expression. Based on the Z. officinale (“Zhugen” cultivar) genome sequence, we screened the corresponding sequences of ZoAP2/ERF genes. Meanwhile, the qRT-PCR primers were designed using Primer Primier 5 software (http://frodo.wi.mit.edu/)  was performed at least three times using a CFX96 Real Time System (Bio-Rad). The reaction was carried out as follows: 95 °C for 30 s, followed by 40 cycles of 95 °C for 10 s and 60 °C for 30 s. Each reaction was performed in three biological replicates.The experimental data were processed by the 2-△△CT method [Samples collected at 12 h after cold, heat, drought, and salt treatments were used for RNA-seq. Total RNA was isolated using the TRIzol kit  and purified using an mRNA purification kit  following the manufacturers’ protocols. Approximately 20 μg of total RNA from each sample was enriched using oligo (dT) magnetic beads and digested into short fragments, and then the first- and second-strand cDNA were synthesized at the BGI . After purification, the fragments were ligated to the sequencing adaptors and sequenced on an Illumina Hiseq 2000 sequencing system. A rigorous algorithm was used to identify differentially expressed genes in the ginger defense response. The false discovery rate (FDR) was set at 5% to determine the T method .All the data were analyzed by analysis of variance using the Sigma Plot v10.0  statistics program, and the means were compared by the Fisher’s least significant difference (LSD) test at significance levels of 0.05 and 0.01, respectively.Additional file 1: Figure S1. Schematic representations for the chromosomal distribution of ginger AP2ERF genes.Additional file 2: Table S1. List of the 163 ZoAP2ERF genes identified in this study.Additional file 3: Figure S2. Alignment of multiple ZoAP2ERF and selected AP2 domain amino acid sequences.Additional file 4: Figure S3. Analysis and distribution of conserved motifs in ginger AP2ERF proteins.Additional file 5: Table S2. Summary of cis-elements is in the promoter regions of ZoAP2/ERF family genes in ginger.Additional file 6: Table S3. GO annotation of ZoAP2/ERF.Additional file 7: Table S4. List of the tandem repeat gene pairs of AP2ERF in ginger.Additional file 8: Figure S4. Analysis and distribution of conserved motif in AP2 subgroup of ginger and other species.Additional file 9: Figure S5. Analysis and distribution of conserved motif in ERF subgroup of ginger and other species.Additional file 10: Figure S6. Analysis and distribution of conserved motif in RAV subgroup of ginger and other species.Additional file 11: Table S5. The lists of ginger and other species AP2ERF synteny gene pairs.Additional file 12: Table S6. Ka/Ks ratio.Additional file 13: Table S7. Expression profile of ZoAP2/ERF family genes in different tissues.Additional file 14: Table S8. Expression profile of ZoAP2/ERF family genes response to abiotic stress treatment.Additional file 15: Figure S7. The Venn map of up regulated genes of ZOAP2/ERF family response to different abiotic stresses.Additional file 16: Figure S8. The Venn map of down regulated genes of ZOAP2/ERF family response to different abiotic stresses.Additional file 17: Figure S9. The primer sequences for qRT-PCR used in this study.