Using RNA-seq, we screened and identified 33 alternative splicing events regulated by SRSF2. Knockdown of SLMAP-L or CETN3-S splice isoform could suppress the growth of colon cancer cells, predicting their role in malignant proliferation of colon cancer cells. Mechanistically, the in vivo crosslinking immunoprecipitation assay demonstrated the direct binding of the RNA recognition motif of SRSF2 protein to SLMAP and CETN3 pre-mRNAs. SRSF2 activated the inclusion of SLMAP alternative exon 24 by binding to constitutive exon 25, while SRSF2 facilitated the exclusion of CETN3 alternative exon 5 by binding to neighboring exon 6. Knockdown of SRSF2, its splicing targets SLMAP-L, or CETN3-S caused colon cancer cells to arrest in G1 phase of the cell cycle. Rescue of SLMAP-L or CETN3-S splice isoform in SRSF2 knockdown colon cancer cells could effectively reverse the inhibition of cell proliferation by SRSF2 knockdown through mediating cell cycle progression. Importantly, the percentage of SLMAP exon 24 inclusion increased and CETN3 exon 5 inclusion decreased in CRC samples compared to paired normal samples. Collectively, our findings identify that SRSF2 dysregulates colorectal carcinoma proliferation at the molecular level of splicing regulation and reveal potential splicing targets in CRC patients.Aberrant expression of serine/arginine-rich splicing factor 2 (SRSF2) can lead to tumorigenesis, but its molecular mechanism in colorectal cancer is currently unknown. Herein, we found SRSF2 to be highly expressed in human colorectal cancer (CRC) samples compared with normal tissues. Both  Ace sites , 5. Aberce sites , 6. Multce sites , 9, 10.Serine/arginine-rich proteins (SR proteins) are well-studied splicing factors that play crucial roles in splicing regulation. As one kind of RNA-binding protein, serine/arginine-rich splicing factor 2 (SRSF2) contains one RNA recognition motif (RRM) and one serine/arginine-rich domain (RS) . SRSF2 ivia mediating cell cycle.The switch in cell cycle progression is one of the main pathways for cell proliferation. Cyclins and cyclin-dependent kinases (CDKs) are key regulatory molecules which are essential for cell transition from G1 to S phase in the cell cycle process , 26, 27.Colorectal cancer (CRC) is one of the most prevalent digestive tumors and is the third most common human cancer according to the statistics . HoweverHere we analyzed the expression of SRSF2 and profiled AS events in CRC samples using RNA sequencing analysis. We further revealed the function of SLMAP-L and CETN3-S splice variants and their regulator SRSF2 in proliferation of colon cancer cells. Cancer-related splice variants in SLMAP and CETN3 were validated by regulation of SRSF2 at the molecular level. Our data provide evidence with clinical implications as candidate prognostic factors in CRC patients.A). In silico analysis of the published datasets from The Cancer Genome Atlas (TCGA)  and GTEx V8 version  including 620 CRC tissues (tumor) and 789 normal colorectal tissues  revealed a significant increase of SRSF2 mRNA expression in CRC tissues compared with normal colorectal tissues . SRSF2 IHC assays confirmed the increased protein expression of SRSF2 in CRC tumor tissues compared with the nontumor adjacent tissues . However, due to the limited number of human CRC samples that we collected from the hospital . Upregulation of SRSF2 mRNA expression (>1.5-fold) occurred in 18 of 51 (35.3%) CRCs compared with normal colorectal tissues. Whereas only 7 of 51 (13.7%) CRCs had >1.5-fold decrease in SRSF2 expression compared to noncancerous samples A. In silhospital  among whhospital .Figure 1APC, TP53, TTN, and KRAS are highly mutated in 556 CRC samples. However, there are rare mutations of SRSF2 in CRC samples (A). Only two patients had SRSF2 mutation, and the somatic mutation rate is only 0.36% in CRC samples, and the mutation sites are D48 G and S187Y (B).To further study whether there are mutations of SRSF2 mRNA in clinical samples, we delved into the somatic mutations in CRC samples from the TCGA datasets. The top ten genes including  samples A. Only tnd S187Y B.Together, these observations strongly indicate that SRSF2 is increased in human colorectal cancer samples and predicted as a potential biomarker for CRC patients. Rare potential mutations of SRSF2 occurred in CRC samples.via Western blot (A). Then two independent shRNAs targeting against SRSF2 (sh-SRSF2#1 and sh-SRSF2#2) and control shRNA (sh-Luci) were designed and synthesized. RKO or HT29 cells were stably knockdown with these shRNAs, and the effects of SRSF2 knockdown were confirmed by Western blot analysis (A). The knockdown of SRSF2 significantly inhibited the growth of both RKO and HT29 cells compared with the cells treated with control shRNA (B and C). Strikingly, SRSF2 knockdown also induced severe inhibition of colony formation of both RKO and HT29 colon cancer cells in vitro (D and E). Moreover, a significant reduction of the known cell proliferating marker 5-ethynyl-2′-deoxyuridine (EdU) was observed in both colon cancer cells’ knockdown of SRSF2 in comparison with the control cells (F). Cell Counting Kit-8 assays verified that SRSF2 knockdown severely inhibited the growth of colon cancer cells (B). However, SRSF2 knockdown did not affect cell death nor apoptosis using Trypan blue staining and cell apoptosis analysis, respectively (C–E). Overexpression of SRSF2 was performed in RKO cells, and the overexpression effect was confirmed by Western blot (A). Significantly, higher expression of SRSF2 promoted both colony formation of RKO cells and the proliferating marker EdU (B–D). These observations indicated that the knockdown of SRSF2 inhibited the proliferation of colon cancer cells, and overexpression of SRSF2 promoted the proliferation of colon cancer cells in vitro.To investigate whether SRSF2 played vital roles in human colon cancer cells, we first examined the expression levels of SRSF2 in CRC cell lines and normal colon cells. Higher levels of SRSF2 protein were verified in CRC cell lines when compared with normal colon cells ern blot A. Then tanalysis A. The knol shRNA , B and Cin vitro , D and Eol cells F. Cell Cer cells B. Howeveectively , C–E. Overn blot A. Signifrker EdU , B–D. Thin vivo xenograft models. The RKO cells with stable knockdown of SRSF2 or the control cells were injected into nude mice independently. Compared with the control group, we observed a significant decrease in both tumor growth rate and tumor weight in SRSF2 knockdown groups, while there was no significance in the body weight between the two groups of mice (G–J). Taken together, these in vitro and in vivo data confirmed that SRSF2 promoted proliferation in colon cancer cells.To further validate the tumorigenic potential of SRSF2, we constructed the  of mice , G–J. TaA). A total of 55,002,320  and 41,118,515  mapped reads were respectively generated after proper optimization. The rates of the unique mapped reads aligned to the reference genome in WT and SRSF2-KD cells were 88.8% and 88.5%, respectively, which indicated a high quality of the sequencing (B). Furthermore, the SRSF2-affected splicing targets were associated with tumor-related functions, such as cell cycle, cell migration, MAPK cascade, and ER to Golgi vesicle-mediated transport (C). Overall, SRSF2 is involved in all common modes of AS, among which cassette exons are the most frequent targets of SRSF2 regulation in colon cancer cells. And the functions of SRSF2-affected AS targets were related to tumorigenesis.As one of the splicing factors, it is well proved that SRSF2 plays a critical role in splicing regulation. We conducted high throughout sequencing of RNA (RNA-seq) on the wildtype (WT) and SRSF2 knockdown (KD) RKO cells to profile the global AS events that might be regulated. Total RNA from si-NC and si-SRSF2 of RKO cells were extracted and the cDNA libraries were constructed after fragmentization. AS events were screened and detected after sequencing by HiSeq X (Illumina) on a 150 bp paired-end run A. A totaquencing . We founquencing  containiquencing B. Furtheransport C. OveralA). The representative 13 validated AS events were shown in B and C. SRSF2 knockdown could change the splicing pattern that either activated the inclusion of the alternative exon in mRNA variants such as RPE, HSF1, GPBP1, OPA1, PAN3, PLEKHA3, NAV2, EIF4H, and CETN3 (B) or repressed the inclusion of the alternative exon in mRNA variants such as MAP3K4, SLMAP, FXR1, and SNX14 (C). We compared the ΔPSI  between RT-PCR experimental validation and RNA-seq AS analysis and found they were highly correlated, reflecting the successful confirmation (D). Together, these endogenous mRNA transcripts were under the control of SRSF2 which could regulate the formation of both the exon-containing and exon-lacking isoforms.To validate SRSF2-dependent AS events in RKO cells, RT-PCR and IGV analysis were performed on these target genes to confirm the splicing pattern of alternative exons for SRSF2 regulation. The AS events affected by SRSF2, and the validated events were involved in the significant inclusion or exclusion of alternative exons A. The rend CETN3 B or reprnd SNX14 C. We comirmation D. Togethin vitro. SLMAP (sarcolemma associated protein) is one part of striatin-interacting phosphatase and kinases complexes. The inclusion of exon 24 introduces a premature termination codon in exon 24, causing the lack of exon 25 in the E24+ protein isoform which contains transmembrane domain TM1 domain in SLMAP. Whereas the exclusion of exon 24 (E24−) containing exon 23 and exon 25 will introduce another transmembrane domain TM2 domain in SLMAP. The splice isoform SLMAP-L represented the inclusion of exon 24 (E24+) and SLMAP-S represented the exclusion of exon 24 (E24−) (A). Similarly, the isoform including exon 5 (E5+) in CETN3 was defined as CETN3-L, and the isoform CETN3-S was with the exclusion of exon 5 (E5−) (A).To identify the roles these validated AS events played in colon cancer cells and whether they were mediated by SRSF2, we first designed the specific shRNAs targeting the validated splice variants and detected the roles they played 4 (E24−) A. Simila 5 (E5−) A.Figure B and S4B). The cell growth of RKO cells was significantly repressed after the knockdown of SLMAP-L or CETN3-S, whereas the knockdown of SLMAP-S or CETN3-L had weak effects on RKO cells proliferation (C–E and C–F). SLMAP-L or SLMAP-S was overexpressed in RKO cells respectively, and the overexpression effect was verified by RT-PCR (A). Overexpression of SLMAP-L was observed to promote the proliferation of RKO cells; however, SLMAP-S overexpression had no significant effect on the growth of RKO cells (B–D). In summary, SLMAP-L and CETN3-S splice variants were identified and proved to play crucial roles in the malignant proliferation of colon cancer cells in vitro.To investigate whether these splice variants were involved in the progression of colon cancer cells, the shRNAs respectively targeting against SLMAP-L (sh-SLMAP-L), SLMAP-S (sh-SLMAP-S), and CETN3-S (sh-CETN3-S), CETN3-L (sh-CETN3-L) were synthesized respectively. SLMAP-L, SLMAP-S, CETN3-S, or CETN3-L splice variant was stably knockdown individually in RKO cells using these shRNAs, and the knockdown effects of SLMAP-L, SLMAP-S, CETN3-S, and CETN3-L were confirmed by RT-PCR analysis B and S4By RT-PCR A. OverexKO cells , B–D. InA and S4A), though the long variant of CETN3 was rarely expressed in untreated RKO cells . To further explore the roles of splicing regulators involved in SLMAP and CETN3 splicing, the deletions of some SR and hnRNP proteins were performed using small-interfering RNA (siRNAs) in RKO cells. The specific primers were designed to detect alternative SLMAP exon 24 or CETN3 exon 5 individually by RT-PCR and the PSI (percent of splicing in) of each RNA product was quantified using Image J software (B and C and A and B). The knockdown effects were confirmed using qRT-PCR analysis (D).Human SLMAP and CETN3 pre-mRNAs are subject to AS regulation, thus long (L) and short (S) variants were generated A and S4Aanalysis D.Figure B and C). Deletion of hnRNPM increased exon 24 inclusion in SLMAP , while deletion of the other splicing regulators except for SRSF1 and SRSF7 repressed the inclusion of exon 24 among which SRSF2 deletion robustly repressed exon 24 inclusion . Furthermore, we found that the knockdown of SRSF1, SRSF2, SRSF5, SRSF6, or SRSF10 could increase exon 5 inclusion in CETN3 to more than 30% , among which SRSF2 deletion activated the inclusion of exon 5 to more than 40%. In contrast, knockdown of SRSF3, SRSF7, hnRNPM, or hnRNPK repressed the inclusion of exon 5 in CETN3 . In total, SLMAP exon 24 or CETN3 exon 5 is subject to multiple splicing factors regulation, and the splicing factors could regulate different AS events in different ways.In untreated RKO cells, the percent of exon 24 inclusion in SLMAP was about 47% (Lane 1 in E). The overexpression of SRSF2 led to a significant increment of exon 24 inclusion in SLMAP, resulting in SLMAP-L isoform compared with the control group. The exogenous overexpression of the RS deletion mutant of SRSF2 had similar effects with SRSF2 WT overexpression in promoting SLMAP exon 24 inclusion. However, the RRM deletion mutant of SRSF2 had no effect on changes of exon 24 inclusion compared with the control group, indicating the crucial effects of SRSF2-RRM domain in regulating SLMAP exon 24 inclusion. Meanwhile, the overexpression of SRSF1 had no effects in regulating exon 24 splicing, hnRNPM could lead to the increment of exon 24 exclusion in SLMAP (F). Similarly, the SRSF2-RRM domain took an essential part in regulating the exclusion of CETN3 exon 5 (C and D).SRSF2 contains one RRM at the N terminus and one arginine/serine domain (RS) at the C terminus. Based on the SRSF2-WT plasmid, we constructed the RRM deletion and RS deletion mutants, respectively E. The ovin SLMAP F. Simila3 exon 5 , C and DG). The in vivo crosslinking immunoprecipitation (CLIP) and RT-PCR analysis were carried out with specific primers for the indicated exons. SLMAP and CETN3 were the cases displayed as SRSF2-dependent exon inclusion and exon exclusion individually. Significantly, SRSF2-WT and RS deletion mutant (SRSF2-ΔRS) were observed to bind with exon 25 in SLMAP pre-mRNA and exon 6 in CETN3 pre-mRNA with high affinity. However, RRM deletion mutant (SRSF2-ΔRRM), SRSF1-WT, or hnRNPM-WT to both SLMAP exon 25 and CETN3 exon 6 were with low or even negligible affinities, similar to exon 23 or 24 in SLMAP and exon 4 or 5 in CETN3 (H and S6E). Indicating that SRSF2 binds to the pre-mRNA in SLMAP or CETN3 through the RRM domain. Both of the binding sites in pre-mRNAs of SLMAP and CETN3 were the flanking constitutive exons.To confirm whether SRSF2 bind to SLMAP or CETN3 pre-mRNA, we transiently overexpressed HA-tagged SRSF2-WT, the RRM, or RS deletion mutants in 293T cells. We also transfected 293T cells using HA-Vector (Vector), HA-SRSF1-WT (SRSF1-WT), or HA-hnRNPM-WT (hnRNPM-WT) plasmids as controls to rule out the experimental artifacts caused by HA antigen G. The inin CETN3 H and S6EBased on the above results, multiple splicing factors were involved in SLMAP and CETN3 splicing, among which SRSF2 is the predominant splicing regulator through the binding of its RRM domain with pre-mRNA of SLMAP or CETN3.C). Cell cycle analysis deduced a significant G1 arrest after SRSF2 knockdown (A and S7A). SLMAP-L or CETN3-S knockdown induced a likewise G1 arrest as SRSF2 knockdown, while SLMAP-S or CETN3-L knockdown had no significant effects on cell cycle progression . Since complex 1 formed by Cyclin D1 and CDK6 are necessary for cell transition from G1 to S phase in the cell cycle (C–E). Likewise, CETN3-S knockdown decreased Cyclin D1 levels, while CETN3-L knockdown had no effects on Cyclin levels (H and I). The Western blot analysis kept consistent with cell cycle analysis. Therefore, we confirmed that SLMAP-L or CETN3-S promotes the proliferation of colon cancer cells like SRFS2 by facilitating cell cycle progression.To our knowledge, previous studies had reported that SRSF2 was involved in the control of cell cycle progression , 27, 29.Fig. 3nockdown A and S7All cycle , C–E. Lin levels , H and IA and S8A). The restoration of SLMAP-L or CETN3-S but not SLMAP-S and CETN3-L, efficiently reversed the effects of SRSF2 knockdown on the proliferation of colon cancer cells (B–F and B and C).We next decided to investigate whether SLMAP-L or CETN3-S splice isoform mediates the effects of SRSF2 on the proliferation of colon cancer cells. We stably introduced SLMAP-L, SLMAP-S, CETN3-L, or CETN3-S into sh-SRSF2 RKO cells respectively to overexpress the corresponding protein and infected the control plasmid into sh-Luci or sh-SRSF2 RKO cells. Overexpression of SLMAP-L/S or CETN3-L/S was verified by RT-PCR analysis A and S8Avia cell cycle progression, cell cycle assays were performed. In consistence with the results above, SRSF2 knockdown severely induced G1 arrest, while much lower G1 arrest was observed when complemented with SLMAP-L or CETN3-S isoform . Moreover, Western blot verified the restoration of SLMAP-L, but not the SLMAP-S splice isoform, which could significantly reverse the reductions in the expression of Cyclin D1 or CDK6 caused by SRSF2 knockdown (H–J). CETN3-S also rescues the reduced expression of Cyclin D1, while CETN3-L could not rescue the reduced Cyclin D1 expression (E and F). In summary, the restoration of SLMAP-L or CETN3-S splice isoform could reverse the suppressions of proliferation affected by SRSF2 knockdown by mediating the cell cycle progression in colon cancer cells.To further explore whether SLMAP-L or CETN3-S mediates the function of SRSF2 in colon cancer cells nockdown , H–J. CEpression , E and FA and B). Furthermore, increased exclusion of CETN3 exon 5 was detected in 14 of 20 CRC samples (G). In conclusion, the increment of SLMAP exon 24 inclusion and CETN3 exon 5 exclusion were observed in human colorectal cancer and predicted as potential targets for CRC patients.The expression patterns of several SRSF2-affected AS events were examined by RT-PCR analysis in 24 paired CRC samples with high SRSF2 mRNA expression. Although there were no significant splicing variations in splicing patterns of GPBP1, OPA1, EIF4H, and NAV2 examined in CRC samples compared with control tissues (data not shown), SLMAP and CETN3 were shown significant AS alterations independently. Of the 24 pairs of CRC samples tested, three pairs of SLMAP AS analysis and four pairs of CETN3 AS analysis were excluded for further statistical analysis because none of these samples had visible bands in the RT-PCR assays. Strikingly, increased inclusion of alternative exon 24 in SLMAP was observed in 17 of 21 CRC samples compared with normal tissues, and the representative images were shown , A and B samples G. In convia mediating cell cycle progression. These findings indicate that abnormal expression of SRSF2 regulates the expression of cancer-related splice variants and further contributes to colorectal cancer development.In the present work, our data presented a comprehensive study of SRSF2 and its downstream AS profile in CRC. We first revealed that SRSF2 was significantly upregulated in clinical CRC samples. Multiple splicing events were identified in colon cancer cells through transcriptome analysis. SRSF2 regulated cancer-related SLMAP and CETN3 to generate different splice variants with distinct functions in cancer. SLMAP-L, CETN3-S, and their splicing regulator SRSF2 stimulated the proliferation of colon cancer cell. SRSF2, SLMAP-L, and CETN3-S splice variants were related to G1 arrest in the cell cycle progression. Rescue of SLMAP-L or CETN3-S in SRSF2-knockdown cells could partially reverse the antiproliferation effects As a well-characterized splicing regulator, SRSF2 regulated AS events in a highly position-dependent manner and has been implicated essential roles in vertebrate development and cancer progression , 13, 30.in vitro and in vivo. Supportively, previous studies showed the essential roles of SRSF2 in cancer cell growth and survival . The knockdown of SRSF2 and its cancer-associated splicing events SLMAP-L or CETN3-S decreased malignant proliferation of colon cancer cells. SRSF2 and its splicing targets SLMAP-L or CETNS were involved in cell cycle progression. Restoration of SLMAP-L or CETN3-S partially abolishes the antiproliferation effects of SRSF2 knockdown via mediating cell cycle progression in colon cancer cells. The increasement of SLMAP-L and CETN3-S splice variants were identified in human colorectal cancer tumor samples compared with the paired normal samples. These findings identify that abnormal high expression of SRSF2 dysregulates the expression of cancer-related splice variants and revealed potential splice variants in patients with CRC.In summary, we observed high expression levels of SRSF2 in CRC samples. Our data provided evidence that SRSF2 activated SLMAP-L splice isoform through binding with exon 25 and promoted CETN3-S splice isoform through binding with exon 6 C. The knFifty-one pairs of clinical CRC samples and the corresponding control tissues were obtained and approved by the Ethics Committee of Nanjing Hospital of Chinese Medicine Affiliated to Nanjing University of Chinese Medicine (KY2021079). The fresh samples were collected without any treatment and placed in RNAlaterTM (Thermo Fisher Scientific) and stored at −80 °C for RNA expression detection and splicing events validation.2 at 37 °C.Human CRC cell lines (RKO and HT29) were purchased from National Collection of Authenticated Cell Cultures . Human embryonic kidney 293T cells were purchased from Beyotime Biotechnology. RKO and 293T cell lines were cultured in Dulbecco’s Modified Eagle Medium (Gibco) supplemented with 10% fetal bovine serum , 100 U/ml penicillin, and 100 mg/ml streptomycin (Gibco). HT29 cells were cultured in McCoy’s 5A (Gibco), supplemented with 10% fetal bovine serum, 100 U/ml penicillin, and 100 mg/ml streptomycin. All cell lines were placed in a humidified incubator containing 5% CO5 cells for each well and maintained for 24, 48, and 72 h. The proliferating cells was measured using EdU (Beyotime Biotechnology) staining . The sequences were listed in The shRNAs specifically targeting SRSF2 (sh-SRSF2), SLMAP-Long isoform (sh-SLMAP-L), SLMAP-Short isoform (sh-SLMAP-S), CETN3-Long isoform (sh-CETN3-L), CETN3-Short isoform (sh-CETN3-S), and negative control (sh-Luci) were independently expressed in the knockdown lentiviruses vector PLKO.1-puro. The designed shRNAs were synthesized by Sangon Biotech. The shRNA sequences were listed in The wildtype SRSF2 (SRSF2-WT), SRSF2 domain deletion mutants (SRSF2-△RRM and SRSF2-△RS), the wildtype SRSF1 (SRSF1-WT), and wildtype hnRNPM (hnRNPM-WT) were constructed into pcDNA3.0 vector containing HA tag. Plasmids expressing SRSF2, SLMAP-L, SLMAP-S, CETN3-L, and CETN3-S transcript were constructed into PCDH-puro vector independently and synthesized by Sangon Biotech.The stable sub-cell lines from colon cancer RKO cell lines were constructed by infecting knockdown or expressing lentivirus expressing PLKO.1-puro or PCDH-puro individually which were selected with puromycin.6 cells each group containing 0.25 v/v matrigel (Corning Incorporated) were subcutaneously injected at each dorsal flank into nude mice. Tumor size was measured every week using a vernier caliper. All nude mice were maintained in specific pathogen-free condition, and all animal experiments were approved by the Animal Ethics Committee of Nanjing University of Chinese Medicine (No. 202112A076).The male BALB/c nude mice at the age of 5 weeks were purchased from Changzhou CAVENS Laboratory Animal Company. The stable RKO sub-cell lines expressing sh-Luci and sh-SRSF2, 3 × 10The total RNAs from indicated cell lines were extracted using Trizol reagent (Beyotime Biotechnology). Reverse transcription from total RNAs were performed using M-MLV reverse transcriptase (Promega Corporation) and oligo-dT primers (Sangon Biotech) following the manufacturer’s instructions. The qRT-PCR was carried out using SYBR Green Master Mix (Yeason). For RT-PCR and qRT-PCR analysis, primers were designed and synthesized by Sangon Biotech and listed in RNA sequencing was performed by NovelBio. Total RNA samples were extracted from si-NC and si-SRSF2 of RKO cells. Each RNA sample was used to construct the cDNA libraries using the TruSeq Stranded mRNA Library Prep Kit  according to the manufacturer’s instructions. The libraries were quality controlled with Agilent 2200 and sequenced by HiSeq X (Illumina) on a 150 bp paired-end run. Clean reads were obtained after the adaptor sequences, and low-quality reads were removed from the raw reads. Reads mapping was carried out after the clean reads obtained. Hisat2 was used to align the clean reads to human genome  . Gene exIn vivo CLIP of SLMAP exon 25 or CETN3 exon 6 pre-mRNA bound to SRSF1, SRSF2, and hnRNPM proteins were performed as described previously  as previously described . Western04916 as https://portal.gdc.cancer.gov/) and GTEx V8 version . The mutation data were downloaded from TCGA database and visualized using the “maftools” package (https://bioconductor.org/packages/release/bioc/html/maftools) and analyzed in R software v4.0.3 , as described previously , and re-suspended in 1 ml cold 70% ethyl alcohol. Then, cells were kept at 4 °C for 24 h and subsequently centrifuged at 1000g for 5 min, followed by washing with 1 ml cold PBS and centrifugation . Cells were stained with 25 μl propidium iodide  and 10 μl rNase  at 37 °C for 30 min. The cell cycle was detected by the Gallios Flow Cytometer .Cells used for cell cycle analysis were collected using 0.25% trypsin, centrifuged at 1000g for 5 min to remove trypsin, washed twice with 1 ml cold PBS, and centrifuged at 300g for 5 min to remove PBS. Then cells were resuspended in 100 μl 1× binding buffer. For each sample, 5 μl of Annexin-V–FITC, and 10 μl propidium iodide were added to the cell suspension and incubated for 15 min at room temperature (25 °C) in the dark. The stained cells were detected by the Gallios Flow Cytometer (A94303) and analyzed using FlowJo (Version 10.8.1).Cells used for apoptosis analysis were harvested and prepared according to the manufacturer’s instructions . Cells were collected using 0.25% trypsin, centrifuged at 300t test. The PSI quantification from RT-PCR was done by ImageJ software. Statistical analyses of clinical CRC samples were performed using SAS software. Gene ontology analysis of SRSF2-affected splicing events were perform using Database for Annotation Visualization and Integrated Discovery   .The data generated in this study are available in the article, tables, and supplementary files.This article contains All the authors declare that they have no conflicts of interest with the contents of the article.


The present study aims to investigate the genotype distribution of Human papillomavirus (HPV) and variations of HPV18 and HPV58 infection among 6538 females in Luoyang city during 2019–2021. The overall positive rate of females with HPV infection was 12.34%, with 9.74% were infected with single HPV and 2.60% with multiple HPVs. The prevalent rate of high-risk HPV (HR-HPV) was 9.85% and the top five HR-HPV genotypes were HPV52 (1.94%), HPV16 (1.93%), HPV58 (1.48%), HPV51 (1.02%) and HPVV39 (0.99%). Two peaks of HPV infections rates were observed in females aged ≤ 20 and 61–65 years old. To characterize mutations, 39 HPV18 and 56 HPV58 L1, E6 and E7 genes were sequenced and submitted to GenBank. In the HPV18 E6-E7-L1 sequences, 38 nucleotides changes were observed with 10/38 were non-synonymous mutations . In the HPV58 E6-E7-L1 sequences, 53 nucleotides changes were observed with 23/53 were non-synonymous mutations . Phylogenetic analysis based on L1 gene showed that 92.3% (36/39) of HPV18 isolates fell into sublineage A1 and 7.7% (3/39) belonged to A5. For HPV58, 75.0% (42/56) isolates belonged to sublineage A1 and 25.0% (14/56) were sublineage A2. There was no association between amino mutation and cervical lesions. The present study provides basic information about the distribution, genotypes and variations of HPV among females population in Luoyang city, which would assist in the formulation of HPV screening and vaccination programs and preventive strategies for HPV-attributable cancer in this region. Females lived in developing countries contributed about 85% cervical cancer and the death rate was higher than developed countries 2. In 2022, there would be approximately 111,820 new cases of cervical cancer and 61,579 cancer deaths in China3.Globally, it was estimated that there were 604,000 cases and 342,000 deaths were caused by cervical cancer in 2020, which ranked the fourth most frequently diagnosed cancers6. HPV is a small circular non-enveloped double-stranded DNA virus that belongs to the family Papillomavirus7. There are more than 200 different HPVs genotypes have been identified and classified into high-risk HPV (HR-HPV) and low-risk HPV (LR-HPV) based on their potential to cause cancer8. Globally, the most common HPV genotypes in invasive cervical cancer were 16, 18, 31, 33, 35, 45, 52 and 589. Meanwhile, it was reported that the distribution of HPV genotypes exhibited significant differences among countries, even in different areas of a country. For example, the top prevalent HR-HPV genotype was HPV52 in Beijing city and HPV16 in Shanghai city of China11. The preventive effect of HPV vaccines on cervical cancer and other HPV-associated diseases has been confirmed in multiple studies13. Currently, commercial 2-valent (HPV16 and HPV18), 4-valent  and 9-valent  HPV vaccines have been approved by the National Medical Products Administration of China. Some provinces of China, such as Guangdong, have piloted the HPV vaccination program among the native females. Understanding the distribution of HPV genotypes in a region will provide baseline information for the implementation of vaccine-based HPV prevention strategies. Until now, there is limited information about the genotype distribution of HPV in Henan province, located in central of China.Human papillomavirus (HPV) are identified in most patients with cervical carcinoma and the persistent infection with one or more genotypes of oncogenic HPV are the necessary causes of cervical cancer14. HPV18 can be divided into three lineages and ten sublineages: (1) A, A1–A6; (2) B, B1, B2 and B3; (3) C15. HPV58 has been classified into four lineages and eight sublineages: (1) A, A1–A3; (2) B, B1 and B2; (3) C; (4) D, D1 and D215. The sublineage of HPV18 and HPV58 had been determined in Hunan and Zhejiang province of China and the predominant sublineage was determined to be sublineage A117. To our knowledge, there are no reports on the sublineages and sequence variations of HPV18 and HPV58 in Henan province.Epidemiological study had showed that except HPV16, HPV18 and HPV58 were the most prevalence genotypes among females with cervical lesions in Henan province of ChinaObject of the present study is to investigate the genotypes distribution and variations of HPV18 and HPV58 among females in Luoyang city, Henan province, located in central China. The investigation would assist on the formulation and development of vaccine-based HPV prevention strategies against cervical cancer.χ2 = 188.673, P < 0.01). The top five HR-HPV genotypes were HPV52 (1.94%), HPV16 (1.93%), HPV58 (1.48%), HPV51 (1.02%) and HPV39 (0.99%). The most prevalent LR-HPV genotypes were HPV61 (0.89%), followed by HPV54 (0.72%), HPV81 (0.60%), HPV6 (0.37%) and HPV11 (0.29%). Among the 807 HPV positive females, 637 were infected with single HPV genotype, 170 were infected with more than one HPV genotype. Among the 637 females infected with single HPV genotype, the HR-HPV infection accounted for 76.45% (487/637). The multiple HR-HPV infection rate was 2.40%, higher than the multiple LR-HPV infection (1.50%) . The top prevalent of the multiple HPV infection was HPV52 (0.70%), followed by HPV58 (0.55%), HPV16 (0.47%), HPV39 (0.41%) and HPV51 (0.41%).A total of 6538 females were included in this study and 807 12.34%) were infected with HPV. As shown in Table 2.34% werχ2 = 149.128, P < 0.01). The highest rate of any HPV type infection was observed in the ≤ 20 year-old group , followed by 61–65 year-old group , while the 31–35 year-old group had the lowest prevalence rate . As shown in Fig. There are significant differences in the positive rates of HPV infection in different age groups  and twenty-four HPV58 (58HNL01-58HNL24) sequences were submitted to GenBank (the accession numbers are OP684028-OP684040 and OP684041-OP684064). The HPV18 (AY262282) and HPV58 (D90400) reference sequences were used as the standard for comparison and position of the gene polymorphism sites, separately. The nucleotide sequence mutations of the studied sequences were shown in Tables For HPV18 L1 gene, 18HNL02 sequence accounted for 48.7% (19/39) and was the predominant strain. Twenty-one variations were observed in HPV18 L1 gene and four were non-synonymous mutations, including G5503A , C5920T , A5796G  and C5875A . The most frequency synonymous mutations were A5832C (6/39) and A5924C (6/39) in HPV18 L1 gene.For HPV58 L1 gene, fifteen nucleotide changes were non-synonymous mutations. The most prevalence synonymous variation was A6560G (27/56), which was found in 26 HPV58 sublineage A1 isolates. The highest rate of non-synonymous mutations was C6688A  and was observed in 24 HPV58 sublineage A1 isolates. The T6434C, A6539G and G6641A variations were only found in sublineage A2. The A6014C synonymous variation was found in 15 HPV58 sequences and 14 were sublineage A2.A total of thirty-nine HPV18 and fifty-six E6–E7 genes were gained and the nucleotide and amino acid sequences variations are summarized in Tables For HPV58, eleven different E6-E7 sequences (58HNE01–58HNE11) were gained and submitted to GenBank . A total of thirteen gene mutations were observed and five were on E6 and eight on E7. The most frequency synonymous mutation was C307T (11/56) in E6 gene and T744G (42/56) in E7 gene. The most prevalent non-synonymous mutations were A388C (K93N) on E6 gene and T803C (V77A) on E7 gene.Phylogenetic trees based on the full length of HPV18/58 L1 genes were constructed, together with those of reference HPV18/58 L1 sequences that represent individual variant lineages/sublineages. As shown in Fig. As each non-synonymous mutation in HPV18 E6–E7 gene has only one sample, thus, the risk association of amino mutation with cervical lesions was estimated on HPV58 only. Among the fifty-six females infected only with HPV58, thirty-one were diagnosed with normal cervix by colposcopy examination and twenty-five were diagnosed with CIN2 or worse. It showed that there was no association between amino mutation and cervical lesions  or Shanghai (18.98%) city18. The most prevalent HR-HPV genotypes in Luoyang city were HPV52, 16, 58, 51 and 39. The 2-valent, 4-valent and 9-valent HPV vaccines cover 28.6%, 34.0% and 73.1% of HR-HPV positive samples in the present study (data not show). Vaccines contained HPV51 and HPV39 would cover 87.6% of HR-HPV infection in Luoyang city (data not show), which should be taken into consideration in future. In China, the knowledge score and proportion of females who were willing to receive HPV vaccine were relative low19. More efforts should be made by the government to increase the awareness and knowledge of HPV vaccine.Cervical cancer is the leading cause of deaths in China and it was estimated that there were 111,820 new cases and 61,579 deaths in Chinese females in 202222. The first peak of HPV infection may be due to the lack of immunity to HPV in ≤ 20 year-old females23. Thus, the adolescent girls should take priority in the HPV vaccine program. The second peak occurred within the age group 61–65, which was assumed to be caused by the physiologic and immunologic deregulation24. In China, though the government has made enormous investment on cancer screen since 2009, more attention should be paid for females around 60 years old25.The prevalence of HPV infection in different age population was calculated. It showed that there were two peaks among females with HPV infections, one was ≤ 20, and the other was 61–65 year-old females. The two peaks of HPV infection age group were also observed in other reports26. HPV58 accounted for 6.4% in invasive cervical cancer worldwide, which was especially higher in Eastern Asia28. It was reported that HPV18 and HPV58 had a significant association with an increased risk for cervical cancer in China31. Genetic variations and sublineage of HPV may affect the pathogenic potential and host immune responses35. In the present study, the L1, E6 and E7 gene sequences of HPV18 and HPV58 were sequenced. Phylogenetic tree based on the L1 genes showed that the most common HPV18 sublineage was A1, which was similar to other provinces of China17. In other countries in Eastern Asia and Pacific, such as Korea and Japan, the predominant HPV18 sublineage was also A137. Compared with HPV18 lineage A, the HPV18 B/C tend to cause higher cancer risks38. Four non-synonymous mutations were found in HPV18 L1 gene, including R25Q, I123V, T149N and A164V, which had been detected in Zhejiang province of China17. The R25Q, T149N and A164V mutations were also prevalent in Korea HPV18 sublineage A1 isolates36. In China, it was reported that the distribution of R25Q mutation differed with geographical region and racial characteristic39. The A5474G, A5741G, A5796G, C5875A, G6089A, G6143C, A6406G, A7079T and G7130A were only found in HNL03, which represented sublineage A5. Compared with the reference HPV18 sublineage A5 (Accession number: GQ180787), A5468G, A5790G, T5914C, A7073T substitutions were found in L1 sequence. For HPV18 E6 and E7 gene, there were twenty-seven HNE01 isolates that shared the same sequence with the reference. The E29Q, E40K and L93R mutations in E6 protein were also reported in Zhejiang province of China17. Due to the limited numbers of HNE02-HNE13 sequences, associations between amino mutation and cervical lesions were not conducted.Globally, HPV18 is the second most carcinogenic HPV genotype and has a higher proportion in cervical adenocarcinomas (ADC)35. The L150F (15/56) was located in the DE loop of L1 protein, which played an important role in the recognition of VLP35. The A6014C, A6416G, T6434C and A6539G in L1 gene variations were present in the whole HPV58 sublineage A2. It was reported that the variations in the fragement of L1 gene (nucleotides 6014–6539) were characterized in HPV58 sublineage A235. In the current research, HPV58 sublineage A1 was the predominant sublineage, which was similar to other provinces of China, such as Zhejiang, Hunan and Liaoning provinces (56.9%)35. In Japan, most HPV58 isolates belonged to lineage A, with more were sublineage A240. Nevertheless, it was reported that there was no association between HPV58 (sub) lineages and cervical lesions34. For HPV58 E6 gene, the A388C (K93N) variation was the most predominant mutation , which have also been reported in Hubei province of China41. In our study, no significant difference was observed among HPV58 (K93N) infected females with normal cervix or low-grade lesions. However, it was reported that the K93N can significantly reduce the risk of cervical lesions in Hongkong and Shanghai43. The most common synomous mutations observed in HPV58 E6 and E7 gene were C307T and T744G, which had also been reported in the past46. The C307T and T744G mutations were also common in other countries, such as Mexico, Korean and Italy49. However, the variations included in the present study had been reported to have no association with cervical lesions49.For HPV58 L1 gene, five non-synomous mutations, including N82T, L150F, F318Y, I325M and T375N, had been detected in Liaoning province of ChinaIn summary, the present study provides basic information about the distribution, genotypes and variations of HPV among females population in Luoyang city, which would assist in the formulation of HPV screening and vaccination programs and preventive strategies for HPV-attributable cancer in this region.From April 2019 to April 2021, 6538 females  who underwent cervical cancer screening in the 989 Hospital of Joint Service Support Force of Chinese PLA  were included in this study. The female was considered if she: (a) had no use of vaginal medication or washing in the previous 72 h; (b) had no sexual activity in the previous 24 h; (c) was not presently during menstruation; (d) had no use of acetic or iodine. Before collection, written informed consent from each participant was obtained. The study protocol adhered to the principles of the Declaration of Helsinki and was approved by the institutional ethics committee (Grant No: LLSC20190305).Cervical specimens were collected by a gynecological practitioner using a cytobrush from the ecto- and endocervix of uterus. The samples were stored at – 20 °C until the HPV genotyping. The HPV genotype was proceed by a commercial gene chip  according to the manufacturer’s instruction. The gene chip contained 37 genotype-specific oligonucleotides designed to detect 18 high-risk human papillomavirus  and 19 low-risk human papillomavirus . The final results were determined by colorimetric change on the chip under direct visualization and blue-purple spots were recognized as HPV positive.Single HPV18 and HPV58 positive samples were chosen and used to amplify the full length of L1, E6 and E7 genes. The primers were designed based on published HPV 18 (AY262282) and HPV58 (D90400) sequences in GenBank and further synthesized by Sangon Biotech, Inc.   and HPV58 (GeneBank D90400) sequences were selected and compared with the studied sequences. The comparison was proceeding by DNAStar  and positions of variations were numbered based on the reference sequence.Phylogenetic trees based on the L1 gene of HPV18 and HPV58 were constructed through Maximum Likelihood method with 1000 bootstrapped replicates using the MEGA (version 6.0). Reference sequences that represent each HPV18 and HPV58 lineage were used to construct the distinct phylogenetic branches.P-value < 0.05 was considered to be statistically significant.SPSS version 19.0  was used to assess the significance of differences in HPV positivity rates among groups. Females with no lesion at colposcopy biopsy were grouped as normal cervices, with CIN2 or worse were grouped as outcome variable. The oncogenic risk association of HPV18 and HPV58 E6/E7 amino acid substitutions was assessed using Chi-squared test or Fisher’s exact test. Females were informed and a written consent was received. The study protocol adhered to the principles of the Declaration of Helsinki and was approved by the institutional ethics committee in the 989 Hospital of Joint Service Support Force of Chinese PLA, Military Training Medical Research Institute of the Whole Army (Grant No: LLSC20190305).


SH3TC2 in colorectal cancer (CRC). Our research revealed that higher expression of SH3TC2 indicated poor disease-free survival and promoted CRC progression and invasion via the MAPK signaling pathway.SH3 domain and tetrapeptide repeat 2 (SH3TC2) is a protein-encoding gene and has previously been described as a critical signaling hub for neurological disorders, but no systematic analysis of SH3TC2 is available in cancer research. We analyzed SH3TC2 in various kinds of cancer to find its tumorigenic role in one or more specific cancers and further explored the mechanism of SH3TC2 in other cancers remain unknown. Thus, this study aimed to analyze SH3TC2 in various kinds of cancer to find its tumorigenic role in one or more specific cancers. In the current study, we analyzed the expression level and prognostic value of SH3TC2 in different tumors in the TCGA-GTEx pan-cancer dataset. Subsequently, the prognostic role and mechanism of SH3TC2 in colorectal cancer (CRC) were further explored via clinical samples and in vitro and in vivo experiments. We observed differential expression of SH3TC2 in colon adenocarcinoma (COAD), acute myeloid leukemia (LAML), READ (rectum adenocarcinoma), SKCM (skin cutaneous melanoma), and TGCT (testicular germ cell tumors). Subsequently, SH3TC2 showed a significant effect on the clinical stage and prognostic value in CRC, LAML, and SKCM. Moreover, we found in the TCGA database and seven GEO datasets that SH3TC2 was significantly highly expressed in tumor tissue. Through enrichment analysis of SH3TC2 and its co-expressed genes, we found that SH3TC2 may play a role in the MAPK signaling pathway. Correlation analysis indicated that SH3TC2 was significantly associated with multiple key factors in the MAPK signaling pathway. Additionally, higher expression of SH3TC2 was found in tumor tissue in our cohort including 40 CRC patients. Overexpression of SH3TC2 may imply poor prognosis. Knockdown of SH3TC2 significantly inhibited tumor invasion, migration, and proliferation. More importantly, knockdown of SH3TC2 inhibited tumor growth in a CRC mouse model. The study preliminarily conducted a pan-cancer study of SH3TC2 and further explored the mechanism of SH3TC2 in CRC. Our research revealed that higher expression of SH3TC2 may promote CRC progression and invasion via the MAPK signaling pathway.SH3 domain and tetrapeptide repeat 2 (SH3TC2) is a protein-encoding gene and has previously been described as a critical signaling hub for neurological disorders. Although increasing evidence supports a vital role of SH3TC2 in the tumorigenesis of various kinds of cancer, no systematic analysis of SH3TC2 is available. The function and mechanism of  Cancers bring heavy burdens to global health. According to global cancer burden data, the number of new cancer patients worldwide in 2020 was 19.3 million, and the number of deaths due to cancer approached 10 million . TumorigSH3TC2) is a protein-coding gene that encodes a protein with two N-terminal Src homology 3 domains and 10 tetratricopeptide repeat motifs [SH3TC2 gene mutations cause autosomal recessive type 4C peroneal muscular dystrophy [SH3TC2 in the cerebellum was regulated by single nucleotide polymorphisms, which may be involved in the pathogenesis of schizophrenia [SH3 domain and tetratricopeptide repeats 2  database [Furthermore, for LAML . Doecke database .The mitogen-activated protein kinase (MAPK) signaling pathway is one of the important pathways in the eukaryotic signaling network and is involved in various biological processes, such as cell proliferation, differentiation, and apoptosis ,19,20,21SH3TC2 significantly inhibited tumor proliferation and tumorigenesis. Knockdown of SH3TC2 in cell lines significantly inhibited MAPK pathways via MAPK8, 9, 13, and 14. Combining comprehensive bioinformatics analysis and in vitro and in vivo experiments, we are the first to report that SH3TC2 is a novel oncogene and may act as a potential prognostic biomarker and therapeutic target for CRC. In the present study, we comprehensively explored the expression profile and prognostic value of SH3TC2 in various kinds of solid tumors via the TCGA database and found that SH3TC2 was more highly expressed in multiple cancer types and was closely related to cancer patient survival. After integrating the analysis from different databases, we demonstrated that SH3TC2 was more highly expressed in CRC and positively related to poor disease-free survival (DFS). In vitro and in vivo experiments revealed that knockdown of https://xenabrowser.net/) database (accessed on 15 March 2022) and consists of screened tumor and normal samples from primary solid tumors. Subsequently, we filtered the samples with an expression level of 0, excluded cancer types with fewer than 3 samples, and finally obtained the expression data of 27 cancer species. The cancer names and their corresponding abbreviations for all cancer types are listed in The standardized pan-cancer dataset (TCGA-GTEx) was acquired from the open-access UCSC  database (http://gepia.cancer-pku.cn/) (accessed on 15 March 2022) [The expression levels of  package  in R sofch 2022) .SH3TC2 on the disease-free survival (DFS) and overall survival (OS) of patients with different tumors via the GEPIA database. All patients of each cancer type were divided into high-expression groups and low-expression groups according to the median value of the SH3TC2 expression level.This study analyzed the effect of SH3TC2 in the TCGA database and Gene Expression Omnibus (GEO) database, including the GSE32323, GSE44076, GSE21815, GSE31279, GSE41657, GSE83889, and GSE87211 datasets, were extracted and log2 transformed. We analyzed the expression difference of SH3TC2 between tumor samples and normal samples in the TCGA database and 7 GEO datasets. The TCGA and GEO databases were assessed on 1 March 2022. The expression values of SH3TC2 in normal tissue samples, tumor tissue samples, and metastatic tissue samples of colon cancer microarray data in the GEO database through the TNM plot database (https://tnmplot.com/) (accessed on 27 May 2022) [SH3TC2 in the different clinical stages of CRC samples from the TCGA database.We analyzed the expression levels of ay 2022) . Furthern = 282), TCGA-READ (n = 91)) from the TCGA database. The deconvo_Cibersoft algorithm of the language IOBR [In this study, the Cibersoft, Timer, and MCPcounter algorithms were used to calculate the infiltration levels of various immune cells in CRC tumor samples (TCGA-COAD (age IOBR  softwareSH3TC2 in colon cancer (TCGA-COAD) and rectal cancer (TCGA-READ) samples in the TCGA database were obtained through the “Similar Genes”. Then, we screened the top 30 co-expressed genes according to the Pearson correlation coefficient (PCC). Subsequently, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on the co-expressed genes by the clusterProfiler package of R software [SH3TC2.The co-expressed genes of software  to exploSurgical resection samples were obtained from 40 CRC patients who underwent surgical resection at Guangdong Provincial People’s Hospital . All of the patients included were undergoing surgical resection and were pathologically diagnosed with adenocarcinoma. All enrolled CRC patients provided written consent, and collection of the clinical sample was approved by the Institutional Review Board of Guangdong Provincial People’s Hospital under approval number GDREC2019504H(R2).To further analyze the potential association between SH3TC2 and CRC, IHC analysis was performed as previously described ,49,50. TSH3TC2 gene expression was investigated via the Cancer Cell Line Encyclopedia (CCLE) database (https://sites.broadinstitute.org/ccle) (accessed on 3 March 2022). Then, the human CRC cell lines HCT116, SW480, and LOVO were purchased from iCell Bioscience . The included cell lines were incubated with RPMI-1640 medium  containing 10% fetal bovine serum  at 37 °C in a humidified incubator containing 5% CO2. SH3TC2 small interfering RNA (siRNA) was synthesized by Sangon Biotech . Three SH3TC2 siRNAs were designed in siDirect version 2.0 (http://sidirect2.rnai.jp/) (accessed on 4 March 2022) [ch 2022) , and theWe performed qRT-PCR analysis as previously described ,49,50. BWestern blot analysis was utilized to examine the protein expression level of SH3TC2 and its related proteins in CRC cell lines. The experiments were performed as previously described ,50. The Cell migration was analyzed by Transwell analysis. Briefly, a total of 500 μL of RPMI-1640 medium containing 10% fetal bovine serum was added to the lower chamber of the Transwell plate. The cells were seeded into the upper chamber of the plate with serum-free medium. After incubation for 72 h, the migratory cells were fixed and stained with 0.1% crystal violet solution . The migrated cells were observed under a light microscope  and were analyzed by ImageJ software.To investigate the cell viability of the cell lines, the cell counting kit 8 (CCK-8) detection kit  was utilized in our study. Briefly, a total of 1500 cells/well in 100 μL complete cell culture medium was seeded in a 96-well plate. After incubation of 0, 24, 48, or 72 h, 10 μL of CCK-8 solution was added to the wells. Absorbance in 450 nm light was assessed on a microplate reader . To detect cell apoptosis after different treatments, a flow cytometer assay was performed with the cell apoptosis kit  following the manufacturer’s instruction. After staining with Annexin-FITC and PI, flow cytometer  was utilized to collect the cell samples. The raw data were analyzed by FlowJo software version 10.6 . Anisomycin was purchased from Beyotime Biotechnology, Shanghai, China (SC0132). 6 HCT116 negative control (HCT116 shNC) cells or HCT116 stable slicing SH3TC2 (HCT116 shSH3TC2) cells were subcutaneously injected into the right flank. Tumor volume was measured every two days and defined as follows: volume = length × width2 × 0.5 mm3. The study was approved by the Ethics Committee of Guangdong Provincial People’s Hospital  under approval number GDREC2019506A. Nude mice (6–8 weeks old) were randomly allocated to two groups. All mice were purchased from Guangdong GemPharmatech  and bred in specific-pathogen-free (SPF) conditions. To establish the CRC model in nude mice, 5 × 10http://www.r-project.org/, accessed on 15 March 2022), GraphPad Prism 9.3 , and Stata 15.0 . Data were presented as mean ± standard deviation (SD). The difference between groups was analyzed by Student’s t-test, unpaired t-test, or one-way ANOVA. Prognostic analysis was performed by log-rank test. Experiments were performed in triplicate, and p < 0.05 was considered statistically significant.The included bioinformatics and statistical analyses were performed using R software version 4.1.3 (SH3TC2 in the 27 types of cancers are shown in p < 0.01), COAD (p < 0.0001), stomach adenocarcinoma (STAD) (p < 0.0001), kidney renal clear cell carcinoma (KIRC) (p < 0.0001), skin cutaneous melanoma (SKCM) (p < 0.0001), bladder urothelial carcinoma (BLCA) (p < 0.0001), READ (p < 0.0001), pancreatic adenocarcinoma (PAAD) (p < 0.0001), and cholangiocarcinoma (CHOL) (p < 0.001). In addition, SH3TC2 expression was significantly downregulated in 12 cancers, including glioblastoma multiforme (GBM) (p < 0.0001), brain lower grade glioma (LGG) (p < 0.001), uterine corpus endometrial carcinoma (UCEC) (p < 0.001), breast invasive carcinoma (BRCA) (p < 0.0001), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) (p < 0.05), lung adenocarcinoma (LUAD) (p < 0.0001), prostate adenocarcinoma (PRAD) (p < 0.0001), liver hepatocellular carcinoma (LIHC) (p < 0.05), thyroid carcinoma (THCA) (p < 0.0001), testicular germ cell tumors (TGCT) (p < 0.0001), uterine carcinosarcoma (UCS) (p < 0.0001), and adrenocortical carcinoma (ACC) (p < 0.0001). Subsequently, expression of SH3TC2 in the TCGA-GTEx database was verified by the GEPIA database  of COAD, READ, SKCM, and TGCT patients in the GEPIA database. The results showed that high SH3TC2 expression predicted worse DFS for colon cancer patients  and rectal cancer patients , while the level of SH3TC2 expression in SKCM  and TGCT  had no significant effect on DFS  of cancer patients . Furthermore, we observed high expression levels of SH3TC2 in CRC samples in the GSE32323 (p < 0.001), GSE44076 (p < 0.001), GSE21815 (p < 0.001), GSE31279 (p < 0.05), GSE41657 (p < 0.001), GSE83889 (p < 0.001), and GSE87211 (p < 0.001) datasets  and correlated with the immune infiltration level of T_cells_CD4_naive  and T_cells_gamma_delta  in READ (p = 0.008) and NK_cells  infiltration levels in COAD and with the infiltration levels of T_cells , CD8_T_cells , and NK_cells  in READ (p = 0.005) in COAD and the infiltration levels of neutrophils  in READ .A total of 40 CRC patients were enrolled in our study. Tumor tissue and non-tumor tissue (NTT) specimens were collected after surgical resection and analyzed after IHC staining. In our cohort, expression of SH3TC2 protein was significantly higher in tumor tissue than in NTT tissue A. ExpresSH3TC2 in CRC cell lines via the CCLE database ) was significantly downregulated in the stable knockdown of SH3TC2 in CRC cell lines. The JNK activator Anisomycin was included in our study to further validate the function of SH3TC2. As shown in 6 HCT116 negative control (HCT116 shNC) cells or HCT116 stable slicing SH3TC2 (HCT116 shSH3TC2) cells were subcutaneously injected into the right flank. The protocol of the in vivo study can be found in To establish the CRC model in nude mice, 5 × 10In previous research, the mutation of SH3TC2 has been considered a critical regulator for diseases of the central nervous system. Mutation or knockdown of SH3TC2 in mice results in peripheral neuropathy, resulting in decreased motor and sensory nerve conduction velocity and hypomyelination . RecentlIn our present study, we analyzed the expression of SH3TC2 by using pan-cancer bioinformatic analysis. By mining the TCGA database, we found that SH3TC2 in tumor tissue was significantly overexpressed in most cancer types. However, SH3TC2 expression is downregulated in 12 types of cancer. The results indicate that SH3TC2 may act as an oncogene or tumor suppressor in different kinds of cancer. Its molecular mechanism still needs to be further explored. After integrating the analysis results in the GEPIA database, we found that SH3TC2 was significantly upregulated in COAD, READ, and SKCM, while SH3TC2 was downregulated in TGCT C. MoreovAlthough SH3TC2 is displayed as a prognostic factor across multiple tumor types via bioinformatic analysis, few studies have focused on the prognostic value of SH3TC2. In LAML, decreased expression of SH3TC2 was associated with MYCN amplification and poor survival . Our stuTo further validate the role of SH3TC2 in CRC, we included 40 CRC patients and analyzed its expression via IHC analysis. In our cohorts, we found that SH3TC2 was significantly highly expressed in tumor tissue, and high expression also implied poor DFS. The in vivo study implied that downregulation of SH3TC2 significantly impaired tumorigenesis and tumor invasion and increased cell apoptosis.In our research, we found that expression of SH3TC2 correlated with the MAPK pathway. The qPCR results implied that MAPK8, MAPK9, MAPK13, and MAPK14 were significantly downregulated after transfection with shSH3TC2. The results implied that SH3TC2 may regulate CRC tumorigenesis via the MAPK pathway. Furthermore, we validated the MAP kinases via Western blot assays. As shown in The MAPK8 (JNK) activator Anisomycin was included in our study to further validate the function of SH3TC2. As shown in JNK, also named MAPK8, is an important member of MAPK pathways, which play a vital role in regulating diverse cell processes . It is iSimilar to the in vitro results, the in vivo study found that stable slicing of SH3TC2 significantly inhibited tumor growth in a CRC nude mouse model. Moreover, the knockdown of SH3TC2 may promote cancer cell apoptosis via multiple molecular mechanisms besides MAPK pathways. As a result, SH3TC2 may act as a therapeutic target for novel CRC treatment. For instance, delivering siRNAs by smart nanoparticles that target SH3TC2 may be a useful way to target CRC ,60,61. HIn summary, through integrated bioinformatics analysis, we identified a novel oncogene, SH3TC2, for CRC. Expression of SH3TC2 was significantly upregulated in tumor tissue, which may imply poor DFS. To our knowledge, this is the first report that focuses on the molecular regulation and prognostic value of SH3TC2 in CRC. SH3TC2 may act as a novel oncogene for CRC. SH3TC2 expression may promote CRC tumor progression via the MAPK pathway. SH3TC2 inhibition impaired tumorigenesis both in vitro and in vivo. Overall, SH3TC2 may be a possible marker to optimize the prognosis of CRC and predict tumor recurrence and metastasis. SH3TC2 may act as a potential therapeutic target for CRC patients. However, larger CRC cohorts and experiments for further study are needed.


Pinus sylvestris) softwood biomass (PWB). The optimization of three process parameters, viz., the incubation time, temperature, and biomass:PIL (BM:PIL) ratio, was performed to determine the best pretreatment conditions for lignin extraction. Under the optimal pretreatment conditions , [N11H(2OH)][LAC] yielded 61% delignification with a lignin recovery of 56%; the cellulose content of the recovered pulp was approximately 45%. Further, the biochemical composition of the recovered lignin and pulp was determined and the recovered lignin was characterized using 1H–13C heteronuclear single quantum coherence (HSQC) nuclear magnetic resonance (NMR) spectroscopy, quantitative 31P NMR, gel permeation chromatography (GPC), attenuated total reflectance (ATF)–Fourier transform infrared spectroscopy (ATR-FTIR), and thermal gravimetric analysis (TGA) analysis. Our results reveal that [N11H(2OH)][LAC] is significantly involved in the cleavage of predominant β–O–4’ linkages for the generation of aromatic monomers followed by the in situ depolymerization of PWB lignin. The simultaneous extraction and depolymerization of PWB lignin favors the utilization of recalcitrant pine biomass as feedstock for biorefinery schemes.Lignin-based chemicals and biomaterials will be feasible alternatives to their fossil-fuel-based counterparts once their breakdown into constituents is economically viable. The existing commercial market for lignin remains limited due to its complex heterogenous structure and lack of extraction/depolymerization techniques. Hence, in the present study, a novel low-cost ammonium-based protic ionic liquid (PIL), 2-hydroxyethyl ammonium lactate [N11H(2OH)][LAC], is used for the selective fractionation and improved extraction of lignin from Scots pine ( As a progressive society, the human race has thrived on the unrestrained usage of natural resources, especially fossil resources, to generate fuels and chemicals. However, the extensive exploitation of fossil fuels creates paramount pollution stress over our planet’s environment, which aids in global warming. Therefore, new strategies/techniques need to be designed to identify sustainable alternatives for the production of fuels and building blocks for chemicals from renewable carbon sources . Lignoceβ-O-4) which constitute <50%, followed by β–β and β–5 carbon linkages. The oxidative disruption of inter-unit linkages and the subsequent oxidation of hydroxyl groups results in lignin degradation to generate high-value, low-molecular-weight aromatic compounds [LAC]. The process parameters for the enhanced extraction of lignin are optimized. The biochemical composition of lignin and residual pulp was determined through a fiber analyzer. The surface morphology of the cellulose-rich pulp was characterized using SEM analysis. Further, the depolymerization properties of the extracted lignin were analyzed using GPC. The recovered lignin was characterized by 1H–13C HSQC NMR, quantitative 31P NMR, GPC, FTIR, and TGA analysis to determine the mechanism of fractionation from biomass.The economic sustainability of ionic-liquid-mediated pretreatment depends upon the optimization of the process parameters for the enhanced selective fractionation of biomass. Specifically, the higher biomass-loading ratio intensifies the pretreatment and decreases the capital expenditure of the process . Hence, Pinus sylvestris) softwood biomass was collected from southern Estonia (58°09′28.4″ N 26°44′27.1″ E). The moisture content of the biomass was determined using Kern MLS-50-3D moisture analyzer . Fiber content of the pine biomass was determined using Ankom 200 fiber analyzer , and composition was estimated following Hasanov et al. [LAC] was synthesized by an acid-base neutralization method .1H and 13C NMR spectra of [N11H(2OH)][LAC] was recorded on a Bruker 400 MHz Avance III NMR  spectrophotometer.Briefly, 226 mL of 2-dimethylamino-ethanol  was added dropwise to a solution of 159 mL racemic lactic acid  and incubated in a water bath for 2 h. After entirely adding the base, the reaction continued for 6 h under constant stirring—the excess of amine produced during the neutralization process was eliminated using a high vacuum. After drying under a vacuum for 16 h, the product was obtained in quantitative yield as a slightly yellow, viscous liquid. The 1H NMR : δ/ppm = 6.23 , 3.87 , 3.63 , 2.84 , 2.55 , 1.18 .13C[1H] NMR  δ /ppm = 178.55 , 66.73 (CHOH-CH3), 59.58 , 56.74 , 43.50 , 21.17 (CHOH-CH3).The collected pinewood was debarked, air-dried, ground to 1–2 mm, maintained at <10% moisture, and stored in an air-tight container. The fractionation and extraction of lignin from PWB using [N11H(2OH)][LAC] were adapted according to the methodology of Gschwend et al. . BrieflyThe delignification of the PWB by [N11H(2OH)][LAC] was determined by estimating the residual lignin in the biomass before and after pretreatment on a dry weight basis . Thus, tThe lignin recovery from [N11H(2OH)][LAC] pretreatment was determined by the amount of lignin retrieved from the precipitation relative to the initial lignin content in the raw PWB . The ligSEM images of the biomass pretreated with [N11H(2OH)][LAC] and the untreated pine wood biomass were obtained using SEM Helios NanoLab 650  at the acceleration voltage of 10 KeV, and the images were obtained at various magnifications.−1 interval range with a resolution of 4 cm−1.The surface functionalities of the lignin obtained from the [N11H(2OH)][LAC] pretreatment were measured using FTIR spectroscopy  with the universal attenuated total reflection (ATR) method. The spectra were recorded with an average accumulation of 16 scans in the 4000–600 cm2 gas with a flow rate of 100 mL/min at a heating rate of 10 °C /min [The thermal degradation properties of the [N11H(2OH)][LAC]-extracted lignin were determined using a NETZSCH STA 449 F3 Jupiter simultaneous (TGA and DSC/DTG) thermal analyzer . Approximately 5 mg of the extracted lignin was heated up to 900 °C under N °C /min .The molecular weight distribution of the [N11H(2OH)][LAC]-extracted lignin was analyzed using high-performance liquid chromatography (HPLC)  equipped with LabSolutions GPC software. The HPLC system contained a pump, an auto-sampler, a set of 2 MCX columns , and a pre-column (8 mm × 50 mm) (Polymer Standards Service (PSS), GmbH, Mainz, Germany) with a UV detector (280 nm). The lignin samples were dissolved in 0.1 M NaOH (5 mg/mL), and isocratic flow was maintained with 0.1 M NaOH solution at a flow rate of 0.5 mL/min with an injection volume of 20 µL. The relative molecular weight of the lignin was determined using polystyrene sulfonate sodium salt standards  ranging from 1100 Da to 100,000 Da.31P NMR and HSQC spectra. All of the NMR measurements  for the determination of the subunit abundance, interunit linkages, and phenolic OH content of the lignin were recorded on an Ascend Neo 500 MHz NMR spectrometer ) equipped with a TCI Prodigy cryo-probe head following the procedure of Wang et al. [The characterizations of the functional groups present in the [N11H(2OH)][LAC]-extracted PWB lignin were determined by g et al. .The effects of the operational process parameters viz., the incubation time (0.5–4 h), temperature (150–210 °C), and BM:PIL ratio  on PWB fractionation were investigated to determine the optimal operational conditions for maximal lignin extraction. The percentage of lignin extracted from the biomass, along with those of cellulose and hemicellulose, were determined by the compositional analysis of the recovered biomass obtained after the [N11H(2OH)][LAC] pretreatment, which was related to the biomass composition of untreated (UT) biomass .Initially, the effect of the incubation time on lignin removal was studied by varying the incubation time and keeping the other parameters, i.e., the temperature and BM:PIL ratio, constant . The ini4] required an optimal incubation period of 8 h [LAC] was visualized using SEM . The untThe visible deformities on the surface can be attributed to the removal of lignin and hemicellulose from PWB . FurtherThe ATR-FTIR analysis of the [N11H(2OH)][LAC]-extracted PWB lignin under optimal conditions was compared with the commercial Kraft lignin to determine the changes in surface functional properties. The ATR-FTIR analysis results of the [N11H(2OH)][LAC]-extracted lignin and Kraft alkali lignin are shown in −1 corresponds to the O=H stretching in the hydroxyl group of phenolic and aliphatic groups of lignin [LAC] pretreatment [LAC]-extracted PWB lignin displays improved surface properties due to the simultaneous extraction and functionalization of lignin by pretreatment. The characteristic band at 3439 cmf lignin . The peareatment . Furtherectively . The appectively . Furtheryl units .Thermogravimetric analysis is used to characterize the thermal properties, such as stability and pyrolytic decomposition, at various temperatures, and to identify the potential relationship between degradation and chemical structure. Thus, the thermal stability of [N11H(2OH)][LAC]-extracted PWB lignin was determined using thermogravimetry (TGA) and derivative thermogravimetry (DTG), as shown in 2, and CH4 [LAC]-extracted lignin was divided into three stages. In the first stage, the initial weight loss observed at 120.6 °C is due to the evaporation of absorbed moisture . The sec and CH4 . The las and CH4 . From DT(355 °C) . The rel(355 °C) ,34. Furt(355 °C) .The development of a high-value product from lignin is significantly influenced by the molecular weight and the polydispersity index (PDI) properties. Therefore, tuning these properties improves the application of lignin in various fields . Thus, t−1 and PDI of 3.28 from PWB compared to Kraft lignin (control). The decreased Mw and PDI of PWB obtained after pretreatment confirms the lignin depolymerization through breakage of interunit linkages [LAC]-extracted PWB lignin were characterized by combining the two-dimensional heteronuclear quantum coherence (HSQC) NMR and quantitative 13C NMR . The assc units) . Scheme 2,C5, and C6 represent G units at 110.9/6.91, 114.9/6.86, and 119.2/6.77, respectively, whereas the S units showed a prominent signal at S2,6 with a correlation at 104.1/6.63. The complete cleavage of the β–O–′ interunit linkage represents in situ depolymerization of PWB lignin during [N11H(2OH)][LAC] pretreatment [LAC] pretreatment. Further, the occurrence of stilbene moieties [(β–′5′)β and (β–′1′)α] due to the reverse aldol reaction of β–′5′ displays modification of interunit linkages of recovered PWB lignin [β–O–4′ motifs [2 cond signal [The aromatic region of the HSQC for [N11H(2OH)][LAC]-extracted PWB lignin after 3 h at 180 °C depicts the characteristic peaks for phenylpropanoids viz., guaiacyl (G) and syringyl (S) units. The correlations for Creatment . FurtherB lignin . The acc′ motifs . The pred signal . However31P NMR  in PWB [LAC]-extracted lignin, is determined by 31P NMR  38]. Af. Af31P Nd groups . The pri) in PWB . The com linkage . The relproducts ,41.1H–13C HSQC NMR and quantitative 31P NMR reveals the cleavage of β–O–4′ linkages and a significant increase in hydroxyl groups. Moreover, the in situ depolymerization of [N11H(2OH)][LAC]-treated lignin was confirmed by TGA and GPC analyses. This implies that [N11H(2OH)][LAC] pretreatment is able to convert the recalcitrant softwood biomass (lignin) into depolymerized aromatic units, which can be extended for other softwood biomasses for value-added chemical production.This study aimed at the synthesis of a novel PIL, 2-hydroxyethyl dimethyl ammonium lactate [N11H(2OH)][LAC], for the extraction of lignin from Scots pine biomass. Under optimized process conditions, [N11H(2OH)][LAC] recovered 56.13% of lignin from PWB. The compositional and SEM analyses of the recovered pulp showed selective removals of lignin. Further, the recovered lignin analyzed through


Following the publication of this article , it was The underlying data for Figs 2–4, 6–9 and S1 are in The Data Availability statement is therefore updated to read: All relevant data are within the manuscript and its Supporting Information files. The cDNA sequence data can be found on GenBank under accession numbers OM258702, OM258703, OM258704 and OM258705.In Fig 9 , the GFPS1 File(XLSX)Click here for additional data file.S2 File(XLSX)Click here for additional data file.S3 File(XLSX)Click here for additional data file.S4 File(XLSX)Click here for additional data file.S5 File(XLSX)Click here for additional data file.S6 File(XLSX)Click here for additional data file.S7 File(XLSX)Click here for additional data file.S8 File(XLSX)Click here for additional data file.S9 File(XLSX)Click here for additional data file.S10 File(XLSX)Click here for additional data file.S11 File(TIF)Click here for additional data file.S12 File(XLSX)Click here for additional data file.S13 File(XLSX)Click here for additional data file.S14 File(TIF)Click here for additional data file.S15 File(TIF)Click here for additional data file.S16 File(XLSX)Click here for additional data file.S17 File(XLSX)Click here for additional data file.


Arachis hypogaea L.) is an important source of edible oil and protein for human nutrition. The quality of peanut seed oil is mainly determined by the composition of fatty acids, especially the contents of oleic acid and linoleic acid. Improving the composition of fatty acids in the seed oil is one of the main objectives for peanut breeding globally. To uncover the genetic basis of fatty acids and broaden the genetic variation in future peanut breeding programs, this study used genome-wide association studies (GWAS) to identify loci associated with target traits and developed diagnostic marker. The contents of eight fatty acid components of the Chinese peanut mini-core collection were measured under four environments. Using the phenotypic information and over one hundred thousand single nucleotide polymorphisms (SNPs), GWAS were conducted to investigate the genetics basis of fatty acids under multi-environments. Overall, 75 SNPs were identified significant trait associations with fatty acid components. Nineteen associations were repeatedly identified in multiple environments, and 13 loci were co-associated with two or three traits. Three stable major associated loci were identified, including two loci for oleic acid and linoleic acid on chromosome A09  and one for stearic acid on B06 (mean PVE: 23%). According to functional annotations, 21 putative candidate genes related to fatty acid biosynthesis were found underlying the three associations. The allelic effect of SNP A09-114690064 showed that the base variation was highly correlated with the phenotypic variation of oleic acid and linoleic acid contents, and a cost-effective Kompetitive allele-Specific PCR (KASP) diagnostic marker was developed. Furthermore, the SNP A09-114690064 was found to change the cis-element CAAT (-) in the promoter of ahFAD2A to YACT (+), leading dozens of times higher expression level. The enhancer-like activity of ahFAD2A promoter was identified that was valuable for enriching the regulation mechanism of ahFAD2A. This study improved our understanding on the genetic architecture of fatty acid components in peanut, and the new effective diagnostic marker would be useful for marker-assisted selection of high-oleic peanut breeding.Peanut ( Arachis hypogea L.) is an important oilseed as well as economic crop in more than 100 countries of Asia, Africa, America. In 2020, world production of peanut (with shell) was 53.64 m tons harvested from an area of 31.57 m ha (FAO 2020) [Peanut , stearic acid (C18:0), arachidic acid (C20:0), behenic acid (C22:0), lignoceric acid (C24:0), and gadoleic acid (C20:1), account for 20% of the total fatty acids [The fatty acids in peanut oil are mainly oleic acid (C18:1) and linoleic acid (C18:2), accounting for about 80% of the total , 4. Sevee mutant , 11. Manty acids , 4. The ty acids , 13.A. duranensis, A. ipaensis [A. hypogaea [Fatty acid components are quantitative characters, which are easily influenced by environment. So far, the studies on the quantitative trait loci (QTL) mapping of fatty acids in peanut are limited, and most of the related QTLs have been obtained by linkage mapping using hybrid segregating populations , 14, 15.ipaensis –23 and Ahypogaea –26 were Our recently study reported identification of 105814 SNP markers from genotyping-by-sequencing (GBS) sequencing data of the Chinese peanut mini-core collection . This stS1 Table in S2 File. Field management followed the recommended cultural practices with manual weeding and no spraying of insecticides and fungicides. After 130 days of sowing, the plants were harvested except marginal ones.A panel of 250 germplasm resources of the Chinese peanut mini-core collection was used for genome-wide association study (GWAS) in this study. The detail information including their origins and botanical subspecies was published previously as supplementary materials . In 2015After the harvested pods were dried, the contents of fatty acids of the population were measured, including C16:0, C18:0, C18:1, C18:2, C20:0, C20:1, C22:0 and C24:0. We randomly selected 10 mature and plump seeds of each plot and ground into powder. Approximately 20 mg powder in triplicate was used for extraction and measurement of fatty acids. First, 20 mg seed powder was blended with 1 mL petroleum ether. Then 400 μL of 0.5 mol/L sodium methoxide (NaOCH3) in methanol solution was added to convert fatty acids to methyl esters. After 1 hour, the organic layer containing the methyl esters was transferred to an autosampler vial for gas chromatograph analysis. Fatty acid composition was determined using an Agilent 7890B gas chromatograph equipped with a flame ionization detector (FID) and an autosampler . Peak separation was carried out on DB-23 capillary column . One μl of sample was injected at a 60:1 split ratio onto the column maintained isothermally at 220°C. The temperature of the inlet and detector were set at 260°C and 280°C, respectively. Fatty acid composition was determined by calculating relative peak areas.H2 = σ2g/(σ2g+ σ2ge/ n + σ2e/ nr), where σ2g was the genetic variance, σ2ge was the variance due to G × E interaction, σ2e was the residual error, n was the number of environments, and r was the number of replications within environment. Correlation coefficients between traits across four environments were also performed on IBM SPSS Statistics software (version 21). The major allele frequency, genetic diversity and polymorphism information content (PIC) were calculated using PowerMarker V3.25 software [Statistical analyses for phenotypic data of fatty acid composition in four environments were performed using IBM SPSS Statistics software (version 21). The broad-sense heritability for each trait across environments was calculated according to Hallauer and Miranda (1998)  as H2 = software .P = 0.05/   as the genome-wide significance threshold after Bonferroni correction [P value (-log104.73×10–7 = 6.33). The Manhattan and quantile–quantile (Q-Q) plots of GWAS results were generated in R program. The phenotypic variation explained (PVE) of significant loci were estimated by ANOVA as described [A total of 105814 high-quality SNPs with minor allele frequency (MAF) ≥ 0.05 in the Chinese peanut mini-core collection  were userrection . In ordeescribed . To idenescribed . The genahFAD2A-F: CATTGCACAAGGCAACCGAA, ahFAD2A-R: CGAACGGCTATGAAACCAGC. The cis-acting regulatory DNA elements was analyzed using http://www.dna.affrc.go.jp/PLACE/. The KASP marker was developed using the flanking 100 bp sequences of the SNP variant [GTAAAATAAATAGTTCCAGTTTAACTTAAGC, Primer_Allele Y: GTAAAATAAATAGTTCCAGTTTAACTTAAGT, Primer_Common: CCAAGAGTCTCTAAAAATAGTGCTAGCAT. Sequences of the KASP primers do not include the tail sequences that interact with the fluor-labelled oligos in the KASP reaction.The base variation of SNP A09_114690064 was confirmed by PCR amplification and sequencing. The specific primers were designed upstream and downstream of the locus were:  variant . Two all with GG alleles and three materials  with AA alleles on the SNP A09_114690064 locus were used to perform expression analysis  stage 7  seeds of stage 7  which sh stage 7 ) with thght pink . Seeds ttin gene .The specS1 Fig in Table 1. The average coefficient of variation (CV) of four environments for each trait varied from 13.50 to 22.24% . The trait of C18:2 content showed the highest CV (22.24%), followed by C20:1 (21.81%) and C18:0 (21.03%). The trait of C16:0 content showed the lowest CV (13.50%). Similarly, the highest and lowest CV in single environment were observed for C18:2 content in E4 (30.15%) and C16:0 content in E1 . The broad-sense heritabilities (H2) for contents of C16:0, C18:0, C18:1, C18:2, C20:0, C20:1, C22:0 and C24:0 across environments were evaluated to be 73.88%, 80.50%, 93.98%, 93.57%, 83.50%, 90.65%, 80.70% and 77.55%, respectively (Table 1).The 250 lines of the Chinese peanut mini-core collection were planted at two locations in two years and the contents of eight fatty acids including C16:0, C18:0, C18:1, C18:2, C20:0, C20:1, C22:0 and C24:0 were measured from seeds harvested from the four environments. The continuous distributions of fatty acid phenotypic values for the mini-core accessions were shown in Table 2). Fourteen trait pairs were significantly negatively correlated, and 11 trait pairs were significantly positively correlated. The top five trait pairs are C18:1-C18:2 , C16:0-C18:1 , C16:0-C18:2 , C18:0-C20:0 , C18:0-C20:1 .Correlation analysis showed that 15 trait pairs were pairwise significantly correlated with each other in four environments, 5 trait pairs were pairwise significantly correlated with each other in three environments, and 6 trait pairs were pairwise significantly correlated with each other in two environments . The larger number of associated loci for single trait in each environment was 14 for C18:0 content in E2, 8 for C18:2 content in E4 and 8 for C20:1 content in E2 and E4. However, no significant associated SNP for C22:0 content was identified in all environments (S3 Table in S2 File). For other single fatty acid components in four environments, a total of 5, 21, 10, 14, 5, 11 and 9 non-redundant associated SNPs were detected for C16:0, C18:0, C18:1, C18:2, C20:0, C20:1 and C24:0, respectively .Association analysis showed that fatty acids had been identified 22 associated loci in E1, 33 associated loci in E2, 12 associated loci in E3 and 34 associated loci in E4 (S2 File). Of which, 19 loci were detected in at least two environments, including one loci for C18:0, six loci each for C18:1, C18:2, C20:1(S2 File). The associated marker A09- 115528661 for C18:1 and C18:2 was consistently identified across four environments. The associated marker A09-114106219 for C18:1 content was repeatedly detected in “E3, E4”, and for C18:2 content in “E2, E3 and E4”. Similarly, the SNP B09- 15848169 was associated with C18:1 content in three environments, and C18:2 content in two environments.As mentioned above, a total of 75 significantly associated SNPs for fatty acids were detected in Chinese peanut mini-core collection (S2 File). Among them, ten markers were significantly associated with both C18:1 and C18:2 contents and seven of them were associated with both traits in multiple environments. Marker A08-13807926 was significantly associated with C16:0, C18:1, C18:2. Markers B06-125245720 and B06-125302832 were significantly associated with C18:0, C20:0 and C20:1, and marker B06-122677790 was significantly associated with C18:0, C20:1. The common associated markers for multi-traits identified by association analysis were consistent with the high phenotypic correlations among these traits.A panel of thirteen SNP loci were detected to be co-associated with two or three fatty acid traits , while the other five loci have major PVE for fatty acid in specific environments.A set of eight associated markers were detected high phenotypic variance explained , which were distributed on the A09, B04 and B06 chromosomes Table 3). The marker A09-114106219 was identified with ~40% PVE for C18:1 content in two environments, similar for C18:2. The marker A09-115528661 averagely explained more than 10% phenotypic variation for C18:1 content in two environments and for C18:2 content in three environments. The marker B06-122677790 for C18:0 content was identified with ~20% PVE in E3 and E4. The results suggested that the genomic regions on A09 and B06 might contain important loci/genes regulating the fatty acids in peanut.Among the associated loci with high phenotypic variance explained, three SNPs  were stable (. The Aradu.G1YNF gene is predicted to encode fatty acid desaturase 2 (FAD2A) which is involved in the triacylglycerol biosynthesis pathway and has previously been identified as the key gene for oleic and linoleic acid [Total 21 candidate genes involved in fatty acid biosynthesis pathway were identified in the genomic regions underlying the three stable major associated loci eic acid , 39. SevAradu.G1YNF (ahFAD2A) which showed that the AA genotype had significant higher (P < 0.05) oleic acid content than that with the GG genotype, while the linoleic acid content showed opposite values in the association panel under four environments .The correlation between base variation in the candidate genes and phenotypic variation were further analyzed in the peanut mini-core population. Unfortunately, we did not find the existing non-synonymous SNP in the candidate genes, but we found eight SNPs in the upstream of the candidate genes underlying the three stable major associations of A09-114106219, A09-115528661 and B06-122677790. The allelic effects of these eight SNPs on target fatty acid components were investigated under “E1-E4” environments. We found one SNP A09-114690064 in the promoter region of ahFAD2A did exist (S1 File). Functional element analysis of promoter found that the base variation changes the cis-element CAAT (-) to YACT (+) . The previously study indicated that tetranucleotide CACT element in the distal region of C4 plants promoter acts as an enhancer-like expression module and sufficient for expression [ahFAD2A displayed dozens of times higher expression level in the AA genotype than in the GG genotype (S2 File), suggesting this SNP played an important role in transcriptional regulation.PCR products sequencing further confirmed that the SNP which leads to a G-A base change at A09-114690064 in the promoter region of pression , while tpression . To exampression . The resFig 3F and 3G). The results showed that the diagnostic marker, Aradu-A09-114690064, amplified the AA alleles in the high oleic and low linoleic acid accessions while the GG alleles in the low oleic and high linoleic acid accessions (S2 File). Based on the results above, the Aradu-A09-114690064 could be used as an effective diagnostic marker for marker-assisted selecting breeding of high oleic peanut varieties.The SNP A09-114690064 was targeted for cost-effective KASP marker development and further validate the effect. The KASP marker was used to genotype 22 accessions with low oleic and high linoleic acid content and 13 accessions with high oleic and low linoleic acid content  and 15 character data  and through cluster analysis , 43. PreS_mqOA_a09 and S_mqLA_a09 in S population (SunOleic 97R× NC94022) [T_mqOA_a09 and T_mqLA_a09 in T population (Trifrunner×GT-C20) [qOle-A09-1 and qLin-A09-1 in FA population (ICGV 06420×SunOleic 95R) [S_mqOA_b09-1 and S_mqLA_b09-1 in S population (SunOleic 97R×NC94022) and QTLs qOle-B09 and qLinB09 in FA population (ICGV06420×SunOleic 95R) [ahFAD2B. Moreover, six fatty acid-related genes were found underlying the associated SNP B09-15848169, including FAD2B encoded by Araip.WI5IC. Moreover, 19 associated loci for fatty acids detected by GWAS were repeatedly identified in at least two environments. The associated loci were identified in different genetic backgrounds or multiple environments, confirming that these loci were stable and reliable.Some of the associated loci identified in this study were consistent with QTLs obtained from segregating populations. The major loci associated with oleic acid and linoleic acid on A09 identified in this study were located in or near the QTL regions identified in three segregation populations. These linkage analysis QTLs were eic 95R) . In addieic 95R) . The cloahFAD2A was identified at the 448th nucleotide of the coding region and the allele mutational peanut showed higher oleic acid content. Previous studies have shown that the 448G>A mutation does not cause significant difference in transcription level but has significant effect on the protein level between high (448A) and normal (448G) oleic acid peanut seeds [FAD2A has a cellular regulatory mechanism by regulating protein level in peanut. In this study, we identified a new variation (G/A) in the promoter region of FAD2A in peanut, which changes cis-element CAAT (-) to enhancer-like expression module YACT (+). Shirsat et al (1989) [ahFAD2A displayed dozens of times higher in the accessions with YACT (+) module than that in the accessions with CAAT (-) element in promoter. This variation of G-A in the promoter led to a remarkable change in the transcription level, suggesting that there was also a transcriptional regulatory mechanism regulating FAD2A expression in peanut. The function of CAAT (-) / YACT (+) element in the promoter of ahFAD2A needs to be ultimately confirmed by follow-up expression analysis and transcription factor-promoter interaction analysis.In previous studies, a SNP (G/A) in ut seeds , 46, shol (1989)  showed tS2 File). These results showed the validity and stability of the allelic effect. In general, KASP markers have many advantages, such as high accuracy, good flexibility and low cost. The KASP diagnostic marker based on A09_114690064 locus in this study could be useful to facilitate high-oleic peanut breeding.Using sequencing-based trait mapping and effect estimation of nucleotide polymorphisms, researchers have identified some loci/genes associated with target traits as well as sequence variations for genes correlated with phenotypic variation . This apS1 File(PDF)Click here for additional data file.S2 File(XLSX)Click here for additional data file.


Hevea brasiliensis serves as an ideal model in this regard. Sucrose metabolism critically influences the productivity of natural rubber, a secondary metabolite of industrial importance. Here, we report on the characterization of the T6P synthase (TPS) gene family and the T6P/SNF1-related protein kinase1 (T6P/SnRK1) signaling components in Hevea laticifers under tapping (rubber harvesting), an agronomic manipulation that itself stimulates rubber production. A total of fourteen TPS genes were identified, among which a class II TPS gene, HbTPS5, seemed to have evolved with a function specialized in laticifers. T6P and trehalose increased when the trees were tapped, this being consistent with the observed enhanced activities of TPS and T6P phosphatase (TPP) and expression of an active TPS-encoding gene, HbTPS1. On the other hand, SnRK1 activities decreased, suggesting the inhibition of elevated T6P on SnRK1. Expression profiles of the SnRK1 marker genes coincided with elevated T6P and depressed SnRK1. Interestingly, HbTPS5 expression decreased significantly with the onset of tapping, suggesting a regulatory function in the T6P pathway associated with latex production in laticifers. In brief, transcriptional, enzymatic, and metabolic evidence supports the participation of T6P/SnRK1 signaling in rubber formation, thus providing a possible avenue to increasing the yield of a valuable secondary metabolite by targeting T6P in specific cells.Trehalose 6-phosphate (T6P), the intermediate of trehalose biosynthesis and a signaling molecule, affects crop yield via targeting sucrose allocation and utilization. As there have been no reports of T6P signaling affecting secondary metabolism in a crop plant, the rubber tree  TPS and TPP genes in Arabidopsis thaliana  ATP , 200 µM AMARA peptide , 5 mM dithiothreitol , 1 µM okadaic acid , 1 mM protease inhibitor cocktail (Sigma-Aldrich P8340), and 3 μL of latex C-serum. After 6 min of incubation at 30 °C, 15 µL was transferred to 4 cm2 squares of Immobilon-P Transfer Membrane (0.22 µm PVDF) (Millipore) that had been treated by soaking in methanol for 5 min and kept wet. After air drying, the membrane squares were washed four times each for 1 min with 800 mL of 1% phosphoric acid , air dried, and transferred to vials containing a 2 mL scintillation cocktail . Incorporation of 32P was determined by liquid scintillation counting . The specific activity of SnRK1 was calculated as nmol of inorganic phosphate (Pi) incorporated into AMARA peptide per min per mg of C-serum protein in the reaction mixture.SnRK1 activity was assayed according to  with modSucrose content in the latex was measured according to . T6P conhttp://linux1.softberry.com/berry.phtml) (accessed on 23 June 2014). Homology analyses (nucleotide and amino acid) of the fourteen HbTPS genes were generated with DNAMAN V6. Phylogenetic relationships were analyzed among the fourteen HbTPS proteins and 34 other previously reported TPS sequences of Populus [Escherichia coli TPS (otsA) used as an outgroup. The protein domains of the HbTPS proteins were searched using the Pfam database (http://pfam.xfam.org/search accessed on 23 June 2014).The analyses for the cDNAs, predicted proteins, and gene structures were performed by GENETYX-WIN version 4.06) and the Softberry software ( Populus , Arabido.06 and t Populus  using MAt-test was performed using the software embedded in Excel 2007 for comparison of HbTPS and HbSnRK1 expression, TPS, TPP and SnRK1 activities, and sucrose, T6P and trehalose levels under different experimental conditions. Differences were accepted as significant at p < 0.05.Student’s