Search

Additional file 1: Fig. S1. Normalized LINP1 expression levels in a variety of tumors analyzed in TCGA database. The original data is from The Cancer Genome Atlas Program (TCGA) database and the diagram shows the expression levels of LINP1 in different types of cancer analyzed by Gene Expression Profiling Interactive Analysis (GEPIA, http://gepia.cancer-pku.cn/ ). Fig. S2. LINP1 promotes cell proliferation, migration and invasiveness in HSC-1 cells. (A) LINP1 expression was detected after LINP1 depletion in HSC-1 cells. Measurements of cell proliferation by CCK-8 assay (B), colony formation assay (C), transwell migration assay (D) and Matrigel invasiveness measurement (E) were performed in HSC-1 cells treated with siRNAs targeting LINP1. (F) Apoptosis assay by Annexin V/PI double staining were performed in HSC-1cells treated with siRNAs targeting LINP1. Scale bars, 500 mm (C), 100 μm (D, E). (G) qPCR validations of key gene expression in endoplasmic reticulum signaling including GRP78, XBP1, DDIT3 and DR5 after LINP1 depletion in HSC-1 cells. (H) Protein levels of GRP78, XBP1, eIF2α, p-eIF2α, DDIT3, DR5, and cleavages of Caspase-8, Caspase-7 and Caspase-3 were detected by Western blot after LINP1 knockdown in HSC-1 cells. GAPDH was using as loading control. Each experiment was performed in at least triplicate and results are presented as mean ± s.d. One-way ANOVA and Dunnett’s multiple comparison test were used to analyze the data (*P < 0.05, **P < 0.01, ***P < 0.001). Fig. S3. LINP1 promotes cell proliferation, migration and invasiveness in A431 cells. (A, F) LINP1 expression was detected after LINP1 depletion or overexpression in A431 cells. Measurements of cell proliferation by CCK-8 assay (B, G), colony formation assay (C, H), transwell migration assay and Matrigel invasiveness measurement (D, E, I, J) were performed after LINP1 knockdown or overexpression in A431 cells. Scale bars, 500 mm (C, H), 100 μm (D, E, I, J). Each experiment was performed in at least triplicate and results are presented as mean ± s.d. One-way ANOVA and Dunnett’s multiple comparison test were used to analyze the data (*P < 0.05, **P < 0.01, ***P < 0.001). Fig. S4. LINP1 functions as oncogene to repress UPR-mediated cell apoptosis in A431 cells. (A) The protein of GRP78, XBP1, eIF2α, p-eIF2α, ATF4 and DDIT3 were detected by Western blot in A431 cells after knockdown of LINP1 or overexpression of LINP1. (B, C) The expression of LINP1, DDIT3, DR5, XBP1 and GRP78 was detected by qRT-PCR in A431 cells after knockdown of LINP1 or overexpression of LINP1. (D) The levels of DR5 and cleaved Caspase-3, cleaved Caspase-7, cleaved Caspase-8 were detected by Western blot in A431 cells after knockdown of LINP1 or overexpression of LINP1. Statistical data of qRT-PCR represent the average of three independent experiments ± s.d. (E, F) Apoptosis assay by Annexin V/PI double staining were performed in LINP1-knockdown or LINP1-overexpression A431 cells. One-way ANOVA and Dunnett’s multiple comparison test were used to analyze the data (*P < 0.05, **P < 0.01, ***P < 0.001). Fig. S5. Peptides of eIF2α binding to LINP1 identified by HPLC-MS. (A) The protein mix precipitated by in vitro-transcribed Biotin-labelled LINP1 RNA was analyzed by high performance liquid chromatography-mass spectrometry (HPLC-MS) to identify the amino acid sequences of eIF2α peptides. (B) The ability of eIF2α to interact with LINP1 is predicted by lncPro ( http://bioinfo.bjmu.edu.cn/lncpro/ ). (C) Predicted secondary structure of LINP1 by RNAfold ( http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi ). Fig. S6. Knockdown of DDIT3 promotes the proliferation, migration but represses apoptosis in cSCC cells. (A) DDIT3 expression was detected after depletion of DDIT3 by siNC and siDDIT3 in cSCC cells. (B-E) Depletion of DDIT3 repressed cell apoptosis and enhanced cell proliferation, migration in cSCC cells by CCK-8 assay, colony formation assay, transwell migration assay and Annexin V/PI double staining measurement. Scale bars, 500 mm (C), 100 μm (D). (F) DR5, cleaved forms of Caspase-8, Caspase-7 and Caspase-3 proteins were detected in cSCC cells by Western blot after DDIT3 knockdown, GAPDH was using as loading control. Each experiment was performed in at least triplicate and results are presented as mean ± s.d. One-way ANOVA and Dunnett’s multiple comparison test were used to analyze the data (*P < 0.05, **P < 0.01, ***P < 0.001). Fig. S7–S9. (A431, HSC-1 and HaCaT cell lines authentication and quality check).

Background Seed flooding stress is one of the threatening environmental stressors that adversely limits soybean at the germination stage across the globe. The knowledge on the genetic basis underlying seed-flooding tolerance is limited. Therefore, we performed a genome-wide association study (GWAS) using 34,718 single nucleotide polymorphism (SNPs) in a panel of 243 worldwide soybean collections to identify genetic loci linked to soybean seed flooding tolerance at the germination stage. Results In the present study, GWAS was performed with two contrasting models, Mixed Linear Model (MLM) and Multi-Locus Random-SNP-Effect Mixed Linear Model (mrMLM) to identify significant SNPs associated with electrical conductivity (EC), germination rate (GR), shoot length (ShL), and root length (RL) traits at germination stage in soybean. With MLM, a total of 20, 40, 4, and 9 SNPs associated with EC, GR, ShL and RL, respectively, whereas in the same order mrMLM detected 27, 17, 13, and 18 SNPs. Among these SNPs, two major SNPs, Gm_08_11971416, and Gm_08_46239716 were found to be consistently connected with seed-flooding tolerance related traits, namely EC and GR across two environments. We also detected two SNPs, Gm_05_1000479 and Gm_01_53535790 linked to ShL and RL, respectively. Based on Gene Ontology enrichment analysis, gene functional annotations, and protein-protein interaction network analysis, we predicted eight candidate genes and three hub genes within the regions of the four SNPs with Cis-elements in promoter regions which may be involved in seed-flooding tolerance in soybeans and these warrant further screening and functional validation. Conclusions Our findings demonstrate that GWAS based on high-density SNP markers is an efficient approach to dissect the genetic basis of complex traits and identify candidate genes in soybean. The trait associated SNPs could be used for genetic improvement in soybean breeding programs. The candidate genes could help researchers better understand the molecular mechanisms underlying seed-flooding stress tolerance in soybean.