Candidate gene discovery for salt tolerance in rice (Oryza sativa L.) at the germination stage based on genome-wide association study.

Salt stress affects rice seed germination and seedling formation, seriously restricting rice production. Screening salt-tolerant rice varieties and analyzing the genetic mechanisms underlying salt tolerance are therefore very important to ensure rice production. In this study, 313 Oryza sativa ssp. japonica germplasm were used to conduct a genome-wide association study (GWAS) using 1% NaCl as a salt stress treatment during germination stage. The germination potential (GP) on different days and the germination index (GI) under salt stress were used as salt tolerance indicators. The results of population structure analysis showed that the 313 germplasm studied could be divided into two subpopulations, consistent with the geographical origins of the materials. There were 52 loci significantly related to salt tolerance during germination, and the phenotypic contribution rate of 29 loci was > 10%. A region on chromosome 11 (17049672-17249672 bp) was repeatedly located, and the candidate gene LOC_Os11g29490, which encodes a plasma membrane ATPase, was identified in this locus. Further haplotype analysis showed the GP of germplasm with different haplotypes at that locus significantly differed under salt stress (p < 0.05), and germplasm carrying Hap2 displayed strong salt tolerance during the germination stage. Two other promising candidate genes for salt tolerance were identified: LOC_Os01g27170 (OsHAK3), which encodes a potassium transporter, and LOC_Os10g42550 (OsITPK5), which encodes an inositol 1, 3, 4-trisphosphate 5/6-kinase. The results of this study provide a theoretical basis for salt-tolerant gene cloning and molecular design breeding in rice.

A lymph node metastasis-related protein-coding genes combining with long noncoding RNA signature for breast cancer survival prediction.

Integrating protein-coding gene (PCG) with long noncoding RNA (lncRNA) expression profiles, our aim is to identify a multidimensional transcriptome model that can predict individual prognosis of patients with breast cancer (BRCA). After diverse bioinformatics and statistical analyses, we obtained gene expression profiles of 1,016 BRCA samples from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) database, and constructed a prognostic signature, which is composed of four PCGs (TFCP2, LRRC75B, PROSER2, and STOML1) and one lncRNA (AL355592.1). In the training set, the multidimensional transcriptome signature could part patients with BRCA into two groups with different survival, defined as high- and low-risk group by Kaplan-Meier (KM) analysis (p < 0.001). In the other five validation datasets, the PCG-lncRNA signature showed a similar predictive performance in BRCA by KM (p < 0.05). The prognostic independence for the PCG-lncRNA model was verified by the multivariable Cox regression analysis. Because Chi-squared test showed the signature was associated with lymph node metastasis status, stratification analysis found that it could further subdivide lymph node metastasis status more precisely in BRCA. The function analysis suggested that the genes from the signature were associated with immunity-related pathways. In summary, we constructed a PCG-lncRNA signature, which could accurately predict survival in patients with BRCA.