BackgroundAs the characteristic functional component in ginger, gingerols possess several health-promoting properties. Long non-coding RNAs (lncRNAs) act as crucial regulators of diverse biological processes. However, lncRNAs in ginger are not yet identified so far, and their potential roles in gingerol biosynthesis are still unknown. In this study, metabolomic and transcriptomic analyses were performed in three main ginger cultivars (leshanhuangjiang, tonglingbaijiang, and yujiang 1 hao) in China to understand the potential roles of the specific lncRNAs in gingerol accumulation.ResultsA total of 744 metabolites were monitored by metabolomics analysis, which were divided into eleven categories. Among them, the largest group phenolic acid category contained 143 metabolites, including 21 gingerol derivatives. Of which, three gingerol analogs, [8]-shogaol, [10]-gingerol, and [12]-shogaol, accumulated significantly. Moreover, 16,346 lncRNAs, including 2,513, 1,225, and 2,884 differentially expressed (DE) lncRNA genes (DELs), were identified in all three comparisons by transcriptomic analysis. Gene ontology enrichment (GO) analysis showed that the DELs mainly enriched in the secondary metabolite biosynthetic process, response to plant hormones, and phenol-containing compound metabolic process. Correlation analysis revealed that the expression levels of 11 DE gingerol biosynthesis enzyme genes (GBEGs) and 190 transcription factor genes (TF genes), such as MYB1, ERF100, WRKY40, etc. were strongly correlation coefficient with the contents of the three gingerol analogs. Furthermore, 7 and 111 upstream cis-acting lncRNAs, 1,200 and 2,225 upstream trans-acting lncRNAs corresponding to the GBEGs and TF genes were identified, respectively. Interestingly, 1,184 DELs might function as common upstream regulators to these GBEGs and TFs genes, such as LNC_008452, LNC_006109, LNC_004340, etc. Furthermore, protein–protein interaction networks (PPI) analysis indicated that three TF proteins, MYB4, MYB43, and WRKY70 might interact with four GBEG proteins (PAL1, PAL2, PAL3, and 4CL-4).ConclusionBased on these findings, we for the first time worldwide proposed a putative regulatory cascade of lncRNAs, TFs genes, and GBEGs involved in controlling of gingerol biosynthesis. These results not only provide novel insights into the lncRNAs involved in gingerol metabolism, but also lay a foundation for future in-depth studies of the related molecular mechanism.

BackgroundHereditary spherocytosis (HS) is a common inherited hemolytic anemia, caused by mutations in five genes that encode erythrocyte membrane skeleton proteins. The red blood cell (RBC) lifespan could directly reflect the degree of hemolysis. In the present cohort of 23 patients with HS, we performed next-generation sequencing (NGS) and Levitt’s carbon monoxide (CO) breath test to investigate the potential genotype-degree of hemolysis correlation.ResultsIn the present cohort, we identified 8 ANK1,9 SPTB,5 SLC4A1 and 1 SPTA1 mutations in 23 patients with HS, and the median RBC lifespan was 14(8–48) days. The median RBC lifespan of patients with ANK1, SPTB and SLC4A1 mutations was 13 (8–23), 13 (8–48) and 14 (12–39) days, respectively, with no statistically significant difference (P = 0.618). The median RBC lifespan of patients with missense, splice and nonsense/insertion/deletion mutations was 16.5 (8–48), 14 (11–40) and 13 (8–20) days, respectively, with no significant difference (P = 0.514). Similarly, we found no significant difference in the RBC lifespan of patients with mutations located in the spectrin-binding domain and the nonspectrin-binding domain [14 (8–18) vs. 12.5 (8–48) days, P = 0.959]. In terms of the composition of mutated genes, 25% of patients with mild hemolysis carried ANK1 or SPTA1 mutations, while 75% of patients with mild hemolysis carried SPTB or SLC4A1 mutations. In contrast, 46.7% of patients with severe hemolysis had ANK1 or SPTA1 mutations and 53.3% of patients with severe hemolysis had SPTB or SLC4A1 mutations. However, there was no statistically significant difference in the distribution of mutated genes between the two groups (P = 0.400).ConclusionThe present study is the first to investigate the potential association between genotype and degree of hemolysis in HS. The present findings indicated that there is no significant correlation between genotype and degree of hemolysis in HS.