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.

The genus Sophora (Fabaceae) includes medicinal plants that have been used in East Asian countries since antiquity. Sophora flavescens is a perennial herb indigenous to China, India, Japan, Korea, and Russia. Its dried roots have antioxidant, anti-inflammatory, antibacterial, apoptosis-modulating, and antitumor efficacy. The congeneric S. koreensis is endemic to Korea and its genome is less than half the size of that of S. flavescens. Nevertheless, this discrepancy can be used to assemble and validate the S. flavescens genome. A comparative genomic study of the two genomes can disclose the recent evolutionary divergence of the polymorphic phenotypic profiles of these species. Here, we used the PacBio sequencing platform to sequence and assemble the S. koreensis and S. flavescens genomes. We inferred that it was mainly small-scale duplication that occurred in S. flavescens. A KEGG analysis revealed pathways that might regulate the pharmacologically important secondary metabolites in S. flavescens and S. koreensis. The genome assemblies of Sophora spp. could be used in comparative genomics and data mining for various plant natural products.

BackgroundReproduction is a key feature of the sustainability of a species and thus represents an important component in livestock genetic improvement programs. Most reproductive traits are lowly heritable. In order to gain a better understanding of the underlying genetic basis of these traits, a genome-wide association was conducted for age at first calving (AFC), first inter-calving period (ICP) and scrotal circumference (SC) within the South African Bonsmara breed. Phenotypes and genotypes (120,692 single nucleotide polymorphisms (SNPs) post editing) were available on 7,128 South African Bonsmara cattle; the association analyses were undertaken using linear mixed models.ResultsGenomic restricted maximum likelihood analysis of the 7,128 SA Bonsmara cattle yielded genomic heritability’s of 0.183 (SE = 0.021) for AFC, 0.207 (SE = 0.022) for ICP and 0.209 (SE = 0.019) for SC. A total of 16, 23 and 51 suggestive (P ≤ 4 × 10-6) SNPs were associated with AFC, ICP and SC, while 11, 11 and 44 significant (P ≤ 4 × 10-7) SNPs were associated with AFC, ICP and SC respectively. A total of 11 quantitative trait loci (QTL) and 11 candidate genes were co-located with these associated SNPs for AFC, with 10 QTL harbouring 11 candidate genes for ICP and 41 QTL containing 40 candidate genes for SC. The QTL identified were close to genes previously associated with carcass, fertility, growth and milk-related traits. The biological pathways influenced by these genes include carbohydrate catabolic processes, cellular development, iron homeostasis, lipid metabolism and storage, immune response, ovarian follicle development and the regulation of DNA transcription and RNA translation.ConclusionsThis was the first attempt to study the underlying polymorphisms associated with reproduction in South African beef cattle. Genes previously reported in cattle breeds for numerous traits bar AFC, ICP or SC were detected in this study. Over 20 different genes have not been previously reported in beef cattle populations and may have been associated due to the unique genetic composite background of the SA Bonsmara breed.

BackgroundImmune traits are considered to serve as potential biomarkers for pig’s health. Medium to high heritabilities have been observed for some of the immune traits suggesting genetic variability of these phenotypes. Consideration of previously established genetic correlations between immune traits can be used to identify pleiotropic genetic markers. Therefore, genome-wide association study (GWAS) approaches are required to explore the joint genetic foundation for health biomarkers. Usually, GWAS explores phenotypes in a univariate (uv), trait-by-trait manner. Besides two uv GWAS methods, four multivariate (mv) GWAS approaches were applied on combinations out of 22 immune traits for Landrace (LR) and Large White (LW) pig lines.ResultsIn total 433 (LR: 351, LW: 82) associations were identified with the uv approach implemented in PLINK and a Bayesian linear regression uv approach (BIMBAM) software. Single Nucleotide Polymorphisms (SNPs) that were identified with both uv approaches (n = 32) were mostly associated with immune traits such as haptoglobin, red blood cell characteristics and cytokines, and were located in protein-coding genes. Mv GWAS approaches detected 647 associations for different mv immune trait combinations which were summarized to 133 Quantitative Trait Loci (QTL). SNPs for different trait combinations (n = 66) were detected with more than one mv method. Most of these SNPs are associated with red blood cell related immune trait combinations. Functional annotation of these QTL revealed 453 immune-relevant protein-coding genes. With uv methods shared markers were not observed between the breeds, whereas mv approaches were able to detect two conjoint SNPs for LR and LW. Due to unmapped positions for these markers, their functional annotation was not clarified.ConclusionsThis study evaluated the joint genetic background of immune traits in LR and LW piglets through the application of various uv and mv GWAS approaches. In comparison to uv methods, mv methodologies identified more significant associations, which might reflect the pleiotropic background of the immune system more accurately. In genetic research of complex traits, the SNP effects are generally small. Furthermore, one genetic variant can affect several correlated immune traits at the same time, termed pleiotropy. As mv GWAS methods consider strong dependencies among traits, the power to detect SNPs can be boosted. Both methods revealed immune-relevant potential candidate genes. Our results indicate that one single test is not able to detect all the different types of genetic effects in the most powerful manner and therefore, the methods should be applied complementary.

In recent years, some common themes in the development of sex-specific traits in different animal lineages have started to emerge since the discovery of the Dmrt (doublesex-mab3-related transcription factor gene) genes. Bivalves are characterized by a diversity of sexual systems, including simultaneous hermaphroditism, sequential hermaphroditism, and strict gonochorism. However, to date, no research has focused on the genome-wide characterization and analysis of Dmrt genes in bivalves. In this study, the identification and analysis of Dmrt genes in 15 bivalves were performed using bioinformatics methods. A total of 55 Dmrt genes were retrieved in the studied bivalve genomes. The number of Dmrt genes in different species ranged from 3 to 5. The phylogenetic tree showed that Dmrt genes in bivalves can be subdivided into 5 classes: the Dmrt2-like class, Dmrt3-like class, Dmrt4/5-like class, Dsx-like class, and scallop-specific Dmrt class. The Ka/Ks ratios suggested that all Dmrt classes underwent purifying selection pressure. Furthermore, the spatiotemporal expression of Dmrt genes in four bivalve species suggested that different Dmrt genes may have different functions, and scallop-specific Dmrt genes may play a key role in sex determination/differentiation. In general, this study provides a molecular basis for in-depth examination of the functions of Dmrt genes and phylogenomic analyses in bivalves.

BackgroundLiver metastasis is the major challenge in the treatment for malignant tumors. Genomic profiling is increasingly used in the diagnosis, treatment and prediction of prognosis in malignancies. In this study, we constructed a gene mutation-based risk model to predict the survival of liver metastases.MethodWe identified the gene mutations associated with survival and constructed the risk model in the training cohort including 800 patients with liver metastases from Memorial Sloan-Kettering Cancer Center (MSKCC) dataset. Other 794 patients with liver metastases were collected from 4 cohorts for validation. Furthermore, the analyses of tumor microenvironment (TME) and somatic mutations were performed on 51 patients with breast cancer liver metastases (BCLM) who had both somatic mutation data and RNA-sequencing data.ResultsA gene mutation-based risk model involved 10 genes was constructed to divide patients with liver metastases into the high- and low-risk groups. Patients in the low-risk group had a longer survival time compared to those in the high-risk group, which was observed in both training and validation cohorts. The analyses of TME in BCLM showed that the low-risk group exhibited more immune infiltration than the high-risk group. Furthermore, the mutation signatures of the high-risk group were completely different from those of the low-risk group in patients with BCLM.ConclusionsThe gene mutation-based risk model constructed in our study exhibited the reliable ability of predicting the prognosis in liver metastases. The difference of TME and somatic mutations among BCLM patients with different risk score can guide the further research and treatment decisions for liver metastases.