Using networks to identify and interpret natural variation

A systems genomics and genetics approach to identify the genetic regulatory network for lignin content in Brassica napus seeds

Seed quality traits of oilseed rape, Brassica napus (B. napus), exhibit quantitative inheritance determined by its genetic makeup and the environment via the mediation of a complex genetic architecture of hundreds to thousands of genes. Thus, instead of single gene analysis, network-based systems genomics and genetics approaches that combine genotype, phenotype, and molecular phenotypes offer a promising alternative to uncover this complex genetic architecture. In the current study, systems genetics approaches were used to explore the genetic regulation of lignin traits in B. napus seeds. Four QTL (qLignin_A09_1, qLignin_A09_2, qLignin_A09_3, and qLignin_C08) distributed on two chromosomes were identified for lignin content. The qLignin_A09_2 and qLignin_C08 loci were homologous QTL from the A and C subgenomes, respectively. Genome-wide gene regulatory network analysis identified eighty-three subnetworks (or modules); and three modules with 910 genes in total, were associated with lignin content, which was confirmed by network QTL analysis. eQTL (expression quantitative trait loci) analysis revealed four cis-eQTL genes including lignin and flavonoid pathway genes, cinnamoyl-CoA-reductase (CCR1), and TRANSPARENT TESTA genes TT4, TT6, TT8, as causal genes. The findings validated the power of systems genetics to identify causal regulatory networks and genes underlying complex traits. Moreover, this information may enable the research community to explore new breeding strategies, such as network selection or gene engineering, to rewire networks to develop climate resilience crops with better seed quality.

LCK, FOXC1 and hsa-miR-146a-5p as potential immune effector molecules associated with rheumatoid arthritis

Rheumatoid arthritis (RA) is a type of systemic immune disease characterized by chronic inflammatory disease of the joints. However, the aetiology and underlying molecular events of RA are unclear. Here, we applied bioinformatics analysis to identify potential immune effector molecules involved in RA. The three microarray datasets were downloaded from the Gene Expression Omnibus (GEO) database. We used the R software screen 115 overlapping differentially expressed genes (DEGs). Subsequently, we constructed a protein-protein interaction (PPI) network encoded by these DEGs and identified 10 genes closely associated with RA - LCK, GZMA, GZMB, CD2, LAG3, IL-15, TNFRSF4, CD247, CCR5 and CCR7. Furthermore, in the miRNA-hub gene networks, we screened out hsa-miR-146a-5p, which is the miRNA controlling the largest number of hub genes. Finally, we found some transcription factors that closely interact with hub genes, such as FOXC1, GATA2, YY1, RUNX2, SREBF1, CEBPB and NFIC. This study successfully predicted that LCK, FOXC1 and hsa-miR-146a-5p can be used as potential immune effector molecules of RA. Our study may have potential implications for future prediction of disease progression in patients with symptomatic RA, and has important significance for the pathogenesis and targeted therapy of RA.

Multiomics approach reveals a role of translational machinery in shaping maize kernel amino acid composition

Maize (Zea mays) seeds are a good source of protein, despite being deficient in several essential amino acids. However, eliminating the highly abundant but poorly balanced seed storage proteins has revealed that the regulation of seed amino acids is complex and does not rely on only a handful of proteins. In this study, we used two complementary omics-based approaches to shed light on the genes and biological processes that underlie the regulation of seed amino acid composition. We first conducted a genome-wide association study to identify candidate genes involved in the natural variation of seed protein-bound amino acids. We then used weighted gene correlation network analysis to associate protein expression with seed amino acid composition dynamics during kernel development and maturation. We found that almost half of the proteome was significantly reduced during kernel development and maturation, including several translational machinery components such as ribosomal proteins, which strongly suggests translational reprogramming. The reduction was significantly associated with a decrease in several amino acids, including lysine and methionine, pointing to their role in shaping the seed amino acid composition. When we compared the candidate gene lists generated from both approaches, we found a nonrandom overlap of 80 genes. A functional analysis of these genes showed a tight interconnected cluster dominated by translational machinery genes, especially ribosomal proteins, further supporting the role of translation dynamics in shaping seed amino acid composition. These findings strongly suggest that seed biofortification strategies that target the translation machinery dynamics should be considered and explored further.

Local adaptation contributes to gene expression divergence in maize

Gene expression links genotypes to phenotypes, so identifying genes whose expression is shaped by selection will be important for understanding the traits and processes underlying local adaptation. However, detecting local adaptation for gene expression will require distinguishing between divergence due to selection and divergence due to genetic drift. Here, we adapt a QST−FST framework to detect local adaptation for transcriptome-wide gene expression levels in a population of diverse maize genotypes. We compare the number and types of selected genes across a wide range of maize populations and tissues, as well as selection on cold-response genes, drought-response genes, and coexpression clusters. We identify a number of genes whose expression levels are consistent with local adaptation and show that genes involved in stress response show enrichment for selection. Due to its history of intense selective breeding and domestication, maize evolution has long been of interest to researchers, and our study provides insight into the genes and processes important for in local adaptation of maize.