Gene networks and pathways for plasma lipid traits via multitissue multiomics systems analysis

Abstract

Genome-wide association studies (GWASs) have implicated ∼380 genetic loci for plasma lipid regulation. However, these loci only explain 17-27% of the trait variance, and a comprehensive understanding of the molecular mechanisms has not been achieved. In this study, we utilized an integrative genomics approach leveraging diverse genomic data from human populations to investigate whether genetic variants associated with various plasma lipid traits, namely, total cholesterol, high and low density lipoprotein cholesterol (HDL and LDL), and triglycerides, from GWASs were concentrated on specific parts of tissue-specific gene regulatory networks. In addition to the expected lipid metabolism pathways, gene subnetworks involved in "interferon signaling," "autoimmune/immune activation," "visual transduction," and "protein catabolism" were significantly associated with all lipid traits. In addition, we detected trait-specific subnetworks, including cadherin-associated subnetworks for LDL; glutathione metabolism for HDL; valine, leucine, and isoleucine biosynthesis for total cholesterol; and insulin signaling and complement pathways for triglyceride. Finally, by using gene-gene relations revealed by tissue-specific gene regulatory networks, we detected both known (e.g., APOH, APOA4, and ABCA1) and novel (e.g., F2 in adipose tissue) key regulator genes in these lipid-associated subnetworks. Knockdown of the F2 gene (coagulation factor II, thrombin) in 3T3-L1 and C3H10T1/2 adipocytes altered gene expression of Abcb11, Apoa5, Apof, Fabp1, Lipc, and Cd36; reduced intracellular adipocyte lipid content; and increased extracellular lipid content, supporting a link between adipose thrombin and lipid regulation. Our results shed light on the complex mechanisms underlying lipid metabolism and highlight potential novel targets for lipid regulation and lipid-associated diseases.

Keywords: GWAS; coagulation factor II; integrative genomics; lipid metabolism; pathway and network analysis.

Fig. 1

Overall design of the study. The statistical framework can be divided into four main parts, including Marker Set Enrichment Analysis (MSEA), merging and trimming of gene sets, Key Driver Analysis (KDA), and validation of the key drivers (KD) using in vitro testing.

Fig. 2

Common KDs and their neighboring genes in the shared lipid-associated subnetworks. A: Adipose KDs and subnetworks. B: Liver KDs and subnetworks. The subnetworks shared by HDL, LDL, TC, and TG are depicted by different colors according to the difference in their functional categories. Nodes are the KDs and their adjacent regulatory partner genes, with KDs depicted as square nodes and their gene symbols labeled in red letters. Only network edges that were present in at least two independent network studies were included. The node size corresponds to the GWAS significance.

Fig. 3

Adipose KDs and subnetworks for each lipid trait. Panels (A)–(D) represent HDL, LDL, TC, and TG subnetworks. Nodes are the KDs and their adjacent regulatory partner genes, with KDs depicted as larger nodes. Different colors indicate genes involved in different pathways.

Fig. 4

Validation of F2's predicted subnetwork and regulatory role in adipocytes. A, B: Time course of F2 expression during adipocyte differentiation in 3T3-L1 cells (A) and C3H10T1/2 cells (B). D-2, D0, D2, D3, D4, D6, D8, D10 indicate 2 days before initiation of differentiation, day 0, day 2, day 3, day 4, day 6, day 8, and day 10 of differentiation, respectively. Sample size n = 2–3/time point. C, D: Visualization and quantification (absorbance value) of lipid accumulation by Oil red O staining in 3T3-L1 adipocytes (C) and C3H10T1/2 adipocytes (D). Sample size n = 5–8/group for adipocytes. E, F: Fold change of expression level for F2 adipose subnetwork genes and negative control genes after siRNA knockdown. At day 7 of differentiation of 3T3-L1 and day 5 and day 7 of differentiation of C3H10T1/2, adipocytes were transfected with F2 siRNA for the knockdown experiments. Ten F2 neighbors were randomly selected from the first- and second-level neighboring genes of F2 in adipose network. Four negative controls were randomly selected from the genes not directly connected to F2 in the adipose network. G, H: The fold changes of adipokine/adipogenesis-related genes in 3T3-L1 (G) and in C3H10T1/2 (H). Gene expression levels were determined by RT-qPCR, normalized to beta-actin. The fold changes were relative to scrambled siRNA control. Sample size n = 4/group. I, J: Lipid profiles: total lipid, triglyceride (TG), total cholesterol (TC), unesterified cholesterol (UC), and phospholipid (PL) in C3H10T1/2 cells (I) and in media (J). Total Lipid was estimated using the sum of the four lipids (TG, TC, UC, PL). Intracellular lipids plotted in (I) were normalized to total cellular protein quantity. Extracellular lipids plotted in (J) are presented as lipid quantity in 1 ml of collected media. Sample size n = 6/group. Results represent mean ± SEM. Statistical significance was determined by two-sided Student's t-test (∗P < 0.05 and ∗∗P < 0.01).

Fig. 5

The associations between lipid-associated supersets and human complex diseases. The edges represent the associations between supersets for the specific lipid classes matched by color and diseases (P value < 0.05; Fisher exact test with Bonferroni correction). AD, Alzheimer's disease; CVD, cardiovascular diseases; T2D, type 2 diabetes.