Integration of Dynamical Network Biomarkers, Control Theory and Drosophila Model Identifies Vasa/DDX4 as the Potential Therapeutic Targets for Metabolic Syndrome

Abstract

Metabolic syndrome (MetS) is a subclinical disease, resulting in increased risk of type 2 diabetes (T2D), cardiovascular diseases, cancer, and mortality. Dynamical network biomarkers (DNB) theory has been developed to provide early-warning signals of the disease state during a preclinical stage. To improve the efficiency of DNB analysis for the target genes discovery, the DNB intervention analysis based on the control theory has been proposed. However, its biological validation in a specific disease such as MetS remains unexplored. Herein, we identified eight candidate genes from adipose tissue of MetS model mice at the preclinical stage by the DNB intervention analysis. Using Drosophila, we conducted RNAi-mediated knockdown screening of these candidate genes and identified vasa (also known as DDX4), encoding a DEAD-box RNA helicase, as a fat metabolism-associated gene. Fat body-specific knockdown of vasa abrogated high-fat diet (HFD)-induced enhancement of starvation resistance through up-regulation of triglyceride lipase. We also confirmed that DDX4 expressing adipocytes are increased in HFD-fed mice and high BMI patients using the public datasets. These results prove the potential of the DNB intervention analysis to search the therapeutic targets for diseases at the preclinical stage.

Keywords: DNB intervention analysis; Drosophila melanogaster; dynamical network biomarkers theory; metabolic syndrome.

Figure 1

Selection of the candidate genes by the DNB intervention analysis. (A) A schematic diagram of the DNB intervention analysis. The DNB intervention analysis involves first detecting the pre-disease stage using high-dimensional low-sample-size (HDLSS) data. This is achieved by identifying a core set of genes (DNB members) with large fluctuations in correlation and variance, signaling an imminent transition to the disease state. High-dimensional statistical analysis is then applied to calculate the intervention index for each gene, ranking potential candidates for intervention based on their degree of fluctuations. The selected top-ranking genes were targeted for intervention, either through experimental or simulation-based methods, with the goal of transitioning the system from a high-fluctuation pre-disease state to a stable, healthy state. Successful intervention is validated by confirming reduced network fluctuations and sustained stability. (B) The result of the DNB intervention analysis. The intervention index expresses absolute values of elements of dominant eigenvector of sample covariance matrix of mRNA expression levels at pre-disease state. The blue circles indicate individual genes. The top 10 genes above the threshold (red line) were selected. (C) A list of eight candidate genes obtained from the DNB intervention analysis.

Figure 2

Fat body-specific vasa knockdown abrogates the effect of HFD on starvation resistance. (A) An experimental flow of the RNAi-mediated metabolic screening for DNB genes by starvation assay. (B) Percent changes in median survival both for ND (RNAi vs. Control) and HFD (RNAi vs. Control) are shown. (C) Percent changes in median survival both for Control flies (HFD vs. ND) and RNAi flies (HFD vs. ND) are shown. (D) Kaplan–Meier survival analysis of S₁106-GS > UAS-vasa RNAi male flies are shown. Control: ND (n = 81) vs. Control: HFD (n = 57); p < 0.0001, Control: ND vs. RNAi: ND (n = 99); p = 0.0318, Control: HFD vs. RNAi: HFD (n = 64); p = 0.0043 by a Log-rank (Mantel–Cox) test. Representative survival curve of three independent assays.

Figure 3

Vasa expressing cells are increased in the fat body in response to HFD. (A) vasa mRNA expression in dissected fat bodies from 3 days old male w1118 flies. Error bars indicate SEM from eight (ND) or nine (HFD) independent biological replicates (ns: no significance by a two-tailed unpaired t test). (B) Immunostaining of Vasa-GFP in dissected fat bodies with anti-GFP antibody. vas::EGFP^KI flies were fed ND (Top) and HFD (Bottom) for 3 days. Representative images (n = 20–21) are shown. Middle and right panels are magnified images of the white square in the left side panels. Scale bars indicate 100 μm (left) and 50 μm (middle and right). (C) Quantification of Vasa-GFP-positive cells from 20 to 21 of whole fat body images. Error bars indicate SEM (** p < 0.01 by a two-tailed unpaired t test). (D) Immunostaining of Vasa in dissected fat bodies with anti-Vasa antibody. S₁106-GS> UAS-EGFP flies were fed ND (Top) and HFD (Bottom) for 3 days with RU486, respectively. Representative images (n = 12) are shown. Scale bars indicate 40 μm. (E) Quantification of Vasa positive cells from 12 of fat body images. Error bars indicate SEM (** p < 0.01 by a two-tailed unpaired t test).

Figure 4

Fat body-specific vasa knockdown enhance upregulation of the fatty acid degradation pathway upon HFD during starvation. (A,B) Bulk RNA-seq analysis of dissected fat bodies from 7 days old male S₁106-GS > UAS-vasa RNAi flies after 48 h starvation. The lists of up-regulated (A) and down-regulated (B) genes in the vasa RNAi flies compared with control flies upon HFD are shown. (C) GSEA pathway analysis of A. (D–F) CG5966 (D), bmm (E), and dHSL (F) mRNA expressions in dissected fat bodies from 7 days old male S₁106-GS > UAS-vasa RNAi flies after 48 h starvation. (D–F) Error bars indicate SD (ns: no significance, ** p < 0.01 by a two-tailed unpaired t test). (G) Lipid (LipidTOX) staining of dissected fat bodies from 7 days old male S₁106-GS > UAS-vasa RNAi flies after 48 h starvation. Representative images (n = 9–12) are shown. Scale bar indicates 50 μm. (H) Quantification of lipid staining (G). (ns: no significance, ** p < 0.01 by a one-way ANOVA, Tukey’s multiple comparisons test).

Figure 5

DDX4/Ddx4 expression are altered in the adipose tissues of HFD-fed mice and the patients with high body mass index. (A–F) scRNA-seq data extracted from a single-cell atlas of human and mouse white adipose tissue of the Single Cell PORTAL (https://singlecell.broadinstitute.org/single_cell (accessed on 13 September 2024)). (A) Ddx4 mRNA expression in mouse adipocytes. Error bars indicate SD (**** p < 0.001 by a two-tailed unpaired t test). (B,C) Number of Ddx4 expressing cells and non-Ddx4 expressing cells calculated from a dataset of A (**** p < 0.001 by a Chi-square test). (D) DDX4 mRNA expression in human adipocytes. Error bars indicate SD (ns: no significance by a two-tailed unpaired t test). (E,F) Number of DDX4 expressing cells and non-DDX4 expressing cells calculated from a dataset of D (* p < 0.05 by a Chi-square test).