Integrating text mining with network models for successful target identification: in vitro validation in MASH-induced liver fibrosis

Abstract

An in silico target discovery pipeline was developed by including a directional and weighted molecular disease network for metabolic dysfunction-associated steatohepatitis (MASH)-induced liver fibrosis. This approach integrates text mining, network biology, and artificial intelligence/machine learning with clinical transcriptome data for optimal translational power. At the mechanistic level, the critical components influencing disease progression were identified from the disease network using in silico knockouts. The top-ranked genes were then subjected to a target efficacy analysis, following which the top-5 candidate targets were validated in vitro. Three targets, including EP300, were confirmed for their roles in liver fibrosis. EP300 gene-silencing was found to significantly reduce collagen by 37%; compound intervention studies performed in human primary hepatic stellate cells and the hepatic stellate cell line LX-2 showed significant inhibition of collagen to the extent of 81% compared to the TGFβ-stimulated control (1 μM inobrodib in LX-2 cells). The validated in silico pipeline presents a unique approach for the identification of human-disease-mechanism-relevant drug targets. The directionality of the network ensures adherence to physiologically relevant signaling cascades, while the inclusion of clinical data boosts its translational power and ensures identification of the most relevant disease pathways. In silico knockouts thus provide crucial molecular insights for successful target identification.

Keywords: disease network; drug discovery; liver fibrosis; metabolic dysfunction-associated steatohepatitis (MASH); systems biology; target discovery; target validation; text mining.

FIGURE 1

Schematic representation of the major steps in the proposed data-driven pipeline for target discovery.

FIGURE 2

Visualization of a growth factor to the extracellular matrix (GF–ECM) pathway. (A) Pathway from GF (TFGB1) to ECM protein (COL1A2). (B) Alternative route of the GF–ECM pathway when EP300 is knocked out.

FIGURE 3

Distribution of target types/families of the 32 proteins resulting from the in silico knockout experiment and selected for quick-scan analysis.

FIGURE 4

Genetic structure of EP300 with its functional domains and exemplified interaction partners. CH, cysteine-/histidine-rich domain; KIX, kinase inhibitory domain, Br, bromodomain. Adapted from Chan and La Thangue (2001).

FIGURE 5

Silencing RNA (siRNA) targeting EP300 shows a significant (p-value < 0.05) reduction in fibrosis. Total collagen levels measured in primary hepatic stellate cells (HSCs) in the presence of siRNA targeting either eGFP (blue, control siRNA) or EP300 (yellow). Aside from the untreated controls, the cells were stimulated with TGFβ (+TGFβ) in the absence or presence of the ALK5 inhibitor (+TGFβ+Alk5-i). The data are presented as mean ± SD (n = 3).

FIGURE 6

Intervention studies with small-molecule inhibitors of EP300 (inobrodib and L002) as a case study for target validation. (A) Inobrodib (Ino) and L002 significantly decrease TGFβ-induced fibrosis in LX-2 cells. The total collagen levels for treatment with Ino, L002, and ALK5 inhibitor (Alk5-i) are shown after TGFβ stimulation compared to the non-stimulated control. (B) Corresponding total protein levels. (C) Ino, L002, and Alk5-i, decreased TGFβ-induced fibrosis in primary HSCs. The total collagen levels for treatment with Ino, L002, and Alk5-i are shown after TGFβ stimulation compared to the non-stimulated control. (D) Corresponding total protein levels. The data are presented as mean ± SD (n ≥ 3). Significance (p < 0.05) is indicated with respect to the TGFβ-stimulated control (*) or Alk5-i (#).