# Multi-omics integration and machine learning reveal gut-immune signatures in idiopathic pulmonary fibrosis: insights from bulk RNA-seq, single-cell profiles, spatial transcriptomics, and experimental validation

## TL;DR

This study uses multi-omics data and machine learning to identify key genes and immune patterns in idiopathic pulmonary fibrosis, revealing a gut-immune-lung connection and potential new treatments.

## Contribution

The study integrates diverse omics data and experimental validation to identify novel diagnostic signatures and therapeutic targets in IPF.

## Key findings

- CXCL13, IL33, TLR4, and IGF1 are core genes linked to immune infiltration and fibrotic remodeling in IPF.
- A four-gene model effectively distinguishes IPF from controls across multiple datasets.
- Mendelian randomization supports a causal gut-immune-lung axis in IPF pathogenesis.

## Abstract

Idiopathic pulmonary fibrosis (IPF) is a progressive, fatal lung disease with limited treatment options and a poor prognosis. Recent studies suggest a critical role for the gut–immune–lung axis in IPF, yet the underlying molecular mechanisms remain unclear.

The current study performed in silico multi-omics integration of publicly available datasets, including bulk RNA-seq, single-cell and spatial transcriptomics, as well as peripheral blood multi-omics data to uncover key molecular signatures in IPF. Furthermore, machine learning techniques were utilized to identify core genes, whereas functional analyses and Mendelian randomization were conducted to evaluate the causal relationships among gut microbiota, immune cells, and IPF. Additionally, experimental validation using qPCR and ELISA assays was conducted in vitro, in vivo, and in patient plasma to confirm the expression patterns of key genes.

Across integrated public bulk, single-cell, spatial, and blood multi-omics, CXCL13, IL33, TLR4, and IGF1 were identified as core IPF genes consistently linked to immune infiltration and fibrotic remodeling. Deconvolution, scRNA-seq, and spatial mapping localized their dysregulation to fibroblasts and immune compartments (notably B-cell, macrophage, and mast-cell axes), highlighting fibroblast–immune crosstalk in fibrotic foci. A four-gene model robustly distinguished IPF from controls across cohorts. Mendelian randomization supported a gut–immune–lung axis, indicating causal effects of specific gut taxa on IPF risk via immune phenotypes. qPCR/ELISA in TGF-β1–stimulated fibroblasts, bleomycin mouse lungs, and patient plasma corroborated upregulation of IL33, CXCL13, IGF1 and downregulation of TLR4. Drug-signature reversal nominated cucurbitacin I and temsirolimus; molecular docking was performed as a preliminary in silico, computer-simulation–based assessment of potential ligand–protein interactions between these compounds and the four core targets.

This study provides new insights into the importance of gut–immune–lung axis in IPF and identifies CXCL13, IL33, TLR4, and IGF1 as diagnostic signatures and therapeutic targets. By integrating public multi-omics resources with experimental validation, our findings offer a foundation for future diagnostic and treatment strategies aimed at modulating the gut microbiota and immune system in IPF.