Meta-Analysis of Integrated Proteomic and Transcriptomic Data Discerns Structure-Activity Relationship of Carbon Materials with Different Morphologies

Adv Sci (Weinh). 2024 Mar;11(9):e2306268. doi: 10.1002/advs.202306268. Epub 2023 Dec 20.

Abstract

The Fiber Pathogenicity Paradigm (FPP) establishes connections between fiber structure, durability, and disease-causing potential observed in materials like asbestos and synthetic fibers. While emerging nanofibers are anticipated to exhibit pathogenic traits according to the FPP, their nanoscale diameter limits rigidity, leading to tangling and loss of fiber characteristics. The absence of validated rigidity measurement methods complicates nanofiber toxicity assessment. By comprehensively analyzing 89 transcriptomics and 37 proteomics studies, this study aims to enhance carbon material toxicity understanding and proposes an alternative strategy to assess morphology-driven toxicity. Carbon materials are categorized as non-fibrous, high aspect ratio with shorter lengths, tangled, and rigid fibers. Mitsui-7 serves as a benchmark for pathogenic fibers. The meta-analysis reveals distinct cellular changes for each category, effectively distinguishing rigid fibers from other carbon materials. Subsequently, a robust random forest model is developed to predict morphology, unveiling the pathogenicity of previously deemed non-pathogenic NM-400 due to its secondary structures. This study fills a crucial gap in nanosafety by linking toxicological effects to material morphology, in particular regarding fibers. It demonstrates the significant impact of morphology on toxicological behavior and the necessity of integrating morphological considerations into regulatory frameworks.

Keywords: bioinformatics in nanosafety; carbon nanomaterials; fiber toxicity assessment; integrative omics meta-analysis; nanofiber rigidity prediction; structure-activity-relationship (SAR).

Publication types

  • Meta-Analysis

MeSH terms

  • Asbestos* / chemistry
  • Carbon* / toxicity
  • Gene Expression Profiling
  • Proteomics
  • Structure-Activity Relationship

Substances

  • Carbon
  • Asbestos