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The Multi-organ Chip - A Microfluidic Platform for Long-term Multi-tissue Coculture
Published on: April 28, 2015
Integrating multi-organ cytokine responses with CTD and AOP-based knowledge to identify disease-relevant hazard
Keon Kang1, Nivedita Chatterjee2, Filipa Lebre2
1School of Environmental Engineering, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02504, Republic of Korea.
Abstract:
Assessment of chemical-induced toxicity has traditionally relied on single-endpoint measurements that provide limited mechanistic resolution for organ-specific disease outcomes. This limitation is particularly evident for CMIT/MIT (chloromethylisothiazolinone/methylisothiazolinone), preservatives linked to severe health effects following South Korea's humidifier disinfectant disaster, where prior studies focused predominantly on Th2-skewed respiratory inflammation with limited cytokine resolution. Using a 46-cytokine Luminex panel, we profiled inflammatory responses across four human cell lines - pulmonary (BEAS-2B), hepatic (HepG2), neuronal (SH-SY5Y), and immune (Jurkat T) - and integrated the resulting profiles with the Comparative Toxicogenomics Database (CTD) and AOP-Wiki for disease-contextualized hazard characterization. CMIT/MIT induced dose-dependent ROS elevation across all cell lines, with cell type-specific cytokine signatures: IL-6 suppression with sustained IP-10 and growth factor elevation in BEAS-2B, and chemokine-dominant activation (MIP-3α, IL-8) in HepG2. Partial least squares-discriminant analysis revealed coordinated, pathway-level cytokine reorganization rather than isolated marker changes. Mapping these signatures onto CTD and AOP key events identified disease-relevant hazard signals for pulmonary fibrosis, hepatocellular carcinoma, and hepatic encephalopathy, supporting mechanistic hypotheses rather than disease causation. These findings demonstrate that multi-cytokine profiling combined with curated database integration provides a reproducible strategy for translating early inflammatory perturbations into mechanistically grounded hazard signals.
Insights
This study reveals how CMIT/MIT (chloromethylisothiazolinone/methylisothiazolinone) affects multiple human cell types by analyzing cytokine profiles. This approach links early inflammatory responses to potential organ-specific diseases like fibrosis and cancer.
Area of Science:
- Toxicology
- Immunology
- Computational Biology
Background:
- Traditional toxicity assessments lack mechanistic detail for organ-specific diseases.
- Preservatives like CMIT/MIT are linked to severe health issues, but their mechanisms remain unclear.
- Previous studies on CMIT/MIT focused narrowly on respiratory inflammation.
Purpose of the Study:
- To investigate the multi-cytokine inflammatory responses induced by CMIT/MIT across different human cell types.
- To integrate cytokine profiles with toxicogenomic databases for disease-contextualized hazard identification.
- To develop a reproducible strategy for translating early toxicological perturbations into mechanistic hazard signals.
Main Methods:
- Utilized a 46-cytokine Luminex panel to profile inflammatory responses in pulmonary, hepatic, neuronal, and immune cells.
- Integrated cytokine data with the Comparative Toxicogenomics Database (CTD) and AOP-Wiki.
- Applied Partial Least Squares-Discriminant Analysis (PLS-DA) to analyze pathway-level cytokine reorganization.
Main Results:
- CMIT/MIT induced dose-dependent reactive oxygen species (ROS) elevation in all tested cell lines.
- Identified cell type-specific cytokine signatures, including IL-6 suppression and IP-10 elevation in pulmonary cells, and chemokine activation in hepatic cells.
- Mapped cytokine profiles to CTD and AOP key events, revealing hazard signals for pulmonary fibrosis, hepatocellular carcinoma, and hepatic encephalopathy.
Conclusions:
- Multi-cytokine profiling combined with database integration offers a robust method for assessing chemical-induced toxicity.
- This approach translates early inflammatory changes into mechanistically grounded hazard signals for various diseases.
- The findings support mechanistic hypotheses for CMIT/MIT-related health risks, moving beyond simple disease causation.
