Coupled Lipidomics and Digital Pathology as an Effective Strategy to Identify Novel Adverse Outcome Pathways in Eisenia fetida Exposed to MoS 2 Nanosheets and Ionic Mo.
Kailun SunJason Christopher WhiteHao QiuCornelis A M van GestelWillie J G M PeijnenburgErkai HePublished in: Environmental science & technology (2023)
Molybdenum disulfide (MoS 2 ) nanosheets are increasingly applied in several fields, but effective and accurate strategies to fully characterize potential risks to soil ecosystems are lacking. We introduce a coelomocyte-based in vivo exposure strategy to identify novel adverse outcome pathways (AOPs) and molecular endpoints from nontransformed (NTMoS 2 ) and ultraviolet-transformed (UTMoS 2 ) MoS 2 nanosheets (10 and 100 mg Mo/L) on the earthworm Eisenia fetida using nontargeted lipidomics integrated with transcriptomics. Machine learning-based digital pathology analysis coupled with phenotypic monitoring was further used to establish the correlation between lipid profiling and whole organism effects. As an ionic control, Na 2 MoO 4 exposure significantly reduced (61.2-79.5%) the cellular contents of membrane-associated lipids (glycerophospholipids) in earthworm coelomocytes. Downregulation of the unsaturated fatty acid synthesis pathway and leakage of lactate dehydrogenase (LDH) verified the Na 2 MoO 4 -induced membrane stress. Compared to conventional molybdate, NTMoS 2 inhibited genes related to transmembrane transport and caused the differential upregulation of phospholipid content. Unlike NTMoS 2 , UTMoS 2 specifically upregulated the glyceride metabolism (10.3-179%) and lipid peroxidation degree (50.4-69.4%). Consequently, lipolytic pathways were activated to compensate for the potential energy deprivation. With pathology image quantification, we report that UTMoS 2 caused more severe epithelial damage and intestinal steatosis than NTMoS 2 , which is attributed to the edge effect and higher Mo release upon UV irradiation. Our results reveal differential AOPs involving soil sentinel organisms exposed to different Mo forms, demonstrating the potential of liposome analysis to identify novel AOPs and furthermore accurate soil risk assessment strategies for emerging contaminants.
Keyphrases
- fatty acid
- quantum dots
- reduced graphene oxide
- human health
- risk assessment
- machine learning
- transition metal
- highly efficient
- visible light
- single cell
- cell proliferation
- genome wide
- high resolution
- climate change
- room temperature
- deep learning
- ionic liquid
- gold nanoparticles
- metal organic framework
- drinking water
- signaling pathway
- oxidative stress
- type diabetes
- artificial intelligence
- gene expression
- endothelial cells
- emergency department
- radiation induced
- adverse drug
- gram negative
- big data
- long non coding rna
- plant growth
- high glucose
- skeletal muscle
- data analysis
- multidrug resistant
- radiation therapy
- simultaneous determination