Induction of Protective Response Associated with Expressional Alterations in Neuronal G Protein-Coupled Receptors in Polystyrene Nanoparticle Exposed <i>Caenorhabditis elegans</i>

Yang, Yunhan; Dong, Wenting; Wu, Qiuli; Wang, Dayong

doi:10.1021/acs.chemrestox.0c00501

Cited by 31 publications

(3 citation statements)

References 68 publications

(124 reference statements)

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“…Interestingly, many stress conditions that involve intestinal FSHR-1 are linked to oxidative stress responses ( e.g. , production of reactive oxygen species, ROS) ( Miller et al, 2015 ; Powell et al, 2009 ; Wang et al, 2023 , Yang et al, 2021a ). Under normal and oxidative stress conditions, fshr-1 mutants also exhibit reduced frequency of body bending in liquid medium, a motility-based readout of neural signaling activity at neuromuscular junctions ( Wei and Kowalski, 2018 ).…”

Section: Evolutionary Conservation Of Glycoprotein Hormone-like Signa...mentioning

confidence: 99%

Evolutionary conserved peptide and glycoprotein hormone-like neuroendocrine systems in C. elegans

Istiban,

De Fruyt,

Kenis

et al. 2024

Molecular and Cellular Endocrinology

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Section: Evolutionary Conservation Of Glycoprotein Hormone-like Signa...mentioning

confidence: 99%

Evolutionary conserved peptide and glycoprotein hormone-like neuroendocrine systems in C. elegans

Istiban,

De Fruyt,

Kenis

et al. 2024

Molecular and Cellular Endocrinology

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“…Decreased insulin signaling leads to altered expression of DAF-16 target genes involved in detoxification response ( Freedman et al, 1993 ; Honda and Honda, 1999 ). Other studies showed the activation on ERK/MAPK or p38/MAPK in the neurons ( Yang et al, 2021 ) and changes to the JNK/MAPK and the insulin signaling in the intestinal cells of C. elegans upon PS-NP exposure ( Qu et al, 2020b ; Liu et al, 2021d ). These pathways are all hallmarks of oxidative stress response.…”

Section: Signaling Pathways Involved In Mp/np-induced Reproductive To...mentioning

confidence: 99%

Microplastics and Their Impact on Reproduction—Can we Learn From the C. elegans Model?

et al. 2022

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Biologically active environmental pollutants have significant impact on ecosystems, wildlife, and human health. Microplastic (MP) and nanoplastic (NP) particles are pollutants that are present in the terrestrial and aquatic ecosystems at virtually every level of the food chain. Moreover, recently, airborne microplastic particles have been shown to reach and potentially damage respiratory systems. Microplastics and nanoplastics have been shown to cause increased oxidative stress, inflammation, altered metabolism leading to cellular damage, which ultimately affects tissue and organismal homeostasis in numerous animal species and human cells. However, the full impact of these plastic particles on living organisms is not completely understood. The ability of MPs/NPs to carry contaminants, toxic chemicals, pesticides, and bioactive compounds, such as endocrine disrupting chemicals, present an additional risk to animal and human health. This review will discusses the current knowledge on pathways by which microplastic and nanoplastic particles impact reproduction and reproductive behaviors from the level of the whole organism down to plastics-induced cellular defects, while also identifying gaps in current knowledge regarding mechanisms of action. Furthermore, we suggest that the nematode Caenorhabditis elegans provides an advantageous high-throughput model system for determining the effect of plastic particles on animal reproduction, using reproductive behavioral end points and cellular readouts.

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“…For example, using meta-analysis, Oh et al extracted 1741 data samples of cadmium-containing semiconductor quantum dots and cellular toxicity from published literature . Much data on the interactions between model animals (such as Caenorhabditis elegans, Daphnia magna, zebrafish, rats, and mice) and NMs are available. − Using meta-analysis to establish databases and then ML algorithms to study the interactions between model animals and NMs has great advantages. For example, ML has been used for the high-throughput screening phenotyping of Caenorhabditis elegans embryos .…”

Section: Investigation Of the Interactions Between Nms And Biologymentioning

confidence: 99%

Machine Learning Boosts the Design and Discovery of Nanomaterials

Jia

Hou

Wang

et al. 2021

ACS Sustainable Chem. Eng.

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Nanomaterials (NMs) have developed quickly and cover various fields, but research on nanotechnology and NMs largely relies on costly experiments or complex calculations (e.g., density functional theory). In contrast, machine learning (ML) methods can address the large amount of time needed and labor consumption in material testing and achieve big-data, high-throughput screening, boosting the design and application of NMs. ML is a powerful tool for NM research; however, large knowledge gaps and critical issues should be promptly addressed to promote NMs from the laboratory to industry. With a focus on the primary NM aspects, enhancements to the design of NM structures, properties, adsorption, and catalysis by ML are reviewed and discussed. Given the emergent challenges in nanobiology, ML predictions of interactions between NMs and biology are also analyzed. Subsequently, this perspective discusses how to improve the interpretability of ML algorithms, which has been a bottleneck of ML in recent years. ML has led to innovations in the development of NMs, but some problems remain, such as imperfect databases and the accuracy of algorithm determination and nanopattern image recognition, which are herein addressed. Overall, this perspective provides insights for the development of ML in NM research.

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Induction of Protective Response Associated with Expressional Alterations in Neuronal G Protein-Coupled Receptors in Polystyrene Nanoparticle Exposed Caenorhabditis elegans

Cited by 31 publications

References 68 publications

Evolutionary conserved peptide and glycoprotein hormone-like neuroendocrine systems in C. elegans

Evolutionary conserved peptide and glycoprotein hormone-like neuroendocrine systems in C. elegans

Microplastics and Their Impact on Reproduction—Can we Learn From the C. elegans Model?

Machine Learning Boosts the Design and Discovery of Nanomaterials

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