Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

Supplementary data related to this article: Supplementary material, available at https://ars.els-cdn.com/content/image/1-s2.0-S0048969722047647-mmc1.docx (Word document35, 5KB)); Supplementary Data 1. These are the chemicals along with their properties and the calculated doses given for the C. elegans (Caenorhabditis elegans), available at https://ars.els-cdn.com/content/image/1-s2.0-S0048969722047647-mmc2.xls (spreadsheet, 68KB); Supplementary Data 2. These are the chemicals and the doses given for the trout gill cell line (Rtgill-WT1). The codes here relate to the same codes on the microarray data, available at https://ars.els-cdn.com/content/image/1-s2.0-S0048969722047647-mmc3.xlsx (spreadsheet; 11KB); Supplementary Data 3. These are the probes found to be significantly different between exposed and unexposed C. elegans for each of the narcotic chemicals tested, available at https://ars.els-cdn.com/content/image/1-s2.0-S0048969722047647-mmc4.xlsx (spreadsheet, 363KB); Supplementary Data 4. The file provide the metabolite composition of the three clusters identified by running a metabolic-based predictive model, available at https://ars.els-cdn.com/content/image/1-s2.0-S0048969722047647-mmc5.xlsx (spreadsheet, 10KB).Copyright © 2022 The Authors. With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical.Unilever Ltd

Data-driven learning of narcosis mode of action identifies a CNS transcriptional signature shared between whole organism Caenorhabditis elegans and a fish gill cell line

With the large numbers of man-made chemicals produced and released in the environment, there is a need to provide assessments on their potential effects on environmental safety and human health. Current regulatory frameworks rely on a mix of both hazard and risk-based approaches to make safety decisions, but the large number of chemicals in commerce combined with an increased need to conduct assessments in the absence of animal testing makes this increasingly challenging. This challenge is catalysing the use of more mechanistic knowledge in safety assessment from both in silico and in vitro approaches in the hope that this will increase confidence in being able to identify modes of action (MoA) for the chemicals in question. Here we approach this challenge by testing whether a functional genomics approach in C. elegans and in a fish cell line can identify molecular mechanisms underlying the effects of narcotics, and the effects of more specific acting toxicants. We show that narcosis affects the expression of neuronal genes associated with CNS function in C. elegans and in a fish cell line. Overall, we believe that our study provides an important step in developing mechanistically relevant biomarkers which can be used to screen for hazards, and which prevent the need for repeated animal or cross-species comparisons for each new chemical

Characterising proteolysis during SARS-CoV-2 infection identifies viral cleavage sites and cellular targets with therapeutic potential.

SARS-CoV-2 is the causative agent behind the COVID-19 pandemic, responsible for over 170 million infections, and over 3.7 million deaths worldwide. Efforts to test, treat and vaccinate against this pathogen all benefit from an improved understanding of the basic biology of SARS-CoV-2. Both viral and cellular proteases play a crucial role in SARS-CoV-2 replication. Here, we study proteolytic cleavage of viral and cellular proteins in two cell line models of SARS-CoV-2 replication using mass spectrometry to identify protein neo-N-termini generated through protease activity. We identify previously unknown cleavage sites in multiple viral proteins, including major antigens S and N: the main targets for vaccine and antibody testing efforts. We discover significant increases in cellular cleavage events consistent with cleavage by SARS-CoV-2 main protease, and identify 14 potential high-confidence substrates of the main and papain-like proteases. We show that siRNA depletion of these cellular proteins inhibits SARS-CoV-2 replication, and that drugs targeting two of these proteins: the tyrosine kinase SRC and Ser/Thr kinase MYLK, show a dose-dependent reduction in SARS-CoV-2 titres. Overall, our study provides a powerful resource to understand proteolysis in the context of viral infection, and to inform the development of targeted strategies to inhibit SARS-CoV-2 and treat COVID-19

Characterising proteolysis during SARS-CoV-2 infection identifies viral cleavage sites and cellular targets with therapeutic potential

SARS-CoV-2 is the causative agent behind the COVID-19 pandemic, responsible for over 170 million infections, and over 3.7 million deaths worldwide. Efforts to test, treat and vaccinate against this pathogen all benefit from an improved understanding of the basic biology of SARS-CoV-2. Both viral and cellular proteases play a crucial role in SARS-CoV-2 replication. Here, we study proteolytic cleavage of viral and cellular proteins in two cell line models of SARS-CoV-2 replication using mass spectrometry to identify protein neo-N-termini generated through protease activity. We identify previously unknown cleavage sites in multiple viral proteins, including major antigens S and N: the main targets for vaccine and antibody testing efforts. We discover significant increases in cellular cleavage events consistent with cleavage by SARS-CoV-2 main protease, and identify 14 potential high-confidence substrates of the main and papain-like proteases. We show that siRNA depletion of these cellular proteins inhibits SARS-CoV-2 replication, and that drugs targeting two of these proteins: the tyrosine kinase SRC and Ser/Thr kinase MYLK, show a dose-dependent reduction in SARS-CoV-2 titres. Overall, our study provides a powerful resource to understand proteolysis in the context of viral infection, and to inform the development of targeted strategies to inhibit SARS-CoV-2 and treat COVID-19

nmrML: a community supported open data standard for the description, storage, and exchange of NMR data

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard. </p

nmrML: a community supported open data standard for the description, storage, and exchange of NMR data.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters and, where available, spectral metadata such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex bio-mixtures i.e. metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker and Agilent/Varian vendor formats. In addition, easy-to-use web-based spectral viewing, processing and spectral assignment tools that read and write nmrML have been developed. Software libraries and web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g. serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI) and we here encourage user participation and feedback to increase usability and make it a successful standard