This research was supported by the National Research Council of Science &#38; Technology (NST) grant by the South Korean government (MSIP) (No. CAP-17-01-KIST Europe) and Project 11911.

1. Equipment
<ol>
	<li>Software: use OECD QSAR Toolbox 4.0 or newer (free download from &#60;https://qsartoolbox.org/download/?) and data analysis software.</li>
	<li>Computer: for the OECD QSAR Toolbox, use: (i) system type: 64 bit, Windows 7 or newer; (ii) processor: I5 at 2.4 GHz, or a faster processor or equivalent AMD CPU; (iii) installed memory (RAM): 6 GB; (iv) hard disk drive (HDD): 20 GB of free hard drive space (OECD QSAR Toolbox 4.3 Release Notes: &#60;https://qsartoolbox.org/file/2019/02/Toolbox-4.3-Release-Notes-1.pdf&#62;).</li>
</ol>
2. Procedure
<ol>
	<li>OECD QSAR Toolbox 
	NOTE: The QSAR Toolbox operates in six consecutive flow modules starting from Input and followed by Profiling, Data, Category Definition, Data Gap Filling, then Report, located at the top of the program interface.
	<ol>
		<li>Explore the aforementioned six stages through six toolbar icons by left-clicking. First, look over the stages of Input, Data Gap Filling, and Report that are necessary to perform the automated workflow &#8220;Ecotoxicological endpoint&#8221; and to document its results.</li>
		<li>Take a short look over optional stages Profiling and Data. The Profiling stage provides an initial insight into the target substance&#8217;s (eco)toxicity potential and environmental fate characteristics. Optional Data stage enables searching for available experimental data related to the target substance.</li>
	</ol>
	</li>
	<li>Input
	<ol>
		<li>Upon starting the QSAR Toolbox, the user begins at the Input toolbox stage by default. The QSAR Toolbox creates a working file named &#8220;Document 1&#8221; automatically, which is displayed in the stage option panel on the left of the program interface. Rename the file, if desired, by right-clicking the working file.</li>
		<li>Click on the CAS# button in the actions toolbar, enter the chemical abstract service (CAS) number of the target substance in the available text field, and click Search. The tool will then search&#160;for the target substance by CAS number.</li>
		<li>If required, choose other search options that are available in the action toolbar such as searching by substance name or simplified molecular-input line-entry system (SMILES) code. SMILES can be entered as 2D non-stereochemical or 3D stereochemical containing forms. Click Name or Structure, respectively. Use the Structure tool to draw the target substance.</li>
		<li>The search tool displays the search results through database records in a pop-up window. Choose the record reporting a &#8220;high&#8221; CAS-SMILES relation (CS Relation field) if multiple records are retrieved for the target substance by checking the box on the left of the record. Click OK. 
		NOTE: Proceeding from this point is possible only if the retrieved record contains a SMILES code, as the SMILES code (2D non-stereochemical containing form) is the basis for computation.</li>
		<li>Batch mode: to perform the in silico assay for multiple target substances, write a simple substance list in a text editor in which each CAS number is listed in a single row (Supplementary Figure S3). Save the text file with an appropriate name and extension .txt on the computer.</li>
		<li>Batch mode: click Data. Then, go to Databases in the stage option panel on the left of the program interface. Make sure databases that are listed under Ecotoxicological Information are checked.</li>
		<li>Batch mode: click Input. Select Query from the actions toolbar. Accept the settings set in step 2.2.6 by clicking Yes in the dialog window.</li>
		<li>Batch mode: choose the CAS tab. Upload the substance list saved as text file through Load list from your computer.</li>
		<li>Batch mode: there are two Add buttons available; click the Add button on the bottom of the pop-up menu and then click Execute. The QSAR Toolbox will display a message on the number of substances that have been retrieved for the search. 
		NOTE: Some substances of the loaded list may not be found by the search tool or that several entries may be available for one CAS number. It is not possible to delete substances from the retrieved set of substances.</li>
	</ol>
	</li>
	<li>Profiling 
	NOTE: The following section is optional. If this is not required, skip to section 2.5.
	<ol>
		<li>Click on the toolbox stage button Profiling. Go to Profiling methods in the stage option panel on the left of the program interface.</li>
		<li>Click Unselect All. Check all profilers listed under Predefined and those related to aquatic toxicity listed under Endpoint specific such as &#8220;Acute aquatic toxicity classification by Verhaar (Modified).&#8221;</li>
		<li>Finish the selection. Then click on the Apply button in the Actions toolbar. 
		NOTE: The QSAR Toolbox provides recommendations on a set of profilers. These are highlighted in green (suitable) and orange (plausible) when choosing Options &#62; Color by: &#62; Endpoint selected in the data matrix in the upper left corner of Profiling methods. Left-click the data matrix field next to the endpoint of interest.&#160;Available endpoints are listed in the endpoint tree next to the stage option panel. The profiler Substance type will indicate whether the target substance is a &#8220;discrete chemical.&#8221; The information is displayed in the expanded endpoint tree &#8220;Profile&#8221;, &#8220;Predefined&#8221;, and &#8220;Substance type&#8221;. Only if the target substance is a discrete chemical can the automated workflow run successfully. &#8220;Acute aquatic toxicity classification by Verhaar (modified)&#8221; provides a first estimate of the acute aquatic toxicity mechanism of the target substance19,20. The information is displayed in the expanded endpoint tree &#8220;Profile&#8221;, &#8220;Endpoint Specific&#8221;, and &#8220;Acute aquatic toxicity classification by Verhaar (modified)&#8221;. Five classes are available: (class 1) inert chemicals (baseline toxicity); (class 2) less inert chemicals; (class 3) reactive chemicals; (class 4) specifically acting chemicals; and (class 5) for chemicals not possible to classify.</li>
		<li>Right-click Parameter in the endpoint tree to run integrated 2D and 3D QSAR models available in the QSAR Toolbox, if desired. Click Calculate/extract all parameters for all chemicals in the pop-up menu.</li>
		<li>2D and 3D QSAR models compiled in Parameter provide numeric values. Use &#8220;Profiling methods&#8221; for qualitative information (see step 2.3.1).</li>
	</ol>
	</li>
	<li>Data 
	NOTE: This section is optional. If it is not required, skip to section 2.5.
	<ol>
		<li>Click on the toolbox stage button Data. Then, click Gather from the Actions toolbar.</li>
		<li>Select All endpoints to gather all experimental data, then Choose to gather endpoint specific experimental data. As an example, if aquatic toxicity is the user&#8217;s focus, click Choose &#62; Ecotoxicological Information &#62; Aquatic toxicity &#62; OK. 
		NOTE: Choosing to gather experimental data for all endpoints may lead to extended processing time. The user can adapt the hierarchy of the endpoint tree to the specific purpose. This changes the manner in which data are displayed.</li>
		<li>If desired, right-click the endpoint of interest in the endpoint tree area. Choose Set tree hierarchy in the pop-up menu. Organize the endpoint tree in the preferred manner using the available terms and arrows and click OK.</li>
		<li>If desired, export the gathered data as an Excel file. Right-click on the endpoint of interest and choose Export Data matrix in the pop-up menu.</li>
		<li>A &#8220;Matrix export&#8221; wizard opens and enables adding other endpoints to the export list. Finish the selection, click Export, and save the file on the computer. 
		NOTE: Exporting data from all databases is not possible. For example, data retrieved from the database &#8220;ECHA CHEM&#8221; cannot be saved.</li>
	</ol>
	</li>
	<li>Data gap filling
	<ol>
		<li>Click on the toolbox stage button Data gap filling. Then, click Automated in the Actions toolbar.</li>
		<li>Select Ecotoxicological Endpoint &#62; Fish, LC50 (lethal concentration, 50%) at 96 h for Pimephales promelas (mortality). Click OK. A &#8220;Workflow Controller&#8221; will appear, and processing will take up to several minutes, especially in batch mode. 
		NOTE: The QSAR Toolbox automatically applies a defined set of profilers when searching for suitable substances with available experimental data for the prediction. The experimental data [e.g., effect concentrations 96 h LC50 (P. promelas) or 96 h EC50 (P. promelas, mortality)] are used to generate the prediction for the target substance by either linear approximation or nearest neighbor method. Note that the methods of linear approximation and nearest neighbor are referred to as trend analysis (labeled as &#8220;T&#8221;) and read-across (labeled as &#8220;R&#8221;), respectively.</li>
		<li>The user will receive a message if the prediction is performed successfully. Click OK and close the workflow controller indicating &#8220;Finished workflow&#8221; by clicking x in the upper right corner.</li>
		<li>Batch mode: upon starting the automated workflow, the user will be asked to specify the range of substances over which to execute the workflow. Accept the full range of substances selected by default in the dialog window by clicking OK.</li>
		<li>Batch mode: the user will not receive a message indicating whether a prediction was run successfully or unsuccessfully. Close the workflow controller indicating &#8220;Finished workflow&#8221; at the end of the batch processing by clicking x in the upper right corner.</li>
	</ol>
	</li>
	<li>Report
	<ol>
		<li>If a prediction was successfully executed, click on the toolbox stage button Report. 
		NOTE: No reports can be generated in batch mode.</li>
		<li>Scroll down and find the prediction value in matrix field located in a yellow highlighted row next to endpoint &#8220;96-h.&#8221; The predicted value is labelled with &#8220;T&#8221; or &#8220;R.&#8221; Activate this specific data matrix field by left-clicking it.</li>
		<li>Click Prediction in the Actions toolbar. Customize the report content and appearance in the pop-up wizard. Three types of reports are available: (i) prediction, (ii) category, and (iii) data matrix.</li>
		<li>The wizard allows the user to fill in the author&#8217;s name and contact details. Write a short summary, provide a detailed explanation of the mechanistic interpretation, or provide justification for the adequacy of the prediction.</li>
		<li>Include additional information related to the executed prediction, if desired. The extent of additional information depends on the user.</li>
		<li>Go through the wizard by clicking Next. Finally, click Create report and save the prediction and category reports as PDF files and the data matrix as an Excel spreadsheet on the computer.</li>
		<li>Find additional details on the functionalities of the QSAR Toolbox and automated workflows in the application manual for the OECD QSAR Toolbox v.4 (F1 help on the keyboard). Details on the algorithms and rationale behind the automated workflow are described by Dimitrov et al.8 and Yordanova et al.9.</li>
	</ol>
	</li>
</ol>
3. Application
<ol>
	<li>If using the predicted effect concentration (i.e., 96-h LC50 of P. promelas) in the environmental risk assessment, use the lower limit of the 95% confidence interval. Find the data on the first page of the saved prediction report (PDF) in &#8220;Prediction summary&#8221;, &#8220;Predicted value: &#60;mean&#62; (from &#60;lower_limit&#62; to &#60;upper_limit&#62;).&#8221; 
	NOTE: The notes given here are based on results of the comparison between predicted and experimental data for a set of target substances reported in this study. Selecting the lower end of the 95% confidence range will increase the likelihood that the predicted effect concentration will not underestimate the real toxicity of the substance (see the representative results). The predicted effective concentration of the lower limit of the 95% confidence interval will therefore present a safer basis for risk assessment.</li>
</ol>

<ol>
	<li>Najarian, K., Najarian, S., Gharibzadeh, S., Eichelberger, C. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Systems Biology and Bioinformatics: A Computational Approach. , (2009).</li><li>Fujita, T., Iwasa, J., Hansch, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+new+substituent+constant,+&#960;,+derived+from+partition+coefficient.">A new substituent constant, &#960;, derived from partition coefficient.</a> Journal of the American Chemical Society. 86, 5175-5180 (1964).</li><li>Roy, K., Kar, S., Das, R. N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Understanding the Basics of QSAR for Applications in Pharmaceutical Sciences and Risk Assessment. , (2015).</li><li>Raies, A. B., Bajic, V. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=In+silico+toxicology:+computational+methods+for+the+prediction+of+chemical+toxicity.">In silico toxicology: computational methods for the prediction of chemical toxicity.</a> WIREs Computational Molecular Science. 6, 147-172 (2016).</li><li>Hayes, T. B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Welcome+to+the+revolution:+integrative+biology+and+assessing+the+impact+of+endocrine+disruptors+on+environmental+and+public+health.">Welcome to the revolution: integrative biology and assessing the impact of endocrine disruptors on environmental and public health.</a> Integrative Compuational Biology. 45, 321-329 (2005).</li><li>Schug, T. T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Minireview:+endocrine+disruptors:+past+lessons+and+future+directions.">Minireview: endocrine disruptors: past lessons and future directions.</a> Molecular Endocrinology. 30, 833-847 (2016).</li><li>Devillers, J., Marchand-Geneste, N., Carpy, A., Porcher, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SAR+and+QSAR+modeling+of+endocrine+disruptors.">SAR and QSAR modeling of endocrine disruptors.</a> SAR QSAR Environmental Research. 17, 393-412 (2006).</li><li>Dimitrov, S. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QSAR+Toolbox+-+workflow+and+major+functionalities.">QSAR Toolbox - workflow and major functionalities.</a> SAR QSAR Environmental Research. 27, 203-219 (2016).</li><li>Yordanova, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Automated+and+standardized+workflows+in+the+OECD+QSAR+Toolbox.">Automated and standardized workflows in the OECD QSAR Toolbox.</a> Computational Toxicology. 10, 89-104 (2019).</li><li>Mombelli, E., Devillers, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+OECD+(Q)SAR+Application+Toolbox+and+Toxtree+for+predicting+and+profiling+the+carcinogenic+potential+of+chemicals.">Evaluation of the OECD (Q)SAR Application Toolbox and Toxtree for predicting and profiling the carcinogenic potential of chemicals.</a> SAR QSAR Environmental Research. 21, 731-752 (2010).</li><li>Devillers, J., Mombelli, E., Samsera, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+alerts+for+estimating+the+carcinogenicity+of+pesticides+and+biocides.">Structural alerts for estimating the carcinogenicity of pesticides and biocides.</a> SAR QSAR Environmental Research. 22, 89-106 (2011).</li><li>Li, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Identifying+unknown+by-products+in+drinking+water+using+comprehensive+two-dimensional+gas+chromatography-quadrupole+mass+spectrometry+and+in+silico+toxicity+assessment.">Identifying unknown by-products in drinking water using comprehensive two-dimensional gas chromatography-quadrupole mass spectrometry and in silico toxicity assessment.</a> Chemosphere. 163, 535-543 (2016).</li><li>Devillers, J., Mombelli, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+OECD+QSAR+Application+Toolbox+and+Toxtree+for+estimating+the+mutagenicity+of+chemicals.+Part+1.+Aromatic+amines.">Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 1. Aromatic amines.</a> SAR QSAR Environmental Research. 21, 753-769 (2010).</li><li>Devillers, J., Mombelli, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+OECD+QSAR+Application+Toolbox+and+Toxtree+for+estimating+the+mutagenicity+of+chemicals.+Part+2.+&#945;-&#946;+unsaturated+aliphatic+aldehydes.">Evaluation of the OECD QSAR Application Toolbox and Toxtree for estimating the mutagenicity of chemicals. Part 2. &#945;-&#946; unsaturated aliphatic aldehydes.</a> SAR QSAR Environmental Research. 21, 771-783 (2010).</li><li>Kulkarni, S. A., Barton-Maclaren, T. S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Performance+of+(Q)SAR+models+for+predicting+Ames+mutagenicity+of+aryl+azo+and+benzidine+based+compounds.">Performance of (Q)SAR models for predicting Ames mutagenicity of aryl azo and benzidine based compounds.</a> Journal of Environmental Science and Health Part C Environmental Carcinogenesis &amp; Ecotoxicology Reviews. 32, 46-82 (2014).</li><li>Craig, E. A., Wang, N. C., Zhao, Q. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Using+quantitative+structure-activity+relationship+modeling+to+quantitatively+predict+the+developmental+toxicity+of+halogenated+azole+compounds.">Using quantitative structure-activity relationship modeling to quantitatively predict the developmental toxicity of halogenated azole compounds.</a> Journal of Applied Toxicology. 34, 787-794 (2014).</li><li>Tebby, C., Mombelli, E., Pandard, P., P&#233;ry, A. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Exploring+an+ecotoxicity+database+with+the+OECD+(Q)SAR+Toolbox+and+DRAGON+descriptors+in+order+to+prioritise+testing+on+algae,+daphnids,+and+fish.">Exploring an ecotoxicity database with the OECD (Q)SAR Toolbox and DRAGON descriptors in order to prioritise testing on algae, daphnids, and fish.</a> Science of the Total Environment. 409, 3334-3343 (2011).</li><li>Mombelli, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluation+of+the+OECD+(Q)SAR+Application+Toolbox+for+the+profiling+of+estrogen+receptor+binding+affinities.">Evaluation of the OECD (Q)SAR Application Toolbox for the profiling of estrogen receptor binding affinities.</a> SAR QSAR Environmental Research. 23, 37-57 (2012).</li><li>Verhaar, H. J. M., van Leeuwen, C. J., Hermens, J. L. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classifying+environmental+pollutants.+1:+structure-activity+relationships+for+prediction+of+aquatic+toxicology.">Classifying environmental pollutants. 1: structure-activity relationships for prediction of aquatic toxicology.</a> Chemosphere. 25, 471-491 (1992).</li><li>Enoch, S. J., Hewitt, M., Cronin, M. T. D., Azam, S., Madden, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Classification+of+chemicals+according+to+mechanism+of+aquatic+toxicity:+an+evaluation+of+the+implementation+of+the+Verhaar+scheme+in+Toxtree.">Classification of chemicals according to mechanism of aquatic toxicity: an evaluation of the implementation of the Verhaar scheme in Toxtree.</a> Chemosphere. 73, 243-248 (2008).</li><li>Burden, N., Maynard, S. K., Weltje, L., Wheeler, J. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+utility+of+QSARs+in+predicting+acute+fish+toxicity+of+pesticide+metabolites:+a+retrospective+validation+approach.">The utility of QSARs in predicting acute fish toxicity of pesticide metabolites: a retrospective validation approach.</a> Regulatory Toxicology and Pharmacology. 80, 241-246 (2016).</li><li>Nendza, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Screening+for+potential+endocrine+disruptors+in+fish:+evidence+from+structural+alerts+and+in+vitro+and+in+vivo+toxicological+assays.">Screening for potential endocrine disruptors in fish: evidence from structural alerts and in vitro and in vivo toxicological assays.</a> Environmental Sciences Europe. 28, 26 (2016).</li><li>Roncaglioni, A., Piclin, N., Pintore, M., Benfenati, E. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Binary+classification+models+for+endocrine+disrupter+effects+mediated+through+the+estrogen+receptor.">Binary classification models for endocrine disrupter effects mediated through the estrogen receptor.</a> SAR QSAR Environmental Research. 19, 697-733 (2008).</li><li>Sosnovcov&#225;, J., Rucki, M., Bendov&#225;, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estrogen+receptor+binding+affinity+of+food+contact+material+components+estimated+by+QSAR.">Estrogen receptor binding affinity of food contact material components estimated by QSAR.</a> Central European Journal of Public Health. 24, 241-244 (2016).</li><li>Walker, J. D., Dearden, J. C., Schultz, T. W., Jaworska, J., Comber, M. H. I., Walker, J. D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QSARs+for+New+Practitioners.">QSARs for New Practitioners.</a> QSARs for Pollution Prevention, Toxicity Screening, Risk Assessment, and Web Applications. , (2003).</li><li>S&#225;nchez-Bayo, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=From+simple+toxicological+models+to+prediction+of+toxic+effects+in+time).">From simple toxicological models to prediction of toxic effects in time).</a> Ecotoxicology. 18, 343-354 (2009).</li><li>Sj&#246;str&#246;m, M., Lindgren, &. #. 1. 9. 7. ;., Uppg&#229;rd, L. L., Chen, F., Sch&#252;&#252;rmann, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Joint+Multivariate+Quantitative+Structure-Property+and+Structure-Activity+Relationships+for+a+Series+of+Technical+Nonionic+Surfactants.">Joint Multivariate Quantitative Structure-Property and Structure-Activity Relationships for a Series of Technical Nonionic Surfactants.</a> Quantitative Structure-Activity Relationships in Environmental Sciences-VII. , (1997).</li><li>Ding, F., Guo, J., Song, W., Hu, W., Li, Z. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparative+quantitative+structure-activity+relationship+(QSAR)+study+on+acute+toxicity+of+triazole+fungicides+to+zebrafish.">Comparative quantitative structure-activity relationship (QSAR) study on acute toxicity of triazole fungicides to zebrafish.</a> Chemistry Ecology. 27, 359-368 (2011).</li><li>Galati, G., O'Brien, P. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+toxicity+of+flavonoids+and+other+dietary+phenolics:+significance+for+their+chemopreventive+and+anticancer+properties.">Potential toxicity of flavonoids and other dietary phenolics: significance for their chemopreventive and anticancer properties.</a> Free Radical Biology in Medicine. 37, 287-303 (2004).</li><li>Russom, C. L., Bradbury, S. P., Broderius, S. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Predicting+modes+of+action+from+chemical+structure:+acute+toxicity+in+the+fathead+minnow+(Pimephales+promelas).">Predicting modes of action from chemical structure: acute toxicity in the fathead minnow (Pimephales promelas).</a> Environmental Toxicology and Chemistry. 16, 948-967 (1997).</li><li>Taysse, L., Troutaud, D., Khan, N. A., Deschaux, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structure-activity+relationship+of+phenolic+compounds+(phenol,+pyrocatechol+and+hydroquinone)+on+natural+lymphocytotoxicity+of+carp+(Cyprinus+carpio).">Structure-activity relationship of phenolic compounds (phenol, pyrocatechol and hydroquinone) on natural lymphocytotoxicity of carp (Cyprinus carpio).</a> Toxicology. 98, 207-214 (1995).</li><li>Nishihara, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Estrogenic+activities+of+517+chemicals+by+yeast+two-hybrid+assay.">Estrogenic activities of 517 chemicals by yeast two-hybrid assay.</a> Journal of Health Science. 46, 282-298 (2000).</li><li>Bolton, J. L., Trush, M. A., Penning, T. M., Dryhurst, G., Monks, T. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Role+of+quinones+in+toxicology.">Role of quinones in toxicology.</a> Chemical Research in Toxicology. 13, 135-160 (2000).</li><li>Panche, A. N., Diwan, A. D., Chandra, S. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Flavonoids:+an+overview.">Flavonoids: an overview.</a> Journal of Nutritional Science. 5, e47 (2016).</li><li>Li, J., Gramatica, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=QSAR+classification+of+estrogen+receptor+binders+and+pre-screening+of+potential+pleiotropic+EDCs.">QSAR classification of estrogen receptor binders and pre-screening of potential pleiotropic EDCs.</a> SAR QSAR Environmental Research. 21, 657-669 (2010).</li><li>Bohl, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Molecular Structure and Biological Activity of Steroids. , (2017).</li><li>Kaminuma, T., Takai-Igarashi, T., Nakano, T., Nakata, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Modeling+of+signaling+pathways+for+endocrine+disruptors.">Modeling of signaling pathways for endocrine disruptors.</a> BioSystems. 55, 23-31 (2000).</li><li>Lillicrap, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Alternative+approaches+to+vertebrate+ecotoxicity+tests+in+the+21st+century:+a+review+of+developments+over+the+last+2+decades+and+current+status.">Alternative approaches to vertebrate ecotoxicity tests in the 21st century: a review of developments over the last 2 decades and current status.</a> Environmental Toxicology and Chemistry. 35, 2637-2646 (2016).</li><li>Kim, J. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Acute+toxicity+of+pharmaceutical+and+personal+care+products+on+freshwater+crustacean+(Thamnocephalus+platyurus)+and+fish+(Oryzias+latipes).">Acute toxicity of pharmaceutical and personal care products on freshwater crustacean (Thamnocephalus platyurus) and fish (Oryzias latipes).</a> Journal of Toxicological Sciences. 34, 227-232 (2009).</li></ol>

The authors have nothing to disclose.

The versatility of the OECD QSAR Toolbox as analytic software for ecotoxicology is shown here with specific interest in the adverse effects of endocrine disrupting chemicals on aquatic vertebrates. In addition, a simple and standard protocol was demonstrated for predicting acute toxicity (96-h LC50) of 74 representative EDs (Table 1) for fish species. This was achieved by applying category building, data gap filling, and ER profiling modules embedded in the QSAR Toolbox (Figure 1, Figure 2).
The linear correlation between log10LC50 and log10KOW with a negative slope (as shown in Supplementary Figure S1) has long been known as a standard quantitative relationship in QSAR analyses25, where higher toxicity is shown the more hydrophobic a given chemical is. As can be seen from a simple calculation, the general mathematical relation that includes Equation S1 and Equation S2&#160;(Supplementary Information) is a converted expression from the following power function26:
<img alt="Equation 1" src="/files/ftp_upload/60054/60054equ01.jpg" />
<img alt="Equation 2" src="/files/ftp_upload/60054/60054equ02.jpg" />
From the plot of (Equation 2), characterizing an intermediate range of KOW26 may be possible by adjusting the parameters a and b, where a certain variation in hydrophobicity (or hydrophilicity) does not significantly change the endpoint of acute toxicity.
Comparative analyses between the computational predictions and experimental observations on the LC50, as shown in Figure 3 and Figure 4, have been typically reported in studies of QSAR for various aquatic toxicants, including technical nonionic surfactants27, triazole fungicides28, and pesticide metabolites21. This type of retrospective validation provides information on how far a given QSAR tool can reach in terms of comparative performance to experimental results. In this study of acute toxicity in fish, the QSAR Toolbox was proven to provide protective predictions for over 90% of tested EDs in all fish and in a single species, Pimephales promelas.
Further identifying the three outlier chemicals in Figure 3 and Figure 4, which showed higher predicted LC50 on average and at a minimum, respectively, is required. First, the 3&#39;,5,7-trihydroxy-4&#39;,6-dimethoxyisoflavone is a type of flavonoid (more specifically, an isoflavone), which is considered to be generally safe and used in herbal pharmaceuticals; however, it still has estrogen-related concerns29 and may cause acute toxicity probably through oxidative phosphorylation uncoupling30. Next, the 1,4-benzenediol, called hydroquinone, is a phenolic compound that can trigger a non-specific and cytotoxic immune response in fish31. Finally, the 4-hexylphenol has been known to exhibit sufficient positive estrogenic activity to be classified as an ED32. It has been well-studied that the main reason of the acute toxicity of hydroquinone is the reduction-oxidation (redox) cycling. The hydroquinone is oxidized to benzoquinone and reduced back to semi-quinone or hydroquinone repeatedly, with depleting cofactors and generating reactive oxygen species33. The other two chemicals may require deeper investigations to reveal their mechanisms of action in acute ecotoxicity using molecular docking approaches such as that used by Panche et al.34, which cannot be covered by the QSAR Toolbox.
EDs interfere with the endocrine system mainly through physicochemical interactions with steroid receptors such as estrogen and androgen receptors, which are of considerable interest in QSAR modeling studies35. Considering this, the QSAR Toolbox is robust in terms of facile and rapid classification of ER binding affinities for a set of chemicals based only on the 2D descriptors of molecular structures. When this ER profiler system was applied to our list of EDs, no clear correlation was found between ER binding affinity and hydrophobicity (Supplementary Figure S2). This result may be explained by the fact that the formation of a steroid-receptor complex is not a direct consequence of a hydrophobic bonding contribution but should be accompanied by a conformational change in the active-site receptor structure36. The receptor binding can be also due to hydrogen-bonding and &#960;-stacking.
Additionally, the position of each chemical group on the molecule may affect the receptor binding, even if the hydrophobicity and number of hydrogen-bond acceptors-donors remain the same. Second, the ER profiler produced contrary trends between predicted and experimental LC50 mean levels with increasing ER binding affinity (Figure 5). This may be because the lethality of parents in an acute toxicity test are not due to ER binding but rather to narcosis in most cases, or to redox cycling in the case of hydroquinone. For example, more extensive analysis, including the chronic toxicity, is required for a larger set of EDs to define predictive limitations of the current version of the QSAR Toolbox.
This preliminary research may also have public health implications because steroids (androgens, estrogens, progestines, and corticoids) and their receptors exhibit similar or even identical macromolecular structures across vertebrates5. These types of analogous endocrine signaling systems may operate using a common mechanism in key events of EDs5. Nevertheless, additional and complementary methodologies are required to illuminate this vast and complex aspect [for example, by performing computational modeling of absorption, distribution, metabolism, and excretion (ADME), and/or adverse outcome pathway (AOP)]38. Furthermore, because most of the scientific and public concerns raised about the adverse effects of EDs are related to their chronic toxicities, improving the databases and algorithms in the QSAR Toolbox and producing reliable long-term ecotoxicology predictions for EDs are both necessary.
This paper demonstrates the application of QSAR Toolbox to compare ecotoxicological LC50 values for fish with log10Kow values of EDs. Throughout the protocol, it results in weak relationships between the two parameters, as it has been revealed by previous studies (e.g., Kim et al.39) that log10Kow is not a good direct predictor of aquatic LC50. In spite of this limitation, this protocol provides a general review or &#8220;vignette&#8221; to describe how to use the dashboard for a given purpose, since it is a valid application to use the QSAR Toolbox for investigating correlations between LC50 (or ER binding affinity) and log10Kow,&#160;or as a tool for rapid acute ecotoxicity screening.&#160;Nevertheless, it should be noted that (1) illuminating the link between estrogen receptor binding and chronic toxicity, rather than acute toxicity (lethality), is more relevant so that clearer correlations may be found, and (2) the androgen receptor, together with that of estrogen, also plays a critical role in reproductive toxicity. Therefore, it is required for the future version of the QSAR Toolbox to improve the prediction functions in light of those two points.

New developments in informatics and computational technology have empowered the biological sciences with quantitative methodologies that offer high precision and reliability1. In particular, algorithms used in molecular taxonomy and property classification have resulted in quantitative structure-activity relationship (QSAR) models2. These models automatically correlate the chemical structures and biological activities of a given chemical database and implement rapid in silico screening of a wide range of chemical substrates according to their medicinal or toxicological actions3. QSAR tools can produce predictive toxicity profiles as a function of feature vectors of molecular descriptors (i.e., physicochemical parameters) of chemicals of interest to numerically create categorical endpoints4. Usually, each quantitative endpoint is displayed as a 2D scatterplot vs. changes in descriptor values. A QSAR model is then generated using (multiple) linear regression analyses. Once a dataset has been fully exploited to construct a QSAR model (called the training set), then the model is statistically validated by predicting the endpoints of a group of chemicals not included in the training set (called the test set). The model can then be used to predict the biological activities of untested compounds3.
Among many harmful chemicals, endocrine disruptors (EDs) have been highlighted as a group of toxicants that may interfere in numerous hormone-related metabolisms in mammals, amphibians, and fish5,6. EDs are known to induce a variety of adverse effects, such as cancers and malformations, by blocking or altering normal hormonal pathways or activating abnormal hormone synthesis/degradation signals. As a consequence, these hormone-mimicking chemicals can perturb endocrine systems such that biological development and reproduction of wildlife animal populations are hampered. In particular, the ecotoxicological effects of EDs have been extensively investigated in aquatic vertebrates, which have nearly identical hormone receptor structures to those of mammals, including humans. Because all hormonal actions occur at low doses in vivo, predicting the potential toxicities of ED candidates using rapid in silico screening is critical to public and environmental health.
QSAR models based on the toxicology of EDs have been conducted utilizing both 2D and 3D descriptors (known as 2D and 3D QSAR, respectively), which reveal the ED ligand binding affinities of estrogen, androgen, and progesterone receptors7. Despite the high-precision advantages of 3D QSAR, in which conformational and electrostatic interactions are considered, 2D QSAR retains its own robustness in direct mathematical algorithms, rapid calculations, and extremely low computational loads. In addition, 2D-QSAR models are flexible for use in a wide range of applications while achieving relatively accurate prediction performance.
The OECD QSAR Toolbox is currently one of the most utilized computer software tools, providing freely available and pre-built QSAR models8,9. Its profiler uses 2D descriptor databases. Since the release of the first version in 2008, the software has been applied in the fields of chemical and biological industries, public health, and environmental safety for full or partial analysis of the potential risks of natural and synthetic compounds, with special interests in carcinogenesis10,11,12, mutagenicity13,14,15, and developmental toxicity16. The application to aquatic toxicology has also been demonstrated, with focus on bioaccumulation and biotransformation17.
The QSAR Toolbox has been proven useful in predicting the short-term toxicity of a broad range of chemicals17, as well as the estrogen receptor (ER) binding affinities of EDs18. However, the acute ecotoxicities of EDs in aquatic vertebrates has not been analyzed using the QSAR Toolbox. In this study, a typical and facile protocol is presented to perform QSAR modeling on the acute adverse effects of EDs with a focus in fish species. The study shows that the QSAR Toolbox is a highly accessible software for calculating and predicting the lethality/mortality of aquatic vertebrates for some representative EDs. Statistical treatment methods for the derived in silico datasets are presented. Figure 1 shows the overall scheme for the general operation of the QSAR Toolbox. The workflow shown in Figure 2 provides straightforward instructions on how to operate the in silico assay to predict acute ecotoxicity of target substances such as endocrine disrupting chemicals.

<table>
	<tbody>
		<tr>
			<td>Adobe Acrobat Reader DC</td>
			<td>Adobe Systems Software Ireland Limited</td>
			<td>NA</td>
			<td>Required to view prediction and category report</td>
		</tr>
		<tr>
			<td>Computer</td>
			<td>System: Microsoft Corporation</td>
			<td>NA</td>
			<td>Recommended system properties: (i) system type: 64 bit, Microsoft Windows 7 or newer, (ii) processor: I5 at 2.4 GHz or faster processor or equivalent AMD CPU, (iii) Installed memory (RAM): 6 GB of RAM, (iv) Hard Disk Drive (HDD): 20 GB free hard drive space</td>
		</tr>
		<tr>
			<td>Microsoft Editor</td>
			<td>Microsoft Corporation</td>
			<td>NA</td>
			<td>Required to upload a substance list of CAS numbers (batch mode) to the OECD QSAR Toolbox as .txt file (text file)</td>
		</tr>
		<tr>
			<td>Microsoft Excel 2016</td>
			<td>Microsoft Corporation</td>
			<td>NA</td>
			<td>Required to export data from OECD QSAR Toolbox as .cvs, .xls or .xlsx files</td>
		</tr>
		<tr>
			<td>OECD QSAR Toolbox version 4.0 or newer</td>
			<td>Organisation for Economic 
			Co-operation and Development</td>
			<td>NA</td>
			<td>Required to run OECD QSAR Toolbox Automated Workflows; free download: 
			https://qsartoolbox.org/download/</td>
		</tr>
		<tr>
			<td>OriginPro 9</td>
			<td>OriginLab Corporation</td>
			<td>NA</td>
			<td>Optional program for data analysis; similar tools possible</td>
		</tr>
	</tbody>
</table>

in silico modeling method for computational aquatic toxicology of endocrine disruptors: a software-based approach using qsar toolbox

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological systems. In particular, quantitative structure-activity relationship (QSAR) models have been increasingly applied to assess the environmental effects of a plethora of toxic materials. In recent years, some more highlighted types of toxicants are endocrine disruptors (EDs, which are chemicals that can interfere with any hormone-related metabolism). Because EDs may significantly affect animal development and reproduction, rapidly predicting the adverse effects of EDs using in silico techniques is required. This study presents an in silico method to generate prediction data on the effects of representative EDs in aquatic vertebrates, particularly fish species. The protocol describes an example utilizing the automated workflow of the QSAR Toolbox software developed by the Organization for Economic Co-operation and Development (OECD) to enable acute ecotoxicity predictions of EDs. As a result, the following are determined: (1) calculation of the numerical correlations between the concentration for 50% of lethality (LC50) and octanol-water partition coefficient (Kow), (2) output performances in which the LC50 values determined in experiments are compared to those generated by computations, and (3) the dependence of estrogen receptor binding affinity on the relationship between Kow and LC50.

Watch this Scientific Journal Video about In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox at JoVE.com

In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Quantitative structure-activity relationship (QSAR) modeling is a representative bioinformatics-assisted method in toxicological screening. This protocol demonstrates how to computationally assess the risks of endocrine disruptors (EDs) in aquatic environments. Utilizing the OECD QSAR Toolbox, the protocol implements an in silico assay for analyzing toxicity of EDs in fish.

Computational analyses of toxicological processes enables high-throughput screening of chemical substances and prediction of their endpoints in biological ...

in-silico-modeling-method-for-computational-aquatic-toxicology

Research

JoVE Journal

Bioengineering

13.7K Views.  KIST Europe Forschungsgesellschaft mbH. Quantitative structure-activity relationship (QSAR) modeling is a representative bioinformatics-assisted method in toxicological screening. This protocol demonstrates how to computationally assess the risks of endocrine disruptors (EDs) in aquatic environments. Utilizing the OECD QSAR Toolbox, the protocol implements an in silico assay for analyzing toxicity of EDs in fish.

Video: In Silico Modeling Method for Computational Aquatic Toxicology of Endocrine Disruptors: A Software-Based Approach Using QSAR Toolbox

Cellular Lipid Extraction for Targeted Stable Isotope Dilution Liquid Chromatography-Mass Spectrometry Analysis

The metabolism of fatty acids, such as arachidonic acid (AA) and linoleic acid (LA), results in the formation of oxidized bioactive lipids, including numerous stereoisomers1,2. These metabolites can be formed from free or esterified fatty acids. Many of these oxidized metabolites have biological activity and have been implicated in various diseases including cardiovascular and neurodegenerative diseases, asthma, and cancer3-7. Oxidized bioactive lipids can be formed enzymatically or by reactive oxygen species (ROS). Enzymes that metabolize fatty acids include cyclooxygenase (COX), lipoxygenase (LO), and cytochromes P450 (CYPs)1,8. Enzymatic metabolism results in enantioselective formation whereas ROS oxidation results in the racemic formation of products.
While this protocol focuses primarily on the analysis of AA- and some LA-derived bioactive metabolites; it could be easily applied to metabolites of other fatty acids. Bioactive lipids are extracted from cell lysate or media using liquid-liquid (l-l) extraction. At the beginning of the l-l extraction process, stable isotope internal standards are added to account for errors during sample preparation. Stable isotope dilution (SID) also accounts for any differences, such as ion suppression, that metabolites may experience during the mass spectrometry (MS) analysis9. After the extraction, derivatization with an electron capture (EC) reagent, pentafluorylbenzyl bromide (PFB) is employed to increase detection sensitivity10,11. Multiple reaction monitoring (MRM) is used to increase the selectivity of the MS analysis. Before MS analysis, lipids are separated using chiral normal phase high performance liquid chromatography (HPLC). The HPLC conditions are optimized to separate the enantiomers and various stereoisomers of the monitored lipids12. This specific LC-MS method monitors prostaglandins (PGs), isoprostanes (isoPs), hydroxyeicosatetraenoic acids (HETEs), hydroxyoctadecadienoic acids (HODEs), oxoeicosatetraenoic acids (oxoETEs) and oxooctadecadienoic acids (oxoODEs); however, the HPLC and MS parameters can be optimized to include any fatty acid metabolites13.
Most of the currently available bioanalytical methods do not take into account the separate quantification of enantiomers. This is extremely important when trying to deduce whether or not the metabolites were formed enzymatically or by ROS. Additionally, the ratios of the enantiomers may provide evidence for a specific enzymatic pathway of formation. The use of SID allows for accurate quantification of metabolites and accounts for any sample loss during preparation as well as the differences experienced during ionization. Using the PFB electron capture reagent increases the sensitivity of detection by two orders of magnitude over conventional APCI methods. Overall, this method, SID-LC-EC-atmospheric pressure chemical ionization APCI-MRM/MS, is one of the most sensitive, selective, and accurate methods of quantification for bioactive lipids.

This protocol will demonstrate the extraction and analysis of free and esterified bioactive fatty acids from cells. Fatty acids are accurately quantified using stable isotope dilution, chiral liquid chromatography, electron capture atmospheric chemical ionization multiple reaction monitoring mass spectrometry (SID-LC-ECAPCI-MRM/MS).

The metabolism of fatty acids, such as arachidonic acid (AA) and linoleic acid (LA), results in the formation of oxidized bioactive lipids, including ...

A Cre-Lox P Recombination Approach for the Detection of Cell Fusion In Vivo

The ability of two or more cells of the same type to fuse has been utilized in metazoans throughout evolution to form many complex organs, including skeletal muscle, bone and placenta. Contemporary studies demonstrate fusion of cells of the same type confers enhanced function. For example, when the trophoblast cells of the placenta fuse to form the syncytiotrophoblast, the syncytiotrophoblast is better able to transport nutrients and hormones across the maternal-fetal barrier than unfused trophoblasts1-4. More recent studies demonstrate fusion of cells of different types can direct cell fate. The "reversion" or modification of cell fate by fusion was once thought to be limited to cell culture systems. But the advent of stem cell transplantation led to the discovery by us and others that stem cells can fuse with somatic cells in vivo and that fusion facilitates stem cell differentiation5-7. Thus, cell fusion is a regulated process capable of promoting cell survival and differentiation and thus could be of central importance for development, repair of tissues and even the pathogenesis of disease.
Limiting the study of cell fusion, is lack of appropriate technology to 1) accurately identify fusion products and to 2) track fusion products over time. Here we present a novel approach to address both limitations via induction of bioluminescence upon fusion (Figure 1); bioluminescence can be detected with high sensitivity in vivo8-15. We utilize a construct encoding the firefly luciferase (Photinus pyralis) gene placed adjacent to a stop codon flanked by LoxP sequences. When cells expressing this gene fuse with cells expressing the Cre recombinase protein, the LoxP sites are cleaved and the stop signal is excised allowing transcription of luciferase. Because the signal is inducible, the incidence of false-positive signals is very low. Unlike existing methods which utilize the Cre/LoxP system16, 17, we have incorporated a "living" detection signal and thereby afford for the first time the opportunity to track the kinetics of cell fusion in vivo.
To demonstrate the approach, mice ubiquitously expressing Cre recombinase served as recipients of stem cells transfected with a construct to express luciferase downstream of a floxed stop codon. Stem cells were transplanted via intramyocardial injection and after transplantation intravital image analysis was conducted to track the presence of fusion products in the heart and surrounding tissues over time. This approach could be adapted to analyze cell fusion in any tissue type at any stage of development, disease or adult tissue repair.

A Cre-Lox P Recombination Approach for the Detection of Cell Fusion In Vivo

A method to track cell fusion in living organisms over time is described. The approach utilizes Cre-LoxP recombination to induce luciferase expression upon cell fusion. The luminescent signal generated can be detected in living organisms using biophotonic imaging systems with a sensitivity of detection of &tilde;1,000 cells in peripheral tissues.

The ability of two or more cells of the same type to fuse has been utilized in metazoans throughout evolution to form many complex organs, including ...

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 &deg;. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.

A methodology to estimate ventricular fiber orientations from in vivo images of patient heart geometries for personalized modeling is described. Validation of the methodology performed using normal and failing canine hearts demonstrate that that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.

Patient-specific  simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered  by the absence of in vivo imaging technology for ...

Characterization of Complex Systems Using the Design of Experiments Approach: Transient Protein Expression in Tobacco as a Case Study

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the additional advantage of short development and production times, but expression levels can vary significantly between batches thus giving rise to regulatory concerns in the context of good manufacturing practice. We used a design of experiments (DoE) approach to determine the impact of major factors such as regulatory elements in the expression construct, plant growth and development parameters, and the incubation conditions during expression, on the variability of expression between batches. We tested plants expressing a model anti-HIV monoclonal antibody (2G12) and a fluorescent marker protein (DsRed). We discuss the rationale for selecting certain properties of the model and identify its potential limitations. The general approach can easily be transferred to other problems because the principles of the model are broadly applicable: knowledge-based parameter selection, complexity reduction by splitting the initial problem into smaller modules, software-guided setup of optimal experiment combinations and step-wise design augmentation. Therefore, the methodology is not only useful for characterizing protein expression in plants but also for the investigation of other complex systems lacking a mechanistic description. The predictive equations describing the interconnectivity between parameters can be used to establish mechanistic models for other complex systems.

We describe a design of experiments approach that can be used to determine and model the influence of transgene regulatory elements, plant growth and development parameters, and incubation conditions on the transient expression of monoclonal antibodies and reporter proteins in plants.

Plants provide multiple benefits for the production of biopharmaceuticals including low costs, scalability, and safety. Transient expression offers the ...

Production, Characterization and Potential Uses of a 3D Tissue-engineered Human Esophageal Mucosal Model

The incidence of both esophageal adenocarcinoma and its precursor, Barrett&#8217;s Metaplasia, are rising rapidly in the western world. Furthermore esophageal adenocarcinoma generally has a poor prognosis, with little improvement in survival rates in recent years. These are difficult conditions to study and there has been a lack of suitable experimental platforms to investigate disorders of the esophageal mucosa.
A model of the human esophageal mucosa has been developed in the MacNeil laboratory which, unlike conventional 2D cell culture systems, recapitulates the cell-cell and cell-matrix interactions present in vivo and produces a mature, stratified epithelium similar to that of the normal human esophagus. Briefly, the model utilizes non-transformed normal primary human esophageal fibroblasts and epithelial cells grown within a porcine-derived acellular esophageal scaffold. Immunohistochemical characterization of this model by CK4, CK14, Ki67 and involucrin staining demonstrates appropriate recapitulation of the histology of the normal human esophageal mucosa.
This model provides a robust, biologically relevant experimental model of the human esophageal mucosa. It can easily be manipulated to investigate a number of research questions including the effectiveness of pharmacological agents and the impact of exposure to environmental factors such as alcohol, toxins, high temperature or gastro-esophageal refluxate components. The model also facilitates extended culture periods not achievable with conventional 2D cell culture, enabling, inter alia, the study of the impact of repeated exposure of a mature epithelium to the agent of interest for up to 20 days. Furthermore, a variety of cell lines, such as those derived from esophageal tumors or Barrett&#8217;s Metaplasia, can be incorporated into the model to investigate processes such as tumor invasion and drug responsiveness in a more biologically relevant environment.

This manuscript describes the production, characterization and potential uses of a tissue engineered 3D esophageal construct prepared from normal primary human esophageal fibroblast and squamous epithelial cells seeded within a de-cellularized porcine scaffold. The results demonstrate the formation of a mature stratified epithelium similar to the normal human esophagus.

The incidence of both esophageal adenocarcinoma and its precursor, Barrett&#8217;s Metaplasia, are rising rapidly in the western world. Furthermore ...

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific mutational background. The model is generated on a decellularized porcine scaffold that reproduces tissue-specific characteristics regarding extracellular matrix composition and architecture including the basement membrane. We standardized a protocol that allows artificial tumor tissue generation within 14 days including three days of drug treatment. Our article provides several detailed descriptions of 3D read-out screening techniques like the determination of the proliferation index Ki67 staining&#39;s, apoptosis from supernatants by M30-ELISA and assessment of epithelial to mesenchymal transition (EMT), which are helpful tools for evaluating the effectiveness of therapeutic compounds. We could show compared to 2D culture a reduction of proliferation in our 3D tumor model that is related to the clinical situation. Despite of this lower proliferation, the model predicted EGFR-targeted drug responses correctly according to the biomarker status as shown by comparison of the lung carcinoma cell lines HCC827 (EGFR -mutated, KRAS wild-type) and A549 (EGFR wild-type, KRAS-mutated) treated with the tyrosine-kinase inhibitor (TKI) gefitinib. To investigate drug responses of more advanced tumor cells, we induced EMT by long-term treatment with TGF-beta-1 as assessed by vimentin/pan-cytokeratin immunofluorescence staining. A flow-bioreactor was employed to adjust culture to physiological conditions, which improved tissue generation. Furthermore, we show the integration of drug responses upon gefitinib treatment or TGF-beta-1 stimulation - apoptosis, proliferation index and EMT - into a Boolean in silico model. Additionally, we explain how drug responses of tumor cells with a specific mutational background and counterstrategies against resistance can be predicted. We are confident that our 3D in vitro approach especially with its in silico expansion provides an additional value for preclinical drug testing in more realistic conditions than in 2D cell culture.

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

We present a three-dimensional (3D) lung cancer model based on a biological collagen scaffold to study sensitivity towards non-small-cell-lung-cancer-(NSCLC)-targeted therapies. We demonstrate different read-out techniques to determine the proliferation index, apoptosis and epithelial-mesenchymal transition (EMT) status. Collected data are integrated into an in silico model for prediction of drug sensitivity.

In the present study, we combined an in vitro 3D lung tumor model with an in silico model to optimize predictions of drug response based on a specific ...

Labeling of Blood Vessels in the Teleost Brain and Pituitary Using Cardiac Perfusion with a DiI-fixative

Blood vessels innervate all tissues in vertebrates, enabling their survival by providing the necessary nutrients, oxygen, and hormonal signals. It is one of the first organs to start functioning during development. Mechanisms of blood vessel formation have become a subject of high scientific and clinical interest. In adults however, it is difficult to visualize the vasculature in most living animals due to their localization deep within other tissues. Nevertheless, visualization of blood vessels remains important for several studies such as endocrinology and neurobiology. While several transgenic lines have been developed in zebrafish, with blood vessels directly visualized through expression of fluorescent proteins, no such tools exist for other teleost species. Using medaka (Oryzias latipes) as a model, the current protocol presents a quick and direct technique to label blood vessels in brain and pituitary by perfusing through the heart with fixative containing DiI. This protocol allows improvement of our understanding on how brain and pituitary cells interact with blood vasculature in whole tissue or thick tissue slices.

The article describes a quick protocol for labeling blood vessels in a teleost fish by cardiac perfusion of DiI diluted in fixative, using medaka (Oryzias latipes) as a model and focusing on brain and pituitary tissue.

Blood vessels innervate all tissues in vertebrates, enabling their survival by providing the necessary nutrients, oxygen, and hormonal signals. It is one ...

Simultaneous Quantification of Selected Kynurenines Analyzed by Liquid Chromatography-Mass Spectrometry in Medium Collected from Cancer Cell Cultures

The kynurenine pathway and the tryptophan catabolites called kynurenines have received increased attention for their involvement in immune regulation and cancer biology. An in vitro cell culture assay is often used to learn about the contribution of different tryptophan catabolites in a disease mechanism and for testing therapeutic strategies. Cell culture medium that is rich in secreted metabolites and signaling molecules reflects the status of tryptophan metabolism and other cellular events. New protocols for the reliable quantification of multiple kynurenines in the complex cell culture medium are desired to allow for a reliable and quick analysis of multiple samples. This can be accomplished with liquid chromatography coupled with mass spectrometry. This powerful technique is employed in many clinical and research laboratories for the quantification of metabolites and can be used for measuring kynurenines.
Presented here is the use of liquid chromatography coupled with single quadrupole mass spectrometry (LC-SQ) for the simultaneous determination of four kynurenines, i.e., kynurenine, 3-hydroxykynurenine, 3-hydroxyanthranilic, and xanthurenic acid in the medium collected from in vitro cultured cancer cells. SQ detector is simple to use and less expensive compared to other mass spectrometers. In the SQ-MS analysis, multiple ions from the sample are generated and separated according to their specific mass-to-charge ratio (m/z), followed by the detection using a Single Ion Monitoring (SIM) mode.
This paper draws the attention on the advantages of the reported method and indicates some weak points. It is focused on critical elements of LC-SQ analysis including sample preparation along with chromatography and mass spectrometry analysis. The quality control, method calibration conditions and matrix effect issues are also discussed. We described a simple application of 3-nitrotyrosine as one analog standard for all target analytes. As confirmed by experiments with human ovary and breast cancer cells, the proposed LC-SQ method&#160;generates reliable results and can be further applied to other in vitro cellular models.

Described here is a protocol for the determination of four different tryptophan metabolites generated in the kynurenine pathway (kynurenine, 3-hydroxykynurenine, xanthurenic acid, 3-hydroxyanthranilic acid) in the medium collected from cancer cell cultures analyzed by liquid chromatography coupled with a single quadrupole mass spectrometry.

The kynurenine pathway and the tryptophan catabolites called kynurenines have received increased attention for their involvement in immune regulation and ...

A MRI-Based Toolbox for Neurosurgical Planning in Nonhuman Primates

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using data extracted from magnetic resonance imaging (MRI). This protocol allows for the generation of 3D printed anatomically accurate physical models of the brain and skull, as well as an agarose gel model of the brain modeling some of the mechanical properties of the brain. These models can be extracted from MRI using brain extraction software for the model of the brain, and custom code for the model of the skull. The preparation protocol takes advantage of state-of-the-art 3D printing technology to make interfacing brains, skulls, and molds for gel brain models. The skull and brain models can be used to visualize brain tissue inside the skull with the addition of a craniotomy in the custom code, allowing for better preparation for surgeries directly involving the brain. The applications of these methods are designed for surgeries involved in neurological stimulation and recording as well as injection, but the versatility of the system allows for future expansion of the protocol, extraction techniques, and models to a wider scope of surgeries.

The method outlined below aims to provide a comprehensive protocol for the preparation of nonhuman primate (NHP) neurosurgery using a novel combination of three-dimensional (3D) printing methods and MRI data extraction.

In this paper, we outline a method for surgical preparation that allows for the practical planning of a variety of neurosurgeries in NHPs solely using ...

A Micropatterning Assay for Measuring Cell Chirality

Chirality is an intrinsic cellular property, which depicts the asymmetry in terms of polarization along the left-right axis of the cell. As this unique property attracts increasing attention due to its important roles in both development and disease, a standardized quantification method for characterizing cell chirality would advance research and potential applications. In this protocol, we describe a multicellular chirality characterization assay that utilizes micropatterned arrays of cells. Cellular micropatterns are fabricated on titanium/gold-coated glass slides via microcontact printing. After seeding on the geometrically defined (e.g., ring-shaped), protein-coated islands, cells directionally migrate and form a biased alignment toward either the clockwise or the counterclockwise direction, which can be automatically analyzed and quantified by a custom-written MATLAB program. Here we describe in detail the fabrication of micropatterned substrates, cell seeding, image collection, and data analysis and show representative results obtained using the NIH/3T3 cells. This protocol has previously been validated in multiple published studies and is an efficient and reliable tool for studying cell chirality in vitro.

We present a protocol for determining multicellular chirality in vitro, using the micropatterning technique. This assay allows for automatic quantification of the left-right biases of various types of cells and can be used for screening purposes.

Chirality is an intrinsic cellular property, which depicts the asymmetry in terms of polarization along the left-right axis of the cell. As this unique ...