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Behavioral fingerprints predict insecticide and anthelmintic mode of action

2021; Springer Nature; Volume: 17; Issue: 5 Linguagem: Inglês

10.15252/msb.202110267

1744-4292

Adam McDermott-Rouse, Eleni Minga, Ida Barlow, Luigi Feriani, Philippa H Harlow, Anthony J Flemming, André EX Brown,

Animal testing and alternatives

Article25 May 2021Open Access Transparent process Behavioral fingerprints predict insecticide and anthelmintic mode of action Adam McDermott-Rouse MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UKThese authors contributed equally to this work Search for more papers by this author Eleni Minga MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UKThese authors contributed equally to this work Search for more papers by this author Ida Barlow orcid.org/0000-0003-1046-9606 MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Luigi Feriani MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Philippa H Harlow Syngenta, Jealott's Hill International Research Centre, Bracknell, UK Search for more papers by this author Anthony J Flemming Syngenta, Jealott's Hill International Research Centre, Bracknell, UK Search for more papers by this author André E X Brown Corresponding Author [email protected] orcid.org/0000-0002-1324-8764 MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Adam McDermott-Rouse MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UKThese authors contributed equally to this work Search for more papers by this author Eleni Minga MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UKThese authors contributed equally to this work Search for more papers by this author Ida Barlow orcid.org/0000-0003-1046-9606 MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Luigi Feriani MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Philippa H Harlow Syngenta, Jealott's Hill International Research Centre, Bracknell, UK Search for more papers by this author Anthony J Flemming Syngenta, Jealott's Hill International Research Centre, Bracknell, UK Search for more papers by this author André E X Brown Corresponding Author [email protected] orcid.org/0000-0002-1324-8764 MRC London Institute of Medical Sciences, London, UK Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK Search for more papers by this author Author Information Adam McDermott-Rouse1,2, Eleni Minga1,2, Ida Barlow1,2, Luigi Feriani1,2, Philippa H Harlow3, Anthony J Flemming3 and André E X Brown *,1,2 1MRC London Institute of Medical Sciences, London, UK 2Faculty of Medicine, Institute of Clinical Sciences, Imperial College London, London, UK 3Syngenta, Jealott's Hill International Research Centre, Bracknell, UK *Corresponding author. Tel: +44 020 8383 8218; E-mail: [email protected] Mol Syst Biol (2021)17:e10267https://doi.org/10.15252/msb.202110267 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Novel invertebrate-killing compounds are required in agriculture and medicine to overcome resistance to existing treatments. Because insecticides and anthelmintics are discovered in phenotypic screens, a crucial step in the discovery process is determining the mode of action of hits. Visible whole-organism symptoms are combined with molecular and physiological data to determine mode of action. However, manual symptomology is laborious and requires symptoms that are strong enough to see by eye. Here, we use high-throughput imaging and quantitative phenotyping to measure Caenorhabditis elegans behavioral responses to compounds and train a classifier that predicts mode of action with an accuracy of 88% for a set of ten common modes of action. We also classify compounds within each mode of action to discover substructure that is not captured in broad mode-of-action labels. High-throughput imaging and automated phenotyping could therefore accelerate mode-of-action discovery in invertebrate-targeting compound development and help to refine mode-of-action categories. Synopsis A combination of imaging and machine learning is used to predict compound mode of action using the unique behavioural responses of the roundworm C. elegans to different pesticides and anthelmintics. Insecticides affect phenotypes in multiple behavioural dimensions. Compounds with the same mode of action have similar effects on behaviour. Combining classifiers by voting enables mode of action prediction. The approach allows mode of action deconvolution within classes. Introduction Invertebrate pests including insects, mites, and nematodes damage crops, decrease livestock productivity, and cause disease in humans. Nematodes alone infect over 1 billion people and lead to the loss of 5 million disability-adjusted life-years annually (Pullan et al, 2014). In livestock, they infect sheep, goats, cattle, and horses causing gastroenteritis that leads to diarrhea, reduced growth, and weight loss. Nematodes that parasitize crops have been estimated to cause well over $100 billion in annual crop losses (Elling, 2013). Crop loss due to insects is measured in tens of metric megatons and is predicted to increase due to climate change (Deutsch et al, 2018). Compounds that kill or impair invertebrates are one of the primary means of defense in human and veterinary medicine and in crop protection. However, resistance is widespread in nematodes and insects and drives continuing efforts to discover new invertebrate-targeting compounds (Sparks & Nauen, 2015; Nixon et al, 2020). To date, most currently approved treatments for infections in humans and livestock and for crop protection in the field have been discovered through phenotypic screens (Geary et al, 2015; Wing, 2020). That is, compounds are first screened for the ability to kill or impair a target species without any hypothesized molecular target. A critical problem is then determining hit compounds’ mode of action, which is important for understanding resistance mechanisms, avoiding pathways where resistance is already common, and subsequent lead optimization. Despite advances in biochemical and genetic methods for determining mode of action, direct observation of the symptoms induced by compounds remains a key step in mode-of-action discovery (Wing, 2020). Because most insecticides and anthelmintics target the neuromuscular system, behavioral symptoms are a particularly important class of phenotypes to consider, but manual observation of behavior is time-consuming, insensitive to subtle phenotypes, and prone to inter-operator variability and bias (Garcia et al, 2010). We therefore sought to develop more automated and quantitative methods to do mode-of-action prediction from phenotypic screens of freely behaving invertebrates. Pioneering work in zebrafish showed that behavioral fingerprints can be used to discover neuroactive compounds and that behavioral fingerprints correlate with compound mode of action (Kokel et al, 2010; Rihel et al, 2010; Laggner et al, 2011). However, this approach has not yet been applied to invertebrate animals—the targets of insecticides and anthelmintics—at a large scale. Furthermore, although previous zebrafish screens were high throughput, their spatial resolution was low and phenotypes were limited to activity levels in response to stimuli. Recent work in computational ethology has shown the power of moving beyond point representations of animal behavior to include information on posture (Anderson & Perona, 2014; Egnor & Branson, 2016; Berman, 2018; Brown & de Bivort, 2018). From previous symptomology work, it is clear that detailed postural information can be useful for resolving mode of action (Sluder et al, 2012; Salgado, 2017). We chose C. elegans as our model system because it is small and compatible with multiwell plates and automated liquid handling. It is sensitive to anthelmintics and insecticides and has played an important role in mode-of-action discovery in the past (Brenner, 1974; Sluder et al, 2012; Buckingham et al, 2014; Burns et al, 2015; Hahnel et al, 2020). To combine the benefits of high throughput and high resolution, we used megapixel camera arrays to record the behavioral responses of worms to a library of 110 compounds covering 22 distinct modes of action. We simultaneously recorded all of the wells of 96-well plates with sufficient resolution to extract the pose of each animal and a high-dimensional behavioral fingerprint that captures aspects of posture, motion, and path. We show that worms have diverse dose-dependent behavioral responses to insecticides and anthelmintics and develop a machine learning approach that shares information across replicates and doses to accurately predict the mode of action of previously unseen test compounds. Furthermore, we show that a novelty detection algorithm can provide an indication that a compound belongs to a mode of action not seen in the training set. This novelty score can be used as a measure of confidence in the class prediction, suggesting a way to prioritize compounds with potentially novel modes of action early in the development process. These results demonstrate that high-throughput phenotyping in C. elegans is a promising approach for assisting target deconvolution in anthelmintic and insecticide discovery. Finally, we show that our prediction accuracy might be limited by uncertainties in the class definition rather than noise or phenotypic dimensionality. Specifically, we show that we can classify compounds even within a mode-of-action class, suggesting that there are limitations in our knowledge of the relevant pharmacology rather than limitations in our ability to reproducibly detect compound-induced phenotypes. Results Insecticides affect phenotypes in multiple behavioral dimensions We assembled a library of 110 insecticides and anthelmintics with diverse targets to sample a range of modes of action used medically and commercially (see Dataset EV1 for full list). The modes of action represented in the library cover 70% of the market of insecticides used in the field (Sparks & Nauen, 2015) and several important classes of anthelmintics used in veterinary and human medicine (Nixon et al, 2020). To quantify the effects of the compounds on behavior, we recorded worms using megapixel camera arrays that simultaneously image all of the wells of 96-well plates (Fig 1A). We recorded at least 10 replicates at three doses for each compound with enough resolution to extract high-dimensional behavioral fingerprints following segmentation, pose estimation, and tracking (Fig 1A). The behavioral fingerprints are vectors of posture and motion features that are subdivided by body segment and motion state including “midbody curvature during forward crawling” or “angular velocity of the head with respect to the tail while the worm is paused”. We have previously shown that similar features can detect even subtle behavioral differences that can be difficult to detect by eye (Yemini et al, 2013) and that the combined feature set has sufficient dimensionality to accurately classify worms with diverse behavioral differences caused by genetic variation and optogenetic perturbation (Javer et al, 2018a, 2018b). Figure 1. Insecticides affect phenotypes in multiple behavioral dimensions We image an entire 96-well plate with a megapixel camera array with enough resolution and high enough frame rate to track, segment, and estimate the posture of C. elegans over time. All compound doses in the speed/tail curvature space, with points and lines showing the mean and standard deviation of biological dose replicates. On average, 12 biological replicates were collected per compound dose, together with 601 DMSO replicates, across at least 3 different tracking days for each condition. Several compounds, including the serotonin receptor antagonist mianserin (blue), glutamate-gated chloride channel activator emamectin benzoate (purple), and vesicular acetylcholine transporter inhibitor SY1713 (red), have a strong effect on the worms' behavioral phenotype. They can be distinguished from the DMSO control (black) and from each other based on speed and tail curvature alone. Not all compounds are well separated in these two dimensions (gray points). Inset images are samples that show postural differences. Sample worm skeletons over time show the effect of the compounds highlighted in (B) on motion. Number of features significantly different from the DMSO control at a false discovery rate of 1% for each compound, grouped by mode of action. The pre-stimulus, blue light stimulus, and post-stimulus data are shown separately (a total of 3,020 features are tested for each assay period). The percentage of significantly different features is highest for the blue light stimulus recording. Download figure Download PowerPoint As expected, several compound classes have strong visible effects on C. elegans behavior including the glutamate-gated chloride channel activator emamectin benzoate, the spiroindoline vesicular acetylcholine transporter inhibitor SY1713, and the serotonin receptor antagonist mianserin. All three compounds at specific doses can be distinguished from DMSO controls and from each other in a simple two-dimensional space defined by speed and body curvature (Fig 1B). The large differences in curvature and in motion caused by some compounds are observable by eye, as shown in the inset images and in Fig 1C. However, not all compounds are well separated in these two dimensions; the gray points in Fig 1B show the dose means and standard deviation of all the compound doses in the speed/tail curvature space, which largely overlap. Some of the screened compounds might not have detectable effects on C. elegans and therefore cannot be used for phenotypic mode-of-action prediction. To find the compounds with no effect, we compared the behavioral fingerprints of treated worms with DMSO controls using univariate statistical tests for each feature and correcting for multiple comparisons with the Benjamini–Yekutieli procedure (Benjamini & Yekutieli, 2001). To account for random day-to-day variation in the experiments, we used a linear mixed model for these statistical tests, where the fixed effect is the drug dose and the day of the experiment is added as a random effect. The number of features that are significantly different at a false discovery rate of 1% between the behavior of worms treated with each compound and the DMSO controls is summarized in a heat map (Fig 1D). To further increase the dimensionality of the behavioral phenotypes, we included a blue light stimulation protocol. Each tracking experiment is divided into three parts: (i) a 5 min pre-stimulus recording, (ii) a 6-min stimulus recording with three 10-s blue light pulses starting at 60, 160, and 260 s, and (iii) a 5 min post-stimulus recording. Blue light is aversive to C. elegans (Edwards et al, 2008) and so it can help to distinguish between animals that are simply pausing and those that are not able to move (Churgin et al, 2017). Behavioral differences are observed in each assay period, but the stimulus period shows the most differences (Fig 1D). Even within mode-of-action classes, compound potency can be highly variable. The largest potency difference is observed for the octopamine agonists where amidine affects 0.08% of features and oxazoline affects 75% of features. Overall, 86% of compounds have a detectable effect on behavior in at least one feature. The 17 compounds that showed no detectable effect in any stimulus period were not included in subsequent analysis. Compounds with the same mode of action have similar effects on behavior Having established that C. elegans shows diverse behavioral responses to insecticides and anthelmintics, we next sought to determine to what extent the responses are mode-of-action-specific. For the initial clustering, we used 256 features from each blue light condition. These features were selected for their usefulness in classifying mutant worms in a previous paper (Javer et al, 2018b). For all clustering and classification tasks, we first z-normalize each feature to put them on a common scale and to prevent arbitrary choices of units from impacting the analysis. We used hierarchical clustering to visualize the relationships between the behavioral responses to different compounds at different doses (Fig 2A). Each row of the heat map is the average of all of the replicates of a given compound at a specific dose. We also included the averages of six subsets of the DMSO replicates randomly partitioned across tracking days as control points. Several of the compound classes show clear clustering, including the AChE inhibitors, vAchT inhibitors, GluCl agonists, and mAchR agonists. The DMSO averages also cluster closely together. The degree of mode-of-action clustering is greater than expected by chance, which can be seen in a plot of the cluster purity observed in the data compared with random clustering (Fig 2B). However, the distance between compounds that share the same mode of action can be large, even for classes that cluster well overall, in part because behavioral fingerprints change with dose. It is also not always possible to align feature vectors using doses because compounds can have very different potencies: A low dose for one compound could be a high dose for another. Furthermore, the compound concentration inside the worm is likely to be much lower than the concentration in the media because of C. elegans’ considerable xenobiotic defenses (Hartman et al, 2021) and the degree of uptake will also vary across compounds even within a mode-of-action class. For this reason, the center-top part of the heat map in Fig 2A is populated with low doses and compounds that either have a low potency or low uptake in the worm, which do not form distinct clusters based on their mode of action, but are rather clustered around the DMSO averages. Figure 2. Clustering and dose response of behavioral fingerprints Hierarchical clustering of behavioral fingerprints highlights structure in the responses to different compounds. Each row of the heat map represents the mean dose fingerprint of a specific compound described by 256 pre-selected features from each blue light condition. Clear clusters can be observed for some compound classes, e.g., AChE inhibitors, vAchT inhibitors, GluCl agonists, and mAchR agonists. Low doses and low potency compounds from different classes cluster together around the DMSO averages at the center-top part of the heat map. Cluster purity as a function of the hierarchical cluster distance shows that the degree of mode-of-action clustering (red) is greater than expected by chance for random clusters (gray). Compounds in the same class can have different dose–response curves. (upper) The three mitochondrial inhibitors cyazofamid, rotenone, and SY1048 all decrease angular velocity, but the concentration at which their effect is measurable is not conserved across compounds. (lower) Different spiroindolines affect body curvature differently. SY1786's dose–response curve is non-monotonic. The central band and box limits show the median and quartiles of the distribution of the biological replicates for each compound dose (on average 12 wells per dose and 601 DMSO wells), while the whiskers extend to 1.5 IQRs beyond the lower and upper quartile. The P-values reported in the legend represent the significance of the drug dose effect and were estimated using linear mixed models with tracking day as random effect and drug dose as fixed effect. The positions of these compounds in the heat map in (B) are marked using the color bar on the right side of the heat map. Download figure Download PowerPoint These effects can be seen in dose–response plots for individual features. The three mitochondrial inhibitors in Fig 2C all decrease angular velocity, but they do it at different doses. At 3 μM, only SY1048 has a strong effect, while at 30 μM, rotenone has a similarly strong effect. Clustering based on angular velocity would lead to qualitatively different conclusions about nearest neighbors at these different doses. For the spiroindolines, similar differences in dose–response are observed for body curvature with the added difference that the effect of SY1786 is non-monotonic and returns to baseline at high doses. These non-monotonic effects can be due to compounds precipitating from solution at high doses or due to intrinsically complex compound effects such as a compound that causes an increase in speed at low doses but is lethal at high doses. Regardless of the cause, complex dose–response curves present challenges for mode-of-action prediction since supervised machine learning algorithms rely on differences in feature distributions to learn decision boundaries and dose–response effects spread out the distributions and increase the overlap between classes. Combining classifiers by voting enables mode-of-action prediction The behavioral fingerprints of compounds with the same mode of action have the same direction in the phenotypic space and can be used for classification in mode-of-action classes. For the classification task, we need a minimum number of compounds per class to get an accurate representation of the class distribution. Out of the compounds with detectable effects in C. elegans, we choose only the classes with at least five compounds (10 classes with 76 compounds). We take advantage of the fact that several replicates are recorded per condition and resample with replacement from the multiple replicates for each dose to create a set of average behavioral fingerprints. This effectively smooths the data reducing the effect of outliers. At the same time, it provides a simple method for balancing classes before classifier training. For classes with fewer compounds, we resample more times so that each class contains the same number of points (see Fig EV1). To partially mitigate the effect of compound potency, we then normalize each behavioral fingerprint to unit magnitude. This normalization is done row-wise on each sample in contrast to the z-normalization described above which is done column-wise on each feature. Rescaling in this way brings compounds with similar effect profiles but different potencies closer together in feature space (Figs 3A and EV2), but because of nonlinearities in the dose–response profiles, the overlap is not perfect even after rescaling. Click here to expand this figure. Figure EV1. The smoothing and balancing procedure using bootstrapped averages reduces the effect of outliers and balances the classes increasing classification accuracy One of the sparsely populated classes (GluCl agonists with 5 compounds) and the most well-populated class (AChE inhibitors with 10 compounds) are shown before and after the smoothing and balancing procedure in the 3 top PCA components. Download figure Download PowerPoint Figure 3. Classifiers trained on behavioral fingerprints can predict the mode of action of unseen test compounds Toy data illustrating the potential benefit of normalization in correcting for potency differences within mode-of-action classes. Following normalization, each behavioral fingerprint exists on a hypersphere in the phenotype space regardless of effect size in the original space. Nonlinear dose–response curves will not collapse perfectly following normalization, which is a linear transformation. The confusion matrix obtained through cross-validation for the best performing feature set (1,024 features) and logistic regression classifier following feature selection and hyperparameter tuning on the training data. The confusion matrix for the classifier trained in (B) applied to previously unseen test compounds without any further tuning. The novelty score assigned to novel test compounds with a mode of action not seen during training compared to the novelty score of compounds from the test set in (C). Novel compounds tend to have higher novelty scores than compounds from previously seen modes of action. The non-novel compounds with high novelty scores include the two incorrectly classified test compounds (in red box). Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Illustration of the effect of normalization with real data The normalization of samples to unit L2 norm brings compounds with different potencies closer together and helps separate the classes in the phenotypic space. PCA of the data from 2 different classes with 3 compounds each before normalization (data simply standardized). PCA of the normalized data. Download figure Download PowerPoint Predictions must be combined across doses and replicates to make a single prediction for the mode of action of a given compound. Inspired by an analogy with the multi-sensor fusion problem (Singh et al, 2019), we use a voting procedure to make a final prediction. However, in contrast to multi-sensor fusion, we cannot train different classifiers for each dose because 1 μM for one compound is not equivalent to 1 μM for another compound. Instead, we train a single classifier for all doses and make predictions for each data point. Each data point contributes a vote for a compound’s class and the class with the most votes wins. We split our data into a training/tuning set consisting of 60 compounds and a hold-out/reporting dataset consisting of 16 compounds containing at least one compound for each mode-of-action class. For mode-of-action prediction, we started with the full set of features output by Tierpsy (3,020 per blue light condition for 9,060 in total) and used the training set to determine an appropriate classifier, select features, and tune hyperparameters using cross-validation. We achieved the highest cross-validation accuracy with 1,024 features selected using recursive feature elimination with a logistic regression estimator. The hyperparameters of the classifier were also tuned using cross-validation, and the best version of the estimator was multinomial logistic regression with l2 regularization and penalty parameter C = 10. Using a regularized linear classifier helps to control overfitting in this high-dimensional feature space, which boosts the cross-validation accuracy. The confusion matrix from cross-validation using the best performing feature set and classifier is shown in Fig 3. To determine whether the classifier could generalize to unseen compounds, we applied it to the test data without further tuning. The classifier predicted the correct mode of action for the unseen compounds 88% of the time (Fig 3C). We generated a null model by partitioning the DMSO data randomly across tracking days to 10 classes. Following the same steps as for the compound-treated data, we obtained a maximum cross-validation accuracy of 10% using the training set, while the prediction accuracy in the test set was 12.5%. In addition to the 10 modes of action that were represented by at least five compounds in our dataset, we had 17 compounds with a detectable effect on C. elegans that belonged to 11 sparsely populated mode-of-action classes. We used these additional compounds to simulate another use case for our approach: detecting screening hits that represent potentially novel modes of action that do not fall into known classes. We use the term “novel test set” to describe these compounds, since their modes of actions are unknown (novel) to the trained classifier. Using a novelty detection algorithm (preprint: Vinokurov & Weinshall, 2016) with some modifications, we assigned a novelty score to each of the test and novel test compounds based on their affinity to each of the existing classes. To obtain the novelty score, we use an ensemble of support vector machine (SVM) classifiers that flag novel compounds based on the confidence values of the main multinomial logistic regression classifier used for the predictions of known classes. The ensemble of SVM classifiers is trained using partitions of the training set into presumed-known and presumed-unknown classes. The novelty score is defined as the weighted average of the output of this ensemble. Most of the novel compounds were assigned novelty scores above 0.8 (Fig 3D). Several of the non-novel compounds—those that come from a class that is present in the training data—have high novelty scores, but this includes the two test compounds that were incorrectly classified. In this case, the high novelty score correctly indicates low confidence in the prediction of the classifier. To explore the origin of the high novelty score for the incorrectly classified compounds, we looked for differences between the effects of compounds within a class. Mode-of-action deconvolution within classes Although compounds are categorized into broad mode-of-action classes, most compounds will have some degree of off-target enga