Neurobehavioural analysis of mouse phenotypes requires the monitoring of mouse behaviour over long periods of time. In this study, we describe a trainable computer vision system enabling the automated analysis of complex mouse behaviours. We provide software and an extensive manually annotated video database used for training and testing the system. Our system performs on par with human scoring, as measured from ground-truth manual annotations of thousands of clips of freely behaving mice. As a validation of the system, we characterized the home-cage behaviours of two standard inbred and two non-standard mouse strains. From these data, we were able to predict in a blind test the strain identity of individual animals with high accuracy. Our video-based software will complement existing sensor-based automated approaches and enable an adaptable, comprehensive, high-throughput, fi ne-grained, automated analysis of mouse behaviour. A utomated quantitative analysis of mouse behaviour will have a signifi cant role in comprehensive phenotypic analyses -both on the small scale of detailed characterization of individual gene mutants and on the large scale of assigning gene function across the entire mouse genome 1 . One key benefi t of automating behavioural analysis arises from inherent limitations of human assessment, namely, cost, time and reproducibility. Although automation in and of itself is not a panacea for neurobehavioural experiments 2 , it allows for addressing an entirely new set of questions about mouse behaviour and to conduct experiments on time scales that are orders of magnitude larger than those traditionally assayed. For example, reported tests of grooming behaviour span time scales of minutes 3,4 , whereas an automated analysis will allow for analysis of this behaviour over hours or even days and weeks.Indeed, the signifi cance of alterations in home-cage behaviour has recently gained attention as an eff ective means of detecting perturbations in neural circuit function -both in the context of disease detection and more generally to measure food consumption and activity parameters 5 -10 . Previous automated systems (see refs 8, 9, 11, 12 and Supplementary Note ) rely mostly on the use of simple detectors such as infrared beams to monitor behaviour. Th ese sensor-based approaches tend to be limited in the complexity of the behaviour that they can measure, even in the case of costly commercial systems using transponder technologies 13 . Although such systems can be used eff ectively to monitor locomotor activity and perform operant conditioning, they cannot be used to study homecage behaviours such as grooming, hanging, jumping and smaller movements (termed ' micromovements ' below). Visual analysis is a potentially powerful complement to these sensor-based approaches for the recognition of such fi ne animal behaviours.Advances in computer vision and machine learning over the last decade have yielded robust computer vision systems for the recognition of objects 14,15 and human actions (see Moeslund et ...
A quantitative linear model accurately (R 2 ؍ 0.88) describes the thermostabilities of 54 characterized members of a family of fungal cellobiohydrolase class II (CBH II) cellulase chimeras made by SCHEMA recombination of three fungal enzymes, demonstrating that the contributions of SCHEMA sequence blocks to stability are predominantly additive. Thirty-one of 31 predicted thermostable CBH II chimeras have thermal inactivation temperatures higher than the most thermostable parent CBH II, from Humicola insolens, and the model predicts that hundreds more CBH II chimeras share this superior thermostability. Eight of eight thermostable chimeras assayed hydrolyze the solid cellulosic substrate Avicel at temperatures at least 5°C above the most stable parent, and seven of these showed superior activity in 16-h Avicel hydrolysis assays. The sequence-stability model identified a single block of sequence that adds 8.5°C to chimera thermostability. Mutating individual residues in this block identified the C313S substitution as responsible for the entire thermostabilizing effect. Introducing this mutation into the two recombination parent CBH IIs not featuring it (Hypocrea jecorina and H. insolens) decreased inactivation, increased maximum Avicel hydrolysis temperature, and improved long time hydrolysis performance. This mutation also stabilized and improved Avicel hydrolysis by Phanerochaete chrysosporium CBH II, which is only 55-56% identical to recombination parent CBH IIs. Furthermore, the C313S mutation increased total H. jecorina CBH II activity secreted by the Saccharomyces cerevisiae expression host more than 10-fold. Our results show that SCHEMA structure-guided recombination enables quantitative prediction of cellulase chimera thermostability and efficient identification of stabilizing mutations.SCHEMA is a computational approach to identifying blocks of sequence that minimize structural disruption when they are recombined in chimeric proteins (1). SCHEMA recombination of eight blocks from three fungal cellobiohydrolase class II (CBH II) 2 genes was used in our previous work to create a library of 3 8 ϭ 6,561 chimeric sequences, all having the native Hypocrea jecorina cellulose binding module and linker and observed to feature a degree of glycosylation similar to that found in native CBH IIs secreted by fungi (2). Synthesis and characterization of selected CBH II chimeras expressed in Saccharomyces cerevisiae revealed enzymes with thermostabilities and cellulose hydrolysis performance superior to those of the parent enzymes from Humicola insolens, H. jecorina, and Chaetomium thermophilum.Our prior analysis showed that a qualitative model based on sequence-stability data from 23 functional chimeras (categorizing blocks as destabilizing, stabilizing, or neutral) could identify highly stable chimeras in the SCHEMA library (2). When studying SCHEMA recombination of a bacterial cytochrome P450, we previously estimated that building a quantitative regression model would require stability measurements for at least 35 re...
We describe an efficient SCHEMA recombination-based approach for screening homologous enzymes to identify stabilizing amino acid sequence blocks. This approach has been used to generate active, thermostable cellobiohydrolase class I (CBH I) enzymes from the 390 625 possible chimeras that can be made by swapping eight blocks from five fungal homologs. Constructing and characterizing the parent enzymes and just 32 'monomeras' containing a single block from a homologous enzyme allowed stability contributions to be assigned to 36 of the 40 blocks from which the CBH I chimeras can be assembled. Sixteen of 16 predicted thermostable chimeras, with an average of 37 mutations relative to the closest parent, are more thermostable than the most stable parent CBH I, from the thermophilic fungus Talaromyces emersonii. Whereas none of the parent CBH Is were active >65°C, stable CBH I chimeras hydrolyzed solid cellulose at 70°C. In addition to providing a collection of diverse, thermostable CBH Is that can complement previously described stable CBH II chimeras (Heinzelman et al., Proc. Natl Acad. Sci. USA 2009;106:5610-5615) in formulating application-specific cellulase mixtures, the results show the utility of SCHEMA recombination for screening large swaths of natural enzyme sequence space for desirable amino acid blocks.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.