Cross-domain text sentiment analysis is a text sentiment classification task that uses the existing source domain annotation data to assist the target domain, which can not only reduce the workload of new domain data annotation, but also significantly improve the utilization of source domain annotation resources. In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset, and accuracy and F1 value are used as evaluation indicators. Compared with other classical algorithms, the experimental results show that the proposed algorithm has better performance. With the vigorous development of social media such as online comments, Weibo, WeChat, and forum communities, a large amount of subjective text data with emotion is presented on the Internet . EmotionSentiment analysis is a very domain-dependent task. The sentiment characteristics of data in different domains are not exactly the same. The sentiment prediction model trained on data in a certain domain cannot usually be used directly in other domains . In the Transfer learning uses labeled training samples in the source domain to build a reliable model to predict unlabeled samples in the target domain with different data distributions . A largeRobert et al.  proposedIn the cross-domain sentiment classification task of text-based social media, it is necessary to deeply understand the mechanism of language expression and the mechanism of emotion transfer. It is very difficult to build large-scale, high-quality labeled datasets; however, deep transfer learning can significantly reduce the demand for labeled data in the target domain. Therefore, deep transfer learning is widely used in cross-domain sentiment classification tasks, and has achieved good results.Zhang et al.  proposedHowever, although the existing work has achieved great success by introducing transfer learning or domain adaptation mechanism to solve cross-domain text sentiment classification tasks, the research on many important issues is not perfect and in-depth, and there are still many theoretical and technical problems need to be explored –20. For In order to effectively achieve the performance of cross-domain text sentiment classification, this paper proposes a BERT-based aspect-level sentiment analysis algorithm for cross-domain text to achieve fine-grained sentiment analysis of cross-domain text. First, the algorithm uses the BERT structure to extract sentence-level and aspect-level representation vectors, extracts local features through an improved convolutional neural network, and combines aspect-level corpus and sentence-level corpus to form a sequence sentence pair. Then, the algorithm uses domain adversarial neural network to make the feature representation extracted from different domains as indistinguishable as possible, that is, the features extracted from the source domain and the target domain have more similarity. Finally, by training the sentiment classifier on the source domain dataset with sentiment labels, it is expected that the classifier can achieve a good sentiment classification effect in both source and target domain, and achieve sentence-level and aspect-level sentiment classification. At the same time, the error pooled values of the sentiment classifier and the domain adversary are passed backwards to realize the update and optimization of the model parameters, thereby training a model with cross-domain analysis capability. Experiments are carried out on the Amazon product review dataset; accuracy and F1 value are used as evaluation indicators. Compared with some existing classical algorithms, the results show that our proposed algorithm has better performance.The organization of this paper is as follows. In BERT is modeled through a self-attention mechanism, which can directly obtain the global information of the text. Since it has no forgetting gate mechanism, the information of all words is preserved, so BERT can better express the complete semantic information of the sentence, and can also directly find the correlation features between words from the global word features.Vt represents the output vector of the Transformer and Xa represents the input vector.The BERT model is composed of multiple transformer layers and uses a multihead attention mechanism. After the input vector is multilayered linearly transformed to obtain different linear values, it is then input to the attention module to calculate the attention weight. The output value of the attention mechanism is combined with the previous linear change again, and the final output of the multihead attention mechanism can be obtained. For any vector input to Transformer, it is processed and output, Trans(.) represents all operations in Transformer, as shown in the following formula:Vb represents the output value of BERT and Xb represents the input vector.BERT is formed by stacking multiple Transformers together, and Bert(.) represents the calculation process in Bert, as shown in the following formula:Aspect-level sentiment analysis is a fine-grained sentiment classification task in sentiment analysis, whose purpose is to identify the sentiment polarity expressed by a sentence on an aspect. There are usually two methods for aspect-level sentiment analysis: (1) Traditional machine learning methods are used, relying on artificially constructed features and rules, but such methods are very time-consuming and labor-intensive. (2) The deep learning method is used to introduce the neural network into the research field of sentiment classification, which can automatically select the features without manual intervention, greatly alleviate the model's dependence on feature engineering, and enable the model to achieve better performance at a lower cost. For example, literature  uses preResearch shows that the above methods only encode text semantic information using word embedding technology, ignoring syntactic structure information and word frequency information, which play an important role in preserving structural information and help shorten the distance between aspect words and opinion words –30. If tTransfer Learning (TL) is a technique that allows fine-tuning of existing model algorithms to apply to new domains or functions . In tranDs={, ysi}i=1Ns, target domain dataset Dt={}j=1Nt, where x represents a sentence, a represents an aspect word in sentence x, ys represents the sentiment label corresponding to aspect word a, Ns represents the amount of data with sentiment labels in the source domain, and Nt represents the amount of data without sentiment labels in the target domain. Cross-domain tasks need to learn a sentiment classifier on Ds to achieve sentiment polarity classification for Dt.Cross-domain text sentiment analysis refers to using only sentiment-labeled data in the source domain to train a sentiment classifier and use it for sentiment classification on the target domain data. Given a source domain dataset Based on the ideas of BERT model, convolution model, and adversarial model, the model structure proposed in this paper is shown in In order to improve the performance of the CNN in During model training, the data in the source domain is extracted into the sentiment classifier after feature extraction, while the data in the target domain is combined with the features extracted in the source domain after extraction, and then used as the input of the domain adversarial classifier. The error pooled values of the sentiment classifier and the domain adversarial classifier are back-passed to enable the updating and optimization of model parameters, thereby training a model with cross-domain analysis capabilities.BERT is modeled through a self-attention mechanism, which can directly obtain the global information of the text. BERT can better express the complete semantic information of the sentence, and can also directly find the correlation features between words from the global word features.In general, BERT is applied to sentence-level sentiment classification, which is defined as a single-sentence classification task. However, in aspect-level sentiment analysis, the same sentence can express different views on different aspects, and express different sentiments. Traditional sentence-level sentiment classification is limited. For example, in the evaluation text, “This restaurant is so delicious, but the attitude of the waiter is too cold”, the emotional polarity for “taste” is positive, and the emotional polarity for “service” is negative, while the emotional polarity for the “environment” aspect is neutral.To address this issue, this paper considers aspect-level sentiment classification as a sentence pair classification task. In text representation, the special token “[CLS]” (classification) is placed at the beginning of the sequence, and the special token [SEP] is placed in front of the sentence. A sequence sentence pair is formed by the combination of aspect-level corpus and sentence-level corpus, which is vectorized as the input value of BERT. The basic idea is as follows:w1, w2,…, wN} and a sentence is represented as {w1`, w2`,…, wN`}. The input sequence is combined using aspects and sentences, and the special token “[CLS]”is placed at the beginning of the sequence, and the special token [SEP] is placed in front of the sentence, forming a sequence of sentence pairs. The expression method is shown in the following formula:First, suppose that an aspect is represented as {Then, the input sequence is encoded with BERT, and the output vector corresponding to “[CLS]” is represented as an aspect-level sentence. The use of BERT is shown in the following formula:Finally, the input of aspect-level sentence representation is performed by a classifier consisting of convolutional layers and Softmax layers for sentiment classification.R of the convolution, then the results are merged together to form the final feature after max pooling. The use of convolution is shown in the following formula:After obtaining the output value of BERT, the text convolution structure is used to convolve the output value of BERT, which can not only extract better local text features, but also reduce the dimension of shared emotional features. For each BERT output vector T[CLS] input to the convolutional neural network, the modified CNN is used for convolution processing. During the convolution process, convolution kernels of different sizes are selected to obtain the output value If only the domain classifier exists, there is no guarantee that the information extracted by the feature extraction module is valid. In order to ensure that the information extracted by the feature extraction module can be used for classification, it is also necessary to rely on a category classifier, which is a sentiment classifier in the current task. The classification accuracy is ensured by supervised training using the sentiment-labeled data in the source domain.The sentiment classifier is only for the source domain dataset, and the text representation of the source domain is the value obtained after convolution pooling, which is input to Softmax for predicting sentiment classification. The classification is performed as shown in the following formula:Domain confrontation is to generalize the feature properties of the source domain to the target domain, so that the classifier cannot distinguish whether the feature is from the source domain or the target domain, so as to realize the confusion of domain features. In this paper, after the features are extracted from the source domain and the target domain through the feature extractor, while training the source domain sentiment classifier, the features extracted from different domains are input together into the domain classifier for domain classification. Logistic regression is used to build a domain classifier as a domain adversarial structure, and the domain classifier cannot distinguish whether the features are from the source domain or the target domain, so as to achieve the effect of domain confrontation and make the extracted shared sentiment features similar. Assuming that the feature vector of the source domain text after passing through BERT-CNN is HS, and the feature vector of the target domain text passing through BERT-CNN is HT, the two are combined according to formula . Then thDuring the training process of the overall model, the two loss functions are merged together to form the final objective function of the model. One loss function is the training objective function of sentiment classifier, used for sentiment classification; one loss function is the objective function used for domain adversarial training, which is used to achieve domain adaptation. The labels of the sentiment classifier can only come from the source domain, while the label information of the domain confrontation is a mixture of the source domain and target domain labels. All parameters are updated through the back-propagation algorithm. The loss function is shown in the following formula:L represents the loss function, d represents the number of domains, Ldomain represents the loss function of the sentiment classifier, β is used to control the magnitude of the error provided by the domain adversarial, and Ldomain represents the objective function of domain adversarial training.Here, The public data set is the Amazon product review dataset provided by Li et al. , which cSince the existing sentiment analysis corpus cannot fully meet the needs of this research, we manually annotated the selected Amazon product review dataset to create a data set suitable for cross-domain aspect-level sentiment analysis tasks. The specific method is to analyze the aspect information and sentiment information on the basis of the sentence-level sentiment analysis public data set, extract the aspect words, and mark the sentiment expressed in the sentence for the aspect. In order to avoid the problems of insufficient training data, different distributions or imbalanced data categories affecting the performance of the model, the corpus created in this research has been manually screened, and the amount of data in each domain and the number of positive and negative labels are basically balanced.In the experiment, one data set in five different fields is used as the source domain data set, and the other four datasets are used as the target domain data set. The data is divided into training set and test set. The source domain and target domain are trained with 2000 positive texts and 2000 negative texts, respectively. All 6000 pieces of target domain data are used for target domain sentiment polarity prediction during testing.In this experiment, Bert-base is set as the basic model, and the learning rate is set as 2e-5, which will be used to fine tune and emotion classification process. The development environment is Python 3.6 and tensorflow 1.12.0.In the experiment of this paper, the accuracy rate Acc and F1 value are used as evaluation indicators. The accuracy rate represents the ratio of the number of samples correctly classified by the classifier to the total number of samples for a given test data set. The F1 value is a concept proposed on the basis of Precision and Recall to evaluate Precision and Recall as a whole. The F1 value is the harmonic mean of precision and recall.yi is the actual label of the data sample, and N is the size of the test set.The calculation of the accuracy rate Acc is shown in the following formula:True Positive (TP): It is judged to be a positive sample, and in fact it is a positive sample.True Negative (TN): It is judged to be a negative sample and in fact it is a negative sample.False Negative (FN): It is judged to be a negative sample, but in fact it is a positive sample.False Positive (FP): It is judged to be a positive sample, but in fact it is a negative sample.The evaluation index of data is generally based on the confusion matrix shown in Precision represents the proportion of true cases among the predicted positive cases . The calculation method is shown in the following formula:The recall rate represents the proportion of true examples in all actual positive examples . The calculation method is shown in the following formula:The F1 value is represented by the harmonic average of the precision rate and the recall rate, which is a comprehensive reflection of the precision rate and the recall rate. The calculation method is shown in the following formula:Ablation study refers to understanding the effect of a component on the entire system by studying the performance of an AI system after removing a component. Ablation study requires the system to exhibit graceful degradation: even if a component is lost or weakened, the system can continue to operate while maintaining functionality.To examine the superiority of aspect-level cross-domain sentiment analysis methods, we conduct two types of ablation experiments on our method. Among them, Experiment 1 explores the advantages of aspect-level sentiment analysis results preprocessed by BERT compared to sentence-level sentiment analysis without BERT preprocessing; Experiment 2 explores the advantages of aspect-level sentiment analysis with gated activation unit compared to sentence-level sentiment analysis without gated activation unit.The results of Experiment 1 are shown in The results of Experiment 2 are shown in   SCL-ML : The met  ITIAD : The met  CGRU : This meIn the experimental dataset, the method proposed in this paper is compared with the experimental results of the following methods.It shows that the model proposed in this paper can better extract the features of text compared with these classic methods. This is because: (1) the model in this paper uses Bert for preprocessing, which can better express the complete semantic information of sentences, and also directly find the correlation features between words from the global word features. (2) The model in this paper improves CNN by adding a gated activation unit, which can improve the weight of emotional words closely related to aspect information and help to improve the accuracy.On the one hand, it verifies the feasibility of fine-grained cross-domain sentiment analysis, and on the other hand, it also verifies the advanced nature of the algorithm in this paper. The problem that it is difficult to obtain good classification results due to less labeled data in the target domain is improved, and the model can perform well in many fields.In this paper, a BERT-based aspect-level sentiment analysis algorithm for cross-domain text is proposed to achieve fine-grained sentiment analysis of cross-domain text. The BERT structure is used to extract sentence-level and aspect-level representation vectors, an improved convolutional neural network is used to extract local features, and domain adversarial neural network is used to make the feature representation extracted from different domains as indistinguishable as possible. The experimental results show that the proposed algorithm has good performance. In many current application scenarios, the pretrained model contains a lot of knowledge, and it is an interesting direction to build an emotional knowledge graph through the pretrained model. Most of the current work focuses on how to build a knowledge graph model, but few researchers focus on building a knowledge graph for sentiment analysis tasks. The following work will focus on the research of sentiment analysis based on knowledge graph.


Ferroic domain dynamics, as a function of external stimuli, can be collectively described as scale-invariant avalanches characterised by a critical exponent that are sensitive to the complexity of the domain microstructure. The understanding and manipulation of these avalanches lies at the heart of developing novel applications such as neuromorphic computing. Here we combine in situ heating optical observations and mean-field analysis to investigate the collective domain behaviour in pure-ferroelastic lanthanum aluminate (LaAlO Ferroelastic switching is characterised by the movement of domain walls in small discrete jerks that emit long ranging elastic fields, which are likely to destabilise surrounding structures and trigger larger switching events known as avalanches, collectively constituting the typical ferroelastic stress - strain hysteresis6.Domain switching has been at the forefront of fundamental and applied research, as it mediates many of the functional properties in advanced applications8; based on the jerky switching in materials with ferroelastic components11, which exhibits similar avalanche dynamics in neuronal activity12.Particularly, the understanding of the physical mechanisms responsible for domain switching, in response to externally applied fields, is critical to the development of novel devices such as neuromorphic computing20, which predicts the power-law distribution of jerks as a scale-free process of the form 21, whereby g(x) describes the probability (g) of a jump (x) and the energy exponent 22. 23  and are independent of the microscopic properties of the system24 allowing for these systems to be characterised into different universal classes. This line of research has been used to determine universality amongst ferroelectrics16 and has demonstrated that the orientation of an applied field will influence the statistical behaviour of the system25.Mean-field theory has successfully characterised the collective movements of domains during switching events26, while studies in smart-memory alloys27 have highlighted the effect of geometry on the material’s microstructure. Moreover, changes in the resultant domain patterning as a function of the aspect ratio have been shown in freestanding BaTiO29. However, systematic studies of ferroelectric and ferroelastic domain dynamics as a function of the aspect ratio are still lacking. Theoretical models for ferromagnets have elucidated how changing boundary conditions can influence the resultant domain configuration in samples with a particular focus on thickness effects30, but these models in part neglect switching behaviours.As mentioned previously, characterising and controlling the response of domains  to external fields has been the main focus within the fields of domain dynamics and domain engineering; however, the system’s geometry is another aspect to device design which is currently poorly addressed when studying ferroelectric and ferroelastic domain dynamics. Indeed, previous reports have suggested that the domain microstructure can be altered due to the system’s elastic anisotropy33.Further to this, studies examining the dynamic behaviour in relation to critical exponent values, and its potential for mixing, as a function of macroscopic parameters, are currently less prominent despite the importance of these factors being highlighted in recent literature35 of the sample, inducing thermal stresses36 which can arise from surface tensions and dead layers29, that effectively manifest as boundary conditions that drive the domain microstructure. Such stresses originate in the anisotropic nature of the crystal structure itself 37 which shows pronounced elastic anisotropy38 and are known to be influenced by temperature40.In this study, in situ heating and optical microscopy are utilised in conjunction with mean-field statistical analysis to characterise the motion of ferroelastic domains  in LaAlOWe determine that stress anisotropy, induced by the sample’ aspect ratio (length over width), not only affects the final domain microstructure but also induces distinct domain dynamics with characteristic critical exponent values. Moreover, we report on the mixing of the critical exponent between accepted mean-field values. Thus, demonstrating that the sample aspect ratio can be implemented as a simple and effective means to induce a change in domain dynamics.In their pristine  state, the two primary sample sets exhibited domains exclusively along the 010]0\documen42. Areas of mixed domain structures refer to the population of the two main domain variants , several spatiotemporal maps showing the avalanche activity as a projection along the x and y axes, as a function of temperature were produced.The x axis corresponds to 010]}pc Fig. a and c. 0}pc Fig.}pc Fig. a. It is In the high aspect ratio sample, the activity increases from the centre of the field of view (long rectangular side) and from the short sides of the field of view  up to a large rate peak  in the high aspect ratio sample was further analysed. In the range of energies  and thus a change in characteristic behaviour occurs. These results were shown to be repeatable and have been reproduced in other sample sets of analogous aspect ratios taken from the same bulk LaAlOAlthough such exponent mixing is widely present in literatureThe aspect ratio range, at which the change occurs between dynamical behaviour and subsequent critical fixed point values, can also be inferred from the regions identified in Fig. 15, which have the ability to de-stabilise other domain walls, precipitating more jerks and thus creating avalanches. As these elastic interactions are non-local, avalanches demonstrate a logarithmic shape-dependency classically26 and can be characterised by a critical exponent value Ferroelastic domain walls propagate as jerks that emit strain fieldsIn this work we use in situ heating combined with in-plane optical microscopy to induce thermal stresses, as the driving force behind domain wall motion, and provide experimental evidence to demonstrate that tuning the aspect ratio of pure-ferroelastic LaAlOMoreover, we report on the distinct domain dynamics between the samples: the lower aspect ratio sample displayed a microstructure that resembled its RT pattern at high temperatures, while the high aspect ratio sample demonstrated a global reconfiguration of the microstructure that was premediated by a large cascading domain wall front, which develops into a sparser and less complex configuration that slowly relaxed into its RT patterning.54, notwithstanding other studies on LaAlO56, validating a universality amongst such materials, although similar dynamical observations appear lacking in ferroelectric systems. This study, to our knowledge, is the first to directly address the correlation between the sample’s aspect ratio, domain structure and critical exponent mixing. We suggest more studies on ferroic systems could benefit from utilising a similar approach to identify such behaviours, which may otherwise be overlooked57, and is critical to further our understanding of ferroic dynamical behaviour and development of future smart devices.Importantly, these in situ optical observations were combined with a statistical analysis by mean-field theory to calculate the critical exponent values and characterise the behaviour observed in each aspect ratio. This exhibited an additional anisotropy in domain activity along the long x-axis of the sample. In the low aspect ratio sample, the field integrated critical value of 60, as well as being translucent and chemically stable, making it an ideal choice for optical studies3.LaAlO15 samples were tested in total, ranging from an aspect ratio of All samples were cut from the same bulk LaAlO61.The optical set up utilised bright-field, transmission imaging with a 10x objective lens  in the light path of the optical set-up  was carried out to analyse changes in the chemical composition at the surface of the sample (22 on the in situ videos based on the difference between consecutive frames (The domain dynamics were investigated using acquired in situ videos of roughly the same area in each HC, with a field of view mentclass2pt{minim22.In order to estimate the power-law exponent (64, in which a series of measured values (E65;This can then be fitted by the maximum likelihood methodSupplementary Information.Supplementary Video 1.Supplementary Video 2.Supplementary Video 3.Supplementary Video 4.Supplementary Video 5.Supplementary Video 6.Supplementary Video 7.Supplementary Video 8.Supplementary Video 9.


Pyruvate carboxylase (PC) is a tetrameric enzyme that contains two active sites per subunit that catalyze two consecutive reactions. A mobile domain with an attached prosthetic biotin links both reactions, an initial biotin carboxylation and the subsequent carboxyl transfer to pyruvate substrate to produce oxaloacetate. Reaction sites are at long distance, and there are several co-factors that play as allosteric regulators. Here, using cryoEM we explore the structure of active PC tetramers focusing on active sites and on the conformational space of the oligomers. The results capture the mobile domain at both active sites and expose catalytic steps of both reactions at high resolution, allowing the identification of substrates and products. The analysis of catalytically active PC tetramers reveals the role of certain motions during enzyme functioning, and the structural changes in the presence of additional cofactors expose the mechanism for allosteric regulation. Pyruvate Carboxylase is a multifunctional enzyme that follows a multi-pathway reaction. Here, the authors, using cryoEM and classification techniques, reveal several catalytic states at reaction sites, the interplay between them, and their relationship with motions in the tetrameric organization. The improvements in the hardware of microscopes and the software used for data analysis have resulted in more detailed maps that frequently arrive to the atomic-level resolution. Cryo-EM maps may also serve to identify multiple conformational states of complexes and their subunits3. The structural heterogeneity in particles can be discrete or continuous and can be separated by 3D classification. The different classes may account for low energy states in thermodynamic equilibrium and describe the functional space accessible to the protein4. This functional space is a limited part of the conformational space which can be modulated with the presence of allosteric regulators. The question is how far cryoEM can go in deciphering the functional structures of oligomeric complexes involved in multi-pathway reactions. This work explores by cryo-EM the functional space for such type of oligomers for the case of a biotin-dependent carboxylase.The number of structures of biomolecules determined by single particle cryo-electron microscopy (cryo-EM) is increasing exponentially in recent years6. Depending on the identity of carboxyl acceptor the enzyme can be pyruvate carboxylase (PC), propionyl-CoA carboxylase (PCC), acetyl-CoA carboxylase (ACC) and methylcrotonyl-CoA carboxylase (MCC). These enzymes are crucial in metabolic processes such as the fatty acid synthesis, gluconeogenesis and amino acid catabolism. In addition, there are two biotin-carboxylases only found in bacteria, geranyl-CoA carboxylase (GCC) and urea carboxylase (UC). Many of the UCs can also play as guanidine carboxylases7. Biotin-dependent carboxylases are usually active in homo- or hetero-oligomeric forms and perform their activity in two sequential steps at two different active sites8. In order to complete the reaction, the biotin co-factor must access the biotin carboxylase (BC) domain where it becomes carboxylated using MgATP and bicarbonate9. The carboxylated biotin is then translocated to the carboxyl transferase (CT) domain where the carboxyl group is transferred to the specific substrate. The biotin prosthetic group is attached to the biotin-carboxyl carrier protein (BCCP) domain that couples both reactions by traveling long distances between catalytic centers. Most of the published crystal structures show BCCP domains far from the catalytic centers or missing due to their flexibility10. The interaction of BCCP with CT domain has been described in PCC11, ACC12 and PC14. However, the interaction between BCCP and BC was only described in the mutant T882A from Rhizobium etli PC (RePC) and contains biotin in a position far from the active site15. The only structure of biotin in the catalytic position of BC corresponds to the Biotin Carboxylase component from the ACC complex and was obtained by co-crystallization of the protein with free biotin16. Thus, a catalytically relevant structure for the interaction between the mobile BCCP and the BC domain is still missing.Biotin-dependent carboxylases are metabolic enzymes that catalyze the transfer of carboxyl groups to different substrates17 and fuels several anabolic reactions as gluconeogenesis, liponeogenesis, insulin secretion and synthesis of glutamate neurotransmitter18. In recent years, the central role of PC as a metabolic hub has been associated with reprograming and flexibility of cancer cells metabolism19.Pyruvate carboxylase (PC) is a biotin-dependent, mitochondrial protein that catalyzes the carboxylation of pyruvate into oxaloacetate. This metabolite is essential in the tricarboxylic acid cycle20. This domain links the other three functional ones and has been named as PC tetramerization domain (PT) due to its contribution to the oligomerization of the enzyme, or allosteric domain (AL), as it is essential to bind the allosteric activator acetyl-CoA22. PC is mostly found as tetramers of four identical subunits, with monomers of ~120–130 kDa in size18. This enzyme possesses a tetrahedral geometry with the tetramer organized in two layers23. In each layer, the monomer pairs run antiparallel to one another and perpendicular between the two faces. The monomer pairs of each face have minimal contacts with each other, and the tetramer is stabilized at each corner through BC-BC and CT-CT homodimer interactions. The BCCP domain is translocated from the BC of its own monomer to the CT domain of the opposite subunit in the same layer24. This pathway seems to be favored by the allosteric regulator acetyl-CoA, but the movement of each BCCP domain can follow different pathways, reaching the four active sites in its own layer25. Kinetic analysis of PC has suggested that both reactions occurring in the active sites of the BC and CT domains are well coordinated23. However, the communication mechanism to trigger the translocation of the BCCP domain between active sites in biotin-dependent carboxylases is not yet resolved.In addition to BC, CT and BCCP, an extra domain was found in crystallographic structures of PC Fig. 14,20. ThLactococcus lactis PC (LlPC) during catalysis in the presence of positive allosteric regulator acetyl-CoA. Using cryo-EM and unsupervised classification techniques, we determine conformations related with PC enzymatic activity. In addition, we analyze a LlPC sample containing the inhibitor cyclic di-3′,5′-adenosine monophosphate (c-di-AMP). Comparison of the different structures reveals insights into the pathway of the reaction and the mechanisms of the opposite allosteric regulations.In this work, we study the structure of We obtained a cryo-EM map of LlPC with acetyl-CoA under reaction conditions with an estimated global resolution of 2.12 Å Fig. . This re26, and in the cryoEM map appears as a disordered density. The absence of a strong signal for the BBCP region prompted us to perform a classification with a soft mask around the BC domain of the particles aligned in the framework of the full tetramer  with a closed B-lid . The B-subdomain is known to be very flexible and oscillates between closed and open states as a molecular lid over the ATP binding sitetin Fig.  in a pose16 Fig.  and apar 15 Fig. .Fig. 2Stopen; two classes with the B-subdomain closed, BCclosed; and six classes showing the BCCP domain oriented towards the BC active site, BCBCCP.The rest of the segregated classes displayed large tilt movements of the BC domain itself. For this reason, the entire population was re-aligned with a refinement focused on this domain. A new classification with a soft mask around the BC domain of the re-aligned set of particles produced several classes with better defined movements of the BC region  Fig. . This rereact is more closed. The helix 827-840 closes approximately 16.2° with respect to the CTempty class. A density for BCCP bound to the mobile region of the funnel subdomain is observed. This binding is stabilized by several interactions between the BCCP β-strand 1105–1111 and the loop 897–900 of the funnel subdomain  where the BC domains of one layer move towards the protein center and the ones at the other layer move outwards . The third vector showed a mix of motions but was very similar to the twist observed in the second one. The fourth vector produces an asymmetric CTtilt where the two CT domains in the same layer move in opposite directions  but contains biotin in a position far from the active site15 33. The position of BCCP and the arrangement of the active site is similar in the three models as the interaction of BCCP with the funnel domain is very stable. CTreact contains a density between the molecule of pyruvate and biotin. This density is probably due to a combination of the carboxyl group bound to biotin or bound to pyruvate.In the same sample we have also found BCCP in the active site of CT domain, in a conformation very similar to the observed for human CT (HsPC)react state the domain funnel closes upon interaction with the inserted BCCP. In this pose there is a large interacting surface between BCCP and CT regions (approximately twice the surface of interaction between BCCP and BC), and the position of the biotin at the tip of BCCP is directed toward the catalytic center. The closing of the CT funnel starts with the binding of pyruvate substrate and reaches its maximum when BCCP enters the site. It is known that the binding of pyruvate induces the formation of a binding pocket for carboxybiotin34, and our results suggest that the presence of pyruvate also triggers the initial closure of the CT funnel and facilitates the interaction with the BCCP domain carrying the carboxybiotin. Thus, the substrate-induced changes play at local (binding pocket for carboxybiotin) and domain (closure of the CT region) levels to facilitate the recruitment of the BCCP-carboxybiotin complex.In the CTThus, at both BC and CT active sites, the recruitment of the BCCP domain and its prosthetic biotin is regulated by changes in the active sites driven by the binding of substrates and cofactors and, presumably, by the different affinities that biotin or carboxybiotin display for the reaction centers. Questions remain as to whether conformational changes in the tetramer drive the movement of the BCCP domain to couple the BC and CT active sites, and the exact mechanism of the allosteric regulation.We have studied the global movements of LlPC using multibody refinement. Our results indicate that PC tetramers display a combination of different motions Fig.  and thatreact and CTreact in the same subunit . This is in disagreement with the postulated negative cooperativity for biotin carboxylase dimers in the so-called half-of-sites reactivity mode, where BC subunits communicate to alternate in their catalytic states39. This model is based on some observations of the alternate mode of action of the biotin carboxylase dimers of the ACC multienzyme complex38. In our data with tetrameric LlPC, a clear negative correlation is observed between the BC domains of the subunits bound by their CT regions for the BCreact state 31, and in the low-resolution cryoEM structure for SaPC23. In the asymmetric configuration, the position of the PT domain is significantly different when subunits from different layers are compared and there is a rotation of about 60–70° between BC and CT regions. In the cryoEM work of SaPC engaged in catalytic activity23, although at low resolution, the asymmetric tetramer was understood as a catalytically relevant state. Our current results for LlPC do not show any asymmetric architecture and most of the relevant stages of the enzymatic activity are in display. The asymmetric quaternary structure has been described in PC enzymes with low intrinsic activity that require the activation by acetyl-coA , and in tetrameric organizations without the activator31 or with only two molecules of acetyl-CoA (or ethyl-CoA) bound to each tetramer20. One possibility is that the asymmetric PC structure appears in tetramers that do not contain the four allosteric activator molecules that are needed to be fully competent. To clarify whether the asymmetric state has any enzymatic relevance, future studies are required.In all the cryoEM maps for LlPC analyzed in this work, the subunits have a symmetrical organization without major differences in their overall architecture. This is relevant since an asymmetric configuration has been described in other PC tetrameric structures, such as in the crystallographic structures for RePCOur results of the effect of the allosteric regulators in the structure of PC suggest that there is a relationship between the activity of PC and the angle between BC and CT domains of the subunits within the tetrameric arrangement. The effect of this angle in PC activity could be mediated by the different accessibility of BCCP to the CT domain as observed in multibody refinement analysis. We do not have access to the analysis of the motions that PC displays while bound to different allosteric modulators, but the clear correlation between the enzymatic activity and the angle that is shown in the hinge between the BC and CT domains Fig.  suggestsIn summary, we have resolved several catalytic states of LlPC with the allosteric activator acetyl-CoA and the motions that LlPC tetramers undergo during catalysis. By crossing these analyses, we conclude that the BCCP domain with the flexible linker can move between active sites driven by the affinity of carboxylated or non-carboxylated biotin and the readiness of the catalytic regions to accept it. The movement of the BCCP domain is not tightly coupled to the conformational changes of the tetramer, but the motions of the oligomer can facilitate its access to the active sites, and allosteric regulators modulate the landscape of these motions, that is, they narrow the conformational space which in turn conditions the functional space of the enzyme Table E. coli and purified following protocols described earlier30. The catalytic activity of LlPC was determined at 10 °C by coupling the production of oxaloacetate to the oxidation of NADH by malate dehydrogenase40. The decreasing concentration of NADH was monitored by measuring the absorbance at 340 nm. The reaction mixture contained 20 mM Tris-HCl, (pH 7.6), 200 mM NaCl, 50 mM sodium bicarbonate, 20 mM pyruvate, 5 mM MgCl2, 2 mM DTT, 2 mM ATP, 350 μM NADH, 15 U of malate dehydrogenase and 0.15 μM of LlPC. Some samples included 2 mM acetyl-CoA and/or 100 μM of c-di-AMP.LlPC was expressed in 2, 2 mM DTT and 2 mM acetyl-CoA at 37 °C for 2 min. Then, a solution of pyruvate in the same buffer was added and incubated on ice. Finally, a reaction buffer containing bicarbonate and ATP was added to the LlPC sample. After mixing, the sample was immediately applied to a Quantifoil grid R2/1 300 Mesh with carbon, incubated for 30 s and vitrified using a Vitrobot (FEI) set to 4 °C and 100% humidity. The final concentrations were 0.1 μM LlPC, 2 mM DTT, 2 mM acetyl-CoA, 20 mM pyruvate, 2 mM ATP, 10 mM KHCO3, 5 mM MgCl2, 200 mM NaCl, 20 mM Tris pH 7.6.LlPC was incubated in a solution containing 20 mM Tris-HCl (pH 7.6), 200 mM NaCl, 5 mM MgClA second sample also containing 100 μM c-di-AMP was vitrified onto a Quantifoil grid R2/1 200 Mesh with carbon in the same conditions.2.Automated data acquisition was carried out at the eBIC facilities  with a Titan Krios electron microscope (FEI) at 300 kV and a K3 direct detector (GATAN) operating in counting mode and using EPU software. In total 10,518 movies were collected each containing 40 frames over a 3.99 s exposure at 81,000X magnification, and yielding a pixel size of 1.06 Å. The total exposure was 48 electrons/Å41 . Each particle of the data set was quadruplicated, and its Euler angles were modified so every asymmetric unit was aligned45. The information on the rotation to which each quadruplicated particle was subjected was stored. A final refinement using local searches yielded a resolution of 2.12 Å with a total of 1,369,092 particles. Local resolution was calculated with RELION.An initial 3D refinement resulted in a resolution of 2.89 Å. This reconstruction was used to initiate several rounds of CTF refinement and Bayesian polishing46 and ranging from 30 to 60 Å2.An unsupervised 3D classification in RELION was performed using a soft mask around the BC domain. In addition, focused refinements of CT and BC domains were performed, followed by 3D classifications using soft masks around the respective domains without particle alignment. Every BC and CT class was refined using local searches. The volumes were B-factor sharpened with values of B-factor automatically calculated in RELIONParticle sets for each class were compared to each other to obtain correlations. The information of rotation of each quadruplicated particle provided additional information on which monomer correlates. Deviations of these correlations from the percentage of particles in each class allow the identification of co-occurrence of classes in different subunits of the protein.47. Two bodies comprised two contacting BC domains from different layers and the corresponding allosteric domains. The other two bodies were composed of two contacting CT domains. The correlations of particles from different classes along these eigenvectors were also analyzed.The quadruplicated data were also refined using multibody refinement in RELION 3.1 using four rigid bodies2. Movie frames were aligned using MotionCor 2 and applying the dose-weighting option, and contrast transfer function (CTF) for each aligned micrograph was estimated using CTFfind 4. A total of 2,172,689 particles were automatically selected using Xmipp auto-picking function48 in the Scipion framework49. Several rounds of reference-free 2D class averaging and unsupervised 3D classification in RELION were used to clean the data until a final number of 522,564 particles. An initial 3D refinement gave a resolution of 3.71 Å. The data were quadruplicated and refined. The new reconstruction was used to initiate several rounds of CTF refinement and Bayesian polishing, and the resulting particles were employed for a new 3D refinement yielding a resolution of 3.37 Å.Data acquisition for LlPC with the inhibitor c-di-AMP was also carried out at the eBIC facilities with Titan Krios electron microscope (FEI) at 300 kV and a Falcon III direct detector. A total of 5869 movies were recorded each containing 39 frames over 1 s exposure at a magnification of 75,000X with a pixel size of 1.085 Å and a total exposure of 95 electrons/Å30 was used as a template for model building. The different domains were rigid body fitted to each density. The maps were sharpened by reference-based amplitude scaling using LocScale50 in the CCPEM suite. Structures were optimized with several iterative cycles of real-space refinement in Phenix51 using secondary structure and Ramachandran restraints followed by manual model building using Coot52. The figures were prepared using Chimera53.The crystallographic model 5VYWFurther information on research design is available in the Supplementary informationPeer Review FileReporting SummaryDescription of Additional Supplementary FilesMovie M1Movie M2


ARHGAP35 which is found mutated in cancers. p190A is a negative regulator of the GTPase RhoA in cells and must be targeted to RhoA-dependent actin-based structures to fulfill its roles. We previously identified a functional region of p190A called the PLS  required for localization of p190A to lamellipodia but also for regulating the GAP activity of p190A. Additional effects of the PLS region on p190A localization and activity need further characterization. Here, we demonstrated that the PLS is required to target p190A to invadosomes. Cellular expression of a p190A construct devoid of the PLS (p190AΔPLS) favored RhoA inactivation in a stronger manner than WT p190A, suggesting that the PLS is an autoinhibitory domain of p190A GAP activity. To decipher this mechanism, we searched for PLS-interacting proteins using a two-hybrid screen. We found that the PLS can interact with p190A itself. Coimmunoprecipitation experiments demonstrated that the PLS interacts with a region in close proximity to the GAP domain. Furthermore, we demonstrated that this interaction is abolished if the PLS harbors cancer-associated mutations: the S866F point mutation and the Δ865–870 deletion. Our results are in favor of defining PLS as an inhibitory domain responsible for masking the p190A functional GAP domain. Thus, p190A could exist in cells under two forms: an inactive closed conformation with a masked GAP domain and an open conformation allowing p190A GAP function. Altogether, our data unveil a new mechanism of p190A regulation.The GTPase-activating protein (GAP) p190RhoGAP (p190A) is encoded by  To thisp190RhoGAP-A  is a GAP that acts on Rho GTPases. p190A plays important roles in actin regulatory pathways in numerous cell types and controls diverse cellular mechanisms, such as cell division, migration, and invasion, endothelial permeability, and cell death . The subp190A is a large (≈1500 aa) multidomain protein with a C-terminal catalytic domain. In addition to this RhoGAP module, p190A contains three other structural domains. The N-terminal part of p190A harbors a GTP-binding domain (GBD), described recently as a class (ii) pseudo-GTPase domain, that is, a nucleotide-binding and catalytically inactive GTPase . There aARHGAP35—the gene encoding p190A–as a new major cancer gene , lung squamous cell carcinoma (5%), lung adenocarcinoma (3%), and head and neck cancer (3%) , encompassing two FF motifs, pG1, and pG2, was previously described as a regulator of p190A GAP function and required for targeting p190A to the lamellipodia of migrating cells (A). We next analyzed the localization of several HA-tagged constructs to check whether the PLS domain is required for p190A localization to invadosomes. Similar to the full-length protein, we found that the PLS alone can localize to invadosomes in NIH-3T3 (B and C) and Huh6 cells (B). On the contrary, the targeting of a p190AΔPLS mutant (p190A lacking the PLS domain) to invadosomes was strongly affected (B and C and S1B). These new data confirmed the importance of the PLS in regulating the localization of p190A in addition to its GAP function.The PLS region A, encompng cells . We analng cells A. We nex NIH-3T3 , B and Ch6 cells B. On theC) and analyzed its impact on p190A localization (D and E). As previously described (D and E). These data revealed that cortactin is not involved in the recruitment of p190A to invadosomes, which is in contrast to its recruitment to extending lamellipodia.We next investigated the role of the actin-binding protein cortactin in p190A targeting to invadosomes because we previously showed that cortactin is required for p190A targeting to lamellipodia . Using tFig. S1lization , D and Eescribed , D and EA).We performed a yeast two-hybrid screen to identify interacting proteins to understand how this PLS domain could regulate p190A. The PLS domain of p190A was used as a bait in a two-hybrid screen of a highly complex human placenta complementary DNA (cDNA) library (92.3 million interactions). Interestingly, p190A itself was identified as interacting with the PLS. Indeed, 35 clones from human p190A were identified  correspoFig. 2A). Using coimmunoprecipitation (co-IP) with anti-HA antibodies, as shown in Figure 2B, we observed a strong interaction between the PLS and the Cter domain of p190A. However, despite identifying the region 2H#1 in the two-hybrid screen, interaction between the Nter domain and the PLS was barely detectable . This weak interaction may be due to the low expression of the HA-Nter construct in HEK293T cells.We coexpressed GFP-tagged PLS with HA-tagged domains of p190A in HEK293T cells to confirm these results. We used previously generated HA-tagged constructs : HA-aminFig. 2C revealed that while the PLS strongly interacted with the C-terminal domain of p190A, the interaction with the so-called p120-binding domain was reduced and the interaction with the GAP domain was almost absent. Among the domains tested in the N-terminal part of p190A, only a weak interaction between the PLS with the GBD and the four FF domains (4FF) was observed (C); no conclusion can be drawn for the two FF domains (2FF) as we were unable to detect 2FF expression. We then generated HA-tagged constructs corresponding to the two regions identified in the two-hybrid screen, that is, regions 2H#1 and 2H#2, and performed co-IPs with GFP-PLS. The 2H#2 region (HA-Cter2h) strongly interacted with the PLS, whereas no interaction with the 2H#1 region was detected (HA-Nter2h) (D). This finding indicated that the minimal region of p190A required for interaction with the PLS domain is between aa 1069 and 1231 in the Cter part. Overall, these results show an interaction between two distinct domains of p190A, involving the region close to the GAP domain and the PLS domain, suggesting thus an effect of this interaction on p190A function.To more precisely map the interacting domains of p190A in the N- and C-terminal parts, we performed co-IP experiments between the PLS and smaller p190A domains. Figure 2observed C; no con-Nter2h) D. This fA and B). We thus questioned whether coexpression of the PLS with the Cter domain could rescue this phenotype by interacting with the Cter domain. To investigate this, we expressed the GFP-tagged PLS domain in Huh7 cells either alone or in combination with the HA-tagged Cter. Expression of the PLS domain alone did not change Huh7 cell phenotype, whereas coexpression of the PLS domain with the Cter domain strongly reduced the dendritic-like phenotype observed with HA-Cter expression (A and B). Some cells also harbored an intermediate phenotype under Cter and PLS domain coexpression, suggesting a partial rescue of the normal phenotype (B). Therefore, our data demonstrate that through its interaction with the Cter domain, the PLS is able to inhibit the GAP activity of p190A.In order to evaluate the involvement of the PLS in the regulation of p190A GAP activity, we analyzed F-actin organization as a read-out of Rho GTPase activity. Huh7 cells were previously used to identify the PLS domain  and are pression , A and Bhenotype B. TherefA). No change in the PLS/Cter interaction was observed using either the WT or mutated Cter domains. This result suggests that phosphorylation of these two specific residues is not implicated in the binding of the Cter domain with the PLS. We next considered whether phosphorylation was generally necessary for this interaction. Figure 4B shows that phosphatase treatment did not modify the interaction between the Cter and PLS domains, implying that phosphorylation is not involved in this molecular interaction.Some amino acids localized in the C-terminal part and notably in the 2H#2 region of p190A have already been recognized as involved in the function and regulation of the protein. In fact, Y1087 and Y1105 were found implicated in p120RasGAP binding , 14. We Fig. 4A), we analyzed their effect on the PLS/Cter interaction. To do so, we introduced each mutation in the GFP-tagged PLS construct and monitored interaction with HA-tagged Cter by co-IP. Interestingly, we showed that introducing the S866F mutation or deleting the five amino acids (Δ865–870) in the PLS abolished the interaction of the PLS domain with the Cter region of p190A (C and D). The same results were obtained when co-IP experiments were performed with the Nter2h corresponding to the 2H#1 region (D). These findings suggest that both mutations could alter the conformation of p190A by modifying domain interactions. This would also imply that these mutations lead to the release of Cter and the PLS domain interaction, in turn unmasking the GAP domain and simultaneously increasing the interaction between the Nter part of p190A and the PLS.The S866F point mutation and the small deletion of five amino acids  were both previously described as cancer-associated mutations and as mutations mimicking PLS deletion in p190A function A, we anaof p190A C and S2.0A C and D. The sa1 region D. These Herein, we confirmed that the PLS of p190A is an important functional domain of p190A. This PLS domain, encompassing two FF motifs, pG1, and pG2, was previously identified in a structure/function analysis as the PLS of p190A . Indeed,versus p190AWT. This strong p190AΔPLS effect arises from the drastic increase in the affinity of p190A for active RhoA  and NIH-3T3-vSrc cells from S. Courtneidge . Cell lines were confirmed for the absence of mycoplasma by PCR and authenticated by STR Matching analysis (American Type Culture Collection).HEK293T, NIH-3T3-vSrc, and Huh7 cell lines were cultured in Dulbecco’s Modified Eagle’s Media containing 4.5 g/l glucose and Glutamax (Gibco) supplemented with 10% fetal calf serum (Eurobio). Hepatoblastoma Huh6 cells were maintained in Dulbecco’s Modified Eagle’s Media containing 1 g/l glucose and Glutamax (Gibco) supplemented with 10% fetal calf serum (Eurobio). All cells were maintained at 37 °C in a 5% COMouse monoclonal (clone 12CA5) and rat monoclonal (clone 3F10) anti-HA antibodies were purchased from Roche. Several anti-HA antibodies were screened, all showing a strong background by Western blot. Mouse anti-p190A (clone D2D6) and rabbit polyclonal anti-GFP antibodies were obtained from Sigma, mouse anti-p190A antibody (clone 30) from BD. Rabbit polyclonal and mouse monoclonal (clone 4F11) anti-cortactin antibodies were purchased from Cell Signaling and Millipore, respectively. Rabbit (FL-335) and mouse (D-6) anti-GAPDH antibodies were obtained from Santa Cruz Biotechnology. Antimouse IRDYE680 and anti-rabbit IRDYE800 secondary antibodies were obtained from Eurobio Scientific. FluoProbes 488-phalloidin and 488-labeled secondary antibodies and 547-labeled secondary antibodies were purchased from Interchim. Hœscht 405  was used to stain nuclei.Rattus norvegicus ARHGAP35 fused to the lexA DNA-binding domain was used to screen for prey clones in a human placenta cDNA library (pP6 prey vector). This screen was performed by Hybrigenics services (Evry) using the ULTImate Y2HTM screen.A bait cDNA construct (pB27 bait vector) expressing the PLS (aa 380–971) from DNA transfections in HEK293T cell line were performed using Lipofectamine 2000 (Thermo Fisher Scientific), Huh6 and Huh7 cells were transfected using PromoFectin-hepatocyte transfection reagent (Promocell), and NIH-3T3-vSrc cells with JetPrime (PolyPlus Transfection) according to the manufacturer’s instructions. siRNA oligos were purchased from Eurofins Genomics and transfected into NIH-3T3-vSrc cells with JetPrime (PolyPlus Transfection) according to the manufacturer’s protocol. Si1 Cortactin targets cortactin mRNA at 5′-GGAACACAUCAACAUUCACTT-3′ and Si2 Cortactin targets at 5′-AAGCUUCGAGAGAAUGUCUUC-3′. Control siRNA corresponds to AllStars Negative control from Qiagen.The pKH3-p190A-WT containing the rat full-length p190RhoGAP (GenBank under accession no. M94721) was a generous gift from I. Macara (Vanderbilt University). The two truncated p190A constructs (Nter2h and Cter2h) were engineered by PCR using the rat full-length p190RhoGAP as matrix and subcloned into pKH3 expression vector using BamHI and EcoRI restriction sites. Point mutations were generated in the parental vector pKH3-p190A using site-directed mutagenesis with the QuikChange II XL kit according to the manufacturer’s protocol (Agilent Technologies). The two mutated PLS constructs (PLS S866F and PLS Δ865–870) were generated by site-directed mutagenesis in the rat GFP-PLS construct. The four mutated Cter constructs  were generated by site-directed mutagenesis in the rat HA-Cter construct. All constructs were verified by DNA sequencing. All other p190 constructs (truncated and/or mutated p190A constructs) were previously generated and published . Activity was visualized with the Odyssey infrared imaging system (LI-COR Biosciences) and the Chemidoc infrared imaging system (Bio-Rad) and analyzed using ImageJ image analysis software. Anti-HA affinity matrix was obtained from Roche for immunoprecipitation assays and used following the manufacturer’s protocol. Briefly, cells were extracted in the Tris–HCl buffer  supplemented with protease and phosphatase inhibitors and centrifuged for 10 min at 10,000g. One milligram of the supernatant was incubated for 30 min with the beads. Bead pellets were washed three times with Tris–HCl buffer, resuspended in loading buffer before analysis by SDS-PAGE.Twenty four hours after transfection, cells were scraped off on ice and homogenized in Tris–HCl lysis buffer  with protease and phosphatase inhibitors. Cell lysates were centrifuged at 10,000Protein lysates were incubated with λ-phosphatase (New England Biolabs) with or without the recommended amount of the Halt Phosphatase Inhibitor Mixture (Thermo Fisher Scientific) for 30 min at 30 °C. Samples were washed six times with buffer, according to the manufacturer’s protocol, prior to co-IP and subsequent SDS-PAGE analysis.To induce linear invadosome formation, coverslips were prepared as previously described . CoverslTwenty four hours after transfection on glass coverslips, cells were fixed with 4% paraformaldehyde for 10 min at RT and permeabilized with 0.2% Triton X-100 for 10 min before incubation with various antibodies, as previously described . After st test. Significance was accepted for values where ∗p < 0.05; ∗∗p < 0.01; ∗∗∗p < 0.001; and ∗∗∗∗p < 0.0001.Statistical analysis was performed with Prism software (GraphPad Software) and data are presented as mean ± SEM of at least three independent experiments. Comparisons between two groups were analyzed by All data are contained in the article.This article contains The authors declare that they have no conflicts of interest with the contents of this article.


Experimental and theoretical studies were carried out on the dynamic deformation and penetration response characteristics of metal ellipsoidal thin curved shells under impact loads. The deformation characteristics of the impacted ellipsoid shell was investigated via the use of a light gas gun to carry out impact loading experiments at different speeds. Ten cases of experiments were conducted with the impact velocities distributed between 25.69 m/s and 118.97 m/s. Stereo digital image correlation (3D-DIC) technology was applied to capture the dynamic deformation and penetration process of the impacted shell. The recovered shells were measured, and the deformation characteristics were analyzed, along with the dynamic evolution, as observed through 3D-DIC analysis. Based on the experimental results, the displacement mode was summarized and the displacement distribution of the locally impacted ellipsoid shell was proposed. The governing equations were derived for the dynamic deformation and penetration of the impacted ellipsoid shell by means of the Lagrange equation. The proposed theoretical model was verified based on the experimental results. Finally, the influence of the curvature distribution on the impact resistance of ellipsoidal shells is discussed. The results indicated that the proposed theoretical model was effective in analyzing the large deformation and the penetration speed. Stretching the axial length of the ellipsoid shell in the impact direction improved its resistance to penetration. Stretching the axial length of the ellipsoid shell perpendicular to the impact direction improved its resistance to deformation, but reduced its resistance to penetration. Maintaining the triaxial ratio and appropriately reducing the size of the ellipsoidal shell improved its resistance to both deformation and penetration. The above research provides a reference for the analysis of the impact resistance of thin-walled curved shell structures in engineering. Thin curved shells are widely used in engineering practices such as aerospace, ships, and civil engineering . They arMost corresponding investigations have been performed based on numerical simulation and experiments ,3,4, andA spherical shell is a special form of ellipsoidal shell, which has been applied as a representation of a free-curved shell, in addition to direct applications in engineering structures. The reason is that mean Gauss curvature majorly governs the stiffness of curved shells . TherefoUnlike a spherical shell, the curvature is not homogeneously distributed in the case of an ellipsoidal shell. Therefore, an ellipsoidal shell can be used to represent the local geometric properties of a free-curved shell. The local dynamic response of a complex curved shell can be shown by examining the dynamic response of a similar ellipsoidal shell. In addition, an ellipsoidal shell can be directly applied in engineering designs. Therefore, the investigation of the dynamic response of an impacted ellipsoidal shell has more extensive engineering application value. Currently, most investigations of ellipsoidal shells have been performed in the fields of forming, stability, and stress analysis. Bushnell  found thIn this study, a series of experimental and theoretical analyses was performed to investigate the large dynamic deformation and perforation behavior of locally impacted thin ellipsoidal aluminum shells. Based on the experimental measurements, the deformation characteristics were determined. This made it possible to describe the displacement distribution in a clear and easy way similar to what was applied to a spherical shell in our previous work . Accordic, which represented the minimum axis of the ellipsoid. The geometric properties of the complete ellipsoid can be described by the relationship between the lengths of all three axes, which were b:a:c = 300 mm:200 mm:100 mm, as applied in the experiments. The ellipsoidal shell, which was a part of the complete ellipsoid, can be described by means of the geometric parameters presented in A light gas gun was applied to launch the projectiles and to perform the impact experiments on the dynamic response of thin ellipsoidal aluminum shells. The diameter of the gun bore was 12.5 mm, and the projectile diameter was 12 mm. Sabot was not used in the test. The highest pressure used in this series of test was 0.9 MPa, and the corresponding launching speed was 118.97 m/s, which was the highest impact speed in this study. A 36.23 m/s impact velocity was achieved by a pressure of 0.1 MPa. A pressure lower than 0.1 MPa sometimes did not work. The velocity of 25.69 m/s was achieved by 0.17 MPa launching, in which the projectile was pushed from the front of the gun to one-quarter length of the gun. The loading and test setup are shown in The MatchID system was used to perform the 3D-DIC measurements for the time-histories of the dynamic response of the impacted ellipsoidal shells and to reconstruct the displacement distribution during the evolution of the ellipsoidal shell deformation. The two cameras used in the DIC measurements needed to be calibrated before the experimental tests, as shown in Four deformation modes were observed on the ellipsoidal shells under local impact loads. According to the shape of the depressed boundary, the deformation modes were classified into four types, including a circular depressed deformation, ellipsoidal depressed deformation, ellipsoidal depressed deformation with buckling in only one direction along the short axis, and ellipsoidal depressed deformation with buckling in two directions along the short axis. These four deformation modes are denoted Mode I, Mode II, Mode III, and Mode IV, respectively, as shown in The results presented in The results of the static analysis and dynamic analysis of high-speed photography showed that under the impact loads, only two deformation modes were observed in the impacted ellipsoidal shell in the deformation development stage, namely the approximate circular deformation (Mode I) in the small deformation stage and the elliptical deformation (Mode II) in the continuous development process of the deformation. Although buckling instability occurred in the case of the large deformation that generated deformation Modes III and IV, the high-speed photography results of the deformation development process showed that these two modes only changed the shape of depressed deformation and had no significant influence on the size of the deformation area. When considering the impact resistance of thin shell structures, the most important factors are the dimple depth, dimple size, and ballistic limit of the thin shell under impact loading. The dimple size is represented by the dimple width. The boundary of the depressed deflection was an ellipse, and the size of this dimple deformation could be described by the long and short axes of the elliptical boundary of the depressed deflection. Normally, dimple width is defined as the short axis of the elliptical defect boundary. If the long axis is discussed at the same time, a label is provided to mark each kind of axis. The largest deflection displacement that occurred during the deformation was the deflection of the center of the shell. The corresponding displacement was defined as the dimple depth. The buckling deformation of the ellipsoidal shell had no considerable influence on the depth or size of the depressed deformation. In addition, the main buckling came from the minor axis direction, and the additional deformation caused by buckling was not prominent relative to the minor axis of the deformation area, which had little impact on the geometric characteristics. This introduced great convenience into the geometric description of the dynamic deformation of the ellipsoidal shell under the impact load. In the theoretical analysis, it was not necessary to consider deformation Modes III and IV. Moreover, the circular deformation boundary was considered as a special case of the elliptical deformation boundary, which could thus be described by means of the elliptical deformation characteristics. Therefore, the dynamic deformation boundary characteristics of the ellipsoidal shell under local impact load could be fully characterized by deformation Mode II.On this basis, all the depressed deformation modes were approximated as ellipses, and the characteristics of the major axis and minor axis of the depressed deformation boundary were analyzed. The measurements were carried out for the static recovered test piece, and the results are shown in As shown in Due to the obvious characteristics of the impact point on the ellipsoidal shell, it was easy to statically measure the final deformation depth. However, it was difficult to obtain the distribution of the dimple depth along a certain direction in order to statically measure the recovered specimen. Manual measurement would introduce many uncertainties. If the specimen was cut, it might cause the release of the residual deformation, and the measured result would not show the depth distribution of the final deformed specimen. The distribution of the dimple depth could be determined, on the other hand, by reconstructing the deformed surface using the DIC method. The reliability of the results obtained from the DIC test was verified by comparing the statically measured dimple depth with the DIC results, and this comparison is shown in The DIC reconstructions of the experimental results are shown in the right-hand image in According to the static measurement of the recovered specimens and the dynamic DIC test results of the impacted ellipsoidal shell, the geometric characteristics of the depressed deformation were obtained for the ellipsoidal shell under local dynamic impact loading. In the case of large depressed deformation, the boundary of the deformation area was almost in the same plane, and the shape of the dimple boundary was an oval. The long and short axes of the elliptical dimple boundary were in the same proportion as the long and short axes in the original ellipsoidal shell surface. In most areas of large deformation, the distribution of dimple depth was the result of the mirror transformation of the original ellipsoid. In a small part of the central area, the dimple depth was linearly distributed. This is summarized in In our previous study , a theorBased on the local dynamic normal impact load experiments using ellipsoidal thin shells, the geometric characteristics of the depressed deformation boundary and surface were measured, and the depressed deformation mode was obtained. Under the local normal impact, the depressed deformation of an ellipsoidal thin shell had distinct characteristics. Most areas of the depressed deformation had a good mirror transformation relationship with the original surface, and only a small part of the depressed deformation surface in the central area had a linear distribution after deformation. In this section, the theoretical model of dynamic deformation of an ellipsoidal thin shell under local normal impact was established based on the energy principle, with particular attention to the dynamic deformation of an ellipsoidal thin shell. In this case, for the final state, the energy involved in a small part of the central area was relatively low, considering that of the entire deformed shell. When the local deformation was small, the kinetic energy of the impact load was dominant for the dynamic evolution process. The kinetic energy of the non-mirror deformation in the central area was not important because of its rather low value compared with the overall energy of the deformed shell. Therefore, an assumption was made for the theoretical model of the dynamic deformation process of an ellipsoidal thin shell that the deformation mode of other areas in the depressed deformation area were equivalent to the mirror transformation of the original surface, except for the edge area. In addition, the edge of the central impact area was considered a shear plug deformation, and shear plug failure and penetration were considered in the final fracture calculations. Furthermore, only the displacement in the impact loading direction was considered, and the displacement in all other directions was ignored. Thus, the deformation mode and displacement distribution of each region were determined. As shown in The displacement of Region 1 is represented by z direction, i.e., the height direction of the ellipsoidal thin shell, all the height descriptions were under the configuration of the undeformed ellipsoidal thin shell, i.e., the height Considering only the displacement in the impact load direction, i.e., axial displacement, the deformed configuration can be easily described based on the original curved surface configuration. Taking the impact direction as the Region 4 was always contained in the displacement mode. That was an undeformed part of shell, and the boundary displacement was zero, which represented a clamped constraint. Therefore, an explicit boundary condition was not necessary in the theoretical model, where the clamped constraint was implied.Since there was no displacement and deformation in Region 4, only the deformation distribution of Regions 1, 2, and 3 needed to be considered. The deformation of Region 1 was considered a shear plug deformation, which was concentrated on the interface between Regions 1 and 2. According to the displacement distribution, Regions 1 and 2 were discontinuous and could not describe the shear deformation. Therefore, it was assumed that a shear deformation region with limited width existed between Regions 1 and 2. The region was a circular ring; the radial width of the ring was terature . The sheterature  and can w indicates displacement along the normal direction. The details in the calculation of the above curvatures is given in Region 2 was an isometric transformation of the original shell, and the corresponding membrane forces were 0, which was different from edge Region 3. In addition, both Region 2 and Region 3 exhibited curvature deformation, and the curvature of thin shell can be calculated based on geometric equations, as expressed in Equation b G is defined in The comparison indicated that general force t is the solving time and In the calculation of the theoretical model, the following initial conditions were applied, according to the experiments.The results shown in In this investigation, it was assumed that the major depressed deformation was an isometric transformation, in order to simplify the model. Based on this method, the original ellipsoid equation could be used to describe the depressed deformation. In fact, according to the comparisons with the experimental results, we found that the theoretical results obtained under this assumption were consistent with the actual measured results in the large deformation and penetration analyses. In practical engineering applications, the large deformation and penetration velocity of ellipsoidal thin shells are key factors that should be considered. Therefore, the theoretical results obtained using this displacement model could provide an important reference for practical applications. The proposed theoretical methodology provided a very efficient model for predicting the general deformation characteristics with an acceptable accuracy. Although not all accurate details of deformation were involved in the theoretical predictions, it was much faster than numerical simulation and more economic than experiments. In addition, it was easy to obtain the influences of various parameters of the shell on the resistance to impact load. This advantage was applied in the following analyses and discussion for the influence of the curvature distribution on the deformation of the impacted ellipsoidal shell.b was fixed to 200 mm, and the impact direction was set to the c axis, respectively. We stretched or compressed their transverse or longitudinal dimensions to investigate the corresponding change in the impact resistance of the ellipsoidal shells.Ellipsoidal shells could provide different curvature distributions by changing the triaxial ratio. In order to explore the influence of curvature distribution on the impact resistance of thin curved shells, the dynamic responses of ellipsoidal shells were theoretically analyzed for shells with different triaxial ratios, starting with the spherical shells with an initial triaxial ratio of b, and the impact speed and dimple width diagram are shown in We gradually increased the ratio of The results shown in In order to explore the influence of the tension or compression of the axis size in the impact direction of the ellipsoidal shell on its deformation and penetration, a, and diagrams of the impact speed and dimple width are shown in The impact resistance of the shell was measured based on the dimple width in the direction of axis The results shown in When the shell was stretched laterally, the integral area of kinetic energy and generalized force was increased at the same time. The membrane force in the edge area provided the major part of the generalized force. The transverse stretching of the shell had a great impact on the generalized force in the edge area, resulting in a reduction of the final depressed deformation. When the shell was stretched longitudinally, it had little effect on the integral region and then had little effect on the kinetic energy term and the generalized force term in the governing equation. Therefore, the longitudinal stretching had little effect on the shell deformation.In the design process of thin curved shells, not only should one consider the triaxial ratio, but one should also consider the influence of its scaling on its performance. Scaling meant that the ratio between all axes was maintained and the lengths of axes were changed. Therefore, the following analysis was conducted to examine the impact resistance capability affected by the scaling of ellipsoidal shell size. We gradually enlarged the size of the ellipsoidal shell to two times that used in the experiment, measured the impact resistance of the shell according to the short semi-axis length of the dimple area, and constructed a diagram of the impact velocity and dimple width, as shown in The results shown in Based on the relationship between the impact velocity and dimple width, it could be observed that the penetration of the shell was induced by the sudden drop in the dimple width, which was the result of the generalized force in the shear region. Region 1 and Region 2 had generalized forces and generalized forces caused by the shear region, respectively. These were a pair of reaction forces, required to prevent the development of the depression in Region 1 and to drive the expansion of the depression in Region 2. When the calculation process indicated that penetration occurred, these two generalized forces failed, resulting in the slow expansion of Region 2, which was manifested in the reduction of the penetration speed. To sum up, the transverse compression, longitudinal tension, and overall reduction in shell size could increase its penetration speed, because these dimensional changes increased the action time of the shear generalized force, which helped the dimple deformation expand.(1)The depressed deformation of an ellipsoidal shell under an impact load is elliptical, and the proportions of the long and short axes of the elliptical dimple boundary are close to those of the initial shell.(2)The isometric transformation was in good agreement with the actual deformation of an ellipsoidal shell in the large deformation region.(3)Enlarging the axial length of an ellipsoidal shell in the impact direction could improve its resistance to penetration.(4)Stretching the axial length of an ellipsoidal shell perpendicular to the impact direction could improve its deformation resistance, but this would reduce its resistance to penetration.(5)Keeping the triaxial ratio unchanged and appropriately reducing the size of an ellipsoidal shell could improve its resistance to deformation and penetration at the same time.In this work, an experimental study of thin-walled metal ellipsoidal shells under the impact of cylindrical projectiles was carried out. Based on the experiments, the final deformation results of ellipsoidal shells under the impact of cylindrical projectiles with different velocities and the key parameters such as the depression depth and width were obtained. At the same time, the dynamic deformation process of ellipsoidal shells was obtained using 3D-DIC technology. The rationality of the use of an isometric transformation in the large deformation region was verified through experiments, and then, a theoretical model based on isometric transformation and the Lagrange principle was established. The theoretical results were in good agreement with the experimental results in the analysis of the large deformation and penetration velocity, which validated the theoretical model established in this paper. Finally, the triaxial dimensions of the ellipsoidal shell in the theoretical model were changed to investigate the influence of the curvature distribution on the impact resistance of the shell. The main conclusions of the experiment can be listed as follows: