As an important component to promote the development of affective brain–computer interfaces, the study of emotion recognition based on electroencephalography (EEG) has encountered a difficult challenge; the distribution of EEG data changes among different subjects and at different time periods. Domain adaptation methods can effectively alleviate the generalization problem of EEG emotion recognition models. However, most of them treat multiple source domains, with significantly different distributions, as one single source domain, and only adapt the cross-domain marginal distribution while ignoring the joint distribution difference between the domains. To gain the advantages of multiple source distributions, and better match the distributions of the source and target domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network. We first map all domains to a shared feature space and then align the joint distributions of the further extracted private representations and the corresponding classification predictions for each pair of source and target domains. Extensive cross-subject and cross-session experiments on the benchmark dataset, SEED, demonstrate the effectiveness of the proposed model, where more significant classification results are obtained on the more difficult cross-subject emotion recognition task. Affective computing is emotion-related computing that helps improve the user experience during a human-computer interaction . As one In recent years, many researchers have gradually focused on the interesting field of brain–computer interfaces. By using neural activity generated by the brain, the brain–computer interface enables harmonious interactions between the user and the computer . This maHowever, the brain presents non-stationary states under the interference of some internal or external factors , which mTo decrease the amount of training data required for test subjects and reduce the time-consuming and laborious data labeling work, transfer learning  or domaiTo take full advantage of the data distributions of multiple source domains, this paper proposes a novel multi-source joint domain adaptation (MSJDA) network to improve the generalization ability of the EEG emotion recognition model across subjects and sessions. We assume that the EEG data in the source domains are fully labeled, and the target domain data are unlabeled, which is congruent with unsupervised domain adaptation. We construct a multi-source EEG emotion recognition network, which is divided into three parts, a domain-shared feature extractor, domain-private feature extractors, and domain-private label predictors. First, a domain-shared feature extractor extracts the general features for all the domains. Second, each pair of source and target domains is further mined by a domain-private feature extractor for specific features that are beneficial for distinguishing emotion categories. Third, with labeled source domain data, a domain-private label predictor can be trained separately for each source domain. Moreover, we employ joint maximum mean discrepancy (JMMD)  to matchThere are two main contributions in this paper. The first is a multi-source joint domain adaptation network proposal, which helps to solve the problem of cross-domain generalization in EEG emotion recognition. The second is our extensive cross-subject and cross-session transfer experiments on a publicly available emotion EEG dataset that verify the effectiveness of the proposed method.The remainder of this paper is organized as follows. Section “Related work” presents some of the research most relevant to this paper. The proposed network model is described in detail in the section “Materials and methods.” The content related to the experiments, such as the dataset and the experimental results, will be given in the section “Experiments.” “Discussion” discusses and analyzes the proposed model. Finally, the section “Conclusion” summarizes our main work.Domain adaptation, especially in unsupervised scenarios of transferring knowledge from a labeled source domain to an unlabeled target domain, has been extensively studied in fields such as computer vision and natural language processing. In this section, we mainly review the domain adaptation algorithms that are most relevant to this paper. To address the challenge of model performance degradation across different subjects or sessions, domain adaptation techniques have been gradually applied to EEG emotion recognition in recent years. Among the numerous previous works, there are two popular types of approaches.One approach employs metrics to measure and minimize the distribution discrepancy across domains. Once the cross-domain distance is reduced to an acceptable level, a model trained with source supervision can better predict target samples. One of the most commonly used metrics is the maximum mean discrepancy (MMD) , which rThe other approach is to extract domain-invariant features through adversarial learning. Generative adversarial nets (GANs)  generateThe above methods validated their performance under different settings, indicating that domain adaptation can generalize models for EEG emotion recognition. In affective brain–computer interfaces, we are usually given labeled data from multiple subjects or sessions. Moreover, the data distributions of the different subjects differ from each other, as do the different sessions of the same subject. However, most of the above methods simply merge different subjects or sessions of the source domains into one single domain, ignoring the differences between them. Many methods assume that after aligning the marginal distributions of source and target domains, their conditional distributions are automatically aligned or remain unchanged throughout the process, which may not always be the case in practical application. Therefore, this paper considers the cross-subject and cross-session scenarios and treats different source subjects or sessions as different source domains, while the target domain contains one subject or one session. We then propose a multi-source joint domain adaptation network that aligns the joint distribution of the deep features and classification predictions for each source domain and target domain.This section introduces a metric for the difference between two joint distributions, followed by a formal description of our proposed multi-source joint domain adaptation network.ℋ by an implicit feature map ϕ, which is called the kernel embedding of distribution. One of its most common applications is MMD  obeyed by m variables Z1,…,Zm, a set n i.i.d. samples is given. Then, using an implicit feature map When two or more random variables are involved, according to Ps and Pt, their corresponding sample sizes are n_s and n_t, respectively. After forward propagation in the neural network, these two domains separately produce activations ℒ. Their joint distributions Ps and Pt are considered to still be different and can act as proxies for the joint distributions Ps and Pt of the original data, respectively  and Pt isTo measure the discrepancy between two joint distributions, ectively . Based oIn the cross-domain EEG emotion recognition problem, it is often necessary to deal with multiple domains with significantly different distributions. To reduce the cost of labeling training data for the target task, experience can be gained from multiple labeled source subjects to help predict the target subject. When data are prone to staleness, the predictions of a new session for one subject can benefit from its known multiple labeled sessions.K distinct source domains Ds={Dsk|k = 1,…,K}, where the k-th source domain n_k i.i.d. labeled examples drawn from the joint distribution Psk, and a target domain n_t i.i.d. unlabeled examples sampled from the joint distribution Pt, are generally given. It is assumed that the joint distributions of different source domains are not equal to each other, and each source domain and target domain also have different joint distributions. Our aim is to reduce the difference between the labeled K source domains and the unlabeled target domain to make the extracted features domain-invariant so that the source learner has better performance when applied to the target domain.Unlike many existing studies that directly combine multiple source domains into one single source domain, we try to explore a more challenging and practical multi-source scenario in which each source domain with a different distribution is treated as an independent source domain. In multi-source unsupervised domain adaptation, We propose a network capable of handling multiple domains with different distributions for the cross-domain EEG emotion recognition problem. As shown in E refers to a shared network structure through which the EEG data of all the source and target domains first pass. Its goal is to enable the neural network to learn abstract representations that are shared by different domains. These representations should be general to all domains for subsequent further processing. For an input sample x from a specific domain, the feature extracted by E is denoted as e=E(x).A domain-shared feature extractor F = {Fk|k = 1,…,K}, is a separate feature extraction structure for each pair of the k-th source domain and the target domain, which follows the domain-shared feature extractor E. In other words, there are K network branches after E, each of which corresponds to one source domain. The role of F_k is to extract some deep representations relevant to the final classification task for each pair of source and target domains. If ek is used to uniformly represent the general features of the k-th source domain or the target domain input to F_k, then the output corresponding to F_k is fk=Fk(ek).Each of the domain-private feature extractors, G = {Gk|k = 1,…,K}, where G_k is connected to F_k. We treat each labeled source domain as an independent source domain. Each source domain has an independent label predictor, which is trained by means of supervised learning and connected to the corresponding private feature extractor. The output of the k-th label predictor can be expressed as gk=Gk(fk).The domain-private label predictors are denoted as Our work is inspired by the work of LCE. The label prediction loss for the k-th source domain is:For a labeled source domain sample, the error between its predicted label and its ground-truth label, which we call the label prediction loss, can be calculated by the cross-entropy loss function fk and label prediction gk, i.e., the activations of the last two layers of each network branch, are used to compute the joint distribution difference (joint adaptation loss) in these layers for each pair of source and target domains. This difference is taken as an approximation of the difference in the original joint distributions of the corresponding source and target domains. Then, the joint adaptation loss between the k-th source and target domains is computed as:After passing through the domain-shared feature extractor, the unlabeled data of the target domain will enter the network branch of each source domain. In this way, we measure the JMMD between each source domain and target domain in a one-to-one manner. The network model proposed here only takes into account the case where each label predictor has one layer, but it can also be easily generalized to the multilayer case. The domain-private feature Therefore, the total loss function is the sum of the label prediction loss and the joint adaptation loss; that is, the objective function of the proposed network iswhere λ > 0 is the tradeoff parameter between the two losses. The objective function is optimized asto improve the performance of the label predictors and reduce the distribution difference between the source and target domains, thereby learning discriminative and domain-invariant feature representations.During training, the EEG data samples of one of the source domains and target domain are input to fit the model in each iteration. At test time, the prediction of each sample in the target domain is determined by the average of the outputs of all the label predictors.We compare the proposed MSJDA network with several baseline methods in cross-subject and cross-session scenarios.To verify the effectiveness of the proposed MSJDA model, we conducted extensive cross-domain EEG emotion recognition experiments on the benchmark dataset SEED . SEED isThe raw EEG data first were preprocessed, including downsampling to 200 Hz, removing artifacts from the signals, and passing through a bandpass filter between 0 and 75 Hz. Next, the preprocessed data were further divided into five frequency bands, namely, delta (1–3 Hz), theta (4–7 Hz), alpha (8–13 Hz), beta (14–30 Hz), and gamma (31–50 Hz). In previous studies , the difX is a random variable with Gaussian distribution N, and its corresponding probability density function is p(x). Finally, the number of samples per session was 3394. Each sample had a dimension of 310 (62 times 5) and was generated from one second of raw EEG data.where We conducted the cross-domain transfer experiments in two scenarios. One scenario was the cross-subject transfer experiment, which takes the new subject as the target domain and the existing subjects as the source domains. The other was the cross-session transfer experiment that used the new session as the target domain and the existing sessions of the same subject as the source domains. In all the experiments, we adopted the leave-one-out cross strategy; that is, each time, one subject (session) was selected as the target domain, and the remaining subjects (sessions of the same subject) were selected as the source domains, so that each subject (session) had a chance to be the target domain. It should be noted that our MSJDA did not have access to the labels of the target domain during the training of the model, which means that it was an unsupervised domain adaptation method.C of the support vector machine (SVM) was fixed to 1, and a linear kernel was chosen for it. The settings of TCA   is a mul0/(1 + βp)γ, is the same as that in 0=0.0001, β=10, γ=0.75, and p (linearly increasing from 0 to 1) represents the training progress of the model. For JAN, MEERNet, and our MSJDA, the value of the tradeoff parameter is computed by λ=2/(1 + ep−η)−1 with η=10 so that it varies from 0 to 1, which also follows the setting in For the neural network-based methods, the setting of the learning rate, α=αIn addition, the differential entropy features for each session of each subject are separately standardized before being fed to the model to speed up convergence.i-th  cross-subject experiment consisted of the i-th session of all the subjects. The mean accuracy (Mean) of the i-th session refers to the average of 15 test accuracies obtained by taking each of the 15 subjects in turn as target domain (corresponding remaining subjects as source domains), where all subjects’ data are from the i-th session. The standard deviation (Std.) corresponding to the mean accuracy is also calculated.First of all, we verify the superiority of the proposed network in cross-subject experiments. Most of the existing studies only select the EEG data of one session from each subject for transfer experiments. For a more comprehensive comparison, we conducted cross-subject experiments in 3 different sessions. Specifically, each subject has EEG data from 3 sessions, and previous studies have shown that the data distribution varies between sessions. Accordingly, it is not appropriate to directly combine all sessions for each subject, so we conducted three cross-subject experiments. The data for the The results of the cross-subject experiments are shown in First, methods based on deep neural networks perform much better than non-deep ones. The most obvious result is that the MLP (76.15%) without the transfer technique is better than the best non-deep method JDA (75.79%), which adopts the transfer technique. This indicates that, although the differential entropy features manually extracted from the raw EEG data are used in the experiments, a deep neural network can still discover some deep representations from such features that are beneficial for distinguishing emotion categories.Second, among traditional methods without deep learning, the transfer learning-based methods (TCA and JDA) are better than SVM without transfer technique; the same finding is also found in the deep methods, that is, JAN, MEERNet, and MSJDA are clearly better than MLP. Therefore, transfer learning can reduce the difference between the source and target domains, making the model perform better in the target domain.Third, both JDA and TCA are better than KPCA, which only performs dimensionality reduction. This shows that there is still a large difference between the source and target domains after only performing dimensionality reduction. By explicitly reducing the differences between domains, the distribution between domains can be better aligned, thereby improving the performance of the model on the target domain data.Fourth, JDA with JDA performs better than TCA with only a marginal distribution adaptation. This shows that, compared to just adapting the marginal distributions of the source and target domains, adapting the joint distributions can reduce more differences between the domains, resulting in better results.p < 0.0001) and 3.11% (p < 0.0001) on average, respectively. Compared with JAN, such an observation suggests that in the multi-source scenario, the target domain acquires more beneficial knowledge from multiple independent source domains than from one single merged source domain. MSJDA narrows the distance between each source domain and target domain by aligning their joint distributions of deep features and predictions, making it better than MEERNet, which only considers single-layer feature adaptation to match marginal distributions. Hence, the superiority of the proposed MSJDA is demonstrated in the cross-subject experiments.Fifth, our MSJDA outperformed JAN and MEERNet by 4.35%  session refers to the average of 15 test accuracies, each obtained by one of the 15 subjects using the i-th session as the target domain.For cross-session experiments, each subject needs to conduct three experiments, respectively, so that the three sessions of the same subject are taken as target domain in turn (the remaining sessions of the same subject as source domains). To better evaluate the performance of the model, we did not average the experimental results for each subject. Instead, the mean accuracy of the The results of the cross-session experiments are presented in First, the results of the cross-session experiments are generally much better than those of cross-subject experiments. It can be seen that the difference in data distribution between different sessions of the same subject is much less than that between different subjects. Intuitively, this is reasonable because the difference in EEG signals between subjects is usually large due to individual factors. In contrast, the data distribution for the same subject varied less over a shorter period of time (approximately a week or longer in the SEED dataset).Then, the same observations as the cross-subject experiment can be obtained; that is, deep learning-based methods perform better than those without deep learning, and transfer learning-based methods (such as MSJDA and JAN) outperform methods (MLP) without transfer techniques. It can be seen that the combination of transfer learning and deep learning can learn more transferable feature representations, thereby improving the effect of knowledge transfer. Likewise, JDA matching the joint distribution clearly outperforms TCA matching the marginal distribution in terms of average accuracy.Additionally, we observe that SVM performed surprisingly well, even better than the transfer learning methods TCA and JDA. There may be two reasons for this. On the one hand, the data distribution of different sessions of the same subject does not vary greatly. On the other hand, SVM covers all source samples, while TCA and JDA only use a subset of the source samples due to limited memory. Under the influence of these factors, the transferability of shallow transfer learning methods is limited.p < 0.01) and 2.62% (p < 0.001) over JAN and MEERNet, respectively. In connection with the previous cross-subject experiments, it can be seen that when there is a large difference across domains and the number of source domains is large, MSJDA can achieve a more significant performance improvement compared to the baseline method. When the number of source domains increases, the framework of MSJDA can be easily extended. This makes a lot of sense in practical applications when we have data from multiple domains with different distributions.Finally, our proposed MSJDA achieved the best results, improving by 1.96%  MSJDA is constructed by a neural network, which can extract more transferable features compared with shallow methods (such as TCA and JDA). (2) MSJDA aligns the joint distribution of features and predictions in the network, which can make the source and target domains more similar than the baseline method, MEERNet that only aligns the marginal distribution. (3) Compared to the baseline methods (such as JAN) that only consider a single source domain, MSJDA is able to exploit multiple available source domains to reduce the distribution difference between each source domain and target domain, thus achieving better performance with the help of multiple label predictors. (4) MSJDA benefits from being able to integrate all these advantages. In other words, MSJDA has the ability to embed a joint adaptation module into the neural network and utilize each of the multiple source domains for adaptation with the target domain separately. Therefore, MSJDA outperforms the baseline methods in both cross-subject and cross-session EEG emotion recognition.In this paper, we address the challenge of the insufficient generalization abilities of the EEG emotion recognition models in cross-subject and cross-session scenarios. To better transfer knowledge from multiple source domains to the target domain, we propose a multi-source joint domain adaptation network that separately matches the joint distributions of deep features and classification predictions for each pair of source and target domains. The cross-domain EEG emotion recognition experiments demonstrate the effectiveness of the proposed method.https://bcmi.sjtu.edu.cn/~seed/index.html.Publicly available datasets were analyzed in this study. These data can be found here: SL, LS, YF, and LW contributed to the conception and design of the study. LW organized the database. YF performed the statistical analysis. SL wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


The retrovirus causing caprine arthritis encephalitis (CAE), a slowly progressive inflammatory disease in goats, belongs to the group of small ruminant lentiviruses (SRLVs) which cause lifelong infections that ought to be avoided for animal welfare as well as economic reasons. SRLV accreditation has been in place for forty years in The Netherlands and is based on the screening of small ruminant sera for specific antibodies. This paper evaluates 38 dairy goat herds that lost CAEV accreditation between 2012 and 2022. The characteristics of these herds are discussed, and specific follow-up scenarios, depending on desired goals, are introduced. The herd size of the participating herds varies from approximately 400 to 4600 adult dairy goats. The larger herds tended to be more prone to lose herd accreditation and had more difficulties regaining accreditation. Possible routes of introduction are lined up. The Royal GD’s tailor-made approach and advice to support livestock farmers with herds that have lost CAE accreditation are discussed in detail. Specific emphasis is placed on the strategic deployment of various diagnostic tests (such as antibody ELISAs and PCR) in different media, such as (pooled) sera, (bulk)milk and tissue samples. Special attention is paid to the added value of retrospective bulk milk testing or the specific testing of groups based on housing and management, which enables the investigation of the moment of viral introduction and route of transmission into a herd. Furthermore, the prospective implementation of bulk milk and strategic pooled milk sample testing in the Dutch SRLV accreditation programs intensifies surveillance and enables the taking of swift action to prevent further transmission within and between herds. An appeal is made to share experiences to improve programs collectively, and to start research into the underlying mechanisms. Over the last decades, the dairy goat industry in The Netherlands has become a serious agricultural sector which has grown significantly. In 2020, on average 670,842 goats were kept on 14,730 registered locations. The average herd size of the 400 professional dairy goat farms has increased to 1411 goats [Within the dairy goat industry, most farmers have their herds accredited for caseous lymphadenitis (CLA)  and caprIn recent years, the dairy goat sector has had to deal with a number of SRLV outbreaks in accredited herds. That is, dairy goat herds that had been CAEV-accredited for consecutive years suddenly faced unfavorable SRLV-seropositive results during the annual surveillance. In 2012, there were 666 CAEV-accredited herds, rising up to 810 in 2022; however, the number of dairy goat herds remained stable at around 400 throughout these years. Since 75% of these dairy goat herds participated in the program, the average annual percentage of herds with a loss of accreditation within this dairy goat herd population was 1.17% (0.33–2.67%).The aim of this paper is to describe GD’s experience with the loss of CAEV herd accreditation in large dairy goat farms, with an emphasis on herd characteristics, diagnostic approach, and specific follow-up scenarios. Infection dynamics using the Röst Freed Model  are discThe month of loss of accreditation depends on the date of registration within the program, and consequently, no timeline analysis had taken place.On affected farms, a huge range exists in the number of test-positive goats, varying from a single reactor  to over 100 individuals . Those single reactors had been confirmed by both resampling four to six weeks later and by confirmatory testing, and in some cases by positive tissue PCR (herd 27).Only thirteen affected herds had used bulk milk testing. In addition, six farmers  had the possibility to test monthly samples in retrospect, and found out that bulk milk ELISA results were positive one (17), four , seven (37), nine (33), or even eleven months (23) prior.Although in the majority of affected herds route of introduction still remains unclear, purchase of goats originating from herds that in retrospect had a CAE infection was most likely in at least six cases . In four cases , contact with SRLV-unaccredited or even known-infected herds was a likely source of infection. In one case (herd 18), the farmer suspected that a substantial spill-over from the milk truck that had just come from another farm with a known CAE infection could have been the cause. The owner of herd 11 suspected the goat buyer who had entered his farm with a truckload of unaccredited goats, and had backed-up his trailer into the stable, as the possible source of infection. Finally, the owner of herd 14 had several dairy goat farms, and not all of them were accredited at the time, but staff and equipment were shared between locations.The second-last column of In very rare situations, qPCR was used within the accreditation system. In six cases , tissue PCRs of lung, udder and spinal cord were conducted. Two herds (3 and 6) that had two goats that came up as PCR-negative in 2012 and 2014, respectively, were consequently considered CAEV-accredited but lost accreditation in 2015 and 2016. Another herd (11) had a similar situation with one PCR-negative in 2012, and a loss of accreditation in 2017. In 2020, herd 27 had one goat that was PCR-negative and one goat that was positive, and herd 30 tested one goat that was PCR-negative. Finally, herd 33 lost accreditation in 2020, and in 2022 had five PCR-positive goats.In six cases, monthly bulk milk samples were tested by antibody ELISA  as shown in The Dutch dairy goat industry has undergone enormous development in a few decades, with numbers increasing from an average of a few dozen animals per farm in the 1980s to an average of two hundred at the end of the 1990s, and finally to more than 1400 on average per farm in 2020 , loss ofThe average number of adult dairy goats over 1 year of age in the 38 herds that lost accreditation is 1686, which is larger than the average total number of 1411 goats on all 400 dairy goat farms in 2020 . This waThe number of positive ELISA results ranged significantly, from a single reactor to 140 out of 145 sampled goats. As shown in In herds with seropositive animals, retrospective bulk milk testing, which revealed seroconversion from four to eleven months prior to surveillance testing, confirmed its added value. Bulk milk represents all lactating goats of a herd and rules out incorrect sampling. Farmers can voluntarily subscribe to bulk milk testing, and logistics are all provided for samples being taken during milk collection.Successfully controlling SRLV depends largely on the optimal use of appropriate diagnostic tools. Immunological and clinical latencies hamper the diagnostic efficacy in SRLVs. The immunological latency causes a so-called serological gap , and thePreviously, GD investigated bulk milk samples in relation to herd SRLV status. Promising results were obtained with a putative detection limit of <3% within-herd prevalence using 1/10 pre-diluted samples, and even <1% within-herd prevalence when samples were tested undiluted. Moreover, out of the 249 bulk milk samples, all SRLV-accredited herds (n = 138) tested negative, while 50% of the samples from herds with an unknown SRLV status (n = 111) were positive . At thatAlthough several attempts have been made to improve the control or eradication of SRLVs by performing PCR in blood samples, it is our experience that a PCR only has a limited added value, and merely in tissue samples but not in blood samples.After loss of CAEV accreditation, different follow-up scenarios are discussed depending on the farmers’ wishes and possibilities. In determining potential future goals, finances play an important role. Since milk yield will most likely decrease as infection progresses, and production is the number one reason for replacements, the turnover of does will increase. Live  trade is also impacted, as goats from accredited herds represent a higher value. Goals might range from no further action and allowing CAEV to become endemic, to levels of control of spread, to total eradication. Once goals have been set, a tailor-made approach is proposed. To GD’s experience, the three main chosen goals are: (A) do nothing and start living with CAEV, (B) accept being non-CAEV-accredited and keep infection pressure low, and (C) aim at regaining CAEV-accreditation. In the latter case, several options are discussed: (1) total herd replacement, (2) artificial rearing of kids, (3) test and cull, and finally, (4) a combination of the three previous options. In general, high biosecurity levels, preferred ‘closed farming’ and in-case replacements should be bought in; quarantine and additional testing, even when goats are purchased from accredited farms, are important, as the consequences of the introduction of CAEV are enormous. Attention for other iceberg diseases such as CLA, paratuberculosis and border disease, and also zoonotic diseases such as chlamydiosis and salmonellosis underline this importance.In large dairy goat herds kept inside, CAEV does not always seem to behave like a lentivirus, and transmission can be very fast as demonstrated in this paper. It is essential for CAEV-accredited herds to detect a possible virus introduction as soon as possible to increase the change of re-accreditation. Based on sensitivity and specificity, the bulk milk ELISA with a cutoff of 0.10, as described in this paper, is a valuable and cost-effective tool for the early detection of CAEV infection.Thirty-eight herds that lost CAEV accreditation between 2012 and 2022 were followed up. The characteristics of these herds were acquired from the legally required national identification and registration data base supervised by The Netherlands Enterprise Agency, and the customer relationship management system of GD. Size of affected herds ranges from 394 to 4589 adult dairy goats. Nine herds had less than 1000 dairy goats, eighteen between 1000 and 2000, four between 2000 and 3000, and four up to 4000, with one ranging over 4000.Following unfavourable laboratory test results, these 38 farms were visited by a small ruminant GD veterinarian. Depending on the availability of samples and/or consent of the farmer, additional samples were tested.The Elitest MVV/CAEV ELISA  was used as a screening test, and the LSIVet Ruminant MAEDI-VISNA/CAEV Serum ELISA Kit  served as the confirmatory test. Based on specificity, sensitivity, and the possibility for sample pooling, these ELISAs were considered to be the most suitable ELISAs for accreditation purposes (according to fitness) for commercially available SRLV antibody tests in The Netherlands . Sera frBulk milk testing has been made available from 2018 onwards on a voluntary basis, since it is not included in the program regulations. Bulk milk testing can be performed as a precautionary measure, in addition to the program. Moreover, it can be applied as a follow-up tool after loss of accreditation. In the latter case, strategic pooled milk for groups housed together, specific age groups, exposed groups, groups at risk, or any chosen number of individual milk samples pooled can be tested.(Bulk) milk samples were preserved with sodium azide. For retrospective testing, monthly taken bulk milk samples from the preceding year were stored at −20 °C until analysis. After unfavourable serum test results, bulk milk samples were analysed to identify the tipping point between the last favourable surveillance outcome and the unfavourable results. Bulk milk samples were tested with the Elitest MVV/CAEV ELISA according to the manufacturer’s instructions, using a cutoff of 0.1.Udder, spinal cord and lung tissue samples of seropositive animals were tested using a qPCR, as described by Brinkhof et al. , until 2A Röst Freed Model describes the dynamics of virus transmission in a herd, dependent on herd size . GD applFour different herd statuses are possible within the CAEV program: unknown, infected, in observation, and accredited. Herds with a status unknown have never participated or no longer participate, and therefore no information regarding SRLV is available. After seropositives have been confirmed, a herd is considered SRLV infected. The status in observation is used for previously accredited herds that aim for re-accreditation after loss of accreditation. Accredited herds have gone through all stages of the accreditation process with a favourable result.Follow-up scenarios start with a voluntary farm visit by a specialised small ruminant GD veterinarian, preferably together with the farm’s own veterinary practitioner. During this initial visit, CAEV backgrounds are shared; possible routes of infection and future impact on farm management as well as finances are discussed; and potential future goals are determined. The farmers are urged to inform owners of herds which have been in contact and therefore are at risk, to provide them with the opportunity to actively act on this information.The characteristics of dairy goat herds with loss of CAEV accreditation show that larger herds seem to be at increased risk. Furthermore, re-accreditation seems more difficult for those larger herds, since only 8 out of the 38 herds acquired re-accreditation. Therefore, CAEV introduction should be avoided at all costs, for instance by biosecurity measures. Bulk milk testing demonstrates potential as a cost-effective tool for the early detection of CAEV infection in dairy herds. In addition to the yearly surveillance scheme, frequent bulk milk testing enables farmers to keep a close eye on potential SRLV introduction in their herd. By noticing a so-called tipping point, an alarm bell should ring that stimulates farmers to perform further investigation. Swift action could potentially reduce viral spread within and between herds, from which not only the farmer but also the industry as a whole could benefit. GD calls on others encountering similar problems to also publish these so that, collectively, programs can be improved.


The utilization of plant by-products as functional food ingredients has received increasing attention in the last decade. One such by-product generated during milk thistle oil pressing is oilseed cakes, which could be used as a novel food ingredient. Therefore, the study aimed at investigating the effects of the addition of milk thistle oilseed cake (MTOC) flour fractions obtained via dry sieving, differing in particle size , on the quality of gluten-free bread and stability of silymarin during breadmaking. The 10% addition of the fractions into gluten-free bread increased the protein, fibre, fat, ash and silymarin content. The breads with the coarse fraction had the highest content of fibre, whereas the breads with the fine fraction excelled in protein, fat and ash content. The medium fraction was characterized as the richest source of silymarin, whilst the fine fraction was the poorest. Silymarin constituents were slightly released during dough rising but also partially decomposed during baking; moreover, silydianin was the most susceptible and degraded the most. The enriched breads had better sensory and textural properties compared to the control bread. The results suggest that MTOC flour fractions can improve the potential health benefits and nutritional profile of gluten-free bread. People suffering from coeliac disease strictly adhere to a gluten-free (GF) diet since it is the only treatment. Unfortunately, this diet may represent a risk for consumers to some extent because it has been reported that it is characterized by a lower content of fibre and protein, on the one hand, and by an excessive content of fat and carbohydrates such as starch, on the other hand ,2,3.Gluten, the protein triggering typical symptoms of coeliac disease, which must be omitted in a GF diet, also plays an important technological role due to its viscoelastic properties that are responsible for the palatability, crunchiness and structural integrity of bakery products. Replacing gluten-containing flour with GF flour makes the resulting bakery product  less sensorially attractive. For example, such bread has a crumbly texture and goes stale faster. The development of GF products is, however, a technological challenge for the food industry . To avoiCoeliac disease, thus, has led scientists to search for GF alternatives to wheat flour for bakery products. As reported by Mir et al. , researcSilybum marianum L.), which is well known for its nutritious oil and presence of silymarin, a complex of polyphenolic compounds composed of six main flavonolignans  and taxifolin, which belongs to the polyphenolic group of flavanonols [A very interesting GF oil crop is milk thistle  of GF bakery products containing silymarin in its non-extracted form, such as oilseed cakes flour fractions, it must be tested whether the enriched GF bread is acceptable for consumers and if silymarin is thermally stable and will remain in baked bread, and, thus, what will be the final content in the bread. It was reported that some of the polyphenolic compounds are sensitive to food processing, mainly to the high temperatures used during baking, which usually lowers their content and alters antioxidant activity–although the rupture of cell walls due to heat can improve mobility and their bioavailability . Polyphei.To assess the effect of the addition of different MTOC flour size fractions into GF bread on the selected qualitative parameters; and,ii.To investigate the effects of bread processing (dough rising and baking) on the antioxidant activity and content of silymarin in GF breads.Considering all the facts given above, the aims of the study were:The study provides information on whether MTOC flour size fractions can be used as an ingredient suitable for GF bread with potentially elevated health benefits.Milk thistle seeds of the Mirel variety, provided by Agritec Plant Research Ltd., were conditioned on a Stephan UM/SK 5 industrial unit , equipped with a 5-litre duplicated stainless steel vessel. Conditioning was done by direct saturated steam injection (2 bar) into the duplicated vessel for 30 s while stirring the seeds with a Teflon wiper. Afterwards, the conditioned material was screw-pressed by a Komet CA59G oil expeller  with an optimal worm shaft frequency of 55 RPM. A nozzle with a diameter of 8 mm was used at the outlet of the screw-pressing unit.The prepared oilseed cakes were then disintegrated and milled using a Grindomix GM 200 knife mill  at 3000 RPM for 2 min. The resulting homogenised material was assigned as unsieved and was subsequently separated into fractions differing in particle size using AS 200 analytical vibrating sieves : coarse (>710 µm), medium (315–710 µm) and fine (<315 µm). Thus, four MTOC flour fractions in total were obtained: unsieved, coarse, medium and fine. Afterwards, all the obtained flour fractions were standardised to the same particle size using a hammer mill equipped with a 60-mesh sieve. Thus, very fine flour fractions were obtained. The reason for the particle size standardisation step was that it would otherwise be impossible to incorporate the coarse fraction into the bread due to the very big pieces and very hard texture of milk thistle achenes. All the flour fractions were then used for the fortification of GF bread and chemical analyses: basic chemical composition, analysis of silymarin and antioxidant activity. Before the analyses were conducted, the flour fractions were lyophilised. The flour fractions’ characteristics are shown in w/w): sorghum flour 23.20%, corn flour 11.60%, corn starch 5.80%, potato starch 5.80%, tapioca starch 5.80%, guar flour 0.52%, sunflower oil 2.32%, salt 0.81%, dried yeasts (S. cerevisiae) 0.58% and water 43.56%. In experimental batches, 10% of the GF flour blend was replaced by the unsieved, coarse, medium or fine MTOC flour fractions. The amount of added oil, salt, yeasts and water remained unchanged. The detailed recipe is shown in MTOC flour fractions were incorporated into the GF control bread based on a recipe that was composed of the following ingredients  were filled into cuboid baking forms (three forms per group) and the dough was allowed to rise at 33 °C for 60 min. The final step was the baking of the bread at 170 °C for 45 min. The process of rising and baking was conducted in a combi oven . Three loaves of bread were made for each group.To determine the effect of dough rising and the baking of the bread on the concentration of silymarin complex, and antioxidant properties, samples of the dough were taken before rising, after rising and after baking.Afterwards, all dough and bread samples intended for chemical analyses  were immediately put into a freezer at −18 °C and then lyophilised and stored at −18 °C until analysis. Fresh bread samples were used for the analyses of specific loaf volume, colour and texture, and also for sensory analysis.The content of crude protein, crude fat, ash, water and non-soluble fibre fractions were analysed in the bread samples and flour fractions.The crude protein content of the samples was measured using a Rapid N Cube elementary analyser  based on a modified combustion Dumas method according to the manufacturer’s instructions. The crude protein content was calculated as nitrogen content multiplied by a factor of 6.25 .The crude lipid content was determined using an ANKOM XT10 extractor  with petroleum ether as a solvent. Firstly, samples (1 g) were placed in filter bags. Then, petroleum ether extraction was conducted using the extractor. The fat content was calculated as the relative difference in weight of the bag containing the sample before and after the extraction .The moisture content was analysed by a gravimetric method by drying the sample at 105 °C until constant weight. The ash content was also gravimetrically determined by combusting the sample at 550 °C for 16 h in a muffle furnace to obtain a light grey ash .The neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) were analysed according to the modified method of Van Soest et al.  using a Based on these analyses, the cellulose, hemicellulose and nitrogen-free extract (NFE) content were calculated as follows:NFE = 100 - .The total energy (kJ/100 g) of the bread samples and flour fractions was calculated using conversion factors according to Regulation (EU) No 1169/2011 of the European Parliament and Council .n = 9). Three samples from each group were analysed twice in the case of crude fat and ash content (n = 6). Each MTOC flour fraction was analysed three times .Each of the three samples in each group was analysed three times in the case of ADF, ADL, NDF and crude protein (v/v) in a 15 mL plastic centrifuge tube. The sample was then extracted in an ultrasound bath  for two hours, and the tubes were gently shaken every 15 min. Then, the extract was centrifuged at 5869× g  for 30 min (20 °C) and stored at −18 °C until analysis.Prior to the extraction of silymarin complex, the dough and bread samples were milled into a fine powder. The MTOC flour fraction samples were lyophilised and directly used for the extraction. Approximately 0.2 g of the lyophilised sample was mixed with 10 mL of 80% methanol (v/v) and B, composed of 100% methanol. The mobile phase flow rate was set at 0.2 mL/min and the elution gradient was set as follows: 0–15 min: 33% B; 15–20 min: 33–38% B; 20–35 min: 38% B; 35–40 min: 38–33% B. Re-equilibration of the column took five minutes before the injection of the next sample.Analysis of the silymarin complex, namely taxifolin, silychristin, silydianin, silybin A, silybin B, isosilybin A and isosilybin B, content in the dough, bread and MTOC flour fraction samples was carried out using a high-performance liquid chromatograph  coupled to a triple quadrupole tandem mass spectrometer equipped with an electrospray ionization source . Five µL of the extracted and appropriately-diluted sample was injected into the system. The separation was accomplished using a Phenomenex Kinetex C18 core-shell column  at 35 °C. Two mobile phases were used: A, composed of 5% methanol with 0.5% formic acid (2) was 11 L/min with a pressure of 35 psi and temperature of 350 °C. The multiple reaction monitoring (MRM) mode was used for the quantification of the target compounds. The optimised parameters of the MRM mode for each compound are presented in After the separation, the samples were introduced into the mass spectrometer operating in negative ionisation mode with the following settings: the capillary voltage was set at −3.5 kV, the flow rate of drying gas (Nn = 3).Results are expressed as mg/g DM (dry matter). Three measurements were conducted per group of dough, bread, or flour  was mixed with 975 µL of methanolic DPPH solution adjusted to an absorbance of 0.8 at 515 nm. The reaction mixture was kept in the dark at room temperature for two hours. Afterwards, the absorbance was measured at 515 nm against methanol using a Thermo Scientific Biomate 5 spectrophotometer . DPPH solution without the sample served as the blank. Trolox was used to prepare a calibration curve and results are expressed as mg of Trolox equivalent per gram of sample on a DM basis (mg TE/g DM). Three samples per group were analysed, thus 2 was dissolved in 20 mL of 5 mM phosphate buffer (pH 7.0) to produce ABTS•+ radicals within 30 min. The solution containing the radicals was then diluted with 5 mM phosphate buffer and the absorbance at 734 nm was adjusted to 0.8. Then, 0.1 mL of the sample extract was added to 1 mL of the working ABTS•+ radical solution. This mixture was then left to react for 1 min in the dark. Finally, the absorbance at 734 nm was measured using the same spectrophotometer as that used in the DPPH method. The results are expressed as mg TE/g DM. Three samples per group were analysed (n = 3).ABTS method: Firstly, ABTS (54.9 mg) reagent together with 1.0 g MnO3/g and was calculated as loaf volume divided by its weight. Three breads per group were analysed (n = 3).The baked bread loaves were left to cool to room temperature, weighed and then their volume was measured by the rapeseed displacement method using an OBK volumeter  . The spen = 6).The colour analysis of the bread crumb was accomplished using a ColorEye XTH spectrophotometer colour analyser  based on the modified method of Bedrníček et al. . The resThe bread samples were put into a high-density polyethylene bag and stored for 96 h at room temperature in the dark to simulate the usual domestic storage conditions. During the storage, the hardness of the bread samples was analysed after the cooling of the bread , and then after 24, 48, 72 and 96 h.n = 3) and the results are expressed as N (newton).The texture hardness of the bread crumb was measured by a TA.XT Plus texturometer  according to Moore et al. . The anan = 14. Water was served as a taste neutraliser and was drank before the judging of the next sample.The sensory properties of the breads were evaluated by a 14-member semi-trained sensory panel based on the modified methodology described by Bedrníček et al. . Bread sp < 0.05, differences were considered as statistically significant.The results of all analyses are presented as mean ± standard deviation. Statistical analysis of obtained data was conducted using Statistica 12 software . To evaluate the statistically-significant effect of flour fraction additions into breads on basic chemical composition, sensory analysis, colour, texture and specific loaf volume analysis, one-way analysis of variance (ANOVA) was used. The same statistical test was used to detect significant differences between flour-fraction samples in all conducted analyses. Two-way analysis of variance was used to determine statistically significant differences between samples (doughs and breads) fortified with the different flour fractions (factor one) and between each processing step  in the case of silymarin analysis and antioxidant activity. Two-way ANOVA was also used for the data analysis in the case of the storage experiment, where the first factor was different flour-fraction addition and the second factor was storage time. Fisher’s LSD post hoc test was then applied to find out the differences between groups. All statistical tests were conducted at a significance level of α = 0.05; therefore, if The chemical composition or nutritional characteristics of foods are a very important aspect in terms of a healthy diet. The basic chemical composition  of the fortified GF breads is presented in p < 0.05) increased up to 8.07% in the case of the fine flour fraction. The lowest increase in crude protein was observed in the bread with the coarse fraction (5.59%). In general, with decreasing particle size of the flour fraction, the crude protein content increased. From this point of view, it seems advisable to use MTOC flour fractions, especially finer fractions, as a source of proteins for GF bread.GF bread often contains lower amounts of protein than gluten-containing bread . In thisRegarding fat content, a similar trend was observed, where the lowest content was measured also in the control bread (4.56%) and the highest in bread containing fine flour fraction, 8.07%, which is an almost two-fold increase compared to the control. The crude fat increased in the enriched breads due to the presence of residual fat in the flour fractions, where crude fat ranged between 9.40 and 16.66%, as shown in p < 0.05) higher levels of insoluble dietary fibre for all fractions . The highest levels of cellulose, hemicellulose and lignin were found in the breads with coarse and medium fractions, while a slightly lower content was found in breads with the unsieved fraction. Bread samples with the fine fraction had the lowest content of fibre among all the fortified breads, although it was still significantly (p < 0.05) more than that in the control bread. Thus, it seems that MTOC, especially the coarse fraction, is a good source of fibre. This finding is in agreement with Bortlíková et al. [Incorporation of the MTOC flour fractions into GF bread led to significantly  after fortification with MTOC flour fractions and the order is opposite to that of crude protein and fibre content. As in the case of fibre, Matos Segura and Rosell [The content of ash in the breads with MTOC flour fractions was found to be in this order: fine > unsieved > coarse ~ medium > control, which again shows that the content increased significantly , more specifically, the content of NFE decreased from 48.43% (control bread) to 40.47% (bread with the fine fraction), whereas the breads with unsieved, coarse and medium flour fractions had 42.22, 41.97 and 41.68% NFE, respectively. However, the fortified samples did not differ from each other (p > 0.05). The recipe of the control bread was based mainly on starchy ingredients  energy content (1058–1084 kJ/100 g) compared to the control samples (1122 kJ/100 g). Although it was reported by Cornicelli et al. [The energy value of food is closely related to its chemical composition. The fortified bread samples showed statistically lower p < 0.0 energy ci et al.  that 10–p > 0.05).The water content in all the breads was very similar (36.31–37.02%) and did not differ significantly (p < 0.05) on the basic chemical composition of the bread samples, except for water content. The changes in bread chemical composition after the addition of the MTOC flour fractions reflect the chemical composition of the flour fractions themselves, as shown in In general, the addition of the MTOC flour fractions had a significant effect  affected the content of silymarin and its individual constituents.Food processing methods can modify both the structure and quantity of polyphenols in foods, which can result in altered properties . Therefop < 0.05) after rising. In particular, the increase was 13% on average, but the highest increase was observed in the case of taxifolin (17%) and the lowest in the case of silydianin and isosilybin B (8% for both). It was reported that bioprocessing, such as fermentation, including dough rising, could release some of the polyphenolic compounds [S. cerevisiae (as a part of the bread recipe in this study) are not the only microorganisms involved in dough rising, since many other microorganisms are naturally present in flour and become active during rising. In addition to microbial enzymatic activity, it is also possible that enzymes of plant origin present naturally in flours can be activated [The rising of the dough at 33 °C for 60 min had a positive effect on all flavonolignans because the content in the doughs grew significantly . Approximately a 20% decrease was observed for the whole silymarin complex, thus 80% remained after baking. Individual flavonolignans, except silydianin, decreased by about 25–17%. An interesting finding was that silydianin exhibited the highest loss during baking and only 52% remained. The results imply that the compounds are moderately thermally stable, but silydianin is sensitive to elevated temperatures. This finding might be related to the different structure of silydianin compared to other flavonolignans, which is unique and somewhat complicated due to the presence of a keto group next to the hemiacetal moiety [The next step in the breadmaking was baking at 170 °C for 45 min. The internal temperature of the dough/bread was monitored using a puncture thermometer. In the first phase of baking, the internal temperature rose from 33 °C (rising temperature) during the first 15 min and then, after stabilization, it ranged between 95 and 100 °C for the remaining 30 min until the baking was done. Baking under these specific conditions affected all silymarin flavonolignans significantly  and the ABTS assay . This means that the samples containing silymarin flour fractions had significantly (p < 0.05) higher antioxidant activity than the control samples. Also, the samples with the medium fraction exerted the highest antioxidant activity, followed by the samples with the coarse fraction, then the unsieved fraction, with the lowest antioxidant activity exhibited by the samples with the fine fraction.Antioxidant activity mirrored the trend in silymarin content in the dough and bread samples. Thus, a very significant correlation was observed for the DPPH assay  increase in antioxidant activity in the fortified dough samples after rising, since only a 4% increase on average was observed. Similarly, baking also did not affect antioxidant activity significantly (p > 0.05) using the DPPH method, because only an 8% loss (on average) was observed with relatively high variability (± 9%) among samples. On the other hand, the ABTS assay showed more precise results which also corresponds with a higher correlation coefficient with silymarin content almost reaching 1 . Moreover, both factors, rising and baking, had a significant effect (p < 0.05) on antioxidant activity. Thus, a significant increase (5%) was observed after rising and a 9% loss after baking. A probable explanation for the discrepancy between the DPPH and ABTS results may be found in the different specificity and sensitivity of these assays. It was reported that ABTS antioxidant capacity is usually higher compared to that measured by DPPH assay [In terms of the influence of the processing phase on the antioxidant activity of the samples, we obtained relatively interesting results. Although a high correlation with silymarin content was found, as mentioned above, the DPPH assay did not show any significant  compared to gluten-containing breads, which has compelled scientists to look for new ingredients or develop new recipes that enhance the quality of GF breads . Thus, wp < 0.05) affected by the addition of the MTOC flour fractions; in particular, the volume decreased. The highest specific loaf volume was observed in the control group (2.76 cm3/g) and the lowest in the unsieved and coarse groups (both having 2.59 cm3/g). However, the specific loaf volume of the samples with the fine flour fraction did not differ significantly (p > 0.05) from that of the control group (2.69 cm3/g). This trend could probably be caused by a higher content of insoluble fibre, which could make the specific loaf volume lower, as summarized by Sabanis et al. [Specific loaf volume was significantly . The control bread was very light, while the fortified breads were much darker and had an appearance similar to rye bread [The colour of the bread was substantially affected by the addition of the MTOC flour fractions. Thus, all three colour parameters  were affected significantly  by both factors (MTOC flour fraction and storage time). Samples containing MTOC flour fractions were much harder than the control bread, which can be linked to the fact that they contained more insoluble fibre, proteins and fat, which usually affect texture [Bread staling is a process deteriorating the quality of a bread that is stored for a longer period and is characterised mainly by a harder texture. In order to assess bread staling during storage at room temperature, the texture of bread samples packed in a high-density polyethylene bag was measured 0 (fresh), 24, 48, 72 and 96 h after baking. The design of the storage experiment was chosen to simulate the usual conditions at home or a supermarket. The texture hardness of the breads was affected significantly  only in the case of the pleasantness of colour and overall impression. Therefore, the differences within the remaining characteristics were insignificant (p > 0.05). Regarding the colour, the control samples received the worst score (4.0), followed by the bread with the fine flour fraction (5.8). On the other hand, the best evaluation was observed in the samples with the unsieved fraction (7.5). This result could be due to the fact that darker breads could be more positively accepted due to the expectation of higher health benefits [A fourteen-member panel evaluated the following sensory characteristics: pleasantness of colour, odour, texture and taste, bitterness and overall impression . The panbenefits , as alreThis paper brings novel findings and describes the possible utilization of milk thistle oilseed cake as an ingredient for GF bread for the first time. Based on the results shown in this study, it seems that milk thistle oilseed cakes are a promising ingredient for GF bakery products. If the oilseed cakes are sieved and separated into different fractions and then added into GF bread, the resulting enriched breads will have different chemical compositions and properties. All enriched breads had a higher content of protein, fat, fibre and silymarin, but, a lower content of NFE, compared to the control GF bread. Differences between the fortified breads were also obvious. The breads with the coarse fraction had a higher content of fibre and a lower content of protein, fat and ash compared to breads with the fine fraction, which showed an opposite trend. Moreover, during bread making, we observed a certain increase in silymarin content after dough rising, but a moderate decrease in silymarin after baking, indicating sensitivity to heat. It should be mentioned that this might also be related to the presence of water and other compounds that can react with silymarin. If rising and baking are compared, then baking has a more significant impact on silymarin quantity and quality, since the most sensitive compound was silydianin. In terms of the sensory properties of the breads, the addition of the fractions caused a much darker colour of the crumb and also a harder texture compared to the control. At the same time, the enriched breads were preferred more than the control bread by the assessor panel.Practical and economic aspects should also be taken into account. Every processing step, including sieving/fractionation, is time- and cost-consuming and producing more than three fractions might be unnecessary. We, therefore, suggest that it might be enough to produce a very-fine fraction, e.g., with a particle size of <200 µm, that can be considered a rich source of proteins and a coarser fraction (>200 µm) with higher content of fibre and silymarin, which also increases the yield of both fractions. The two fractions might have different applications in the food industry. The finer fraction might enhance the nutrition profile of GF bread recipes that are mainly based on starchy ingredients, or can be used, for example, to produce “high-protein” products. On the other hand, the coarser fraction might be used for breads as a source of fibre-rich material that also delivers high amounts of silymarin to the final product. However, we wouldn’t recommend an addition level higher than 10% for any fraction.After considering all the results, a few questions arise from the present study that should be examined in the future, e.g., firstly, what is the bioavailability of silymarin from the GF breads, and secondly, can the fortified bread enhance liver function, or possess other health benefits related to silymarin?


Levansucrase LevS from Leuconostoc mesenteroides B-512F is a multidomain fructansucrase (MD-FN) that contains additional domains (ADs) to the catalytic domain. However, the understanding of the effect that these ADs have on enzyme activity remains vague. To this aim, structure-function relationship studies of these LevS ADs were performed by evaluating both biochemical properties and the enzymatic capacity of truncated versions of LevS. Joint participation of the N- and C-terminal domains is essential for stability, activity, specificity, and polymerization processes. Specifically, the N-terminal region is involved in stability, while the transition region plays an essential role in the transfructosylation reaction and polymer elongation. Based on our results, we suggest that ADs interact with each other, adopting a U-shaped topology. The importance of these ADs observed in the MD-FN of the Leuconostocaceae family is not shared by the Lactobacillaceae family. Phylogenetic analysis of LevS AD suggests that MD-FN from Lactobacillaceae and Leuconostocaceae have different evolutionary origins. This is the first study on the structure-function relationship of multidomain levansucrases from the Leuconostocaceae family. Our results point towards the functional role of AD in MD-FN and its involvement in fructan synthesis. Fructansucrases (FNs) are enzymes that are responsible for the synthesis of fructan, a fructose polymer with important applications in the food, cosmetic, and pharmaceutical industries ,3,4,5,6.FNs belong to the family 68 of glycosyl hydrolases, whose members display a catalytic domain with a five-bladed β-propeller architecture. Each β-sheet adopts a “W” topology with four antiparallel β-strands. The β-propeller contains a negatively charged funnel-like structure in the center. This structure has two aspartic acid residues acting as the nucleophile and transition state stabilizer, and one glutamic acid serves as a general acid/base residue . The traThe complete levan synthesis pathway has been recently identified, pinpointing the amino acids that make up the five product/acceptor binding subsites. These subsites help the enzyme carry out fructan elongation . FNs canMost FNs consist of a single catalytic domain with an approximate molecular mass of 45 to 65 kDa. However, a cluster of FNs from Gram-positive bacteria, specifically from the Lactobacillales order, have a multidomain structure. Consequently, their molecular mass ranges from 80 kDa to 170 kDa. Multidomain FNs (MD-FNs) are composed of three regions: (I) an N-terminal domain, (II) a conserved catalytic domain, and (III) a C-terminal domain. The MD-FN architecture resembles the structure observed in glucansucrases (GNs), which are enzymes that belong to family 70 of glycosyl hydrolases, which also have a multidomain structure. These enzymes use sucrose as a substrate to synthesize a glucose polymer, denominated glucan . SeveralLactobacillus johnsonii. The structure of this version contains the catalytic domain and a minimal region containing the additional domains. The catalytic domain displays a typical β-propeller structure, with the catalytic site in the center and bottom of the funnel-like structure. Thirty-three residues of the N-terminal region were identified in the structure, which make up two motifs, α1 and α2 helices. The N-terminal domain extends away from the catalytic domain and is connected to it through 12 residues. Based on this structure, the author suggests that the N-terminal and C-terminal domains of InuJ are far from the active-site pocket. Therefore, it is unlikely that they are critical for the transfructosylation process , the trisaccharides 1-kestose [β-D-Fruf-(2→1)-β-D-Fruf-(2→1)-α-D-Glcp], 6-kestose [β-D-Fruf-(2→6)-β-D-Fruf-(2→1)-α-D-Glcp], and neo-kestose [β-D-Fruf-(2→6)-α-D-Glcp-(1→2)-β-D-Fruf] as transfer products to sucrose, and levantriose [β-D-Fruf-(2→6)-β-D-Fruf-(2→6)-β-D-Fruf], a trisaccharide of the oligolevan series. Additionally, two series of levan-type fructooligosaccharides (FOS) were identified by comparison with standards: one derived from the elongation of 1-kestose  products . The comThe LMW product profile synthesized by LevSΔN and LevSΔC remained un-changed. In contrast, for LevSΔTnC, the production of 1K-FOS and 6K-FOS series was detected up to approximately DP10, corroborating the effect on the elongation capacity of the enzyme observed in HPSEC analysis. Finally, for LevSΔNC and LevS/Cat enzymes, no LMW products were observed, supporting the observation that sucrose was only hydrolyzed .By comparing the distribution of fructose in the reaction products, it was possible to quantify the hydrolytic activity and the effect on the transferase activity to high- and low-molecular weight products between the different versions of LevS. As shown in Proteins are stabilized by different intermolecular forces: hydrogen bonds, hydrophobic interactions, disulfide bonds, charge–charge interactions, salt bridges, and π* interactions. Subtle changes in the displacement of the atoms due to amino acid deletion change the conformation of the protein, increasing or decreasing its stability. Thus, in the case of an enzyme, the elimination of several amino acids can change the optimum conditions of reaction such as stability, leading to enzyme denaturation .Leuconostoc, and only a few studies have addressed the AD impact in MD-LV from Lactobacillales. Van Hijum et al.  and Ni et al. (2021) performed and characterized C- and N-terminal domain truncated versions of MD-IN and MD-LV from Lactobacillus reuteri; however, they did not compare the effects with the WT versions (both MD-IS and MD-LV) [Lactobacillus johnsonii [Our results showed that the truncated versions of LevS displayed modified biochemical properties. In the case of optimal pH, both the N- and C-terminal domains were implicated in maintaining the optimal pH of 6. It is important to highlight that the role of the additional domains has not been studied in other MD-LV from d MD-LV) ,31,32. Dd MD-LV) . Similarohnsonii .Leuconostoc citreum CW28. Nonetheless, removal of the C-terminal domain and the Tn region caused a decrease in the optimal temperature by 5 °C in the truncated version IslA3. This change was maintained in the truncated version IslA4, which maintains only the catalytic domain and 100 amino acids from the N-terminal [Lactobacillus johnsonii NCC 533 MD-IS InuJ, the truncated versions did not show optimal temperature changes (55 °C) [Regarding optimal temperature, the C-terminal domain does not seem to be related to this property; however, both the N-terminal domain and the Tn region are involved in the optimum biocatalysis temperature. In a similar way, Del Moral et al. (2008) observed that the optimum temperature (35 °C) was unchanged when eliminating the C-terminal domain from the MD-IS IslA from L. citreum, where it was observed that the N- and C-terminal domains contribute to the stabilization of the catalytic domain, since the elimination of both domains resulted in a threefold decrease in stability compared to the WT enzyme [Leuconostoc mesenteroides NRRL B-512F, where truncated versions in the C-terminal domain were found to be more susceptible to thermal denaturation [L. reuteri 121, in accordance with those observed in the MD-IS IslA [To demonstrate if the observed changes in the biochemical properties were related to enzymatic stability, we analyzed this parameter. Our results indicate that the N- and C-terminal domains are jointly essential for enzyme stability, but not independently. These results resemble those reported for the MD-IS IslA of T enzyme ,28. Howeturation . Recentl-IS IslA .In general, we can conclude that the N- and C-terminal domains are involved in the enzymatic stability of LevS, probably through intermolecular forces from the amino acids that compose them, with the N-terminal having a more relevant role. A destabilization of the enzyme folding is perhaps responsible for the changes in biochemical properties in the truncated versions.Leuconostococacea family, which are more efficient for transfructosylation reactions, have a more complex structure than single-domain enzymes [It has been demonstrated that the enzyme nature and the reaction conditions are important aspects of fructansucrase catalysis . The MD- enzymes . In thisRegarding reaction specificity, although LevS has a predominant transfructosylase activity, the enzyme became mainly hydrolytic when one of the terminal domains was missing, including the Tn region . These rInterestingly, the strong difference observed in transfructosylase activity between the LevSΔC and LevSΔTnC versions, both C-domain-lacking enzymes with and without the Tn region, respectively, highlights the importance of the Tn region in the reaction specificity. In this case, stability was not compromised. Therefore, the Tn region has an important role in the enzymatic catalysis. Currently, these regions have not been studied in other MD-FNs.Lactobacillus gasseri [Two mechanisms of polymer elongation have been reported for FNs. The processive mechanism is characterized by a low synthesis of FOS with low DP, and the predominant synthesis of HMW polymers. In contrast, the non-processive mechanism involves the formation of oligosaccharides that gradually grow and form a distribution of LMW products . Accordi gasseri .L. citreum, no change was observed in the distribution of the HMW polymer produced by IslA and its truncated versions in the N- and C-terminal domains [Lactobacillus reuteri 121; however, in the MD-IS of this same microorganism, the elimination of this region results in some minor changes to the product profiles [L. reuteri also did not show any changes in the distribution of synthesized HMW polymers [Removal of both the N- and C-terminal domains in the truncated version LevSΔNC drastically affected HMW polymer synthesis capacity, as well as the elimination of all the additional domains in the truncated LevS/Cat version . In cont domains . In enzyprofiles . Similarpolymers .L. johnsonii, which synthesizes FOS from inulin with a DP15, while the WT enzyme synthesizes only kestose and HMW polymers [L. sanfranciscensis TMW 1.392 did not show a change in the FOS distribution concerning the WT enzyme [The LevS enzyme produces FOS up to DP8; however, the truncated versions LevSΔNC and LevS/Cat are hydrolytic since no LMW products were observed, showing that the N- and C-terminal domains are necessary for the transfructosylation reaction. These results contrast with those reported for the N- and C-terminal domain truncated version of the inulosucrase InuJ from polymers . On the T enzyme .We estimated the specificity of the transfructosylation reaction to produce HMW polymers or LMW products for LevS and the different truncated versions . We obseOur results support the idea that LevS ADs are associated and interact with the catalytic domain. Particularly, the N-terminal and Tn region could interact jointly with the catalytic site and participate essentially in the transference of the fructosyl unit to the acceptor molecule. This role resembles those observed in the additional domains present in the glucansucrases of the GH 70 family. These multidomain enzymes have a U-shaped fold where the additional N- and C-terminal domains interact with each other, giving rise to five structural domains. Domain A is the catalytic domain, domain B contributes several amino acid residues to the substrate acceptor and donor binding sites, and domain C is the base of the “U”. Domain IV functions as a hinge, helping domain V to bring the acceptor closer to the catalytic site . TherefoLactobacillales order. For instance, in the case of MD-FNs from the Lactobacillaceae family, Van Hijum et al. (2002) studied a truncated version of MD-IN from Lactobacillus reuteri, which lacks the C-terminal region implicated in anchoring the enzyme to the cell wall. The authors found no difference in the reaction products synthesized by the C-terminal truncated version compared to those produced by purified enzymes from the wild-type strain [Lactobacillus sanfranciscensis that lacks the N-terminal repeats. They found no effect on the kinetic properties of the MD-LS or the product profile generated from sucrose when removing the N-terminal repeating units [L. reuteri, which showed the highest activity among all the truncated versions studied. The authors conclude that N-terminal truncation has little influence on fructan biosynthesis [As we mentioned previously, very few researchers have studies the structure–function relationship of additional domains in MD-FNs of the e strain . On the ng units ,37. Ni eynthesis . These rLeuconostocaceae family have shown the importance of the N- and C-terminal regions in the catalytic capacity of these enzymes. Olivares-Illana et al. (2003) identified and characterized the MD-IS IslA from Leuconostoc citreum CW28. They also constructed two truncated versions lacking the C-terminal region and found a negative impact on transferase activity and enzyme stability [L. mesenteroides. Finally, our work supports the hypothesis that these additional domains have allowed these proteins to evolve towards enzymes with a better transfructosylation capacity.In contrast, studies regarding the structure-function relationship of MD-IS from the tability . Subsequtability . Our anaThe similar architecture of the MD-FNs from the Lactobacillaceae family suggests that these domains have a common evolutive origin. However, the difference in the functions of additional domains from MD-FNs in Lactobacillaceae and Leuconostocaceae families suggests that these domains diverged their evolutive function at some point in time. A phylogenetic analysis of the C-terminal and N-terminal additional domains for both the Leuconostocaceae and Lactobacillaceae families demonstrates that the additional domains from the Leuconostocaceae family belong to other clads, and its roots diverged recently in time from the AD belonging to MD-FNs from the Lactobacillaceae family . Based oLeuconostoc mesenteroides B-512 F. Our analyses showed the participation of the additional domains in several biochemical properties and enzymatic catalysis. The N-terminal is relevant to stability; however, the conjunction of both N- and C-terminal domains is critical to the stability and, therefore, the enzymatic activity. This domain conjunction is also involved in specificity and the polymerization reaction. On the other hand, the Tn region is implicated in the transfructosylation reaction and polymer elongation, highlighting the importance of this region for catalysis developed by the LevS enzyme. The relevance of the conjunction of additional domains in catalysis leads us to suggest that the MD-FNs of the Leuconostocaceae family adopt a U-shaped topology where the additional domains interact with each other, similar to those described in the MD-GN enzymes. Finally, the importance of the additional domains observed in the FNs of the Leuconostocaceae family is not shared among MD-FNs of the Lactobacilleaceae family. Therefore, we suggest that although the architecture of the MD-FNs for the order of Lactobacillales is similar, the additional domains diverged evolutionarily, giving them different functions.In this work, we studied the role of the additional domains of MD levansucrase LevS from


We used RoseTTAFold to assemble full-length models for the six human ferlin proteins . Our full-length ferlin models were used to obtain objective domain boundaries, and these boundaries were supported by AlphaFold2 predictions. Despite the differences in amino acid sequence between the ferlin proteins, the domain ranges and distinct subdomains in the ferlin domains are remarkably consistent. Further, the RoseTTAFold/AlphaFold2 in silico boundary predictions allowed us to describe and characterize a previously unknown C2 domain, ubiquitous in all human ferlins, which we refer to as C2-FerA. At present, the ferlin domain-domain interactions implied by the full-length in silico models are predicted to have a low accuracy; however, the use of RoseTTAFold and AlphaFold2 as a domain finder has proven to be a powerful research tool for understanding ferlin structure.Ferlins are complex, multi-domain proteins, involved in membrane trafficking, membrane repair, and exocytosis. The large size of ferlin proteins and the lack of consensus regarding domain boundaries have slowed progress in understanding molecular-level details of ferlin protein structure and function. However,  Six parll types . Three oll types . Otoferlll types , and is ir cells , 7. Mutair cells . Myoferlir cells  and has ir cells , 11. Ferir cells –17; howeir cells –20 and Fir cells –23 are aβ-strands that fold around a central Greek key motif with two β-sheets that form a β-sandwich [2+ binding pocket and are labeled loops 1, 2, and 3 [2+, the C2 domains that do bind Ca2+, such as the two tandem C2 domains of synaptotagmin-1, possess up to six conserved acidic residues within the binding pocket with which to coordinate divalent cations [2+ binding pocket form the basis of the Ca2+-dependent peripheral phospholipid interaction that is characteristic of many C2 domains [The defining feature of ferlin proteins is their multiple tandem C2 domains. The prototypical C2 domain is approximately 130 residues in length and possesses eight sandwich . There asandwich . Three l2, and 3 . While n cations . Hydroph domains .β-strands per C2 domain and ferlin molecules dominated by unstructured linker sequences. With the advent of AlphaFold2 and RoseTTAFold [Early analysis of dysferlin’s primary sequence concluded that there were at least two C2 domains in the protein . Later aeTTAFold , 36, we Overlapping primary sequences of each of the ferlin family members were submitted to the Robetta server for the structural assessment using the RoseTTAFold algorithm . Assuminβ-strands that fold with a characteristic C2 domain topology [β-strand of the domain through to the last residue that contributes a backbone hydrogen bond to the eighth β-strand of the domain. Loops that significantly deviate from the typical Type-II C2 pattern, as defined by the C2A domain of dysferlin, and are unique to the ferlin family were defined as subdomains. A more extensive definition of each subdomain is provided in the results section. A table with ferlin domain boundaries is in the supplementary section  algorithm was used to calculate electrostatic interactions. Equilibration runs used the NVT ensemble at 300 K. Molecular dynamics production runs were performed under NPT conditions at 303.15 K. The domain models were extracted and analyzed using VMD.Molecular dynamics (MD) systems were prepared using Charmm-GUI  to regulTen aligned snapshots were extracted from the MD simulations. From the set of 10 snapshots, a representative model of each domain was selected based on its near-zero QMEAN Z-score as computed by SWISS-MODEL . Violatihttp://imed.med.ucm.es/Tools/sias.html) for each domain  fusion tag and a tobacco etch virus (TEV) cleavage site. The human dysferlin C2-FerA domain expression plasmid was transformed into Mix & Go BL21 Gold (DE3) competent cells prepared using Zymo Research’s Mix & Go! E.coli Transformation Kit and Buffer Set (T3002). The plasmid/competent cell mixture was plated on LB-agar plates supplemented with 50 μg/mL kanamycin and incubated at 37 ℃ overnight. Multiple colonies were used to inoculate 100 mL of Luria Broth (LB) containing 50 μg/mL kanamycin in a 250 mL baffled Fernbach flask. The inoculated media was grown in a shaking incubator at 250 rpm and 37 ℃ for 12–16 hours. 15 mL of the LB starter culture was used to inoculate a 2800 mL baffled Fernbach flask containing 1 L of terrific broth (TB) supplemented with 50 μg/mL kanamycin. The inoculated media was grown in a shaking incubator at 250 rpm and 37 ℃ for four hours or until the culture reached an OD600 of at least 2.0. Next, the shaking incubator was cooled to 18 ℃, and protein expression was induced by the addition of 400 μL of 1 M IPTG. After induction, the culture was grown for an additional 24 hours in the shaking incubator (250 rpm and 18 ℃). The culture with the expressed dysferlin C2-FerA domain was harvested by centrifugation and the cell pellets were flash-frozen in liquid nitrogen and stored at -80 ℃ until needed.A codon-optimized human dysferlin cDNA gene was purchased from Addgene (Plasmid 67878) . The DNA® column using a Bio-Rad NGC chromatography system. The TEV protein did not bind and most of the MBP did not bind to the Q-Sepharose® resin. C2-FerA was eluted from the ion-exchange resin by applying a gradient from 0 M to 1 M NaCl over 30 minutes at a flow rate of 5 mL/min. The fractions containing the dysferlin C2-FerA domain were pooled and concentrated 1–0.5 mL using an Amicon®Ultra-15 Centrifugal Filter Unit with a 10 kDa molecular-weight cut off (UFC901024). The concentrated protein was further purified by size exclusion chromatography using Superdex-75 resin. Purity was assessed using SDS PAGE Any-kD Mini-PROTEAN TGX Stain-Free gels from Bio-Rad and protein concentration was quantified using absorbance at 280 nm and the calculated extinction coefficient of 46870 M-1 cm-1 from ProtParam on the ExPASy web server [Fig 7A).To begin the purification, cells were thawed in lysis buffer , ruptured using a Microfluidics M-110EH microfluidizer, and cell debris was pelleted using a Beckman JA-20 rotor at 19,500 rpm and 4 ℃ for 1 hour. The clarified supernatant was applied to a 10 mL Ni-IDA (His6-Ni Superflow Resin) affinity column, which was equilibrated with lysis buffer, and incubated at 4 ℃ overnight. The supernatant was allowed to pass through the column and 200 mL lysis buffer including 20 mM imidazole was used to wash the column. Finally, human dysferlin C2-FerA was eluted with 35 mL lysis buffer including 250 mM imidazole. The resulting fusion protein was cleaved with 4 mg of Tobacco Etch Virus (TEV) protease at 4 ℃ overnight. Next, the TEV-cleaved protein was loaded onto a 30 mL GE-Healthcare Q-Sepharose Fast Flowb server  μM C2-FerA using a J-850 spectropolarimeter from JASCO Corp. Data collection was performed at room temperature with an acquisition rate of 1 nm/sec and a data pitch of 0.1 nm. Additionally, CD spectra of 10 mM sodium phosphate pH 7.4 alone were collected and subtracted from the C2-FerA CD spectra to produce the final CD spectra used for fitting. The C2-FerA buffer-subtracted CD spectra were normalized to mean residue ellipticity and the secondary structure was estimated using the BeStSel webserver [Fig 7B).Purified dysferlin C2-FerA was buffer exchanged using a disposable PD 10 desalting column (Cytiva 17–0851-01) into a 10 mM sodium phosphate pH 7.4 buffer for circular dichroism (CD) spectroscopy. The CD spectrum was collected from 250–180 nm with 4 ebserver , 50. We 2+ and phospholipid membranes without a defined 3D structure [Ferlin proteins have generally been thought of as flexible proteins that interact with Catructure . In facttructure , 52. Thetructure . HoweverAn early prediction of the C2 domain topology in dysferlin suggested the presence of both Type-I and Type-II C2 domains . The incInterestingly, our ferlin models also show long conserved insertions within the predicted loops of many of the C2 domains. We refer to these loop insertions as subdomains. Here, we define subdomains as any C2 domain loop insertion greater than 20 amino acids (relative to the reference dysferlin C2A domain) that possess conserved secondary structural elements across at least two human ferlin paralogs.2+-binding pocket, so it cannot coordinate Ca2+ [2+ binding region (CBR) typically used to coordinate Ca2+. Therefore, we predict that the human Fer1L5 C2A domain does not possess the capacity to bind Ca2+. β-strand 3 of the Fer1L5 C2A model has a poly-basic region similar to β-strand 3 of dysferlin C2A  is the longest linker sequence modeled in the human ferlin molecules. Our models predict this region to be mostly unstructured due to a high fraction of proline and glycine residues. For example, the A-B linker sequence in dysferlin is 93 amino acids in length and is 22% proline. The A-B linker sequence from myoferlin is 73 amino acids in length and is 13.7% proline and 13.7% glycine. Otoferlin has the longest A-B linker sequence in the ferlin family with 132 amino acids but possesses a lower fraction of proline and glycine, 8.3% and 9.1% respectively. Fer1L5 has the shortest A-B linker of the human ferlin family, 42 amino acids, and a lower fraction of proline and glycine, 4.8% and 7.1%, respectively. Despite the prevalence of proline and glycine in the A-B linkers among all ferlins, our RoseTTAFold models predict two nd A170E . P132L oIn dysferlin, there are 14 consensus phosphorylation sites predicted within the helices of the A-B linker . Interes2+ binding motif .The reported boundaries for the C2B domain of dysferlin are the most consistent among all of the previous C2B domain predictions , 33, 34 The FerI region is a unique ferlin primary sequence motif that is described as being tightly sandwiched between C2B and C2C . Despiteβ-strand subdomain insertions than any other ferlin C2 domain (β-strands 1 and 2 (β1–2), introducing a long, amphipathic α-helix within loop 1 of the C2 domain . The β1–2 subdomain of C2C is the only example of a loop 1 or β1–2 subdomain in the ferlin family. A loop 1 insertion is particularly notable as this loop typically participates in Ca2+-dependent phospholipid binding in many C2 domains. In fact, the β1–2 subdomain of C2C shares similarity to the loop 1 (CBR1) insertion observed in cytosolic phospholipase A2 [2+ and phospholipid-binding activity as cytosolic phospholipase A2. In the Ca2+-dependent C2 domains, the domain itself contributes at least five of six total coordinating oxygen atoms to bind Ca2+; a phospholipid headgroup contributes the remaining coordinating oxygen. All six human ferlin C2C domains possess most of the requisite divalent cation coordination residues to bind Ca2+  . The overall structure of the β6–7 subdomain of C2C is uncertain; but, it may contain at least two α-helices. The exon 17 exclusion of dysferlin abbreviates the β6–7 subdomain to a single Val residue, making it a similar size to the other human ferlin C2C domains , and it is approximately 20 residues in length across all human ferlins. There is no obvious sequence similarity or discernible function that can be inferred from our models or alignment of β7–8 .The C2C domains of dysferlin and myoferlin possess an additional 30-amino acid subdomain between α-helical propensity of the dysferlin amino acid region located between C2C and inner DysF [2+-dependent manner [β-strands from secondary structure analysis on either side of the FerA four-helix bundle [β-strands, we could assemble a complete C2 domain with the FerA four-helix bundle modeled as a subdomain between β-strand 4 and β-strand 5 (β4–5). The new C2 domain has been independently modeled in our AlphaFold2 and RoseTTAFold ferlin analysis (β-strands 2–3 (β2–3) . Loop 3 of Fer1L5 also possesses two bulky hydrophobic residues (Fer1L5: Phe-1157 and Trp-1158) with which to interact with the phospholipid membrane. In contrast to the β1–2 subdomain in C2C, the Fer1L5 C2D loop 1 helix does not possess residues that could participate in Ca2+ coordination, so Ca2+-dependent phospholipid interaction is unlikely   occurs within the β6–7 subdomain of C2E, but only in the exon40a insertion of human dysferlin [β-strands from the C2E domain and leave two β-strands as remnants on the N-terminus of the mini-dysferlinC72 fragment.The ferlin C2E domain contains a large subdomain between 7 (β6–7) . The C2Eysferlin . Proteol2+ in the divalent binding pocket; however, the requisite constellation of acidic residues that are normally used to coordinate Ca2+ is currently unclear  domains  s of 2.7Å , and the of 2.7Å . It is u2+ are present within each ferlin C2G domain; therefore, we predict that all of the C2G domains should bind calcium ions . The β4–5 subdomain of C2G gives the domain an overall ‘L’ shape. Due to the conservation of large hydrophobic residues at the tip of the C2G β4–5 subdomain throughout all ferlins  spans were predicted from the full-length RoseTTAFold ferlin models and confirmed with the TMHMM server  Table 2Table 2. α-helical segments on either side of the membrane-spanning helix  essential for fertilization in C. elegans [2+-dependent membrane fusion of membranous organelles during spermatogenesis [dys-ferlin’ to reflect the deleterious role that mutations within the DYSF gene play in Limb-Girdle Muscular Dystrophy-2B (LGMD-2B) [fer-1 paralogs were subsequently annotated as ferlins in mammalian genomes. Phylogenetic analysis revealed that the ferlin family likely has an ancestral function as regulators of vesicle fusion and receptor trafficking [Ferlins were first identified in 1980 as a gene  . Five adfficking , 86. In fficking , 87. HowKnowledge of the domain structure of the ferlins is essential to begin to understand its mechanism of action. Since at least 1998,  research2+-dependent membrane binding [2+ binding activity has been abrogated by the lack of loop 1. [2+-binding residues typical of other C2 domains; therefore, we conclude that C2B cannot bind Ca2+ in a manner similar to dysferlin or myoferlin C2A domains ipase A2 . In phosubdomain . The β4– surface . The Dys surface , 68 that surface . Similared above . Any Ca2nd dsRNA . Currentfunction . The preβ-strand loops on the opposite side of the domain from the Ca2+ and phospholipid-binding pocket. The rationale for these modifications is not yet clear, but it may have to do with preserving the C2 domain’s overall shape and the C2 domain’s ability to interact with other domains and proteins [Ferlins are part of the multiple-C2-domain-containing proteins (MC2Ds) superfamily classified as proteins containing two or more C2 domains , 89. To proteins .RoseTTAFold- and AlphaFold2-derived models provide an excellent tool to decipher the domain structure of the ferlins, but further questions remain. For example, what are the ligand-binding residues of each domain, how do ferlins associate with membranes, how do pathogenic mutations affect the function of the protein, what is the function of the various subdomains, and do domain-domain intra-molecular interactions contribute to the overall activity? Understanding the structures and molecular mechanisms of large multi-functional ferlin proteins will be an important step toward understanding and treating ferlin-associated diseases such as Limb-Girdle Muscular Dystrophy type-2B (dysferlin), auditory neuropathy (otoferlin), and perhaps in the treatment of metastatic tumors (myoferlin).S1 File(PDF)Click here for additional data file.S1 Raw image(PDF)Click here for additional data file.