Mycobacterium avium subspecies paratuberculosis (Map). Map is mainly transmitted between herds through movement of infected but undetected animals. Our objective was to investigate the effect of observed herd characteristics on Map spread on a national scale in Ireland. Herd characteristics included herd size, number of breeding bulls introduced, number of animals purchased and sold, and number of herds the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds. A stochastic individual-based model was used to represent herd demography and Map infection dynamics of each dairy cattle herd in Ireland. Data on herd size and composition, as well as birth, death, and culling events were used to characterize herd demography. Herds were connected with each other through observed animal trade movements. Data consisted of 13 353 herds, with 4 494 768 dairy female animals, and 72 991 breeding bulls. We showed that the probability of an infected animal being introduced into the herd increases both with an increasing number of animals that enter a herd via trade and number of herds from which animals are sourced. Herds that both buy and sell a lot of animals pose the highest infection risk to other herds and could therefore play an important role in Map spread between herds.Bovine paratuberculosis is an endemic disease caused by The online version contains supplementary material available at 10.1186/s13567-022-01066-5. Mycobacterium avium subspecies paratuberculosis (Map). Johne’s disease is endemic in the dairy sector worldwide. It has a large economic impact due to milk losses, early culling and increased mortality [st year of life [Map is mainly transmitted between herds through movement of these infected but undetected animals [Map between herds and its subsequent persistence within infected herds can be very informative for control purposes. Once Map is introduced into a herd, model results suggest that a herd can remain infected for over 10 years [Bovine paratuberculosis, or Johne’s disease, is a disease caused by ortality . Suscept of life . Clinica of life . However of life . Tests t of life , 6. Thus animals . Underst10 years , 9. IndeMap spread between Irish dairy herds. In Ireland, the dairy industry is pasture-based to optimize the use of grass as the primary feed source for lactating cattle [Map, (2) Reduce the level of infection in infected herds, (3) Provide additional assurance to the marketplace, and (4) Improve calf health and farm biosecurity [In this study we aim to investigate g cattle . For morg cattle , 12. Hersecurity , 14. At security . Herd prsecurity  and 28% security .Map spread at the national scale in Ireland are still barely understood. Epidemiological modelling is relevant to answering this question, as it enables us to integrate both the within-herd population and infection dynamics, and to link herds through trade movements [Map spread between cattle herds at a large scale, including a model adapted to the dairy farming system of western France [Map spread in the predominantly seasonal Irish dairy farming system [Although trade movements are well described in Ireland , the conovements . So far,n France , 20, a mn France , and a mn France , 23. Then France , 25. Theg system .Map spread. Our objective was to investigate the effect of herd characteristics on Map spread in dairy herds in Ireland. Herd characteristics that could be calculated from the movement data included herd size, number of male animals introduced for breeding, number of animals purchased and sold, and number of herds that the focal herd purchases from and sells to. We used these characteristics to classify herds in accordance with their probability of becoming infected and of spreading infection to other herds.We used an adapted version of the French transmission model to simulate Map infection dynamics of each dairy cattle herd in Ireland [A stochastic individual-based model was used to represent herd demography and  Ireland , 24. Dat Ireland . A comprst January 2009–31st December 2018.Data were extracted from the Animal Identification and Movement system (AIM) of the Irish Government’s Department of Agriculture, Food and the Marine. The AIM database comprises records on bovine births, movements and movement types , 27. MovHerds included in our dataset, also referred to as the studied metapopulation, were dairy herds classified as such using a similar methodology to Brock et al. . In geneMap spread and were excluded from the dataset. Only female animals were modelled, with the exception of the purchase of some male animals for breeding (see below). Hence, herds that did not exchange female animals with other herds were also excluded. Finally, herds were selected on the basis of size: herds with 15 animals or less, or five adults or less, were considered to be non-commercial dairy herds and were excluded. A total of 13 353 herds, with 4 494 768 dairy female animals, were included in the metapopulation. The retained herds belonged to one of six possible herd types . To differentiate bulls introduced for fattening purposes from bulls introduced for breeding purposes, only bulls that met the following criteria were included: bulls had to be pure bred dairy or beef, they had to enter the herd via trade, and beef bulls had to come from a non-dairy breeder herd. Bulls had to be at least 1 year of age upon entering a herd, and they had to remain in a herd for at least 5 months (one breeding season). The maximum length of stay in a herd was 4 years for beef bulls and 2 years for dairy bulls. It was assumed that dairy bulls were only used for breeding for a maximum of 2 years because after that these animals had the potential to mate with their daughters . The number of bulls introduced per year was calculated based on the assumption that about half the cows would be serviced by a bull, with 20 cows per bull. The other half of the cows will be artificially inseminated. The number of cows present in a herd was determined on the first of May of a given year. Based on this calculation, if more bulls were needed than present in the trade data, dairy bulls that were born into a herd and kept until breeding age were selected. A total of 72 991 bulls were selected for 11 121 herds of the metapopulation and added to the dataset.In Ireland, the use of natural service bulls following rounds of artificial insemination is common practice . To accoT), latently infected (IL), moderately infectious (IM), and highly infectious and with the possibility to also be clinically affected (IH). Animals are assumed to be most susceptible at birth, with susceptibility decreasing exponentially with age [L, IM, IH) shed Map in their faeces and, after calving, in their colostrum and milk. The quantity shed depends on the health state and is heterogeneous between animals of the same state [Map load present was removed per week, representing the effect of removing manure [Map load occurred weekly [Map load [The within-herd model is adapted from Camanes et al.  and Biemwith age . Infectime state , 32. Anig manure . Similard weekly . AdditioMap load , 30. DetTo represent Irish dairy farm management, which is highly seasonal , animalsst of January 2009. For each age class, seasonal exit rates  were defined because it resulted in the best agreement between observed and modelled herd size compared to the use of yearly or monthly exit rates . The out-strength measures the number of animals sold by the herd of interest (outgoing movements). Polarity is defined as ng herds . The herMap introduction in the metapopulation when they were not the ones initially infected. On this basis, for 1000 replicates, 25% of the herds were randomly chosen to be initially infected irrespective of their herd characteristics. The within-herd prevalence is low in the majority of herds based on earlier analyses of field data [Map transmission was calculated for each herd given that it was not initially infected. The 30% most likely herds to be infected were chosen as candidate herds to be initially seeded with Map in the subsequent simulations.Which herds were chosen to be infected at the start of the simulations had an effect on model predictions  and as numerator the subset of these replicates in which no infected animal entered the herd within the 10-year simulation period. A generalized linear model with a logit link was used to connect (the logarithm of) each herd characteristic to the probability of a herd being infected, model fit was assessed by Akaike information criterion (AIC). Fourthly, we investigated the infection risk posed by each selling herd type. We determined the number of buying herds infected following outward movements of infected animals from each selling herd, and summarised as the average number of herds infected by each selling herd type. A buying herd was considered to be infected by the selling herd if at least one infected animal was introduced and up to that moment no infected animals had been present in the herd. Lastly, we determined the number of unique infection sources per herd. A herd is an infection source if it introduces an infected animal in the herd of interest in at least one replicate.Results were analysed and visualized in R  using thFor all replicates, herd prevalence increased over time Figure A. After n = 2). On average, herds classified as DnR-C  have a higher out-degree and out-strength compared to the other herd types. Furthermore, herds classified as DnR-C or DnR-nC (non-rearing dairy herds which do not use contract rearing) have a higher in-strength compared to the other herd types.Table Figure Figure Of the 13 353 herds in the metapopulation, 1969 herds escaped infection in more than 90% of the replicates. Figure n = 2) was not significantly different from any other herd type.Figure Figure Figure Map spread between dairy cattle herds in Ireland, with the aim of investigating the effect of herd characteristics on the risk of becoming infected and spreading infection on a national scale. We showed that the probability of an infected animal being introduced into the herd increases both with increasing in-strength  and in-degree . Furthermore, herds that both buy and sell a lot of animals pose the highest infection risk. Non-rearing dairy herds trade more animals and with more dairy herds than other herd types and could therefore play an important role in Map spread between dairy cattle herds.We used a stochastic individual-based transmission model to simulate Map spread between dairy cattle herds.We found that herds classified as non-rearing dairy herds infected on average more herds compared to other herd types. These herds (DnR-C and DnR-nC) do not rear their own female dairy calves. Calves are moved to contract rearing herds and pregnant heifers are bought back from the same contract rearing herd (DnR-C). Or cows are bred to beef bulls and the calves are sold, replacement heifers are bought from other herds (DnR-nC). The in-strength and out-strength (and to a lesser extent in-degree and out-degree) of these herd types was high compared to other herd types. DnR-C and DnR-nC herds also infected on average more herds compared to other herd types, and DnR-nC herds had the highest number of unique infection sources. Over 84% of the DnR-C and over 52% of the DnR-nC herds infected at least one other herd after they became infected themselves. Furthermore, 89% of the DnR-C herds and 40% of the DnR-nC herds had a probability greater than 97.5% of becoming infected during the 10-year simulation period. Because of these herd characteristics, it could be that these herd types play an important role in disease spread between herds. Although the trading structures are very different, pig herds in the pork supply chain with a high out-degree or long outgoing infection chain were of particular importance during an epidemic with regard to disease spread . The in-We observed a strong positive correlation between in-degree and in-strength (0.80), meaning that herds that bought from a small number of other herds were also likely to buy a small total number of animals and vice-versa. Furthermore, we found a high correlation between in-strength and out-strength (0.72), meaning that herds that bought a lot of animals were likely to also sell a lot of animals, and vice-versa. Tratalos et al. , who anaWe found that the probability of a herd becoming infected during the 10-year simulation period increased rapidly with increasing number of animals purchased per year. Herds that bought more than 8 animals per year had, on average, a probability of over 95% of becoming infected. Also for France the probability of becoming infected increased with the number of animals purchased; French farms that bought three or more animals per year had a probability of over 50% of becoming infected within the 9-year simulation period . HoweverWe used polarity as a measure to investigate the probability of a herd becoming infected and thereafter being a risk of spreading infection to other herds. Results are presented separately for herds with an average in-strength of less than 4 and herds with an average in-strength of 4 or more. Herds with an average in-strength of less than 4 often escape infection and herds with an average in-strength of 4 or more have a high probability to become infected Figures C. There Map infection was very persistent, as shown by herd prevalence increasing over time for all replicates. Model predictions in dairy herds in France showed that, even with low herd prevalence and within-herd prevalence, extinction was impossible without intervention strategies [Map introductions in free herds [rategies . Howeveree herds , 25. In ee herds . In the Map introduction. After their introduction in the herd, bulls were assumed to behave like, and reside in the same location as, female animals of their age group. Transmission via semen [We included breeding bulls in the dataset because we wanted to account for the associated risk of ia semen –48, whetMap spread. A disadvantage of using real data is that the simulated period and the number of herds modelled are constrained by the data, and simulations are based on past events. Farm numbers declined over the studied period [The model is data-driven, meaning that herd-specific parameters such as initial herd size, exit rate, calves born over time and animal movements between herds were based on real trade data. An advantage of using real data is that there is a close resemblance to reality; actual herd demographics resemble simulated herd demographics, and herds interacting in reality are also interacting in the model. It was, therefore, possible to identify which herds with associated herd characteristics were most exposed to infection, had a higher chance of escaping it, or contributed most to d period , 50, andIn the model, several assumptions are made. Validating the realism of these assumptions is not always easy because detailed data is not available. However, model components and the parameters used in the model are based on research, and updated when new information becomes available. For the within-herd part of the model, the transmission rates are the most uncertain. Two previous studies using a similar model, performed a sensitivity analysis on these parameters. For transmission within Irish dairy herds, only variation in the transmission rate parameter for the general indoor environment had an effect on prevalence over time, but the conclusion that the general indoor environment was the most important transmission route remained unchanged . SimilarMap substantially in the presence of young dairy female calves is low, and therefore, their contribution to Map spread is minimal. Slightly less prevalent were the non-rearing dairy herds (DnR-C and DnR-nC). These herds sell most of their calves, with female dairy calves being moved to external contract rearing herds ). The difference between DnR-C herds and DnR-nC herds is that in DnR-C herds the female calves return to their birth herd as pregnant heifers (contract rearing), whereas in DnR-nC herds there is no contract rearing and replacement animals are bought from other herds with a surplus of cows or pregnant heifers. In SdR herds, female dairy calves are reared and inseminated before returning to their birth herd (the DnR-C herds). In these herds, animals are usually of young age. Since herds with less than five adults were too small to be taken into account by the model, only two SdR herds were included in the metapopulation.Brock et al.  identifiNon-dairy herds were connected to the dairy herds as well. However, 63% of the movements were from a dairy herd in the metapopulation to a herd outside of the metapopulation. Only 20% of the movements were from a herd outside of the metapopulation to a herd in the metapopulation. Brock et al.  investigIn the model, we assumed that herd management was similar for all farms, even though the characteristics of the herd types, especially with regard to which age groups are present, are different. In the model, animals were divided into age classes, each with their own properties, and therefore all herds could be modelled in the same way. For example, for all herds we assumed that cows would go to pasture in the beginning of March, whereas calves stay indoors to the end of April. Thus, properties between age classes were different but for a given age class properties were the same across farm types.Map introduction into herds that are currently free of infection.We have shown that herds that buy many animals from a lot of different source herds have the highest probability of becoming infected, whereas herds that buy only a few animals from a few sources have the highest probability of escaping infection. Furthermore, on average, herds classified as non-rearing dairy herds are at greatest risk of infecting other herds, compared to other herd types. These findings could be used to identify herds on which the control programme could particularly focus, including those at highest risk of being infected and those at highest risk of spreading infection to other herds. For example, by combining intervention strategies with risk based trading, there is the possibility to prevent Additional file 1. List of dairy breeds and table containing model parameters.Additional file 2. Schematic representation of the within-herd Map transmission model.Additional file 3. Simulated versus observed herd sizes, when simulations are based on seasonal exit rates.Additional file 4. Visualisation of the range of the in-degree, out-degree, in-strength, and out-strength span per herd.Additional file 5. Explanation of the importance of choosing the right herds to seed with infection at the start of the simulations. Showing that the results obtained are affected by which herds are selected to be initially infected.Additional file 6. Probability distribution from which the initial within-herd prevalence was sampled.


Transcription of endogenous retroviral elements are tightly regulated during development by members of the KRAB‐containing zinc finger proteins (KRAB‐ZFPs) and the co‐repressor Trim28 . KRAB‐ZFPs form the largest family of transcription regulators in mammals and initiate transcriptional silencing by tethering Trim28 to a target locus. Subsequently, Trim28 recruits chromatin modifying effectors resulting in the formation of heterochromatin. In the present study, we identify surface exposed residues on the central six turns of the Trim28 coiled‐coil region forming the binding interface for the KRAB domain. Using AlphaFold2 (AF2) we provide high confidence models of the interface between Trim28 and the KRAB domain and identified leucine 301 on each chain of the Trim28 monomer to act as a pin extending into a hydrophobic pocket on the KRAB domain surface. Site directed mutations in the Trim28‐KRAB binding interface abolished binding to the KRAB domain. Our work provides a detailed understanding of the specific interactions between the KRAB domain and the Trim28 coiled‐coil and how this interaction may be regulated during silencing events. KRAB‐ZFPsKRAB‐containing zinc finger proteins1The human genome contains endogenous retroviral elements (ERVs) as relics of historic retroviral infections that have been maintained in the germline and inherited by future generations. In human ancestral lineages it is estimated there were over 50 waves of retroviral endogenization events.The KRAB‐ZFPs contain an N‐terminal KRAB domain followed by an array of between three and 40 DNA binding C2H2 zinc finger domains.Trim28 (or KAP1 or TIF1β) belongs to the TRIM protein family of RING E3 ubiquitin ligases consisting of over 70 members in humans. Family member's share a conserved N‐terminal domain organization termed the RBCC, consisting of a RING finger domain, one or two B‐box domains, and a coiled‐coil region. One defining feature of TRIM proteins is their ability to oligomerize as homo‐dimers and higher order oligomers. The coiled‐coil regions of TRIM proteins forms an extended ~170 Å long α‐helix that exhibits a conserved heptad repeat forming a constitutive anti‐parallel homodimer.in vitro investigations on Trim28 binding to the KRAB domain of KRAB‐ZFPs independently determined that the binding stoichiometry is 1:2 KRAB‐ZFP: Trim28, indicating the KRAB binds at the two fold symmetry axis of the coiled‐coil domain.Two recent Protein structure prediction from the amino acid sequence has been a developing field for the past decades with the CASP14 experimentIn this study we demonstrate the surface exposed residues of the central six turns of the Trim28 coiled‐coil region form the binding interface for the KRAB domain. We provide high confidence models of the interface between Trim28 and the KRAB domain and identified leucine 301 on each chain of the Trim28 monomer to act as a pin extending into a hydrophobic pocket on the KRAB domain surface. We also validate the proposed model through site‐directed mutagenesis of interfacing residues on the Trim28 coiled‐coil that result in a reduction in binding affinity.22.1The coiled‐coil region of Trim28 is comprised of two 30‐turn amphipathic helices followed by a hairpin loop with the protein chain extending back toward the center of the helices and connecting to a small helix forming a four‐helix bundle.Using a series of pull‐down experiments we identified K297 in this central region as being critical for binding. To expand on this work we hypothesized that there are likely other mutations that, rather than abolishing binding completely, would result in a reduction in binding affinity that could be assessed using our surface plasmon resonance (SPR) binding assay.We undertook Biacore SPR on the Trim28 mutants to examine their binding to the immobilized KRAB domain from ZFP932. The KRAB domain was expressed with an N‐terminal Avitag resulting in a monobiotinylated construct that was then immobilized onto the surface of a biacore Sensor Chip CAP. Data were analyzed by either equilibrium or kinetic analysis.The interaction between Trim28 and ZFP932 has an affinity of 14.6 ± 1.1 nM consistent with our previous experiments. Analysis of the Turns 1–2 mutant binding to ZFP932 has an affinity of 2.7 ± 0.1 μM, a 185‐fold decrease in affinity. Additionally, K297 was located in the Top Face 6 mutant and was binding‐deficient suggesting that this is the key binding interface. This is supported by mutations on the opposing face of the coiled coil that showed minimal effect on binding with affinities of 34.5 ± 11.3 nM and 52.9 ± 7.8 μM for the bottom face 2/6 mutants, respectively. We were unable to use a kinetic analysis for Turns 3–4 and Turns 5–6 due to the fast on/off kinetics of binding. Equilibrium analysis of Turns 3–4 gave an affinity of 1.1 ± 0.1 μM and Turns 5–6 gave an affinity of 14.0 ± 0.7 μM showing mutations in these turns of the coiled‐coil can disrupt binding to ZFP932 as severe as the K297 mutations. The Turns 7–8 mutant displayed the same affinity as the wild‐type protein placing this area of the coiled‐coil outside the binding interface. Taken together, our binding data confirms that the KRAB domain binds the top face of the Trim28 coiled‐coil and spans the central region including residues between V293 and R311. Furthermore, it demonstrates that residues located on the top face of the Turns 1–6 are involved in the interaction expanding the binding interface compared to previous studies2.2Recent advances in protein folding algorithms have made it possible to model proteins with a high degree of accuracy. Alphafold has been shown to accurately predict the structure of proteins from sequence alone. Based on the conserved nature of the Trim28‐KRAB interaction, we hypothesized that sufficient co‐evolutionary information would be present for Alphafold2 to provide an accurate model of the interaction that could then be verified either by site‐directed mutagenesis or mutations present in existing literature.Models for individual protein subunits were accessed via the EBI AlphaFold 2 database. The AF2 predicted structure for Trim28 domains is consistent with published structures (UniProt Q13263) albeit as a model of the monomer rather than the dimer Figure . HoweverModels of the KRAB‐ZFP KRAB domain from multiple models in the AF2 database converge on a common fold consisting of four helices Figure . The N‐tTo understand the molecular determinants for binding of the KRAB domain with Trim28 we modeled Trim28 in complex with the KRAB domain from ZFP809 using AF2. This modeling is complicated by the 2:1 stoichiometry of the TRIM‐KRAB complex. To generate the model we initially used a sequence whereby the Trim28 RBCC (58–418) was duplicated with a 40‐glycine linker placed between monomers, the KRAB‐AB domain of ZFP809 (residues 1–74) was then appended to the sequence after another 40aa glycine linker to generate the appropriate 2:1 stoichiometry of the interaction.The model for the complex of Trim28 with the ZFP809 KRAB domain comprised of two similar chains of the Trim28 RBCC  and a single KRAB domain. AF2 recreated the expected anti‐parallel Trim28 dimer with the Bbox and RING domains placed in positions consistent with the published structures Figure . In our 2 from each molecule. The binding interface with Trim28 spans residues Lys290—Arg312 of chain A, and Val294—Asn308 for chain B  to be 445‐fold weaker than wildtype, and L301S (Kd = 30.8 ± 1.0 μM) to further diminished binding by 2,112‐fold. This mutagenesis confirms the importance of L301 with mutations to surrounding residues also resulting in a reduction in binding affinity but to a lesser degree.Our modeling of Trim28 in complex with KRAB domains produced high accuracy predictions consistent with known structures Trim28 and the KRAB domain in isolation.The asymmetric interface formed by the Trim28:KRAB complex is extremely tight in fitting with its biological role, the KRAB domain must recruit Trim28 to a single site within the genome. The two pseudo symmetric hydrophobic pockets on the KRAB domain allows it to bind in either orientation with essentially identical interactions. Comparison with the structure of the Trim28—SMARCAD1 Cue1 complex shows no overlap in the binding interface allowing both interactions to occur simultaneously. This is consistent with the Trim28 dimer functioning as a scaffold tethering multiple epigenetic regulators to KRAB‐ZFP binding sites.Consistent with the models presented, mutation of conserved amino acids within the KRAB‐A domain have previously been shown to abrogate Trim28‐mediated transcriptional repression.The KRAB‐B box has previously been shown to enhance the repression activity of the KRAB‐A box.Our study provides insights into the initial events during Trim28‐mediated transcription silencing. The affinity of Trim28 for the KRAB domain is low nanomolar ranging from 2 to 200 nM.44.1glutathione S‐transferase [GST] tag in pET49b with an maltose binding protein [MBP] tag) by a ligation independent cloning (LIC) method.DNA encoding murine Trim28 (1–834) and ZFP932_KRAB (5–76) was codon optimized and synthesized (GeneArt). The coding sequences for T28_RBCC (58–418) and ZFP932 (5–76) were amplified by PCR. These constructs were cloned into pET‐49b(+)‐MBP  cellsD‐1‐thiogalactopyranoside (IPTG) to log phase cultures prior to subsequent growth overnight at 18°C. Cells were lysed by cell disruption in 20 mM Tris–HCl (pH 8.0), 200 mM NaCl, 0.5 mM Tris(2‐carboxyethyl)phosphine (TCEP), 10% (v/v) glycerol, 0.1% (v/v) Triton X‐100. The MBP‐His‐tagged Trim28 constructs were purified using amylose affinity chromatography. The N‐terminal purification tags were removed by cleavage with 3C‐protease prior to further purification by size exclusion chromatography (SEC). The AviTagged ZFP932 KRAB domain was co‐expressed with pBirA in E. coli BL21 (DE3) LOBSTR grown in LB at 37°C to approximately 0.5 OD600. Cells were then induced with 1 mM IPTG and supplemented with 20 μM D‐biotin (Sigma) and grown overnight at 18°C. The MBP‐His‐tagged ZFP932_KRAB (5–76) was then purified as described above.All Trim28 constructs were expressed in 4.3Size‐exclusion chromatography coupled to multi‐angle laser light scattering (SEC‐MALLS) was used to determine the solution molecular weight and assess protein heterogeneity. Samples (100 μl) were applied to either a Superdex S200 Increase 10/300GL column equilibrated in 10 mM Tris/HCl pH 7.8, 150 mM NaCl, 0.1 mM TCEP mounted on a Dionex HPLC with a PSS SLD7000 7‐angle MALLS detector and Shodex RI‐101 differential refractive index detector. The weight average molecular weight was determined using PSS winGPC Unichrom software.4.4d was determined by fitting a two‐state reaction model using BiacoreX100 Evaluation Software version 2.0.1. For equilibrium analysis, a protein concentration series was prepared by serial diluting proteins with SPR buffer. Each concentration (from low to high) was injected over the reference and sample surfaces for 60 s, followed by a 600 s wash with SPR buffer. The data was fitted using BiacoreX100 Evaluation Software version 2.0.1.SPR was undertaken using a Sensor Chip CAP in a Biacore X100 instrument. CAPture reagent (GE) was loaded onto both the reference and sample surfaces at a flow rate of 5 μl/min for 120 s, achieving a resonance unit (RU) of ~2,500. Monobiotinylated KRAB domain was then loaded onto the sample surface at 10 μl/min achieving ~100 RU. Exactly, 1 μM of RBCC_R184D or RBCC coiled‐coil mutants were injected over the reference and sample surfaces for 60 s, followed by a 600 s wash with SPR buffer . K4.5Models were produced using AlphaFold2_advanced on Google Colab GPUs. The input were sequences of Trim28 RBCC (residues 58–418) and KRAB domains with the stoichiometry set to 2:1 Trim28:KRAB. No templates were included in the modeling and the models were relaxed using Amber‐Relax as part of the AF2 modeling process. Five models were produced for each complex and the rank1 model that has the highest overall confidence was chosen for analysis. No differences were observed in the Trim28:KRAB interface between the 5 models. Residues with pLDDT less than 50 and the B1 Bbox domains were removed prior to analysis.Jamie R. H. Taka: Conceptualization (supporting); investigation ; writing – original draft ; writing – review and editing . Yunyuan Sun: Investigation (supporting); writing – review and editing (supporting). David C. Goldstone: Conceptualization ; investigation ; supervision ; writing – original draft ; writing – review and editing .The authors declare no competing financial interests.Figure S1. Structural comparison between the modeled Trim28‐KRAB complexes indicates a uniform binding site. Predicted aligned error plots for the four models of the Trim28‐KRAB complexes with Trim28 consisting of chain A and B, and the KRAB domains consisting of chain C. The plots indicate a high confidence in the relative position of residues within the domains of the Trim28 RBCC. There is high confidence in the relative position of the KRAB domains to the coiled‐coil regions of each chain of Trim28.Figure S2. SEC‐MALLS analysis of the Trim28 RBCC coiled‐coil mutants. Protein elution profile is shown by differential refractive index traces (solid lines) and MW is represented by open circles. (a–c) Mutation of coiled‐coil residues to alanine and change of function mutations show a consistent elution profile and Mw as wildtype Trim28. (d) Mutation of K305E has weak concentration dependent self‐association.Figure S3. Mutation of conserved residues in the KRAB‐A box disrupts the interaction with Trim28 and transcriptional repression. The KRAB‐A box is highly conserved and responsible for the interaction with Trim28. Residues involved in the Trim28‐KRAB binding interface with from our AF2 models are marked by an asterisk. GST‐pulldown binding assay shows mutations of conserved KRAB‐A box residues disrupt the interaction with the Trim28 RBCC  and are congruent with a disruption of the binding interface identified from our structural models. GAL4‐KRAB‐A box mutations also disrupt the transcriptional repression activity of KZFPs . Exchanging the PRDM9 residues for amino acids occurring at the respective positions in ZFP10 confers repression activity, whereas wildtype PRDM9 is unable to repress transcription. (B) ZFP809 KRAB domain model showing location of conserved “D8V9,” “E16E17W18,” and “M33L34E35” motifs involved in the interaction with Trim28Click here for additional data file.


A category of metamaterials consisting of chiral cytosolic elements assembled periodically, in which the introduction of a rotatable annular structure gives metamaterials the ability to deform in compression–shear, has been a focus of research in recent years. In this paper, a compression–shear coupling model is developed to predict the compressive deformation behaviour of chiral metamaterials. This behaviour will be analysed by coupling the rotation of the annular node and the bending characteristics of ligament beam, which are obtained as a function of the length of ligament beam and the angle of rotation at the end of the beam. The shape function of the ligament beam under large deformation is obtained based on the elliptic integral theory; the function characterises the potential relationship between key parameters such as displacement and rotation angle at any point on the ligament beam. By simulating the deformation of cells under uniaxial compression, the reasonableness of the large deformation model of the ligament beam is verified. On this basis, a chiral cell-compression mechanical model considering the ductile deformation of the annular node is established. The compression–shear deformation of two-dimensional planar chiral metamaterials and three-dimensional cylindrical-shell chiral metamaterials was predicted; the offset displacements and torsion angles agreed with the experimental and finite element simulation results with an error of less than 10%. The developed compression–shear coupling model provides a theoretical basis for the design of chiral metamaterials, which meet the need for the precise control of shapes and properties. Metamaterials focus on the design of microstructure for different functions; for example, Lakes designed porous structures in two dimensions and analysed the negative Poisson’s ratio properties and deformation behaviour for these structures . SubsequChiral metamaterials are formed by tangentially connected annulars and elastic ligaments with periodic distributions to form a honeycomb topology, and to study their unique properties, the chiral single-cell , the desThe current research objects mainly focus on mesh structures after cell combination and analyse its overall characteristics. For example, a prior study has examined the relationship between elastic modulus within a surface and Poisson’s ratio with geometrical parameters of structure in chiral metamaterials , the anaThe derivation of the chiral structure formulation is mostly based on the following assumptions: Annular nodes are completely rigid and deformation of annular nodes is not considered; forces perpendicular to the direction of applied load are neglected; internal forces depend on the observed deformation of the structure; based on the assumption of small deformation, only the elastic bending deformation of ligaments is considered and plastic deformation is neglected, and therefore the rotation of annular nodes is neglected . ConsideIn this paper, mesh structures made with three-dimensional printing (additive manufacturing) technology, a compression–shear coupling model, which considers the rotation of annular nodes, is established based on the design of tetrachiral cells to analyse the large deformation behaviours of ligament beam. The validity of the model is verified by comparison with finite-element simulations and experiments. Based on this model, the influence of geometric parameters on the deformation of meshes structure is investigated, and the deformation mechanism of meshes structure in the compression-shear process is characterised. The establishment of a compression–shear coupling model will provide a theoretical basis for the prediction and on-demand design of large deformations in chiral metamaterials.Under uniaxial compressions, the deformation characteristics of tetrachiral metamaterials may be expressed as two adjacent annular nodes centered by axial forces From The micro-element Define In the deformation of chiral metamaterials under uniaxial compression, the two annular nodes are assumed to be rigid annulars which rotate around their centres and do not undergo any deformation. In order to convert the ligament beam-bending equilibrium equation as a function of Due to the complexity of the equation solution, this section solves the large deformation equation of the ligament beam based on the elliptic integral theory. After expanding the vector product of Equation (2) and taking one integral, define the relationship As shown in Therefore, when the end of the ligament beam is subjected to horizontal force As an example of uniaxial compression, the effective stress and effective strain are defined as follows :(8)σeff=When the rotation of unit chiral cell annular node is small  for nonlinear finite element simulation. The beam cell (B22 element in ABAQUS) with refined meshes is adopted to ensure the accuracy of the calculation, the cross-sectional dimensions of the beam cells are 5 mm × 5 mm, and the radii of the annular nodes are all 20 mm.The material’s elastic modulus is 1404.70 MPa, Poisson’s ratio is 0.3, and a 4 × 6 combination of cell is used. Based on Equation (6), the variation of From the figures, it may be observed that the turning angle increases with increasing load values for the same ligament length. Under the same load, the rate of increase in node-turning angle is higher with increases in the ligament’s beam length. The ligament beam angles showed a “J” shape with the increased load, and the larger the ligament beam, the greater the slope. Comparing this finding with The relationship between rotation angle and strain for different directional forces is shown in Taking the ligament beam length of 4 cm as an example, the effective strain of chiral metamaterials is calculated by Equation (8), and the effective strain–stress relationship is obtained, as shown in It is shown in Based on the theoretical values of ligament beam deformations under small deflection deformation calculated by Equation (9), the finite element simulation results are positive, as shown in With the compression of the tetrachiral cell, the ligament beam will undergo significant deformation, in addition to the large deformation of the annular nodes of the cell. Microsegments are selected at the angle of In a microsegment, the relationship between radial internal force When the micro-segment is subjected to a radial force From the above equation, introduce the relationship between bending moment, tangential internal force, and deformation .(14)N=EThe higher-order non-simultaneous differential equations used to express the deformation of annular joints under the action of radial force are obtained as follows.Among them, we have At the position of the polar axis, the annulus is not deformed in the radial or tangential direction. There is no tangential deformation at tal axis , as VRθ=To solve the radial displacement and tangential displacement under the action of tangential force Among them, we have The analytical equations for the radial and tangential deformations of the annular node under the action of the bending moment are as follows .(19)UR=The chiral cell has a simple and variable structure (few parameters and easy to control) with guided deformation, and it exhibits properties of circular deformation and rotation under compression, as shown in Based on elliptical integration theory, To investigate the controlled and versatile properties of chiral metamaterials, to achieve a change from passive deformation to controlled directional deformation, and to meet a range of specific deformation requirements, find the combined structure of two types of chiral cells and analyse their deformation properties.To achieve the controlled deformation of chiral metamaterials, the corner and displacement of key nodes are two main judging factors . Based oThis example relies on three-dimensional printing technology to place the cured sample in a universal testing machine for compression testing. The loading rate of the compression test is 5 mm/min. The instrument used in this experiment is shown in The torsion angle For chiral metamaterials, a non-linear theoretical model is established in this paper for predicting the deformation of structure and solving shape function for the large deformation of ligament beam. Based on the theoretical solutions of elliptic integral and finite element simulation, the relationship curves of rotation angle, strain, and load under different ligament lengths are obtained, which verifies that the model can effectively couple the two mechanical responses of ring node rotations and ligament beam bending. The theoretical values are in good agreement with the numerical simulations.For the study of chiral cell deformation, it is assumed that the ring nodes are completely rigid; that is, the deformation of the annular nodes is not considered in this paper. Based on the established compression–shear coupling deformation model, large deformations of ligament beam, accounting for the ductile deformation of cell annular nodes coupled with the vector additivity of the deformation of cells under unidirectional loading, enable metamaterials to achieve specific deformations. Compression tests of two types of chiral-cell combined structures are based on three-dimensional printing technology. The results show that the theoretical model is able to fit the experimental results and can be used as a theoretical basis for achieving a controlled deformation mechanical response of chiral metamaterials, providing a systematic guide for solving large deformations of chiral structures.


Distribution discrepancy is an intrinsic challenge in existing fingerprint-based indoor positioning system(s) (FIPS) due to real-time environmental variations; thus, the positioning model needs to be reconstructed frequently based on newly collected training data. However, it is expensive or impossible to collect adequate training samples to reconstruct the fingerprint database. Fortunately, transfer learning has proven to be an effective solution to mitigate the distribution discrepancy, enabling us to update the positioning model using newly collected training data in real time. However, in practical applications, traditional transfer learning algorithms no longer act well to feature space heterogeneity caused by different types or holding postures of fingerprint collection devices (such as smartphones). Moreover, current heterogeneous transfer methods typically require enough accurately labeled samples in the target domain, which is practically expensive and even unavailable. Aiming to solve these problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed for FIPS, which can realize higher positioning accuracy and robustness against environmental changes without reconstructing the fingerprint database repeatedly. Specifically, the source domain samples are mapped into the feature space in the target domain, then the marginal and conditional distributions of the source and target samples are aligned in order to minimize the distribution divergence caused by collection device heterogeneity and environmental changes. Moreover, the utilized co-occurrence fingerprint data enables us to calculate correlation coefficients between heterogeneous samples without accurately labeled target samples. Furthermore, by resorting to the adopted correlation restriction mechanism, more valuable knowledge will be transferred to the target domain if the source samples are related to the target ones, which remarkably relieves the “negative transfer" issue. Real-world experimental performance implies that, even without accurately labeled samples in the target domain, the proposed HTL-CD can obtain at least  An indoor positioning system is a vital technique as it enables a series of position-based Internet of things (IoT) applications, including smart cities ,2, healtTransfer learning , for theIn the community of heterogeneous transfer learning, in order to establish the connection between source and target domains with different feature dimensions, a small number of accurately labeled samples in the target domain is normally required, which contributes to semi-supervised learning ,25. In tCo-occurrence data are known for their ability to capture higher-order relationships between classes, concepts, and labels , which aOriented by the aforementioned problems, a heterogeneous transfer learning framework based on co-occurrence data (HTL-CD) is proposed. First, the source domain data were mapped into the target feature space, enabling us to fulfill the positioning task in the same feature space. Within our framework, the marginal and conditional probability distributions between the source and target domains were narrowed down through a maximum mean discrepancy (MMD) restriction, ensuring a higher positioning precision. Meanwhile, co-occurrence data acted as the bridge linking the source and target domains, based on which the cross-domain correlation could be calculated even with different feature spaces. Subsequently, a topology correlation restriction was imposed on each sample, such that the mapped distance between two highly related samples was particularly small, in which case, more knowledge from the source domain was transferred to the target domain. Contrarily, little knowledge transfer took place between rarely related samples, avoiding the so-called “negative transfer” problem.The main contributions of our proposed algorithm can be summarized asWithin the HTL-CD framework, homogeneous knowledge transfer and knowledge transfer between heterogeneous domains can be attained. To cope with the heterogeneous problem caused by the collection device heterogeneity or real-time environmental changes, a projection from the source feature space to the target feature space was considered. In addition, by imposing a MMD restriction on the mapping matrix, the cross-domain marginal and conditional distributions were both aligned, giving rise to higher positioning accuracy and robustness in uncertain environment dynamics.The cross-domain correlation was evaluated with a correlation coefficient, which could precisely reflect whether and how the given source domain is effective in knowledge transferring to the target domain in both homogeneous and heterogeneous cases. Hence, it is possible to take full advantage of a fingerprint database and avoid the “negative transfer” issue.Through extending the current definition of co-occurrence data to a general-sense version, it was dramatically easier to collect co-occurrence fingerprint samples in FIPS. With the help of general-sense co-occurrence fingerprint data, joint sources, and target domains, the correlation between two arbitrary samples can be fairly evaluated, such that even in the existence of significant cross-domain divergence, an accurate and robust positioning performance can be achieved without resorting to common features or accurately labeled samples in the target domain.The remainder of the paper is structured as follows. Related works are reviewed in In this section, some related works are briefly reviewed, mainly concerning classical indoor positioning algorithms and transfer learning in FIPS.Geometric-based indoor positioning typically utilizes geometric principles  in  collectsDue to the rapid development of computational vision techniques , vision-PDR  recursivFIPS are defined as signal features that are valuable for position classifications, such as fingerprints, RSS ,37, and Transfer learning, as a different kind of machine learning method, concentrates on storing knowledge gained while solving one problem and applying it to a different (but related) problem . SpecifiCo-occurrence data are known for their extraordinary abilities in jointing source and target samples even from heterogeneous domains, which provide potential solutions for us to solve feature space heterogeneity. In , by resoOriented by the aforementioned problems, we propose the HTL-CD framework for FIPS, capable of narrowing down cross-domain distribution differences, capturing the degree of correlation in both homogeneous and heterogeneous cases, and facilitating better learning performances. Therefore, higher positioning precision and robustness against uncertain dynamics can be obtained for FIPS.ith fingerprint sample in the database, representing the feature of the ith position. ith fingerprint. Similarly, ith test sample.In the first place, we define the samples from the fingerprint database as the source domain data Traditionally, the key principle of FIPS is training a classifier In this article, our objective was to design accurate and robust positioning FIPS using In this section, the HTL-CD will be elaborated with the overall architecture illustrated in Assume that there exists a mapping matrix c in the source and target domain. C is the total number of different labels. Respectively, ith sample with label c in the source and target domain.The subsequent positioning is completely based on the mapped source data pired by , the croFor description conciseness, we denote at yt in  cannot bCase 1: In the first iteration,   Equation , the disCase 2: During the subsequent iterations, pseudo-labels for target samples were achieved via the last iteration, enabling us to minimize both marginal and conditional distribution differences, with the objective function expressed in the form ofquations  and 4)  Case 2: Co-occurrence data define a data set simultaneously containing source sample features as well as target sample features, which can be formulated as In the field of FIPS, heterogeneity is a rather complex problem, resulting from the heterogeneity of distributions, terminal device types, detected sensor numbers, and other environmental variations, such that there is no reasonable and specific definition for the co-occurrence data in heterogeneous FIPS. Borrowing the co-occurrence data  from theIt is required that the collection devices, collection times, and labels of Due to the different types and holding postures of terminal collection devices, some sensors may not be detected in the target positioning phases, hardly allowing us to seek a collection moment containing all of the involved APs. Consequently, collecting narrow-sense co-occurrence data is unpractical in the presence of real-time environmental changes, and a more general definition of co-occurrence data should be adopted.The rigorous device and time consistencies between In the sequel, unless declared otherwise, the so-called co-occurrence data refer to wide-sense co-occurrence data, which are much easier to obtain than the traditional narrow-sense co-occurrence data.Firstly, the correlation definition between two samples is divided into homogeneous and heterogeneous cases:Case 1: Correlation coefficient of two homogeneous samples Theoretically, the closer Case 2: Correlation coefficient of two heterogeneous samples In the field of transfer learning, there exists an inherent problem as to how effective the source domain data are in transferring knowledge to the target domain. Aimed at addressing this problem, the following constraint is imposed on Evidently, by resorting to the topology constrain in , the higjth column elements of Furthermore,  can be rIn order to restrict the complexity of matrix ong with  and 10)A and eliLet Setting Subsequently, the analytical expression of Algorithm 1: HTL-CD framework for fingerprint positioningRequire: TEnsure:   1:Compute N using ;  2:Compute M using  and H=M+  3:Compute AT using  and X^s=  4:Compute   5:while Iteration times do  6:       Compute M using  and upda  7:       Update AT using  and X^s=  8:       Update   9:end while 10:return Employing or (KNN)  and suppor (KNN) . A complIn this section, the positioning performance comparison between the proposed HTL-CD and existing FIPS are provided, along with the robustness analyses.In order to validate the robustness of our HTL-CD against the environmental changes, the experiment is conducted using UJI-DB , consistith month define the APs detected in the ith month (and were not detected in the 1st month); the missing APs represent those detected in the 1st month (and were not detected in the ith month). Effective APs tremendously changed, especially after the 11th month, which severely deteriorated the positioning accuracies because of different feature spaces. Moreover, the number of the same APs from the 1st month and the average positioning errors directly using KNN are presented in We set the fingerprint samples from the 1st month as the source domain and samples from other months as the target domain. In The average localization error (ALE) was adopted to benchmark the accuracy capabilities for positioning algorithms, which can be formulated asTo thoroughly and fairly compare the positioning performance, the following two cases were considered to conduct positioning experiments using the proposed HTL-CD along with current state-of-the-art schemes, including JDA , balanceCase 1: positioning with partly different features.This case is designed to simulate a scenario with some sensors updated in the online positioning phase, i.e., the source and target domains share partially different features. Utilizing the samples from the 1st month as the source domain, the samples from the 12–25th months were the target domains to conduct our experiment. In addition, 100 co-occurrence samples were randomly selected by combining a source sample and a target sample with the same but unknown labels.The ALEs of these algorithms are depicted in Case 2: positioning with totally different features.As a further illustration, an extreme circumstance was taken into account, where all the sensors were changed in the online positioning phase and the source and target domains owned totally different features. In accordance with case 1, with 2880 samples from the 1st month set as the source domain samples, and the samples from the 12th to 25th month being target domain samples, positioning tasks were fulfilled with the help of 100 randomly chosen co-occurrence samples.The ALEs of our proposed HTL-CD along with state-of-the-art methods are presented in Notice that the design parameters and β in  may affeOn the other hand, to study the dependence of the proposed HTL-CD upon specific co-occurrence samples, 10 experiments were further conducted, each with 100 randomly selected co-occurrence samples. The other design parameters were set in accordance with the last section. The ALEs of these experimental results are depicted in In this article, an HTL-CD framework is proposed, aimed at solving the time-varying distribution difference and feature space heterogeneity for FIPS, which can realize accurate and robust positioning performances without repeatedly reconstructing fingerprint databases. To solve the feature space heterogeneity issue, a cross-domain projection was considered, allowing us to design FIPS in a homogeneous space. Then, a MMD restriction was imposed on the projection; both marginal and conditional distribution differences could be minimized. Wide-sense co-occurrence data were used for joint sources and target domains, which are much easier to capture than traditional narrow-sense co-occurrence data in FIPS. Hence, correlation coefficients between both homogeneous and heterogeneous domains can be calculated and based on the employed correlation restriction; more knowledge transfer occurs between more correlated samples, which take full advantage of source samples and avoid the “negative transfer” issue. Subsequently, the MMD and correlation restriction are simultaneously embedded in an overall objective function; by iterative optimizations, optimal projection and positioning results can be obtained. An experimental performance implies that the proposed HTL-CD can obtain at least


Oryza sativa) protein OsJAC1 enhances resistance against different bacterial and fungal plant pathogens in rice, barley, and wheat. Recently we reported possible carbohydrate interaction partners for both domains of OsJAC1  and a dirigent (DIR) domain), however, a mechanistic understanding of its function is still lacking. Here, we report crystal structures for both individual domains and the complex of galactobiose with the DIR domain, which revealed a new carbohydrate binding motif for DIR proteins. Docking studies of the two domains led to a model of the full-length protein. Our findings offer insights into structure and binding properties of OsJAC1 and its possible function in pathogen resistance.Pesticides are routinely used to prevent severe losses in agriculture. This practice is under debate because of its potential negative environmental impact and selection of resistances in pathogens. Therefore, the development of disease resistant plants is mandatory. It was shown that the rice ( Different abiotic and biotic factors are responsible for global losses in the harvesting of important agricultural crops, such as wheat (29% harvest loss) or rice (40%) . In ordeAsCrs-1 [Agrostis stolonifera), PeD-J [Phyllostachys edulis), TaVer2 [TaHFR1 [Triticum aestivum), SbSL [Sorghum bicolor), ZmBGAF [Zea mays), ShDJ [Saccharum hybrid cultivar), and OsJAC1 [Oryza sativa). Recently, Ma and Han (2021) identified 46 MCJ genes from the wheat genome and divided the class into three subfamilies [ShDJ from sugarcane also showed drought resistance properties along with the ability to improve biomass production and to increase saccharification [OsJAC1 [TaJA1 [N-acetylgalactosamine [N-acetylglucosamine [A protein involved in resistance against different pathogen groups, which is only present in monocot plants, is referred to as monocot chimeric jacalin (MCJ) ,3. As itAsCrs-1  in creep), PeD-J  in Moso , TaVer2  and TaHF [TaHFR1  in wheatm), SbSL  in sorgh, ZmBGAF  in maizes), ShDJ  in sugard OsJAC1  in rice families . Consistfamilies . The homfication . Anotherfication . However [OsJAC1  or TaJA11 [TaJA1 . Among t1 [TaJA1 ,11,12,151 [TaJA1 ,12, N-actosamine , or N-acZmBGAF was abolished [In contrast, there is little physiological information available about the properties of the DIR domains in the full-length MCJs. So far, it was reported that after the deletion of the DIR-domain the agglutination activity of bolished , the sugbolished , and thebolished . Howeverbolished ,18, arombolished , as wellbolished ,21. Furtbolished ,23 and tbolished . Howeverbolished . NeverthOsJAC1 biochemically, revealing new binding properties in either case [OsJAC1, we have now determined the crystal structures of its two domains. These structures enabled in silico docking studies giving insights into the architecture of the full-length protein. Furthermore, we were able to obtain a complex structure featuring the DIR domain and a putative carbohydrate binding partner , which revealed the binding site. Finally, the interaction with cell surface carbohydrates was investigated through hemagglutination assays for the full-length protein and its two domains.Recently, we characterized both domains of her case . We demoher case . This waher case  but are OsJAC1 and its two individual domains were prepared as previously described in Huwa et al. (2021) [2O. Laminaribiose and 1,4-β-d-galactobiose were purchased from Megazyme . Dithiothreitol (DTT) was purchased from Fisher Chemical . Rabbit blood in Alsever’s solution was purchased from Fiebig-Nährstofftechnik GbR . Screening sets for protein crystallization were purchased from Merck  and Qiagen .. (2021) . The iniOsJAC1 domains was performed at 20 °C on a Freedom Evo robotic system  in sitting-drop geometry using a 2:1 ratio of protein stock and reservoir solution. Screening conditions which gave rise to the crystals reported in this work are listed in OsJAC1 DIR domain samples were recorded at 100 K on beamline ID30A-3 (OsJAC1-DIR) and beamline ID23-1  of the European Synchrotron Radiation Facility . The OsJAC1-JRL datasets were acquired at 100 K on ESRF beamlines ID23-1 and ID29. For data collection statistics, refer to Prior to crystallization experiments, the DIR domain His-tag was removed by incubation with thrombin  at room temperature for 48 h. Quantitative cleavage was verified by SDS-PAGE and no further purification steps were conducted. Crystallization screening for the two individual OsJAC1-DIR, independent solutions based on the structure of PTS1 from Pisum sativum (PDB-ID 6OOD) were found by applying MoRDa [OsJAC1-JRL-cf1 was obtained with MOLREP [All X-ray structures presented in this work were determined by molecular replacement. For the two crystal forms of ng MoRDa . An inith MOLREP  using thh MOLREP  with inth MOLREP . Final ah MOLREP , unless x,y,z) for putative binding site I and position 38.5, 46.4, and 24.0 Å  for putative binding site II; box dimensions used were 24.8 × 28.1 × 21.98 = 15,310 Å³.Ligand–protein docking was conducted with AutoDock Vina . Prior t+ [Domain interaction was analyzed in silico by docking both domains through the web-based docking program HADDOCK V2.4 . Prior t+ .g for 5 min at 4 °C, followed by removal of the supernatant. The washing step was repeated three times and/or until the supernatant was clear. For the determination of the hemagglutination activity, the proteins (OsJAC1 and its domains) were serially diluted in a 96-well microtiter plate . The final protein concentration of the first row for each protein was 0.5 mg/mL. The assay was conducted with a 1% (v/v) rabbit erythrocyte suspension. The results of the assay were documented after 1 h incubation at room temperature by visual determination of the hemagglutination. The following controls were included: buffer without any protein as negative control and the OsJAC1-JRL domain as positive control. The OsJAC1-JRL domain has previously been shown to agglutinate rabbit erythrocytes [The hemagglutination assay was conducted based on the protocol of  with slihrocytes .Greiner Bio-One GmbH, Frickenhausen, Germany). LB media containing 0.1 mM ampicillin was inoculated towards an OD600 of 0.1 with an overnight culture of Escherichia coli BL21(DE3). OsJAC1 was serially diluted from 3.5 mg/mL to 27 µg/mL. The total assay volume was 150 µL (135 µL LB media + 15 µL protein solution). The plate was sealed with an air-permeable foil  and incubated on a plate shaker for 4 h at 30 °C and 800 rpm. After the incubation, the OD600 was determined by an Infinite M1000 Pro plate reader . Following subtraction of control absorbance, OD600 values of the culture were plotted against the substrate concentration.The assay was conducted in triplicate on a 96 × 300 µL microtiter plate  distance of 0.87 Å for Cα atoms and 1.24 Å for all non-hydrogen atoms . The dimer itself features C2 symmetry with constituent chains being more similar in crystal form (cf) 1 of the OsJAC1-JRL domain (Cα RMS distance 0.58 Å) than in JRL-cf2 (Cα RMS distance 1.13 Å). As indicated by PISA analysis [2 of solvent-accessible surface area on either protomer and is classified as stable in solution. The structure of the JRL domain of OsJAC1 is highly conserved and shares structural similarities with classical mannose-selective JRLs (mJRLs), such as artocarpin from Moraceae [Artocarpus incisa (PDB entry 4WOG), with a sequence similarity of approximately 30%. In contrast to all other mJRLs from plants, the two chains of the non-crystallographic dimer of OsJAC1-JRL are linked via a symmetric disulfide bond formed by a cysteine residue close to the N-terminus (Cys161). So far, only for the JRL homolog in the pearl shell (Pteria penguin) such an inter-subunit disulfide bond has been reported, but in this case bond formation was mediated by cysteines at the C-termini [The two domains of analysis , the extMoraceae  or fruta-termini . The pos-termini . OverallOsJAC1 presents as a trimer in the asymmetric unit of both crystal forms  and PsPTS1 (PDB: 5OOD), revealed RMS distances of approximately 1 Å for the core β-barrel structure. Increased deviations are observed for the loops as well as the N- and C-terminal regions of the proteins. In particular, the loops lining the substrate binding sites differ in their lengths and conformations  variant of galactobiose, which we had previously identified as a high-affinity OsJAC1 ligand. The soaking procedure resulted in an overall increase of disorder, as evidenced by elevated B-factors and a reduction of useable resolution. Nevertheless, the disaccharide could be clearly identified by newly appearing difference density in all three chains of the DIR domain trimer  revealed two different conserved patterns  [AtDIR6, on the other hand, contains a signature similar to that in PsPTS1. These observations suggest that the DIR domains of the MCJ1 subgroup not only exhibit the same tertiary structure as the OsJAC1-DIR domain but are likely to also feature similar carbohydrate binding properties. TaMCJ2 and TaMCJ3 exhibit a slightly different sequence pattern at the binding area of the OsJAC1-DIR domain, which indicates a more distant evolutionary relation and possibly a different function.r PsPTS1  and OsJAn (2021) ; AtDIR6,OsJAC1 is selective for mannose [Classical JRLs are categorized according to their binding selectivity to either mannose (mJRL) or galactose (gJRL) , and the mannose . A highe mannose .OsJAC1-JRL domain, docking experiments with laminaribiose were performed. The described binding sites of Banlec [In order to investigate the binding site of the f Banlec  were useOsJAC1-JRL domain and Banlec (PDB: 2BN0) of 2.1 Å. According to Meagher et al. (2005) [3-Asp motif loop. The docking of laminaribiose toward site II of the OsJAC1-JRL domain involves an interaction with both mentioned loops ; additionally, Asp224 and Tyr295 might interact with the ligand and stabilize it further (see OsJAC1-JRL domain (site I) shows a different interaction pattern (see OsJAC1-JRL domain has a Gly-X3-Asn motif loop (residues 293–297) mediating similar interactions as observed for site II. However, in the other loop of site I, one glycine of the Gly-Gly motif is substituted with a glutamic acid (Glu174), which might alter the ligand interaction pattern and orientation compared to site II.We were able to identify two small, separated carbohydrate-binding pockets A, which . (2005) , the twother see C. The dotern see B. Site IOsJAC1-JRL domain with different MCJ and mJRL proteins. The bars correspond to the loops and the arrows to the β-strands of the OsJAC1-JRL domain. It should be noted that the automatic assignment of β9 is imperfect in OsJAC1-JRL, although the angles φ and ψ are in the Ramachandran area of β-strands. The residues of sites I and II described above are framed in black and yellow, respectively. The highest sequence similarity in regard to the proposed ligand binding motif of OsJAC1-JRL is found for the proteins TaMCJ1, ShDJ, and AcmJRL (homolog of Banlec). These three proteins should thus exhibit two carbohydrate binding sites as already shown for AcmJRL [TaMCJ3 and SalT might be monovalent using site II only, which was shown for SalT [TaMCJ2 does not share the above-described binding motif and might belong to another subclade of JRL proteins.r AcmJRL . On the for SalT . TaMCJ2 OsJAC1 protein. To identify if or how both domains are able to interact with each other, we therefore conducted an in silico analysis by docking both domains through the web-based docking program HADDOCK [OsJAC1. Hence, we excluded these antiparallel β-strands of the JRL domain from docking with the DIR domain. All other predicted active residues were supplied to the HADDOCK algorithm as potential attractors and are listed in OsJAC1 domains. This model, which essentially represents the full-length OsJAC1 protein, was subsequently subjected to an additional round of docking, in order to determine potential modes of self-association  [OsJAC1 and the DIR domain, the JRL domain relies on dimerization via the inter-subunit disulfide bond to achieve optimal hemagglutination activity.The full-length 5 µg/mL) . IntriguOsJAC1 and reported new properties, including the binding of galactose containing sugars to the DIR domain [OsJAC1 by providing the first crystal structures of its two domains. The domains showed the characteristic architectures of their respective protein families, i.e., the β-prism-I fold for JRLs [OsJAC1-JRL domain is likely to exhibit two closely apposed carbohydrate-binding sites. The increased hemagglutination activity of the OsJAC1-JRL domain under oxidizing conditions points to a crucial role of the intermolecular disulfide bond that is consistently observed in both crystal forms. In the absence of a covalent linkage the multivalency required for hemagglutination may be achieved either via the existence of duplicate binding sites, the short distance of which may limit efficiency, or by transient dimerization. A disulfide-bridged JRL dimer, on the other hand, would constitute a stable tetravalent carbohydrate binder with favorable distance between interaction sites, which would be expected to promote networking between carbohydrates of the erythrocytes, as illustrated in Monocot chimeric jacalins are reported to be involved in different plant stress responses, such as drought , or pathR domain . The galR domain , and thefor JRLs  or the βfor JRLs . Based oOsJAC1-DIR domain revealed an eight-stranded antiparallel β-barrel fold with a wide-open putative binding area, which is surrounded by five loops  or laminaribiose (β1-3) emerged as favorable target structures [OsJAC1 is involved in the abiotic stress response to DNA damage from gamma radiation [N- and O-glycans of plant proteins contain, among other sugars, also galactose [OsJAC1-overexpressing plants showed a growth inhibiting effect on Xanthomonas oryzae pv. oryzae and Escherichia coli [OsJAC1 did not result in any growth inhibition of Escherichia coli (see OsJAC1 to exert its antibacterial properties, and a yet-to-be-identified DIR domain substrate represents an obvious candidate. Moreover, either the DIR domain or the substrate may need to be activated. As an example of the first case, the DIR domain might only have its auxiliary enzyme activity upon binding of the JRL domain to its target. Recall that, according to our docking model, loop L2 of the DIR domain is positioned close to the carbohydrate binding sites of the JRL domain (see AtDIR6 [PhDIR [GhDIR4 [OsJAC1 JRL domains but also for activation of a putative DIR domain substrate.R domain , indicatructures . The potadiation  and hencalactose , which chia coli , whereascoli see . These rmain see  and mighe AtDIR6 , PhDIR [6 [PhDIR , or GhDI [GhDIR4 , which g [GhDIR4 ; these cOsJAC1 presented in this work, obtained by X-ray crystallography and in silico docking, provide us with a solid basis for understanding the protein’s architecture, which is crucial for follow-up studies to elucidate the relevant function of OsJAC1 and other chimeric jacalins in the pathogen resistance response.We were looking into possible oligomerization of the full-length protein. This analysis resulted in two plausible dimer models A,B. We n


The fusion of the secretory vesicle with the plasma membrane requires the assembly of soluble N-ethylmaleimide-sensitive factor attachment protein receptor (SNARE) protein complexes formed by synaptobrevin, syntaxin-1, and SNAP-25. Within the pathway leading to exocytosis, the transitions between the “open” and “closed” conformations of syntaxin-1 function as a switch for the fusion of vesicles with the plasma membranes; rapid assembly and disassembly of syntaxin-1 clusters on the plasma membrane provide docking and fusion sites for secretory vesicles in neuroendocrine cells; and the fully zippered trans-SNARE complex, which requires the orderly, rapid and accurate binding of syntaxin-1 to other SNARE proteins, play key roles in triggering fusion. All of these reactions that affect exocytosis under physiological conditions are tightly regulated by multiple factors. Here, we review the current evidence for the involvement of syntaxin-1 in the mechanism of neuroendocrine cell exocytosis, discuss the roles of multiple factors such as proteins, lipids, protein kinases, drugs, and toxins in SNARE complex-mediated membrane fusion, and present an overview of syntaxin-1 mutation-associated diseases with a view to developing novel mechanistic therapeutic targets for the treatment of neuroendocrine disorders. Syntaxin-1 (STX1), a member of the syntaxin family is a nervous system-specific membrane protein responsible for fusion . MoreoveThis review focuses on the mechanisms and factors that regulate the functions of syntaxin-1, with an emphasis on the key regulatory pathways of SNARE assembly, such as the transitions in the conformations of syntaxin-1, syntaxin-1 clusters, and precise assembly process.22.1Syntaxin-1 is a helical protein with multiple domains that is localized on the plasma membrane. Syntaxin-1 consists of a short N-peptide aa 1-9 or 1-28), the Habc domain , the linker region aa 156-187), followed by the SNARE motif , a juxtamembrane region (aa 258-270), and a C-terminal transmembrane region  Figure 16-187, fo or 1-28,2.2One of the most extensively characterized functions of syntaxin-1 is the assembly of the SNARE complex, which triggers exocytosis. Exocytosis occurs through the rapid and regulated fusion of secretory vesicles with the plasma membrane, and this fusion is driven by the assembly of soluble N-ethylmaleimide-sensitive factor (NSF) attachment protein (SNAP) receptor (SNARE) protein complexes . Many st2+-dependent membrane fusion, which are likely to include preparation of the secretory vesicle and presynaptic membrane prior to vesicle docking. The critical events are listed below: The SNARE complex is disassembled by the joint action of α-SNAP and NSF with Mg-ATP  triggers release by stimulating synaptotagmin binding to a core fusion machinery composed of SNARE and SM proteins that mediate membrane fusion during exocytosis   . In cont release . During  release , 13.Among other functions, syntaxin-1 is believed to interact with the “synprint” site of the calcium channels, which regulates N- and P/Q-type channels . Most sy33.1Syntaxin-1 constantly switches between the freely diffusing (mobile) and clustered (immobile) states at the plasma membrane, which serves as a mechanism for the rapid assembly and disassembly of syntaxin-1 nanoclusters in response to the transformation of syntaxin-1 mobility at the plasma membrane. Syntaxin-1 clusters in NE cells are functional docking and fusion sites for exocytic vesicles, which cluster and diffuse at these sites . SyntaxiSyntaxin-1 is highly mobile at the plasma membrane, which allows the formation of clusters of syntaxin-1. The interaction between syntaxin-1 and other SNARE proteins affects the mobility of syntaxin-1 at the plasma membrane. Recent research has shown that syntaxin-1 is present more frequently at the synaptic site, and the mobility of syntaxin-1 at the synaptic region is significantly reduced. In addition, interference with syntaxin-1 and the interactions between syntaxin-1 and other SNARE proteins could alter the frequency of movement and dwelling of single syntaxin-1 molecules . One stuThe domain 3a hinge-loop of Munc18-1 controls the conformational change of syntaxin-1 and the mobility of syntaxin-1 in the presynaptic membrane. Studies have shown that the expression of Munc18-1 with the domain 3a hinge-loop defects can eliminate the decrease in the mobility of Syntaxin-1 caused by wild-type Munc18-1 under certain stimulation. Further studies showed that botulinum toxin e (hydrolyzed SNAP-25) also eliminated the inhibition of the mobility of syntaxin-1 by Munc18-1 . These fWeak homogeneous interactions between syntaxin-1 molecules allow syntaxin-1 to cluster at the plasma membrane and maintain a dynamic equilibrium between the free and clustered subgroups of syntaxin-1 molecules . Notably3.2Most syntaxin members, including syntaxin-1, can adopt a non-activating “closed” conformation. When syntaxin-1 is not involved in the assembly of the SNARE complex, the Habc domain folds back into the SNARE domain, forming a closed conformation. At this point, molecular interactions between the Habc and SNARE domains prevent the SNARE domain from interacting with other proteins. In the open conformation, the Habc domain is released from the H3 domain, facilitating the interaction of syntaxin-1 with other proteins and participating in the formation of the SNARE complex   can bind to the corresponding domain of Munc13-1 to regulate the release of synaptic transmitters.Munc18-1 is a member of the Sec1/Munc18 (SM) protein family. The domain 3a hinge-loop of Munc18-1 can catalyze the conversion of syntaxin-1A from a “closed,” fusion-incompetent conformation to an “open,” fusion-competent conformation. Munc13-1 is an isoform of Munc13 in major mammalian neurons. Munc13-1 contains four protein kinase C (PKC) homology domains: a calmodulin-binding domain (CaMb) and an extended tethering module called the MUN domain . Ca2+ anMunc18-1 interacts with syntaxin-1 in two modes. In mode I, free syntaxin-1 is protected by Munc18-1 into a closed, non-activated state, preventing the formation of the ternary SNARE complex and inhibiting neurotransmitter release . Mode IIUNC-13, a homolog of Munc13 in mammals, is a highly conserved syntaxin-binding protein that promotes the release of neurotransmitters. UNC-13 recruits synaptic vesicles for their release and also binds to syntaxin-1A for its open conformation, and the C2A domain of UNC-13L is identical to the corresponding domain of Munc13-1. Notably, inhalation of general anesthetics impairs the catalytic action of UNC-13 and promotes the closed conformation of syntaxin-1A .3.33.3.1The assembly of SNARE proteins into the pathway product requires the involvement of various accessory proteins, and without regulatory factors, SNAREs will incorrectly assemble into a variety of off-path products that do not mediate membrane fusion, limiting the speed and accuracy of SNARE assembly . For exaMunc18-1 and Munc13-1 “template” SNARE complex assembly to ensure proper assembly orientation . Munc18-3.3.2in vivo, SNARE complex formation is completed within 1 ms; during this period, a number of factors stabilize the SNARE complex in some intermediate states to ensure that the complexes bind together in a similarly ordered manner   . Synaptoand C2B) . The trie fusion . Moreovee fusion . Synaptoe fusion .Complexin, also known as synaphin, mediates the localization of proteins and the binding of synaptic vesicles, facilitates the fusion of highly curved membranes, and regulates SNARE complexes and neurotransmission. The central α-helix of the complex can also form part of the tripartite interface of the “primed, locked” synaptotagmin-complex-SNARE complex, which enhances the rapid release of neurotransmitters in response to stimulation by calcium signals , 45. Com3.3.3In addition to the synaptotagmin and complexin mentioned above, a number of protein factors can accelerate or inhibit the process of SNARE complex formation and regulate vesicle docking and fusion directly or indirectly, including tomosyn, proline-rich transmembrane protein 2 (PRRT2), Sept5, Syntaphilin, α-SNAP and NSF.Tomosyn is a syntaxin-1A-binding protein divided into tomosyn1 and tomosyn2, which inhibit the initiation steps of neurosecretory cells and neurons. The C-terminus of all isoforms of tomosyn contains a C-terminal domain homologous to VAMP2, which forms a tight complex with t-SNAREs and prevents VAMP2 from entering the SNARE complex . VAMP2 cin vitro and inhibits liposome docking and synaptotagmin-dependent proteoliposome fusion  is associated with the components of the membrane fusion apparatus, and contains a GTP-binding motif as well as a predicted coiled-coil domain. Syntaxin binds to both ends of Sept5, including the N-terminal portion encompassing the GTP-binding region . Sept5 iSyntaphilin is a brain-enriched protein that includes 13 repeats of proline-rich motifs  and numerous consensus protein phosphorylation sites /cGMP-dependent protein kinase (PKG), and eight sites for CaMKII), suggesting that it may be the target for modulation of synaptic transmission through signal transduction pathways at nerve terminals . TransieNSF is a soluble cytosolic hexamer protein and SNAPs are a family of cytoplasmic proteins including α, β, and γ homologs . α-SNAP 44.12+-calmodulin-dependent kinase II (CaMKII), Serine/threonine kinase casein kinase 2α (CK2α), Death-associated protein kinase (DAPK), cAMP-dependent protein kinase (PKA), and Protein kinase C (PKC) activation in the presynaptic terminus have been shown to correlate with the protein binding of syntaxin-1.The reversible phosphorylation of proteins is a general control mechanism that regulates almost all aspects of cell life, including the protein interactions within the exocytosis machinery . Cyclin-in vitro and in vivo, and S327 of Sept5 is a phosphorylation site. A serine (S)-to-alanine (A) 327 mutant of Sept5 bound syntaxin more efficiently than the wild type. Additionally, the phosphorylation of Sept5 by Cdk5/p35 decreased its binding to syntaxin-1. Moreover, the mutant non-phosphorylated Sept5 potentiated exocytosis further than the wild type when expressed in PC12 cells  domain, and a death domain , 68. DAPin vitro and in vivo. PKA catalyzes the phosphorylation of Thr-138 in SNAP-25, and overexpression of the Thr-138 mutant SNAP-25a eliminated the effects of PKA inhibitors on the vesicle-priming process  is concentrated at the junction of secretory vesicles and is associated with syntaxin-1. The interaction between PIP2 and syntaxin-1 allows syntaxin-1 to cluster in PC12 cells. Calcium can also act as a charge bridge connecting multiple syntaxin-1 and PIP2 complexes and can allow PIP2 junctions to form a larger domain to regulate the size of syntaxin clusters . One stuDrosophila larvae NMJs was found to show smaller clusters than wild-type syntaxin-1A-mEos2. Syntaxin-1AKARRAA-mEos2 shows higher mobility in NMJs than wild-type syntaxin-1A-mEos2 and does not vary with thermogenic stimulation  also promotes syntaxin-1 clusters. The interaction of PIP3 with syntaxin-1 is required for syntaxin-1 clusters and neurotransmitter release in NMJs . The PIPmulation .Cholesterol controls the stability of different protein assemblies on the plasma membrane and also regulates syntaxin-1 clustering. Cholesterol is thought to be a key regulator of syntaxin-1 cluster stability, and its depletion at the plasma membrane markedly reduced syntaxin-1 clustering at the plasma membrane of PC12 cells and pancreatic β-cells . In addi4.3In addition to the endogenous factors mentioned above, certain exogenous factors can have an effect on syntaxin-regulated exocytosis, including botulinum neurotoxins (BoNTs), antiepileptic drugs, anesthetic drugs and antidepressant drugs.BoNTs are metalloproteinases that can be endocytosed into neuron nerve terminals and translocation of the light chain across the membrane, and then cleaves SNARE proteins , 82. How2+-evoked release, and their stimulatory actions are predominantly inhibited by inhibitors of N-type voltage-sensitive calcium channels (VSCC) and syntaxin. In addition, CBZ and ZNS reduce VAMP-regulated K+-evoked release, an action predominantly inhibited by inhibitors of P-type VSCCs and synaptobrevin  is one of approximately 28 genes within a commonly deleted region in Williams Syndrome , a multi-system developmental syndrome with features such as connective tissue abnormalities, cardiovascular disease, mild intellectual disability, delayed motor development, unique social and personality characteristics, and growth and endocrine abnormalities (Syntaxin-1A knockout (KO) mice are viable and fertile. These mice display normal spontaneous and evoked release, basal transmission and paired-pulse ratio, but impaired long-term potentiation  , 94. Cormalities , 100.In contrast, syntaxin-1B KO mice show severely disrupted motor coordination, impaired brain development, and death within two weeks of age . Heteroz6Syntaxin-1 mediates the docking and fusion of synaptic vesicles and presynaptic membranes by facilitating the assembly of the SNARE complex, and a growing body of evidence indicates that regulation of syntaxin-1 is an important event for proper synaptic function. The changes in the open and closed conformations of syntaxin-1, nanoclusters formed by syntaxin-1, and the orderly, rapid, and accurate assembly of SNARE complexes involving syntaxin-1 are the main mechanisms of action of multiple cofactors Figure 4In the course of the study, altered syntaxin-1 function was also shown to cause NE disorders, leading to various metabolic and neurological diseases. However, NE toxicities may also produce clinical therapeutic effects, such as antiepileptic and anesthetic effects. Syntaxin-1 is widely distributed in the nervous and endocrine systems and is a key protein in studies of various neurological and psychiatric diseases and drugs. However, research on the exact mechanism of action of syntaxin-1 as a drug target is limited, indicating new directions for future studies.XY, JZ: original draft preparation. XY: software, figure, and table. XG, WT and QZ: revising the manuscript critically. QZ, JG and JZ: reviewing and editing. All authors contributed to the article and approved the submitted version.


The purpose of the present study was to evaluate the mechanical resistance of elastodontic devices (ED): their maximum compression loads and plastic deformation under loading (percentage). An Instron universal machine  was employed with a 100 N load cell and with Bluehill software for loading analyses. Each device was submitted to a five-cycles test. The following ED were evaluated: A.M.C.O.P.  in red color, in orange color, and in blue color; HealthyStart , and T4K™ phase 1 . During the five-cycles test, the Ortho-Tain device delivered the greatest compression load (7.56 N), with the lowest percentage of deformation (0.95%). For all devices, a slight plastic deformation of the material was registered, ranging from 0.95% to 1.75%. For the T4K device it was not possible to complete the five-cycles test. For all the analyzed ED, a slight plastic deformation under loading was registered, that in all cases can be considered clinically acceptable. Further studies are needed to test the appliances after clinical usage. Interceptive orthodontics represents a preventive approach for treating malocclusion at pediatric age . It has ED produce light and biological elastic forces to correct malocclusions, changing the position of the teeth and potentially affecting growth of the bone bases, by stimulating the perioral muscles  and remoToday, orthodontists have access to a wide range of ED.One of the most popular is the so-called trainer for kids, the T4K™ , a polyurethane appliance, suggested to correct class II malocclusions ; furtherAnother recently developed elastodontic approach is represented by the bio-activator A.M.C.O.P.™  . These appliances consist of two flanges, one vestibular and one lingual, which delimit a free central area, allowing an orthopedic effect, while moving without pressure ,16. It cAnother popular ED is the HealthyStart™ , specifically suggested for oral breathing children to reduce this muscular dysfunction, while developing the jaw naturally and straightening teeth.These appliances have been described to be clinically capable of improving skeletal patterns as Class II malocclusions, at an early age in a relatively cost-effective manner, by acting on muscular dysfunction and repositioning the mandible . In the Despite the existence of studies of their clinical effectiveness, their mechanical characteristics—the assumption of their clinical effectiveness, with respect to other types of functional appliances—have not been analyzed in the literature as yet . Only onThus, the purpose of the present study is to evaluate the compression resistance of the most popular ED, through the analysis of maximum compression loads and plastic deformation under loading.-A.M.C.O.P.  in red color (Ref. ΦS3–55 mm).-A.M.C.O.P. in orange color (Ref. SC3–55 mm).-A.M.C.O.P. in blue color (Ref. S3–55 mm).-HealthyStart  (Ref. 7–11 PAD).-T4K phase 1 (Ref. T4K).This in-vitro study was aimed at evaluating the maximum compression resistance of some popular EDs, as well as their degree of plastic deformation before breaking. The study was carried out at the Engineering Department of the Leone Company . The following ED were evaluated in this study:The engineering drawings for every ED are represented in A compression test was performed for the mechanical characterization of the devices. An Instron universal traction machine  was employed with a 100 N load cell, with Bluehill software for the analyses.Preliminarily, the devices were positioned on a stainless-steel plate in such a way as to have their median line parallel to the crossbar of the traction machine; a synthetic paste was used to hold the device in place and avoid its movement on the transversal plane. For testing, the displacement was applied with a punch on a steel slide  that allowed the force to be applied on the same region of the device during the whole test (it was a 5-cycles test). Thanks to its thread, the punch was installed directly on the crossbar of the traction machine, as represented in A displacement method was developed, as represented in The variables investigated were the maximum compression load and the plastic deformation under loading. These variables are useful to understand the elastic/plastic behavior of the appliances when subjected to a compressive load and displacement. The variables are described in the following sentences.2). This formula, that was obtained for the calculation of the tensile stress, can be identically applied to compressed bodies. In the present study the maximum loads necessary for compression of each device were calculated as the higher level of force recorded at the maximum stroke during the 5-cycles test for each device. The maximum compression load can be evidenced in a graph where X-axis represents the displacement expressed in millimeters, and the Y-axis shows the applied load, expressed in N.Compression is one of the elementary stresses that a body can be subjected to, along with traction, bending, shear and torsion. A body is subject to compression when a system of converging forces acts upon it. In a generic section of a beam subject to compression, the unit stress is calculated via a relation, in which compression force = N/A  = Start dimension (mm) – Dimension after test (mm) × 100 Start dimension (mm). The software used for the measurements were Rhinoceros , and Netfabb  with specific tools.The research experimental workflow is represented in It can be seen from In the present study, EDs were studied to analyze their mechanical characteristics during a compression test with a 100 N load cell and displacement. Maximum compression loads and plastic deformation as a percentage were investigated comparing measurements among the five EDs.From a clinical point of view, mechanical characteristics of these devices, such as the maximum compression loads and the subsequent plastic deformation, are strategic in terms of their clinical effectiveness in intercepting and treating malocclusion at an early age. When these mechanical characteristics are altered, it leads to a reduction in the clinical effect of the device. Ideally, an ED should not show any deformation under compressive loading during displacement. Obviously, it is expected that a deformation, although minimal, can occur for each device, but the scientific literature does not report standard and/or clinically acceptable data for the percentage of deformation under compressive load for the ED in the market. Thus, in the present study, no standard values were compared to the present ones.This is because their elastic potential energy, that is the energy stored by their elastic deformation, is the principle on which their mechanism of action is based during their clinical usage. These appliances may be applied in several different clinical situations, and can potentially be useful for moving individual teeth, blocks of teeth, and aiding growth modification as functional conventional appliances . Thus, tTherefore, the elastic potential energy can be different for every ED, potentially explaining the clinical results achieved after their usage.In the present study, for the T4K™ it was not possible to complete the test due to the need to insert metallic elements.For the other considered EDs, during the five-cycles test, the appliance that brought the greatest compression load was the HealthyStart Ortho-Tain. Regarding plastic deformation, all devices analyzed in this study were subjected to slight plastic deformation during the whole five-cycles test, ranging from 0.95% to 2.55%, with the Ortho-Tain registering the lowest percentage of plastic deformation.The A.M.C.O.P. device is made of a polymer/elastomer combination, aimed at being elastic and non-deformable. Another characteristic of this appliance lies in the soft consistency of the material, which allows the possibility of performing myofunctional exercises for the oral and lingual musculature while wearing the device during the day. In addition, it is thermoactivated, and extremely adaptable to different arch shapes. At the same time, being soft, possible interferences from flanges can be modified with heat-appropriate instruments; in addition, the patient can also expand the device by soaking it in hot water at about 70 °C for 30 s. Then, to fix it in its new form, it can be soaked in cold water . These cDifferently, the HealthyStart appliance is made of a vinyl resin whose primary characteristic is resilience. Indeed, it is usually recommended for obtaining minor tooth movements after an orthodontic treatment because of its elastomeric material . In chilIn the previous literature, these appliances have never been compared for these variables, but only in their tensile strength, for which the different materials showed almost the same values: high temperature vulcanizing silicone appliances evidenced similar values compared with urethane and vinyl materials .Regarding deformation under loading, an in-vitro study was performed in the literature , but notIn that study, urethane, white rubber, and elastocryl  were compared and the deformation was established during a compression test by compressing cylinders of materials for given periods of time, and then measuring the level of deformation. This study showed that all three materials manifested slight permanent deformation.Although with a different methodology, the findings agree with that study, and the conclusion is that, from a clinical point of view, that the percentage of deformation was <1.75% for all the analyzed ED can be considered clinically acceptable for their use. The slight difference among the appliances relates to the recommended clinical use.Regarding the clinical relevance of the present findings, it can be highlighted that mechanical properties and slightly different mechanical characteristics of different EDs can explain their clinically recommended usage. Thus, from the present study it can be stated that the HealthyStart appliance is made of a vinyl resin whose primary characteristic is resilience . Indeed, it is usually recommended for obtaining minor tooth movements after an orthodontic treatment because of its elastomeric material.Differently, A.M.C.O.P. is made of a thermo-activable polymer that requires a plasticity of the material and ensures extreme adaptability to different arch shapes, allowing myofunctional exercises during the day, thanks to its soft consistency.The present study was limited, as the appliances were not preliminary subjected to clinical use or to thermocycling to simulate intra-oral use. Thus, the results could not be the same under the two conditions. The characteristic of elasticity for all the materials could be much lower after clinical usage, or after immersion in the water bath, as assessed for other elastics in orthodontics ,28,29. AED were studied to analyze their mechanical characteristics during a compression test with a 100 N load cell and displacement. During the five-cycles test, the HealthyStart (Ortho-Tain) showed the lowest percentage of deformation (0.95%) with the highes5 maximum compression load (7.56 N) but, for all devices, only a slight plastic deformation of the appliances was registered, ranging from 0.95% to 1.75%, that in all cases can be considered clinically acceptable. Further studies are needed to test more appliances and evaluate data after clinical usage.