The RTX domains found in some pathogenic proteins encode repetitive peptide sequences that reversibly bind calcium and fold into the unique the β-roll secondary structure. Several of these domains have been studied in isolation, yielding key insights into their structure/function relationships. These domains are increasingly being used in protein engineering applications, where the calcium-induced control over structure can be exploited to gain new functions. Here we review recent advances in the use of RTX domains in the creation of calcium responsive biomaterials.  Several excellent reviews are available on different types of crosslinking moieties that are responsive to environmental cues and their applications ,3,4,5,6. and KCl . The Repeats-in-Toxin (RTX) domains undergo a conformational change and fold into the β-roll secondary structure specifically upon calcium binding. Proteins with RTX domains are produced by a variety of Gram-negative bacteria and these domains are employed in the translocation of the protein across the membrane . As suchSerratia marcescens) and proteases . RTX proteins share two common features. First, they have repeats of 9 amino acid (nonapeptide) sequences rich in glycine and aspartic acid residues at their C-termini, which are involved in calcium binding. Additionally, they are secreted across the bacterial envelope via the type I secretion system. Typically, the C-terminal repetitive sequence is composed of several blocks of GGXGXDXUX, whereby U represents an aliphatic amino acid and X can be any residue. RTX domains become biologically activated upon calcium binding, as they serve as molecular chaperons or as the folding nuclei. Inside the cell at low calcium concentration (<1 μM), they likely remain in their disordered state. Upon translocation into the extracellular space, the relatively higher calcium concentrations (>1 mM) enable the RTX domains to fold into functional globular proteins. The interaction of most virulence factors, such as Escherichia coli α-hemolysin (HlyA), with their target cells depends on the proper secretion and folding mechanism of RTX domains [E. coli and adenylate cyclase (CyA) from Bordetella pertussis [Proteins with repeats-in-toxin (RTX) domains represent a large family of virulence factors, with over 1000 family members, produced by Gram-negative bacteria with diverse biological functions. These domains are present in many lipases  has been isolated from the CyA parent protein and has been extensively investigated as a model RTX domain. It is composed of 152 amino acid residues with a molecular weight of 15.9 kDa. This peptide has nine repeats of the nine-residue consensus motif along with a capping group at the C-terminus, which is necessary for proper folding of the isolated peptide [The CyA protein of  peptide ,35,36,37S. marcescens is a 50 kDa metalloprotease with an N-terminal catalytic domain with zinc-binding motif HEXXHXXGXXH, where histidine residues are the zing ligands, and a single C-terminal RTX domain composed of 6 repeats of 9 amino acid sequences (nonarepeats) [B. pertussis and S. marcescens, cytotoxic proteins with RTX domains are present in many bacteria including E. coli. Among the best-characterized RTX proteins is the alkaline protease from Pseudomonas aeruginosa, which is another zinc-dependent metalloprotease with a calcium-dependent RTX domain composed of 6 nonarepeats [E. coli has a C-terminal RTX domain with 11 nonarepeats [The serralysin protein of repeats) ,39,40. Trepeats) . Other tarepeats ,42,43; tarepeats .RTX domains have been explored as building blocks for creating hydrogel materials, as the calcium dependent disorder-to-order transition can be used as a means of controlling the network assembly and hydrogel formation. Stimulus responsive hydrogels were constructed, whereby RTX domains were utilized in combination with other cross-linking domains, such as leucine zippers and as stand-alone cross-linking domains. In the calcium-bound, folded conformation, the Block V RTX domain of CyA forms a β-roll domain with a C-terminal capping group with two parallel β-sheet faces, each composed of 4 β-strands with 8 amino acids projecting outward into the solvent , eliminating the α-helical leucine zipper domain . A clearThe DLeuβ-roll was utilized in another hydrogel study, where this peptide was fused to an additional RTX domain mutant that was evolved to be capable of calcium-dependent target capture, the PN406β-roll . The PN4RTX domain-based recombinant polypeptides were constructed by fusing the WTβ-roll peptide from CyA to GB1 domains and resilin-like sequences, whereby the former and latter provided superior solubility and crosslinking sites for network assembly, respectively . In thisS. Marcescens. Electron microscopy was used to show that in the presence of calcium, the PGAL proteins were close to each other, as the RTX domain spacer remained short in its calcium bound state. Following the removal of calcium ions via EDTA, the proteins became separated by a range of distances from 3 nm to 13 nm, indicating that the RTX domain transitioned to its unfolded state, extending the space between the PGAL molecules. This study showed that RTX domains could be used to serve as calcium-responsive molecular springs, whereby the distances between network components can be controlled by calcium addition.In another study focusing on the design of different protein mesh networks, an RTX domain was fused between two 6-phospho-β-galactosidase (PGAL) molecules . The RTXA hydrogel responsive to both calcium and pH was constructed by single-tail peptide amphiphiles (PA) composed of a short RTX sequence and hydrophobic palmitic acid. These molecules were capable of self-assembly into nanofibers and subsequent gelation . Three dThere has been a good deal of interest in developing new protein-based biomaterials using native biomolecular interactions. RTX domains are useful for this approach, as they exhibit a well-defined and tunable conformational transition that is very specific to calcium. These domains have been used as controllable spacers between proteins, and as controllable domains in peptide amphiphile designs. We have shown that the mutagenesis of the exposed side chains on the faces of the β-roll domain can be used to design hydrophobic interfaces that assemble in a calcium-dependent fashion. These domains are contributing to the toolbox available for responsive biomaterial development and it is clear that the combination of these domains with other responsive components will lead to the development of new materials capabilities.


Aedes spp. mosquitoes in its transmission is a risk factor for outbreaks of greater proportions. The virus causes a potentially disabling illness known as Mayaro fever, which is similar to that caused by the chikungunya virus. The cocirculation of both viruses, with their clinical and structural similarities, and the absence of prophylactic and therapeutic measures highlight the need for studies that seek to understand the Mayaro virus. Using approaches in silico, we identified an antigenic and specific epitope (p_MAYV4) in domain A of the E2 glycoprotein of the Mayaro virus. This epitope was theoretically predicted to be stable and exposed on the surface of the protein, where it showed key properties that enable its interaction with neutralizing antibodies. These characteristics make it an interesting target for the development of immunodiagnostic platforms. Molecular dynamics simulation-based structural analysis showed that the PHE95 residue in the E1 fusion loop region is conserved among Alphavirus family members. PHE95 interacts with the hydrophobic residues of the E2 glycoprotein to form a cage-shaped structure that is critical to assemble and stabilize the E1/E2 heterodimer. These results provide important insights useful for the advancement of diagnostic platforms and the study of therapeutic alternatives.The Mayaro virus is endemic to South America, and the possible involvement of  Togaviridae family and Alphavirus genus. It has a wildlife cycle that involves transmission between Haemagogus spp. mosquitoes and animal reservoirs2. MAYV is endemic to South America and has been reported in Central America4. Imported human cases have been detected in European and North American countries2. Climate and environmental changes may have contributed to its silent dispersion throughout Brazil and worldwide7. Detection of MAYV infections in dengue, Zika and chikungunya outbreaks, together with possible involvement of Aedes spp. in MAYV transmission, observed under laboratorial conditions, warns of the risk of outbreaks in naive populations10.The Mayaro virus (MAYV) is a neglected arbovirus belonging to the 12. These symptoms are similar to those of other important arboviral diseases, such as chikungunya, dengue, Mayaro and Zika, suggesting a new term for this arboviral infection: “the ChikDenMaZika syndrome”2. More than 50% of patients develop debilitating and persistent joint pain during the chronic phase of the disease, similar to that caused by CHIKV infection2. Thus, developing sufficiently accurate diagnostic tests to distinguish infections caused by MAYV would be an important advance in regions where the arboviruses cocirculate.MAYV causes an acute and nonspecific febrile illness characterized by short viremia that can be accompanied by prodromal symptoms such as fever, headache, retro-orbital pain, vomiting, diarrhea, maculopapular rash, myalgia and arthralgia13. Structurally, the E1 and E2 glycoproteins have three domains interconnected by β-connectors. The E1 glycoprotein has 436 amino acids and three domains  distributed throughout the protein. Domain II is at the amino-terminal region, domain III is at the carboxy-terminal region and domain I is between domains II and III. The E2 glycoprotein has 422 amino acids and three domains . Domain B is positioned at the amino-terminal region of the protein; domain C is positioned at the carboxy-terminal region; and domain A is positioned between domains B and C17.The MAYV genome is composed of a positive-strand RNA approximately 11.5 kb in length and two open reading frames (ORFs). The first ORF is located in the genome 5′-end and encodes nonstructural viral proteins  involved in viral replication and pathogenesis. The second ORF, positioned in the genome 3′-end, synthesizes the structural proteins of Capsid (C), envelope glycoproteins 1, 2 and 3 (E1/E2/E3) and a small 6 K protein, which are important for infection and protection of viral genetic materialAlphavirus infectious process. The E2 glycoprotein recognizes and binds to a target receptor on the cell membrane to promote endocytosis21. The importance of the E2 glycoprotein was demonstrated by mutation studies in domain B of CHIKV and Semliki Forest virus (SFV)19. In Alphavirus, the acidic endosomal environment exposes and inserts the E1 fusion loop into the endosome membrane to trigger infection. This fusion results in viral capsid release into the cell cytoplasm, where it is disassembled and the viral genetic material is released, thereby inducing viral replication and polyprotein translation20. Cryogenic electron microscopy (cryo-EM) images of CHIKV22, Venezuelan Equine Encephalitis virus (VEEV)23, Barmah Forest virus (BFV)24 and Sindbis virus (SINV)25 show that E1/E2 glycoproteins form dimeric spicules on the membrane surface with regions exposed to the extracellular medium24. Exposure of E1/E2 glycoprotein residues to the extracellular environment enables their interaction with neutralizing antibodies, making them interesting targets for therapeutic and diagnostic studies29.The E1/E2 glycoproteins are directly involved in the 1. The immediate obstacle in the development of this platform is the determination of a specific protein region that is adequate for making an accurate distinction among infectious agents29. However, studies have shown success by using small peptide sequences as antigens for diagnosing some Flavivirus and Alphavirus32. Bioinformatics and immunoinformatics tools can facilitate the rational search for these small peptide sequences within the viral proteome. Bioinformatics has been used in the rational design of drugs and vaccines to reduce the time and cost of their discovery and in the exploration of diagnostic platform designs for viral infections34.The development of a diagnostic platform for MAYV will be helpful in the clinical management of patients and especially for epidemiological screenings of a virus circulation pattern to prevent its spreadTo propose a peptide for application in MAYV immunodiagnostic tests, glycoprotein amino acid sequences from the MAYV and CHIKV E2 glycoproteins were analyzed according to their antigenic properties. Then, the three-dimensional structure of the E1/E2 heterodimer was predicted by molecular modeling and protein-protein docking strategies. Finally, the heterodimer produced was subsequently subjected to molecular dynamics (MD) simulations to analyze the heterodimer stability and understand the properties that could support a rationally designed peptide suitable for immunodiagnostic tests.The full amino acid sequences of MAYV and CHIKV E1 and E2 glycoproteins, isolated from different endemic countries, were retrieved from the Virus Pathogens Resource (ViPR) database  were not predicted as antigenic regions in CHIKV. However, the peptide regions p_MAYV5 and p_MAYV6 may cross-react due to the presence of conserved residues in the CHIKV glycoprotein. The p_MAYV2 and p_MAYV11 peptides showed no antigenicity for MAYV according to the VaxiJen server. With the exception of the p_MAYV4 peptide, residues 107–116 in domain A, and p_MAYV9 peptide residues 266–274 in domain C, all the residues predicted for MAYV are conserved in the CHIKV E2 glycoprotein sequence , and no sequence overlap with any other Alphavirus was identified. Residue 107 (PRO) was not searched because its insertion in the sequence decreased the antigenicity score of the peptide (VaxiJen Score: 0.9877).Thus, we consider that p_MAYV4 peptide (PGEVISVSFV) is a promising target for the development of a specific MAYV immunodiagnostic assay. To improve the antigenicity of the peptide, we hypothesized that the elongation of p_MAYV4 between residues 108–120 (GEVISVSFVDSKN), which showed an antigenicity score of 1.1595 according to VaxiJen. This peptide was named p_MAYV4a. The peptide was analyzed on the BLASTp online server (http://web.expasy.org/protparam/). According to this prediction analysis, the peptide is 1,33 kDa, has acidic features (pI 4.37) and is probably hydrophobic, even though the index was low (GRAVY score: 0.079). In addition, p_MAYV4a is possibly stable under natural conditions (the instability score was 6.92). The yeast half-life time in vivo exceeded 20 hours.The physicochemical properties of the p_MAYV4a peptide were predicted using the ProtParam tool  model was produced for each protein using the I-TASSER server. The five major templates used for MAYV E1 glycoprotein modeling were VEEV (3J0C), SINV (3J0F), CHIKV (3N42), EEEV (6MUI), and BFV (2YEW), and for E2 glycoprotein, they were VEEV (3J0C), BFV (2YEW), SINV (3J0F), CHIKV (3N40) and CHIKV (2XFB)  values of 2.7 Å and 3.1 Å, respectively. The TM-score for both proteins was 0.99. The E1 glycoprotein monomer had 82.7% residues in favorable regions and 97.7% in allowed regions, while the E2 monomer had 81.9% in favorable regions and 96.0% in allowed regions. The values predicted by these analyses indicate that the models determined for the E1 and E2 glycoprotein monomers have a great chance of representing their expected native structures. Thus, from these monomers, it was possible to obtain the dimeric structure from molecular docking simulations performed by ClusPro 2.0. Among the ten structures produced by the ClusPro 2.0 server, we selected the one that presented the highest similarity with the resolved structures of the MD simulations of 150 nanoseconds (ns) for the predicted MAYV E1/E2 glycoprotein dimer and its behaviors were assessed by trajectory analysis. RMSD (root mean square deviation) was used to evaluate the deviation of the predicted models from the original states during the simulation Fig. . High-maAlphavirus, such as CHIKV, VEEV and BFV.A complementary measure of this parameter is performed by evaluating the fluctuations resulting from movements of each of the residues in a protein, which highlights the most flexible chain segments. Therefore, we evaluated the fluctuations of the residues in two ways: the mean fluctuation throughout the trajectory and that for the duration of each simulation. The root mean square fluctuation (RMSF) analysis of each residue was performed to determine which residues may have caused an increase in the RMSD values see Fig. . SignifiThe MAYV E1/E2 glycoproteins were formed mainly by conserved β-sheet and α-helix secondary structural components interspersed with small structures of bend, turn and coil in the regions that connected the E1 and E2 domains  is located in domain A of the E2 glycoprotein (108–120) Fig. . It is sory Fig. . The pepory Fig. , and a l2), retaining the fusion loop in the binding crevice between domains A and B , thus forming a staple-like structure that fixes the E1 fusion loop to the E2 glycoprotein.The PHE95 residue in the E1 fusion loop (domain I) is conserved among alphaviruses and interacts with the GLN226, TYR228 and ARG178 residues of E2 glycoprotein (domain B) in the binding crevice  are transmitted by arthropod vectors, such as mosquitoes and ticks37. The rational design of antigenic peptides using prediction tools in silico is a promising option in the development of immunodiagnostic platforms32. Therefore, this study was developed to fill two gaps related to MAYV knowledge: identify antigens and specific epitopes on the surface of the MAYV E2 glycoprotein for application in immunodiagnostic tests and provide a three-dimensional model of the E1/E2 glycoprotein dimer.The lack of accurate and simplified diagnostic platforms and prophylactic alternatives are obstacles to overcomein silico identified two potential antigenic linear peptides, the p_MAYV4 peptide present in domain A (residues 107–116) and the p_MAYV9 peptide inserted into domain C (residues 266–274). The p_MAYV4 peptide is more likely to be recognized by neutralizing antibodies produced during MAYV infection because domain A of the E2 glycoprotein has greater solvent exposure than domain C35. It is known that the neutralizing antibodies against Alphavirus are directed mainly to targets present in domains A and B of the E2 glycoprotein36. Therefore, due to the location of the p_MAYV4a peptide in domain A of the E2 glycoprotein, close to the cellular receptor binding site (THR58)39, we hypothesize that antibody binding in this region is associated with inhibited cell recognition.The results from the analyses of MAYV E2 glycoprotein 40. This interaction is important to ensure that the loop region of the peptide is continuously exposed to the solvent and to enable access by the antibody41. The loop region increased the antigenicity score of the p_MAYV4 peptide according to the VaxiJen server (score of 1.1595). This region is composed of four hydrophilic amino acid residues  arranged in a β-turn secondary structure at the amino-terminus. β-Turn secondary structures are more accessible to solvent and are more hydrophilic and flexible than β-sheets or α-helices. In addition, they have greater flexibility potential and hydrophilic scores, which are desired in the construction of an antigenic peptide43. The four residues were constantly exposed to solvent during the MD simulation. However, the insertion of the segment should be performed with caution since other Alphaviruses, such as CHIKV and VEEV, have a similar secondary structure44. Although these viruses have the same spatial arrangement in terms of the E2 glycoprotein structure, the primary sequence of the amino acid residues identified in CHIKV30 and VEEV is completely different from the one proposed for MAYV (p_MAYV4a).The side chains of the hydrophobic amino acids in the β-sheet region (residues 110–115) of the peptide form hydrogen interactions with the neighboring antiparallel structure and provide stability to the peptide in the protein25; therefore, we proposed a structural model of the MAYV heterodimer generated by I-TASSER and submitted it for an MD simulation of 150 ns. The high protein RMSD value along the trajectory indicates that a significant change in the molecule, compared to the original structure, has occurred. However, the stability reached by the protein after 75 ns, persisting at a value of approximately 1 nm, illustrates its stability after the conformational adjustment. While RMSD is an analysis of the protein as a whole, the RMSF analysis describes the individual residue behavior along the trajectory. In this case, the RMSF analysis showed fluctuations in important regions of the molecule, which exemplifies the flexibility and mobility of the protein under study. The cluster analysis corroborates the RMSD profile when the stability indicated by the RMSD value is reached, and the time for the number of clusters to decrease is similar. It is noteworthy that, after 75 ns, only three clusters appear, and cluster number 1 is the most prevalent among them. This shows that cluster number 1 represents the most stable protein structures. In view of this finding, the central structure is determined to be cluster number 1, which is defined as the structure with the smallest average RMSD compared to all other structures of the cluster and resulted in a protein similar to the crystal structures of other species of Alphavirus24; thus, it was used as the MAYV E1/E2 model for this study.The envelope proteins of Alphaviruses (E1/E2 glycoproteins) are fundamental during the infection process15 or by the absence of glycoprotein E3, positioned next to domains A and B. When E3 glycoprotein is present, domain B remains stable and protects the fusion loop, but after cleavage, this domain moves to the membrane fusion22. The absence of the trimeric structure in the presence of neutral conditions (pH 7.0) during the simulation may have triggered instability in domain B and in the fusion loop. To evaluate this possibility, further studies of MD with the MAYV heterotrimer  will be carried out in different pH conditions to accurately determine the position and behavior of domain B in the viral particle throughout the infectious stages.Structurally, domain B is located at the distal end in relation to the membrane, positioned above domain II of E1 and protecting the fusion loop at neutral pH. The fluctuation observed in this domain may be due to a natural flexibility caused by its long β-linkerAlphavirus, such as CHIKV, Sindbis virus and SFV, is necessary for them to bind to cellular receptors during the infectious process. In this scenario, domain B and its β-connectors move to expose the E1 fusion loop. The high values of RMSD and RMSF obtained in this study show equal flexibility of domain B of the MAYV. Our results also indicate that the movement observed in the regions of β-connectors may be useful for the protein to adapt to the environment during the fluctuations of domain B until a moment of stability is found.Cryo-EM studies show that the flexibility of domain B in Alphavirus, the movement described in domain B of the MAYV is an interesting target to be explored for the development of vaccines using neutralizing antibodies. Neutralizing antibodies produced against the epitope described in this work or that recognize other regions of domain B, domain A, or the β-connectors may be sufficient to prevent their movement, exposure of the E1 fusion loop and consequent viral infection. Currently, some works explore the development of antibodies against Alphavirus and describe that antibodies produced against domain B, domain A or β-linkers are highly effective in neutralizing the infection36. However, these works do not exploit peptide regions of the glycoproteins for the determination of targets for neutralizing antibodies. In this study, we explored an approach in silico that enabled us to characterize the E1/E2 glycoprotein structures, to identify important movements and behaviors of the virus, and to identify an antigenic region in domain A.Since there are no vaccines available that neutralize the infection by 14. Two of those residues (HIS29 and HIS73) are conserved in MAYV, but not HIS226, substituted by H226Q. Our analysis adds that the amino acid PHE95, present in the E1 fusion loop, is conserved among Alphavirus and is important for dimer fixation when interacting with the amino acids TYR228, ARG178 and GLN226 that are present in domain B and in the β-linker of the E2 glycoprotein. The MD simulation results show that these three residues form a cage-like structure that theoretically imprisons the nonpolar residue PHE95. This result will be evaluated by alanine scanning and experiments in vitro in the future. If the peptide predicted in this work is confirmed, then PHE95 is a promising therapeutic target candidate.A previous CHIKV study showed that residues HIS29, HIS73 and HIS226 of the E2 glycoprotein contribute to dimer stabilityAlphaviruses. Finally, we emphasize that these results will be applied in subsequent confirmatory in vitro tests.In summary, our results highlight an antigenic sequence in the MAYV E2 glycoprotein with potential for the development of immunodiagnostic platforms. In addition, they suggest a structural model of the MAYV E1/E2 heterodimer with insights for a better understanding of its structure and behavior, making it a hot topic for further studies involving mutagenesis and drug therapy against MAYV and CHIKV, major arthritogenic https://www.viprbrc.org) and aligned using Muscle software implemented in the Mega 7.0 program45. Representative sequences of MAYV (GenBank KM400591.1) and CHIKV (GenBank KP164567) were selected for further analysis.The methodological flowchart is shown in Supplementary Fig. 46 (http://tools.immuneepitope.org/bcell/). The Kolaskar and Tongaonkar antigenicity scale is a semiempirical epitope prediction method with more than 75% prediction accuracy46. Peptides that reached or crossed the threshold of 1.05 were classified as potential antigenic epitopes47. Potential antigenic sequences specific for MAYV as determined through the use of the antigenicity scale of Kolaskar and Tongaonkar were submitted to a second online antigenicity prediction platform, VaxiJen (http://www.ddg-pharmfac.net/vaxijen)48. The VaxiJen server is an alignment-independent antigen predictor with 87% viral epitope prediction accuracy47. These antigen prediction methodologies are based on the physicochemical properties of amino acid residues and their frequency of identification in previous experimental studies46.MAYV linear B-cell epitopes were predicted using the Kolaskar and Tongaonkar antigenicity scale49 (http://web.expasy.org/protparam/). The half-life prediction estimates how long a peptide remains stable in prokaryotic and eukaryotic organisms. A protein is considered stable-when the value obtained is lower than the cut-off value of 40, while the hydropathicity index evaluates the probability of a region being hydrophobic  or hydrophilic 32. The secondary structure of the peptide was assessed by a graphic representation generated by the MD.Physicochemical properties of the MAYV antigenic sequences, including half-life, instability index, aliphatic index, theoretical pI and the hydropathicity value, were predicted using the ProtParam online tool50 (https://zhanglab.ccmb.med.umich.edu/I-TASSER/). Based on the primary amino acid sequences of select E1 and E2 glycoproteins (KM400591.1), the I-TASSER determines the 3D structure by iterative simulations of segmentation assembly52.Due to the absence of resolved dimer and monomer structures of the MAYV E1/E2 glycoprotein in the PDB (Protein Data Bank), a three-dimensional (3D) structural model was generated using a threading modeling methodology on the I-TASSER online prediction server53 (http://molprobity.biochem.duke.edu/index.php). This server uses two measures to evaluate the quality of the produced model: the clashscore, which evaluates the number of severe steric overlays per 1,000 atoms, and the MolProbity value, which combines the clashscore, rotameter and Ramachandran ratings into a single score that is normalized such that it is on the same scale as the X-ray resolution. These combined measures enable the quality assessment of a geometric model53.The quality of the structural model generated by I-TASSER was evaluated with the MolProbity server54 (https://cluspro.bu.edu/login.php). The server performs molecular docking according to free energy parameters and produces 10 models ordered according to the structure requiring the lowest free energy for docking. The best output hits from ClusPro were submitted for refinement and analysis of the MD simulation53.For the formation of the E1/E2 dimeric structure of the MAYV envelope glycoprotein, the best models produced by the I-TASSER were submitted to the ClusPro 2.0 online server56. The sugar residues were fixed using the GLYCAM_06j-1force field57 (http://glycam.org/) and histidine residues predicted by the H++ server58 (http://biophysics.cs.vt.edu/credits.php) were protonated using the AmberTools55. The protein format was converted using ACPYPE59, and the system was minimized and equilibrated with a TIP3P water model and CL− ions60. Transmembrane protein domains at residues 397–436 in the E1 glycoprotein and 346–422 in E2 glycoprotein were restricted throughout the simulation. The MD simulation was performed using GROMACS 5.1.2 software61 in the AMBER ff99SB-ILDN force field62. The system was subjected to 150 ns simulation at 300 k temperature and 1 bar pressure, with restriction confirmed for only the transmembrane domain. The trajectory analysis was performed using RMSD and RMSF, calculated from GROMACS tools package, in the gromos algorithm. The RMSD is a measure of the spatial difference between two static structures, and in the simulation, the calculation was performed on the basis of the initial structure and all succeeding trajectory frames. On the other hand, the RMSF profile calculates the flexibility of a residue based on the fluctuation around an average position among all MD simulations63. The g_cluster (GROMACS) program, based on the RMSD profile with a cut-off of 0.25 nm, was used to determine the conformations that were found most frequently along the trajectory. In this case, all structures with RMSD values less than 0.25 nm for any element in a cluster are added to the primary cluster. It is unlikely that a molecule with an RMSD value higher than 0.25 nm from another cluster would be considered a structure. UCSF Chimera64 and Visual Molecular Dynamics (VMD)65 were used to visualize protein behavior throughout the simulation. The quality of the proposed model was evaluated using the MolProbity server51.Correct disulfide bonds between cysteine residues in the I-TASSER 3D model were determined by the tleap tool using the AMBER ff14SB force field from the AMBER18 packageVideo 1Supplementary Information


Transfer learning can enhance classification performance of a target domain with insufficient training data by utilizing knowledge relating to the target domain from source domain. Nowadays, it is common to see two or more source domains available for knowledge transfer, which can improve performance of learning tasks in the target domain. However, the classification performance of the target domain decreases due to mismatching of probability distribution. Recent studies have shown that deep learning can build deep structures by extracting more effective features to resist the mismatching. In this paper, we propose a new multi-source deep transfer neural network algorithm, MultiDTNN, based on convolutional neural network and multi-source transfer learning. In MultiDTNN, joint probability distribution adaptation (JPDA) is used for reducing the mismatching between source and target domains to enhance features transferability of the source domain in deep neural networks. Then, the convolutional neural network is trained by utilizing the datasets of each source and target domain to obtain a set of classifiers. Finally, the designed selection strategy selects classifier with the smallest classification error on the target domain from the set to assemble the MultiDTNN framework. The effectiveness of the proposed MultiDTNN is verified by comparing it with other state-of-the-art deep transfer learning on three datasets. In the past two decades, machine learning has dramatically progressed, and it has become a practical technology from laboratory to widespread commercial use . CurrentTransfer learning, also known as domain adaptation, provides an effective means to solve the above problems. On the one hand, it no longer requires training and test data to satisfy independent and identical distribution conditions. On the other hand, when the training data in the target domain is scarce and not enough to obtain a good classifier, the data from the source domain (often containing a large number of labeling samples) is similar to the target domain and can be used to assist the learning tasks in the target domain. Transfer learning has achieved remarkable results in resisting this challenge by transferring knowledge from source to target domains with different distributions . TherefoThe above transfer algorithms can only handle a single source domain, but in many real-world applications, data from more than one source domain can be collected. Therefore, transfer learning algorithms with multi-source domains are naturally researched, and the classification effect is better than that using only one source domain . RecentlNo matter multi-source or single-source transfer learning algorithms, although the classification effect of the above transfer learning algorithms can be accepted, in fact these algorithms belong to a shallow structure. Therefore, they cannot find deeper and more complex knowledge behind the data, and then find more common information between domains to further improve the classification effect in target domain.With the emergence of deep learning, it has more powerful expression ability than the learning algorithms of shallow structure, and has gained a lot of attention for the advantage of better representation features. Consequently, deep learning can generate more domain-invariant features for knowledge transfer between domains. At present, there are many research works on the combination of transfer learning and deep learning. Huang et al.  proposedIn this paper, inspired by previous researches on the combination of deep neural networks and transfer learning, we propose a new multi-source deep transfer neural network algorithm (MultiDTNN) based on convolutional neural networks and multi-source transfer learning. The core idea of this work is as follows: First, to enhance the feature transferability in specific layers in deep neural networks by reducing the domain differences between each source and target domain with using joint probability distribution adaptation (JPDA). Then, we train Convolutional Neural Networks (CNN) on each source and target domain to get a set of classifiers. Finally, for the sake of gaining MultiDTNN, the second stage of the TaskTrAdaBoost  algorithOur threefold contributions are highlighted as follows: (1) the deep transfer structures are constructed based on JPDA and a convolutional neural network which can transfer more features of data in the source domain to the target domain; (2) more knowledge in multi-source domains are provided to assist in building the learning model of target domain, so the classification effect of the model is better; (3) ensemble system of classifiers is more advantageous than a single classifier in terms of prediction effectiveness and stability.The remaining parts of the paper are organized as follows: In Transfer learning has been extensively studied for many years since it was proposed in NIPS-95 in 1995 . HoweverIn Multi-source transfer learning can be divided into two categories: the boosting-based methods ,25 and rIn the past few years, deep learning has achieved good performance in solving various problems. CNN has been extensively studied in different types of deep neural networks . In 2006In Equation (1), Since the proposed MultiDTNN needs to measure the distribution differences between domains, it is necessary to choose a suitable measurement method of distribution distance. It has recently been demonstrated that the maximum mean deviation (MMD) in the regenerative kernel Hilbert space is a valid method for estimating the distance between two distributions . For theGiven a labeled dataset in a source domain In Equation (3), the differences of distribution between two domains is the distance between the two data distributions. The smaller of MMD value, the closer the two domains are. If the value is 0, the two domains match. At present, MMD have been widely used in transfer learning algorithms ,29,30,32This section describes the multi-source deep transfer neural network algorithm in detail. For convenience, we only consider the binary classification problem. Given In this paper, the goal of our proposed MultiDTNN is to use knowledge from multi-source domains to assist learning tasks of target domains to create an efficient classifier model, which can accurately label unlabeled samples in target domains. In MultiDTNN, knowledge transfer from the source to target domains is achieved through transfer learning . TransfeIn practical applications, each source and target domains are not only different in marginal probability, but also have significant differences in conditional probability. If only the marginal probability between the source and target domains is considered, the negative transfer phenomenon may occur, and the better classification performance cannot be achieved in transfer learning. Therefore, in order to make the proposed MultiDTNN a better classification effect, we simultaneously consider both the marginal and conditional probability. Literature ,46 pointIn Equations (4) and (5), Equation (4) is to minimize the data distribution distance between the target and source domains. We apply MMD (Equation (3)) to calculate Equation (4):The conditional distribution in (5) is intractable because of unknown In order to solve the problems of the unknown sample label of the target domain, the literature ,31 propoIn Equation (9), Based on JPDA in From  that in  to choos that in . Such syA. Construction of The structure of By minimizing Equation (10), we can adapt the pre-trained CNN to the classification task of the target domain. We use a mini-batch stochastic gradient (SGD) ,30 and aThe detailed formations of The training procedure mainly consists of two subprocesses: (1) pre-trained CNN on each labeled source domain data; (2) network adaptation in target domain using labeled data of source domain data and unlabeled data of target data by training CNN classification of (1). Therefore, we can get a collection of classifiers B. Strategy of selectionIn order to get a powerful set of classifiers, we are inspired by  to impleAccording to In this section, in order to analyze the effectiveness of the proposed MultiDTNN, we evaluate it on three cross-domain standard datasets. First, the experimental setup is introduced in The following state-of-the-art transfer learning methods are chosen as benchmark algorithms for comparison with MultiDTNN: ARTL , STLCF , TaskTrBOffice-31, Office-10+Caltech-10 and Office+Home ,31,32 arwww.amazon.com, and the samples in Webcam and DSLR are obtained through web cameras and digital SLR cameras in different environments. We construct six cross-domain tasks A->D, A->W, W->A, W->D, D->A, and D->W from source to target domains. On each of the above-mentioned cross-domain, the proposed multi-source MultiDTNN algorithm uses A, W, and D as the source domain.Office-31 is a standard dataset that contains 4,652 images from the domains Amazon (A), Webcam (W), and DSLR (D). These images can be divided into 31 categories. Among them, the samples in Amazon are from 2 datasets, which have been widely used in domain adaptation methods. As with the method of constructing cross-domain tasks on Office-31, we construct 12 cross-domain tasks. The number of source domain is 4 in MultiDTNN.Office-10+Caltech-10 contains 10 common objects shared by Office-31 and Caltech-256 (C)www.clipart.com), Product  and Real-World . The dataset has 65 objects with15500 image samples. Similarly, we constructed 12 cross-domain tasks in a similar way to Office-31, with MultiDTNN using 4 source domains simultaneously on each task.Office+Home collects objects from 4 domains: Art , Clipart  On the cross-domain tasks of three datasets, the average accuracy rate of the based deep learning methods outperform the common transfer learning algorithms ARTL and STLCF, which shows that the based deep learning methods are obviously superior to the shallow transfer learning algorithm.(2) CNN-based deep transfer learning algorithms  can use the knowledge of source domain to assist in learning tasks in target domain, so their classification performance is better than standard deep learning method (CNN). This indicates that the data in source domain can be used to improve the learning task of target domain with unlabeled data on the deep neural network model combined with transfer learning, so their experimental results are better.(3) In the benchmark algorithms, TaskTrBoost, FastDAM, and IMTL can utilize the sample features of multiple source domains to help learning tasks of target domain create classifier models, so their classification effect is better than ARTL and STCF, which are non-deep single source domain transfer learning algorithms, and even are obviously superior to CNN-based deep transfer learning algorithms in some cases.(4) Comparing with Office-31 and Office-10+Caltech-10, Office+Home contains more categories and the distribution between categories is larger, so all algorithms cannot achieve promising performance. However, from the experimental results we could notice that our proposed model obtain better performance in most cases. Especially in Office+Home, MultiDTNN can achieve better performance than the benchmark algorithms.(5) Comparing with the benchmark algorithms, our proposed MultiDTNN model can transfer knowledge from more than one source domain, so it can help the learning tasks of the target domain to build a more efficient classifier model. For example, for cross-domain task A->W of the dataset Office-31, the transfer deep neural network algorithms DTLC, DAN, SDT, DHN, D-CORAL, and DDC of the benchmark algorithms can only transfer the knowledge of one source domain A to the target domain W. Nevertheless, the proposed MultiDTNN can simultaneously use the knowledge of three source domains A, W, and D for the learning task of the target domain. Similarly, the number of source domains that MultiDTNN can utilize on the Office-10+Caltech-10 and Office+Home datasets is 4. We carefully analyzed all the experimental results on the three datasets, and see that MultiDTNN works best. In addition, the experimental results fully demonstrate that in deep neural networks, multi-source transfer can effectively compensate for the lack of single-source transfer.From A. Convergence analysisThe training process of MulDTNN in B. Parameter analysisThe parameter In this paper, we design a new deep transfer neural network framework: a multi-source deep transfer neural network, which integrates multi-source transfer learning, CNN, and JPDA into an optimization program. Multi-source transfer can provide more knowledge that is transferred into the target domain by using knowledge from multiple source domains, and the classification models of the target domain are built; CNN extracts more complex features of the dataset; JPDA is used to reduce the difference of probability distribution between domains and increases the transferability of features in source domains. Specifically, for the purpose of enhancing the transferability of features in deep neural networks, MultiDTNN utilizes JPDA to reduce the difference of domain probability distribution between each source and target domains. Then, on each source and target domains, we train CNN to obtain a set of deep learning classifiers. Finally, in order to select the classifier with the smallest classification error in the target domain from the classifier set, inspired by TaskTrAdaBoost a selection strategy is designed to obtain the MultiDTNN framework. The experimental results on the three cross-domain benchmark datasets demonstrate the effectiveness of our proposed model and have certain advantages over the benchmark algorithms. Although the experimental results show that the MultiDTNN has better classification performance than the benchmark algorithms, it still needs to work in the following aspects: further improve the convergence efficiency of the MultiDTNN model; in addition, it is also an interesting challenge to increase the number of source domains to more than 10.


Cyanobacteria produce hydrocarbons corresponding to diesel fuels by means of aldehyde-deformylating oxygenase (ADO). ADO catalyzes a difficult and unusual reaction in the conversion of aldehydes to hydrocarbons and has been widely used for biofuel production in metabolic engineering; however, its activity is low. A comparison of the amino acid sequences of highly active and less active ADOs will elucidate non-conserved residues that are essential for improving the hydrocarbon-producing activity of ADOs.Escherichia coli and quantifying the hydrocarbon yield and amount of soluble ADO. We demonstrated that the activity was highest for the ADO from Synechococcus elongatus PCC 7942 (7942ADO). In contrast, the ADO from Gloeobacter violaceus PCC 7421 (7421ADO) had low activity but yielded high amounts of soluble protein, resulting in a high production level of hydrocarbons. By introducing 37 single amino acid substitutions at the non-conserved residues of the less active ADO (7421ADO) to make its sequence more similar to that of the highly active ADO (7942ADO), we found 20 mutations that improved the activity of 7421ADO. In addition, 13 other mutations increased the amount of soluble ADO while maintaining more than 80% of wild-type activity. Correlation analysis showed a solubility-activity trade-off in ADO, in which activity was negatively correlated with solubility.Here, we measured the activities of ADOs from 10 representative cyanobacterial strains by expressing each of them in We succeeded in identifying non-conserved residues that are essential for improving ADO activity. Our results may be useful for generating combinatorial mutants of ADO that have both higher activity and higher amounts of the soluble protein in vivo, thereby producing higher yields of biohydrocarbons.The online version of this article (10.1186/s13068-019-1409-8) contains supplementary material, which is available to authorized users. The biosynthesis of hydrocarbons has provided a remarkable means of producing substitutes for petroleum-based fuels . Biohydrn) into corresponding alkanes or alkenes (Cn−1) and formate, respectively . In addition, several mutations at residues that compose the substrate binding channel have been reported to change the substrate specificity of ADO  divided by the amount of the soluble form of ADO [quantified using sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) or western blotting]. The same method has been successfully applied to measure the activity of AAR, and we found that AAR from Synechococcus elongatus PCC 7942 (7942AAR) had the highest activity among the examined AARs  produced in the culture were normalized to the amount of the soluble form of the ADO protein expressed in The amounts of the soluble and insoluble forms of ADO proteins were measured by SDS-PAGE or western blotting as previously described , 24. BriA total of 134 orthologs of ADO were found on the BLAST server  using thhttp://microbedb.jp/), CyanoBase (http://genome.microbedb.jp/cyanobase/) [http://cyanobacteria.web.pasteur.fr/). Ninety-three and 31 species are freshwater and marine cyanobacteria, respectively, and 10 species can live in both freshwater and marine environments.The habitats of 134 cyanobacterial species were examined using MicrobeDB (nobase/) , and theAdditional file 1: Table S1. Amino acid sequence identities (%) among the ADO sequences used in the present study.Additional file 2: Figure S1. Phylogenetic tree of cyanobacterial ADOs based on ADO amino acid sequences. ADO sequence groups 1, 2, and 3 are shown in hatched yellow, cyan, and magenta, respectively. Host strains of ADOs used in the present study are indicated in red. Vertical bars show habitats of derived cyanobacteria: marine (blue), freshwater (red), and both marine and freshwater environments (green). The scale of the phylogenetic tree is shown in the upper right corner.Additional file 3: Figure S2. GC–MS profiles of extracts from E. coli cell cultures coexpressing 7942AAR and ADO from one of 10 representative cyanobacteria. a Whole GC–MS profiles. Pentadecane (C15:0), heptadecene (C17:1), and heptadecane (C17:0) were eluted at retention times of 14.15, 16.32, and 16.54 min, respectively. The region in the black square is expanded and shown in Additional file b Peaks of pentadecane. c Peaks of heptadecene and heptadecane. Color codes for various ADOs are shown in panel (a).Additional file 4: Figure S3. GC–MS profiles of fatty aldehydes and corresponding fatty alcohols. a, b GC–MS profiles of fatty aldehyde and alcohol standards, including hexadecanal , hexadecanol , octadecanal , and octadecanol , which were eluted at retention times of 17.69, 18.35, 19.74 and 20.32 min, respectively. The concentrations of the standards were 50 mg/L (a) and 10 mg/L (b). c, d GC–MS profiles of the extracts from E. coli cell culture expressing neither AAR nor ADO (c) and from E. coli coexpressing 7942AAR and 73102ADO (d). In panel (d), peaks 1, 2, and 3 correspond to hexadecanal, hexadecanol, and octadecenal, respectively, while these peaks are not present in panel (c).Additional file 5: Table S2. The absolute amounts of hydrocarbons (in milligrams) produced in E. coli coexpressing ADO and 7942AAR per liter of E. coli cell culture using M9 medium.Additional file 6: Figure S4. Solubility and protein expression levels of ADO and 7942AAR in E. coli. a Western blotting analysis of the supernatant and pellet fractions of the E. coli cell culture lysates. Images of triplicate experiments are shown. The E. coli cell culture coexpressing ADO and 7942AAR was sonicated and centrifuged to separate the supernatant (S) and pellet (P) fractions. M denotes the molecular weight markers. The bands for AAR (38.6 kDa) and ADO (26.5–28.3 kDa) are indicated by arrows. The results for E. coli transformed with the pETDuet-1 vector containing neither the AAR nor ADO gene are shown in lanes 17 and 18 as a control. In lanes 9 and 10, the 7425ADO protein migrated more slowly than the other ADOs (see text for details). b The amount of the soluble form of 7942AAR relative to that observed in E. coli coexpressing 73102ADO.Additional file 7: Table S3. Charge and molecular weight of each ADO used in the present study.Additional file 8: Figure S5. Correlation analysis among various properties of 10 representative ADOs. The relative amount of total hydrocarbon plotted against relative activity (a), relative amount of soluble ADO (b), solubility (c), and relative protein expression level of ADO (d). The relative activity of ADO plotted against the relative amount of soluble ADO (e), the relative protein expression level (f), and solubility (g). The relative amount of soluble ADO plotted against the relative protein expression level (h) and solubility (i). Solubility plotted against the relative protein expression level (j). In each panel, the red continuous line indicates a linear regression. The corresponding correlation coefficients, r, and the p values are shown.Additional file 9: Figure S6. Correlation analysis of various properties of ADO with amino acid sequence identities. a The relative activity of ADO plotted against sequence identity (%) with the amino acid sequence of 7942ADO. b, c The solubility (b) and protein expression level (c) of ADO plotted against sequence identity (%) with the amino acid sequences of TeADO and 7421ADO, respectively. d The amount of total hydrocarbon produced in E. coli coexpressing 7942AAR and ADO from one of 10 representative cyanobacteria plotted against sequence identity (%) with the amino acid sequence of TeADO. e The relative amount of soluble ADO plotted against sequence identity (%) with the amino acid sequence of 7421ADO. In each panel, the red continuous line and blue dotted line respectively indicate a linear regression obtained using all data and that obtained without the data point for the highest value. The corresponding correlation coefficients, r, and the p values are shown.Additional file 10: Figure S7. Correlation analysis among various properties of the 7421ADO mutants. a, b The relative amounts of hydrocarbons plotted against the solubility (a) and relative protein expression level of 7421ADO (b). c The relative activity of 7421ADO plotted against the relative protein expression level. d The solubility of ADO plotted against the relative protein expression level. In , the data point for R181E is highlighted. The blue dotted line indicates a linear regression obtained without using the data for R181E.


Mycobacterium bovis MLVA genotype richness at three analytical scales; breakdown level, herd level, and patch level, and compared the results between dairy and non-dairy production types.We used genetic Multi-Locus VNTR Analysis (MLVA) data gathered from surveillance efforts to better understand the ongoing bovine tuberculosis (bTB) epidemic in Northern Irish cattle herds. We modelled the factors associated with In 83% of breakdowns and in 63% of herds, a single MLVA genotype was isolated. Five or more MLVA genotypes were found in less than 3 % of herds. Herd size and the total number of reactors were important explanatory variables, suggesting that increasing MLVA genotype richness was positively related to increases in the number of host animals. Despite their smaller relative size, however, the highest MLVA genotype richness values were observed in non-dairy herds. Increasing inwards cattle movements were important positive predictors of MLVA genotype richness, but mainly in non-dairy settings.M. bovis infection in cattle herds. We hypothesise that these mechanisms will maintain, but may not explicitly increase, MLVA genotype richness. The presence of elevated MLVA richness is relatively rare and likely indicates beef fattening enterprises, which purchase cattle from over long distances. Cattle movements were furthermore an important predictor of MLVA genotype richness in non-dairy herds, but not in dairy herds; this may represent reduced cattle purchasing levels in dairy enterprises, compared to beef. These observations allude to the relative contribution of different routes of bTB infection between production types; we posit that infection associated with local factors may be more evident in dairy herds than beef herds, however in beef herds, inwards movements offer additional opportunities for introducing M. bovis into the herd.The principal finding is that low MLVA genotype richness indicates that small-scale epidemics, e.g. wildlife, contiguous farms, and within-herd recrudescence, are important routes of The online version of this article (10.1186/s12866-019-1530-7) contains supplementary material, which is available to authorized users. Mycobacterium bovis is the causative agent of bTB, a serious bacterial disease of cattle and livestock. The animal and human health implications of bTB infection are considerable, and far-reaching economic costs are associated with disease control. As yet, however, eradication has remained elusive throughout much of the United Kingdom (UK) and Republic of Ireland (ROI) [Genetic approaches such as spoligotyping, MLVA genotyping, and more recently, whole genome sequencing (WGS) are routinely used to help answer epidemiological questions –4. The mnd (ROI) , 10.M. bovis isolates from culture-confirmed bTB cases, including reactor cattle which test positive to the Single Intradermal Comparative Cervical Tuberculin (SICCT) test, and lesioned animals identified during meat inspection [M. bovis genotypes are associated with relatively different proportions of lesioned reactors in cattle [M. bovis genotype richness (i.e. numbers of different MLVA genotypes) is linked to prolonged and recurrent bTB breakdowns [MLVA genotyping is carried out on spection . The infn cattle  and increakdowns .M. bovis MLVA genotype data also reveals that the genetic structure of the M. bovis population in NI is spatially aggregated [M. bovis populations in cattle elsewhere [M. bovis MLVA genotypes are co-localised between cattle hosts and a known wildlife reservoir, the European badger (Meles meles) [M. bovis MLVA genotype richness. Such transmission routes may include spillback from infected wildlife in the area [M. bovis MLVA genotypes within-herd [M. bovis MLVA genotype richness have also been observed within herds [M. bovis MLVA genotypes may also be introduced via infected wildlife [The gregated , 15 a phlsewhere –19. Furtthe area , 20, rechin-herd , infectihin-herd , or fromhin-herd , 24. Notin herds , alludinin herds , 26, or in herds , 28 alsowildlife ; badgerswildlife . Howeverwildlife .post-mortem than non-dairy animals [ante-mortem diagnostics [M. bovis MLVA genotype richness across multiple analytical scales in NI, in both dairy and non-dairy settings. We determine the factors associated with this observed richness, and use the results to better understand infection dynamics across both production types.Different mechanisms of infection may furthermore be associated with different herd management practices. In GB, there is evidence that beef fattening farms may be at more risk of acquiring infection via inwards cattle purchases, whereas dairy farms are more likely to spread infection via the onwards sale of animals . In NI,  animals , 34. Dai animals , 35, 36,gnostics . Togethes) = 0.52, p < 0.05) and every unit increase in breakdown level MLVA richness was associated with an increase in herd level MLVA richness of 38%  was identified between individual herd level richness and patch level MLVA richness, with a 2.1% increase in patch level richness for a unit increases in herd level richness  and a greater effect size . Measures of MLVA genotype richness at the breakdown, herd and patch level varied spatially across NI. Fig. Final datasets were derived from 29,473 animal-level tests, associated with 7478 bTB breakdowns from 5378 herds. There was moderate correlation between breakdown level MLVA richness and herd level MLVA richness (Spearman’s Rank (Rn = 2964), there were two or fewer reactors disclosed; the majority of these breakdowns  occurred in herds without a milk licence. However, the total number of reactors variable did not exhibit the same distribution between dairy and non-dairy herds; increases in the number of reactors disclosed during a breakdown had a positive association with the presence of a milk licence . The presence of a milk licence was also significantly negatively associated with inwards movement intensity .Exploratory analysis revealed differences in the distribution of explanatory variables between herds with milk licences, and herds without. Whilst the mean herd size at time of breakdown was 141 animals (Standard Deviation (SD) ±146), herds with milk licences  were significantly larger than beef herds . In 40% of breakdowns (n = 6168), only one MLVA type was isolated. The maximum MLVA genotype richness observed was 12 (n = 1), and 49 breakdowns (0.66%) yielded 5 or more different MLVA types. Whilst the presence of a milk licence was a positive predictor of MLVA richness, it was not statistically significant . Furthermore, non-dairy herds exhibited greater accumulation of MLVA genotypes. For example, the maximum number of different MLVA genotypes isolated from a breakdown in a non-dairy herd was 12, with 41 breakdowns yielding five or more different MLVA genotypes. In breakdowns in dairy herds however, the maximum number of MLVA genotypes isolated was seven, and only eight breakdowns yielded five or more different MLVA genotypes . However only one breakdown occurred in the majority of herds in the dataset . In those herds with more than one breakdown (n = 1575), no increase in MLVA genotype richness was observed in 26.3% of cases (n = 414). In 57 herds (3.62%) which experienced more than one breakdown, the number of MLVA genotypes in the breakdown level analysis and the herd level analysis increased by five or more.After data were aggregated to herd level, no increases in MLVA genotype richness were observed in 70.6% of herds (n = 3382) and the maximum number of MLVA genotypes isolated was 19 (n = 1), with 159 herds (2.96%) yielding five or more MLVA genotypes. In this analysis, the presence of a milk licence also exhibited a slight association with increasing MLVA genotype richness . Despite this, there was greater accumulation in the overall number of MLVA genotypes in non-dairy herds, as 133 non-dairy herds (3.5%) were associated with between five and 19 different MLVA genotypes, compared to the 26 dairy herds (1.7%) that were associated with between five and 10 MLVA genotypes  were associated with 10 or fewer different MLVA genotype isolates, and 82.1% of patches (n = 101) were associated with 20 or fewer different MLVA genotype isolates. Comparing patch level MLVA richness between dairy and non-dairy herds  experienced only one breakdown during the study period. It would therefore appear that the occurrence of breakdowns associated with newly introduced MLVA genotypes is less common. Furthermore, in 26% of herds which did experience more than one breakdown, there were no increases in MLVA genotype richness over successive restriction periods. These results further highlight the relative importance of small-scale, local effects in maintaining the epidemic.The results reveal that a single MLVA genotype was isolated in the majority of cases . The accumulation of MLVA genotype richness at the breakdown level and at the herd level was less common; less than 1 % of breakdowns and 3 % of herds were associated with five or more MLVA genotypes, respectively. Our principal finding is, therefore, that us farms , contamius farms , 36, 47.s spread ; our resM. bovis population in NI is spatially clustered, with MVLA genotypes localised to geographic areas [M. bovis genotypes. We posit that elevated MLVA genotype richness within-herd implies that these herds are beef fattening enterprises. These herds operate via the purchase of a large number of cattle, which are then raised for sale, straight to slaughter. This affords the opportunity for the accumulation of MLVA genotype richness with less risk of spreading infection to other herds via the onwards sale of cattle. In GB, beef fattening herds are indeed shown to have a higher turnover of animals than dairy herds [M. bovis infection, in addition to wildlife, nearby farms, within herd recrudescence and environmental contamination.The ic areas . Consequic areas , or possic areas . Howeverry herds  which maM. bovis population [The low MLVA genotype richness observed in most herds does not mean that cattle movements do not contribute more widely to the epidemic; cattle movements are a known risk factor for bTB in NI . Indeed,pulation  means thpulation .Whilst there was a moderate correlation between breakdown level and patch level metrics of MLVA genotype richness, individual herd level MLVA richness values exhibited only weak correlation with the overall patch level MLVA richness. Higher patch level MLVA richness will indeed partially reflect the larger host population size in patches compared to herds, but it also further shows that herds within a patch share some, but not all, of the same MLVA genotypes. This further hints at the importance of localised infection mechanisms in maintaining MLVA richness within herds. In addition, we found that the mean herd level MLVA richness in the patch did indeed exhibit a stronger association with patch level MLVA richness. This is likely due to the influence of herds with elevated MLVA type richness (i.e. beef fattening herds) impacting on the mean.M. bovis MLVA genotypes is likely to be related to increases in the number of hosts. Increases in herd size were also positively associated with MLVA genotype richness; this may be a further manifestation of the positive relationship between MLVA genotype richness and increasing cattle numbers. However, large herds may also be linked to other factors which afford more opportunities for the accumulation of new M. bovis MLVA genotypes e.g. inwards cattle movements, or a larger geographic footprint.In all models, the total number of reactors disclosed during a breakdown was an important explanatory variable for MLVA genotype richness. Thus, at least in part, the accumulation of different The results furthermore demonstrate that dairy herds were both larger, and contained more reactors, than herds without a milk licence. There was also evidence that at the herd level, the presence of a milk licence had a slight positive association with increased MVLA genotype richness. In spite of this, the highest MLVA genotype richness values were not found in dairy settings. Previous studies show that dairy herds in NI are associated with elevated risk of bTB breakdown compared to other herd types  and the The herd-level analysis suggested that increasing numbers of the most common MLVA genotype found in dairy herds was negatively associated with MLVA genotype richness at the herd level. We consider that rapid propagation of a single MLVA genotype within-herd could arise if within-herd transmission dynamics were different between beef and dairy herds . In dairM. bovis hosts, the maximum MLVA genotype accumulation was not observed in dairy herds. We therefore posit that in dairy herds, localised infection routes which do not explicitly introduce MLVA genotypes from outside the area, e.g. within herd recrudescence, are more evident. Non dairy herds, however, may also be exposed to additional infection via cattle purchasing. Future eradication should focus on understanding the contribution of infection pathways in different production types. An enhanced understanding of the NI cattle movement network will provide a better understanding of the risk associated with cattle purchasing in different settings. In addition, we advocate that future work should focus on the infection risk associated with farm fragmentation, contiguous herds and shared grazing between different herd types in the Northern Irish context.This work alludes to the fact that different routes of infection may be of greater relative importance to different production types in NI. In the majority of herds, there was relatively low MLVA genotype richness, indicating that highly localised factors operating over a small geographical extent were primarily influencing the epidemic, e.g. infection from neighbouring herds, wildlife or within herd recrudescence. Only a limited number of herds were identified with elevated MLVA richness, and these were likely beef fattening enterprises, wherein long range cattle purchases may also represent an important source of new MLVA genotypes. Indeed, inwards movement intensity was an important predictor of increasing MLVA genotype richness in non-dairy settings. Cattle movements appear to be relatively less important for introducing MLVA genotypes into dairy herds, which may be a consequence of less movement intensity associated with dairy herds more generally, and/or the purchase of cattle from the local area which are more likely to share the same MLVA genotypes. Despite the larger number of potential . The area of NI is approximately 14,000 km2 and mean patch size is approximately 110 km2 (SD ± 53). Data on confirmed breakdowns which occurred between January 2003 to April 2016 were made available from the Animal and Public Health Information System (APHIS) database [M. bovis genotype surveillance data (MLVA) from skin-test reactors and lesioned non-reactor animals identified at slaughter [https://www.mbovis.org/ was applied for all spoligotypes in the dataset. Eight VNTR loci across the M. bovis genome were genotyped [This retrospective analysis used data collected between 2009 and 2016 inclusive, and was conducted at three analytical scales; breakdown level, herd level and patch level. Cattle herds in NI reside within one of 123 administrative patches, which are situated within 10 Divisional Veterinary Offices  were analysed separately from herds without a milk-licence . Nine models were therefore constructed in total. Seven variables were considered for inclusion in the breakdown level analysis  , initial package  and Mora package , howeverAdditional file 1Figure S1. Patches and Divisional Veterinary Offices (DVOs) within NI. Table S1. Summary statistics for herd level variables. Table S2. Summary statistics for patch level variables. Table S3. AIC values for each of the models, compared to null models containing only the “total reactors” variable. Table S4. Final full model for the breakdown level analysis constructed using all data. Table S5. Final full model for the breakdown level analysis constructed using only data from herds with milk licences. Table S6. Final full model for the breakdown level analysis constructed using only data from herds without milk licences. Table S7. Final full model for the herd level analysis constructed using all data. Table S8. Final full model for the herd level analysis constructed using data from herds with milk licences. Table S9. Final full model for the herd level analysis constructed using data from herds without milk licences. Table S10. Final full model for the patch level analysis constructed using data from all herds. Table S11. Final full model for the patch level analysis constructed using only data from herds with milk licences. Table S12. Final full model for the patch level analysis constructed using only data from herds without milk licences. (DOCX 187 kb)