Studies of between-herd contacts may provide important insight to disease transmission dynamics. By comparing the result from models with different levels of detail in the description of animal movement, we studied how factors influence the final epidemic size as well as the dynamic behaviour of an outbreak. We investigated the effect of contact heterogeneity of pig herds in Sweden due to herd size, between-herd distance and production type. Our comparative study suggests that the production-type structure is the most influential factor. Hence, our results imply that production type is the most important factor to obtain valid data for and include when modelling and analysing this system. The study also revealed that all included factors reduce the final epidemic size and also have yet more diverse effects on initial rate of disease spread. This implies that a large set of factors ought to be included to assess relevant predictions when modelling disease spread between herds. Furthermore, our results show that a more detailed model changes predictions regarding the variability in the outbreak dynamics and conclude that this is an important factor to consider in risk assessment. SuchTheoretical considerations may assume a homogeneous contact pattern with equal probabilities of infections between all herds, known as mass action mixing (MAM), but more realistic models need to include mixing heterogeneities. Perhaps most obvious, the number of contacts may be expected to differ between herds ,14. InfeIn this study, we focus on three factors at herd and between-herd levels that may cause heterogeneous contact patterns. First, clustering is typically the effect of a strong spatial component in the contact pattern. Consequently, if contacts are more likely to occur at shorter distance, we may expect a more rapid decrease in the number of new infections owing to depletion of local susceptible herds. Secondly, a large herd may be expected to have more contacts and may thereby be more susceptible to infections as well as potentially infecting many other herds. Thirdly, the production type of a herd is expected to influence both the number of contacts and which herds that will be part of these contacts. Heterogeneity due to different production types is therefore expected to result in heterogeneous disease transmission as well as clustering patterns in the contact structure. In addition, the number of movements from herds of one type to another may be very different from the number of reversed movements ,5.et al. [Lindström et al.  presente2.S), infected (I) or recovered (R). A herd is defined as a group of animals that mix homogenously and spread of disease occurs at a much faster rate within than between herds. We further assume that recovered herds remain in state R for the time of the outbreak (hence become immune) and no new herds are added during the time of an outbreak. If all herds are equal and have equal probability of contacts, disease transmission could be modelled as an MAM process. In this study, we are not interested in the dynamics of any specific disease, but rather the influence of the observed contact structure, and we therefore want to make as few assumptions as possible about the disease. In order to observe relevant dynamics, we do however need to make some assumptions about the recovery of infected herds. We formulate a discrete model where each step consists of one movement  which results in a new infection if the source herd is infected and the destination herd is susceptible. Hence, we assume that there is a constant rate of movements It and with the assumption of the MAM, the probability of the source farm being infected is ((1 − r)It)/N, where N is the total number of herds, and the probability of the destination herd being susceptible is given by St/N where St is the current number of susceptible herds. Hence, the expected number of susceptible, infected, and recovered (R) herds at time t + 1 is given byt = 0, then I0 = 1, S0 = N − 1 and initial increase of infected herds is expected if I1 = (1 − rc)(1 + 1/N) > 1, or rewrittenrc is the critical value for when an initial increase in the number of infected herds is expected. In the analysis of pig movements in Lindström et al. [N = 3084 in equation (2.2), rc = 3.24 × 10−4. The expected time that a herd remains infected, g, is given as g = 1/λ, where λ = − log(1 − r). In order to calculate gc, the critical expected time a herd must remain infected for an expected initial increase in the number of infected herds, we set r = rc and by the assumption of 55.4 daily movements we estimate gc at 55.7 days.In this study, we study infections at the herd level. We follow the assumptions of SIR models, where a herd is considered to be either susceptible (ovements ). Note t [et al.  consistem et al. , 3084 he3.et al. [The pig industry has a pyramidal structure with transports predominantly going downward in the system; from nucleus (breeder) herds to multiplying (gilt producing) herds, from multiplying herds to piglet producing herds and farrow-to-finish herds, and finally from piglet producing herds to fattening herds . Farrow-et al. , it was 4.et al. [In Lindström et al. , a modelet al.  for detaet al.  for furts to d, P, where θ refers to a set of parameters yet to be specified, can be written asI and J, referring to the production types of the source and destination herd, respectively. Schematically, the model is written asP is given as the probability of d conditional on s and production types I, J as P, and the probability of source herd P is conditional on the production-type I. The distribution P is defined such that I, taken into account that a herd is a fraction I. Similarly, J, but adjusted to exclude the probability of the destination herd being the same as the source herd. For modelling of a system with K production types, h is a matrix of dimensions K × K and is defined as hIJ is a measurement of how common movements between production types I and J are relative to the abundance of the types. In our model, we included the seven production types described in §3, and in addition used a pseudo-type for farms that had not reported a production type, giving K = 8. If hIJ = 1/K2 for all IJ, then movements between all production types are equally probable.It is assumed that the probability of a movement from herd P was assumed to be dependent on the production-type I and herd size. The latter was given by the maximum capacity, which is reported separately for fattening pigs and sows. This was modelled asS1s and S2s are the maximum capacity of fattening pigs and sows, respectively, reported for herd s, and S refers to the maximum capacity of both demographic groups on all herds. The parameters S1 and S2, respectively, for production-type I. The probability of movements was assumed to depend on herd size with a power-law relationship and G was given asSuf + 1  rather than simply Suf was used to avoid zero probability of movements from herds with maximum capacity reported as zero for any of the demographic groups. If u and the probability of movements starting at a herd for production-type I.The probability d of a movement was assumed to depend on both herd size of d and the distance between herds s and d, Dsd, and given asc̈1J and c̈2J are equivalents of destination herd depending on herd sizes. Consequently, c̈uJ = 0  means that there is no relationship between maximum capacity of demographic group u and the probability of movements starting at a herd for production-type J. The function F is a spatial kernel function and describes how probability of movements decreases with distance between herds,κIJ and VIJ are the kernel kurtosis and variance, respectively, asΓ is the gamma function while the parameters b and a regulate κIJ and VIJ of the spatial kernel (for more details see [The probability of the destination herd ails see ). In equails see .5.g = 91 days (three months). This generation time was chosen because it is larger than gc and hence at least for MMAM results in an initial increase of infected herds, yet it is small enough to avoid outbreaks where all herds are infected. Hence it allows for observing relevant aspects of the dynamics. Thousand replicates were simulated with each model and the 3084 herds included in the analysis of Lindström et al. [In order to test the effect of the factors included in the contact model on outbreak dynamics, we simulated disease transmission via animal movements with different models. Each replicate was seeded with a single, randomly picked index herd and a herd was assumed to instantly become infected if a movement was simulated from infected herd A to susceptible herd B, and any subsequent movement from B to a susceptible herd was assumed to result in a new infected herd. The mean expected time a herd remains infected (and infective) was set to m et al.  were useet al. [Mfull: the full model as described in §4 and [— n §4 and .M1: by setting F  = 1, distance dependence is removed. Hence, probabilities of contacts depend only on production types and herd sizes.— M2: here hIJ = 1/K2 for all I, J, thereby removing the production-type structure of the contact pattern.— M3: the probability of contacts depending on herd size was removed by setting I, J.— MMAM: all herds have the same probability of sending and receiving movements.— By implementing reduced versions of the full model, we aim to show what difference the included factors have on the dynamic and thereby indicate what aspects may be missed if these factors are excluded in modelling of spread of livestock disease via between-herd contacts. The full model is by no means a ‘true model’, but it is the most detailed description of the system.If nothing else is stated, all models were parametrized by random draws (one draw for every replicate) from the full posterior distribution estimated with the MCMC as given in Lindström et al.  and schet = 1552, corresponding to the expected number of movements of four weeks. This was chosen because at this time, the number of infected herds was large enough to make relevant comparisons.To compare the dynamics predicted by the different models, we plot the mean number of infected individuals versus time from first infection. We are also interested in the variability in the initial stage of an outbreak and therefore also compare the final epidemic size as well as the distribution of currently infected and recovered herds at τ = Δτ, 2Δτ, 3Δτ, … using Δτ = 776 (corresponding to the movements of two weeks) and defined τpeak of each replicate as the time where the most herds are in the infected state at this scale. For τ < τpeak, corresponding to the phase of initial increase, we identified and counted the number of time steps satisfying Iτ < Iτ−Δτ and for τ > τpeak the number of time steps satisfying Iτ > Iτ−Δτ, where Iτ is the number of infected herds at time τ. We define ρx,m as the proportion of time steps satisfying any of these two inequalities. This proportion gives an estimate of marked fluctuations of the rate of spread. We found that the choice of Δτ = 776 was most suitable to capture marked changes in the number of infected herds rather than small changes due to individual herds becoming infected or recovered. At slower time scales the fluctuations evened out while at faster the fluctuations from the inherent randomness were still present. Such random events are also more apparent when there is a small number of infected herds [Iτ > 10.Assuming no interventions, the typical behaviour of an outbreak involves an initial increase of the number of infected herds because infections are generated faster than herds are recovering. As more herds are infected, the susceptible herds are depleted and eventually the rate of recovery is faster than the rate of new infections. When contacts between herds are not randomly distributed, we expect deviations from this pattern making the course of an outbreak less regular and to show fluctuations in the rate of spread during the initial increase as well as during the decrease . Hence wed herds , therefo6.M2, has the highest impact when comparing the curves to the full model, Mfull. However, other factors are also essential as M2 differs from MMAM, most noticeably in the peak time of the curves. The maximum value for M2 occurs earlier, and there is initially a more rapid increase in the number of infected herds. Such dynamics is expected if some actors, in this case  larger herds, are found to have more contacts [M2 than MMAM , but if they occur they may rapidly generate a large number of infections. Model M1, where distance dependence was removed, also showed a significantly lower ρ than Mfull  would result in more predictable dynamics than removal of just one of this factors removed in M1 or M2. To avoid the false impression that unpredictability implies low accuracy of the model, it needs to be stressed that the heterogeneous dynamics, here in particular caused by the production-type structure, is a realistic and important feature of disease spread modelling.The production-type structure also causes unpredictable dynamics in the later stages of the outbreak scenario. The lowest median of n et al.  were main et al. . The estan Mfull . We may The aim of this study was to perform an in-depth analysis of animal movement heterogeneities and consequences for disease transmission dynamics. Rather than using expert opinions of contact patterns, which is the main means of parametrization in other simulation models, such as Interspread and Intercsf , our modet al. [In our simulation model, each replicate was initialized with the index herd picked randomly. A simulation model applied to a specific disease would also likely include deviations from this assumption. Arguments may be made that perhaps larger farms are more likely index herds because they have more contacts. However, Nöremark et al.  showed tThe approach of this study was to use a model that captures as much details of the contact structure as possible and by removing of heterogeneities one may test the effect of these. A slightly different approach would be to estimate the parameters of the reduced models separately. While this may seem more intuitive, it would make it more difficult to see the effect of the heterogeneous contact structure because herd size and production types are not independent. From a phenomenological point of view, the models could possibly behave similar but it would give less insight into the effect of the observed heterogeneities and make the results less relevant to other systems. It is however also important to point out that our results may not hold for all animal movement networks. For instance, in less-structured industries, such as sheep or most cattle production, production types may have far less importance. We suggest that similar analyses as the one presented here should be performed on other movement data in order to generate a more detailed and specific knowledge regarding the importance of heterogeneous contact structures. This will give deeper insight into what data need to be collected in order to make epidemiological predictions. Our results do point out the importance of correct data on production types in these databases and in general stresses that there is a need for continuous validation of the data on production type.7.Of the factors included in the full model, the production-type structure is the factor most influential on the dynamics and hence the most important factor to obtain valid data for and include for modelling. Exclusion of this structure overestimates the final epidemic size but underestimates the variability of outbreaks, both in initial and later stages. However, contact heterogeneities due to herd sizes and between-herd distances also contribute substantially to the dynamics. Exclusion of the former leads to underestimation of the initial growth of the epidemic but slightly overestimate the final epidemic size. Also, if contact probabilities are modelled as independent of between-herd distances, the final epidemic size is somewhat overestimated. Exclusion of production-type differences in contact probabilities due to distance however underestimated the epidemic size in the analysed system. While the presented work only takes into account disease transmission via animal movements it still demonstrates that detailed and updated registers on the animal population in which a model is applied is essential. For a structured farming industry, such as the pig farming considered here, production types are particularly important. This may not be the most important factor in less-structured industries, but our result show that it primarily have to be considered and tested before applying modelling and risk assessment.


A heme-containing transmembrane ferric reductase domain (FRD) is found in bacterial and eukaryotic protein families, including ferric reductases (FRE), and NADPH oxidases (NOX). The aim of this study was to understand the phylogeny of the FRD superfamily. Bacteria contain FRD proteins consisting only of the ferric reductase domain, such as YedZ and short bFRE proteins. Full length FRE and NOX enzymes are mostly found in eukaryotic cells and all possess a dehydrogenase domain, allowing them to catalyze electron transfer from cytosolic NADPH to extracellular metal ions (FRE) or oxygen (NOX). Metazoa possess YedZ-related STEAP proteins, possibly derived from bacteria through horizontal gene transfer. Phylogenetic analyses suggests that FRE enzymes appeared early in evolution, followed by a transition towards EF-hand containing NOX enzymes (NOX5- and DUOX-like). An ancestral gene of the NOX(1-4) family probably lost the EF-hands and new regulatory mechanisms of increasing complexity evolved in this clade. Two signature motifs were identified: NOX enzymes are distinguished from FRE enzymes through a four amino acid motif spanning from transmembrane domain 3 (TM3) to TM4, and YedZ/STEAP proteins are identified by the replacement of the first canonical heme-spanning histidine by a highly conserved arginine. The FRD superfamily most likely originated in bacteria. As a result, the absorption of bioactive and water soluble ferrous ion (Fe2+) became a challenge for all forms of life and left a great impact on evolution All aerobic living organisms face a dilemma when confronted with the need to assimilate the essential element iron. Indeed, iron is the second most abundant metal on earth, yet the primary form found in the environment is the water insoluble and metabolically inactive ferric ion (Fe3++e− = Fe2+ (ferric reductase) and O2+ e− = O2− (NADPH oxidase).Ferric reductases (FRE) and NADPH oxidases (NOX) are homologs NOXs transfer electrons to oxygen to produce short-lived superoxide which is the primary reactive oxygen species (ROS), which is then transformed into various other ROS, such as hydrogen peroxide, hypochlorite or ozone Detailed bioinformatics analyses highlighted both the gene phylogeny and the structure of family members In the present study, we characterized the evolutionary history of the FRD superfamily. By adding homologs of species from deep-branching nodes of the species tree, we showed that families and subfamilies emerged earlier than had been previously thought. The gene phylogeny is discussed in the context of structural and functional features. Findings to be highlighted include 1) conserved residues predicted to be crucial for NADPH-oxidases; 2) the probable lateral inheritance of the metazoan STEAP family from bacteria and the synchronous loss of the ancient ferric reductases in this clade; 3) the emergence of the NOX family from EF-hand containing superfamily members; 4) the origin of the FRD superfamily from a bacterial homolog that consists solely of a ferric reductase domain.Eukaryotic homologs of the FRD superfamily were obtained from UniProtKB (release 2011_07) Sequences were aligned using MAFFT (version 6) http://www.phylosoft.org/archaeopteryx).For the analysis of the superfamily, the sequence redundancy of the predicted ferric reductase domains was reduced to 90% using Jalview. Truncated and dubious sequences were removed and sequences were realigned with MAFFT, using settings optimized for data with one conserved domain and long gaps (L-INS-i) and the scoring matrix JTT200. No other default parameter values were changed. The alignment was edited again with Jalview. This time, the data model was selected in a less stringent way to keep a maximum number of positions for the analysis. Sequence redundancies above 82% were removed from the data model. An ML tree was constructed with PhyML using the same evolutionary model as described above. Phylogenetic trees were inspected and drawn using Archaeopteryx , NOX-EF group (EF-hand(s) containing members of the NOX group), ppFRE group (protist and plant homologs of the FRE group), and fuFRE  families, as well as the bacterial short and long forms and the STEAP family were obtained from the UniProt server and previous datasets. Sequences of each family were aligned using MAFFT. The alignments were then inspected manually and sequences that possessed atypical insertions within the transmembrane domains were removed from the MSA. The monophyletic group of YedZ-like proteins was removed from the MSA of bacterial short forms; in the following the group was treated as a group on its own. Well-aligned regions – including the predicted transmembrane domains (TM3–TM5) – were extracted from the alignments. Finally, sequence conservation logos were constructed from the eight MSAs using the WebLogo server (weblogo.threeplusone.com). The alignments used for this purpose included 443 , 107 , 75 (STEAP), 22 , 123 (fuFRE), 54 (ppFRE), 66 (NOX-EF), and 54 (NOX) sequences, respectively.For the identification of conserved sites within transmembrane domains and conserved adjacent regions, representative homologs of the NOX family , mafft.cbrc.jp/alignment/server (MAFFT), guidance.tau.ac.il (GUIDANCE), darwin.uvigo.es/software/prottest.html (ProtTest), www.phylogeny.frwww.cbs.dtu.dk/services , phobius.sbc.su.se (Phobius), pfam.sanger.ac.uk (Pfam), www.ebi.ac.uk/interpro (InterPro), weblogo.berkeley.edu (WebLogo) and local computers.The analyses were performed on the Vital-IT High Performance Computing Center , Diplomonadida (3), Euglenozoa (2), Microsporidia (4), Parabasalia (1), stramenopiles (1), Platyhelminthes (2) and Arthropoda (2). For bacteria, FRD homologs are annotated in only 37% of all complete proteomes.At the highest level of structural organization, members of the FRD superfamily are characterized by a common transmembrane ferric reductase domain (Pfam: PF01794) with structural similarity to cytochrome b of the mitochondrial bc(1) complex. Our analysis initially focused on eukaryotic homologs, whose superfamily members also possess a dehydrogenase module consisting of an FAD-binding domain (Pfam: PF08022) and an NADPH-binding domain (Pfam: PF08030), both oriented towards the cytoplasm. Conserved blocks of these homologous regions account for the data model used in the phylogenetic analyses of the eukaryotic genes; the phylogeny of the FRD superfamily is inferred based on the homologous regions of the ferric reductase domain . The manNematostella vectensis currently represents the most basal metazoan, and its proteome contains three gene copies, i.e. two ortholog to the NOX1-3 subfamily and one ortholog to the NOX4 subfamily. There is even some evidence that the divergence of these two subfamilies took place prior to the emergence of animal – NOX homologs of the choanoflagellates Monosiga brevicollis and Salpingoeca sp. (strain ATCC 50818) cluster steadily with the NOX1-3 clade. Furthermore, the NOX family includes the independently duplicated genes from fungi, Amoebozoa and Naegleria gruberi. The presence of these co-orthologs reveals that a gene of the NOX family was probably already present in very early eukaryotes.The phylogenetic tree of the FRD superfamily suggests three major protein groups. Clade 1 includes homologs of the ROS-generating protein families (NOX group) as well as homologs of ferric reductases (FRE) predominantly from plants and protists (ppFRE). This group is of special interest in this study, because – based on functionally characterized family members – we presume that the functional shift from a metalloreductase to a ROS-generating protein took place within this branch in an ancestral gene of very early eukaryotes. The phylogeny of the ROS-generating NADPH oxidases  was analyzed previously What is more challenging is the interpretation of the evolution of the EF-hand containing protein group . It has been suggested that the EF-hand domain was acquired only once At the base of clade 1, metalloreductases predominantly from protists and plants (ppFRE) diverge . Minor cClade 2 includes solely fungal metalloreductases (fuFRE), and the topological placement of this branch outside other eukaryotic metalloreductases is notable . Our anaSo far, no metazoan eFRE orthologs have been identified. Indeed, animals possess enzymes with the conserved ferric reductase domain and a ferric reductase activity, but this family (STEAP) is probably xenolog to eFRE .Acaryochloris marina (strain MBIC 11017) (UniProtKB: B0CEP3), which could have been obtained from an ancestral gene of the NOX5 family . This clade is composed of bacterial ferric reductases (bFRE) including the bacterial YedZ family and the eukaryotic STEAP family. Bacterial members can be roughly classified into short and long forms; the short forms consist basically of the ferric reductase domain, and the long forms of all three domains conserved in the eukaryotic families . Accordi5 family . UniProt5 family .The basic structural component of this superfamily is the transmembrane ferric reductase domain that is present in all the analyzed superfamily members. This domain includes four canonical conserved heme-coordinating histidines in transmembrane domain 3 (TM3) and transmembrane domain 5 (TM5). Their positions in the human NOX2 sequence are 101 and 115 (TM3), and 209 and 222 (TM5). The heme close to the cytoplasm is expected to bind to His-101 and His-209, while the heme close to the cell surface is expected to bind to His-115 and His-222. Most of the bFRD clade homologs consist solely of this domain, but seven distinct modules have been identified, which contribute to the extension of this simple ‘bacterial structural core’ . InteresIn the following sections, the different domain architectures are discussed along with examples of well-studied proteins . An overProteobacteria, which are involved in redox regulation and transmembrane electron transfer E.coli homolog of this family has been shown to form an oxidoreductase complex with the soluble catalytic molybdoenzyme YedY Typical representatives of the ‘one-domain’ homologs from bacteria are members of the YedZ family found in Most structural modifications are found in orthologs of closely related species, thus indicating that variations of the basic core are generally not persistent in bacteria, a fact which is typical for the evolution of prokaryotic proteins. Hence, the enduring bacterial ‘long form’ might be more efficient than functional equivalents or could have a unique function.Saccharomyces cerevisiae homologs are among the best-studied examples of this group. As they are important components of the high affinity uptake system for iron and copper ions, FRE1 and FRE2 are involved in both ferric and cupric ion reduction. There is evidence that FRE3-6 homologs are specific iron reductases, whereas the FRE7 homolog is a specific copper reductase Most, if not all, fungal metalloreductases (fuFRE) contain an additional predicted transmembrane domain that precedes the three conserved domains of the eukaryotic structural core. The nine Viridiplantae and stramenopiles. A well-studied example is the tissue-specific expression of the eight paralogs of the ferric chelate reductase (FRO) family from Arabidopsis thalianaIn some ways similar to the fungal metalloreductases, the majority of protist and plant homologs (ppFRE) in this group bear predicted transmembrane regions in addition to those of the ferric reductase domain. Two or six transmembrane domains are found in the N-terminal of these homologs, but additional transmembrane helices are also predicted close to the C-terminus, within the NADPH-binding domain in clade members from Heterolobosea N. gruberi and homologs from the stramenopiles Albugo laibachii (UniProtKB: F0X089) and Phytophthora infestans (UniProtKB: D0MUZ3) – contain one or two extracellular DOMON domains (Pfam: PF03351). The DOMON domain was predicted to possess a hydrophobic pocket that binds heme and sugar Besides additional transmembrane domains, a single clade of the eFRE group has acquired a new structural module. Members of a subfamily – including seven paralogs from the Note that no FRE-EF forms were observed in bacteria and eukaryotes, suggesting that the regulatory EF hand domains were acquired by superoxide-producing NOX enzymes.Rhodophyta, stramenopiles and Viridiplantae contain four predicted additional transmembrane helices between the FAD-binding and NADPH-binding domains. One study revealed a potential ROS-generating NADPH oxidase activity for the homolog of the red algae Chondrus crispus, At the base of the NOX group, homologs from 2+ to EF-hands can cause conformational changes linked to regulatory functions, as for instance in calmodulin 2+, representative family members were inspected manually . All the studied homologs possessed at least two EF-hands, of which at least one was canonical and hence assumed to be Ca2+-binding. Fungal members of the paraphyletic group of NOX-EF have a single Ca2+-binding EF-hand motif. They seem to exhibit ROS-generating NADPH oxidase activity and have been suggested to be regulated by Ca2+2+-regulated ROS-generation The functional shift from metalloreductases to ROS-generating NADPH oxidases probably occurred early on in the NOX group and, to some extent, seems to be linked to the occurrence of EF-hands in the N-terminus. The binding of CaD. discoideum. Preliminary studies using a NOXC knockout strain showed reduced levels of stimulated ROS generation in vegetative and starved cells . We therefore assume, that the second conserved residue, Thr-178, is equally important for NADPH oxidase activity, but a gating function seems more likely than oxygen-binding. According to an analysis of the functional roles of catalytic residues A comparison of the eFRE metal reductases with expected NADPH oxidases indicated two positions that are conserved in members of the latter group: a histidine in addition to the two heme-binding histidines in transmembrane domain 3 (TM3), and a conserved threonine in transmembrane domain 4 (TM4) . In accostramenopiles and rhodophytes – but not the genes of the chlorophytes analyzed here – are likely to possess a ROS-generating activity that is not regulated by Ca2+. All other homologs of the NOX-EF group possess two or more EF-hands. In addition, two other structural components are found in members of this group: a domain of unknown function that encompasses the N-terminus of the NADPH oxidase  and the heme-containing peroxidase-like domain (Pfam: PF03098). These two additional domains are the hallmark of two separate NOX families. The cytoplasmic NADPH-oxidase domain is attached to the N-terminus of the EF-hands in the RBOH family of land plants. Interestingly, closely related homologs from stramenopiles (RBOH-like) seem to possess EF-hands, but show no predicted domains at the N-terminus. Members of the DUOX family comprise up to three predicted EF-hand motifs, and a further transmembrane domain links to an extracellular peroxidase-like domain. The predicted signal peptide might be required to translocate the peroxidase-like domain to the extracellular space. Members from both families have been biochemically characterized as ROS-generating and Ca2+-regulated enzymes, strongly suggesting a conserved regulation pattern within the NOX-EF group  and Naegleria. The loss of the EF hand-containing N-termini occurred concomitantly with the emergence of separate cytosolic and membrane bound regulatory subunits. An advantage of multi-domain proteins is the colinear expression of functional units that are essential for a specific cellular task, bypassing the need for coordinated expression of separate gene products. However, a more fine-grained regulation is possible when multiple differentially expressed and regulated subunits assemble into a functional complex.Analyses of the extremely divergent domain architectures suggest that two distinct series of events appear to have led to an increased complexity of regulation of ferric reductase domain enzymes. First, as detailed in previous sections, during the early evolution of the superfamily, NOX enzymes acquired longer N-termini with various regulatory and functional domains. Second, the N-terminus was lost subsequent to the gene duplication event that gave rise to the emergence of the NOX1-4 family, which includes the meatozoan NOX1-4 homologs, and the independently duplicated co-orthologs of fungi , Developmental steps to a higher regulation complexity from NOX4, to NOX3, NOX1 and NOX2 can be traced in the NOX family. Of all these, the best studied NOX ortholog is NOX2 and its regulatory network has shown to be particularly complex. Indeed, its fine regulation requires an interaction both with the membrane component p22phox and the cytosolic components p47phox, p67phox, and p40phox. Finally, it also associates with a Rac GTPase that is responsible for the activation of the protein complex Heterolobosea N. gruberi, for instance, contain an SH3 domain (Pfam: PF00018) within their C-terminal NADPH binding domain. Usually, the SH3 domain mediates protein complex assembly via binding to proline rich regions (PRR) of binding partners N. gruberi NOX homologs suggests that some unknown PRR-containing protein partner is involved in their regulation Dictyostelium discoideum carries a functionally analogous WW domain for protein-protein interactions (Pfam: PF00397) Pezizomycotina, including Aspergillus nidulans, Neurospora crassa and Epichloe festucaeD. discoideum NOXA and NOXB, Rac1A, -B and -C show the greatest homology with Rac2 in human neutrophils. In addition, all of the Rac2 residues at the interface with p67phox are conserved, suggesting that a similar activation process might also have occurred in D. discoideumPrecursors of the domains that interact with these essential regulatory subunits can already be found in the NOX homologs of unicellular organisms. The two orthologs of Domain architectures evolve at different modes in prokaryotes and eukaryotes and our results are characteristic for the two superkingdoms. Prokaryotic proteins generally possess a less complex domain composition than eukaryotes, but undergo fusion and fission events more frequently. Hence, proteins with a more complex domain organization are found in small clades . In contAt the sequence level, divergent evolution can be observed at functional sites, which are assumed to reflect changes in substrate selectivity and possibly the reaction mechanism. The functional shift from metal reductase to NADPH oxidase activity probably occurred early on in evolution at the base of the NOX clade. Four amino acids have been identified to distinguish metalloreductases from ROS-generating NADPH oxidases. The replacement of the first canonical heme-spanning histidine by a highly conserved arginine in members of the prokaryotic YedZ family and the eukaryotic STEAP family may indicate a change in the reaction mechanism.The ferric reductase domain may well have emerged from cytochrome b. The phylogeny of the FAD-binding domain suggests the fusion of a ferric reductase domain to a dehydrogenase module . Based oThis study reveals that the ferric reductase domain superfamily probably originated from a short bacterial homolog that consisted solely of a ferric reductase domain. During evolution these ‘one-domain’ homologs were extended by different modules that either participate in redox systems or are regulatory components. The C-terminal fusion of an FAD- and NADPH-binding domain probably resulted in the core of the long homologs, which are mostly found in eukaryotes. Phylogenetic analysis reveals that the highly diverse number of gene copies per species is derived by extensive lineage-specific gene gain and gene loss throughout evolution. The functional shift from metal reductase to NADPH oxidase activity probably occurred early in evolution at the base of the NOX clade. The generation of potentially toxic ROS is accompanied by increased complexity of regulatory systems. One such regulatory component is p22phox, which appears with the emergence of the NOX family; its origin is still unknown.Four amino acids have been identified to distinguish metalloreductases from ROS-generating NADPH oxidases. We believe that it might be possible to detect further sites for the construction of even more specific function predictors based on the comparison of clade-specific conservation signatures. Finally, we plan to use conservation signatures for the revision of unresolved nodes. Preliminary results suggest taking the option of classifying the superfamily into three main groups, namely NOX, FRE and YedZ/STEAP. These and other related issues will be the subject of future interesting studies.File S1Sequence data and domain predictions.(XLSX)Click here for additional data file.File S2Maximum likelihood phylogeny of eukaryotic gene families of the FRE group and the NOX group. The phylogenetic tree is a detailed presentation of (TIF)Click here for additional data file.File S3Exploration of the tree space. Phylogenies of the FRD superfamily from multiple analyses. Gene families are colored in the phylogenetic tree and family names are given. Branch support values of major internal nodes are indicated.(PDF)Click here for additional data file.File S4Analysis of bacterial FRD superfamily members.(PDF)Click here for additional data file.File S5Evolution of the FRD superfamily from the view of its core domains.(PDF)Click here for additional data file.


Physical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms and a significant fraction of PPIs are the result of domain-domain interactions. Exon shuffling, intron-mediated recombination of exons from existing genes, is known to have been a major mechanism of domain shuffling in metazoans. Thus, we hypothesized that exon shuffling could have a significant influence in shaping the topology of PPI networks.Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Cryptococcus neoformans and Arabidopsis thaliana. For all four metazoan species, genes enriched in exon shuffling events presented on average higher vertex degree (number of interacting partners) in PPI networks. Furthermore, we verified that a set of protein domains that are simultaneously promiscuous (known to interact to multiple types of other domains), self-interacting (able to interact with another copy of themselves) and abundant in the genomes presents a stronger signal for exon shuffling.We tested our hypothesis by compiling exon shuffling and PPI data from six eukaryotic species: Exon shuffling appears to have been a recurrent mechanism for the emergence of new PPIs along metazoan evolution. In metazoan genomes, exon shuffling also promoted the expansion of some protein domains. We speculate that their promiscuous and self-interacting properties may have been decisive for that expansion. In 1978, Walter Gilbert speculated that the presence of introns in eukaryotic genes would lead to non-homologous recombinations and the creation of new exon combinations  reasonedPhysical protein-protein interaction (PPI) is a critical phenomenon for the function of most proteins in living organisms, and the recent accumulation of PPI data from numerous small-scale and a few recent large-scale experiments now allow us to build proteome-wide PPI networks -15. TheyThis last relationship, together with the role of exon shuffling as an evolutionary process to make possible the reuse of domains, led us to the natural hypothesis that exon shuffling may have contributed significantly to the evolution of PPI networks. This present work is an effort to verify this hypothesis in several eukaryotic species and to discover what concrete and specific type of influence exon shuffling has had on PPI networks.With the purpose of generating PPI networks representative of the state-of-the-art knowledge, we compiled data from several public databases (a detailed description is available in the Methods section). Because of the incompleteness of data comparing interaction properties of several protein products of a gene we merged all data concerning proteins from a gene in just one vertex of the network, a strategy that has been adopted by others . Given tHomo sapiens), mouse (Mus musculus), the fruit fly Drosophila melanogaster, the nematode worm Caenorhabditis elegans, and the flowering plant Arabidopsis thaliana. We added to the analysis a sixth case: a predicted PPI network that was obtained by projecting the binary interactions of the exhaustively studied yeast Saccharomyces cerevisiae (an extremely intron-poor species) PPI network into the nearest ortholog genes in Cryptococcus neoformans, a fungal species where exon shuffling analysis was possible because of less extensive intron loss [We included the five species that we found to be more balanced concerning a reliable PPI network and a considerably sized intron density: human  genes. Homologous regions were then analyzed in regard to the presence of introns in four windows adjacent to their extremities . APP is present in vertebrates and has several isoforms resulting from alternative splicing, some of them (like APP751 and APP770) including an exon containing a Kunitz-type protease inhibitor (KPI) domain, which is absent from other isoforms (like APP695). APP usually suffers one out of two major types of processing by proteases: either it is cleaved by an alpha-secretase , or is cleaved in succession by a beta- and a gamma-secretase ,37. BothWe next evaluated a special type of PPI, known as self-interaction, which refers to the capacity of a protein to interact with another copy of itself. The self-interacting proteins are noteworthy because they usually present higher degrees than non-self-interacting ones . Indeed,Our three shuffling profile gene sets were compared in humans regarding their self-interaction properties. ES genes presented a higher frequency of self-interaction proteins than SS and WS genes Table . This reThe observed relationship between exon shuffling and self-interactions led us to search for a mechanism by which exon shuffling might have promoted the emergence of self-interacting proteins. The gain of a domain capable of interacting with another copy of itself may be the simplest, most economical and most effective evolutionary solution to generate homodimeric proteins. We hypothesized that, if this mechanism had a significant participation in the evolution of the abundant self-interactions in the human PPI network, a stronger intron flanking signal  would be expected to exist around self-interacting domains.A number of studies have tried to disclosure what pairs of domains establish physical interactions, either based on structural data ,46, or bNematostella vectensis. In fact, we found a similar pattern for all other analyzed metazoan species (data not shown). In contrast, for the choanoflagellate Monosiga brevicollis , as wela Tables  and 5, tGiven the evidence that we found for the role of exon shuffling in the spread of self-interacting domains, we speculate about the evolutionary advantage that this type of exon shuffling might have. The gain by a protein of a domain that mediates PPIs might in principle make it possible for the acceptor protein to interact with proteins bearing the domains (and motifs) that interacted with the shuffled domain in the donor protein. We think that this kind of chemical affinity consequence is much more likely to have a functional consequence when the exon shuffling event involves self-interacting domains. Indeed, the abundance of self-interactions in humans and the widespread exon shuffling of self-interacting domains in metazoans may be molecular testimonies in favor of this hypothesis.On the other hand, the same rationale would also predict that the gain of a domain by exon shuffling, whether self-interacting or not, would be more likely to have functional effects if the domain could interact with a higher number of other domains, leading us to expect a higher frequency of exon shuffling involving “promiscuous domain types”, that is, domain types that have the capacity to interact with many other domain types. We observed this to be true in all metazoan species that we analyzed, with data for some selected eukaryotic species seen in Tables The observation that the exon shuffling gene set is enriched with promiscuous domains provides an explanation for the observed association between vertex degree and exon shuffling. An illustration of the effect upon the PPI network topology of an exon shuffling event leading to the gain of a domain with promiscuous property is seen in Figure Another domain property that seems to be correlated to exon shuffling events is abundance (number of occurrences of a domain in a given proteome). It is already known that abundant domains have on average stronger intron flanking signal . We obseCause and effect may be difficult to disentangle for the above-mentioned properties, and clearly more studies are necessary for this purpose. We speculate that the interaction properties of a domain are important in determining the probabilities for that domain to be spread by exon shuffling. In this sense, self-interaction and promiscuity would be properties to promote the abundance of domains along large evolutionary time frames.In this work we have presented evidence for a relevant role of exon shuffling in the evolution of metazoan PPI networks. We also showed that some interaction properties of domains might make them more likely to take part into exon shuffling phenomena that promote the evolution of new genes bearing novel PPIs. This idea, added to the fact that metazoan exon shuffling genes specifically show higher degree, suggests that the role of exon shuffling in PPI networks is primarily to promote the creation of novel PPIs. Furthermore, the strong signal of exon shuffling in the set of promiscuous domains suggests a possible role of exon shuffling in the origin of hubs in PPI networks.It is important to add a final remark concerning models normally used for PPI networks. Our findings suggest that an important evolutionary process contributing to the emergence of PPI networks of extant species has been overlooked in most graph growth models used for PPI network study in the literature. It has been repeatedly proposed that duplication models can explain the degree distribution of PPI networks -54, and Recently, a work has addressed the resilience of the powerlaw distribution produced by duplication models to shuffling phenomena interfering in the topology of the PPI network, and concluded that these phenomena did not significantly affect the powerlaw . HoweverHomo sapiens, Mus musculus, Drosophila melanogaster, and Caenorhabditis elegans, from JGI site, for Nematostella vectensis  and Monosiga brevicollis , and from NCBI site, for Cryptococcus neoformans  and for Arabidopsis thaliana (December 2007 version). Protein domain occurrences were identified using Pfam A (release 22), and an e-value smaller than 1e-2 was required for an occurrence to be accepted.We used information concerning aminoacid sequences and intron positions from Ensembl version 48, for S. cerevisiae  and A. thaliana, more recent versions of some databases were used , because we verified that updates in these versions were particularly relevant for these species, but not for the remaining ones. For all species, these databases include both high-throughput experiments and low-throughput ones curated from the literature. Several filters were adopted to reduce possible noise sources. When a description of the technique used to discover the interaction was available, those registers with an entry that referred to one of the several mass spectrometry-based methods were excluded, in order to avoid including indirect interactions. Exclusively functional interactions, for example numerous exclusively genetic interactions present in BIOGRID, were also excluded. Conversion from Refseq and Uniprot protein identifiers and Ensembl and species-specific gene identifiers to Entrez gene identifiers were made using conversion tables available in the Gene section of NCBI site , with some conversions also using tables uniprot_trembl.dat and uniprot_sprot.dat available at the Uniprot site. The used versions for these four conversion tables were of November 2008  or February 2008 versions . For the metazoan networks, vertex degree was attributed based on a network were each vertex corresponded to a unique Entrez Gene ID; also, vertices with degree equal to zero were discarded. Given the comprehensive conversion tables for protein identifiers made available by NCBI , we considered that this would be a good strategy for maximizing the use of information present in the six raw networks for the process of degree estimation. By means of this pipeline, we obtained networks for H. sapiens (9897 vertices e 41981 edges), D. melanogaster (7647 vertices e 25988 edges), C. elegans (3191 vertices e 5295 edges), M. musculus (2565 vertices e 3302 edges), and A. thaliana (1283 vertices and 2381 edges). However, for the purposes of analyzing shuffling profile categories in metazoan species, we considered only those vertices with a corresponding Ensembl Gene ID in the gene_info NCBI table, after what remained: 8934 vertices (degree average 8.3) for H. sapiens; 2444 vertices (degree average 2.5) for M. musculus; 7576 vertices (average degree 6.8) for D. melanogaster; 3186 vertices (average degree 3.3) for C. elegans. For Cryptococcus neoformans, a PPI network based on Entrez Gene ID was obtained as described above for Saccharomyces cerevisiae, and the Inparanoid algorithm [C. neoformans genes will interact if and only if their corresponding S. cerevisiae orthologs also do. From this procedure resulted a network containing 2395 vertices, with degree average equal to 16.0. For both C. neoformans and A. thaliana, shuffling gene categories analysis was made using RefSeq identifiers. All network genes had a corresponding maximal length Ensembl or RefSeq protein, and the length of this protein was also considered to be the protein length of the network vertex.Given the existence of several incomplete public primary PPI databases with different standards, we adopted the common practice of merging their data ,56-58. TH. sapiens and M. musculus. For D. melanogaster, C. elegans, A. thaliana and C. neoformans, species with more extensive intron loss, just two windows with introns were required. Regardless of which genes presented the flanking introns, any two genes whose corresponding aligned proteins met the above requirement were considered to belong to the exon shuffling (ES) gene set. Genes with aligned regions that met the homology criteria but such that none of these regions had the adequate intron flanking were classified as sequence (SS) genes, if they had not been included in the ES set the previous step. The remaining genes, without any homologous region to a non-homologous gene, were classified as part of the without-shuffling (WS) gene set.At first, we considered the set of all protein-coding genes for the species under analysis. For each gene, we just considered one protein of maximal length. Using Blastp 2.2.17, local alignments were performed between any two proteins, and putatively homologous regions in non-homologous genes were identified by the following criteria: alignments that presented e-value less than 1e-3, and covered less than 50% of the shorter protein. We sought for the presence of introns in the four windows flanking the aligned coding sequence. Counting in relation to the extremities of the aligned region, each window extended from the third nucleotide toward the region interior to the twentieth nucleotide toward the region exterior. We required that at least three of these windows to contain introns for When possible, paralogy was defined through lists made publicly available in Ensembl site. For the species for which this was not possible, we made all-against-all Blastp 2.2.17 alignments of proteins of maximal length from each protein-coding gene and defined as paralogous any two genes that had an alignment with added non-superimposable high-scoring sequence pairs comprising at least 70% of the length of the longest of the two proteins. We also considered paralogy to be a transitive property: if gene A is paralogous to gene B, and gene B is paralogous to gene C, then gene A is also paralogous to gene C.Comparisons that evidenced statistically significant differences among ES, SS and WS gene sets were subjected to a confirmatory procedure to rule out the possibility of the result to be due only to an unanticipated expansion of some gene families through gene duplication. The procedure consisted in making a confluence of all sets of genes that were paralogous among themselves and that belonged to the same of the three shuffling sets (genes in distinct sets were not merged). Each subset of vertices corresponding to these paralogous genes was replaced by a single new vertex with degree and protein length equal to the average degree and average protein length of the subset, respectively.For analyzing association between gene presence in PPI networks and shuffling profile categories, we used chi-square test with correction for continuity. For evaluating degree distribution among the ES, SS, and WS sets, because of the strongly asymmetric and non-gaussian nature the data, we made use of the following comparison techniques: resampling procedure for estimating p-value for the arithmetic and geometric degree mean differences, with or without a control for length; U test. Given the fact that we made three tests for each species , in all above-mentioned cases we conservatively used a Bonferroni correction. The resampling procedure without control for length consisted of just randomly selecting samples with the size of the smaller of two sets under comparison from the union of these sets, and counting the fraction of samples that had more extreme degree arithmetic/geometric means than the ones present in the real data. The alternative hypothesis assumed that these means were different, so this fraction was multiplied by 2. The control for length in the resampling procedure involved making a one-to-one correspondence between vertices of the real sets and vertices of randomly selected sets, restricting the random selection of each vertex to a set of vertices with protein length average equal to the protein length of a corresponding vertex in the real set. This assured that the randomly drawn sets had protein length averages approximately equal to the real set under analysis. Similar resamplig procedures were used for investigating the association between shuffling profile categories and protein self-interactions, and the association between vertex degree in PPI networks and gene duplication.We sought for the presence of introns in the two windows flanking the domains.Counting from the domain extremities, each window extended from the third nucleotide toward the domain interior to the thirty-second nucleotide toward its exterior. We required both windows to contain at least one intron each. A paralog confluence procedure was also used when counting the number of both total and flanked domains: a domain in a gene containing k paralogs in the genome was counted as if it was only 1/k of a domain.Possible domain-domain interactions were obtained from DOMINE (version 1.1 of February 2008). Only structural and high confidence predicted interactions were considered. Given this selected set of domain-domain interactions, Pfam domain types were arbitrarily classified in three categories: promiscuous domain-interacting domains, if they had six or more domain-domain interactions, non-promiscuous domain-interacting domains, if they had between one and five domain-domain interactions, and non-domain-interacting domains, if they had no domain-domain interaction. This same selected set was used to define if the domain-interacting domains (those with at least one domain-domain interaction) were self-interacting or not.Domains types were arbitrarily classified as having “low abundance” or “high abundance” if their total number of occurrences in the genome was up to three, or four or more, respectively.The association between domain properties  was statistically evaluated by chi-square test with correction for continuity. When multiple tests were performed tables  and 7, pThe authors declare that they have no competing interests.DVC wrote most of the code and drafted the manuscript. GSF wrote the code for identifying exon shuffling cases by means of sequence alignment and created the illustrations. SJS conceived the investigation, revised the manuscript and supervised the work. All authors read and approved the final manuscript.Influence of shuffling upon presence in PPI networks in selected species. Percentage of protein-coding genes in PPI networks according to shuffling profile category in mouse, worm, fly, fungus, and plant species (bar charts), and chi-square values and p-values for comparisons among shuffling profile groups concerning presence in PPI networks (tables). Numbers above bars indicate the absolute number of genes in the corresponding PPI network, and the total number of protein-coding genes of the species.Click here for fileVertex degree in PPI networks according to shuffling profile category for selected species. Data come from the following species: human (A/E), mouse (F and B/G), D. melanogaster (H and C/I), C. elegans (J and D/K), C. neoformans (L), and A. thaliana (M). Boxplots and tables display, respectively, vertex degree distributions and p-values for group comparisons. Tables F, H, J, L, and M make use of all genes in PPI network, and present the results of statistical analyses concerning main text figures 3B, 3C, 3D, 3E and 3F, respectively. In contrast, boxplot/table pairs A/E, B/G, C/I, and D/K consider genes in PPI network after a paralog confluence procedure for vertices in order to control for the effect of gene duplications. In boxplots, X and O indicate arithmetic and geometric mean, respectively, whiskers mark percentiles 10 and 90, and protein length for each group is reported as mean ± standard deviation.Click here for fileVertex degree in PPI networks for genes with and without paralogs. In boxplots of vertex degree, analyzed species were human (A), mouse (B), D. melanogaster (C), C. elegans (D), C. neoformans (E), and A. thaliana (F). In metazoan species (A to D), genes of the ES subsets were previously excluded. Estimated p-values for group comparisons are found in tables G (human and mouse), H (fly and worm) and I (fungus and plant). In boxplots, X and O indicate arithmetic and geometric mean, respectively, whiskers mark percentiles 10 and 90, and protein length for each group is reported as mean ± standard deviation.Click here for fileSelf-interactions according to shuffling profile category in human. Percentages and explicit fractions of self-interacting vertices, for the human PPI network subjected to a paralog confluence procedure, are seen in part 4A, whereas chi-square values and p-values regarding comparisons among groups are shown in part 4B.Click here for file


Takifugu rubripes is teleost fish widely used in comparative genomics to understand the human system better due to its similarities both in number of genes and structure of genes. In this work we survey the fugu genome, and, using sensitive computational approaches, we identify the repertoire of putative protein kinases and classify them into groups and subfamilies. The fugu genome encodes 519 protein kinase-like sequences and this number of putative protein kinases is comparable closely to that of human. However, in spite of its similarities to human kinases at the group level, there are differences at the subfamily level as noted in the case of KIS and DYRK subfamilies which contribute to differences which are specific to the adaptation of the organism. Also, certain unique domain combination of galectin domain and YkA domain suggests alternate mechanisms for immune response and binding to lipoproteins. Lastly, an overall similarity with the MAPK pathway of humans suggests its importance to understand signaling mechanisms in humans. Overall the fugu serves as a good model organism to understand roles of human kinases as far as kinases such as LRRK and IRAK and their associated pathways are concerned. Takifugu rubripes is a teleost fish native to northwest pacific seas. It belongs to the family Tetraodontidae and order Tetraodontiformes. The fugu genome is rather compact with a size of ~400 Mb although the number of genes is comparable to that of higher eukaryotes indicating a considerable reduction in the intergenic regions [ regions . Detaile regions  and in c regions , synteny regions  have bee regions .http://www.fugu-sg.org/. The latest version (v-5) was released in 2010 which covers about 392 Mb and 72% of genome being organized into chromosomes. In 2002, the first draft sequence of the fugu genome was reported by the International fugu genome Consortium using the “whole-genome shotgun” strategy. Subsequently many versions of the genomic data have been made available at Response to environmental stimulus via complex signaling systems is a central feature of all living cells. Phosphorylation is one such posttranslational modification employed in signaling circuits which usually results in a functional change in the substrate by changing enzyme activity, cellular location, or association with other proteins. Thus, protein kinases have implication in regulation of various cellular processes encompassing metabolism, stress responses, cell cycle control, organ development, and intercellular communication , 29. AbnThe fugu genome has been used in a myriad of comparative genomic studies to elucidate the function of proteins involved in neurodegenerative diseases , 34, sigTakifugu rubripes fifth assembly genome has been obtained from http://www.fugu-sg.org/. We have adopted sensitive sequence profile matching algorithms to identify and examine Ser/Thr and Tyr kinases encoded in the genome. The protocol used is identical to that adopted for analysis of kinases of other organisms earlier in this laboratory [E-value cutoff of 0.0001 which is decided on the basis of previous prototypic study [http://www.sbg.bio.ic.ac.uk/phyre/) [The complete set of predicted proteins from the ORF's of the boratory –49. Brieboratory , MulPSSMboratory –41 involboratory  which maboratory  which inic study . Truncatic study . These s/phyre/) , 52 to eDrosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae. Hanks and Hunter have proposed classification of kinases based on sequence analysis . In ordeDomain assignments have been made for protein kinase catalytic domain containing gene products using the HMMer method by querying each of the kinase domain containing proteins against the protein family HMMs available in the Pfam database  and MULPhttp://www.genome.jp/kegg/pathway.html [E-value cutoff of 0.001.The pathway was then generated as a network using CYTOSCAPE2.6.3 [Sequences of MAPK proteins involved in the MAPK pathways in human were obtained from KEGG pathway way.html . The seqway.html  with an APE2.6.3 .http://king.mbu.iisc.ernet.in/. In addition, these 519 kinases are listed in the supplementary data file 1 in Supplementary Material available online at doi. 10.1155/2012/258284. The 15 sequences lacking the critical aspartate are unlikely to be functional as kinases though they are likely to adopt the kinase fold. Though the exact roles of these PKLNK's (protein kinase like non kinases) is not entirely clear, such sequences have been reported to have implications in various signaling pathways [The genome of fugu encodes 534 PK-like sequences. Of the 534 PK-like sequences 519 them possess the critical aspartate residue at the location characteristic of catalytic base and have at least one glycine conserved in the “glycine rich” loop GXGXXG present in the subdomain I of the kinase catalytic domain. A list of all these 519 sequences identified in this work along with classification and domain combinations are deposited in the KinG database  which wapathways . Out of pathways , 61. ThiAmong the putative PKs, 135 are likely to be tyrosine kinases and 296 are likely to be Ser/Thr kinases. 17 of the 296 Ser/Thr kinases are predicted to have membrane spanning regions. Similarly, 50 of the tyrosine kinases are predicted to be receptor tyrosine kinases. List of these kinases including the information on predicted transmembrane region is included in the supplementary data file 1.Drosophila melanogaster, Caenorhabditis elegans, and Saccharomyces cerevisiae by performing similar analysis . These may be fugu-specific sequences which are functionally divergent compared to those paralogs which show significant similarity to the human sequences. Similar expansions have been seen in other organisms as well [We have analyzed the nature of clustering of these fugu kinases with respect to those in other organisms. For most sub-families, fugu kinases group quite closely to those of human and in a few cases with other organisms. A similar trend is observed in the underrepresented sub-families with different clusters being observed that are not organism specific. The example of CDK subfamily is depicted in  as well  that helThe domain combinations for all the predicted kinases of fugu are provided in supplementary data file 1. Most of the domain combinations observed have been observed in various higher eukaryotes according to the Pfam database . HoweverIn the first case, a galectin binding domain has been associated with protein kinase domain. The galectin domain is a carbohydrate binding domain and its function has been attributed to regulation of immunity and inflammatory responses, progression of cancer, and in specific developmental cascades . These d Tetraodon nigroviridis, however, without the YkyA domain. The YkyA domain has been reported only in bacterial species and is a putative lipoprotein binding domain occurring as a single-domain protein which aids in virulence [The second case involves a YkyA domain tethered to the protein kinase, CNH, PBD, PH, DMPK_Coil, C1_1, and the M protein repeats. Such a combination has been predicted also in another closely related fresh water species of Puffer fish,irulence . It is lirulence . HoweverEight fugu kinase sequences are predicted to have two protein kinase domains each containing all the prerequisites for a functional kinase. This feature is distinct from that of Janus kinase which comprises a nonfunctional kinase domain  apart from a functional kinase domain. Five of these 8 cases correspond to the AGC group while the rest belong to the CAMK group. Such twin kinases have been reported in other higher eukaryotes in proteins like MAPKAP-K1  wherein Given the slight unusual distribution of fugu kinases in terms of subfamilies we wanted to investigate the overall effect on a signaling pathway by considering distribution of all the proteins involved (including nonkinases) in the pathway. Although we did not observe any significant skewing in the clustering of the MAPK subfamily we chose to work on the MAPK pathway as the proteins involved in this pathway are extremely well characterized in other eukaryotes, especially for humans. Both kinases and non-kinases (including upstream factors and downstream effectors) were considered for the analyses. The extent of sequence similarity is an approximate indicator of the similarities of the functions of human and fugu proteins involved in MAPK pathway. Results are depicted pictorially in The current analysis on the fugu genome indicates a kinase repertoire of ~3% of the total genome which is slightly higher than an average of around 2% in the other model organisms. All the groups of eukaryotic protein kinases are found to be present in this genome with comparable numbers to that of humans. The observed distribution of few kinase subfamilies is fugu specific. The presence of unique domain combinations gives an insight into possibility of new regulatory functions of kinases. Finally, the similarity to signaling pathways in human may not only provide a platform for studying signaling systems but can also be used in kinase drug screening.Supplementary data 1 provides a list of all the 519 protein kinase like sequences encoded in the fugu genome along with their subfamily distribution, domain combination and information on presence of transmembrane helices. Supplementary data 2 presents percentage-wise distribution of all the subfamilies of protein kinases encoded in fugu genome in comparison to other model organisms. Supplementary data 3 shows a dendrogram depicting clustering of kinases of Dual specificity tyrosine regulated kinases (DYRK) subfamily from fugu, human, yeast, Drosophila, C.elegans and Zebrafish. Supplementary data 4 gives the results of BLAST search, in SWISSPROT database, for each of fugu protein kinase sequences belonging to DYRK subfamily.Click here for additional data file.Click here for additional data file.Click here for additional data file.Click here for additional data file.


Bos TaurusandCaenorhabditis elegans. Using the method of steered molecular dynamics, the key salt bridges responsible for the interdomain interactions were identified: Arg171: Glu516 and Arg416: Glu218. Based on the performed molecular modeling, the scheme of the mechanism triggering ATP hydrolysis and leading to the separation of ATPase and the substrate-binding domains was proposed.Hsp70 is a chaperone protein that participates in the folding of de novo synthesized proteins, protection of the hydrophobic regions of denaturated proteins, the regulation of apoptosis, the immune response, and several other cellular processes. Despite the large number of publications devoted to the functioning and structure of Hsp70, a reliable full-size 3D structure of this protein remains currently unavailable. Several probable full-size models of human Hsp70 have been constructed based on the structures of individual domains and their components from different organisms and using molecular modeling methodology. The stability of the obtained structures was studied using molecular dynamics. As a result of such an analysis, the most adequate model was selected. The model was built on the basis of Hsp70 elements from Hsp70 (Heat shock 70 kDa protein 1A/1B) is a two-domain ATP-dependent chaperone. Its function is to protect from aggregation the hydrophobic surfaces of proteins denaturated under stress and to promote their correct folding or direct them toward degradation. Proteins of this class are involved in the transport of peptides through the mitochondrial and cellular membranes. Depending on whether their localization is extracellular or intracellular, Hsp70s are involved, respectively, in the autoimmune labeling of tumor cells or in the protection of cells from stress and apoptosis . Hsp70 cThe substrate-binding domain (SBD) consists of a β-sheet subdomain (βSBD), which binds the substrate peptide, and an α-helical “cap” (αSBD), formed by five α-helices A, B, C, D and E see . Due to Mediated by Hsp40, ATP hydrolysis and substrate binding lead to separation of the Hsp70 substrate-binding domain from the ATPase domain. As a result, they remain connected only by the hydrophobic linker. It is known that Hsp70 is capable of forming oligomers in solution. Most likely, this is due to the fact that the substrate-binding domain of Hsp70 is able to bind the hydrophobic linker of another Hsp70 molecule when its domains are separated , 9.ATP hydrolysis is induced by the J-domain of cochaperone Hsp40. A study of the binding of Hsp70 and Hsp40 showed that Asp35 of yeast Hsp40 J-domain interacts with Arg171 of Hsp70. This leads to ATP hydrolysis and subsequent separation of Hsp70 domains from each other. The signal for ATP hydrolysis is assumed to pass through the chain J-domain of Hsp40 → Arg171 (interdomain linker of Hsp70) → Tyr371 (Hsp70 ATPase domain) → Ile181 (Hsp70 ATPase domain) -12. ThenIt is assumed that the structure of the substrate-binding domain of mammalian Hsp70 is similar to the structure of the analogous domain of its prokaryotic homolog DnaK see  [7]. Alt2 . The visual analysis of three-dimensional structures was performed using VMD 1.8.6 [Homology modeling was performed using the Swiss Model , 16. TheMD 1.8.6 . Modeling the full-size structure of Hsp70 Escherichia coli ; ATPase Model hHsp70_1dkz  Modeling was conducted in three steps: B. taurus – Hsc70 (1YUW), a model was built describing the ATPase domain, βSBD and the helix ‘A’ of αSBD. The identity of the model with the template was 88.6%. To obtain a full-size structure, we had to construct the remaining part of the αSBD.1. Based on a primary amino acid sequence of human Hsp70 (hHsp70) (PID: P08107) and a tertiary structure of its close homologue from  2. To construct the αSBD the structure of the prokaryotic homolog of Hsp70, DnaK (1DKZ) was used as a template. In 1DKZ, the α-subdomain, in a complex with the β-subdomain, forms a full-size substrate-binding domain. The identity of the constructed structure with the template was 44.7%.  3. The resulting structures were superimposed by βSBD. The model was equilibrated using molecular dynamics simulation for 6.5 ns. E. coli .In the resulting construct, the ATPase domain and βSBD were built by homology modeling with the close eukaryotic relative of Hsp70; and αSBD, by homology modeling with the prokaryotic chaperone DnaK from  An analysis of molecular dynamic trajectories showed that the spatial structures of the ATPase domain and βSBD were stable, while the segment of αSBD, formed by the α-helices ‘B’-’E’, was prone to unfolding.  Model hHsp70_2p32 Caenorhabditis elegans ; PDB ID: 2P32) was taken as an alternative template for homologous modeling of the unstable segment. The identity of the resulting structure and the template was 63%. The created construction was superimposed on hHsp70_1dkz by the α-helix ‘B’, after which part of the α-helix ‘B’, as well as helices ‘C’, ‘D’ and ‘E’, was replaced by the corresponding elements constructed by homology modeling with the 2P32. The resulting model was equilibrated for 8.5 ns.The structure of αSBD from nematode ( The model hHsp70_2p32 (after equilibration) is shown in  Study of interdomain interactions in hHsp70  In the process of chaperone functioning, its domains diverge with a disruption of all noncovalent interactions. As a result, they remain connected only by a cross-domain linker. The process of domain separation was studied using the method of steered molecular dynamics. The salt bridges that are crucial in this process were identified. It was shown that inverse convergence of the domains is not dependent on conformational changes in the ATPase domain.  Disruption of salt bridge Arg171 : Glu516 According to , separat The resulting structure was equilibrated for 1 ns. Signs of a restoration of the disrupted salt bridge Arg171: Glu516, as well as further domain separation, were not observed.  Disruption of salt bridge Arg416:Glu218  Disruption of the salt bridge Arg171: Glu516 led only to a partial divergence of the domains, but the final separation has not occurred. In this regard, it was interesting to identify the most stable noncovalent bonds whose disruption could lead to a complete separation of the hHsp70 domains.  Initially, the salt bridge Arg155: Glu523 connecting the α-helix ‘A’ with the subdomain IA was selected, but its disruption has not led to the domains’ separation and has shown no signs of positive development of the process. The negative result led to the conclusion that the salt bridge Arg416: Glu218 is the bond most likely to be key in domain divergence. It links one of the βSBD loops with the subdomain IIA of the ATPase domain. The initial distance between the Cα-atoms of the residues forming the bond was 12.2 Å; after simulation of its disruption, it was - 22.2 Å. The total amount of energy spent on the process was 33.4 kcal/mol. On a curve representing the process, a leap was observed: an increase in distance from 13.4 to 14.7 Å, accompanied by an immediate rupture of the salt bridge, required about 7 kcal/mol .The energy spent on breaking the salt bridge was equal to the energy released in ATP hydrolysis; it could, therefore, be assumed that breakage of the macroergic ATP bond in the ATPase domain  is required to break the salt bridge Arg416: Glu218. Glu218 is part of the β-sheet (formed by the amino acid residues 192 – 226 and 332 – 338;  As expected, simulation of the breakage of the bond Arg416: Glu218 has led to a complete separation of domains. The resulting structure was equilibrated for 1.5 ns, during which no significant changes in the structure were observed. An interesting fact was observed in the simulation of domain separation: the residues Arg416 and Glu516 liberated from salt bridges later formed a new salt bridge with each other see . Formati Based on these data we can draw a conclusion on the important role of interdomain salt bridges in the functioning of hHsp70: the salt bridges Arg171: Glu516 and Arg416: Glu218 connect ATPase and substrate-binding domains, but as the domains diverge from each other the partners are rearranged and can form the salt bridges Arg171: Glu218 and Arg416: Glu516, which stabilize the new structural state of the chaperone.  Simulation of separation of subdomains I and II  In the process of nucleotide exchange, ATPase and substrate-binding domains converge back, but the force involved in this process has yet to be established. A stochastic nature of domain convergence seems unlikely. Because nucleotide exchange occurs while domains are separated and is accompanied by the interaction of the ATPase domain with the nucleotide exchange factor, it is reasonable to assume that pulling the subdomains I and II apart by the nucleotide exchange factor, theoretically, can affect the convergence of ATPase and the substrate-binding domains. We conducted a simulation of the process. In simulating pulling apart subdomain I from subdomain II, points of application of force were taken to be the nitrogen atoms belonging to residues Thr14 and Gly203, directly interacting with ATP / ADP. The initial distance between them was 7.3 Å; the final one, 14.3 Å. Despite the theoretical possibility that nucleotide exchange affects the convergence of domains, we obtained a predictable result: change in the distance between the subdomains I and II did not affect the interaction of the domains with each other. This result suggests that the convergence of domains is most likely mediated by some protein which may be another chaperone hHsp70. Theoretically, it can bind the hydrophobic linker released in domain divergence as a substrate and thus bring the diverged domains closer. However, this hypothesis requires further verification.  Basing on research, the following scheme of hHsp70 functioning can be suggested: at the interaction with the J-domain of cochaperone Hsp40, the salt bridge Arg171: Glu516 is broken. This leads to a convergence of the loops fixing ATP and promotes ATP hydrolysis. ATP hydrolysis, in turn, is accompanied by a shift of the β-sheet containing one of the loops fixing ATP and a shift of the residue Glu218 that is located in the site of interdomain contact, which leads to the disruption of the salt bridge Arg416: Glu218. Disruption of the salt bridge Arg416: Glu218 results in separation of the ATPase domain from the substrate-binding domain. Arg416 forms a salt bridge with Glu516 that belongs to the substrate-binding domain. Arg171, in turn, forms a new salt bridge with Glu218. Thus, regrouping of the salt bridges that previously connected ATPase and the substrate-binding domains of hHsp70 occurs.  The role of the nucleotide exchange factor in the functioning of hHsp70, apparently, is not only in performing the exchange of ADP for a new molecule of ATP, but also in the fixation of the subdomains I and II in separated states, which is accompanied by the convergence of ATPase and substrate-binding domains with formation of the salt bridge Arg171: Glu516 that can be figuratively described as “cocking the hammer” for the next round of ATP hydrolysis. The “finger” that can pull the trigger is the J-domain of cochaperone Hsp40. An equilibrated model of human Hsp70 can be found in the PMDB depositary , 21 (PMD