MaMyD88 and MaTRAF6 genes identified from the blunt snout bream  transcriptome profile. MaMyD88 is 2501 base pairs (bp) long, encoding a putative protein of 284 amino acids (aa), including the N-terminal DEATH domain of 78 aa and the C-terminal TIR domain of 138 aa. MaTRAF6 is 2252 bp long, encoding a putative protein of 542 aa, including the N-terminal low-complexity region, RING domain (40 aa), a coiled-coil region (64 aa) and C-terminal MATH domain (147 aa). Coding regions of MaMyD88 and MaTRAF6 genomic sequences consisted of five and six exons, respectively. Physicochemical and functional characteristics of the proteins were analysed. Alpha helices were dominant in the secondary structure of the proteins. Homology models of the MaMyD88 and MaTRAF6 domains were constructed applying the comparative modelling method. RT-qPCR was used to analyse the expression of MaMyD88 and MaTRAF6 mRNA transcripts in response to Aeromonas hydrophila challenge. Both genes were highly upregulated in the liver, spleen and kidney during the first 24 h after the challenge. While MyD88 and TRAF6 have been reported in various aquatic species, this is the first report and characterisation of these genes in blunt snout bream. This research also provides evidence of the important roles of these two genes in the blunt snout bream innate immune system.MyD88 and TRAF6 play an essential role in the innate immune response in most animals. This study reports the full-length  C-type lectin receptors (both transmembrane proteins), retinoic acid-inducible gene (RIG)-I-like receptors and NOD-like receptors (both cytoplasmic proteins) [N-terminal leucine-rich repeats and a transmembrane region followed by a cytoplasmic Toll/IL-1R homology (TIR) domain [C-terminal TIR and an N-terminal DEATH domain [The immune system protects an organism against diseases by identifying and eliminating the pathogen . Innate roteins) . TLRs arroteins) , leadingroteins) . TLRs ar) domain . The TIR) domain ), MyD88-) domain . While T) domain ,13, most) domain . MyD88 s) domain ,14. MyD8H domain ,15. The H domain . ActivatH domain .Chlamys farreri) [Cyprinus carpio) [Miichthys miiuy) [Litopenaeus vannamei) (MyD88) [Fenneropenaeus chinensis) [Danio rerio) [Epinephelus coioides) [Ctenopharyngodon idella) [Megalobrama amblycephala). Based on the available data from other (related) species, we hypothesize that both genes are present in blunt snout bream and that their structure and functions are conserved. Herein, the transcripts of MaMyD88 and MaTRAF6 were identified from the transcriptomic profile of blunt snout bream and their expression levels after challenge with Aeromonas hydrophila were investigated. Additionally, the entire coding regions of MaMyD88 and MaTRAF6 genes were amplified and sequenced from the genomic DNA. Computational tools were used to analyse the physicochemical characteristics and predict the structure of the encoded proteins. This study provides an insight into the role of MaMyD88 and MaTRAF6 in antibacterial response mechanisms of the blunt snout bream immune system, which can facilitate the utilisation of molecular tools for selective breeding of disease-resistant blunt snout bream broodstock in order to reduce mortality and increase productivity during the cultivation.Both genes have been identified and their functions studied in a broad range of aquatic animals: such as Zhikong scallop (farreri) , common  carpio) , miiuy cs miiuy) , whitele (MyD88) , penaeidinensis) , zebrafio rerio) , orange-oioides) , whiteleoioides)  and gras idella) . HoweverMaMyD88 transcript, deposited in NCBI GenBank database under the accession number KP192128, was 2501 base pairs (bp) long, containing a 239 bp 5' untranslated region (UTR), an open reading frame (ORF) of 855 bp encoding a putative protein of 284 amino acids (aa) and a 1407 bp 3' UTR. The 3' UTR contained a single typical polyadenylation signal (AATAAA) at the position (1454) and six mRNA instability motifs (ATTTA)  and the C-terminal TIR domain consisting of 137 aa (Thr184–Pro284) were found in the protein  were found within the TIR domain (MaMyD88 shares high amino acid sequence similarity (>60%) with homologs in other teleosts, particularly cyprinids: common carp (94%), crucian carp (Carassius carassius) (93%) and zebrafish (90%) (The full  (ATTTA) A. Start  protein B. N-termR domain A. MaMyD8sh (90%) .MaTRAF6 transcript, deposited in the NCBI’s GenBank database under the accession number KP192129, was 2252 bp long, containing an 79 bp 5' UTR and a 547 bp 3' UTR. A polyadenylation signal sequence AAAAAA was found at the position 1762. Four mRNA instability motifs (ATTTA) were found in the 3' UTR. Three potential N-linked glycosylation sites  were located in the coding region. The start (ATG) and stop (TGA) codons were determined from the 5' end of the cDNA at positions 80 and 1706, respectively. The ORF of 1629 bp is encoding a putative polypeptide chain of 542 aa with molecular weight of 61.8 kDa. Sequence analysis results revealed that MaTRAF6 consists of a low complexity region of 12 aa (Pro41–Pro52), an N-terminal zinc finger type protein structural domain, named RING domain (Cys71–Asp109), a coiled-coil region (Gln311–Gln374) and a conserved C-terminal meprin and TRAF homology (MATH) domain (Trp379–Leu503) , particularly cyprinids: grass carp (99%), common carp (94%) and zebrafish (91%).The complete –Leu503) . No N-teMaMyD88 and MaTRAF6 genes were amplified and sequenced from the genomic DNA. The analysis revealed that both are composed of a chain of exons, separated by introns. MaMyD88 gene contains five exons  and 4 introns . The first exon encodes the DEATH domain, while exons 3, 4 and 5 encode the TIR domains. MaTRAF6 gene sequence consists of six exons , separated by five introns . The RING domain and coiled-coil region are encoded by the first two exons, while the MATH domain is encoded by the sixth exon. While no differences were found for MaTRAF6, the coding regions of the genomic MaMyD88 translate into a putative 289 aa protein, due to the insertion of four amino acids at the position 204 (204QVCL207).The entire coding regions of the MaMyD88 and MaTRAF6 very closely related to homologs from other cyprinids (MaMyD88 shares the highest similarity with common carp (ADC45019.2) and crucian carp (AGO57937.1) MyD88 sequences. MaTRAF6, however, is the most similar to a grass carp (AGI51678.1) TRAF6 sequence.Both MyD88 and TRAF6 analyses revealed monophyletic teleost and cyprinid clusters, with yprinids . MaMyD88MaMyD88 and MaTRAF6 protein sequences without the signal peptide were used as the templates for physicochemical characterisation analyses  and 40,910 and 28,880 M−1 cm−1 , respectively. The estimated half-life of both proteins is 30 h in mammalian reticulocytes in vitro, >20 h in yeast in vivo and >10 h in Escherichia coli in vivo. Based on the instability index (II) value, which is a measure to evaluate the stability of proteins in a test tube, MaMyD88 protein (II = 33.5) is probably stable (II < 40), while MaTRAF6 (II = 58.6) is probably not stable (II > 40) [MaMyD88 (87.18) and MaTRAF6 (72.14) indicate high thermostability of these proteins [Mature analyses . The theII > 40) . High AIMaMyD88 protein is rich in leucine, aspartic acid, lysine and threonine, while MaTRAF6 is rich in leucine, glutamic acid, and serine. Both proteins are classified as soluble. Twelve cysteine residues were found in MaMyD88 and 32 in MaTRAF6. The most probable pattern of cysteine residue pairing predicted in MaMyD88 is Cys105–Cys221 and Cys125–Cys268, while no cysteine pairing was predicted in MaTRAF6.In both proteins, alpha helix was predominant among the secondary structure elements, followed by random coil, extended strand and beta turn. The rest of the structure elements were not predicted .MaMyD88 DEATH domain model ; MyD88 (PDB ID: 4eo7.1.A) [MaMyD88 TIR domain ; N-terminal of TRAF6 (PDB ID: 3hcs.1.A) [MaTRAF6 RING domain  and TRAF6 (PDB ID: 1lb5.1.A) [MaTRAF6 MATH domain .Based on the sequence and structural similarity, four different human protein structure templates available from the Protein Data Bank (PDB) were picked to build three-dimensional protein models : MyD88 (eo7.1.A)  for the hcs.1.A)  for the lb5.1.A)  for the MaMyD88 and MATH domain of MaTRAF6 have over 90%  of residues in the most favoured region, suggesting a good quality of the homology models  indicating acceptable quality. The overall average G-factor of dihedral angles and main-chain covalent forces for the models ranged from −0.15 to 0.18, indicating a very good quality (>−0.5) of the proposed models [MaTRAF6 RING model (2.436), which is “correct” (>1.5). MaxSub validation measure indicates “very good” quality (>0.8) of the MaMyD88 TIR domain, “correct” quality (>0.1) of MaTRAF6 RING domain and “good” quality of the remaining two models (>0.5) [MaMyD88 and MaTRAF6 domains can be accepted as relatively accurate.Ramachandran plot analysis results reveal that the models for TIR domain of y models . Both red models . LGscores (>0.5) . The Z-Ss (>0.5)  . In comparison to the control group , MaMyD88 gene was significantly (p < 0.01) overexpressed in liver (38.6-fold) at 4 hpi, the expression was then reduced at 12 hpi (<5-fold), just to rise again significantly  at 24 hpi. Expression in kidney followed a similar pattern: 15.9-fold (p < 0.01) at 4, <5-fold at 12 and >10-fold (p < 0.01) at 24 hpi. Expression in spleen was significantly (p < 0.01) upregulated at all three time-points, but the pattern was different: less than 5-fold at 4 hpi, 20.3-fold at 12 and then reverting to <5-fold level at 24 hpi. Expression at 72 and 120 hpi in all tissues was close to, or even below, the normal (control group) levels (RT-qPCR was used to detect the changes in expression of ) levels A.MaTRAF6 in liver followed a pattern somewhat similar to MaMyD88, albeit the over-expression levels were not nearly as high: significantly (p < 0.01) over-expressed (6.4-fold) at 4 hpi, significantly (p < 0.05) underexpressed at 12 hpi and then again over-expressed (2.8-fold) at 24 hpi. Expression then tailed off to the normal level at 72 hpi and to downregulated at 120 hpi (0.2-fold). Expression in kidney and spleen roughly followed a similar, bell curve pattern: peaking at 12  and 24  hpi, respectively, and then tailing off towards the normal level . However, alignment with other sequences revealed the absence of this insertion  as its homologs in other animals [The presence of conserved functional domains in  animals ,28. The  animals , whereas animals . Three h animals . Further animals ,38.MaTRAF6 primary and secondary structure is comparable to those reported in other fish species, such as zebrafish [MaTRAF6 amino acid sequence. The RING and/or zinc finger regions of TRAFs are critical for signalling, probably due to their association with downstream kinase molecules such as MEKK1 [MaTRAF6 and at the position 201—lysine (K/AAG) was changed to threonine (T/ACC). However, the second zinc finger appears identical in both species, suggesting that the reported differences could have actually been caused by different parameters used by the employed software, and not by different functional properties of the proteins. To further corroborate this, a common carp TRAF6 sequence, where no zinc fingers were reported [MaTRAF. Furthermore, MaTRAF6 RING and MATH domains share very high similarity with the homologous domains in other teleosts  in liver, kidney and spleen after a challenge with Vibrio anguillarum [et al. [MyD88 transcription was induced in orange-spotted grouper spleens eight hours after a challenge with Singapore grouper iridovirus; it was also highly expressed in all the examined tissues in large yellow croaker (Pseudosciaena crocea) in response to formalin-inactivated Vibrio parahaemolyticus challenge [Regulation patterns of both uillarum ; Yan et  [et al.  reportedhallenge .TRAF6 expression in aquatic animals was also previously found to be highly responsive to pathogen challenges [TRAF6 was up-regulated in spleen and head kidney six hours after a challenge with Ichthyophthirius multifiliis, while the expression was significantly down-regulated in head kidney at 72 hpi [TRAF6 was upregulated in tiger shrimp (Penaeus monodon) haemocytes and lymphoid organ at the late stages of WSSV and poly I:C infection [MaMyD88 and MaTRAF6 play key roles in the blunt snout bream innate immunity. A good understanding of the blunt snout bream immune system is necessary for the prevention of diseases relevant for the farming of this species, however, further studies of the specific functions of these two genes are needed in order to fully confirm these assumptions.In agreement with results presented here, allenges ,24,25,26nfection . These rAll animals and experiments were conducted in accordance with the “Experimental Animal Regulation in Hubei Province”. The regulation was approved in the 16th plenary session the Standing Committee of People’s Congress of Hubei Province. The announcement of the Standing Committee of People’ Congress of Hubei Province is code No. 50 (issued date: 29 July 2005 and effective date: 1 October 2005).3 tank with aeration at about 28 °C for 2 weeks in the laboratory at the College of Fisheries, Huazhong Agricultural University. Fish were fed commercial pelleted feed twice a day. For the analysis of mRNA expression, fish were challenged with A. hydrophila. Fish were divided into two groups: 87 specimens in the control group and 151 specimens in the experimental group. Fish from both groups were intraperitoneally injected equal volumes (0.1 mL) of sterile physiological saline (control) and a bacterial suspension at 1.8 × 106 cfu/mL . Six specimens from each group were sampled at 4, 12, 24, 72 and 120 hpi anesthetised in MS-222  at 100 mg/L concentration and immediately killed. Liver, spleen and kidney were separately sampled from all specimens, immediately flash-frozen in liquid nitrogen and stored at −80 °C.Healthy blunt snout bream specimens (average body weight: 27.3 ± 6.8 g) used in this study were collected from the farm in Tuanfeng, Huanggang, Hubei, China. Fish were maintained in a 1 m® RT reagent Kit with gDNA Eraser  following the manufacturer’s instructions, serially diluted 10-fold and used as the template for RT-qPCR.The total RNA was extracted from each sample with RNAisoPlus Reagent , according to the manufacturer’s instructions. Quality and quantity of the extracted RNA were estimated using electrophoresis in 1% agarose gel and Nanodrop 2000 spectrophotometry . Equal amounts of the total RNA of six specimens sampled at each time-point were pooled. cDNA library was synthesised using PrimeScriptMaMyD88 and MaTRAF6 were obtained from the blunt snout bream transcriptome profile, constructed using Solexa/Illumina technology. The unigenes were firstly obtained via the de novo assembling of short reads from the transcriptome of blood, liver, gill, intestine, spleen and kidney. Then, the transcripts were annotated and identified through BLAST homology search against the GenBank database [MaMyD88 and MaTRAF6 were obtained using SMART™ RACE cDNA Amplification Kit , following the manufacturer’s instructions. The specific primers are described in The full-length cDNAs of database . The fulMaMyD88 (284 aa) and MaTRAF6 (542 aa) polypeptide chains: molecular weight, amino acid composition, theoretical isoelectric point, total number of positive and negative residues, extinction coefficient, instability index, aliphatic index and grand average of hydropathicity. SOSUI [Expasy’s ProtParam prediction server  was emply. SOSUI  was usedy. SOSUI  to prediMaMyD88 and MaTRAF6 was predicted using the SOPMA server [Secondary structure of A server , with deA server . The modA server . StereocA server ,58, ERRAA server , ProQ [3A server  and ProSA server .® Premix Ex Taq™  in a Rotor-Gene Q real-time PCR cycler . The total reaction volume of 10 mL contained 5 µL of SYBR® Premix Ex Taq II (2×), 0.4 µL of each primer (10 µM), 0.8 µL of cDNA template and 3.4 µL of dH2O. The primers  (Qiagen). The Ct values were obtained using a threshold value of 0.05 for all three genes. Relative gene expression was quantified by the comparative Ct method, expressed as 2Ct−ΔΔ [18S rRNA), where ΔCt was calculated as CtTest − Ct18S rRNA, and ΔΔCt was calculated as ΔCtTest − ΔCtControl. The RT-qPCR data were analysed statistically with Microsoft Excel and with one-way analysis of variance (ANOVA) in the SPSS 16.0 software. Differences were considered statistically significant at p < 0.05 and p < 0.01.RT-qPCR was performed with SYBR primers  were dess 2−ΔΔCt . The rel


Through evaporation of dense colloids of ferromagnetic ~13 nm ε-Co particles onto carbon substrates, anisotropic magnetic dipolar interactions can support formation of elongated particle structures with aggregate thicknesses of 100–400 nm and lengths of up to some hundred microns. Lorenz microscopy and electron holography reveal collective magnetic ordering in these structures. However, in contrast to continuous ferromagnetic thin films of comparable dimensions, domain walls appear preferentially as longitudinal, i.e., oriented parallel to the long axis of the nanoparticle assemblies. We explain this unusual domain structure as the result of dipolar interactions and shape anisotropy, in the absence of inter-particle exchange coupling. One of the few practical avenues for creating superstructures of nanoparticles (NPs) is self-assembly (SA). The process can be considered akin to crystallization, where elements rearrange themselves into a more or less periodic crystal structure. Under appropriate conditions, colloidal NPs can self-assemble into structures with long-range periodic order1134579121316171819A major physics question related to magnetic assemblies of NPs is which magnetic order, i.e. which domain structure, and which magnetic properties may be associated with these structures, but few techniques have the ability to reveal this at the scale of the structures. Magnetic domains that occur over several particles due to collective magnetic ordering in NP assemblies may at first be expected to be similar to magnetic domains in more conventional materials, with the only difference being that atomic spins are replaced by the magnetic moments of the single-domain NPs. However, this is not necessarily the case. In traditional continuous magnetic materials, properties and domains structures are largely determined by the intrinsic exchange coupling, but in magnetic NP assemblies, where particles contain a surfactant layer, inter-particle exchange interactions are negligible, and hence different properties may emerge depending on particle types and particle arrangements161718Here we report fabrication of extensive, anisotropic, “rope-like” NP structures by drying-mediated aggregation of ligand-capped ferromagnetic ~13 nm ε-Co particles onto a carbon-based substrate. We show by use of transmission electron microscopy (TEM) based techniques for magnetic imaging2(CO)8 precursor in a boiling (181 °C) mixture of organic solvent  and surfactants (trioctylphosphine oxide and oleic acid)3Dense colloids of fairly monodisperse ε-Co NPs with diameters of approx. 13 nm (12.5 ± 1.1 nm) were synthesized in an Ar atmosphere by thermal decomposition of the CoNanoparticle assemblies were achieved by depositing drops of the colloidal solution onto amorphous carbon on standard 400-mesh copper TEM grids from Ted Pella INC and letting the colloid dry in oxygen-free conditions inside a glove box. During the drying process the samples were covered by a Petri dish in order to slow down evaporation of dichlorobenzene and allow more time for the particles to arrange during drying.et al. in their studies of evaporating magnetic NP solutions onto a similar amorphous carbon substrateWe applied Lorentz electron microscopy to address magnetic ordering, in particular areas of different particle magnetization directions (domains) and in-field domain behavior in the ropes. Lorentz microscopy captures the Lorentz deflection of electrons traveling within a magnetic field, and it has been used extensively to reveal magnetic domain structures in thin ferromagnetic films. Here we applied the Fresnel mode of Lorentz microscopyi.e., the angle between sample plane and field is 60° and the field in the plane of the sample was ≥0.5 T). Subsequently, while maintaining the sample tilt, the objective lens was used to obtain reverse field magnetizations while imaging. Starting from zero-field, the value of the objective lens was varied, in steps of 2 mT, until it reached a reverse field value of 40 mT. Fresnel images acquired during magnetization reversal are shown in The ropes were initially magnetized in one direction in the TEM by applying a large magnetic field (>1 T) in the direction of the electron beam using the objective lens and with the sample tilted 30° . However, in a hexagonal (close-packed) lattice, the nearest neighbor coupling is always ferromagnetic, and consequently ferromagnetic ordering is favored locally26In order to understand how a longitudinal domain wall is stabilized in an elongated nanoparticle structure, we first note that, in general, the absence of inter-particle exchange coupling introduces a magnetic “softness”: There is no exchange energy cost of the domain wall itself and the energy is solely determined by the interactions between the magnetic dipoles. It may therefore appear favorable to introduce longitudinal domain walls in elongated structures as that keeps the magnetic field inside the structure and hence lowers the global magnetostatic energy. On the other hand, there is a dipolar energy cost in forming a domain wall. Due to the spatial anisotropy of the dipolar interaction, this cost depends on the particle arrangements: In a simple square lattice of magnetic moments, the favored magnetic structure is “ferromagnetically” ordered chains along the dipole direction, with adjacent chains being “antiferromagnetically” ordered  a single-domain ferromagnetic state (denoted the F-state) where all magnetic dipole moments are aligned parallel to the length of the assembly, 2) a T-state with a single transversal domain wall , and 3) an L-state with a single longitudinal domain wall . The domain wall in the T- and L-states is placed symmetrically to yield zero net moment.The magnetic moments of the three states  were allowed to relax using damped Newtonian dynamics see . SimilarThe simulations represent simplifications of the real NP systems, which have loop-structures and typically consist of more than one monolayer of particles. However, the magnetic ordering in the simulated systems, which only rely on dipolar interactions, lean support to interpreting the observed superferromagnetic ordering and longitudinal domain walls as being a consequence of inter-particle dipolar interactions. We note that the simulations also support our observation  of fullyA feature of the real NP assemblies, which we have not been included in our simulations summarized in We further used simulations to investigate the domain wall evolution in a magnetic field applied parallel to the long axis of the NP assemblies. We started the calculations from the configuration of a disordered structure of a relaxed F- and L-state  and thenIn conclusion, we have described the physical behaviour of self-assembled low-dimensional superstructures of magnetic nanoparticles . Magnetic nanoparticle systems may in general be considered a new class of magnetic materials, since their properties may be very different from conventional magnetic materials, and can be tailored by means that are not easily accessible in other systems. We find that a fundamental difference in magnetic response emerges from the combination of exchange-decoupling between magnetic building blocks and their dipolar magnetostatic interactions. In particular, we have verified that longitudinal domain-walls can be stabilized by dipolar inter-particle interactions, in the absence of inter-particle exchange interactions. The energy of transverse/vortex walls is comparable in continuous film and nanoparticle assemblies while the energy of longitudinal walls is very large  in continuous films and very low in nanoparticle assemblies. Therefore, while vortex walls do indeed appear in nanoparticle assemblies with comparable lateral dimensions , longitudinal walls are always cheaper to form in elongated nanoparticle assemblies, but they are the most costly in continuous strips. Moreover, we find that domain wall propagation occurs in the transverse direction with reverse field for longitudinal domain walls in elongated nanoparticle assemblies and that domain structures are stabilized during reversal by imperfections and packing disorder. The domain behaviour has been captured visually by electron microscopy experiments, and the results interpreted on the basis of simulations, providing a coherent and consistent physical picture that highlights how the domain behavior of nanoparticle-materials does not involve transverse (or vortex) domain walls typically observed in thin film strips of Co of comparable dimensions.How to cite this article: Varón, M. et al. Longitudinal domain wall formation in elongated assemblies of ferromagnetic nanoparticles. Sci. Rep.5, 14536; doi: 10.1038/srep14536 (2015).


Aspergillus as the most diverse genus in terms of exclusive domain content. In addition, we also investigated which domains could be considered promiscuous in the different organisms. As an application of this analysis, we explored three different ways to detect potential targets for antifungal drugs. First, we compared the domain and domain architecture content of the human and fungal proteomes, and identified those domains and domain architectures only present in fungi. Secondly, we looked for information regarding fungal pathways in public repositories, where proteins containing promiscuous domains could be involved. Three pathways were identified as a result: lovastatin biosynthesis, xylan degradation and biosynthesis of siroheme. Finally, we classified a subset of the studied fungi in five groups depending on their occurrence in clinical samples. We then looked for exclusive domains in the groups that were more relevant clinically and determined which of them had the potential to bind small molecules. Overall, this study provides a comprehensive analysis of the available fungal proteomes and shows three approaches that can be used as a first step in the detection of new antifungal targets.Over the past several years fungal infections have shown an increasing incidence in the susceptible population, and caused high mortality rates. In parallel, multi-resistant fungi are emerging in human infections. Therefore, the identification of new potential antifungal targets is a priority. The first task of this study was to analyse the protein domain and domain architecture content of the 137 fungal proteomes (corresponding to 111 species) available in UniProtKB (UniProt KnowledgeBase) by January 2013. The resulting list of core and exclusive domain and domain architectures is provided in this paper. It delineates the different levels of fungal taxonomic classification: phylum, subphylum, order, genus and species. The analysis highlighted  Some fungi have become pathogenic to plants and in a lesser extent to animals. Under certain conditions their presence in the human body can prove a threat for human health, especially for immunocompromised patients. Yet, some fungi can also infect healthy individuals. The low sensitivity of the antifungal drugs available together with the clinically observed resistance of some fungi raises the demand for new alternative treatments. Proteins are biological molecules which perform essential functions within the living organisms. Many of those functions are attributed to the varying folded structure of each protein. These configurations are composed of functional units -also called domains- each one independently responsible for a fraction of the overall biological function. Understanding how the different block combinations are distributed across members of the same or similar families of organisms is important. For instance, exclusive domain combinations can hold particular acquired functions. Blocks displaying a high mobility can play major roles for the organism's survival. The biological goal of this study was to analyse the functional implications of protein domains and domain combinations in the available fungal proteomes. This information can be used to highlight proteins and pathways that could be potentially used as drug targets. Candida spp. followed by Aspergillus spp. There has been a significant rise in the incidence of fungal infection over the last few years. This has been partially due to an increase in the susceptible population as the result of blood cancer, intensive care, solid organ transplantation, or chronic granulomatous disease, in addition to a growing number of patients receiving high doses of corticosteroids or other immunosuppressive treatments The rationale in the search for new antifungal targets in the pre-genomics era was based on the molecular study of genes associated with fungal viability or virulence. The advent of massive parallel sequencing technologies and their progressively reduced cost has enabled the sequencing of large numbers of genomes in a short period of time. As a result, it is now possible to look for broad-spectrum antifungal targets by detecting homologous proteins present in most of the available fungal proteomes In the late 90s, the concept of protein domain was coined by Branden and co-workers to define an independent, compact and stable protein structural unit that folds independently of other such units Whereas the domain repertoires of different organisms can be relatively similar, there can be a high variation in the number of the domain combinations. Therefore, the number of different domain architectures is more variable and is related to the organism complexity and lifestyle in silico the potential of ligands and small molecules included in the ChEMBL database Protein domain information can be used for many different purposes. For instance, the domain architecture can determine the overall protein function and be used to transfer genomic annotations in newly sequenced genomes in silico approaches to detect potential candidate drug targets, using information coming from different public resources and previous studies. In addition, we also classify some fungal species according to their occurrence in clinical samples and provide the related exclusive domain/domain architecture related information.In this manuscript, we first perform an analysis of the protein domains and domain architectures derived from all the available complete fungal proteomes. We identify the core and exclusive domains and domain architectures at different levels of the fungal taxonomical classification  and characterize which domains are found to be promiscuous. Then, we use the obtained protein domain information to explore Saccharomyces cerevisiae (13 strains) or Ajellomyces capsulata (4 strains). For a complete list of species and strains, see Proteins from all the available complete fungal proteomes (137 organisms) were retrieved from UniProtKB . The 137 fungal proteome sets represented 111 species of the following four fungal phyla: Ascomycota, Basidiomycota, Chytridiomycota and Zygomycota. In addition, the phylum Microsporidia was also considered. Microsporidia include a controversial group of eukaryotic organisms of fungal origin, without mitochondria and peroxisomes. Microsporidia have been considered in this study since they are considered the earliest-diverging clade of sequenced fungi Melampsora laricis-populina (strain 98AG31/pathotype 3-4-7), the poplar leaf rust fungus, and Puccinia graminis sp. tritici (CRL 75-36-700-3/race SCCL), the causal agent of wheat and barley stem rust. These fungi only had a 36.5% and 39.5% of Pfam coverage, respectively. The likely explanation for this lower coverage is that some of the domains are not found in the traditional model organisms and therefore not included in Pfam. On the contrary, the fission yeast Schizosaccharomyces pombe (strain 972/ATCC 24843) and its close relative species S. japonicus (strain yFS275/FY16936) showed a coverage over 87.5%. The complete coverage information per organism can also be found in The related domain information for each protein was obtained from Pfam (release 27.0). On average, 67.0% of the proteins had at least one Pfam domain assigned. However, the Pfam coverage was not evenly distributed among the studied species. On the low coverage side, there were plant pathogens like On average, each fungal protein in the studied set had 1.55 domains, a proportion that did not vary significantly between species. Overall, 5,279 different Pfam domains were found among the studied proteomes . Among them, 131 domains (2.5%) were found in every fungus analysed (they are ‘core’ domains), whereas 612 domains (11.6%) were found only in one organism. Interestingly, when Microsporidia organisms were not considered in the analysis, the number of fungal ‘core’ domains rose to 268. Overall, 70.8% of all domain types  appeared in both single and multidomain proteins. In addition, 964 domains (18.3%) were not seen in combination with any other domain, whereas 575 domains (10.9%) appeared exclusively in multidomain proteins see . No lineProtein domain architectures were defined throughout this study as the ordered tuples of Pfam domains, listed from the protein N-terminus to C-terminus. Architectures with either different domain counts or order were computed as independent domain combinations, even if they had the same domain types. For instance, the representation of three potential different example domain architectures with just two domains could be: 1) D1∼D1∼D2; 2) D1∼D2; and 3) D1∼D2∼D1. As indicated, all three would be considered different domain architectures because either the number of domains, the order of the domains and/or the number of domains were different.In total, 21,853 unique domain architectures were identified in our set of 137 fungal proteomes, with 10,206 of them (46.7%) appearing exclusively in a single proteome and only 56 ‘core’ architectures (0.2%) that were present in all the fungi. However, when Microsporidia species were not taken into account, the number of ‘core’ architectures almost doubled: 107 domain architectures were found. Gene Ontology (GO) term annotations were added to give an insight of their molecular function and/or biological activity see ‘’. ApproxMost of these 56 ‘core’ domain architectures  were comRhizopus delemar) had more proteins  than the total amount of proteins from all the Microsporidia strains together . When Zygomycota were removed, the number of ‘core’ domains rose just from 131 to 132, whereas the number of ‘core’ architectures did not change.The effect of removing Microsporidia from the analysis of ‘core’ domains and architectures highlights how different Microsporidia organisms can be, when compared with the rest of the organisms analysed. To measure how significant this increase is, we compared it to the effect induced by removing the species from other phylum (Zygomycota). Note that the only strain from Zygomycota  tend to have a larger ratio of architectures per domain, whereas yeasts  in general hold less combinatorial power to create new architectures. Interestingly, the thermo-dimorphic (found in both filamentous and unicellular form) fungal pathogens A. capsulata (Histoplasma capsulatum), Paracoccidioides brasiliensis Pb01  were found in the middle section of the figure with an architecture/domain ratio value close to 1.The same pattern of modest variance in the number of domains shown in the previous section was also observed at the level of the domain architectures . On the At the species level, 637 domains and 10,582 domain architectures were identified as exclusive for one of the 111 fungal species analysed. In this case, proteomes from multiple strains belonging to the same species were grouped. Throughout the rest of the manuscript, organisms belonging to the phylum Microsporidia were considered as any other fungus. In any case, the different analyses were also performed without Microsporidia. However, the results are not shown for simplification purposes since we believe they do not add much novel information, unlike the information about ‘core’ domains and architectures, included in the previous sections.Batrachochytrium dendrobatidis, the only representative species of the Chytridiomycota subphylum had most of the exclusive domains (36.6%), but with a modest combinational power (only 4.2% of exclusive architectures).The average number of exclusive architectures per species was 96, ranging from 10 to 467 (see below). All the exclusive architectures for each species are provided as Encephalitozoon intestinalis and E. cuniculi, with only 10 species-specific domain architectures. Then, there were two Saccharomycotina species: Candida dubliniensis (12 architectures) and C. glabrata (15 architectures). Cryptococcus gattii serotype B was the species with the lowest number of exclusive architectures in the phylum Basidiomycota (32 architectures).The organisms with the least number of exclusive architectures were those from the primitive Microsporidia species R. delemar (467 architectures) and Batrachochytrium dendrobatidis (443), which were the only representatives of the Zygomycota and Chytridiomycota phyla, respectively. Remarkably, two Ascomycetes: the pathogen F. oxysporum (424) and A. capsulata (301), the causative agent of histoplasmosis Mixia osmundae (262), a Basidiomycete genetically highly divergent compared with other members of the subphylum Pucciniomycotina As a simple observation, species without other closely related species in the dataset tended to have larger numbers of exclusive domain architectures. The highest numbers were found in Batrachochytrium dendrobatidis, 233 domains) and Zygomycota . In addition, the Microsporidia member with the highest number of exclusive domains was E. bienueusi, with 68. Finally, the Ascomycete model organism Sordaria macrospora had 23, and the tree pathogen Basidiomycete Moniliophthora perniciosa had 11 exclusive domains.The repertoire of exclusive protein domains looked slightly different. For a complete list of exclusive domains per species see Aspergillus, the best represented genus with 7 species and 9 strains in total, had the largest set of exclusive architectures (943), containing 104 architectures shared by at least two Aspergillus species  and their related domain architectures. The specificity and exclusiveness or these domains to certain taxonomical groups could help to elucidate the underlying biological functions.Since we were interested in knowing which domains were essential for the different fungi, next we identified the list of promiscuous domains per organism, as explained in ‘The promiscuous domain found in a highest number of organisms was the ‘ATPases Associated with cellular Activities’ (‘AAA’ or ‘AAA+’) domain, found to be promiscuous in 133 out of the 137 organisms studied. It belongs to a large and intensively studied protein superfamily. ‘AAA’ domains usually have a ring shaped oligomeric complex that conveys them several activities through the energy-dependent unfolding of macromolecules via protein-protein interactions Interestingly, after ‘AAA’ and ‘GATase’, the most promiscuous domains identified were ‘SH3_1’ (found in 122 organisms) and ‘PX’ (117 organisms), two related domains which can interact with each other. ‘SH3 ‘domains are typically 40–60 amino acids long It has been previously pointed out that protein domain promiscuity is a volatile feature throughout evolution in silico potential targets for antifungal agents. It is well-known that undesired side effects are one of the main issues of the currently used antifungal drugs So far, in this study we have performed a detailed analysis of the protein domain and architecture content of the available fungal proteomes. The resulting information can be used with different purposes in mind. In our case, we decided to further mine the data and explore approaches to detect Protein domain information of the human reference proteome (UniProtKB release 01_2013) was then analysed and compared with the information available for all the fungal organisms. At least one Pfam domain was retrieved for 72.8% of the human proteins, with an average of 2.06 domains per protein. The number of distinct Pfam domains found  was slightly higher than the sum of the different domains available for all the fungal proteomes analysed . However, on the other hand, almost three times more architectures were found in the combined fungal proteomes when compared with human . The number of domains and domain architectures either exclusive or shared between human and fungi are represented in In order to have a second view on the human and fungal domain contents, we again analysed the protein domain information, but this time at the level of the Pfam domain clans. Pfam clans consist of a series of evolutionary related Pfam families which are believed to share a common ancestor Based on the criterium of drug side effects, all proteins containing protein domains belonging to those 48 clans (corresponding to 77 Pfam domains) are in our opinion, potentially good initial candidates to be considered as antifungal targets. Using the already available information about the exclusive domains per taxonomic level, the list of potential targets could then be tailored to e.g. particular species, genera or phyla.Aspergillus. The ‘DinB’ domain was named after dinB, one member of the DNA damage-inducible din genes. The three din loci  were originally described in Bacillus subtilis as components of a global SOS-like regulatory network called SOB system Aspergillus and might then represent a good potential target for anti-Aspergillus drugs.Just as one example, one of those detected 77 Pfam domains was the domain ‘DinB’ , which was exclusively found in proteins from species from the genus The second approach was to search for biological pathways that could be potential targets for antifungal compounds, focusing on proteins with promiscuous domains, since they might play a role in maintaining network stability Overall, 3,675 fungal proteins in UniProtKB contained at least one of these 8 domains. We then looked for representation of these proteins in fungal pathways available in the public domain. Overall, three pathways present in UniPathway, containing these five proteins, were identified as potential targets for antifungals:Aspergillus terreus and is a precursor of other successful anti-cholesterol drugs Lovastatin biosynthesis (for the domain ‘CBM_1’). Lovastatin is commonly used as cholesterol-lowering agent. In a fungal context, it is a compound that blocks the first step of the terpene biosynthesis for the production of ergosterol (the main component of cell membranes in fungi). It was first discovered in Xylan degradation (for the domain ‘KR’). Diverse microorganisms including filamentous fungi secrete enzymes capable to digest xylan, a polysaccharide constituent of the plant cell walls. These enzymes have been used in industrial production of animal food, textiles and production of biofuels S. cerevisiaeBiosynthesis of siroheme (for the domain ‘Sirohm_synth_M’). Siroheme is a heme-like prosthetic group used by sulfite and nitrite reductases to convert sulfite into sulfide and nitrite into ammonia, respectively. This process is essential for the assimilation of sulfur and nitrogen by plants and consequently, for life. Sulfite reductases are found in bacteria, plants and fungi but not in animals. Assimilation of all inorganic sulfur and the majority of nitrogen in the biosphere depend on the availability of siroheme. Without it, there would be no reduced sulfur available for the synthesis of cysteine and methionine. Those amino acids are essential for animals, which are unable to reduce sulphate, and thus require to include sulfur-containing amino acids in the diet Unfortunately, the amount of pathway information from fungal organisms in the public domain is quite limited at present. It was not possible to find comparable ready-to-use information in other resources apart from UniPathway see ‘’. This cSince annotation of fungal pathways in resources like UniPathway is limited to the most popular species, the protein domain architecture of the five proteins identified was searched in the rest of fungal organisms to know how common these three pathways were throughout the fungal kingdom. Assuming that proteins with the same domain architecture can have a similar function and can be involved in the same or similar pathways, the most ubiquitous pathway among the three was the “biosynthesis of siroheme”, present in 96 out of the 111 fungal species studied . AnalogoThe 111 fungal species used in this study were classified in five groups taking into account the frequency in which they had been found in clinical samples  was characterized by 140 exclusive architectures. Among those, 11 architectures contained DUFs. Group 3 contained 674 unique domain architectures. The least clinically relevant groups, groups 2 and 1, had 1,801 and 3,048 architectures, respectively. They were the ones with the highest number of exclusive architectures.Mycobacterium tuberculosisWhen the analysis was performed at the level of the protein domains, 11 exclusive domains were found for at least one species in Group 3, whereas only two domains were found to be exclusive of Group 4. Extended information about all domains exclusively found in different at least one species from the described groups according to their occurrence in clinical samples can be found in As a third approach to identify potential targets for drugs, the list of protein domains produced by Kruger and colleagues in a previous study F. oxysporum and R. delemar (closely related to Rhizopus oryzae).When we compared directly this list with the generated list in the previous section , we found that none of the exclusive domains from proteins of the Groups 3 or 4 were identified to have small-molecule binding potential. Only seven domains exclusive for organisms belonging to the less relevant group 2 appeared to have small-molecule binding potential: ‘BH3’ (PF15285), ‘Cons_hypoth698’ (PF03601), DUF2146 (PF10220), ‘Gb3_synth’ (PF04572), ‘NCD2’ (PF04905), ‘bact-PGI_C’ (PF10432) and ‘TTKRSYEDQ’ (PF10212). Interestingly, these exclusive domains were present mainly in proteins of the multi-resistant species Based on the structural similarities, we decided to include in the comparison all the Pfam domains belonging to the same clans than the list of 215 domains initially studied .A. terreus whereas ‘Uma2’ was exclusively identified in Trichophyton rubrum (protein F2SXP0). Both are putative uncharacterized poorly annotated proteins. Q0CDT4 was predicted to have a signal peptide using SignalP As a result of this second analysis, three domains were exclusively found in proteins exclusive of species of the Group 3: ‘CTP_transf_3’ (PF02348), ‘HI0933_like’  and ‘Uma2’ (PF05685). ‘CTP_transf_3’ is a domain found in cytidylyl-transferase membrane proteins, which are important regulatory enzymes in the synthesis of phospholipids in eukaryotic cell membranes in silico to detect potential candidate targets for antifungal drugs. Throughout the study, we have considered and taken into account information coming from both individual domains and complete protein domain architectures. In the case of the latter, independent architectures were defined as those ones in which the order or number of domains were different (taking into account domain duplications).In this study, we have characterized the protein domain and domain architecture content of the available fungal proteomes (including the phylum Microsporidia) and we have shown how that information can be used In analogous previous studies, protein domain order and domain repetitions were considered in a different way, assigning the same domain arrangement regardless of the number of consecutive copies of a single given domain Another reason for the consideration of the architectures as they were, is that part of the approach developed in this project is planned to be used to improve automatic protein sequence annotation in UniProtKB S. cerevisiae (strain ATCC 204508/S288c). From this observation two applications of the work presented here could take place: on one hand, proteins containing the same domain composition could be similarly annotated. On the other hand, the importance of these particular proteins and the corresponding domains (like DUF3818) could be further investigated given their membrane localization and the important cell communication process in which these proteins are believed to play a role.For example, knowing that the fungal promiscuous domain ‘PX’ is usually involved in targeting proteins to cell membranes, domain architectures including ‘PX’ domains might be of interest. Looking into this particular case, we identified the domain architecture ‘PXB∼PX∼DUF3818’, present in 106 proteins of all fungal phyla. The only protein manually annotated in UniProtKB with this domain architecture is Q06839, a peripheral membrane protein believed to be involved with cell communication process (GO:0007154) in As a result of this comprehensive study, we provide access to the full list of ‘core’ and exclusive domains and domain architectures at different levels of the taxonomic classification and also identify the promiscuous domains. This information can be a very valuable resource for researchers interested in comparative studies between different fungal organisms. Here, we have only highlighted some examples of how this information could be used, but it is clear for us that more focused studies could be performed on particular groups of organisms, using all the generated information here. This information could also be combined with genome features such as gene clusters  or synteny.However, in this study we decided to focus on the possible application of this information in the detection of antifungal targets. We then followed three different approaches. First of all, we identified those protein domains and domain architectures that were present in fungi but not found in the human proteome. Secondly, using the promiscuous domains, we identified three pathways whose components could be targeted. Last, we created five groups of organisms depending on their occurrence in clinical samples and then inferred small-molecule protein domain binding information obtained in a recent study involving small molecules stored in the ChEMBL database. The results coming from these three approaches constitute just a first step and should be taken with caution, since they have different inherent limitations. It is also expected that these approaches will provide new information when new data (e.g. pathway related information) is made available in the public domain.Analogous studies where the interaction between protein domains and small molecules is assessed, are becoming more popular in the last few years. For instance, recently, using data from Protein Data Bank (PDB) structures, more than thirteen thousand physical interactions between small molecules and protein domains were identified M. laricis-populina (coverage of 36.5%) and P. graminis sp. tritici (coverage of 39.5%). In the future, this study could be extended by using Pfam-B domains and protein domain information coming from other databases, for instance from other protein domain/family resources that are also part of the InterPro consortium Throughout this study we have used domain information coming from Pfam-A domains, so all the conclusions are limited to that context. One fact to consider is that protein sequence coverage in Pfam for fungi is lower than for other organisms, especially for species like https://github.com/alexbarrera/fungidomDB). We hope that some of these strategies can be refined and applied in the practice, followed by in vivo validation.Overall, this manuscript provides a comprehensive analysis of protein domain and domain architectures in the available fungal proteomes and shows three approaches that can be used as a first step in the detection of new antifungal targets. These approaches could also be used for organisms with clinical interest other than fungi e.g. bacteria. Therefore, analogous analyses could be performed for different groups of pathogenic bacteria using as a starting point the scripts provided (available at http://www.uniprot.org/) The proteomes used in this study were obtained from UniProtKB (via FTP (ftp://ftp.sanger.ac.uk/pub/databases/Pfam/releases/Pfam27.0/). Only 425 protein sequences (0.035%) from UniProtKB release 2013_01 were not present in Pfam 27.0. For those proteins, Pfam domain information was obtained directly from UniProtKB using a tailored extended version of UniProtJAPI, the Java Application Programming Interface (API) to access UniProtKB Domain information was obtained from Pfam http://www.geneontology.org) Pfam2GO.txt’ (version 2013/07/24) was used to map GO terms to the Pfam domains. These expert manual annotations are originally assigned by curators in the InterPro team based on the function of particular domains rather than the function of domain families Additional functional annotation for Pfam domains was retrieved using Gene Ontology (GO) terms (http://www.r-project.org) version 2.15.3 and scripts developed in Python. The charts and diagrams were produced using the following R packages: ‘ggplot2’ https://github.com/alexbarrera/fungidomDB.A local MySQL database was developed to store the protein sequence and domain information. The analysis of domain and architectures was performed using R . The taxonomical classification used was the one provided by the National Centre for Biotechnology Information (NCBI) A method to measure the weighted bigram frequency (WBF), introduced by Basu and colleagues Information in http://www.unipathway.org/) http://www.genome.jp/kegg) or MetaCyc (http://metacyc.org) due to the comprehensive mapping to UniProtKB entries and the hierarchical representation of this information. In addition, entities from UniPathway are cross-linked to other major pathway resources such as KEGG, MetaCyc and ‘The SEED’ (http://www.theseed.org).Metabolic pathway information was retrieved from the public resource UniPathway .Group 3: Species with more than 50 isolates in the study carried out by Puig-Asensio Group 2: Species isolated at least once in any of the previously cited epidemiological studies.Group 1: Species not isolated in the cited epidemiological studies, but present in clinical samples of the collection of fungal strains from the National Centre for Microbiology (Spain).Group 0: Neither isolated in the two epidemiological studies cited nor present in the Spanish collection of fungal strains from the National Centre for Microbiology.in silico predicted protein domains that can bind small molecules was collected from a recent study http://www.ebi.ac.uk/~fkrueger/mapChEMBLPfam/) was used to cross-check this capability for the fungal domains included in this study.A list of Figure S1Number of domains and domain architectures for each species, grouped by subphylum. From top to bottom, species and strains are sorted in descending order according to the proportion of domain architectures per domain. Endemic dimorphic fungi are marked with an asterisk.(PNG)Click here for additional data file.Table S1Fungal species overview. Excel file containing a detailed list of the species and strains used in this study, along with their basic proteome information.(XLS)Click here for additional data file.Table S2Core domains and domains found in single and multidomain proteins. Excel file containing the exclusive domain lists for single and multidomain proteins. The information is split in five tabs: (i) core domains found in all species; (ii) exclusive domains in single domain proteins; (iii) exclusive domains in multidomain proteins; (iv) domains found in both single and multidomain proteins; and (v) core domains found in all species without including Microsporidia species in the analysis.(XLS)Click here for additional data file.Table S3Core domain architectures. Excel file containing the core domain architectures represented in every organism analysed. The first tab contains the core domain architectures found in all species whereas the second one shows the core domain architectures in all fungal species without including Microsporidia species in the analysis. Notice that there is an entry for each distinct domain found in a multidomain domain architecture.(XLS)Click here for additional data file.Table S4Exclusive domain architectures per species. Tab-delimited file listing the Pfam domains architectures and fungal species in which those domain architectures were found exclusively.(CSV)Click here for additional data file.Table S5Exclusive domains per species. Tab-delimited file listing the Pfam domains and fungal species in which those domains were found exclusively.(CSV)Click here for additional data file.Table S6Exclusive and core domains by taxonomical categories. Tab-delimited file listing the Pfam domains that are found to be exclusive at least in one fungal phylum. Columns named after a taxonomical level  contain a value different than 0 if the corresponding domain was only found in proteins from such taxonomic level. Additionally, for each category, a column named core with a value of 1 indicates the presence of that domain in proteins of all species under that given category.(CSV)Click here for additional data file.Table S7Exclusive and core domain architectures by taxonomical categories. Tab-delimited file listing the Pfam domain architectures that are found to be exclusive at least in one fungal phylum. Columns named after a taxonomical level  contain a value different than 0 if the corresponding domain architecture was only found in proteins from such taxonomic level. Additionally, for each category, a column named core with a value of 1 indicates the presence of that domain architecture in proteins of all species under that given category.(CSV)Click here for additional data file.Table S8Clans containing fungal domains but no human domains. Excel file listing the 48 clans for which no Pfam domain identified in a human protein was associated.(XLS)Click here for additional data file.Table S9Domain architectures exclusive by clinical groups. Tab-delimited file listing the exclusive domain architectures per clinical isolate.(CSV)Click here for additional data file.Table S10Domains exclusive by clinical groups. Tab-delimited file listing the exclusive domains per clinical isolate.(CSV)Click here for additional data file.


Alphacoronavirus, has caused huge economic losses for the global pork industry recently. The spike (S) protein mediates PEDV entry into host cells. Herein, we investigated the interactions between the S protein and its receptor porcine aminopeptidase N (pAPN) or co-receptor sugars. The C-terminal domain (CTD) of the S1 domain is bound to pAPN. The prototype strain demonstrated similar receptor-binding activity compared with the variant field isolate. Three loops at the tips of the β-barrel domains did not play crucial roles in the PEDV S-pAPN association, indicating that PEDV conforms to a different receptor recognition model compared with transmissible gastroenteritis virus (TGEV), porcine respiratory CoV (PRCV), and human coronavirus NL63 (HCoV-NL63). The N-terminal domain (NTD) of the PEDV S1 domain could bind sugar, a possible co-receptor for PEDV. The prototype strain exhibited weaker sugar-binding activity compared with the variant field isolate. Strategies targeting the receptor binding domain (RBD) may be helpful for developing vaccines or antiviral drugs for PEDV. Understanding the differences in receptor binding between the prototype and the variant strains may provide insight into PEDV pathogenesis.Porcine epidemic diarrhea virus (PEDV), a member of  Coronaviridae family consists of four genera known as the α-, β-, γ-, and Deltacoronaviruses [Betacoronavirus genus uses angiotensin-converting enzyme 2 (ACE2) as its receptor, and its receptor-binding domain (RBD) is located within CTD of the S1 domain [Betacoronaviruses are located in the N-terminus of the S1 domain. However, carcinoembryonic antigen cell adhesion molecule (CEACAM) and sugar serve as the receptors for MHV and BCoV, respectively, despite their high sequence homology [Coronaviruses are enveloped, single-stranded, positive-sense RNA viruses. They are globally important infectious agents that are associated with respiratory, digestive, and neurological diseases in humans and animals . The Coraviruses ,2. These1 domain . ACE2 is1 domain . No stru1 domain . The RBDhomology ,7.Alphacoronavirus genus and can cause an acute and highly contagious enteric disease. Its clinical symptoms include watery diarrhea, severe enteritis, vomiting, and weight loss; the mortality rate can reach 50%–90% in suckling piglets [PEDV belongs to the  piglets ,9,10. In piglets . Subsequ piglets . CurrentThe genome of PEDV contains 28 kb which encodes the polyproteins ORF1a and ORF1b, S, open reading frame 3 (ORF3), envelope (E), membrane (M), and nucleocapsid (N) proteins ,13. The The major genetic variations of coronaviruses have been reported to be concentrated in the S1 portion of the S gene. Even a single amino acid mutation can change the virulence of a given virus ,19. The In this study, the RBD of the PEDV S1 domain was identified, and the receptor-binding activity was compared between the variant strain CHGD-01 and the prototype CV777 strain. The motifs responsible for receptor binding were predicted and verified using mutagenesis. Finally, the sugar-binding domain of PEDV was determined, and the binding activity was compared between the CHGD-01 and CV777 strains.2. CV777 is a PEDV prototype strain [Insect cells were cultured in Sf-900 II SFM  at 27 °C. HEK-293T cells were grown in RPMI 1640 medium  supplemented with 10% fetal bovine serum at 37 °C with 5% COe strain , and CHGe strain .4 Fc tag and an N-terminal honeybee melittin signal peptide. The S1 fragment of CV777 containing residues 249–634 was cloned in the same way. Meanwhile, the IgG4 Fc fragment was cloned into the pFastBac1 vector. The full-length S1 and the S1 truncated fragments of CHGD-01 containing residues 1–543, 1–434, 1–324, 1–252, 253–631, and 477–629, which were designed based on previous studies for PEDV and TGEV [6 tag and an N-terminal honeybee melittin signal peptide. The full-length S1 and the S1 truncated fragment of CV777 containing residues 1–320 were cloned in the same way. The pAPN ectodomain  was amplified and cloned into the pFastBac1 vector with a C-terminal His6 tag and an N-terminal honeybee melittin signal peptide.The S1 sequences of the CHGD-01 strain  and the CV777 strain  were codon-optimized and synthesized . All of the PEDV S1-truncated variants used in this study were generated based on those templates. The S1 fragments of CHGD-01 containing residues 19–638, 1–434, 253–533, 253–638, and 477–629, which were designed based on previous studies for PEDV, TGEV, and PRCV RBDs ,18, wereand TGEV , were cl4 Fc tag and an N-terminal honeybee melittin signal peptide.The residues 521–534, 556–563, and 607–615 from the CHGD-01 S1 C-terminus (residues 477–629) were singly mutated to the HCoV-NL63 sequences corresponding to residues 493–506, 534–541, and 585–593, respectively; alternatively, these residues were singly mutated to the “SGSGS” motif. Residues 493–513, 531–541, and 585–590 of the HCoV-NL63 RBD form three loops that are responsible for its ACE2 binding but that do not have pAPN-binding activity [4 Fc proteins were expressed and purified using a previously described protocol [4 Fc region were expressed in insect cells using the Bac-to-Bac expression system . The cell culture supernatants were harvested for protein purification using a HiTrap Protein G HP column . Then, a Superdex 200 pg gel filtration column  was applied. The same procedure was used for protein expression of the C termini of various fragments of the PEDV S1 domains containing His6 tags, a HisTrap HP column  and a Superdex 200 pg gel filtration column were used for purification. The pAPN ectodomain containing a C-terminal His6 tag was expressed and purified using the same procedure as that used for the His6-tagged PEDV S1 domains.The PEDV S1 fragment proteins and IgGprotocol . Briefly4 Fc-tagged PEDV S1 or mutant proteins  overnight at 4 °C. After washing, the plates were blocked with bovine serum albumin (BSA) for 2 h at room temperature (RT). The plates were washed and incubated with His6-tagged pAPN protein (50 μg/mL) for 2 h at RT. Mouse anti-His monoclonal antibody  was added to the plates after washing and was incubated for 1 h at 37 °C. After washing, the plates were incubated with horseradish peroxidase (HRP)-conjugated goat anti-mouse IgG at 37 °C for 45 min . The 3,3′,5,5′-tetramethylbenzidine (TMB) substrate was added after the incubation. The reaction continued for 7 min and was subsequently stopped by adding 50 μL of 0.25% HF. The ELISA signal was measured using the ELISA plate reader SpectraMax 190 .The binding affinity of pAPN with different fragments of the PEDV S1 or PEDV S1 C-terminus mutated proteins was detected by ELISA ,23. Brie6-tagged PEDV S1 domain. After washing, the ELISA was performed as described above [To detect the binding affinity between sugars and different PEDV S1 domain fragments, ELISA plates were coated with 60 μg/mL bovine submaxillary gland mucin (BSM) at 4 °C overnight. After washing, the plates were blocked with BSA for 2 h at RT and incubated with 1 μM of the different fragments of the His4 Fc-tagged PEDV S1 fragment proteins and S1 C-terminus mutant proteins were spotted onto nitrocellulose membranes. The membranes were then completely dried, blocked with BSA at 4 °C overnight and incubated with 20 μg/mL His6-tagged pAPN for 4 h at RT. After washing three times with TBST, the blots were incubated with mouse anti-His monoclonal antibody at RT for 2.5 h. After another three washes with TBST, the blots were incubated with HRP-conjugated goat anti-mouse IgG antibody  at RT for 2 h. Finally, the results were detected using a chemiluminescence reagent  and visualized with the G: Box Chemi XT4 .Receptor-binding assays of the PEDV S1 fragment proteins were performed as previously described . Briefly6-tagged pAPN was mixed with 25 μg of each of the IgG4 Fc-tagged PEDV S1 fragment proteins and S1 C-terminus mutant proteins. The formed complexes were precipitated using Ni Sepharose™ 6 Fast Flow  by incubation for 1 h at 4 °C. After washing with RIPA lysis buffer, the His6-tagged pAPN and IgG4 Fc-tagged PEDV S1 fragment proteins were separated by SDS-PAGE and detected using anti-His antibodies and anti-human IgG4 Fc antibodies , respectively.The protein pull-down assay was performed as previously described . Briefly6) were then harvested with PBS containing 5 mM EDTA and were aliquoted into microcentrifuge tubes. The pellets were subsequently resuspended in PBS containing 10% fetal bovine serum (FBS) and various fragments of IgG4 Fc-tagged PEDV S1 proteins. The cells and proteins were incubated for 1 h at RT and washed once in PBS with 2% FBS. The pellets were then resuspended in 100 μL of PBS with 2% FBS containing FITC-labeled anti-human IgG4 Fc antibodies  and incubated for 1 h at 4°C. Following this, fluorescence-activated cell sorting (FACS) was performed using a FACScan instrument .HEK-293T cells were transfected with a pAPN gene-containing plasmid to obtain receptor-expressing cells 48 h prior to their use in a binding assay. The cells (1 × 10The CHGD-01 (residues 477–629), TGEV  and HCoV-NL63  RBD sequences were aligned using ClustalW. The structure model of the PEDV S1 C-terminus motif was predicted using SWISS-MODEL and the PRCV S protein was chosen as a template for modeling, then compared PEDV S1 C-terminus with the RBD structures of TGEV (PDB code No. 4F2M) and HCoV-NL63 (PDB code no. 3KBH).19–638, S253–638, S1–434, S253–533, S477–629) of the CHGD-01 strain with a C-terminal IgG4 Fc tag , which corresponds to the sequences of TGEV, PRCV, and HCoV-NL63 RBDs using SWISS-MODEL. The PEDV S1 C-terminus was predicted to contain eight β-strands and several loops A, left. 477–629 fragment from CHGD-01 strain exhibited pAPN-binding activity in the ELISA and flow cytometry assays. To determine whether PEDV uses the same receptor recognition model as TGEV, PRCV, and HCoV-NL63, the three loops from the CHGD-01 S477–629 fragment were individually replaced with those of HCoV-NL63 or the sequence “SGSGS.” The mutants were then expressed in insect cells. Three proteins were successfully expressed: RBM1-1 (residues 521–534 were mutated to the HCoV-NL63 sequence corresponding to residues 493–506), RBM2-1 (residues 556–563 were mutated to the HCoV-NL63 sequence corresponding to residues 534–541), and RBM3-2 (residues 607–615 were mutated to “SGSGS”). These proteins were purified by Protein G affinity chromatography  corresponded to the RBDs of TGEV, PRCV, and HCoV-NL63. Coincidentally, the Stography A. An ELI protein B. The sa protein C.The dot-blot hybridization assay was used to compare the pAPN-binding activities between the wild-type and the three mutant proteins. The results revealed that the RBM1-1, RBM2-1, and RBM3-2 proteins did not completely lose their pAPN-binding activities D.477–629 fragment protein , 5-N-glycolylneuraminic acid (Neu5Gc), and 5-N-acetylneuraminic acid (Neu5Ac). The results showed that the S1–793, S1–324, S1–434, and S1–543 fragments bound to mucin with high affinity, while the S253–631 fragment bound to mucin with a much lower affinity, and the S477–629 fragment had little binding activity  at the tips of the β-barrel domains are responsible for receptor binding . In our i.e., Neu5,9Ac2) can be recognized by BCoV and HCoV-OC43 in the genus Betacoronavirus [α- and Gammacoronaviruses, such as TGEV and IBV [1–434 and S253–533 fragments could weakly bind to pAPN-expressing cells, and that the S19–638 fragment revealed a much higher binding affinity, which indicates that except for pAPN recognized by the CTD of the PEDV S1 domain, other cellular molecules, such as sugars, can be recognized by the NTD of the S1 domain as receptors or co-receptors. Similar to TGEV, which uses Neu5Gc and Neu5Ac as co-receptors [Coronaviruses use a variety of cellular molecules as receptors or co-receptors, including proteins and sugars. A sugar moiety on cell-surface glycoproteins or glycolipids (onavirus ,32. More and IBV ,34. The  and IBV . Thus, b and IBV . In the eceptors ,37, we cThe variations in coronaviruses are concentrated in the S1 domain . SequencIn summary, we demonstrated that the C-terminal region of the PEDV S1 domain is responsible for binding to pAPN as its major receptor and it conforms to a different receptor recognition model compared with TGEV, PRCV, and HCoV-NL63. The N-terminal region of the PEDV S1 domain is responsible for binding to sugar, which may act as its co-receptor. The sugar-binding activities of the variant field isolate are stronger than those of the prototype strain. The first and crucial step of virus infection is virus–receptor interaction; thus, investigations on receptor recognition are important for understanding the mechanisms of virus invasion and pathogenesis and will provide rational strategies for developing antiviral drugs, vaccines, and monoclonal antibodies for the treatment and prevention of viral infections.


Domains are predicted using a library of profile HMMs representing 2737 CATH superfamilies. Gene3D has previously featured in the Database issue of NAR and here we report updates to the website and database. The current Gene3D (v14) release has expanded its domain assignments to ∼20 000 cellular genomes and over 43 million unique protein sequences, more than doubling the number of protein sequences since our last publication. Amongst other updates, we have improved our Functional Family annotation method. We have also improved the quality and coverage of our 3D homology modelling pipeline of predicted CATH domains. Additionally, the structural models have been expanded to include an extra model organism (Drosophila melanogaster). We also document a number of additional visualization tools in the Gene3D website.Gene3D  Protein structural domains are compact structural modules within proteins and can be grouped into sometimes very large clusters of relatives showing clear evolutionary relatedness, termed homologous superfamilies. The CATH database takes structures from the Protein Data Bank (PDB), and from these identifies individual protein domains which are subsequently assigned to one of a few thousand homologous families . In the The Gene3D resource predicts domain superfamily assignments for tens of millions of protein sequences in UniProKB  and EnseIn summary, we have updated Gene3D domain assignments using the latest release of CATH (v4.1) and this has considerably expanded the numbers of protein sequences and genomes in the database. We have improved the functional annotations of these sequences using the FunFHMMer algorithm and have used the more accurate FunFams to improve our structural modelling pipeline. For the first time, we have incorporated Ensembl genomic coordinates into the database as a valuable aid to examine alternative splicing processes and we have incorporated knowledge of protein regions and mutations that affect protein interactions. We have also expanded our coverage of human mutations to the much larger set integrated by UniProt and included additional visualization tools in the website. Additionally, we have added in predictions of both disordered and foldable regions to help annotate protein regions currently missing domain assignments.The number of sequences in the database has increased by over 2-fold since our last release to 43 378 462 million sequences with over 19 471 cellular genomes now present. The number of CATH domain sequence assignments has increased from 26 to 54 million sequences belonging to 2737 CATH superfamilies.The expansion in protein sequences in the database continues to be driven mainly by new bacterial genome sequences. Because of this continuing trend to more bacterial sequences it is essential to use a set of protein sequences common to both releases when comparing domain coverage between releases. For Ensembl sequences common to both releases (which predominantly consists of vertebrate genomes with a few non-vertebrate model organisms), there is a small but positive increase in domain coverage (i.e. the percentage of sequences with at least one domain annotated) from 70.8 to 71.4%. On a larger set of pan-taxonomic compara taxons from Ensembl Genomes  . The newAs mentioned, FunFams are sub-classifications of CATH superfamilies derived using sequence signature analysis. Analysis of the FunFams produced by FunFHMMer shows that they represent structurally cohesive clusters well suited to be used for homology modelling . The strThere is a manuscript in preparation describing the homology pipeline and its performance in more details. In brief, our analyses of the performance in using the new FunFams to build homology models for uncharacterized sequences show that they produce more good quality models (TM-score >0.50) than BLAST for close homologues (sequence identity ≥30%). TM-scores have previously been shown to be useful in assessing model quality . AlthougDrosophila melanogaster genomes. The FunFam modelling pipeline generates 24 and 10% more human models compared to the classical BLAST and PSI-BLAST approaches, respectively . In cases where FunFams cannot obtain a good template match, HHsearch is used to obtain extra coverage. Compared to the previous release, we have added in structural models for the D.melanogaster genome and we are in the process of adding structural models for an additional eight model organisms. These are expected to be completed by the time this manuscript is published.Our FunFam modelling pipeline has been applied to model domain sequences in Human and As another new feature in this release of the Gene3D database, we now integrate the full set of mutations stored in UniProtKB. This includes both manually curated mutations and those automatically imported from the Ensembl genomes project .Alternative splicing is an important process for expanding the functional repertoire of complex organisms. We have imported genomic coordinate data for Ensembl genomes, which allows protein features such as domains to be mapped back to their parent exons.For binary protein interactions we have extracted information on sub-regions of a protein sequence where there is some evidence in IntAct , that thThe website has been augmented with additional datasets and visualization tools, for brief descriptions of these additions see below.A large amount of mutation data has become available in recent years. It has been shown that domain-based analysis of mutation data can help when interpreting how a mutation contributes to disease . To helpDisordered regions predicted by IUPRED-long  are now http://msa.biojs.net/) which allows for much improved visualization options and can cope with large alignments . AdditiIf a structural model is available, clicking on a residue in the top bar of the MSA viewer highlights the selected residue on the structural model. It is possible to highlight many other features stored in Gene3D onto the structure. For example, we have added the ability to display the position specific data in UniProt which contains over 20 different feature types, such as active-sites. Additionally, any regions annotated with information on their role in mediating proteins interaction can be displayed using data imported from IntAct. The IntAct imported data also contains information on mutations which can alter the strength of the interaction Figure .For a modelled structure it is possible to highlight on the structure which exons code for different stretches of protein sequence. Clicking on an exon highlights that section on the structure whilst clicking the ‘colour by exons’ alternates the colour of consecutive exons in the structure. CRISPR-CAS9 developments  are makiWe have improved our genomic comparison tools making using of D3.js  based trhttp://gene3d.biochem.ucl.ac.uk/examples).There are many high quality protein interaction resources available with advanced network display tools ,22. It iIn the interaction networks the query proteins domains are displayed as circular nodes. Inside the nodes we show an image of a representative structure from the domain superfamily to which the domain belongs. A link between a domain node and a protein node indicates there is experimental annotation indicating that a sub-region of the protein, which overlaps with the domain, affects the interaction with that protein. The links between domains and proteins become thicker as a greater proportion of the annotated sub-region is covered by the domain. A red link indicates terms such as ‘sufficient to bind’ etc. and clicking on an edge joining a protein and domain gives further details along with links back to the source resource that provided the annotation. A blue link indicates instances where a mutation is known to affect the interaction. A sliding bar allows the user to filter out those links between domains and proteins where the sub-region interaction annotation has a relatively small overlap with the domain.As an example, for the amyloid protein A4_HUMAN ,there is evidence for its C-terminal domain having a role in mediating interactions with other proteins Figure . As anotClicking on the ‘Splicing Browser’ link in the navigation bar at the top of the page produces a table of entries where there is some isoform specific functional annotation from UniProt. Entering text in the search box of this section filters the table and highlights the matched text. For example, entering search terms ‘location’ and ‘nucleus’ shows isoform annotations where there is information that this isoform may have a role in nuclear localization.ftp://ftp.biochem.ucl.ac.uk/pub/gene3d_data/CURRENT_RELEASE/). We have added domain annotations for many key organisms in the ‘TAXONS’ sub-directory of the FTP site.We provide the same downloads as for previous releases at the usual ftp site  data. As the datasets grow, improvements in visualization tools are needed to make best use of the data. It is notable that we make use of several BioJS  componen


The mixolab, a rheological device developed recently, combines approved farinograph and amylograph test procedures. Analysing wheat flour composites with hemp, teff, or chia in terms of all three mentioned rheological methods, correspondence of farinograph, and amylograph versus mixolab features was examined by principal component analysis. The first two principal components, PC1 and PC2, explained 75% of data scatter and allowed a satisfying confirmation of presumed relationships between farinograph or amylograph and mixolab parameters. Dough development time and stability were associated with gluten strength (C1 torque point) and also dough softening (mixing tolerance index) had a link to protein weakening (C1-C2 difference). In the second mentioned case, amylograph viscosity maximum and amylase activity (C3-C4) closeness was verified. Starch and starch gel properties during mixing  affect dough viscosity (C1) and rheological behaviour (dough development time and stability). Another important finding is unequivocal distinguishing of the composite subsets  by the used rheological methods and statistical treatment of multivariable data. Addition of nontraditional components offers new possibility of wheat flour properties and could be performed by milling product from hemp, teff, and chia seeds. By the way, mentioned components also improve nutritional value of products owing to protein content and further positive ingredients for health.Hemp (Cannabis sativa) is planted as two subspecies, namely, ssp. culta and ssp. indica. The latter is called hash hemp and belongs to forbidden raw material with respect to intoxicating substances production. Hemp flour composition depends on variety and planting locality and differs according to preparation and defatting. Protein, fat, and starch rates are known to be 30–33%, 7–13%, and approximately 40%, respectively. It contains a significant level of beta-carotene and vitamins B1 and E. Considering mineral component aspect, a benefit could be found in higher portion of iron and zinc [Teff (Eragrostis tef) is classified into cereal group of the Poaceae (Gramineae) family. As reported by Gomez and Gusta [injera (ingera) dominates among other culinary treatments, and it is manufactured from thin fermented dough with a portion of wheat flour. Because of its tiny seeds, whole meal flour is characterised by high rate of coating layers and sprout, resulting into higher content of insoluble polysaccharides. Proteins have nongluten nature and owing to prevailing portion of prolamins belong to easily digestible ones. From a nutritional benefit viewpoint, high minerals content is cited  and B1 vitamin.nd Gusta , main prChia  is an annual herb of the Labiatae family, producing seeds which were one of the basic foods from the Aztec civilization. Now, the main producers belong to Argentine, Columbia, and Peru. The composition includes minerals, about 20% protein, 30–32% fat, 30–40% polysaccharides (insoluble fibre as the important portion), and 2-3% fructooligosaccharides [A goal of the presented work was an investigation of farinograph and amylograph versus mixolab features comparability, performed by correlation and hierarchical cluster analyses. Torque points C1 and C2, describing mixing part of the Mixolab test, could have relationships to for example, farinograph dough development time or mixing tolerance index, while a difference C3-C4 should be correlated with amylograph viscosity maximum. Secondly, three tested wheat flour composite subsets  should be distinguished by statistical means according the influences of alternative flour on rheological behaviour. For this purpose, principal component analysis was used, studying influences of alternative flour and addition level factors.Wheat flour used as composites base was of commercial origin, obtained from industrial mill Delta Prague in 2010, and is characterised by low ash (0.59%) and quite high protein contents 12.97%. Hemp flour samples K1, K2, and K3 were produced also in the year 2010. The former two hemp flour samples were gained as conventional planting regime commodity . On the contrary, the sample K3 was prepared from hemp grain bred under bioregime conditions .Wheat flour replacement was selected on levels of 5%, 10%, 15%, and 20% identically for all three hemp materials.Teff flour R1 and R2 samples were produced by Tobia Teff UK Ltd., and they could be identified according their white and brown botanical type. Wheat flour replacement ratios were 10, 20, and 30% for both teff types.Chia flour Ch1 and Ch2 samples were prepared from conventionally and organically produced white and dark chia seeds, bought from Aida Organic and Country Life CZ companies, respectively. Chia seed samples originated in Mexico. With respect to EU legislation, substitution levels were chosen as 2.5% and 5.0% of wheat flour.Mixed composites were signed by combination of alternative flour type and its addition level, for example, K2-15 means ratio of 85%/15% (w/w) of wheat and K2 hemp flour, respectively.−1, kneader temperature 30°C, heating rate 2°C/min, and total analysis time 45 min. Course of wheat dough behaviour as well as the composite dough ones was described by five basic torque points  and four pair differences .Using factor 5.7, protein content (PRO) was determined according to the Kjeldahl's method (ČSN ISO 1871). The farinograph and the amylograph test was performed following ČSN ISO 5530-1 and ICC 126/1, respectively. Observed features were dough development time and dough stability in min  together with mixing tolerance index ; DDE and DST express “protein strength” at dough mixing and protein resistance against dough overmixing. From amylograph curve record, the amylograph viscosity maximum  was considered; it has a relation to starch physical-chemical stage and it corresponds to amylases activity. On the mixolab, predefined protocol “Chopin+” was used, with the test settings corresponding to user's manual and published earlier [t-test on P = 95%. By linear correlation analysis, correspondence between mixolab versus farinograph and amylograph data was explored . For variables closeness examination, both principal component analysis (PCA) and hierarchical clustering were employed. Histogram, the second method output, was built in Euclidean space according to the farthest neighbourhood clustering algorithm.For statistical treatment of measured data, the software Statistica 7.0  was used. Within the pairs hemp-teff, teff-chia, and hemp-chia composites, differences between recorded rheological feature means as well as their scatters similarity were tested by pair Student's Composite K1 and K2 subsets are characterised by PRO slightly higher than common level of the Czech wheat, that is, from 13.11 to 14.82% for samples with 5% and 20% of hemp flour. Blends containing hemp K3 type were somewhat richer in the component; determined limits were 13.46% and 15.94% for adequate blends. Pure teff flours R1 and R2 had the lowest PRO between tested nontraditional materials; with respect to this, their addition did not meant a nutritional benefit—in the former case, PRO oscillated around 12.57% and around 13.06% in the latter. According to smallest amount added (2.5% and 5.0%), wheat flour enhanced by chia ones demonstrated very slight increase of PRO from 10.97% to 11.21%, respectively, independently to Ch1 or Ch2 type.Farinograph data in During the amylograph test, viscosity of flour suspensions containing both tested chia types was recorded on approximately half level than other composites. Comparing hemp and teff subsets, suspensions viscosity maximum increased in the former and reversely diminished in the latter case as addition level of alternative flour increased .Interpretation of Mixolab curves determined in “Chopin+” measurement mode was published previously –7. The fIndirect examination of anylases activity is represented by further three torque parameters, C3 as well as C4 during a heating and C5 during a cooling phase of the mixolab test. ANOVA results in r = −0.46, P = 95%).With respect to the paper aim, Pearson correlation matrix (as an exploration method) was calculated firstly. Considering three probability levels , the farinograph test is satisfyingly represented by the DST and the MTI features . No signr = 0.877, 0.794 and 0.907) and their differences  within the set of 60 Romanian bread flour samples.According to international standards, amylases activity could be described by the reference amylograph test, so by the screening method “Falling Number”. Codină et al.  mentionet-test. Measured feature averages of hemp-teff, hemp-chia, and teff-chia were not different in 9 cases, and dissimilar subset scatters were found in 11 cases of 39 in total (data not shown). Summarised, the PCA method could explain variability within the set correctly. As known, data for the PCA must be intercorrelated and observed subsets should be characterised by a similar data variance. Links among all 13 determined variables were presented above, and subset scatter comparability was tested by the pair Student's Data variance was explained from 89% by the first three principal components (PCs), from 56%, 19%, and 14% by PC1, PC2, and PC3, respectively . The mixStudying effect of composites chemical constitution, also all three tested subsets were successfully distinguished by combination of three employed rheological tests and the multivariable explorative statistics. A dominant influence of tested alternative crop is unequivocal in the PC1 × PC2 plot, over the crops conventional/organic origin, white/brown type or added amount factors . As discPossible substitutions within the rheological feature groups were further tested by cluster analysis. Based on data pretreated by standardisation , a histogram was built in the Euclidean space using the farthest neighbourhood algorithm. Within Presented study experimental design included comparison of three rheological methods result, assessed for wheat flour blends with hemp, teff, and chia, and the PCA method used for features closeness determination as well as discrimination of the three tested composite subsets. Farinograph, amylograph, and mixolab data have shown basic differences in composite dough rheological behaviour, that is, in a bakery potential of these mixtures.Correlation analysis partially signified some links between parameters of these three tests, and principal component analysis confirmed presumed relationships in agreement with conclusion published by other authors. Within 75% of explained variability (PC1 × PC2 plane) and considered farinograph characteristics, tight associations were revealed out between dough development time or stability and C1, and between mixing tolerance index and the difference C1-C2. Amylograph viscosity maximum was connected with C3, C4 and C5 torque points , which correspond to amylases activity and starch gel properties during heating and cooling phase of the mixolab test. Levels of those torque points  corresponded to dough consistency changes , so relationships to farinograph dough development time and stability.All these findings were confirmed by hierarchical clustering in Euclidean space, because mentioned features groups were found in built histogram.