Phytophthora infestans using a crucifer-infecting strain of the plant virus tobacco mosaic virus (TMVcr) and its derivative TMVcr-Δ122 that is mutated in the sequence of the p122 replicase subunit and thus inhibited in RNA suppression activity. In this study we provide evidence that replication of TMVcr-Δ122 but not of TMVcr was impaired in P. infestans as well as in tobacco plants used as positive control. The interference was associated with induction of high transcription of dicer-like genes Pidcl2 and NtDCL2 and of RNA-dependent-RNA-polymerase Pirdr1 and NtRDR1 in P. infestans and tobacco, respectively. These high transcription levels suggest an RNAi-based response that TMVcr-Δ122 mutant was not able to suppress. Taken altogether, results of this study demonstrated that an antiviral silencing activity operates also in P. infestans and that a plant virus could be a simple and feasible tool for functional studies also in oomycetes.RNA interference (RNAi) is a sequence identity-dependent RNA degradation mechanism conserved in eukaryotic organisms. One of the roles of RNAi is as a defense system against viral infections, which has been demonstrated in filamentous fungi but not in oomycetes. We investigated the virus-RNAi interplay in the oomycete  In some organisms, a host RNA-dependent RNA polymerase (RdRp) uses the single-stranded RNA (ssRNA) templates removed from the primary siRNAs to synthesize mobile secondary siRNA duplexes for amplification and spread of the silencing signal throughout host tissues6.RNA interference (RNAi) is a sequence identity-dependent RNA degradation mechanism involved in the innate immunity response in plants, invertebrates, fungi and oomycetes. Double-stranded RNAs (dsRNA) derived from transgenes, endogenous genes, viruses and transposable elements activate the mechanism by processing the dsRNAs into 21–30 nucleotides (nt) long duplexes, called primary small interfering RNAs (siRNA) via a type III ribonuclease of the Dicer-like (DCL) protein family. Then members of the Argonaute protein (AGO) family recruit siRNAs to assemble RNA-induced silencing complexes (RISCs) that are the effectors of the process. RISCs remove one of the two strands from primary siRNAs and use the other as a sequence-specific guide to direct degradation or silencing of complementary RNAs in different subcellular compartments10 and sequence similarities with plant viruses is being reported7. Thus, a population of virus-derived small interfering RNAs (vsiRNAs) to target viral nucleic acid would be a predictable component also of fungi and oomycetes RNAi pathways in response to the accumulation of genomic dsRNA or of dsRNA intermediates of viral replication16. The importance of RNAi as defense mechanism against viruses is reflected in the fact that most viruses encode RNAi silencing suppressors (VSRs) that interfere with different but specific steps of the silencing pathway17 or contribute to keep a balance between host development and virus accumulation levels18. The use of counter defense strategies based on suppression of RNAi has been documented for the S10 gene of the Rosellinia necatrix mycoreovirus 3 (RnMyRV3) and for the p29 multifunctional protein encoded by Cyphonectria hypovirus 1(CHV1-EP713)20 but to the best of our knowledge to date this evidence has not been provided for oomycetes. The Rosellinia necatrix/RnMyRV3 and Cyphonectria parasitica/CHV1 systems are robust models for studying host defense-viral counterdefense interactions in filamentous fungi. RnMyRV3 is an encapsidated dsRNA mycoreovirus replicating inside the virions and its nucleic acid is never exposed to cytoplasm while CHV1-EP713 is a naked ssRNA hypovirus. Despite this remarkable difference both the viruses are sensitive to RNAi and express a VSRs16. The suppressor activity of the S10 gene coded by RnMyRV3 was demonstrated in a R. necatrix strain carrying a constitutively silenced green florescent protein (GFP). In this host, RnMyRV3 infection reduced the accumulation of siRNAs derived from GFP and increased the accumulation of GFP dsRNA, suggesting an interference with the dicing step of R. necatrix RNAi21. Agroinfiltration in plants validated the VSR role of the S10 gene of RnMyRV321. Similarly, the papain-like protease P29 encoded by the hypovirus CHV1-EP713 suppressed RNAi either in C. parasitica or in plant. The P29 product is phylogenetically and functionally affine to potyvirus HC-Pro as a protease, a symptom determinant, and a VSR12.Filamentous fungi and oomycetes host the replication of a large number of mycoviruses and oomycete viruses, the majority of which have dsRNA genomes although an increasing number of viruses with plus-sense ssRNA genomesPhytophthora infestans that encodes the three core components involved in RNAi comprising two DCL genes , five AGO genes (Piago1–5) and one RNA-dependent RNA polymerase gene (Pirdr1)23. Comparative genomics led to identification in P. infestans of other genes potentially involved in gene silencing such as class 1 RNase III, chromodomain proteins, DEAD-box helicases, histone deacetylases and histone methyltransferases, coming to the conclusion that proteins similar to Dicer, Ago and Histone deacetylases are essential for gene silencing in the oomycete23.Gene silencing in oomycetes is most well characterized in the potato late blight pathogen P. infestans to the infection of the plant virus tobacco mosaic virus and its mutant defective in the ability of the p122 protein (TMVcr-Δ122) to suppress RNAi24. In particular, in this manuscript we show that TMVcr and its VSRs-defective mutant are able to replicate and express in P. infestans and that, similarly to plants, the p122 VSRs coded by TMVcr functions also in this oomycete to suppress RNAi triggered by viral infection. The rationale for using a plant virus derives from the observation that TMV could enter and persist in the mycelia of the soil-inhabiting phytopathogenic oomycete Pythium sp.25 and particles of TMV and tobacco necrosis virus could be transfected in P. arrehnomanes26. These early studies were followed up and more thoroughly discussed in a recent paper27 where it was demonstrated that a wild-type isolate of TMV was able to enter, replicate and persist up to seventh subculture in cells of the phytopathogenic fungi Colletotrichum acutatum, C. clavatum and C. theobromicola and that C. acutatum responded to the TMV infection by activating an RNAi-based mechanism. Compared to current strategies to investigate RNAi in filamentous fungi and oomycetes, the approach used in this study is more direct, easy to do, and feasible since it simply entailed the addition of a purified virus preparation to liquid cultures of P. infestans.This study investigated the RNAi-based response of the oomycetes P. infestans. Oomycete liquid cultures were inoculated with a purified preparation of the TMV-based vector TMV-GFP-1056, expressing green fluorescent protein (GFP) as reporter. Before mycelia inoculation, the vector was tested in tobacco plants to verify the biological expression of GFP  and 178 million raw single reads were produced from nine libraries with an average of 19.7 million reads per sample. Reads quality was assessed using FastQC29 and reads were trimmed with Trimmomatic, version 0.3630 that, after filtering for rRNA and adapters, yielded 175 million of 51 nt high quality reads. Consensus sequences of TMVcr and TMVcr-Δ122 were reconstructed on the available TMVcr genome (Z29370.1) by aligning reads by HISAT2 2.0.5 software31. BLASTx searches in the GenBank Virus Reference Sequence Database showed that the consensus sequences from mycelia inoculated with TMVcr and TMVcr-Δ122 had 97.5%, 98.1% and 97.5% amino acid identity with putative replicase, movement protein and coat protein, respectively, encoded by TMVcr or TMVcr-Δ122. The consensus sequence of TMVcr-Δ122 confirmed that, in mycelia of P. infestans, the amber stop codon of p122 at position 3390 retained the mutation to a tyrosine codon as in the original preparation used for viral inoculation , and NtDCL2 (up to 38-fold) and NtRDR1 (up to 20-fold), respectively. Upregulation was also observed in the transcription of Piago1 and NtAGO1 but to a lesser extent (approx 2-fold for both genes at 20 dpi)  Fig. .Phytophthora spp transformants involves accumulation of siRNAs homologous to the sequence of the target gene33. We therefore tried to isolate vsiRNAs in RNA preparations extracted from mycelia of P. infestans taken at 10 dpi with TMVcr or TMVcr-Δ122. The siRNAs were identified via northern blot analysis using DIG-RNA probes for TMVcr replicase and CP. The results indicate that mycelia of P. infestans infected by TMVcr or TMVcr-Δ122 but not of the mock-inoculated WT culture contain vsiRNAs specific for TMVcr and TMVcr-Δ122 ) and its Δ122 mutant (P.i. + TMVcr-Δ122). Two biological replicate samples for each condition were sequenced yielding an average of 96 million redundant reads. After parsing from adaptors, reads that aligned without mismatches to P. infestans genome had an overall alignment rate of 86.5%, for P. infestans mock-inoculated WT, and of 85.98% and 84.46% for P.i. + TMVcr and P.i. + TMVcr-Δ122, respectively . To date, most of the mechanisms for such adaptation are still unknown.This study reveals also the ability of a plant virus to infect P. infestans by inducing the overexpression of the dicer and rdrd genes and synthesis of vsiRNAs through an RNAi mechanism that is sensitive to the VSRs coded by the virus. Thus, the evidence reported here may expand the information about RNAi and VSRs as trans-kingdom complementary mechanisms. In addition, results from this study highlight the utility of plants viruses for functional genomic studies in filamentous fungi40. Besides of being a new piece of work, the system described here pushes forward also the understanding on mechanisms of pathogen specialization and adaptation to new host species35. In this framework, this study improves the knowledge base on the biological and molecular aspects of the TMV and P. infestans interactions and condenses this knowledge base into outputs delivered through the TMV-P. infestans model, which is expected to be a new and easy-to-use tool for understanding mechanisms that can be useful for the biocontrol or at least shutting down virulence genes of this important plant pathogen.In conclusion results from this study revealed that, similarly to plants, a plant virus infection activates an RNAi pathway in Phytophthora infestans isolate 96.9.5.1 (mating type A1) used in this study was kindly provided by Dr. David Cooke  and was maintained (16 °C) on pea agar (PA) medium41. To set up liquid cultures, sporangia were collected from 3-week-old cultures on 90 mm PA plates by adding sterile distilled water (7 ml) and by gently rubbing mycelium with a glass rod. The suspension was adjusted to 1 × 105 sporangia/ml using a haemocytometer and zoospores released by chilling (4 °C for 2 h) followed by incubation (1 h) at room temperature. Zoospores suspension was subsequently inoculated in liquid pea medium41 supplemented with ampicillin (50 mg/ml) and rifampicin (30 mg/ml) and grown under continuous shaking .The Agrobacterium tumefaciens C58C1/pBIN61 harboring the plasmid pBIN-p122 were kindly provided by Dr. József Burgyán . The TMVcr-Δ122 had the amber stop codon TAG of the 122-kDa replicase subunit (p122) sequence replaced with the TAA codon for tyrosine to inhibit the siRNA binding activity of p12224. Inocula of TMVcr and TMVcr-Δ122 were prepared in Nicotiana occidentalis by rub-inoculation of a biologically active transcript synthesized from the PmlI-linearized plasmids pUC-TMVcr and pUC-TMVcr-Δ12223 with T7 RNA polymerase and the mMessage mMachine kit (Ambion), following the protocol of the manufacturer. Two true leaves of four plants of N. occidentalis were inoculated each with the transcription (30 μl) mixture obtained from pUC-TMVcr whereas to activate replication of the transcript of TMVcr-Δ122 we followed the protocol of Csorba et al.24. Briefly, leaves of N. occidentalis were first agroinfiltrated with a preparation of A. tumefaciens C58C1/pBIN-p122 to express a functional p122 protein and after two days the same leaves were inoculated with the transcription mixture (30 μl) obtained from pUC-TMVcr-Δ122. Plants agroinfiltrated with a preparation of A. tumefaciens carrying a pBIN empty plasmid and after two days with the transcription mixture (30 μl) obtained from pUC-TMVcr-Δ122, served as control. All the plants were grown in a greenhouse  and monitored daily for symptom appearance. Plant sap extracted in phosphate (Na2-K) buffer  from systemically infected leaves collected at 10 days post inoculation (dpi) was used to inoculate plants of N. tabacum cv Samsun for virus maintenance and purification. Viral particles of TMVcr and its mutant TMVcr-Δ122 were purified from tobacco leaves as described by Lot et al.42 and stored at −20 °C in 50 mM NaCl, 30% glycerol solution until used. Both the strands of an amplified product from the viral replicase (see below) were sequenced by an external sequencing service to assess conservation of the TAA mutation after multiplication in plants and purification. Prior to inoculation in liquid cultures of P. infestans, infectivity of purified virus preparations of TMVcr and its mutant TMVcr-Δ122 was evaluated by rub-inoculation onto N. tabacum cv Samsun.Full-length clones of the crucifer-infecting strain of tobacco mosaic virus (TMVcr) and of its substitution mutant TMVcr-Δ122 and a culture of P. infestans, purified preparations of TMVcr or TMVcr-Δ122  were added to sporangia growing in pea broth medium41 (100 ml) supplemented with ampicillin (50 mg/ml) and rifampicin (30 mg/ml). Cultures mock-inoculated with 50 μl of NaCl-glycerol solution  served as wild-type (WT) negative control. All cultures were prepared in triplicates and incubated under continuous shaking .To inoculate P. infestans co-incubated with purified virus preparations or systemically infected leaves of N. tabacum were collected at 4,10 and 20 dpi with TMVcr or TMVcr-Δ122. To remove virus particles adhering externally to hyphae, mycelia were harvested by filtering through sterilized Whatman No.1 filter paper, treated (30 s) with a commercial solution of sodium hypochlorite (2% vol/vol) and washed extensively with sterile distilled water26. To assess the efficacy of sodium hypochlorite treatment in removing virus particles adhering externally to mycelia of P. infestans, wash samples were rub-inoculated onto N. occidentalis and Samsun tobacco plants after dialysis  to eliminate residues of sodium hypochlorite.Mycelia of et al.43. The ChemiDoc system apparatus and Quantity One software (Bio-Rad Laboratories) were used to detect the chemiluminescent signals.Total RNA was extracted with Eurogold RNA pure (Euroclone) from freeze-dried mycelia or tobacco leaves (100 mg) ground to a fine powder with liquid nitrogen, following the protocol of the manufacturer. RNA was suspended in RNase-free water and the concentration was adjusted to 1 μg/μl. RNA preparations were separated by electrophoresis  and stained with Gel-red (Biotium). When necessary, northern blots were prepared and subjected to hybridization with digoxigenin (DIG)-labeled DNA probes specific for the sequence coding for the coat protein (CP) of TMVcr (see below) following the protocol of Minutillo P. infestans or plants. Reactions were carried out with primer pairs mapping to TMVcr sequences (Acc. no. Z29370.1) coding for coat protein  or TMVcr replicase . PCR conditions were: denaturation  followed by 35 cycles of denaturation , annealing  for TMV-cr coat protein and 55 °C for TMV-cr replicase and synthesis . Final elongation step was for 5 min at 72 °C. Purity and size of the amplified products was estimated by electrophoresis . For probe synthesis, amplified products (324 bp) from TMVcr replicase (REP) and from TMVcr coat protein (CP) (227 bp) were ligated to p-GEM-T- Easy vector (Promega) and cloned in DH5α cells to yield pREPcr and pCPcr plasmids, respectively.Standard reverse-transcription polymerase chain reactions (RT-PCR) were used throughout to detect the TMVcr and TMVcr-Δ122 in mycelia of 27. Hybridizations and washes were conducted at 42 °C and chemiluminescent signal acquired by ChemiDoc (Bio-Rad Laboratories). Two synthetic DNA oligonucleotides of 21-, and 25-nt long were used as molecular size markers.For detection of vsiRNAs, these were extracted using either Eurogold RNA pure (Euroclone) or the mirVana miRNA kit (Ambion) from freeze-dried mycelia (100 mg) collected at 10 dpi and ground to a fine powder with liquid nitrogen. RNA was separated by electrophoresis through denaturing gels , and electroblotted for 1 h at 15 V in 0.5TBE to positively-charged nylon membranes (Roche). Membranes were probed with UTP-DIG–labelled probes for TMVcr REP and CP transcribed from pREPcr and pCPcr plasmids, respectively, and hydrolyzed as described29, parsed to remove 5′TGGAATTCTCGG3′ adaptors with Trim Galore! and aligned to P. infestans genome using BOWTIE2 . The annotated genome for P. infestans was downloaded from the Broad Institute website44.The same small RNA preparations were also sent to an external sequencing service (Genewiz) to prepare libraries for sequencing on the Illumina Hiseq platform. Sequencing reads produced from six libraries with an average of 96 million reads per sample, were quality assessed with FastQCP. infestans and N. tabacum. Samples were run in triplicate and reactions were set up in a total volume of 10 μl using 66 ng of cDNA (66 ng) and primers (250 nM each) described above. Three replicates of serially diluted preparations (5-fold) of the pREPcr plasmid were used to construct standard curves. The cycling conditions were: 95 °C for 20 sec; 40 cycles at 95 °C for 3 sec and 60 °C for 30 sec. Specificity of the qPCR products was confirmed by analysis of the dissociation curves. Results were evaluated using the StepOne™ Software v2.3.Quantitative real-time PCR (qPCR) was conducted using the Applied Biosystems StepOne™ Real-Time PCR System and Fast SYBR®Green Master Mix (Applied Biosystems). Absolute qPCR was performed to monitor TMVcr CP accumulation at 4, 10 and 20 dpi in N. tabacum and in mycelia of P. infestans at 4, 10 and 20 dpi with TMVcr or TMVcr-Δ122 using the ΔΔCT method45 and primer pairs described by Vetukuri et al.23. Relative expression of Dicer-like 2 (NtDCL2) (Acc. n. KF006308.1), Argonaute 1 (NtAGO1) (Acc. n. AB542739.1) and RNA-dependent RNA polymerase 1 (NtRDR1) (Acc. n. AJ011576) in N. tabacum was estimated using glyceraldehyde phosphate dehydrogenase (GAPDH) (Acc. n. AJ133422) as a housekeeping gene46. Expression levels of Dicer-like 1 (Pidcl1) (Acc. n. EEY55353), Argonaute 1-2 (Piago1/2) (Acc. n. EEY67432) and RNA-directed RNA polymerase 1 (Pirdr1) (Acc. n. EEY56917) in P. infestans were estimated using actinA (PiactA) as housekeeping gene23. Relative abundance of the transcripts was compared with mock-inoculated samples collected at the same time points from the two hosts.Transcript level of candidate genes encoding components of the silencing pathway were quantified in leaves of P. infestans were confirmed by observing the green fluorescent protein (GFP) emitted fluorescence in mycelia and sporangia of P. infestans inoculated with the recombinant vector TMV-GFP-1056. The vector was grown in N. occidentalis inoculated with biologically active RNA transcripts synthesized in vitro from the plasmid TMV-GFP-105647 obtained from Dr. Peter Palukaitis . The TMV-GFP-1056 plasmid was linearized at the KpnI site and the recombinant vector transcribed using T7 RNA polymerase and the mMessage mMachine kit (Ambion), following the protocol of the manufacturer. Observations were made using AxioPlan epifluorescent microscope (Zeiss) equipped with a mercury vapor lamp light source HBO 50 and filter combination BP450-490/FT510/LP520. Fluorescent images were acquired also from an inverted Leica LSM TSC SP2 AOBS confocal microscope (www.leica-microsystems.com) equipped with a 63X oil-immersion objective. Bright field images were acquired from the same areas.TMV replication and expression in P. infestans were collected from liquid cultures at 10 dpi with TMVcr or TMVcr-Δ122, treated with sodium hypochlorite and crushed in Na2-K phosphate buffer . The slurry was rub-inoculated on celite-dusted leaves of Samsun tobacco. Plants inoculated with slurry obtained from mycelia of mock-inoculated wild type (WT) cultures of P. infestans collected at 10 dpi served as control. All plants were maintained in greenhouse .For virus infectivity test in plants, mycelia of P. infestans pathogenicity test, mock-inoculated WT mycelia or mycelia inoculated with TMVcr or TMVcr-Δ122 were grown on PA plates. Sporangia were collected by washing 2-week-old cultures with distilled water and used as inoculum on UC82 tomato leaflets excised from the plants. Leaflets were surface-sterilized with sodium hypochlorite solution (2% vol/vol), washed, dried and transferred on moist filter paper in Petri dishes. Each leaflet was inoculated on abaxial side with droplets (30 μl) of sporangia suspension (1 × 104) near the main vein and kept in growth chambers . Three leaflets placed in the same Petri dish were used as a replicate, and three plates were used for each inoculum. Disease severity was estimated by measuring the area of the developed lesion from two separate experiments.For the Supplementary materials


The β phase is randomly distributed in the lamellar microstructure, indicating that the β phase cannot be directionally solidified. The directional solidification of γ/α2 lamellae was not affected by the precipitation of the β phase. Hot compression tests show that the deformation behavior of the β-containing lamellar microstructure also exhibits the anisotropic characteristic. The deformation resistance of the lamellae is lowest when the loading axis is aligned 45° to the lamellar interface. Microstructural observation shows that the decomposition of the lamellar microstructure tends to begin around the β phase, which benefits from the promotion of a soft β phase in the deformation. Moreover, the deformation mechanism of the lamellar microstructure was also studied. The bulging of the γ phase boundaries, the decomposition of α2 lamellae and the disappearance of γ/γ interfaces were considered as the main coarsening mechanisms of the lamellar microstructure. A β-solidifying Ti–43Al–2Cr–2Mn–0.2Y alloy was directionally solidified by the optical floating zone melting method. The microstructure is mainly characterized by γ/α Hig2) two-phase TiAl alloys. The directional solidification and mechanical properties of (γ + α2) TiAl alloys have been well studied. However, limited research has been conducted on the directional solidification of β-solidifying TiAl alloys, and has been mainly focused on the creep properties of β-solidifying γ-TiAl alloys. Compared with conventional TiAl alloys, the β-solidifying TiAl alloy contains α, γ and β phases. The effect of the β phase on the directional solidification of the lamellar microstructure is unclear. Moreover, the deformation resistance and deformation behavior of lamellae would also be influenced by the introduction of the β phase. Thus, further studies are needed to clarify these problems mentioned above.The lamellar microstructure is a typical feature of TiAl alloys. The mechanical behavior of TiAl alloys are directly relevant to the lamellar orientation . Fully lIn this paper, a directionally solidified β-solidifying Ti–43Al–2Cr–2Mn–0.2Y alloy was produced by optical floating zone melting. The morphology and distribution of the β phase were observed. Moreover, hot compression tests were carried out, and the hot deformation behavior, microstructural evolution and deformation mechanisms of the β-containing lamellae were investigated.−1 under high-purity argon. XRF showed that the actual composition of the bar was Ti–43.2Al–1.8Cr–2.1Mn–0.19Y. The dimension of the sample employed for the compression test was Φ4 × 6 mm. Hot compression specimens were electro-discharge machined parallel to the lamellar boundary of the middle columnar crystal. All compression specimens were polished using 2000 grit emery paper. Isothermal compression tests were conducted using a Gleeble-1500D simulator  at different temperatures with a constant strain rate of 0.01 s−1. The phase composition was checked using X-ray diffraction . Microstructural observation was conducted by scanning electron microscopy  in the back-scattered electron (BSE) mode. In order to study the microstructural evolution and deformation behavior of the lamellar microstructure, transmission electron microscopy  was also employed. TEM foils were prepared through mechanical polishing and twin-jet electropolishing by using a solution of 6% perchloric acid + 34% butanol + 60% methanol at −20 °C and 25 V.A Ti–43Al–2Cr–2Mn–0.2Y ingot (Φ110 × 250 mm) was fabricated by vacuum induction melting. X-ray fluorescence  spectrometry revealed that the actual composition of the ingot was Ti–43.4Al–1.9Cr–2.05Mn–0.18Y. A master batch (Φ6 × 100 mm) was cut from the ingot and mechanically polished to a 0.05 μm finish. A directionally solidified TiAl bar (Φ6 × 100 mm) was then prepared by the optical floating zone melting method at a growth rate of 20 mm•h2 lamellae. The average lamellar colony size is in the range of 200–600 μm. Moreover, the β phase was introduced by adding 2% Cr and 2% Mn. The XRD pattern of as-cast Ti–43Al–2Cr–2Mn–0.2Y alloy has been reported in the literature [0 phase with a B2 structure (CsCl) at room temperature. For convenience of expression, both disordered β and ordered β0 are denoted by β in this paper. It can be seen in 0 phases during cooling [2 lamellae exhibit relatively straight interfaces.terature . It shou cooling . The TEM2 lamellae was not influenced by the β phase. Moreover, it can also be observed that a block-shaped γ phase precipitated around the β phase in the directionally solidified microstructure, which is consistent with the as-cast alloy [The microstructure of the directionally solidified alloy is shown in st alloy . As show2 lamellae, which exhibit straight boundaries. The selected area electron diffraction (SAED) patterns from α2 + γ colonies in 2 phases still obey the Blackburn orientation relationship, which is consistent with the conventional TiAl alloy [f domain embedded in the γL lamellar structure was often observed. Detailed research on γf and γL has been reported in the literature [TEM observation was also conducted to investigate the directionally solidified microstructure. The bright-field image in Al alloy . Moreoveterature . The hig−1. The values of the angle η between the loading axis and the lamellar interface were set to 0°, 45° and 90°. Fully dynamic recrystallization (DRX) tends to occur for the lamellar microstructure when the deformation temperature reaches 1200 °C [2 phases. Shear deformation occurs more easily on the (111) close-packed plane parallel to γ/α2 lamellar boundaries, which is beneficial to the plastic deformation and the decrease of the deformation resistance. Thus, DRX generally tends to occur in the shearing area, as shown in 2 lamellar boundaries, leading to an increase in the deformation resistance. As shown in To study the effect of the lamellar orientation on the deformation resistance of β-containing lamellae, hot compression tests were conducted at 1100 °C/0.01 s 1200 °C . Thus, a−1. The stress axis is aligned parallel to the lamellar orientation of the middle columnar crystal. The microstructure of TiAl alloys after 20% and 40% hot deformation are shown in 2 lamellae exhibit only small bending and coarsening when the deformation amount is 20%. Only a few new grains formed around the initial β phase, indicating that the decomposition of the lamellar microstructure tends to occur around the β phase. This is because a soft β phase contributes to hot deformation and promotes DRX. As can be seen from To study the hot deformation behavior and the microstructural evolution of β-containing lamellae, the directionally solidified alloy was isothermally compressed with different deformations at 1200 °C/0.01 s2 lamellae in the deformed microstructure (20% reduction). Compared to the initial microstructure, the uniformity of the lamellar width significantly decreased. The corresponding SAED pattern identified that the coarsening mainly occurred on γ lamellae. Several neighboring fine lamellae are easily transformed into coarse γ lamellae [2 phase between γ phases also occurred, thereby merging two thin γ lamellae on either side of the α2 phase into new thick γ lamellae. A similar phenomenon was also observed in other deformed alloys [2 phase. A metastable α2 phase transforms into a γ phase under high temperature and large stress, leading to the decomposition of α2 lamellae. Moreover, it can be seen from 2 lamellae can be obtained, including the bulge of the γ phase boundaries, the decomposition of α2 lamellae and the disappearance of unstable γ/γ interfaces. Previous studies have made great progress in understanding the microstructural evolution of TiAl alloys during hot working. However, the deformation mechanism is not yet clear. Further TEM investigation was conducted on the microstructure of TiAl alloys with different deformations to study the deformation mechanism. As shown in lamellae ,25. As sd alloys ,27. The 2 phase, and the activation of dislocations in the α2 phase also required higher shear stress. Thus, the γ phase mainly affords the plastic deformation of TiAl alloys. As can be seen from TEM images of the microstructure of the TiAl alloy after 40% hot deformation are shown in 2 lamellae can be controlled by directional solidification, which was not influenced by the precipitation of the β phase.(1) A novel β-solidifying TiAl alloy was fabricated by directional solidification. Microstructural observation showed that the β phase was distributed randomly in the lamellae, indicating that the β phase cannot be directionally solidified. γ/α(2) The deformation behavior of the β-containing lamellar microstructure also exhibited an anisotropic characteristic. When the angle η between the loading axis and the lamellar interface was 45°, the lamellae exhibited the lowest deformation resistance.2 lamellae tended to begin around the β phase. The bulging of the γ phase boundary, the decomposition of α2 lamellae and the disappearance of γ/γ interfaces are the main coarse mechanisms of γ lamellae.(3) The decomposition of γ/α


Domain-domain interactions in multi-domain proteins play an important role in the combined function of individual domains for theoverall biological activity of the protein. The functions of the tethered domains are often coupled and hence, limited numbers of domainarchitectures with defined folds are known in nature. Therefore, it is of interest to document the available fold-fold combinations and theirpreference in multi-domain proteins. Hence, we analyzed all multi-domain proteins with known structures in the protein databank andobserved that only about 860 fold-fold combinations are present among them. Analyses of multi-domain proteins represented in sequencedatabase result in recognition of 29,860 fold-fold combinations and it accounts for only 2.8% of the theoretically possible 1,036,080 (1439C2)fold-fold combinations. The observed preference for fold-fold combinations in multi-domain proteins is interesting in the context ofmultiple functions through structural adaptation by gene fusion. Domains are the structural, functional, and evolutionary units ofproteins. Majority of the proteins encoded in genomes of all thetaxa consists of multiple domains We retrieved 3-D structures of proteins solved by X-ray diffractionmethod or Nuclear Magnetic Resonance (NMR) from the ProteinData Bank (PDB) Domain architecture was retrieved from Pfam database There are 1439 folds reported in SCOPe version 2.07. In the datasetof multi-domain proteins of known structure, only 504 folds areobserved for constituent domains. On enumerating the fold-foldpreferences in multi-domain proteins, only 860 fold-foldcombinations are observed out of 1,036,080 theoreticalcombinations calculated for 1439 folds . The obsInterestingly, domains corresponding to ~28% (155 folds out of 504folds) of the folds interact with domains of same fold  preferring to interact with manydomain folds. For this, we used the functional annotations fromSUPERFAMILY database Majority of the folds follow the trend of higher the number offunctions associated with a fold, higher is the number of foldcombinations .Folds thThe structural information available on multi-domain proteins islimited due to the technical difficulties in crystallising multidomainproteins and the size limitation on the structure elucidationby nuclear magnetic resonance (NMR). Since only a minor fractionof PDB represents multi-domain proteins (~35%) in contrary to theproportion of multi-domain proteins encoded in genomes (~75% onaverage), it is imperative to include sequence data, which is muchlarger, in the our analysis dataset. For this, we mapped theinformation on sequence domains available in the Pfam database(ver. 31) Majority of the proteins encoded in genomes of all the life formshave more than one domain. The presence of multiple domains in aprotein confers stability (e.g. by forming stabilising interactionsbetween domains The results discussed here suggest that a limited set of fold-foldcombinations have been selected for multi-domain proteins duringevolution. Our analyses also highlight the disparity in the numberof multi-domain proteins for which structure has been elucidatedand the number of multi-domain proteins encoded in the genomesof all life forms.Authors declare no conflict of interest.


In sub-Saharan Africa, livestock transhumance represents a key adaptation strategy to environmental variability. In this context, seasonal livestock transhumance also plays an important role in driving the dynamics of multiple livestock infectious diseases. In Cameroon, cattle transhumance is a common practice during the dry season across all the main livestock production zones. Currently, the little recorded information of the migratory routes, grazing locations and nomadic herding practices adopted by pastoralists, limits our understanding of pastoral cattle movements in the country. GPS-tracking technology in combination with a questionnaire based-survey were used to study a limited pool of 10 cattle herds from the Adamawa Region of Cameroon during their seasonal migration, between October 2014 and May 2015. The data were used to analyse the trajectories and movement patterns, and to characterize the key animal health aspects related to this seasonal migration in Cameroon.Several administrative Regions of the country were visited by the transhumant herds over more than 6 months. Herds travelled between 53 and 170 km to their transhumance grazing areas adopting different strategies, some travelling directly to their destination areas while others having multiple resting periods and grazing areas. Despite their limitations, these are among the first detailed data available on transhumance in Cameroon. These reports highlight key livestock health issues and the potential for multiple types of interactions between transhumant herds and other domestic and wild animals, as well as with the formal livestock trading system.Overall, these findings provide useful insights into transhumance patterns and into the related animal health implications recorded in Cameroon. This knowledge could better inform evidence-based approaches for designing infectious diseases surveillance and control measures and help driving further studies to improve the understanding of risks associated with livestock movements in the region.The online version of this article (10.1186/s12917-018-1515-z) contains supplementary material, which is available to authorized users. In sub-Saharan Africa (SSA), transhumance of livestock is a common practice for pastoralist communities to cope with local environmental constraints, and fully exploit seasonal availabilities of grazing and water resources –3. TransIn Cameroon, transhumance is an established practice among cattle herders to overcome the constrains of the dry season , 8, whicSince 1997, several studies have investigated the movement behaviour of wildlife and livestock animals in Africa using global positioning system (GPS) technology –12 and, Movements and contact patterns within and between animal populations are known to be central drivers of livestock disease dynamics  and empiHere, we present the first formal study of transhumance patterns in Cameroon, while assessing the feasibility of applying GPS collar devices on cattle herds for the entire duration of the migration. Cattle herds originally located in the Adamawa Region of Cameroon were tracked for a period of over six months. Upon their return from seasonal pastoral movements, a questionnaire-based survey was used to collect further information on tracked herds’ experience during their migration. The objectives of this study were (1) to characterise the seasonal transhumance routes and daily movement patterns of a restricted pool of cattle herds normally grazing in the Adamawa Region, and (2) to describe the main animal health related issues and interaction patterns during this long-distance migration.The Adamawa Region is mainly an open woodland Guinea savannah ecotype above 1000 m, covering an area of approximately 64,000 km2. It is considered to be the main cattle production area of Cameroon with a reported cattle population of about 1.25 million head of cattle .Between October and November 2014, a convenience sample of ten cattle herds whose owner/herdsman were prepared to participate in the study were identified. It was possible to select herds originating/normally grazing in three different Divisions (one administrative level below Region) of the Adamawa Region. This convenience sample was the only applicable due to the nature of the context and the complexity of identifying suitable and available candidates willing to participate in the study. In similar settings, cattle herds have been observed to synchronise their behaviour, to a large extent, to more socially dominant individuals, particularly during travelling and grazing activities –29. In eLightweight 320 g) GPS collars  with global system for mobile (GSM) communications network access and on-board backup data storage were fitted to each selected animal. The data collection schedule and data recording parameters were set through an on-line software interface. The GPS sampling frequency was set to every 2 h and transmission over the GSM network was set for once daily in accordance with other studies tracking cattle in similar settings , 15, 30.0 g GPS cThe GPS collars were retrieved upon return from transhumance in May 2015 and sent back to Savannah Tracking Ltd. in Kenya to retrieve the stored data that could not be downloaded through the GSM network. The lightweight (320 g) GPS collars were easily retrieved from the tracked animals of each herd that after the study continued to be part of their herds. The datasets from each collar unit were downloaded as csv files and included the complete record of the latitude and longitude and the travelling speed (km/hour) at each recording point and an estimation of the accuracy of the location. In addition, at the time of recovering the collar, a structured interview was carried out with the returning herdsmen. The questionnaire took 15–20 min to administer and aimed at collecting information on the daily routines of the herd and herd management practices during the transhumance period, including animal health conditions, interactions with livestock or wildlife populations and on trading activities. The hard copies of the questionnaires were manually transcribed to pre-designed Excel 2007 (Microsoft) spreadsheet and stored as a csv file.A combination of descriptive analytical approaches was applied to characterize the transhumance trajectories of the tracked cattle and to assess the general characteristics of the period of movements of the tracked herds. The recorded GPS coordinates and speed of travel of the tracked cattle were assessed using simple descriptive tools such as histograms and interquartile box plots.The distance traveled between any two consecutive recorded GPS locations was estimated in kilometers using latitude/longitude (degree) georeferences and calculating the Euclidean distance between these GPS locations. The distances travelled were also aggregated at daily and weekly intervals in order to assess the variability of the distances travelled by the different tracked herds over different intervals. This was then used to characterize the range of daily distances traveled and to estimate the daily mean distances travelled during each of the weeks of observation.A hot spot analysis  was carrAll analyses and graphics were performed using the raster , rgdal , ks 38]38] and gThis research was authorised by the Ministry of Livestock, Fisheries and Animal Industries (MINEPIA) (Research permit number: 0119/MINRESI/B00/C00/C010/nye), and approved by the Cameroon Academy of Sciences . In the United Kingdom approval was given by the Veterinary Ethical Review Committee (VERC) of the Royal (Dick) Veterinary School of the University of Edinburgh .All methods were performed in accordance with the relevant guidelines and regulations and informed consent was obtained from all subjects. Interviewers were trained to provide the information regarding the consent process to be communicated to the participants and the informed consent was obtained from all subjects. Oral consent was obtained due to the variable level of literacy of the respondents. Prior to interviewing, the study objectives, procedures and the content of the questionnaires were also explained to the participants who were made aware that they were under no obligation to participate if they did not want to.Out of the 10 deployed GPS collar units, all of them were successfully retrieved from the animals in May 2015, including seven with complete records of spatial locations and three collars with partial records. Among the seven collars with complete records, one collar belonged to a herd whose herder finally decided not to go on transhumance. Partial recordings from the three collars were due to the memory being overwritten with later locations as a consequence of poor GSM network coverage which resulted in excessive use of the on-board memory storage. In one case (collar 1350) the recordings were almost entirely unavailable, likely due to a concomitant technical failure of the GPS device. Data retrieved from collars 1303 and 1307 were only partially complete, 42 and 44% of the transhumance days, respectively. As a consequence, these three collars were excluded and, along with the herd that failed to leave on transhumance, left data from 6 collars for analysis.The six herds that went on transhumance showed different migratory patterns Fig. . In mostThe speed of movement of the tracked herds, as recorded every 2 h, ranged between 0.1 and 7.8 km/hour Fig. . The oveOverall, herds showed consistent patterns of movements across the 24 h cycle. The mean speed of the herds tended to increase between 06:00 and 18:00 h. Similarly, the absolute peaks of speed were recorded within this time window , goats (4/9 herdsmen), horses and dogs (3/9 herdsmen) and pigs (1/9 herdsmen). The most frequently encountered wildlife species were the antelopes (4/9 herdsmen), followed by warthogs (2/9 herdsmen) and buffaloes (1/9 herdsmen) and another unspecified animal (1/9 herdsmen) Fig. .Among the reported health problems faced by the cattle herds during the transhu- mance period, the most commonly reported was trypanosomiasis (7/9 herdsmen) followed by liver fluke (4/9 herdsmen), foot and mouth disease (FMD) (3/9 herdsmen), dermatophilosis (2/9 herdsmen) and plant intoxication (1/9 herdsmen) Fig. .When assessing the causes of death of cattle during transhumance, accidents were reported as having caused the loss of at least one animal (5/9 herdsmen), followed by disease (4/9 herdsmen) and by plant intoxication (3/9 herdsmen) (Table None of the herdsmen (0/9) reported having acquired cattle during the transhumance period, either from livestock markets or from outside the trading system. However, four (4/9) herdsmen reported having sold cattle at livestock markets during the transhumance, while two (2/9) herdsmen reported having sold cattle outside the trading system Table . When trIn SSA, seasonal livestock mobility is an important adaptation mechanism for pastoralist communities, and a key strategy to manage the variability of the natural resources in the ecosystem . In CameLong daily distances were relatively rarely traveled (approximately 15% of the recorded days), typically at the beginning and the end of the transhumance. During these periods of more active mobility, an average speed of movement compatible with traveling behavior (between 3 and 4 km/hour) was mostly recorded during the daylight, consistently to reports in the East African rangelands . HoweverOver the six GPS-tracked herds we found three cattle herds having multiple repeated traveling and grazing periods through different temporary transhumance locations. Similarly to previous findings in SSA , 43, henMultiple grazing locations during transhumance increase exposure of herds to geographically limited or seasonally abundant diseases . In our Because of the complexity of engaging stakeholders in this study, it was not possible to use a statistically robust sampling approach. Instead, we used a convenient sampling of a limited number of cattle herds. Clearly, this is a major shortcoming of this survey and additional studies, with increased number of herds, are required to confirm our findings. In particular, our survey provides information of only a few diseases affecting cattle herds through anecdotal reporting and without any supporting laboratory evidence. Although this list is certainly not exhaustive, we believe that it represents the list of infectious diseases which are perceived by herdsmen as the most important for their herds. It is also worth noting that the reported health conditions included infectious diseases for which direct contacts between animals are key transmission mechanisms (e.g. FMD and dermatophilosis), and which are well recognized by livestock stakeholders in the study areas .During transhumance, particularly while traveling towards grazing areas, cattle herds tended to have more frequent contacts with other herds and with wildlife, compared to when they are sedentary at grazing locations. Despite this variability in contact rates, the interactions between cattle herds, both during traveling and at grazing areas, were reported to have relatively short durations (< 1 h). Nevertheless, a short contact time, particularly if at close proximity, could be sufficient for the transmission of highly infectious diseases, especially during the peak of the infectious periods , 48.Over the eight months of study, two tracked herds, which originated from very distant areas, were recorded in contact at the same grazing location for 4 to 6 h. This observation confirms that opportunities for close interactions occur not just locally, at transhumance destinations with communal grazing, but also through the transhumance migration routes. This finding further reinforces the potential strategic role of veterinary surveillance and control points along the migratory routes, or transhumance corridors. Veterinary check points would represent key locations for designing and implementing efficient surveillance and control measures against infectious and parasitic diseases, including other priority livestock diseases in Cameroon other than the ones highlighted in this study, such as pasteurellosis, Contagious Bovine Pleuropneumonia (CBPP) and tick-borne diseases .During the transhumance period, herdsmen of six of the nine herds under study reported to have sold cattle, either within or outside the formal trading system. Although livestock markets are known to greatly influence the spread of multiple infectious diseases throughout livestock industries –51, theyAlthough infectious diseases are the major animal health problem for the livestock sector in Cameroon , 45, theThe potential tension then arising could pose additional security challenges to herds, as informally reported during the data collection of the current study.This limited study provides a general characterization of cattle transhumance pat- terns and of the key associated issues in Central Cameroon. The spatial and temporal overlapping between tracked cattle herds highlighted the opportunity for direct contacts and interactions between herds of distant origins, as well as with other domestic and wildlife species, both during traveling and grazing periods. The recorded speed of movement and interaction frequencies, and durations, hence, could potentially inform parametrization of further epidemiological studies.Specific infectious and parasitic diseases were reported affecting the migrating herds, however physical accidents and environmental hazards (e.g. plant intoxications) were also reported as key factors impacting these herds. Importantly, the transhumant herds have also been shown to connect to the formal cattle trading system, highlighting the complexity of the pastoral, but increasingly market-orientated, livestock system in the country.The overall characterization of transhumnce patterns in the study areas, and the related key aspects, represent a preliminary step for better understanding their implications in the epidemiology of livestock infectious diseases, and for their potential applications to inform surveillance and control strategies.The further confirmation of some of the characterized migratory routes as common transhumance corridors, would provide the evidence to strategically design robust and efficient surveillance and control interventions at key locations.Increased knowledge and understanding of pastoral movements and contacts between livestock populations at local and long-distance levels is essential for supporting the veterinary services in designing and planning more effective surveillance and control strategies of infectious diseases. Considering the key role of livestock transhumance as a key adaptation and ecological management mechanism for pastoralist communities in the region, this seasonal migration should be increasingly addressed in animal health management.Additional file 1:Figure S1. Mean and ranges of the speed over the 24 h period. For each tracked cattle the speed of movements was recorded every two hours of the observation period. The middle line represents the mean speed at that hour of the day while the upper and lower lines represent, respectively, the fastest and slowest speed recorded at that specific time during the observation period. Figure S2. Reported duration of interaction with other cattle herds. On the x axis the reported usual duration of interaction with other cattle herds during transhumance and on the y axis the number of interviewees. Figure S3. Recorded encounter between 2 tracked herds. The herdsmen of these 2 herds (1301 and 1305) reported having met each other at the time of interview. The analysis of the GPS recordings enabled to identify the exact time and location of this encounter. The herds were recorded interacting for about 4 h between 8 am and 12 am of the 23rd April 2015, while returning to their respective grazing locations for the rainy season. (PDF 710 kb)


Saccharomyces cerevisiae can switch from a unicellular lifestyle to a multicellular one. A crucial step in multicellular lifestyle adaptation is self-recognition, self-interaction, and adhesion to abiotic surfaces. Infectious yeast diseases such as candidiasis are initiated by the adhesion of the yeast cells to host cells. Adhesion is accomplished by adhesin proteins that are attached to the cell wall and stick out to interact with other cells or substrates. Protein structures give detailed insights into the molecular mechanism of adhesin-ligand interaction. Currently, only the structures of a very limited number of N-terminal adhesion domains of adhesins have been solved. Therefore, this review focuses on these adhesin protein families. The protein architectures, protein structures, and ligand interactions of the flocculation protein family of S. cerevisiae; the epithelial adhesion family of C. glabrata; and the agglutinin-like sequence protein family of C. albicans are reviewed and discussed.The ability of yeast cells to adhere to other cells or substrates is crucial for many yeasts. The budding yeast  Cellular adhesion is fundamental in many biological processes such as the development of multicellular organisms, as well as in a variety of contexts that are important to the life cycles of unicellular organisms ,2. Many Saccharomyces cerevisiae usually grows as a unicellular microorganism, but it can switch to a wide range of multicellular phenotypes, such as flocs, flors, filaments, and biofilms in response to changes in the environment and its genetic background [S. cerevisiae to colonize various habitats, forage for nutrients, and escape unfavorable conditions [The budding yeast ckground . These pnditions ,7,8,9. CCandida species belong to the human microbiome of healthy humans [Candida infection (candidiasis). Immunocompromised persons are very susceptible to fungal infections. Malnutrition, which encompasses both undernutrition and overnutrition, is responsible for an enormous health burden globally [Fungal infections are an extremely important health problem. About 1.2 billion of people are infected every year by fungi. Nevertheless, their contribution to the global burden of disease is largely unrecognized ,11. Somey humans . Impairmglobally ,14. Resuglobally . Defectsglobally ,17. Undeglobally ,19. Infeglobally . The incglobally . Well-doCandida  account for more than 90% of all of the diagnosed cases of candidemia and invasive candidiasis, but their relative frequency varies depending on the population involved, geographical region, previous anti-fungal exposure, and patient age [C. albicans is the most common etiological agent of candidiasis [C. glabrata and other non-albicans Candida species are nowadays considered as emerging opportunistic organisms, since they represent an increasing number of fungal infections, which depends on the resistance of these yeasts to several antimicrobial agents [C. glabrata is ranked second in isolation frequency among the Candida infections, and together with C. albicans, they are responsible for approximately 65–75% of all systemic candidiasis [Five species of ient age . C. albididiasis ,24. C. gl agents ,26. C. gdidiasis .Candida adhesion to host cells  represents the onset of infection, which is next followed by invasion. The strategies of C. albicans and C. glabrata to attach and invade into the host cells, obtain nutrients, and evade the host immune response show significant differences [C. albicans follows an aggressive strategy to subvert the host response and obtain nutrients for its survival; C. glabrata evolved a strategy that is based on stealth, evasion, and persistence, without causing severe damage.ferences . C. albiS. cerevisiae, the epithelial adhesin protein family of C. glabrata, and the agglutinin-like sequence protein family of C. albicans. The protein architectures of these yeast adhesins are compared based on the described architectures in the Pfam database. Next, the ligand–adhesin interaction mechanism is explained based on the protein structures of the N-terminal domains, which account for the adhesion domains of the adhesin. Finally, cellular adhesion mechanisms are discussed, i.e., cell-cell binding based on S. cerevisiae–lectin–flocculation interaction, cell-cell binding based on S. cerevisiae–Flo11p interaction, C. albicans–Als protein interactions, and C. glabrata–Epa protein interactions.Despite the intensive research on yeast adhesion and unraveling the molecular mechanisms of adhesion over the last 30 years, only a few adhesin structures have been solved. Therefore, this review focuses on these adhesin protein families: the flocculation protein family of S. cerevisiae can be subdivided into two groups [FLO1, FLO5, FLO9, and FLO10. The gene products of FLO1, FLO5, FLO9, and—to a lesser extent—FLO10 [FLO10 and FLO11, but not FLO1, also promote both adhesion to agar and plastic, and filamentation. The second group of the Flo family, which includes Flo11p, Fig2p, and Aga1p, has a domain structure that is similar to that of the first group, but with quite unrelated amino acid sequences [The Flo adhesin protein family of o groups . The firequences . Flo11p equences ,11, and equences . N-Flo11equences ,15. Fig2equences . These pequences ,34,35,36equences . The abiequences .The PA14-Flo proteins architecture. These adhesins have a modular configuration that is composed of three domains: the N-terminal, central, and C-terminal domain [l domain   [20, which is a fragment of the protective antigen (PA) from anthrax toxin [Bacteria , Eukaryota , and Archaea   [C. albicans, C. tropicalis, Clavispora lustinae, and Cryptococcus neoformans, and the yeast Brettanomyces bruxellensis, which is involved in the spontaneous fermentation of lambic beer. Some human proteins also contain a PA14 domain, such as N-acetylgalactosamine transferase and fibrocystin.The N-terminal domain of the Flo proteins (including Lg-Flo1p) in this group contains the PA14 domain    . The insax toxin . The preax toxin ,27. Thisax toxin ; and theax toxin . The PA1ax toxin ,25. The species) A (Pfam vPF00933) . ExampleS. cerevisiae in two populations: one with an apparent molecular mass of 36 kDa and one of 100 kDa [8-14GlcNAc oligosaccharides (core type) and large Man>50GlcNAc N-glycans (hyperglycosylated type) but in different ratios, i.e., two oligomannoses and one hyperglycosylated structure for the 36-kDa population, and one oligomannose and two hyperglycosylated structures for the 100-kDa population [As is the case for many cell wall proteins, the N-terminal domain of the PA14-Flo proteins is N-glycosylated and O-glycosylated ,32. For pulation . Electropulation .Saccharomyces proteins [The central domain of Flo proteins contains many tandem repeats, which are rich in serine and threonine . These rproteins , but notThe Flo11 protein architecture. The flocculin Flo11p is 37% similar to Flo1p  [Saccharomycetales ”  domains close to the N-terminal Flo11 domain. For Flo11p from S. cerevisiae, the C-terminus is a GPI anchorage site [entical) . Flo11p sion 31, ). The Flsitaniae B. In somgisporus , which iage site   proteins such as Epa1p, Epa6p, and Epa7p, which are the best characterized Epa adhesins [C. glabrata depends on the strain: there are 17 in strain CBS138 and 23 in strain BG2 [C. glabrata—other species of the Nakaseomyces genus such as the human pathogenic C. bracarensis and C. nivariensis, which contain respectively 12 and nine EPA-like genes, and the non-pathogenic Nakaseomyces delphensis harbors a single copy, C. castelli contains three homologs of the EPA genes, and N. bacillisporus presented only one distant homolog [C. glabrata.The adhesion of the opportunistic human pathogenic yeast adhesins ,44,45,46rain BG2 ,48. Epa- homolog . These dS. cerevisiae [S. cerevisiae Flo proteins [C. glabrata, where it is involved in carbohydrate binding; and it is comparable to the VSWGT pentapeptide in Flo1p from S. cerevisiae [The N-terminal adhesion domain of Epa proteins contains a lectin activity, which is calcium-dependent and shows a certain similarity to domains within Flo1p and Agαp from revisiae ,42. Initproteins . An EYDGproteins . It is previsiae . The VSWrevisiae . Adhesinrevisiae ,46. Howerevisiae . The GLErevisiae , which wrevisiae ,44.The central domain of Epa proteins is rich in serine and threonine and has a structural function: it extends the N-terminal domain into the extracellular space outside of the cell wall mannoprotein layer, where it can interact with its ligand .Ashbya gossypii AFL095Wp contains many Flocculin repeats (PF00624) in the central region. It has been shown that this gene AFL095W is a homolog of S. cerevisiae FLO5 [Zygosaccharomyces rouxii and KLLA0A11935p of Klyveromyces lactis. A flocculation protein Flo1 is also present in Scheffersomyces stipites, where the central domain is composed of “Candida agglutinin-like (ALS)”  domains. An uncharacterized protein of the pathogenic yeast Clavispora lusitanae contains 1 “Flocculin type 3 repeat”  at its C-terminal end.The GLEYA domain is present in 135 species and in 55 protein architectures A. A few iae FLO5 . SimilarC. albicans have the typical three-domain modular design: an N-terminal region that contains a signal peptide and a conserved threonine-rich (T) region; a central domain with multiple tandem repeats; and a long, highly glycosylated Ser/Thr-rich C-terminal stalk region with a 13–20-residue signal sequence at the end for the attachment of a GPI anchor [The agglutinin-like sequence (Als) family includes eight members (Als1p-Als7p and Als9p) that share a high degree of sequence conservation . The AlsI anchor ,53,54. TI anchor ,56. The I anchor . It was I anchor . The T-rI anchor ,59  of about 36 amino acids in length, which play a critical role in the stabilization and proper presentation of the binding domain ,57. BaseThe C-terminus is the least conserved in length and sequence among the Als proteins, and is extensively glycosylated due to a large number of serine and threonine residues . This paSaccharomycetales  in the Pfam database. The Candida_ALS_N domain is present in 38 architectures, and only within the ascomycetal orders of the ycetales A. The arbidities ,48,49,61bidities , and hasadhesins B.C. glabrata adhesins Epa1p [S. pastorianus had been crystallized and diffracted to high-resolution using X-ray radiation [The first structure of the flocculation proteins that was solved was the N-terminal adhesion domain N-Flo5p  Table 1Table 1. ns Epa1p ,44 and lns Epa1p   by cis peptides aspartic acid 160 (D160) and D161 (indicated as “DcisD” motif), and on CBL2 by the asparagine 224 (N224) side chain and the carbonyl groups of valine 226 (V226) and W228. Residues D160, D161, and N224 are strongly conserved in the Flo and Epa adhesin families due to their importance for metal binding [The overall atomic structures of N-Flo5p, N-Flo1p, N-Lg-Flo1p, N-Epa1p, N-Epa6p, and N-Epa9p are very similar. These structures confirmed the topological link between the flocculins and the PA14 domain ,53,54. T binding ,53. The  binding . There ivice versa, phylogenetically closely related adhesins (such as N-Epa6p and N-Epa7p or N-Epa3p and N-Epa3p) possess distinct ligand-binding affinities, which indicates that functionally related Epa variants might have repeatedly developed independently. A comparison of the variability/conservation of amino acid residues located on the protein surfaces of the adhesin domains of the different Epa members or their ligand-binding pockets revealed that all of the Epa adhesin domains have conserved PA14/GLEYA-like cores and a highly variable surface composition. Two signatures, i.e., the “DD-N” and “W-R” signatures, constitute an invariable core of the binding pocket that are essential for the efficient binding of the terminal hexose moiety in most Epa adhesin domains [cisD motif of CBL1 and an asparagine of CBL2 that confer coordination of the Ca2+ ion (see above). The W-R signature refers to a highly conserved tryptophan from loop L3 at the surface, and an arginine from a corner of the inner binding pocket (CBL2) [Recently, a large-scale functional analysis of the N-terminal adhesion domains of 17 Epa paralogs in combination with three-dimensional structural studies of N-Epa1p and Epa6p with cognate ligands was performed, and revealed that most Epa paralogs possess individually tailored ligand-binding properties . Most Ep domains . The DD-t (CBL2) . The Epat (CBL2) ,52. Satut (CBL2) . Glycan S. cerevisiae cells can switch to complex multicellular structures such as flocs, filaments, mats, and flors [FLO11  gene expression renders haploid cells adherent and invasive into semi-solid agar medium  [FLO11 expression is also associated with the formation of mats, which are complex colony-like structures on a low-density semi-solid medium [FLO11 is also involved in cell–cell interaction (floc formation) [FLO11 and flocculation activity such as the cell density, surface charge, and pH, and environmental factors such as oxygen limitation, nutrient limitation, and cell surface hydrophobicity [FLO11 alleles, and do not depend on quantitative differences in FLO11 expression or surface hydrophobicity [Planktonic nd flors ,55. The nd flors ,56,57,58growth”) ,60. In dgrowth”) ,67. FLO1d medium ,69; the d medium ,70,71; td medium ,72. The rmation) ,74,75,76hobicity ,77,78,79hobicity ,74,75. Ehobicity .The structure of the N-terminal adhesion domain of the Flo11 adhesin has been recently solved using X-ray crystallography  Table 1Table 1. The FN3-like domain is girdled by two surface aromatic bands at the apical region and the neck subdomain . HydrophStaphylococcus epidermis [S. aureus [The first structure of the Als proteins that was determined was the N-terminal domain of Als9 (N-Als9-2p) (18–329 amino acids) ,83 Tabl. Nuclearpidermis ,85 and C. aureus . The pepals3 deletion strain. The N-Als C-terminus that contains a conservative amyloid-forming region (AFR), which also contains adhesive properties [C. albicans with destroyed Als3p amyloidogenic potential showed little contribution of the AFR to adhesion, and suggested an aggregative function of the AFR.The N-terminal adhesion domain of Als proteins, in particularly Als3p , can binoperties ,93, was operties . C. albiC. albicans hyphae to epithelial cells [Als3p and Hwp1  are involved in the binding of al cells ,99,100. al cells , since tal cells ,102. Theal cells ,107,108.al cells ,96,109. PA14-lectin flocculins recognize the disaccharidic ends of glycans. N-Flo1p interacts specifically with D-mannose glycans , which iN-Flo1p also binds to N-Flo1p; this homophilic interaction has millimolar affinity  Table 2Table 2. 2+. On flocculating cells, Flo proteins are the dominating cell wall proteins that stick out of the cell wall [cis interactions that immobilize the N-terminal domain at the cell surface, while the other binding site is responsible for the trans interaction [2+ is also involved in N-Flo1p glycan-glycan interactions. This points to a two-step cell-cell adhesion process, where in a first step the long, flexible glycans have a high probability of interaction when cells are moving close to each other and initially serve to stabilize cell-cell interactions. In a next step, the non-reducing glycan ends enter the binding pocket of the Flo lectin, and binds to the protein. In both steps, Ca2+ plays a crucial role in the interactions. This flocculation model unifies the historically first-proposed model “Ca2+-bridge” hypothesis [2+-bridge” hypothesis stated that flocculation is based on ionic interactions stabilized by hydrogen bonds and the involvement of Ca2+ ions that could form bridges between flocculating cells by linking the carboxyl groups present on the cell surface [2+ could bridge cells through glycan-glycan interactions via negatively charged phosphates that are present in hyperglycosylated N-glycans , including many pathogenic yeasts, and they appear in various protein architectures. Three-dimensional structural determination of fungal adhesins using classical X-ray diffraction is still difficult, since they are large and highly glycosylated. Nuclear magnetic resonance (NMR) spectroscopy is performed in solution, and can help with understanding the binding mechanisms of ligands, as was demonstrated for N-Als9-2p [Escherichia coli was recently determined at 4.2-Å resolution using cryo-EM [As discussed in this review, only three-dimensional (3D) structures of a few adhesins belonging to three protein families of the yeast species -Als9-2p . Future -Als9-2p . For exa cryo-EM .Candida adhesins Als and Epa, and the bacterial uroepithelial FimH adhesin from E. coli [Structural studies have to be complemented with biophysical interaction studies at the molecular and cellular level to determine the adhesion mechanism. In the future, fungal adhesion data in structural and functional databases will be more and more linked and used to unravel the complex interactions of various microbial  pathogens that are involved in many infectious diseases. Recently, a database strategy, i.e., the lectin–glycan interaction network strategy, was set up to predict interacting host receptors for the  E. coli . In this


Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes. Protein domains are basic functional units of proteins, yet domain-based pathway annotations for proteins are challenging tasks because many domains are pervasive among diverse pathways. Therefore, we developed a network-based scoring scheme to measure pathway specificity of domains, and then used it to identify pathway-specific domains. Surprisingly, we observed substantially more disease mutations in pathway-specific domains than non-specific domains. We found evidences that mutations of pathway-specific domains tend to perturb pathway integrity via disrupting within-pathway protein-protein interactions. We also demonstrated prediction capacity of pathway-specific domains for complex diseases with experimental validations. Our study demonstrated the usefulness of pathway information for protein domains in interpreting non-random distribution of disease mutations among domains and identification of disease genes and variants. The lower reliability of sequence-based annotation for pathways are partly due to the fact that many domains are pervasive among diverse pathways. For example, the ‘winged helix-turn-helix DNA-binding’ domain occurs in many DNA-binding proteins that are involved in diverse pathways. Nevertheless, some domains may be associated with certain pathways with high specificity. Therefore, domain-based annotation of pathways requires a quantitative method which can incorporate not only sequence similarity but also specificity of domain-pathway associations.Protein domains are the structural, evolutionary, and functional units of proteins. Because domains are the basic building block of protein structure and an evolutionary module  that incHuman complex diseases are generally viewed as pathway-level disorders. Given that a large portion of disease-associated genes are also strongly associated with specific pathways , proteinHere, we present a network-based scoring scheme to quantify pathway specificity of protein domains, which can be used to identify domains associated with specific pathways. We first generated domain profiles for human proteins then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). Interestingly, we observed a significant enrichment of disease-associated mutations for PSDs, where mutations tend to disrupt interfacing domains that mediate within-pathway protein-protein interactions (PPIs) and to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in pathway disorders. Finally, we demonstrated the utility of pathway-specific domains in predicting disease-associated genes with experimental validations in zebrafish.WMI, which assigns more weight to rarer domains in calculating the MI [Fig 1A) was highly predictive for proteins that operate the same human GOBP pathways. Using a Bayesian statistics framework, we assigned LLSs [We previously found that human protein interactions can be accurately retrieved by associations between domain profiles with a scoring scheme based on information theory, g the MI . The resned LLSs  to the lFig 1B). We therefore measured domain-pathway associations  based on the network connections from a domain to the member proteins of the pathway. However, overall strength of the network connections for a domain-pathway association does not guarantee their specificity. We thus accounted for the distribution of each domain across pathways using the Gini Index (GI). In summary, the network-based scoring scheme PS quantifies pathway-specific associations for each protein domain .Because the network links were based on domain-sharing patterns among proteins for the same pathway, domains enriched for a pathway also likely connect to other proteins for the same pathway . The significance of the agreements with gold-standard associations was also assessed by Fisher’s exact test. We observed similarly high correlations between PS and gold-standard data, where the top 16,000 domain-pathway associations significantly overlapped (P ≤ 0.01) with gold-standard data . We defined 4,506 InterPro domains from the top 16,000 significant domain-pathway associations as pathway-specific domains (PSDs) and the remaining 3,856 InterPro domains as non-specific domains (NSDs). The PS threshold for the division between the two domain classes (P ≤ 0.01) was 0.056.We calculated erPro2GO  annotatiNVR), which represents the probability of a variant occurring in a PSD or NSD normalized to the number of variants observed in both types of domains. Notably, we observed an approximately 1.5-fold higher NVR in PSDs than in NSDs for all three pathogenic variant sets . We found that the observed enrichment of PSD for disease-associated variants was not significantly affected by moderate changes in PS threshold for defining PSDs . In contrast, variants with neutral effects derived from the HumVar neutral training set for PolyPhen-2 [NVRs between PSDs and NSDs. We also performed a similar analysis using cancer somatic mutations from the TCGA for several cancer types including breast cancer [NVRs between PSDs and NSDs. Notably, germline cancer variants from the GWASdb set exhibited an approximately 1.5-fold higher NVR for PSDs than for NSDs (1.5 for PSDs and 0.6 for NSDs). These results indicate that PSDs are more susceptible to the diseases by inherited genetic variants, but not by somatic mutations.Recently, investigations into the protein domain-level landscapes of cancer somatic mutations have revealed domains that are enriched for somatic and germline mutations, and domain-level mutational hot spots, which facilitate the identification of novel cancer genes and functional mutations, and provide mechanistic insights regarding mutational consequences –13. To ilyPhen-2  exhibitet cancer , and fouTo provide mechanistic insight for the higher frequency of pathogenic variants in PSDs than in NSDs, we investigated the relationship among the disease-associated mutations, the pathway specificity of the domains, and the domain-level interaction network. Our analysis was motivated by the following three recent observations: (i) the majority of disease-associated variants exert pathogenic effects via perturbations of PPIs rather than on protein folding or stability ; (ii) diNVR for PSDs than for NSDs . We also found approximately 2-fold enrichment of PSD for nonsynonymous variants affecting on physical protein interactions recently published by IMEx consortium [PS threshold for defining PSDs did not significantly influence enrichment of PSD for nonsynonymous variants affecting on physical protein interactions by IMEx . In contrast, the NVR of the quasi-WT class of mutations was approximately 1.5-fold higher for PSDs than for NSDs, which was similar to the fold change for other disease-associated variant sets , indicating that PSDs are enriched for disease mutations that cause loss of wildtype PPIs. PPIs are mediated by domain-level interactions. Therefore, these results suggest that PSDs are more important than NSDs for PPIs, whose perturbations can result in phenotypic changes.For example, a recent large-scale characterization of disease-associated mutations revealed that most missense disease mutations do not severely altered protein structure or stability, but rather that they tend to perturb PPIs in the majority of the wildtype proteins . In the nsortium . We conf2(Domain Ratio) score—for interfacing domains with a moderate number (2–120) of domain interactions, whereas PSD is depleted—indicated by negative log2(Domain Ratio) score—among interfacing domains with either a single interaction or more than 121 interactions . To explain the observed higher frequency of PSDs among interfacing domains with a moderate number of domain interactions, we proposed a model of mutational consequences via the disruption of interfacing domains with different degrees of connectivity . Mutations on interfacing domains with a single protein interaction (green nodes) may result in the functional failure of a single protein and no detectable pathogenic effect. Consequently, these mutations are not detected among patients. If mutations occur in a hub-interfacing domain (blue node), the interactions toward many proteins involved in diverse pathways may be disrupted, which may result in the functional failure of the global system. In this case, mutations would generally cause lethal phenotypes, resulting in purifying selection of the mutation. In contrast, mutations on interfacing domains with a moderate number of domain interactions (yellow nodes), which likely corresponds to the range of the pathway size, disrupt the interactions of proteins within that pathway, which may result in the functional failure of local systems. Because the majority of disease genes are enriched for pathways [To further investigate the impact of PSDs on phenotype via PPIs, we next performed domain-level network analyses based on the human structural interaction network (hSIN) , which mpathways , these mp ≤ 10−7). However, GWASs usually detect a large number of candidate genes with moderate associations that have p-values above this stringent threshold (e.g. 10−7 < p ≤ 10−3). Additional candidate genes, i.e., those with moderate GWAS significance, may be rescued by meta-analyses with larger sample sizes, but such studies are costly to conduct. We hypothesized that an additional disease-associated feature would enable us to distinguish disease genes from non-disease genes among those with moderate GWAS significance. Therefore, we tested whether disease-associated PSDs could identify disease genes among candidates with moderate GWAS significance derived from two publicly available data sets: CARDIoGRAM [Fig 3A). To conduct gene-centric analyses, we identified SNPs with moderate GWAS significance that were located within 10 kb upstream or downstream of the gene, resulting in 204 and 1,044 genes moderately associated with CAD and SCZ, respectively. We then identified PSDs associated with CAD or SCZ. PSD-pathway relationships were converted into PSD-disease relationships based on significant overlap (P < 0.01 by Fisher’s exact test) between disease-associated genes and pathway-associated genes. We compiled 212 disease-associated genes for CAD and 233 disease-associated genes for SCZ from OMIM [p < 0.01), we identified 2,664 PSDs for CAD (S2 Table) via 97 CAD-associated GOBP pathways (S3 Table), and 2,517 PSDs for SCZ (S4 Table) via 61 SCZ-associated GOBP pathways (S5 Table). For GOBP pathways, we considered only those that contained at least five member genes. We further selected CAD and SCZ candidate genes with moderate GWAS significance based on the number of disease-associated PSDs in each gene  for each disease phenotype, but identify only a few candidates due to highly stringent significance thresholds  and on the number of disease-associated PSDs among genes with both moderate and low GWAS significance (PSD set). A clear benefit of using PSDs was observed for CAD, as approximately 30% more CAD genes were identified in the GWAS∩PSD set than in the GWAS set . An even greater benefit was observed for SCZ . Interestingly, the PSD set was more predictive for SCZ than the GWAS set. For both CAD and SCZ, the combination of GWASs and PSDs outperformed GWASs only and PSDs only, indicating that GWASs and PSDs contributed largely complementary information about the diseases.Next, disease predictions made by PSDs were validated using independent disease annotations from two disease-specific databases: 604 CAD-associated genes from CADgene V2.0  and 936 tram1, apod, cypna1, and slc22a2. Unfortunately, the zebrafish model for CAD has not been well established. However, we found that 207 human orthologs of zebrafish genes for heart or blood vessel development by GO annotations were significantly associated with CAD genes indicated by OMIM or DO annotations  or by CADgeneDB annotations , indicating significant associations between CAD and heart/vessel development at the pathway level. These results indicate that zebrafish genes validated by abnormal heart or blood vessel phenotypes during embryonic development may have implications for human CAD. To confirm the feasibility of CAD gene validation based on heart/vessel phenotype, atp2a2b, which has been implicated in CAD [Fig 4A and S2 Fig). We found that the majority of embryos with morpholino injections exhibited abnormal heart or blood vessel phenotypes, not only in the CAD-associated atp2a2b group, but also in three of the four candidate gene groups, including the tram1, cypna1, and slc22a2 groups , strongly implicating the association of these genes with CAD.Next, we experimentally validated the predictions of a GWAS∩PSD set using a morpholino-based loss-of-function phenotype analysis in zebrafish. Although the majority of human disease genes have zebrafish orthologs , some did in CAD , was incThe network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.Stratification of coding regions by different susceptibilities to heritable pathogenic variations may improve the assessment of genomic risk for complex human diseases based on exonic variations. For example, if we can identify PSDs for a particular disease as described in this work, more weight may be assigned to the mutations located in the PSDs for the disease than those located in the NSDs in assessing disease risk. Additionally, disease-associated PSDs would be useful predictors for disease gene candidates. The insufficient statistical power of GWASs often omits a large number of SNPs with moderately significant disease associations. Thus, in theory, we may apply the demonstrated procedure of candidate gene selection with moderate significance based on disease-associated PSDs to all GWASs, which may reveal many disease gene candidates that are missed during conventional GWAS analyses. Therefore, PSDs will significantly contribute to the genetic dissection of human diseases.PS, which quantifies specificity of domain-pathway associations. Although the given quantification strategy was demonstrated in human only, its application to other organisms is conceptually straightforward: (i) generate domain profiles for proteins using InterPro databases, (ii) construct a co-pathway protein network based on the associations between domain profiles, (iii) calculate PS and identify PSDs using gold-standard domain-pathway pairs, (iv) infer GOBP pathway terms of proteins or predict proteins with phenotypic effects using the identified PSDs.In this study, we present a scoring scheme PS can prioritize GOBP terms for an InterPro domain, which may facilitate manual curation for novel entries in the InterPro2GO database. Furthermore, probabilistic models of pathway involvement of proteins could be developed based on PS.This network-based scoring scheme to quantify specificity of domain-pathway associations may be a significant addition to our current computational tool box for pathway annotation of domains and proteins. For example, http://www.ensembl.org/biomart/martview) from the InterPro [Information regarding domain occurrence for human proteins was downloaded using the BioMart search tool (InterPro  databaseMI), which considers the entropy (i.e. complexity) of profiles. The MI does not require an a priori model, and has high robustness and accuracy for a wide variety of applications. Additionally, the amount of information across individual domains seemed to vary. We observed a power-law distribution of domain occurrence among proteins, from which we hypothesized that rare domains were associated with relatively specific biological processes and prevalent domains contributed to diverse functions. Therefore, we assigned higher weights to rarer domains during the MI calculation, resulting in weighted mutual information (WMI). The weight for each domain was calculated as described in the following definitions.A co-pathway protein network was constructed based on the association between domain profiles as described in our previous study  and summDefinition 1. Domain-specific weight 𝜔jn proteins and m domains, the domain-specific weight 𝜔j for each domain j (1 ≤ j ≤ m) is defined as:ckl represents the occurrence value (0 or 1), assigned by whether kth protein contains lth domain.Given Definition 2. Weighted mutual information IωX and Y,Hω(X) and Hω(Y) represent the weighted entropy of protein X and protein Y, respectively, and can be calculated as follows:t represents domain profile value of protein X, which can be {0,1} because we adopt Boolean domain profile.Given two proteins Hω represents the weighted joint entropy between X and Y, and can be calculated as follows:t1 and t2 represents domain profile value of protein X and protein Y, respectively.Additionally, LLS), which was based on a Bayesian statistical framework as previously described [The weight of a protein-protein link or a domain-pathway link was measured by a log likelihood score (escribed .Definition 3. Log likelihood score of a protein-protein link or a domain-pathway linkP(L|E) and P(⌐L|E) represent the frequencies of positive (L) and negative (⌐L) gold-standard links observed in the given evidences (E), while P(L) and P(⌐L) represent the prior expectations . In Bayesian words, P(L)/P(⌐L) is prior odds and P(L|E)/P(⌐L|E) is posterior odds. The posterior odds are the prior odds times the Bayes factor, likelihood. For protein-protein links, we first sorted them by confidence score (e.g. WMI), then computed LLS for each bin of 1000 protein pairs. For the given size of binning, we hardly encountered with P(L) = 0 or P(⌐L|E) = 0. However, if so, we could avoid the problem by taking larger bin size. Protein-protein pairs or domain-pathway pairs with positive LLS values are more likely to be associated with each other for the given evidence than those by random chance. For this study, the positive gold-standard protein-protein links were generated by pairing two proteins annotated for the same GOBP terms [BP terms  and negaPS), to quantify the specificity of domain-pathway associations, based on the combination of connectivity from a domain to the member proteins of the pathway and domain-specific weights. For the computing PS, we defined a domain-based co-pathway network by taking protein-protein links with only positive LLS. As a first step in the PS calculation, the protein-pathway association (PPA) score of each protein for a specific pathway was calculated via summation of LLSs to the protein in the domain-based co-pathway network. Subsequently, we transformed the association score based on the LLS into the probability score. We observed that the sum of LLSs of protein pairs followed a power-law distribution. Thus, we modeled the sum of the LLSs as a Pareto distribution, which is a power-law probability distribution that coincides with social, scientific, geophysical, and many other types of observable phenomena. The p-value of the Pareto distribution is calculated as follows:xi is sum of LLS of protein i and xmin is the scale parameter empirically plugged in by the minimum of sum of LLS values and α is the shape parameter that determines the steepness of the slope. As α increases, the p-value of the Pareto distribution is exponentially distributed with intensity α. We wanted to reduce the skewness of sum of LLS distribution by transformation into PPareto, which is subsequently used to compute pathway association of each protein. The number of proteins with sum of LLS is subject to the size of pathways. If a pathway has a small number of member proteins, α tends to be very small. We found that if α < 1, the skewness of the sum of LLS distribution did not significantly improved. Therefore, we empirically take 1 as the minimum value of α to calculate protein-pathway association score as in Definition 4.We developed a metric, Pathway Specificity (Definition 4. Protein-pathway association (PPA) score for a specific pathway fi, the PPA score PPAi (f) is defined as follows:For a given protein si (f) was calculated via summation of the LLSs as follows:llsik denotes the LLS of a link between protein i and protein k, Gi indicates a set of all proteins connected to gene i in the network, and F indicates a set of proteins annotated for pathway f. We have assigned probability scores on edges of protein-protein interaction network using Pareto distribution. The si(f) for each protein was calculated based on degree of association to each pathway by summation of the assigned probability scores of all links to known proteins for the pathway. Using the PPA score and domain profile matrix, we then defined the domain-pathway association (DPA) score as in Definition 5.Here, Definition 5. Domain-pathway association (DPA) score for a specific pathway fj, the DPA score DPAj (f) is defined as follows:Given a specific domain PS as described in Definition 6.Then, we finally calculate Definition 6. Pathway Specificity (PS) for a domain j and a pathway fDPAj(f) is the association score of domain j for a specific pathway f, and GIj is the Gini Index of a domain j over all pathways, and is defined as following:GI, which is a common impurity measure for classification-type problems, is maximized when the DPA of a domain for all pathways are equal, and is equal to zero when a domain has a DPA for only one pathway.PSs for a total of 49,636 domain-pathway associations between 5,253 InterPro domains and 407 GOBP pathways (S1 Table). Using manually curated associations between InterPro domains and GOBP pathways derived from InterPro2GO [PS scores. We observed strong positive correlations between PS and the likelihood of gold standard domain-pathway association. The significance of the agreements with gold-standard associations was also assessed by Fisher’s exact test. We divided domain-pathway associations into two classes by high significance of agreement with gold-standard associations (P ≤ 0.01), by which 4,506 InterPro domains from the top 16,000 significant domain-pathway associations were defined as pathway-specific domains (PSDs) and the remaining 3,856 InterPro domains as non-specific domains (NSDs).We calculated erPro2GO  as gold p < 10−7) associated with nearly 1,610 GWAS traits from GWASdb, which mapped them to dbSNP Build 142 and Genome Assembly, GRCh37/hg19, resulting in 26,342 disease-associated SNPs. Only 966 of these SNPs (~3.6%) were located in the protein-coding regions, and of these, 569 SNPs were located in InterPro domain regions. For the analysis of cancer germline variants, we compiled 51 germline variants associated with cancer studies from GWASdb, and 20,945 somatic variants from breast cancer patients from The Cancer Genome Atlas (TCGA) consortium. We also compiled 1,779 and 10,778 variants for OMIM disease genes from SwissVar (http://swissvar.expasy.org) and dbSNP , respectively, to generate an OMIMVar set of 11,024 OMIM disease-associated variants. We found that 9,050 of these variants were located in protein domain regions. ClinVar is another major public archive of relationships among human variants and phenotypes. We obtained 13,465 ClinVar variants for the clinical significance term of ‘pathogenic’, and found that 10,680 of them were located in protein domain regions. To generate the null model, we employed variants expected to have a neutral effect, which were derived from the HumVar neutral training set from Polyphen-2 [et al. [We compared the occurrence of disease-associated variants between PSDs and NSDs using pathogenic germline variants compiled from three independent sources: (i) SNPs from the GWASdb ; (ii) OM [et al. : (i) quaVR), was calculated as follows:To compare occurrence of neutral or disease-associated variants between PSDs and NSDs, the total number of variants in the entire genomic region for each domain class, i.e. the variation rate (BVR) for all domain regions, including both PSDs and NSDs, was calculated as follows:The background variation rate  to calculate the normalized variation rate (NVR) as follows:NVR differences between PSDs and NSDs were evaluated by binomial tests.Statistical significance of et al. [Wang et al.  provided−7 < p ≤ 10−3) were associated with CAD and SCZ, respectively. We assigned each SNP to genes that were located within 10 kb of the gene (downstream or upstream), resulting in the assignment of 3,188 SNPs to 204 genes for CAD and 54,688 SNPs to 1,044 genes for SCZ. These genes were further filtered by the number of PSDs relevant to the diseases.We used GWAS data for coronary artery disease (CAD) and schizophrenia (SCZ), which were publicly available from the CARDIoGRAM consortium  and Psyc2, and 24.1 mM MgCl2) containing 1% methylene blue  at 28.5°C. For observation and photography, the embryos were raised (24 hours after fertilization) in the E3 medium containing 0.2 mM N-phenylthiourea  to block melanin formation.Adult zebrafish were maintained at 28.5°C with a 13:11 h light:dark cycle in the Zebrafish Auto System . Zebrafish embryos were collected after natural breeding and incubated in clean petri dishes with E3 medium  candidate genes were designed and synthesized by Gene Tools . Each MO was diluted in distilled water at a concentration of 2 μg/μL and then injected into the yolk of zebrafish embryos at 1–4 cell stages using a gas-based microinjection system .Overall morphology, heart asymmetry, and vascular phenotypes of the Tg(flk1:EGFP) zebrafish were observed using a fluorescent stereomicroscope . Images were captured using a camera  and analyzed using NIS-Elements imaging software .S1 Fig(TIF)Click here for additional data file.S2 Fig(a) The sequence of translation-blocking MOs targeting candidate CAD genes used for this study. (b) Tg(flk1:EGFP) zebrafish embryos were injected with morpholinos (MOs) for candidate CAD genes and compared with control MO-injected embryos (morphants). The majority of morphants, except for apod morphants, exhibit gross morphological abnormalities, including a small brain, heart edema, and curved tail, at 3 days post-fertilization . (c) Diagrams show the representative heart defects, such as no asymmetry (midline) and reversed asymmetry between ventricle and atrium, at 3 days post-fertilization.(TIF)Click here for additional data file.S3 FigIn addition to the set of PSDs used for the analysis (4506 PSDs with 16k DPAs by PS threshold of 0.056), we also tested a smaller set of PSDs by more stringent PS threshold (0.066) resulting in 14k DPAs and 4341 PSDs and a larger one by more loose PS threshold (0.05) resulting in 18k DPAs and 4654 PSDs. We found that moderate changes in PS threshold for defining PSDs did not significantly affect enrichment of PSD for disease-associated variant by GWASdb (a) and for nonsynonymous variant affecting physical protein interactions by IMEx (b).(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.