Plants regulate intrinsic gene expression through transcription factors (TFs), transcriptional regulators (TRs), chromatin regulators (CRs), and the basal transcription machinery. An understanding of plant gene regulatory mechanisms at a systems level requires the identification of these regulatory elements on a genomic scale.Here, we present PlantTFcat, a high-performance web-based analysis tool that is designed to identify and categorize plant TF/TR/CR genes from genome-scale protein and nucleic acid sequences by systematically analyzing InterProScan domain patterns in protein sequences. The comprehensive prediction logics that are included in PlantTFcat are based on relationships between gene families and conserved domains from 108 published plant TF/TR/CR families. These prediction logics effectively distinguish TF/TR/CR families with common conserved domains. Our systematic performance evaluations indicate that PlantTFcat annotates known TF/TR/CR families with high coverage and sensitivity.http://plantgrn.noble.org/PlantTFcat/.PlantTFcat provides an analysis tool to identify and categorize plant TF/TR/CR genes on a genomic scale. PlantTFcat is freely available to the public at  Plants regulate intrinsic gene expression through transcription factors (TFs), transcriptional regulators (TRs), chromatin regulators (CRs) and the basal transcription machinery to control development and respond to environmental changes. To understand the fundamental mechanisms of plant development and environmental responses, we need a systematic way to identify and categorize TFs, TRs, and CRs on a genomic scale.e.g., TRs. A classic example of the control of gene expression via TFs and TRs is the control that the auxin signal has over all major aspects of plant development. Auxin enhances the proteolysis of auxin/IAA (Aux/IAA) transcriptional repressors, which heterodimerize with the auxin response factor (ARF) TFs to block the transcriptional activation of auxin-responsive genes [TFs are proteins that bind to specific DNA sequences, usually to a motif in the target gene promoter, to control the transcription of the target gene. TFs may perform this function with other proteins, ve genes . This exArabidopsis thaliana, mutations of histone mono-ubiquitination enzymes lead to premature flowering as well as defects in plant size and leaf development. These changes indicate that histone mono-ubiquitination enzymes are master regulators of plant development [CRs are also essential components in the gene expression regulation machinery. CRs are divided into two categories: 1) chromatin remodelers that reposition and restructure nucleosomes and 2) chromatin modifiers that add or remove covalent marks from histones. Chromatin remodeling complexes are widely conserved among plants, animals, and fungi. The maintenance of the chromatin structure conserves epigenetic marks on the histones, which are required for both proper plant development and the maintenance of perception to environmental cues such as light and temperature [elopment .http://www.transcriptionfactor.org), features PFAM [Over the past several years, next-generation sequencing (NGS) technologies have enabled life scientists to collect genome sequences both rapidly and economically. However, although plant gene transcription regulatory elements, including TFs, TRs, and CRs, have been broadly studied for several years, only a few tools have been developed to systematically categorize and analyze published TF/TR/CR gene families on a genomic scale. One popular database, DBD (res PFAM  and superes PFAM  domain ires PFAM . Furtherhttp://plntfdb.bio.uni-potsdam.de/), hosts sequences and PFAM domain models of 84 TF/TR families in plants [http://bioinfo.bti.cornell.edu/cgi-bin/itak/index.cgi) that has adopted the PFAM domain rules described in PlnTFDB to predict TF and TR genes. However, although the web version of ITAK is user-friendly, this tool can analyze a maximum of 50 sequences for each submission, which makes it impractical for analyzing sequences on a genomic scale. The standalone version of ITAK may be capable of large-scale data analyses if the Linux command line program is optimized and deployed on a parallel computing cluster. However, this implementation poses a challenge for most biologists.Another popular database, PlnTFDB (n plants . Howeveri.e. PS50097) or the SMART [i.e. SM00225). As another example, the Chromo and shadow domains of the CHROMO-DOMAIN family are also found only in the PROSITE and SMART domain pattern databases.The above analysis tools were developed on the basis of domain patterns from PFAM and superfamily databases, which are not sufficient for the systematic identification and categorization of TF/TR domains because some TF/TR domains are not covered by either the PFAM or the superfamily databases. For example, the BTB/POZ-like domain is the featured domain of the BTB-POZ, BTB-POZ-MATH, and ABTB families, but the domain model is available only in the PROSITE profile database   scanned all Medicago truncatula genome sequences using InterProScan and identified TF/TR gene families from a curated mapping table between InterProScan domains and TF/TR families [et al (2010) also developed a TF/TR prediction protocol using InterProScan and the mapping table previously described in their soybean database, SoyDB [InterProScan  is a comfamilies . Wang ete, SoyDB . Howeverhttp://plantgrn.noble.org/PlantTFcat/), which utilizes InterProScan to systematically search proteins for TF/TR/CR-related domain signatures. Furthermore, we have developed comprehensive prediction logics based on relationships between gene families and conserved domains to effectively distinguish families with common conserved domains, which are often overlooked in traditional BLAST-based searches. PlantTFcat therefore systematically predicts and categorizes plant TF, TR, and CR genes with high coverage and sensitivity.We have developed a web-based analysis server, PlantTFcat  in less than 10 minutes.To provide high-throughput genome-scale analysis capability, we customized InterProScan to include only relevant domain information. We further accelerated the back-end prediction module by deploying PlantTFcat on our in-house BioGrid parallel computing platform, which is equipped with approximately 300 AMD processor cores at 2.5GHz clock frequency. With these optimizations, PlantTFcat is able to analyze the must-have domain” pattern and a “prohibited domain” pattern, respectively. The domain signatures of a protein in InterProScan that meet the former pattern may satisfy the criterion for such a family. However, any signature that meets the latter pattern will lead to a negative prediction.The InterProScan domain patterns of 108 TF/TR/CR families were compiled in a back-end database by mining the published datasets ,11 and rmust-have domain” pattern was designed to handle logical relationships such as “AND” and “OR”. For example, the ARF TF family is expected to include either IPR010525 or IPR011525 (“OR”) as well as IPR003340 (“AND”). We therefore use a logical expression (IPR010525|IPR011525)&IPR003340 to represent the “must-have domain” pattern for the ARF TF family.The “prohibited domain” pattern was designed to divide superfamilies into more specific families based on the existing literature. For example, experimental evidence implicates the BTB/POZ-like domain, which is a protein-protein interaction module, in the regulation of gene expression through the local control of chromatin conformation. In some cases, the BTB-POZ domain has been observed with MATH (meprin and TRAF-C homology), which is known to be involved in the regulation of protein processing and ubiquitination. However, the BTB-POZ domain has also been associated with the ankyrin repeat, which is another protein-protein interaction domain [prohibited domain” pattern. As another example, TFs with the DNA-binding domain MADS box (IPR002100) can be subdivided into two families: MADS type1 and MADS-MICK. The MADS type1 family includes only the MADS box. However, the MADS-MICK family includes both the MADS box and a keratin-like box (K-box) (IPR002487) that promotes protein dimerization. To discriminate between these two families, the K-box domain can be included in the “prohibited domain” pattern of the MADS type1 family.The “n domain ,14. Thermust-have domain” and “prohibited domain” patterns, are available in Additional file http://plantgrn.noble.org/PlantTFcat/familylist.do).The comprehensive prediction logics, including the “PlantTFcat web interface was written in the Groovy language and the back-end pipeline was written in both Java and Groovy.The PlantTFcat consists of an intuitive web interface that allows users to submit large numbers of sequences and retrieve analysis results as well as a sophisticated back-end high-performance prediction module that uses InterProScan to search potential domains from the user-submitted sequences. This back-end module screens potential TFs/TRs/CRs by referring to the featured conserved domain patterns of each family. The PlantTFcat web server runs on an Oracle Java Virtual Machine and Resin J2EE server. The high-performance prediction module is deployed on our in-house BioGrid parallel computing platform, which runs on a CentOS 6 Linux Operating System. The Arabidopsis thaliana genome  within 10 minutes.InterProScan is a computationally intensive program that completes the search for the domains of a typical protein sequence in approximately 5 minutes. To analyze genome-scale sequences, we reduced the domain pattern libraries in InterProScan to include only TF/TR/CR-related domains, which accelerated the search by 2 to 3 orders of magnitude. This customized InterProScan installation coupled with our in-house BioGrid parallel computing deployment allows PlantTFcat to analyze the entire User-submitted jobs are scheduled into the back-end pipeline by a queue management system on a first-come first-serve basis. Only two sessions can run simultaneously in the back-end pipeline. All other sessions wait in the queue until the current jobs have been completed. An exception is made for small jobs (<20 kb), which are submitted and run directly without waiting in the queue.Analysis” tab  and the Zea mays genome release 3b.50 (http://www.maizesequence.org/). PlantTFcat missed 199 genes out of 2,757 TF/TR gene models from the Arabidopsis thaliana data in PlnTFDB. Moreover, PlantTFcat reported 1,744 additional gene models as TF/TR/CR candidates. Of these identified gene models, only 95 were not confirmed, because they were annotated as either unknown or non-TF/TR genes per the TAIR8 annotation  was run against the TAIR8 TF/TR genes that were downloaded from PlnTFDB. The BLAST search correctly hit all of the 2,757 TF/TR reference genes because the test dataset contains these reference genes. In addition, the BLAST-based method reported 3,870 more homologous genes as TF/TR candidates that had been excluded by PlnTFDB Programming language: Groovy, JavaOther requirements: A web browserLicense: None for usage.Any restrictions to use by non-academics: None.The authors declare that they have no competing interests.PZ conceived the project and supervised the development of the PlantTFcat system as well as the analyses. XD developed the PlantTFcat system and performed the system performance evaluations. SS and MU provided in-depth insights into chromatin regulators, transcription factors, and transcriptional regulators, curated the mapping table between the InterProScan domains and the TF/TR families, and assisted with the compilation and fine-tuning of the prediction logics. XD, SS, and PZ wrote the manuscript. All authors read and approved the final manuscript.List of comprehensive prediction logics for all TF/TR/CR families included in the PlantTFcat.Click here for filePerformance analysis by systematically comparing PlantTFcat predictions and the PlnTFDB database on Arabidopsis thaliana genome.Click here for filePerformance analysis by systematically comparing PlantTFcat predictions and the PlnTFDB database on Maize (Zea mays) genome.Click here for filePerformance comparisons between PlantTFcat and traditional BLAST method on Arabidopsis thaliana genome.Click here for file


Group E members of the SOX transcription factor family include SOX8, SOX9, and SOX10. Preceding the high mobility group (HMG) domain in each of these proteins is a thirty-eight amino acid region that supports the formation of dimers on promoters containing tandemly inverted sites. The purpose of this study was to obtain new structural insights into how the dimerization region functions with the HMG domain. From a mutagenic scan of the dimerization region, the most essential amino acids of the dimerization region were clustered on the hydrophobic face of a single, predicted amphipathic helix. Consistent with our hypothesis that the dimerization region directly contacts the HMG domain, a peptide corresponding to the dimerization region bound a preassembled HMG-DNA complex. Sequence conservation among Group E members served as a basis to identify two surface exposed amino acids in the HMG domain of SOX9 that were necessary for dimerization. These data were combined to make a molecular model that places the dimerization region of one SOX9 protein onto the HMG domain of another SOX9 protein situated at the opposing site of a tandem promoter. The model provides a detailed foundation for assessing the impact of mutations on SOX Group E transcription factors. Upon the discovery of SRY (Sex-Determining Region Y), a transcription factor required for the expression of male sex-specific traits, a search for similar proteins revealed a large family of over thirty related transcription factors termed SOX (SRY-related HMG box). Sequence similarity among SRY/SOX proteins within the High Mobility Group (HMG) DNA binding domain typically exceeds 50%. Several structural and biophysical studies of the SOX4 ,2, SOX5 The Group E proteins SOX8, SOX9, and SOX10 are known for their ability to dimerize in a DNA-dependent manner –13 at si0) proximal promoter and collagen Col9a1 enhancer demonstrated that the dimerization region helps SOX Group E transcription factors function at a variety of tandemly inverted promoters that vary in binding site spacing and affinity [Early studies of the myelin glycoprotein Protein zero  via NdeI and XhoI restrictions sites into the expression vector pET28 (Novagen) resulting in a 6xHis-tagged protein. The high solubility variant was used as a framework for making additional mutants in the dimerization region using the Quikchange method (Agilent). All constructs were confirmed by sequencing at the York University Core Facility.Portions of the human SOX9 gene  encoding amino acids 110–184 (the HMG domain) and 71–184 (the dimerization region plus HMG domain) were amplified by PCR and inserted into the expression vector, pET15b (Novagen) via 600nm of 0.8, cultures were grown five hours further. As all expressed proteins were insoluble, the cell pellet was dissolved in a denaturing solution of TP300  supplemented with 6 M urea and lysed further with a French press. The solubilized SOX9 protein was bound to a 10 mL nickel-NTA column (Qiagen) in the same buffer, successively washed with TP300 + 4 M urea, TP300 + 2 M urea, TP300 + 1 M urea and TP300 + 15 mM imidazole and eluted with ice cold TP300 + 20 mM EDTA + 1 mM phenylmethylsulfonylfluoride. After a brief concentration step, the protein was purified further by size exclusion chromatography using a Sephacryl S100 16/60 HiLoad column (GE Biosciences) equilibrated with 10 mM sodium phosphate buffer + 100 mM NaCl, pH 6.0. Protein concentrations were determined from their extinction coefficient at 280 nm as calculated by ProtParam [Typically, 1–2 L cultures were grown at 25°C in LB media. Upon induction with 1 mM isopropylthiogalactoside at an ACACAAAGCCGGCTTTGTGTAGGATCCC) Annealing of CC36 was achieved by slow cooling a 100 μM solution in phosphate buffered saline from 95°C to room temperature. A binding reaction typically contained 2 μM oligonucleotide duplex and a 0.5–4 μM concentration of protein in a buffer of 10 mM sodium phosphate pH 6.0, 100 mM NaCl, 5 mM EDTA. After incubation on ice for 30 min, complexes were resolved using a 10% Tris-borate-EDTA gel. After soaking the gel for 15 min in a 1:10000 SYBR-Green-I solution (Invitrogen), visualization was performed using an Alpha Imager HP system (Alpha-Innotech).An EMSA was used to directly visualize monomeric or dimeric binding of SOX9 proteins towards a 36 bp palindromic, oligonucleotide probe with two binding sites spaced 4 bp apart  fused amino terminally to a non-native glycine followed by amino acids 71–85 of the dimerization region (VSIREAVSQVLSGYD). This peptide was added to 1 μM stoichiometric SOX9 protein / CC36 DNA complexes and incubated on ice for 30 min. DNA-protein-peptide complexes were resolved using a 10% polyacrylamide gel containing 0.5x TBE buffer  and visualized using a Bruker-Carestream 4000 MM Pro Image Station at wavelengths specific for FITC .The previously described EMSA assay was used to determine if a peptide derived from the SOX9 dimerization region could bind HMG domain / CC36 DNA complexes in 32P-γATP with a specific activity of 3000 Ci/mmol (Perkin Elmer). The specific oligonucleotide probes used were a CC36 double site described previously and a single site probe termed S9WT (GGGTTAACGAACAATGGAATCTGGTAGA). Each binding assay contained a constant 9 nM concentration of DNA, which was approximately one order of magnitude lower than the estimated Kd of the SOX9/DNA complex. Gels were imaged with a Typhoon 9400 imager (GE Life Sciences). Bands were integrated with GelEval v1.37  and normalized to the observation from a highest protein concentration used in the titration. The data were fit to a four parameter logistic curve .An EMSA was also performed only radiolabeled oligonucleotides prepared by treatment with T4 polynucleotide kinase and A molecular model of the dimeric SOX9 / DNA complex was assembled in six stages. All scripts and intermediate models used in these calculations are available at the York University Open Access Repository.Stage 1: The SOX9 HMG domain sequence (101–184) was threaded into the crystal structure of SOX17/DNA complex  with SWISS-MODEL [SS-MODEL . Steric SS-MODEL  to make Stage 2: A palindromic tandem site with 4 bp spacing was made using the software, 3DNA [re, 3DNA . To mainStage 3: Using the combined distance, angle and planarity restraints from the first two stages, a model of two SOX9 HMG domains bound to one tandem site DNA was calculated using a standard simulated annealing schedule in CNS akin to what is used in an NMR structure calculation.Stage 4: The two SOX9 HMG domains from the model in Stage 3 were extended amino-terminally to include the dimerization region sequences as extended segments. A set of backbone torsion angle restraints and hydrogen bond distance restraints for helix α0 was synthesized using standard parameters for this secondary structure. Helix α0 was folded by subjecting the amino-terminally extended model to an additional round of simulated annealing.Stage 5: At this stage, the dimerization helix α0 needed to be interfaced with the SOX9 HMG domain. Since the orientation of helix α0 on the HMG was not known, a study was performed separately on a simple system consisting only of helix α0 and α1/α2 helices of the HMG domain. Orientations of the dimerization helix α0 on the HMG domain were sampled by performing a rigid body docking simulation with HADDOCK v2.2 [http://haddock.chem.uu.nl). According the mutagenesis data, AIRs were created between I73, A76, V77, V80, L81 and Y84 in the dimerization region and A119 and L142 in the HMG domain. As recommended by the HADDOCK protocol, docking outcomes were improved by allowing the α0 helix to interact with a larger hydrophobic surface on the HMG domain. Thus, additional AIRs were made by amino acids near to A119 and L142 which were W115, L123, L130, T138, L139 and T146. From an ensemble of 400 trial structures, 20 lowest energy structures were chosen for assessment and clustered into three bins according to the orientation of α0 relative to α1 and α2. A candidate was the selected from the predominant bin and a set of fifty random distance restraints between helix α0 and the HMG domain were calculated.OCK v2.2  and a seStage 6: All distance restraints from the previous stages were amalgamated and one final round of refinement with CNS was performed to produce a complete model of a dimeric SOX9-DNA complex. The linker region between the putative dimerization helix and HMG domain was left unrestrained throughout this stage.Initially, a protein fragment SOX9(71–184) was expressed to study dimerization. While this fragment was suitable for binding studies, it could not be concentrated to greater than 0.1 mM, thereby diminishing its suitability for future structural studies. Since a shorter SOX9(101–184) protein fragment spanning the HMG domain was soluble over a wide range of pH and ionic strength, we hypothesized that the solubility issues of the larger protein were due to several potentially solvent exposed hydrophobic amino acids in a linker region between the most essential amino terminal amino acids of the dimerization region and the HMG domain. We tested this hypothesis by expressing an optimized protein that incorporated the dimerization region from SOX10 (the only difference is that K82 in SOX9 is S82 in SOX10), and a new 12 aa. glycine/serine rich linker. One further substitution, C72S, was made to ensure that dimerization could be assessed in the absence of oxidation. Overall, these alterations resulted in a substantial increase in solubility (~0.8 mM was highest concentration tested). The sequence of this optimized, dimerizing protein fragment, termed SOX9 D-HMG, and several single amino acid substitution mutants made within that high solubility framework, are summarized in 32P-labelled probe was represented by S9WT, an engineered, high-affinity 29 bp sequence bearing one canonical AACAATG sequence [32P-labelled probe was represented by CC36, a 36 bp palindromic sequence bearing two CACAAAG sequences from the stronger of two sites comprising the P0 proximal promoter [Kd. From that study, a refined range of protein concentrations from 10–450 nM was selected  were calculated and presented in An electrophoretic mobility shift assay (EMSA) was used to study the binding of SOX9 D-HMG and SOX9 HMG towards single site and double site DNA duplexes. The single site sequence . The doupromoter . An initFig B in . Bands cRegardless of the protein (dimerizing or non-dimerizing) and DNA probe used (single- or double-site), the observed affinities were similar. This result may seem surprising since the dimerization region promotes the filling of double sites to such an extent that half-filled double sites are not observed. A closer examination of the plots in Comparing the S9WT single-site CC36 double-site probes, affinity was favored slightly towards S9WT. This difference may reflect a preference for adenine (in S9WT) over cytosine (in CC36) at the first position of the binding site. Interpreting this result from another perspective, the SOX9 HMG domain is versatile enough to read out two different base pairs and still maintain a high affinity interaction.0 proximal promoter C/C' tandem site [koff = 0.693 / t1/2, the estimated half-lives are equivalent to off-rates of 1.6x10-3 s-1 and 1.6x10-2 s-1. Amalgamating these observations for SOX10 with the Kd values for SOX9 determined in this report, we estimate that the SOX9 D-HMG and SOX9 HMG proteins would have on-rates of 1.6x105 M-1s-1 and 1.6x106 M-1s-1, respectively. Thus, the available experimental evidence indicates that the dimerization region promotes both a slower on-rate and a slower off-rate relative to the HMG domain alone. The slower on-rate may be a consequence of two SOX Group E proteins requiring more time to scan DNA, find their respective binding sites, and come sufficiently close together to make new protein-protein contacts. Once two SOX Group E proteins are coupled to each other and to DNA, the slower observed off-rate is likely the consequence of the requirement for protein-protein and protein-DNA contacts to be severed before two Group E proteins can exit the binding site.As part of a comprehensive, early study on SOX Group E dimerization, a pulse-chase competition experiment was used to demonstrate that SOX10 D-HMG had a much longer residency on DNA than SOX10 HMG at the Pdem site . From thBy replacing the amino acids 85–101 with an unrelated sequence to produce the SOX9 D-HMG variant used in this study, solubility was improved and the boundaries of the dimerization region were defined in accordance with earlier studies ,25. To dA plot of the dimerization region on a helical wheel suggested a possible amphipathic helix with a hydrophobic face consisting of I73, A76, V77, V80, L81, and Y84 . To asseOne important early insight into the role of the HMG domain in dimerization came from helix swapping experiments between SOX10 and a non-dimerizing Group C protein, SOX11 . From thtrans. A analysis of the integrated fluorescent signals, demonstrated a linear relationship up to 2 μM, where a stoichiometric peptide-protein-DNA complex would be achieved. As a result, an affinity of the D-peptide for the for preassembled HMG-DNA complexes could not be determined.To determine if the dimerization region could directly interact with the HMG domain, a fluorescein tagged peptide (D-peptide) corresponding to amino acids 71–85 of SOX9 was used as a probe. From a titration of SOX9 HMG at concentrations up to 15 μM, no changes in fluorescence anisotropy were observed in a solution of 20 nM peptide indicating if there was a peptide-protein interaction, it was very weak (data not presented). This observation led us to hypothesize that the D-peptide may only interact with the HMG domain in preassembled HMG/DNA complexes since the HMG domain only folds completely in its DNA bound state ,27. To vTowards obtaining the first high resolution view of the SOX Group E dimerization, the mutagenesis data was used as input in the form of ambiguous distance restraints for molecular docking experiment using the HADDOCK protocol . To simpBuilding upon the results of the dimerization helix / HMG domain docking simulation, a complete molecular model of a dimeric SOX9-DNA complex was calculated using a combination of intramolecular protein-protein restraints to build an HMG domain and then duplicate it, intermolecular protein-DNA restraints to dock the HMG domains on tandem promoter, protein-protein restraints to dock the dimerization region on an opposing HMG domain, and DNA-DNA restraints to create a bend and open the minor groove . The resThe observed affinity of SOX9 towards the single site and double site oligonucleotide probes used in this study is weaker by nearly two orders of magnitude than earlier reports for SOX4 (0.03 nM) and SOX5 (1 nM) ,31. ThisSimilar dissociation constants were observed for dimerizing and non-dimerizing variants (substitution and deletion mutants) leading us to speculate that aside from creating a specific architecture through the binding of two proteins, the unseen consequences of dimerization may be to modify the kinetics of the interactions occurring at a given promoter or enhancer. Dimerization of SOX Group E proteins at tandem promoters may also promote the dimerization and activation of accessory factors that are coupled to them.The mutagenesis based survey of the SOX9 dimerization region and HMG domain presented in this report builds upon several studies ,17,18,25Kd of the SOX9 D-HMG and SOX HMG proteins are similar, it suggests that there is no major structural or kinetic consequence of self-binding. Thus, on single sites, SOX9 is free to function like any other non-dimerizing SOX family member.From the modeling of the dimerization helix α0 with the HMG domain, there remains the possibility that on a single site, helix a0 could bind helices α1/α2 of its HMG domain in the absence of a partner. Since the The MADS domain transcription factor family also employs short secondary structure elements to link proteins already bound to DNA. At adjacent binding sites, a β-sheet in the MCM1 MADS domain is extended by a sequence donated by MATα2, an unrelated protein from the homeodomain family of DNA binding proteins . MEF2 faS1 FileFig A is comparison of the HMG / DNA molecular model used in this study with the crystal structure of the SOX9 HMG / DNA complex. Fig B presents the gel images used to determine protein-DNA binding affinities. Fig C is a workflow of the clustering and selection of a model describing the possible interaction of the dimerization region with the SOX9 HMG domain.(PDF)Click here for additional data file.


In insulinoma cells when pitrilysin expression was decreased to 5% of normal levels, there was a 60% increase in islet amyloid polypeptide-induced apoptosis. In contrast, overexpression of pitrilysin protects insulinoma cells from human islet amyloid polypeptide-induced apoptosis. Since pitrilysin is a mitochondrial protein, we used immunofluorescence staining of pancreases from human IAPP transgenic mice and Western blot analysis of IAPP in isolated mitochondria from insulinoma cells to provide evidence for a putative intramitochondrial pool of IAPP. These results suggest that pitrilysin regulates islet amyloid polypeptide in beta cells and suggest the presence of an intramitochondrial pool of islet amyloid polypeptide involved in beta-cell apoptosis.Amyloid formation and mitochondrial dysfunction are characteristics of type 2 diabetes. The major peptide constituent of the amyloid deposits in type 2 diabetes is islet amyloid polypeptide (IAPP). In this study, we found that pitrilysin, a zinc metallopeptidase of the inverzincin family, degrades monomeric, but not oligomeric, islet amyloid polypeptide  Type 2 diabetes is characterized by a loss of beta cell mass and function, along with the deposition of amyloid in pancreatic islets –4. The mSimilarities exist between the relationships of hIAPP to type 2 diabetes and human amyloid beta peptide (hAβ) to Alzheimer’s disease . In bothin vivo.The mitochondrial peptidase, pitrilysin, cleaves mitochondrial Aβ , howeverhIAPP transgenic mice (FVB/N-Tg(Ins2-IAPP)RHFSoel/J)  accumulate amyloid in the pancreas and develops hyperglycemia , 21. PanHuman islets were purchased from Prodo Laboratories .Cell culture reagents were from Invitrogen . hIAPP was from Anaspec . Adenoviruses (Adv) expressing green fluorescent protein (GFP), prepro-rIAPP-GFP, and prepro-hIAPP-GFP were generously provided by Dr. Christopher Rhodes  and expanded as described . For immS1 Fig).Pitrilysin was produced in Sf9 cells  and purified as described. . The purMonomeric hIAPP (20 μM) was incubated with 40 nM pitrilysin in 50 mM Tris/HCl buffer (pH 7.4) containing 200 mM NaCl at 37°C for various times and analyzed by reverse phase HPLC , 23. hIAhIAPP was incubated at 37°C for 1 hr. with gentle shaking to induce hIAPP oligomerization. To produce low molecular weight oligomers, hIAPP oligomerization was initiated in saline solution at 37°C using 50 μM recombinant hIAPP and then incubated with myocytes for 2 hours at room temperature . SimilarThe rat insulinoma cell line INS 832/13 was kindly provided by Dr. Christopher Newgard  and cultured as described , 29.INS 832/13 cells were plated on coverslips and transfected with pcDNA3.1-pitrilysin-flag  or pCMV6GIPZ lentiviral shRNAmir vectors against rat pitrilysin (V3LMM_494519), rat IDE (V3LHS_312518) and a non-silencing control (RHS4346) were from Open Biosystems . These and other lentiviruses were produced according to the manufacturer’s instructions. For selection of INS 832/13 stable cells in which the level of pitrilysin or IDE was knocked-down, cells were transduced with the appropriate lentivirus and then selected with 10 μg/ml puromycin. Knockdown of pitrilysin or IDE was confirmed by Western blot analysis.pcDNA3.1 vectors containing cDNAs of human pitrilysin (PITRM)  or its iINS 832/13 cells were transduced with adenovirus expressing prepro-hIAPP-GFP at a multiplicity of infection (MOI) of 100 and cultured for 48 hours. Adenovirus expressing prepro-rIAPP-GFP and GFP alone transduced at the same MOI were used as controls. Apoptosis was determined by the amount of activated caspase-3 , 31.Mitochondria were isolated from INS 832/13 cells by differential centrifugation followed by Percoll purification [Purified mitochondria were resuspended in mitochondria resuspension buffer  at 0.4 mg total protein/ml and incubated for 30 min at 37°C with 10 μg/ml trypsin in the presence or absence of 0.1% Triton X-100. Trypsin digestion was terminated by addition of 50 μg/ml trypsin inhibitor. SDS-PAGE loading buffer was then added to the reaction followed by boiling and analysis by SDS-PAGE and immunoblotting as detailed below.Protein concentration was determined using the BCA protein assay kit . Cell lysates were separated on 4–12% Bis-Tris NuPAGE gels (Invitrogen) and transferred to polyvinylidine difluoride membranes. For Western blotting of IAPP membranes were boiled in PBS for 5 min before blocking. The following primary antibodies were used: rabbit anti-amyloid oligomer , rabbit anti-cleaved caspase-3 , mouse anti-β-actin , rabbit anti-IDE (1:5000), rabbit anti-pitrilysin (1:5000), goat anti-calnexin (1:300), rabbit anti-cytosolic thiolase , rabbit anti-mitochondrial malate dehydrogenase , or rabbit anti-hIAPP . Following incubation with the appropriate primary antibody membranes were incubated with the corresponding horseradish peroxidase-conjugated secondary antibody  followed by SuperSignal West Pico Chemiluminescent Substrate (Thermo Scientific) and then detected by luminescence. In some cases membranes were reprobed after stripping with stripping buffer .Data are expressed as the mean ± SEM and compared using the Student’s t-test. Values are considered to be statistically significant with a p<0.05.Animal protocols were approved by the University of Kentucky Animal Care and Use Committee and performed in accordance with University guidelines.Fig 1A. To confirm that the observed degradation of hIAPP was mediated by pitrilysin, the purified enzyme was incubated with rabbit anti-pitrilysin antibody followed by protein A/G Sepharose to immunodeplete the enzyme from solution. As shown in Fig 1B, hIAPP degradation was abolished when pitrilysin was immunodepleted with rabbit anti-pitrilysin antibody, but not with normal rabbit IgG or a vehicle control, confirming that hIAPP degradation is indeed catalyzed by pitrilysin. Pitrilysin also degrades monomeric rat IAPP (rIAPP), similar to monomeric hIAPP (data not shown).As noted in the introduction, the zinc metallopeptidase pitrilysin has been shown to cleave amyloid beta peptides . Since I by the inability of pitrilysin to reduce the amount of hIAPP oligomers of 12 kDa, 28 kDa, 36 kDa, or 40 kDa. These likely correspond to hIAPP trimers, heptamers, nonomers, and decamers, respectively. The same results were obtained using a different antibody rabbit polyclonal, rabbit anti-IAPP T4157. Pitrilysin is also not able to degrade large hIAPP large oligomers . It is worth noting that the amount of pitrilysin used to test for the degradation of hIAPP oligomers would have completely degraded monomeric hIAPP under the reaction conditions employed. As expected rIAPP did not form large oligomers under our experimental conditions. Thus pitrilysin degrades monomeric, but not oligomeric IAPP.Based on the crystal structure of pitrilysin  we wouldFig 1A), manually collected and subjected to mass spectral analysis. The identified degradation products are listed in S1 Table while a schematic representation of the cleavage sites is shown in Fig 1C. Initial cleavage of hIAPP by pitrilysin  occurs between Phe15-Leu16, Gly24-Ala25 and Ala25-Ile26. Products were subsequently cleaved at Arg11-Leu12, Leu12-Ala13, Ala13-Asn14 and Asn14-Phe15 . The degradation of hIAPP by pitrilysin resulted in the production of several 9–15 amino acid fragments with the most abundant being the carboxy-terminal peptide 25-37-amide. Two of the major cleavage sites (Gly24-Ala25 and Ala25-Ile26) are within the GAILS sequence believed to be important for hIAPP aggregation and amyloidogenesis. Since five of the cleavage products were clustered we assayed for carboxypeptidase activity in the pitrilysin preparation with hippuryl-L-phenylalanine as a general carboxypeptidase substrate [To identify the pitrilysin dependent peptide cleavage sites in hIAPP, reaction products were first separated by HPLC  using a molecular mass of 117 kDa for pitrilysin. The KM for hIAPP was determined by using it as an alternate substrate inhibitor of the hydrolysis of the fluorogenic peptide Abz-GGYRRGQ-EDDnp [cat and KM values of pitrilysin with hIAPP are 13.5 ± 0.1 min-1 and 0.38 ± 0.01 μM, respectively (Table 1). Since pitrilysin cleaves amyloid β peptide 1–40 (Aβ1–40) [1–40 to that of hIAPP and found that pitrilysin has a ~4 to 4.5 times higher cleavage rate and higher affinity for hIAPP compared to Aβ1–40  suggesting that hIAPP may be a physiologically relevant substrate for pitrilysin.Having established IAPP as a substrate of pitrilysin, the kinetic properties of the reaction were determined. The rate of hIAPP hydrolysis was determined by measuring the disappearance of the substrate by HPLC, while GQ-EDDnp . The kcantration  μM using(Aβ1–40) , we compectively . Thus thcat and KM values for IDE with hIAPP were determined as above yielding values of 294 ± 0.4 min-1 and 3.11 ± 0.20 μM, respectively. Compared to IDE, the catalytic efficiency of pitrilysin towards hIAPP is similar, exhibiting 40% of the catalytic efficiency of IDE . Although IDE is a major insulin degrading enzyme in vivo, pitrilysin was shown to be very inefficient in degrading insulin [Table 1). Thus relative to IDE, pitrilysin is a more specific IAPP degrading enzyme.Insulin-degrading enzyme (IDE) has been shown to hydrolyze hIAPP . We ther insulin  . We also demonstrated a mitochondrial location of pitrilysin in human islets by its colocalization with the mitochondrial voltage-dependent anion channel 1 . Western blot analysis of human islet extracts also showed the presence of pitrilysin .Pitrilysin has been shown to be located within the mitochondrial matrix ,25. We cFig 4A shows that in INS832/13 cells pitrilysin levels were reduced ~95% by the shRNA, while IDE levels were reduced by 80%. Cells were then transduced with adenovirus Adv- prepro-hIAPP-GFP or Adv-prepro-rIAPP-GFP as a negative control, and apoptosis was evaluated by measuring the cleavage of caspase-3.To determine whether endogenous pitrilysin regulates IAPP in cells we measured the effect of pitrilysin on preventing hIAPP induced apoptosis. We used RNA interference (RNAi) to decrease pitrilysin and then measured apoptosis produced by hIAPP. We introduced recombinant hIAPP through an adenovirus vector  since thFig 4B, but not in control cells. This finding confirms hIAPP induced apoptosis. IDE knockdown (IDEi) also increased hIAPP-induced apoptosis as expected. The nonsilencing control shRNA (NSi) did not affect hIAPP-induced apoptosis . Apoptosis was not induced by knockdown of pitrilysin or IDE in the absence of hIAPP expression, nor by treatment with Adv-propro-rIAPP-GFP. These results strongly support a role for pitrilysin in the degradation of hIAPP in vivo.When the pitrilysin-depleted cells expressed Adv-prepro-hIAPP-GFP, apoptosis significantly increased relative to naïve cells, Fig 5A shows that lentivirus transformation increased pitrilysin levels by 80% for PITRMx and 60% for PITRM. The cells with increased active pitrilysin were more resistant to Adv-prepro-hIAPP-GFP-induced apoptosis compared to naïve cells, cells treated with empty lentiviral vector, as well as cells treated with inactive pitrilysin . As an additional control, cells treated with Adv-prepro-ratIAPP-GFP did not exhibit significant apoptosis.To further examine the role of pitrilysin in regulating IAPP, we used lentivirus carrying human pitrilysin to overexpress the enzyme. Lentivirus carrying an inactive E107Q mutant pitrilysin (PITRMx) and empty lentiviral vector were used as negative controls. Pitrilysin is known to be located within the mitochondrial matrix of cells , 23, andFig 6A). Similarly, the colocalization of hIAPP with mMDH was also indicated in pancreas sections of hIAPP transgenic mice, although the resolution of the images were insufficient to distinguish the sub-organelle localization. In this case the immunofluorescence corresponding to mitochondrial hIAPP was ~9% of total hIAPP . In contrast and as a control, considerably less insulin appeared colocalized with mMDH  representing background staining .To determine whether IAPP accumulates in the mitochondria of pancreatic beta-cells, the pancreatic beta-cell line INS 832/13  was tranFig 7A). Although the majority of cellular IAPP appears to be located in the ER, the importance of mitochondrial IAPP cannot be underestimated since as described below mitochondrial pitrilysin likely keeps its steady-state concentration low under normal conditions. Under pathological conditions, the mitochondrial IAPP concentration could increase and be an important factor in inducing mitochondrial dysfunction, similar to the role of mitochondrial amyloid β-peptide in Alzheimer’s disease [Fig 7B). Thus these data indicate a previously unidentified mitochondrial pool of IAPP, which is analogous to the pool of Aβ localized inside mitochondria in Alzheimer’s disease [To provide additional evidence for a mitochondrial location of a pool of IAPP in pancreatic beta-cells and eliminate any confounding effects of hIAPP oligomerization / assemblies on its subcellular location, we examined the localization of endogenous rat IAPP in INS 832/13 cells employing cell fractionation and Western blot analysis [ disease . Importa disease , 17.25–37-amide. Cleavage of hIAPP by pitrilysin produces non-toxic products, likely due to cleavages in the region of hIAPP known to be important for its oligomerization. Pitrilysin is ~20 times more efficient in cleaving hIAPP than Aβ. Thus, if pitrilysin is able to degrade Aβ in vivo [in vivo. Our demonstration of pitrilysin in human islets suggests pitrilysin plays a role in regulating IAPP levels in humans.Pitrilysin was initially described as a metallopeptidase cleaving the opioid peptide leumorphin  and lateGiven that knockdown of pitrilysin increased hIAPP-induced apoptosis and that overexpression of pitrilysin protects against hIAPP-induced apoptosis, this data strongly suggest that pitrilysin contributes to the regulation of cellular IAPP levels. The fact that pitrilysin is known to be a mitochondrial enzyme and the data presented here that suggests co-localization of a fraction of IAPP with the mitochondrial marker mitochondrial malate dehydrogenase, these findings raise the intriguing possibility that there exists an intramitochondrial pool of IAPP which contributes to IAPP induced beta-cell death. Since pitrilysin degrades monomeric, but not oligomeric IAPP, this putative mitochondrial pool of hIAPP must contain monomeric IAPP. However, this mitochondrial hIAPP could aggregate to form toxic oligomers within the mitochondrion. How monomeric IAPP is transported into mitochondria is currently unclear.et al. [IAPP is synthesized in the ER as a precursor protein, which is then processed to its mature form and secreted into the extracellular space . hIAPP cet al. , one canS1 FigRecombinant pitrilysin was purified as described in the Methods section and analyzed by SDS-PAGE on a 10% polyacrylamide gel stained with Coomassie blue. The purity of recombinant pitrilysin is greater than 97%.(TIF)Click here for additional data file.S1 Table20μM hIAPP was incubated with 40 nM recombinant pitrilysin at 37°C and the degradation of hIAPP was analyzed by HPLC. Peaks were collected manually and subjected to mass spectral analysis for identification. Peak designations are shown in (DOCX)Click here for additional data file.


L) of the linker in the finally active mode and the optimal number (Nopt) of the residues in the linker satisfy relation L ≈ αNopt, with α = 0.24–0.27 nm being a constant insensitive to the system.DNA polymerase I (PolI), T7 primase and DNA polymerase IV (Dpo4) have a common feature in their structures that the two main domains are connected by an unstructured polypeptide linker. To perform their specific enzymatic activities, the enzymes are required to rearrange the position and orientation of one domain relative to the other into an active mode. Here, we show that the three enzymes share the same mechanism of the transition from the inert to active modes and use the minimum numbers of residues in their linkers to achieve the most efficient transitions. The transition time to the finally active mode is sensitively dependent on the stretched length of the linker in the finally active mode while is insensitive to the position and orientation in the initially inert state. Moreover, we find that for any enzyme whose two domains are connected by an unstructured flexible linker, the stretched length ( A lot of proteins or enzymes have the structures with two domains being connected by an unstructured polypeptide linker. To perform the specific enzymatic activity, the enzyme is required to rearrange the position and/or orientation of one domain relative to the other domain into an active mode by stretching the flexible linker. Thus, it is an interesting topic of how the linker affects this rearrangement. Here, we take DNA polymerase I (PolI), bacteriophage T7 primase and DNA polymerase IV (Dpo4) as examples to study the effect of the linker that connects the two domains of the enzyme on this transition from the inert to active modes, determining how many residues in the linker are required to make the enzyme achieve the most efficient transition.PolI is a well-characterized enzyme involved in DNA replication and repair. During the replication of the lagging strand DNA, it plays a particularly important role in the processing of Okazaki fragment1346710T7 primase, which can synthesize RNA primers, is located in the N-terminal half of the multifunctional gene 4 helicase-primase protein16Dpo4 is a prototypical Y-family DNA polymerase that can catalyze translesion DNA synthesisL) of the linker in the finally active state and the optimal number (Nopt) of the residues in the linker satisfy relation L ≈ αNopt, with α = 0.24–0.27 nm being a constant insensitive to the enzyme.To study the effect of the linker on the dynamics of the transition from the inert to active modes in these enzymes, we first determine the elasticity of the linker by using atomic molecular dynamic (MD) simulations. Then, with the determined elasticity of the linker we study the dynamics of the transition by using the simplified model of the system. Our results show that the wild-type (wt) linkers of the three enzymes contain the minimum numbers of residues to achieve the most efficient transitions. More interestingly, we find that for any enzyme whose two domains are connected by an unstructured flexible linker, the stretched length  of the 5′-domain and impose a series of constant forces on some residues (residues 306–448) of the core domain along the x axis. We then calculate the distance between the two terminal alpha carbons of the linker (residues 290 and 305) after the system reaches equilibrium. To make simulations of only the linker, we keep residues 289 through residue 306, where the residues 290–305 constitute the linker. We adjust the line connecting residues 289 and residue 306 along the x axis. We fix residue 289 and impose a series of constant forces on residue 306 along the x axis. We then calculate the distance between the two terminal alpha carbons of the linker after the equilibrium. Each simulation is carried out in a time period of 60 ns. The calculated data of the force-extension relations of these linkers can be fitted by the worm-like-chain (WLC) modelTo make simulations of the complete PolI (1TAQ.pdb)Lc is the contour length and Lp is the persistence length of the linker  linker of 16 residues (deviation from 16 by 0). ΔN = −1, …, −5 correspond to the linkers with 1, …, 5 residues deleted from the terminus (residue 305) of the wt linker , respectively. ΔN = + 1, …, +5 correspond to the linkers with 1, …, 5 residues added to the terminus (residue 305) of the wt linker , respectively, where the types of the added residues are chosen randomly. The calculated data of the force-extension relations of these linkers can also be fitted by the worm-like-chain (WLC) model, i.e., Here, we calculate the force-extension curves for 11 linkers of different numbers of residues, which are denoted by Δr . The coordinate xoy is shown in x, y) and the rotation by its rotational angle θ, where θ is defined as the rotation angle of the line connecting the center-of-mass position and the active site relative to the y axis, with θ = 0 in the initial state. According to the structural data (1TAQ.pdb), the initial state of the 5′-domain is at  =  (x0 > r) , the end-to-end distance of the linker, L, is calculated by L, the pulling force imposing on the 5′-domain by the stretched linker, f (L), is calculated by x and y directions having forms x, y, θ) = , where d is the center-of-mass position of the 5′-domain in the y direction, which is equal to the end-to-end distance L of the linker at the finally active state , V2(y) and V3(θ) have the Morse-like formswhere rB = A = 0.5 nm that is consistent with the Debye length in the order of 1 nm in solution.where The motion and rotation of the 5′-domain in viscous solution can be described by Langevin equationsx = Γy = 6πηr = 6.59 × 10−11 kg ⋅ s−1 and Γθ = 8πηr3 = 1.0766 × 10−9 nm2 ⋅ kg ⋅ s−1, where to be consistent with the size of the enzyme (1TAQ.pdb) we take r = 3.5 nm, and the viscosity of the aqueous medium η = 0.01 g ⋅ cm−1 ⋅ s−1. The Langevin forces satisfy 〈ξi(t)〉 = 0 , 〈ξi(t) ξj(t')〉 = 0 (i ≠ j) and 〈ξi(t) ξi(t')〉 = 2kBT Γiδ(t−t'). Without loss of generalityV0 = 18kBT. Here, we solve 22Here, the drag coefficients Γd = 4 nm. It is seen that the 5′-domain moves and rotates randomly until it binds stably to the flap at about 2.5 μs, implying that under this realization of simulations the transition time of the 5′-domain from the initial to final positions is about 2.5 μs. By averaging the transition times of 1000 realizations we obtain the mean transition time Tm. It is noted that Tm is only sensitively dependent on the final state while is nearly independent of the initial state , the transition time Tm is kept to the minimum value; and if the residue number is smaller than 16 the transition time Tm increases significantly. It is noted that if the transition time is too long, the DNA substrate can be dissociated from PolI before the 5′-domain transits to the finally active mode. Thus, the 5′-domain should use the shortest time to transit to the active mode. Based on the above results, we see that for the finally stretched length of L = 3.9 nm, the number of residues in the linker should be N ≥ 16, with N = 16 being the minimum number. Thus, for the wt linker having 16 (the minimum number) residues, it is expected that the linker should be stretched to a length of about 3.9 nm in the finally active mode. On the other hand, from the available structural data it is inferred that in the finally active mode the end-to-end distance of the linker is approximately 3.92 nm , in the following we will make simulations in 2D space to save simulation time.It should be mentioned here that for simplicity, in the above we have made calculations in two-dimensional (2D) space. Since in reality the 5′-domain moves in 3D space, to verify if our results in 2D space are applicable, we also make calculations in 3D space and compare with those in 2D space , where residues are deleted from or added to the terminus (residue 70) of the wt linker and the added residues are chosen randomly. The calculated data of the force-extension relation can be fitted with WLC model  =  (x0 > r) and the final condition is  = , where θ is defined as the rotation angle of the line connecting the center-of-mass position of ZBD and the DNA-binding site relative to the y axis, with θ = 0 in the initial state, d represents the distance between the center-of-mass position of ZBD and that of RPD in the finally active state, and xf = 2 nm. Based on the structure  residues, the linker is stretched to a length of about In tructure .Dpo4 has a polymerase core consisting of a palm, fingers and thumb domain in addition to a LF domain. The polymerase core and the LF domain are connected by an unstructured polypeptide linker of 11 residues 21. Upon N = −2, …, +2), where residues are deleted from or added to the terminus (residue 244) of the wt linker and the added residues are chosen randomly. The calculated data of the force-extension relation can be fitted with WLC model  we should also consider the azimuthal rotation, which is described by ϕ  = V0V1(x)V2(y)V3(θ)V4(ϕ), where V1(x), V2(y) and V3(θ) are still described by ϕ ≤ 2π). The motion and rotation of LF domain are described by ϕ dϕ/dt = −∂V/∂ϕ + ξϕ(t), with ξϕ being the Langevin force. Here, considering that the linker is flexible, with a persistent length of only Lp = 0.3~0.4 nm  =  (x0 > r) and the final condition is  = , where d is the center-of-mass position of LF domain in the y direction, which is equal to the end-to-end distance of the linker at the finally active state. As done above, we take d as a variable parameter.To be consistent with the size of the enzyme (2RDI.pdb)ructures ,b, the iTm versus ΔN for different values of d, which are similar to d = 2.7 nm, the critical value ΔNc = 0. This implies that the number of residues in the linker should be N ≥ 11 for the end-to-end distance of the linker to be about d = 2.7 nm in the finally active state, with N = 11 being the minimum number. Thus, we expect that for the wt linker of 11 (the minimum number) residues, the linker is stretched to a length of about L = d = 2.7 nm, which is consistent with the value of about 2.64 nm measured from the available structure (3QZ7.pdb)In L = 3.9 nm. Thus, on average, each residue has a length α = L/N = 0.244 nm along the direction that the linker is stretched, which is slightly smaller than the average size of about 0.33 nm for a residue. This implies that the linker is not completely stretched in the finally active position of the 5′-domain and thus, only a small internal force or a small increase in the internal free energy is induced, as noted from L of the linker in the finally active conformation and the optimal number (Nopt) of the residues in the linker has the simple relationThe linker of PolI is composed of 16 residues. In the active mode of the 5′-domain, the length of the linker is about α ≈ 0.25 nm. This can also be clearly seen from L with Nopt by summarizing our results shown in where L with Nopt has the same form as that for PolI and T7 primase. In addition, from the various previous studies27282930Lc of different linkers have nearly the same value for a fixed number of residues and the persistence lengths Lp are almost always within a range of 0.3~0.6 nm27282930Lp = 0.3~0.4 nm, which is within the range of 0.3~0.6 nm. Furthermore, we check that even for the linkers with Lp = 0.3 nm and Lp = 0.6 nm, the length L and optimal number Nopt can still satisfy the relation given by α ≈ 0.243 nm and α ≈ 0.269 nm, respectively  of the residues in the linker can always be calculated by the simple relation of α = 0.24–0.27 nm being a constant insensitive to the system. If the number N is smaller than Nopt, the transition from the inactive to active states would take too long time so that the substrate  bound to the enzyme is dissociated. If the number N is larger than Nopt, the time for the enzyme to transit from the inactive to active states would be nearly the same as that with N = Nopt. Thus, the redundant (N−Nopt) residues would evolve to disappear.To generalize our conclusion, it is necessary to make further study of the effect of the sequence of residues in the linkers. For this purpose, we also calculate the force-extension relations of three linkers by mutating the wt linker of DNA polymerase I . It is sectively  and S5. L) of the linker of the three enzymes in their active states and the optimal number (Nopt) of the residues in the linker satisfy the relation L ≈ αNopt, with α being a constant of about 0.25 nm. Furthermore, our analysis show that for any enzyme whose two domains are connected by a flexible linker, the finally stretched length L and the optimal number Nopt of the residues in the linker satisfy the same relation, with α = 0.24–0.27 nm.We study the dynamics of transition from the inactive to active states for PolI whose core and 5′-domains are connected by a linker of 16 residues, T7 primase whose ZBD and RPD domains are also connected by a linker of 16 residues, and Dpo4 whose polymerase and LF domains are connected by a linker of 11 residues. We show that the three enzymes share the same mechanism of transition from the inactive to active states. Moreover, we show that they use the minimum number of residues in the linker to achieve the most efficient transitions. More interestingly, we show that the finally stretched lengths (The structures of PolI (1TAQ.pdb)How to cite this article: Fu, Y.-B. et al. Optimal numbers of residues in linkers of DNA polymerase I, T7 primase and DNA polymerase IV. Sci. Rep.6, 29125; doi: 10.1038/srep29125 (2016).


This article presents a historical review of the protein structure classification database CATH. Together with the SCOP database, CATH remains comprehensive and reasonably up-to-date with the now more than 100,000 protein structures in the PDB. We review the expansion of the CATH and SCOP resources to capture predicted domain structures in the genome sequence data and to provide information on the likely functions of proteins mediated by their constituent domains. The establishment of comprehensive function annotation resources has also meant that domain families can be functionally annotated allowing insights into functional divergence and evolution within protein families. •We present a historical review of the protein structure database CATH.•We review the expansion of the CATH and SCOP resources with sequence data and functional annotations.•How functional annotation resources allow insights into functional divergence and evolution within protein families. Several studies had shown the extent to which protein structures are conserved during evolution which suggested that 3D structure was a valuable fossil capturing the essential features of an evolutionary protein family and making it possible to identify even very remotely related proteins through similarities in their structures.The first protein structure, myoglobin, was solved in 1958 and for the following three decades the number of structures solved and deposited in the Protein Databank Although global structural characteristics (ie the folds of homologous proteins) are largely conserved during evolution The development of structure comparison algorithms by Rossmann and Argos Pioneering studies of groups of related proteins, by Chothia and Lesk Lesk and Chothia also published a seminal study on the relationship between sequence identity and structural similarity These studies prompted the question of whether other general trends could be gleaned from reviews of protein families. Furthermore, the concurrent expansion of the PDB and development of more robust techniques for comparing structurally distant homologues led several groups to begin classifications of protein families based on structure. The domain was the focus of these studies as it was clear this represented the primary unit of evolution and proteins evolved through duplication, shuffling and fusion of domain sequences in genomes Early structure comparison algorithms exploited rigid body algorithms to superimpose the structures based on the 3D coordinates and the methods struggled to achieve an optimal superimposition between distant homologues. Generally, they failed to converge on a solution where substantial insertions/deletions (indels) and changes in secondary structure orientations had occurred. Therefore in the late 1980s, more sophisticated approaches were explored  could easily be confirmed using pairwise algorithms like BLAST This was a considerable task but arguably more reliable than relying entirely on completely automated approaches. Over the last decade or more, much more sophisticated sequence comparison techniques have been developed that can confirm homology even in the midnight zone of sequence similarity (<20% sequence identity). These are discussed in more detail below.In 1997 the SSAP algorithm was modified to increase the speed for large scale comparisons within the PDB, by employing a filter that only allows comparison of proteins having sufficiently similar secondary structure arrangements and connectivity in their common structural core The SCOP classification was largely constructed using manual evaluation of domain relationships although available algorithms such as BLAST Both SCOP and CATH further classified the domain superfamilies and fold groups according to their architecture, where architecture describes the orientation of the secondary structure elements in 3D regardless of their connectivity. However, in CATH this was a formal level in the hierarchy whilst in SCOP architecture was simply an annotation. Finally domains were assigned to protein classes depending on the composition of secondary structure elements ie whether they were all-alpha, all-beta, or mixtures of alpha and beta see  SCOP use3ab-initio methods . This is because the linking regions between domains can be quite small and the domain interfaces quite large and complex, especially if one of the domains is dis-contiguous. Therefore, it is difficult to optimize parameters in a way that doesn't lead to under-chopping or over-chopping of large complex multi-domain proteins. The difficulty in capturing the heuristic rules defining domains in an algorithm is illustrated by the fact that an analyses of the performance of these methods, judged using a manually validated benchmark set, showed that all three approaches only agreed about 10% of the time and that frequently there were considerable differences in the boundaries assigned SCOP employs the same physical criteria in recognising domains by expert curation. Furthermore, a putative domain must be found to occur in at least two different independent contexts ie with different domain partners. This means that some protein regions deemed to be domains in CATH are not recognized as such by SCOP until additional data confirms the existence of this domain fused to different domain partners.In 1995 both CATH and SCOP publicly launched their classifications via the web 4Perhaps the most interesting revelation to emerge from the structural classification data was the highly uneven distribution observed in the populations of the fold groups. In 1994 Orengo, Jones and Thornton reported the existence of ten ‘superfolds’ accounting for nearly 50% of all domain relatives in CATH Based on the number of diverse sequence families found across the SCOP classification and the proportion of all known sequence data that this represented, Chothia postulated that there could be fewer than 1000 fold groups in nature In fact these predictions appear to have been largely borne out by the current data. Twenty years on, there are still only 1300 folds identified in CATH, and sensitive structure prediction tools suggest that CATH and SCOP capture nearly 80% of all protein domains in completed genomes with the remaining 20% being largely membrane associated domains which are likely to adopt relatively few diverse structural arrangements because of the physical constraints imposed by their location. The remaining sequences suggest highly disordered proteins that are not expected to adopt globular folds.The structural classification data and large scale comparisons of domain structures also revealed novel folding motifs  common to a large proportion of alpha–beta domain superfamilies in which structures comprise a central antiparallel beta-sheet covered by a layer of alpha-helices  a hypothesis that had been speculated as a ‘russian doll effect’ in the original CATH classification Development of faster structural comparison algorithms like GRATH 5The international genome initiatives which started in the late 1990s and exploited rapid sequencing techniques, enabled the completion of the human genome by the millennium, and resulted in an explosion of sequence data by the turn of the century. This expansion of the sequence data combined with much more sensitive tools for recognizing sequence similarities prompted the recruitment of sequence relatives into the domain superfamilies of SCOP and CATH. Both resources exploited powerful sequence profiles – known as Hidden Markov Models (HMMs) – which were built from multiple alignments of sequence clusters in domain superfamilies and used to recognize domain relatives in the genome sequences. Two sister resources were established – Gene3D These strategies brought about an ∼100-fold increase in the number of domains assigned to CATH and SCOP allowing more detailed analyses of evolutionary relationships and in particular an understanding of the divergence of sequences and functions within particular superfamilies (discussed more below). Currently more than 50 million sequences are assigned to CATH superfamilies in Gene3D. SUPERFAMILY which has explicitly incorporated completed genome data not yet deposited in public repositories like UniProt and ENSEMBL, has more than 40 million sequences from 2500 completed genomes. This data is likely to expand further in the near future as the metagenome projects bring in millions more relatives from species in diverse environments from around the globe.By recognizing CATH or SCOP domains within protein sequences it was possible to trace the emergence of novel proteins resulting from different domain fusions or fissions. For example, Vogel and Chothia examined changes in domain partnerships in proteins linked to the immunoglobulin superfamily. In worm, expansions in multi-domain architectures were linked to the expansion of structural proteins whilst in fly, expansions involving related domains enhanced the immune system repertoire 6The expansion of CATH superfamilies with sequence data considerably increased the amount of functional data too, allowing large scale studies of the divergence of function within superfamilies during evolution. These studies showed that whilst relatives in most superfamilies shared a common function, in the most highly populated superfamilies considerable divergence of sequence and function had occurred. A detailed study of 31 such diverse superfamilies revealed the molecular mechanisms by which functions had changed Studies of enzyme superfamilies revealed that changes in function were usually associated with variations in the substrate specificity. Changes in chemistry were much less frequent presumably because it is harder to engineer a novel chemistry than to alter the binding site and change the compound on which that chemistry is performed More recent collaborations between the Thornton and Orengo groups have led to the establishment of a new resource, FunTree FunTree enabled much more comprehensive studies of functional divergence in domain superfamilies and confirmed the previously observed trends of conservation of chemistry between parent and child nodes in the phylogenetic tree but also highlighted frequent divergence in substrate specificity within some promiscuous enzyme superfamiles. However, significant changes in chemistry can still occur As regards the structural mechanisms mediating functional change, analyses of some structurally and functionally divergent CATH superfamilies revealed substantial insertion of residues often resulting in additional secondary structural features packed against the common structural core of the domain and appearing as embellishments to that highly conserved core. In some large CATH superfamilies, relatives differ in size by three-fold in the number of residues or more 7More recently, the extreme divergence of functional properties of relatives in some highly populated CATH superfamilies prompted the development of protocols to sub-classify superfamilies into functional families . Comparisons of interfaces across superfamilies showed that functionally diverse relatives were exploiting different surface patches on the structure ie distinct sites, depending on their interaction partner, and that paralogues shared few common interactors. However, quite frequently there was one location that was more frequently exploited by diverse paralogues Recent analyses of changes in catalytic residues in different functional families across 101 well annotated CATH enzyme superfamilies showed that in the majority of superfamilies (∼70%) at least two FunFams had significant differences in their catalytic machineries . Unsurprisingly, most of the time these led to changes in the chemistries of the relatives or the substrates being acted upon. However, in 25% of the cases examined the same chemistry was associated with completely different catalytic machineries. This may be a consequence of evolutionary drift from a common ancestor with a particular function, resulting in modified efficiency in one relative which is then compensated by further mutations to optimize the physico-chemical features of the active site 8The SCOP and CATH classifications organize the 3D structure of proteins into evolutionary classifications that have enabled detailed studies of the molecular mechanisms by which new protein structures and functions evolve. The sequence patterns and fold libraries that they provide have enabled prediction of structural relatives thereby providing structural annotations for more than 50 million domain sequences, available on their sister sites  and in InterPro Although ∼20–25% of domain sequences in the genomes do not currently map to any structural superfamilies in CATH or SCOP, the analyses of structures solved by the structural genomics initiatives in the States – which targeted structurally uncharacterized domain families in Pfam for structural determination – showed that once the structures of these superfamilies had been solved they revealed a structural or evolutionary relationship with an existing fold group or superfamily in SCOP or CATH. In fact, nearly 98% of all new structures deposited in the PDB can be classified in an existing CATH superfamily Over the last few years collaborations between SCOP and CATH have led to mappings between these resources that help to confirm detection of very remote homologues


Caenorhabditis elegans as a model to show that water-soluble cranberry extract standardized to 4.0% proanthocyanidins (WCESP), a major component of cranberries, can enhance host innate immunity to resist against Vibrio cholerae ) infection. Supplementation of WCESP did not significantly alter the intestinal colonization of V. cholerae, but upregulated the expression of C. elegans innate immune genes, such as clec-46, clec-71, fmo-2, pqn-5 and C23G10.1. Additionally, WCESP treatment did not affect the growth of V. cholerae and expression of the major bacterial virulence genes, and only slightly reduced bacterial colonization within C. elegans intestine. These findings indicate that the major components of WCESP, including proanthocyanidins (PACs), may play an important role in enhancing the host innate immunity. Moreover, we engaged C. elegans mutants and identified that the p38 MAPK signaling, insulin/IGF-1 signaling (IIS), and HSF-1 play pivotal roles in the WCESP-mediated host immune response. Considering the level of conservation between the innate immune pathways of C. elegans and humans, the results of this study suggest that WCESP may also play an immunity-promoting role in higher order organisms.Botanicals are rich in bioactive compounds, and some offer numerous beneficial effects to animal and human health when consumed. It is well known that phytochemicals in cranberries have anti-oxidative and antimicrobial activities. Recently, an increasing body of evidence has demonstrated that cranberry phytochemicals may have potential benefits that promote healthy aging. Here, we use  Vaccinium macrocarpon) is rich in various polyphenols, including anthocyanins, flavonols, proanthocyanidins (PACs), phenolic acids, stilbenes, etc. Escherichia coli to uroepithelial cells water-soluble cranberry extract standardized to 4.0% PACs (WCESP) can promote animals' response to abiotic stress accompanied with a prolonged lifespan A broad array of evidence demonstrates a positive link between plant phytochemicals and health-promoting effects, such as cancer prevention, bone health, a reduced risk of cardiovascular and metabolic diseases, etc. Caenorhabditis elegans (C. elegans) has been engaged as an infection model to study host-pathogen interactions. Since C. elegans can be easily exposed to a variety of pathogens without ethical issues, this model has been successfully used to identify and assess virulence factors of several human pathogens, e.g., Pseudomonas aeruginosa, Yersinia pseudotuberculosis, Salmonella enterica, and Vibrio choleraeC. elegans also makes a good model for studying host innate immunity from an evolutionary perspective C. elegans has evolved a complex innate immunity to survive in the natural environment. Their immune responses include physical barriers, avoidance behaviors, secretion of antimicrobial molecules, etc. C. elegans are evolutionarily conserved, such as the p38 mitogen-activated protein kinase (MAPK) pathway, the insulin/insulin-like growth factor-1 signaling (IIS) pathway, the transforming growth factor β (TGF-β) signaling pathway The nematode Vibrio cholerae (V. cholerae), is a severe public and individual health problem around the world. To understand the biology of this disease, it is equally critical to 1) identify and understand the major virulence factors of V. cholerae and 2) understand the host gene expression response to V. cholerae infection. Although many virulence factors of V. cholerae, such as cholera toxin (CT), toxin-coregulated pili (TCP), and hemolysin, have been identified and researched extensively, questions regarding the host response to V. cholerae infection still remain unanswered C. elegans has been introduced to study the interactions with V. cholerae by several research groups. Previous studies showed that V. cholerae can cause lethal infection in C. elegans and this lethality was associated with the bacterial hemolysin HlyA and the extracellular protease PrtV, which are regulated by the quorum sensing LuxO-HapR regulatory pathway C. elegans host model have identified many innate immune genes which are induced upon V. cholerae infection C. elegans infection model as a powerful platform to investigate the protection mechanisms of nutraceuticals and pharmaceuticals against V. cholerae and other bacterial pathogens.Cholera, mediated by V. cholerae and its underlying mechanisms in C. elegans. We found that supplementation of WCESP prolonged the survival time of worms exposed to V. cholerae, implying the protective effects of WCESP against V. cholerae. Utilizing worm mutants, our genetic epistasis analyses suggested that WCESP may act through the p38 MAPK signaling pathway, IIS and HSF-1, but independent of DAF-16, to prevent killing by V. cholerae. Notably, WCESP administration significantly modulated the expression of host innate immune genes, but not the expression of key virulence genes in V. cholerae. Moreover, WCESP consumption did not distinctly decrease the intestinal colonization of V. cholerae in C. elegans. Taken together, our findings unveil the immune-enhancing activity of cranberry and the molecular mechanisms by which WCESP promotes C. elegans innate immunity against V. cholerae infection.In the present study, we attempted to examine the potential effects of WCESP on host immune response to E. coli OP50, V. cholerae wild type C6706 (O1 El Tor isolated from Peru) V. cholerae strain expressing green fluorescence protein (GFP)) Staphylococcus aureus , Pseudomonas aeruginosa , Enterococcus faecalis , Salmonella typhimurium , and E. coli O157:H7 (ATCC#700927). The E. coli, V. cholerae, P. aeruginosa, and S. typhimurium strains were cultured in Luria-Bertani (LB) medium. The S. aureus and E. faecalis strains were cultured in brain-heart infusion (BHI) medium.The bacterial strains used in this experiment were C. elegans strains were maintained at 20°C on nematode growth medium (NGM) seeded with E. coli OP50 feeding strain. 100 µl of OP50 was dropped on the center of 60 mm NGM plates, which were allowed to dry overnight before the culture assays were carried out. Strains used in this study were: N2 Bristol (wild type), sek-1(ag1), pmk-1(km25), daf-2 (e1370), age-1(hx546), daf-16 (mgDf50), hsf-1(sy441), fmo-2(ok2147), dod-22(ok1918), and CL2070(hsp-16.2::GFP). All the strains were obtained from the Caenorhabditis Genetics Center (CGC), University of Minnesota, USA.All Naturex-DBS, LLC  and was described in previously published studies The cranberry extract used in this study was obtained from C. elegans strains were routinely maintained at 20°C on nematode growth medium (NGM) agar plates seeded with E. coli OP50 feeding strain. Synchronized worm populations were acquired by allowing 10–15 hermaphrodites to lay eggs for 4 hours at 20°C, and then the parents were removed. V. cholerae was grown in freshly prepared LB medium overnight with shaking at 37°C. The overnight culture was then 10-fold concentrated and dropped (20 µl) on the center of 35 mm NGM plates (containing 50 µg/ml FUDR to prevent the growth of worm progeny) p-values were calculated using the log-rank test. p-value <0.05 was accepted as statistically significant.The All RNA samples were prepared using RNAzol RT reagent  and stored at −80°C. Complementary DNA was synthesized using the Invitrogen Superscript first strand synthesis system for RT-PCR (Invitrogen).C. elegans immune response genes, the L1 stage nematodes were grown on NGM plates supplemented with or without 2 mg/ml WCESP at 25°C, until they reached the young adult stage. The worms were collected with M9 buffer. Transcription levels of act-1 were used as internal controls to normalize the expression levels of target transcripts.For V. cholerae virulence genes, the wild type C6706 strain was grown overnight at 37°C in LB medium supplemented with or without 2 mg/ml WCESP. The bacteria were collected and washed with PBS buffer. 16S rRNA was used as the endogenous control to normalize the expression levels of target transcripts For −ΔΔCT) method p<0.05 was accepted as statistically significant. The qPCR primers used in this study are listed in qPCR was performed using SensiFAST SYBR No-Rox Kit (Bioline) and the CFX96 real-time PCR detection system according to the manufacturer's suggested protocol (Bio-Rad). The qPCR conditions were: 95°C for 3 minutes, followed by 40 cycles of 10 s at 95°C and 30 s at 60°C. Relative fold-changes for transcripts were calculated using the comparative CT  plates to dwell for one hour.GFP-expressing C. elegans intestine, worms pre-treated with or without WCESP from above LB gentamicin (50 µg/ml) plates were placed on a 2% agarose pad in a 5 µl drop of M9 buffer with 25 mM sodium azide as an anesthetic. When the worms stopped moving, a fluorescence microscope  was used to examine the GFP signal.To determine the fluorescent signal of bacteria in p-value <0.05 was accepted as statistically significant.To enumerate the bacteria in the gut, 10 worms pre-treated with or without WCESP which had spent one hour on a LB gentamicin plate were collected into a 1.5 ml Eppendorf tube and washed twice with 500 µl of sterile M9 buffer for 5 minutes with gentle rotation. Thereafter, the worms were settled and then re-suspended in 250 µl fresh M9 buffer. Silicon carbide particles  were added to disrupt the worms as previously described Whole worm extracts were electrophoresed on a 10% SDS-PAGE gel and then transferred to a Nitrocellulose membrane using a Bio-Rad semi-dry transfer apparatus according to the manufacturer's protocols. After 30 min pre-incubation with 5% nonfat milk in TBST  at room temperature, the membrane was transferred and incubated with primary antibodies in 5% nonfat milk TBST at 4°C for 12 h. Thereafter, membrane was washed three times for 10 min and incubated with horseradish peroxidase (HRP)-conjugated secondary antibodies at room temperature. After 1.5 hours, membrane was washed with TBST three times and developed with the ECL system (Thermo Scientific) according to the manufacturer's protocols. Anti-phospho-p38 MAPK monoclonal antibody 28B10 (1∶500) was purchased from Cell Signaling. Anti-Actin monoclonal antibody MAB1501  was purchased from EMD Millipore. The goat anti-mouse IgG antibody (H&L) [HRP]  was purchased from GenScript.V. cholerae killing assays in C. elegans. Specifically, wild-type C. elegans N2 worms were supplemented with WCESP on NGM plates from the early L1 stage until L4/young adult. Thereafter, the L4/young adult wild type N2 worms were transferred and exposed to V. cholerae C6706 infection in the presence of WCESP. The survival of these worms was monitored every 24 hours until the last worm died. Worms cultured without WCESP supplementation served as the control. It was found that WCESP treatment resulted in significantly prolonged survival time (p<0.001) than that of the control, suggesting the protective effects of WCESP on V. cholerae infection  V. cholerae was recovered from the WCESP treated worms than that from the control worms (79±10 vs. 107±13 CFUs/worm), and this difference is statistically significant  that are known for worm lethality by comparing their transcriptional levels in the presence and absence of WCESP using quantitative real-time PCR (qRT-PCR). As seen in ctxA, tcpA, toxT, and toxR genes, which are critical for human infection but dispensable for worm killing. No apparent changes of gene expression were observed in the presence of WCESP  that are known to respond to V. cholerae infection i.e. without V. cholerae exposure . Under the non-infectious condition, five tested immune genes  were consistently induced by 2- to 10-fold in the presence of WCESP (P<0.05)  and dod-22(ok1918), when exposed to V. cholerae in the presence of WCESP. It was known that WSESP supplementation did not alter the expression of dod-22, while fmo-2 was up-regulated with WCESP treatment  and fmo-2  deletion mutant worms when exposed to V. cholerae infection. This finding may suggest that the immune genes regulated by WCESP function cohesively to promote host immunity in response to V. cholerae infection. Hence, a single gene alteration in this network may not be able to dramatically alleviate immunity induced by WCESP. Taken together, our findings suggest a general health-promoting effect of WCESP in C. elegans under non-infection condition.Expression of immune response genes may be induced by the WCESP, thereby enhancing worm's defense against (P<0.05) , while treatment . IntriguC. eleganspmk-1 (km25) mutant worms with 2 mg/ml WCESP and found that WCESP did not extend the survival time of the pmk-1 (km25) mutant when exposed to V. cholerae, relative to the controls without WCESP supplementation  worms when exposed to V. cholerae infection compared to the genotype matched non-supplemented controls  and found that these worms supplemented with WCESP showed stronger GFP signal relative to non-treated controls. Subsequently, we performed qRT-PCR to measure the mRNA level of hsp-16.2 in wild-type N2 worms. In consistent with our GFP observation, WCESP treatment resulted in at least two-fold increase of hsp-16.2 at transcriptional level as compared to the non-supplemented controls. These findings suggested that WCESP contribute to the protection by increasing the transactivity of HSF-1.DAF-16 and heat-shock transcription factor (HSF) -1 are two important transcription factors that have been reported to modulate nfection , Table 2cholerae , Table 2V. cholerae infection daf-2 and age-1, two major components of IIS pathway. Mutant worms, daf-2(e1370) and age-1(hx546), were treated with WCESP and their survival was monitored under infection with V. cholerae. As compared to the genotype matched non-supplemented controls, neither daf-2(e1370) nor age-1(hx546) showed significantly prolonged survival time with WCESP supplementation  and endoplasmic reticulum (ER) stress response contribute to the regulation of host immunity V. cholerae infection of C. elegans induced expression of a subset of unfolded protein response (UPR) genes, which are generally regulated by ER stress C. elegans pqn-5, an ER stress regulated response gene C. elegans innate immunity in concert with UPR. Previous studies reported that increased activities of HSF-1, either by overexpression or reducing IIS, lead to an enhanced C. elegans innate immunity C. elegans lifespan C. elegans. In support of this, our results indicated that IIS (DAF-2 and AGE-1) is required for WCESP-induced immunity promotion (i.e. WCESP supplementation reduced the activity of IIS, thereby resulted in an elevated activity of HSF-1 and enhanced worm innate immunity against V. cholerae. As IIS can regulate both HSF-1 and DAF-16, the specificity of HSF-1 for WCESP's immunity promoting effects not only emphasized the importance of HSF-1 on C. elegans innate immunity as compared to DAF-16, but also profoundly implied the biological role of HSF-1 in regulating C. elegans innate immunity.It was shown that romotion . This fiV. cholerae infection and highlighted the innate immunity-promoting properties of WCESP. Our findings on the requirement of p38 MAPK pathway and HSF-1 not only revealed the molecular mechanisms by which WCESP promotes C. elegans innate immunity (C. elegans to mammals, the WCESP-induced innate immunity promotion against V. cholerae may have interesting implications in higher order organisms. Interestingly, our latest studies showed that WCESP supplementation in C. elegans could also provide significant protection, against several other bacterial pathogens, such as Staphylococcus aureus and Pseudomonas aeruginosa (This study systematically analyzed the genetic requirements for WCESP-mediated protective effects against immunity , but alsruginosa . Whether