Diabetes increases the risk of cardiovascular complications including arrhythmias, but the underlying mechanisms remain to be established. Decreased conduction velocity (CV), which is an independent risk factor for re-entry arrhythmias, is present in models with streptozotocin (STZ) induced type 1 diabetes. Whether CV is also disturbed in models of type 2 diabetes is currently unknown.
We used Zucker Diabetic Fatty (ZDF) rats, as a model of type 2 diabetes, and their lean controls Zucker Diabetic Lean (ZDL) rats to investigate CV and its response to the anti-arrhythmic peptide analogue AAP10. Gap junction remodeling was examined by immunofluorescence and western blotting. Cardiac histomorphometry was examined by Masson`s Trichrome staining and intracellular lipid accumulation was analyzed by Bodipy staining.
CV was significantly slower in ZDF rats (56±1.9 cm/s) compared to non-diabetic controls (ZDL, 66±1.6 cm/s), but AAP10 did not affect CV in either group. The total amount of Connexin43 (C×43) was identical between ZDF and ZDL rats, but the amount of lateralized C×43 was significantly increased in ZDF rats (42±12 %) compared to ZDL rats (30±8%), p<0.04. Judged by electrophoretic mobility, C×43 phosphorylation was unchanged between ZDF and ZDL rats. Also, no differences in cardiomyocyte size or histomorphometry including fibrosis were observed between groups, but the volume of intracellular lipid droplets was 4.2 times higher in ZDF compared to ZDL rats (p<0.01).
CV is reduced in type 2 diabetic ZDF rats. The CV disturbance may be partly explained by increased lateralization of C×43, but other factors are likely also involved. Our data indicates that lipotoxicity potentially may play a role in development of conduction disturbances and arrhythmias in type 2 diabetes.
Diabetic cardiomyopathy; Arrhythmia; Lipotoxicity; Conduction velocity; Gap junctions; Type 2 diabetes; Zucker Diabetic Fatty (ZDF) rats
Mycophenolic acid (MPA) is a fungal secondary metabolite and the active component in several immunosuppressive pharmaceuticals. The gene cluster coding for the MPA biosynthetic pathway has recently been discovered in Penicillium brevicompactum, demonstrating that the first step is catalyzed by MpaC, a polyketide synthase producing 5-methylorsellinic acid (5-MOA). However, the biochemical role of the enzymes encoded by the remaining genes in the MPA gene cluster is still unknown. Based on bioinformatic analysis of the MPA gene cluster, we hypothesized that the step following 5-MOA production in the pathway is carried out by a natural fusion enzyme MpaDE, consisting of a cytochrome P450 (MpaD) in the N-terminal region and a hydrolase (MpaE) in the C-terminal region. We verified that the fusion gene is indeed expressed in P. brevicompactum by obtaining full-length sequence of the mpaDE cDNA prepared from the extracted RNA. Heterologous coexpression of mpaC and the fusion gene mpaDE in the MPA-nonproducer Aspergillus nidulans resulted in the production of 5,7-dihydroxy-4-methylphthalide (DHMP), the second intermediate in MPA biosynthesis. Analysis of the strain coexpressing mpaC and the mpaD part of mpaDE shows that the P450 catalyzes hydroxylation of 5-MOA to 4,6-dihydroxy-2-(hydroxymethyl)-3-methylbenzoic acid (DHMB). DHMB is then converted to DHMP, and our results suggest that the hydrolase domain aids this second step by acting as a lactone synthase that catalyzes the ring closure. Overall, the chimeric enzyme MpaDE provides insight into the genetic organization of the MPA biosynthesis pathway.
The interaction between antibodies and antigens is one of the most important immune system mechanisms for clearing infectious organisms from the host. Antibodies bind to antigens at sites referred to as B-cell epitopes. Identification of the exact location of B-cell epitopes is essential in several biomedical applications such as; rational vaccine design, development of disease diagnostics and immunotherapeutics. However, experimental mapping of epitopes is resource intensive making in silico methods an appealing complementary approach. To date, the reported performance of methods for in silico mapping of B-cell epitopes has been moderate. Several issues regarding the evaluation data sets may however have led to the performance values being underestimated: Rarely, all potential epitopes have been mapped on an antigen, and antibodies are generally raised against the antigen in a given biological context not against the antigen monomer. Improper dealing with these aspects leads to many artificial false positive predictions and hence to incorrect low performance values. To demonstrate the impact of proper benchmark definitions, we here present an updated version of the DiscoTope method incorporating a novel spatial neighborhood definition and half-sphere exposure as surface measure. Compared to other state-of-the-art prediction methods, Discotope-2.0 displayed improved performance both in cross-validation and in independent evaluations. Using DiscoTope-2.0, we assessed the impact on performance when using proper benchmark definitions. For 13 proteins in the training data set where sufficient biological information was available to make a proper benchmark redefinition, the average AUC performance was improved from 0.791 to 0.824. Similarly, the average AUC performance on an independent evaluation data set improved from 0.712 to 0.727. Our results thus demonstrate that given proper benchmark definitions, B-cell epitope prediction methods achieve highly significant predictive performances suggesting these tools to be a powerful asset in rational epitope discovery. The updated version of DiscoTope is available at www.cbs.dtu.dk/services/DiscoTope-2.0.
The human immune system has an incredible ability to fight pathogens (bacterial, fungal and viral infections). One of the most important immune system events involved in clearing infectious organisms is the interaction between the antibodies and antigens (molecules such as proteins from the pathogenic organism). Antibodies bind to antigens at sites known as B-cell epitopes. Hence, identification of areas on the surface antigens capable of binding to antibodies (also known as B-cell epitopes) may aid the development of various immune related applications (e.g. vaccines and immunotherapeutic). However, experimental identification of B-cell epitopes is a resource intensive task, thereby making computer-aided methods an appealing complementary approach. Previously reported performances of methods for B cell epitope predictive have been moderate. Here, we present an updated version of the B-cell epitope prediction method; DiscoTope, that on the basis of a protein structure and epitope propensity scores predicts residues likely to be involved in B-cell epitopes. We demonstrate that the low performances to some extent can be explained by poorly defined benchmarks, and that inclusion of additional biological information greatly enhances the predictive performance. This suggests that, given proper benchmark definitions, state-of-the-art B cell epitope prediction methods perform significantly better than generally assumed.
In all vertebrate animals, CD8+ cytotoxic T lymphocytes (CTLs) are controlled by major histocompatibility complex class I (MHC-I) molecules. These are highly polymorphic peptide receptors selecting and presenting endogenously derived epitopes to circulating CTLs. The polymorphism of the MHC effectively individualizes the immune response of each member of the species. We have recently developed efficient methods to generate recombinant human MHC-I (also known as human leukocyte antigen class I, HLA-I) molecules, accompanying peptide-binding assays and predictors, and HLA tetramers for specific CTL staining and manipulation. This has enabled a complete mapping of all HLA-I specificities (“the Human MHC Project”). Here, we demonstrate that these approaches can be applied to other species. We systematically transferred domains of the frequently expressed swine MHC-I molecule, SLA-1*0401, onto a HLA-I molecule (HLA-A*11:01), thereby generating recombinant human/swine chimeric MHC-I molecules as well as the intact SLA-1*0401 molecule. Biochemical peptide-binding assays and positional scanning combinatorial peptide libraries were used to analyze the peptide-binding motifs of these molecules. A pan-specific predictor of peptide–MHC-I binding, NetMHCpan, which was originally developed to cover the binding specificities of all known HLA-I molecules, was successfully used to predict the specificities of the SLA-1*0401 molecule as well as the porcine/human chimeric MHC-I molecules. These data indicate that it is possible to extend the biochemical and bioinformatics tools of the Human MHC Project to other vertebrate species.
Recombinant MHC; Peptide specificity; Binding predictions
In this paper, we describe the methodologies behind three different aspects of the NetMHC family for prediction of MHC class I binding, mainly to HLAs. We we have updated the prediction servers servers, NetMHC-3.2, NetMHCpan-2.2, and a new consensus method, NetMHCcons, which, in their previous versions, have been evaluated to be among the very best performing MHC:peptide binding predictors available. Here we describe the background for these methods, and the rationale behind the different optimisation steps implemented in the methods. We go through the practical use of the methods, which are publicly available in the form of relatively fast and simple web interfaces. Furthermore, we will review results optained in actual epitope discovery projects where previous implementations of the described methods have been used in the initial selection of potential epitopes. Selected potential epitopes were all evaluated experimentally using ex vivo assays.
The prediction of the network of protein-protein interactions (PPI) of an organism is crucial for the understanding of biological processes and for the development of new drugs. Machine learning methods have been successfully applied to the prediction of PPI in yeast by the integration of multiple direct and indirect biological data sources. However, experimental data are not available for most organisms. We propose here an ensemble machine learning approach for the prediction of PPI that depends solely on features independent from experimental data. We developed new estimators of the coevolution between proteins and combined them in an ensemble learning procedure.
We applied this method to a dataset of known co-complexed proteins in Escherichia coli and compared it to previously published methods. We show that our method allows prediction of PPI with an unprecedented precision of 95.5% for the first 200 sorted pairs of proteins compared to 28.5% on the same dataset with the previous best method.
A close inspection of the best predicted pairs allowed us to detect new or recently discovered interactions between chemotactic components, the flagellar apparatus and RNA polymerase complexes in E. coli.
Prediction methods as well as experimental methods for T-cell epitope discovery have developed significantly in recent years. High-throughput experimental methods have made it possible to perform full-length protein scans for epitopes restricted to a limited number of MHC alleles. The high costs and limitations regarding the number of proteins and MHC alleles that are feasibly handled by such experimental methods have made in silico prediction models of high interest. MHC binding prediction methods are today of a very high quality and can predict MHC binding peptides with high accuracy. This is possible for a large range of MHC alleles and relevant length of binding peptides. The predictions can easily be performed for complete proteomes of any size. Prediction methods are still, however, dependent on good experimental methods for validation, and should merely be used as a guide for rational epitope discovery. We expect prediction methods as well as experimental validation methods to continue to develop and that we will soon see clinical trials of products whose development has been guided by prediction methods.
CTL; epitope; HLA; MHC; prediction; T cell; vaccine
Several studies have shown that cancers actively regulate alternative splicing. Altered splicing mechanisms in cancer lead to cancer-specific transcripts different from the pool of transcripts occurring only in healthy tissue. At the same time, altered presentation of HLA class I epitopes is frequently observed in various types of cancer. Down-regulation of genes related to HLA class I antigen processing has been observed in several cancer types, leading to fewer HLA class I antigens on the cell surface. Here, we use a peptidome wide analysis of predicted alternative splice forms, based on a publicly available database, to show that peptides over-represented in cancer splice variants comprise significantly fewer predicted HLA class I epitopes compared to peptides from normal transcripts. Peptides over-represented in cancer transcripts are in the case of the three most common HLA class I supertype representatives consistently found to contain fewer predicted epitopes compared to normal tissue. We observed a significant difference in amino acid composition between protein sequences associated with normal versus cancer tissue, as transcripts found in cancer are enriched with hydrophilic amino acids. This variation contributes to the observed significant lower likelihood of cancer-specific peptides to be predicted epitopes compared to peptides found in normal tissue.
A large panel of methods exists that aim to identify residues with critical impact on protein function based on evolutionary signals, sequence and structure information. However, it is not clear to what extent these different methods overlap, and if any of the methods have higher predictive potential compared to others when it comes to, in particular, the identification of catalytic residues (CR) in proteins. Using a large set of enzymatic protein families and measures based on different evolutionary signals, we sought to break up the different components of the information content within a multiple sequence alignment to investigate their predictive potential and degree of overlap.
Our results demonstrate that the different methods included in the benchmark in general can be divided into three groups with a limited mutual overlap. One group containing real-value Evolutionary Trace (rvET) methods and conservation, another containing mutual information (MI) methods, and the last containing methods designed explicitly for the identification of specificity determining positions (SDPs): integer-value Evolutionary Trace (ivET), SDPfox, and XDET. In terms of prediction of CR, we find using a proximity score integrating structural information (as the sum of the scores of residues located within a given distance of the residue in question) that only the methods from the first two groups displayed a reliable performance. Next, we investigated to what degree proximity scores for conservation, rvET and cumulative MI (cMI) provide complementary information capable of improving the performance for CR identification. We found that integrating conservation with proximity scores for rvET and cMI achieved the highest performance. The proximity conservation score contained no complementary information when integrated with proximity rvET. Moreover, the signal from rvET provided only a limited gain in predictive performance when integrated with mutual information and conservation proximity scores. Combined, these observations demonstrate that the rvET and cMI scores add complementary information to the prediction system.
This work contributes to the understanding of the different signals of evolution and also shows that it is possible to improve the detection of catalytic residues by integrating structural and higher order sequence evolutionary information with sequence conservation.
Coevolution; Mutual information; Specificity determining position; Catalytic residues; Functional sites; Sequence analysis
Starch is the most important source of calories for human nutrition and the majority of it is produced by cereal farming. Starch is also used as a renewable raw material in a range of industrial sectors. It can be chemically modified to introduce new physicochemical properties. In this way starch is adapted to a variety of specific end-uses. Recombinant DNA technologies offers an alternative to starch industrial processing. The plant biosynthetic pathway can be manipulated to design starches with novel structure and improved technological properties. In the future this may reduce or eliminate the economical and environmental costs of industrial modification. Recently, many advances have been achieved to clarify the genetic mechanism that controls starch biosynthesis. Several genes involved in the synthesis and modification of complex carbohydrates in many organisms have been identified and cloned. This knowledge suggests a number of strategies and a series of candidate genes for genetic transformation of crops to generate new types of starch-based polymers. However transformation of cereals is a slow process and there is no easy model system available to test the efficiency of candidate genes in planta.
We explored the possibility to use transgenic barley callus generated from immature embryo for a fast test of transgenic modification strategies of starch biosynthesis. We found that this callus contains 4% (w/w dw) starch granules, which we could modify by generating fully transgenic calli by Agrobacterium-transformation. A Green Fluorescent Protein reporter protein tag was used to identify and propagate only fully transgenic callus explants. Around 1 – 1.5 g dry weight of fully transgenic callus could be produced in 9 weeks. Callus starch granules were smaller than endosperm starch granules and contained less amylose. Similarly the expression profile of starch biosynthesis genes were slightly different in callus compared with developing endosperm.
In this study we have developed an easy and rapid in planta model system for starch bioengineering in cereals. We suggest that this method can be used as a time-efficient model system for fast screening of candidate genes for the generation of modified starch or new types of carbohydrate polymers.
Malaria during pregnancy in Plasmodium falciparum endemic regions is a major cause of mortality and severe morbidity. VAR2CSA is the parasite ligand responsible for sequestration of Plasmodium falciparum infected erythrocytes to the receptor chondroitin sulfate A (CSA) in the placenta and is the leading candidate for a placental malaria vaccine. Antibodies induced in rats against the recombinant DBL4ε domain of VAR2CSA inhibit the binding of a number of laboratory and field parasite isolates to CSA. In this study, we used a DBL4ε peptide-array to identify epitopes targeted by DBL4ε-specific antibodies that inhibit CSA-binding of infected erythrocytes. We identified three regions of overlapping peptides which were highly antigenic. One peptide region distinguished itself particularly by showing a clear difference in the binding profile of highly parasite blocking IgG compared to the IgG with low capacity to inhibit parasite adhesion to CSA. This region was further characterized and together these results suggest that even though antibodies against the synthetic peptides which cover this region did not recognize native protein, the results using the mutant domain suggest that this linear epitope might be involved in the induction of inhibitory antibodies induced by the recombinant DBL4ε domain.
Enzymes play a fundamental role in almost all biological processes and identification of catalytic residues is a crucial step for deciphering the biological functions and understanding the underlying catalytic mechanisms. In this work, we developed a novel structural feature called MEDscore to identify catalytic residues, which integrated the microenvironment (ME) and geometrical properties of amino acid residues. Firstly, we converted a residue's ME into a series of spatially neighboring residue pairs, whose likelihood of being located in a catalytic ME was deduced from a benchmark enzyme dataset. We then calculated an ME-based score, termed as MEscore, by summing up the likelihood of all residue pairs. Secondly, we defined a parameter called Dscore to measure the relative distance of a residue to the center of the protein, provided that catalytic residues are typically located in the center of the protein structure. Finally, we defined the MEDscore feature based on an effective nonlinear integration of MEscore and Dscore. When evaluated on a well-prepared benchmark dataset using five-fold cross-validation tests, MEDscore achieved a robust performance in identifying catalytic residues with an AUC1.0 of 0.889. At a ≤10% false positive rate control, MEDscore correctly identified approximately 70% of the catalytic residues. Remarkably, MEDscore achieved a competitive performance compared with the residue conservation score (e.g. CONscore), the most informative singular feature predominantly employed to identify catalytic residues. To the best of our knowledge, MEDscore is the first singular structural feature exhibiting such an advantage. More importantly, we found that MEDscore is complementary with CONscore and a significantly improved performance can be achieved by combining CONscore with MEDscore in a linear manner. As an implementation of this work, MEDscore has been made freely accessible at http://protein.cau.edu.cn/mepi/.
CD4+ T cells orchestrate immunity against viral infections, but their importance in HIV infection remains controversial. Nevertheless, comprehensive studies have associated increase in breadth and functional characteristics of HIV-specific CD4+ T cells with decreased viral load. A major challenge for the identification of HIV-specific CD4+ T cells targeting broadly reactive epitopes in populations with diverse ethnic background stems from the vast genomic variation of HIV and the diversity of the host cellular immune system. Here, we describe a novel epitope selection strategy, PopCover, that aims to resolve this challenge, and identify a set of potential HLA class II-restricted HIV epitopes that in concert will provide optimal viral and host coverage. Using this selection strategy, we identified 64 putative epitopes (peptides) located in the Gag, Nef, Env, Pol and Tat protein regions of HIV. In total, 73% of the predicted peptides were found to induce HIV-specific CD4+ T cell responses. The Gag and Nef peptides induced most responses. The vast majority of the peptides (93%) had predicted restriction to the patient’s HLA alleles. Interestingly, the viral load in viremic patients was inversely correlated to the number of targeted Gag peptides. In addition, the predicted Gag peptides were found to induce broader polyfunctional CD4+ T cell responses compared to the commonly used Gag-p55 peptide pool. These results demonstrate the power of the PopCover method for the identification of broadly recognized HLA class II-restricted epitopes. All together, selection strategies, such as PopCover, might with success be used for the evaluation of antigen-specific CD4+ T cell responses and design of future vaccines.
Placental malaria infections are caused by Plasmodium falciparum–infected red blood cells sequestering in the placenta by binding to chondroitin sulfate A, mediated by VAR2CSA, a variant of the PfEMP1 family of adhesion antigens. Recent studies have shown that many P. falciparum genomes have multiple genes coding for different VAR2CSA proteins, and parasites with >1 var2csa gene appear to be more common in pregnant women with placental malaria than in nonpregnant individuals. We present evidence that, in pregnant women, parasites containing multiple var2csa-type genes possess a selective advantage over parasites with a single var2csa gene. Accumulation of parasites with multiple copies of the var2csa gene during the course of pregnancy was also correlated with the development of antibodies involved in blocking VAR2CSA adhesion. The data suggest that multiplicity of var2csa-type genes enables P. falciparum parasites to persist for a longer period of time during placental infections, probably because of their greater capacity for antigenic variation and evasion of variant-specific immune responses.
Epitope mapping from affinity-selected peptides has become popular in epitope prediction, and correspondingly many Web-based tools have been developed in recent years. However, the performance of these tools varies in different circumstances. To address this problem, we employed an ensemble approach to incorporate two popular Web tools, MimoPro and Pep-3D-Search, together for taking advantages offered by both methods so as to give users more options for their specific purposes of epitope-peptide mapping. The combined operation of Union finds as many associated peptides as possible from both methods, which increases sensitivity in finding potential epitopic regions on a given antigen surface. The combined operation of Intersection achieves to some extent the mutual verification by the two methods and hence increases the likelihood of locating the genuine epitopic region on a given antigen in relation to the interacting peptides. The Consistency between Intersection and Union is an indirect sufficient condition to assess the likelihood of successful peptide-epitope mapping. On average from 27 tests, the combined operations of PepMapper outperformed either MimoPro or Pep-3D-Search alone. Therefore, PepMapper is another multipurpose mapping tool for epitope prediction from affinity-selected peptides. The Web server can be freely accessed at: http://informatics.nenu.edu.cn/PepMapper/
Seq2Logo is a web-based sequence logo generator. Sequence logos are a graphical representation of the information content stored in a multiple sequence alignment (MSA) and provide a compact and highly intuitive representation of the position-specific amino acid composition of binding motifs, active sites, etc. in biological sequences. Accurate generation of sequence logos is often compromised by sequence redundancy and low number of observations. Moreover, most methods available for sequence logo generation focus on displaying the position-specific enrichment of amino acids, discarding the equally valuable information related to amino acid depletion. Seq2logo aims at resolving these issues allowing the user to include sequence weighting to correct for data redundancy, pseudo counts to correct for low number of observations and different logotype representations each capturing different aspects related to amino acid enrichment and depletion. Besides allowing input in the format of peptides and MSA, Seq2Logo accepts input as Blast sequence profiles, providing easy access for non-expert end-users to characterize and identify functionally conserved/variable amino acids in any given protein of interest. The output from the server is a sequence logo and a PSSM. Seq2Logo is available at http://www.cbs.dtu.dk/biotools/Seq2Logo (14 May 2012, date last accessed).
The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.
Deciphering the cellular immunome of a bacterial pathogen is challenging due to the enormous number of putative peptidic determinants. State-of-the-art prediction methods developed in recent years enable to significantly reduce the number of peptides to be screened, yet the number of remaining candidates for experimental evaluation is still in the range of ten-thousands, even for a limited coverage of MHC alleles. We have recently established a resource-efficient approach for down selection of candidates and enrichment of true positives, based on selection of predicted MHC binders located in high density “hotspots" of putative epitopes. This cluster-based approach was applied to an unbiased, whole genome search of Francisella tularensis CTL epitopes and was shown to yield a 17–25 fold higher level of responders as compared to randomly selected predicted epitopes tested in Kb/Db C57BL/6 mice. In the present study, we further evaluate the cluster-based approach (down to a lower density range) and compare this approach to the classical affinity-based approach by testing putative CTL epitopes with predicted IC50 values of <10 nM. We demonstrate that while the percent of responders achieved by both approaches is similar, the profile of responders is different, and the predicted binding affinity of most responders in the cluster-based approach is relatively low (geometric mean of 170 nM), rendering the two approaches complimentary. The cluster-based approach is further validated in BALB/c F. tularensis immunized mice belonging to another allelic restriction (Kd/Dd) group. To date, the cluster-based approach yielded over 200 novel F. tularensis peptides eliciting a cellular response, all were verified as MHC class I binders, thereby substantially increasing the F. tularensis dataset of known CTL epitopes. The generality and power of the high density cluster-based approach suggest that it can be a valuable tool for identification of novel CTLs in proteomes of other bacterial pathogens.
Binding of peptides to major histocompatibility complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC genomic region (called HLA) is extremely polymorphic comprising several thousand alleles, each encoding a distinct MHC molecule. The potentially unique specificity of the majority of HLA alleles that have been identified to date remains uncharacterized. Likewise, only a limited number of chimpanzee and rhesus macaque MHC class I molecules have been characterized experimentally. Here, we present NetMHCpan-2.0, a method that generates quantitative predictions of the affinity of any peptide–MHC class I interaction. NetMHCpan-2.0 has been trained on the hitherto largest set of quantitative MHC binding data available, covering HLA-A and HLA-B, as well as chimpanzee, rhesus macaque, gorilla, and mouse MHC class I molecules. We show that the NetMHCpan-2.0 method can accurately predict binding to uncharacterized HLA molecules, including HLA-C and HLA-G. Moreover, NetMHCpan-2.0 is demonstrated to accurately predict peptide binding to chimpanzee and macaque MHC class I molecules. The power of NetMHCpan-2.0 to guide immunologists in interpreting cellular immune responses in large out-bred populations is demonstrated. Further, we used NetMHCpan-2.0 to predict potential binding peptides for the pig MHC class I molecule SLA-1*0401. Ninety-three percent of the predicted peptides were demonstrated to bind stronger than 500 nM. The high performance of NetMHCpan-2.0 for non-human primates documents the method's ability to provide broad allelic coverage also beyond human MHC molecules. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan.
MHC class I; Binding specificity; Non-human primates; Artificial neural networks; CTL epitopes
Recent advances in high-throughput technologies have made it possible to generate both gene and protein sequence data at an unprecedented rate and scale thereby enabling entirely new “omics”-based approaches towards the analysis of complex biological processes. However, the amount and complexity of data that even a single experiment can produce seriously challenges researchers with limited bioinformatics expertise, who need to handle, analyze and interpret the data before it can be understood in a biological context. Thus, there is an unmet need for tools allowing non-bioinformatics users to interpret large data sets. We have recently developed a method, NNAlign, which is generally applicable to any biological problem where quantitative peptide data is available. This method efficiently identifies underlying sequence patterns by simultaneously aligning peptide sequences and identifying motifs associated with quantitative readouts. Here, we provide a web-based implementation of NNAlign allowing non-expert end-users to submit their data (optionally adjusting method parameters), and in return receive a trained method (including a visual representation of the identified motif) that subsequently can be used as prediction method and applied to unknown proteins/peptides. We have successfully applied this method to several different data sets including peptide microarray-derived sets containing more than 100,000 data points.
NNAlign is available online at http://www.cbs.dtu.dk/services/NNAlign.
Assigning functions to newly discovered genes constitutes one of the major challenges en route to fully exploiting the data becoming available from the genome sequencing initiatives. Heterologous expression in an appropriate host is central in functional genomics studies. In this context, filamentous fungi offer many advantages over bacterial and yeast systems. To facilitate the use of filamentous fungi in functional genomics, we present a versatile cloning system that allows a gene of interest to be expressed from a defined genomic location of Aspergillus nidulans. By a single USER cloning step, genes are easily inserted into a combined targeting-expression cassette ready for rapid integration and analysis. The system comprises a vector set that allows genes to be expressed either from the constitutive PgpdA promoter or from the inducible PalcA promoter. Moreover, by using the vector set, protein variants can easily be made and expressed from the same locus, which is mandatory for proper comparative analyses. Lastly, all individual elements of the vectors can easily be substituted for other similar elements, ensuring the flexibility of the system. We have demonstrated the potential of the system by transferring the 7,745-bp large mpaC gene from Penicillium brevicompactum to A. nidulans. In parallel, we produced defined mutant derivatives of mpaC, and the combined analysis of A. nidulans strains expressing mpaC or mutated mpaC genes unequivocally demonstrated that mpaC indeed encodes a polyketide synthase that produces the first intermediate in the production of the medically important immunosuppressant mycophenolic acid.
Epitopes from all available full-length sequences of yellow fever virus (YFV) and dengue fever virus (DENV) restricted by Human Leukocyte Antigen class I (HLA-I) alleles covering 12 HLA-I supertypes were predicted using the NetCTL algorithm. A subset of 179 predicted YFV and 158 predicted DENV epitopes were selected using the EpiSelect algorithm to allow for optimal coverage of viral strains. The selected predicted epitopes were synthesized and approximately 75% were found to bind the predicted restricting HLA molecule with an affinity, KD, stronger than 500 nM. The immunogenicity of 25 HLA-A*02:01, 28 HLA-A*24:02 and 28 HLA-B*07:02 binding peptides was tested in three HLA-transgenic mice models and led to the identification of 17 HLA-A*02:01, 4 HLA-A*2402 and 4 HLA-B*07:02 immunogenic peptides. The immunogenic peptides bound HLA significantly stronger than the non-immunogenic peptides. All except one of the immunogenic peptides had KD below 100 nM and the peptides with KD below 5 nM were more likely to be immunogenic. In addition, all the immunogenic peptides that were identified as having a high functional avidity had KD below 20 nM. A*02:01 transgenic mice were also inoculated twice with the 17DD YFV vaccine strain. Three of the YFV A*02:01 restricted peptides activated T-cells from the infected mice in vitro. All three peptides that elicited responses had an HLA binding affinity of 2 nM or less. The results indicate the importance of the strength of HLA binding in shaping the immune response.
Foot-and-mouth disease (FMD) continues to be a significant threat to the health and economic value of livestock species. This acute infection is caused by the highly contagious FMD virus (FMDV), which infects cloven-hoofed animals, including large and small ruminants and swine. Current vaccine strategies are all directed toward the induction of neutralizing antibody responses. However, the role of cytotoxic T lymphocytes (CTLs) has not received a great deal of attention, in part because of the technical difficulties associated with establishing a reliable assay of cell killing for this highly cytopathic virus. Here, we have used recombinant human adenovirus vectors as a means of delivering FMDV antigens in a T cell-directed vaccine in pigs. We tested the hypothesis that impaired processing of the FMDV capsid would enhance cytolytic activity, presumably by targeting all proteins for degradation and effectively increasing the class I major histocompatibility complex (MHC)/FMDV peptide concentration for stimulation of a CTL response. We compared such a T cell-targeting vaccine with the parental vaccine, previously shown to effectively induce a neutralizing antibody response. Our results show induction of FMDV-specific CD8+ CTL killing of MHC-matched target cells in an antigen-specific manner. Further, we confirm these results by MHC tetramer staining. This work presents the first demonstration of FMDV-specific CTL killing and confirmation by MHC tetramer staining in response to vaccination against FMDV.
In Plasmodium falciparum malaria endemic areas placental malaria (PM) is an important complication of malaria. The recurrence of malaria in primigravidae women irrespective of acquired protection during childhood is caused by the interaction between the parasite-expressed VAR2CSA antigen and chondroitin sulfate A (CSA) in the placental intervillous space and lack of protective antibodies. PM impairs fetal development mainly by excessive inflammation processes. After infections during pregnancy women acquire immunity to PM conferred by antibodies against VAR2CSA. Ideally, a vaccine against PM will induce antibody-mediated immune responses that block the adhesion of infected erythrocytes (IE) in the placenta.
We have previously shown that antibodies raised in rat against individual domains of VAR2CSA can block IE binding to CSA. In this study we have immunized mice, rats and rabbits with each individual domain and the full-length protein corresponding to the FCR3 VAR2CSA variant. We found there is an inherently higher immunogenicity of C-terminal domains compared to N-terminally located domains. This was irrespective of whether antibodies were induced against single domains or the full-length protein. Species-specific antibody responses were also found, these were mainly directed against single domains and not the full-length VAR2CSA protein.
Binding inhibitory antibodies appeared to be against conformational B-cell epitopes. Non-binding inhibitory antibodies reacted highly against the C-terminal end of the VAR2CSA molecule especially the highly polymorphic DBL6ε domain. Differential species-specific induction of antibody responses may allow for more direct analysis of functional versus non-functional B-cell epitopes.