NetMHCpan,a method for MHC class I binding prediction beyond humans

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 . Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

Limitations of Ab Initio Predictions of Peptide Binding to MHC Class II Molecules

Successful predictions of peptide MHC binding typically require a large set of binding data for the specific MHC molecule that is examined. Structure based prediction methods promise to circumvent this requirement by evaluating the physical contacts a peptide can make with an MHC molecule based on the highly conserved 3D structure of peptide:MHC complexes. While several such methods have been described before, most are not publicly available and have not been independently tested for their performance. We here implemented and evaluated three prediction methods for MHC class II molecules: statistical potentials derived from the analysis of known protein structures; energetic evaluation of different peptide snapshots in a molecular dynamics simulation; and direct analysis of contacts made in known 3D structures of peptide:MHC complexes. These methods are ab initio in that they require structural data of the MHC molecule examined, but no specific peptide:MHC binding data. Moreover, these methods retain the ability to make predictions in a sufficiently short time scale to be useful in a real world application, such as screening a whole proteome for candidate binding peptides. A rigorous evaluation of each methods prediction performance showed that these are significantly better than random, but still substantially lower than the best performing sequence based class II prediction methods available. While the approaches presented here were developed independently, we have chosen to present our results together in order to support the notion that generating structure based predictions of peptide:MHC binding without using binding data is unlikely to give satisfactory results.     

TiArA: A Virtual Appliance for the Analysis of Tiling Array Data

Background

Genomic tiling arrays have been described in the scientific literature since 2003, yet there is a shortage of user-friendly applications available for their analysis.

Methodology/Principal Findings

Tiling Array Analyzer (TiArA) is a software program that provides a user-friendly graphical interface for the background subtraction, normalization, and summarization of data acquired through the Affymetrix tiling array platform. The background signal is empirically measured using a group of nonspecific probes with varying levels of GC content and normalization is performed to enforce a common dynamic range.

Conclusions/Significance

TiArA is implemented as a standalone program for Linux systems and is available as a cross-platform virtual machine that will run under most modern operating systems using virtualization software such as Sun VirtualBox or VMware. The software is available as a Debian package or a virtual appliance at http://purl.org/NET/tiara.     

Deciphering the MHC-associated peptidome: a review of naturally processed ligand data

Introduction: The availability of big data sets (‘OMICS’) has greatly impacted fundamental and translational science. High-throughput analysis of HLA class I and II associated peptidomes by mass spectrometry (MS) has generated large datasets, with the last decade witnessing tremendous growth in the breadth and number of studies.

Areas covered: For this, we first analyzed naturally processed peptide (NP) data captured within the IEDB to survey and characterize the current state of NP data. We next asked to what extent the NP data overlap with existing T cell epitope and MHC binding data.

Expert commentary: The current collection of NP data represents a large and diverse set of class I/II peptides mostly derived from self-antigens. These data overlap only marginally with existing immunogenicity and binding data and it is thus difficult to ascertain the correspondence between the different assay methodologies. This highlights a need for unbiased studies benchmarking in model antigen systems how well MHC binding and NP data predicts immunogenicity. Going forward, efforts at generating an integrated process for capturing all NP, curating associated metadata and accessing NP data from an immunological viewpoint will be important for development of novel methods for identifying optimal target antigens and for class I and II epitope prediction.     

Primary rat hepatocyte culture on 3D nanofibrous polymer scaffolds for toxicology and pharmaceutical research

Primary rat hepatocytes are a widely used experimental model to estimate drug metabolism and toxicity. In currently used two‐dimensional (2D) cell culture systems, typical problems like morphological changes and the loss of liver cell‐specific functions occur. We hypothesize that the use of polymer scaffolds could overcome these problems and support the establishment of three‐dimensional (3D) culture systems in pharmaceutical research. Isolated primary rat hepatocytes were cultured on collagen‐coated nanofibrous scaffolds for 7 days. Cell loading efficiency was quantified via DNA content measurement. Cell viability and presence of liver‐cell‐specific functions (albumin secretion, glycogen storage capacity) were evaluated. The activity of liver‐specific factors was analyzed by immunofluorescent staining. RNA was isolated to establish quantitative real‐time PCR. Our results indicate that primary rat hepatocytes cultured on nanofibrous scaffolds revealed high viability and well‐preserved glycogen storage. Albumin secretion was existent during the entire culture period. Hepatocytes remain HNF‐4 positive, indicating highly preserved cell differentiation. Aggregated hepatocytes re‐established positive signaling for Connexin 32, a marker for differentiated hepatocyte interaction. ZO‐1‐positive hepatocytes were detected indicating formation of tight junctions. Expression of cytochrome isoenzymes was inducible. Altogether the data suggest that nanofibrous scaffolds provide a good in vitro microenvironment for neo tissue regeneration of primary rat hepatocytes. Biotechnol. Bioeng. 2011; 108:141–150. © 2010 Wiley Periodicals, Inc.     

Functional classification of class II human leukocyte antigen (HLA) molecules reveals seven different supertypes and a surprising degree of repertoire sharing across supertypes

Previous studies have attempted to define human leukocyte antigen (HLA) class II supertypes, analogous to the case for class I, on the basis of shared peptide-binding motifs or structure. In the present study, we determined the binding capacity of a large panel of non-redundant peptides for a set of 27 common HLA DR, DQ, and DP molecules. The measured binding data were then used to define class II supertypes on the basis of shared binding repertoires. Seven different supertypes (main DR, DR4, DRB3, main DQ, DQ7, main DP, and DP2) were defined. The molecules associated with the respective supertypes fell largely along lines defined by MHC locus and reflect, in broad terms, commonalities in reported peptide-binding motifs. Repertoire overlaps between molecules within the same class II supertype were found to be similar in magnitude to what has been observed for HLA class I supertypes. Surprisingly, however, the degree to which repertoires between molecules in the different class II supertypes also overlapped was found to be five to tenfold higher than repertoire overlaps noted between molecules in different class I supertypes. These results highlight a high degree of repertoire overlap amongst all HLA class II molecules, perhaps reflecting binding in multiple registers, and more pronounced dependence on backbone interactions rather than peptide anchor residues. This fundamental difference between HLA class I and class II would not have been predicted on the basis of analysis of either binding motifs or the sequence/predicted structures of the HLA molecules.     

Insights into HLA-restricted T cell responses in a novel mouse model of dengue virus infection point toward new implications for vaccine design

The frequency of dengue virus (DENV) infection has increased dramatically in the last few decades, and the lack of a vaccine has led to significant morbidity and mortality worldwide. To date, a convenient murine system to study human T cell responses to DENV has not been available. Mice transgenic for HLA are widely used to model human immune responses, and it has been shown that mouse-passaged DENV is able to replicate to significant levels in IFN-α/βR(-/-) mice. To cover a wide range of HLA phenotypes, we backcrossed IFN-α/βR(-/-) mice with HLA A*0201, A*0101, A*1101, B*0702, and DRB1*0101-transgenic mice. A DENV proteome-wide screen identified a total of 42 epitopes across all HLA-transgenic IFN-α/βR(-/-) strains tested. In contrast, only eight of these elicited responses in the corresponding IFN-α/βR(+/+) mice. We were able to identify T cell epitopes from 9 out of the 10 DENV proteins. However, the majority of responses were derived from the highly conserved nonstructural proteins NS3 and NS5. The relevance of this model is further demonstrated by the fact that most of the epitopes identified in our murine system are also recognized by PBMC from DENV-exposed human donors, and a dominance of HLA B*0702-restricted responses has been detected in both systems. Our results provide new insights into HLA-restricted T cell responses against DENV, and we describe in this study a novel murine model that allows the investigation of T cell-mediated immune mechanisms relevant to vaccine design.     

Defining the herpes simplex virus-specific CD8+ T cell repertoire in C57BL/6 mice

HSV type 1 (HSV-1) expresses its genes sequentially as immediate early (α), early (β), leaky late (γ1), and true late (γ2), where viral DNA synthesis is an absolute prerequisite only for γ2 gene expression. The γ1 protein glycoprotein B (gB) contains a strongly immunodominant CD8(+) T cell epitope (gB(498-505)) that is recognized by 50% of both the CD8(+) effector T cells in acutely infected trigeminal ganglia (TG) and the CD8(+) memory T cells in latently infected TG. Of 376 predicted HSV-1 CD8(+) T cell epitopes in C57BL/6 mice, 19 (gB(498-505) and 18 subdominant epitopes) stimulated CD8(+) T cells in the spleens and TG of HSV-1 acutely infected mice. These 19 epitopes identified virtually all CD8(+) T cells in the infected TG that represent all or the vast majority of the HSV-specific CD8(+) TCR repertoire. Only 11 of ～84 HSV-1 proteins are recognized by CD8(+) T cells, and most (～80%) are expressed before viral DNA synthesis. Neither the immunodominance of gB(498-505) nor the dominance hierarchy of the subdominant epitopes is due solely to MHC or TCR affinity. We conclude that the vast majority of CD8(+) T cells in HSV-1 acutely infected TG are HSV specific, that HSV-1 β and γ1 proteins that are expressed before viral DNA synthesis are favored targets of CD8(+) T cells, and that dominance within the TCR repertoire is likely due to the frequency or expansion and survival characteristics of CD8(+) T cell precursors.     

Bi-specific aptamers mediating tumor cell lysis

Antibody-dependent cellular cytotoxicity plays a pivotal role in antibody-based tumor therapies and is based on the recruitment of natural killer cells to antibody-bound tumor cells via binding of the Fcγ receptor III (CD16). Here we describe the generation of chimeric DNA aptamers that simultaneously bind to CD16α and c-Met, a receptor that is overexpressed in many tumors. By application of the systematic evolution of ligands by exponential enrichment (SELEX) method, CD16α specific DNA aptamers were isolated that bound with high specificity and affinity (91 pm-195 nm) to their respective recombinant and cellularly expressed target proteins. Two optimized CD16α specific aptamers were coupled to each of two c-Met specific aptamers using different linkers. Bi-specific aptamers retained suitable binding properties and displayed simultaneous binding to both antigens. Moreover, they mediated cellular cytotoxicity dependent on aptamer and effector cell concentration. Displacement of a bi-specific aptamer from CD16α by competing antibody 3G8 reduced cytotoxicity and confirmed the proposed mode of action. These results represent the first gain of a tumor-effective function of two distinct oligonucleotides by linkage into a bi-specific aptamer mediating cellular cytotoxicity.