Steric Inhibition of 5' UTR Regulatory Elements Results in Upregulation of Human CFTR.

Cystic fibrosis (CF) is an autosomal recessive monogenic disease caused by mutations in the CFTR gene. Therapeutic approaches that are focused on correcting CFTR protein face the challenge of the heterogeneity in CFTR mutations and resulting defects. Thus, while several small molecules directed at CFTR show benefit in the clinic for subsets of CF patients, these drugs cannot treat all CF patients. Additionally, the clinical benefit from treatment with these modulators could be enhanced with novel therapies. To address this unmet need, we utilized an approach to increase CFTR protein levels through antisense oligonucleotide (ASO)-mediated steric inhibition of 5' UTR regulatory elements. We identified ASOs to upregulate CFTR protein expression and confirmed the regulatory role of the sites amenable to ASO-mediated upregulation. Two ASOs were investigated further, and both increased CFTR protein expression and function in cell lines and primary human bronchial epithelial cells with distinct CF genotypes. ASO treatment further increased CFTR function in almost all CF genotypes tested on top of treatment with the FDA approved drug Symdeko (ivacaftor and tezacaftor). Thus, we present a novel approach to CFTR therapeutic intervention, through ASO-mediated modulation of translation.

Oncogenic Role of THOR, a Conserved Cancer/Testis Long Non-coding RNA.

Large-scale transcriptome sequencing efforts have vastly expanded the catalog of long non-coding RNAs (lncRNAs) with varying evolutionary conservation, lineage expression, and cancer specificity. Here, we functionally characterize a novel ultraconserved lncRNA, THOR (ENSG00000226856), which exhibits expression exclusively in testis and a broad range of human cancers. THOR knockdown and overexpression in multiple cell lines and animal models alters cell or tumor growth supporting an oncogenic role. We discovered a conserved interaction of THOR with IGF2BP1 and show that THOR contributes to the mRNA stabilization activities of IGF2BP1. Notably, transgenic THOR knockout produced fertilization defects in zebrafish and also conferred a resistance to melanoma onset. Likewise, ectopic expression of human THOR in zebrafish accelerated the onset of melanoma. THOR represents a novel class of functionally important cancer/testis lncRNAs whose structure and function have undergone positive evolutionary selection.

Antisense oligonucleotides targeting translation inhibitory elements in 5' UTRs can selectively increase protein levels.

A variety of diseases are caused by deficiencies in amounts or activity of key proteins. An approach that increases the amount of a specific protein might be of therapeutic benefit. We reasoned that translation could be specifically enhanced using trans-acting agents that counter the function of negative regulatory elements present in the 5' UTRs of some mRNAs. We recently showed that translation can be enhanced by antisense oligonucleotides (ASOs) that target upstream open reading frames. Here we report the amount of a protein can also be selectively increased using ASOs designed to hybridize to other translation inhibitory elements in 5' UTRs. Levels of human RNASEH1, LDLR, and ACP1 and of mouse ACP1 and ARF1 were increased up to 2.7-fold in different cell types and species upon treatment with chemically modified ASOs targeting 5' UTR inhibitory regions in the mRNAs encoding these proteins. The activities of ASOs in enhancing translation were sequence and position dependent and required helicase activity. The ASOs appear to improve the recruitment of translation initiation factors to the target mRNA. Importantly, ASOs targeting ACP1 mRNA significantly increased the level of ACP1 protein in mice, suggesting that this approach has therapeutic and research potentials.

Identification and Validation of PCAT14 as Prognostic Biomarker in Prostate Cancer.

Rapid advances in the discovery of long noncoding RNAs (lncRNAs) have identified lineage- and cancer-specific biomarkers that may be relevant in the clinical management of prostate cancer (PCa). Here we assembled and analyzed a large RNA-seq dataset, from 585 patient samples, including benign prostate tissue and both localized and metastatic PCa to discover and validate differentially expressed genes associated with disease aggressiveness. We performed Sample Set Enrichment Analysis (SSEA) and identified genes associated with low versus high Gleason score in the RNA-seq database. Comparing Gleason 6 versus 9+ PCa samples, we identified 99 differentially expressed genes with variable association to Gleason grade as well as robust expression in prostate cancer. The top-ranked novel lncRNA PCAT14, exhibits both cancer and lineage specificity. On multivariate analysis, low PCAT14 expression independently predicts for BPFS (P=.00126), PSS (P=.0385), and MFS (P=.000609), with trends for OS as well (P=.056). An RNA in-situ hybridization (ISH) assay for PCAT14 distinguished benign vs malignant cases, as well as high vs low Gleason disease. PCAT14 is transcriptionally regulated by AR, and endogenous PCAT14 overexpression suppresses cell invasion. Thus, Using RNA-sequencing data we identify PCAT14, a novel prostate cancer and lineage-specific lncRNA. PCAT14 is highly expressed in low grade disease and loss of PCAT14 predicts for disease aggressiveness and recurrence.

Immune epitope database analysis resource (IEDB-AR).

We present a new release of the immune epitope database analysis resource (IEDB-AR, http://tools.immuneepitope.org), a repository of web-based tools for the prediction and analysis of immune epitopes. New functionalities have been added to most of the previously implemented tools, and a total of eight new tools were added, including two B-cell epitope prediction tools, four T-cell epitope prediction tools and two analysis tools.

A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection.

Many components contribute to immunodominance in the response to a complex virus, but their relative importance is unclear. This was addressed using vaccinia virus and HLA-A*0201 as the model system. A comprehensive analysis of 18 viral proteins recognized by CD8(+) T cell responses demonstrated that approximately one-fortieth of all possible 9- to 10-mer peptides were high-affinity HLA-A*0201 binders. Peptide immunization and T cell recognition data generated from 90 peptides indicated that about one-half of the binders were capable of eliciting T cell responses, and that one-seventh of immunogenic peptides are generated by natural processing. Based on these results, we estimate that vaccinia virus encodes approximately 150 dominant and subdominant epitopes restricted in by HLA-A*0201. However, of all these potential epitopes, only 15 are immunodominant and actually recognized in vivo during vaccinia virus infection of HLA-A*0201 transgenic mice. Neither peptide-binding affinity, nor complex stability, nor TCR avidity, nor amount of processed epitope appeared to strictly correlate with immunodominance status. Additional experiments suggested that vaccinia infection impairs the development of responses directed against subdominant epitopes. This suggested that additional factors, including immunoregulatory mechanisms, restrict the repertoire of T cell specificities after vaccinia infection by a factor of at least 10.

A community resource benchmarking predictions of peptide binding to MHC-I molecules.

Recognition of peptides bound to major histocompatibility complex (MHC) class I molecules by T lymphocytes is an essential part of immune surveillance. Each MHC allele has a characteristic peptide binding preference, which can be captured in prediction algorithms, allowing for the rapid scan of entire pathogen proteomes for peptide likely to bind MHC. Here we make public a large set of 48,828 quantitative peptide-binding affinity measurements relating to 48 different mouse, human, macaque, and chimpanzee MHC class I alleles. We use this data to establish a set of benchmark predictions with one neural network method and two matrix-based prediction methods extensively utilized in our groups. In general, the neural network outperforms the matrix-based predictions mainly due to its ability to generalize even on a small amount of data. We also retrieved predictions from tools publicly available on the internet. While differences in the data used to generate these predictions hamper direct comparisons, we do conclude that tools based on combinatorial peptide libraries perform remarkably well. The transparent prediction evaluation on this dataset provides tool developers with a benchmark for comparison of newly developed prediction methods. In addition, to generate and evaluate our own prediction methods, we have established an easily extensible web-based prediction framework that allows automated side-by-side comparisons of prediction methods implemented by experts. This is an advance over the current practice of tool developers having to generate reference predictions themselves, which can lead to underestimating the performance of prediction methods they are not as familiar with as their own. The overall goal of this effort is to provide a transparent prediction evaluation allowing bioinformaticians to identify promising features of prediction methods and providing guidance to immunologists regarding the reliability of prediction tools.

Structural prediction of peptides binding to MHC class I molecules.

Peptide binding to class I major histocompatibility complex (MHCI) molecules is a key step in the immune response and the structural details of this interaction are of importance in the design of peptide vaccines. Algorithms based on primary sequence have had success in predicting potential antigenic peptides for MHCI, but such algorithms have limited accuracy and provide no structural information. Here, we present an algorithm, PePSSI (peptide-MHC prediction of structure through solvated interfaces), for the prediction of peptide structure when bound to the MHCI molecule, HLA-A2. The algorithm combines sampling of peptide backbone conformations and flexible movement of MHC side chains and is unique among other prediction algorithms in its incorporation of explicit water molecules at the peptide-MHC interface. In an initial test of the algorithm, PePSSI was used to predict the conformation of eight peptides bound to HLA-A2, for which X-ray data are available. Comparison of the predicted and X-ray conformations of these peptides gave RMSD values between 1.301 and 2.475 A. Binding conformations of 266 peptides with known binding affinities for HLA-A2 were then predicted using PePSSI. Structural analyses of these peptide-HLA-A2 conformations showed that peptide binding affinity is positively correlated with the number of peptide-MHC contacts and negatively correlated with the number of interfacial water molecules. These results are consistent with the relatively hydrophobic binding nature of the HLA-A2 peptide binding interface. In summary, PePSSI is capable of rapid and accurate prediction of peptide-MHC binding conformations, which may in turn allow estimation of MHCI-peptide binding affinity.

HLA class I-restricted responses to vaccinia recognize a broad array of proteins mainly involved in virulence and viral gene regulation.

We have analyzed by ex vivo ELISPOT the anti-vaccinia cytotoxic T lymphocyte responses of peripheral blood mononuclear cells from humans vaccinated with Dryvax vaccine. More than 6,000 peptides from 258 putative vaccinia ORFs predicted to bind the common molecules of the HLA A1, A2, A3, A24, B7, and B44 supertypes were screened with peripheral blood mononuclear cells of 31 vaccinees. A total of 48 epitopes derived from 35 different vaccinia antigens were identified, some of which (B8R, D1R, D5R, C10L, C19L, C7L, F12, and O1L) were recognized by multiple donors and contain multiple epitopes recognized in the context of different HLA types. The antigens recognized tend to be >100 residues in length and are expressed predominantly in the early phases of infection, although some late antigens were also recognized. Viral genome regulation and virulence factor were recognized most frequently, whereas few structural proteins were immunogenic. Finally, most epitopes were highly conserved among vaccinia virus Western Reserve, variola major and modified vaccinia Ankara, supporting their potential use in vaccine and diagnostic applications.

Characterization of the peptide-binding specificity of Mamu-A*11 results in the identification of SIV-derived epitopes and interspecies cross-reactivity.

The SIV-infected Indian rhesus macaque is the most established model of HIV infection, providing insight into pathogenesis and a system for testing novel vaccines. However, only a limited amount of information is available regarding the peptide-binding motifs and epitopes bound by their class I and class II MHC molecules. In this study, we utilized a library of over 1,000 different peptides and a high throughput MHC-peptide binding assay to detail the binding specificity of the rhesus macaque class I molecule Mamu-A*11. These studies defined the fine specificity of primary anchor positions, and dissected the role of secondary anchors, for peptides of 8-11 residues in length. This detailed information was utilized to develop size-specific polynomial algorithms to predict Mamu-A*11 binding capacity. Testing SIVmac239-derived Mamu-A*11 binding peptides for recognition by peripheral blood mononuclear cells (PBMC) from Mamu-A*11-positive, SIV-infected macaques, identified five novel SIV-derived Mamu-A*11 epitopes. Finally, we detected extensive cross-reactivity at the binding level between Mamu-A*11 and the mouse H-2 class I molecule Kk. Further experiments revealed that three out of four Mamu-A*11 binding peptides which bound Kk and were immunogenic in Kk mice were also recognized in Mamu-A*11-infected macaques. This is the first detailed description of mouse-macaque interspecies cross-reactivity, potentially useful in testing novel vaccines in mice and macaques.