Human Leukocyte Antigen Ligandome Research Articles

Cisplatin resistance driver claspin is a target for immunotherapy in urothelial carcinoma.

Bladder cancer is a major and fatal urological disease. Cisplatin is a key drug for the treatment of bladder cancer, especially in muscle-invasive cases. In most cases of bladder cancer, cisplatin is effective; however, resistance to cisplatin has a significant negative impact on prognosis. Thus, a treatment strategy for cisplatin-resistant bladder cancer is essential to improve the prognosis. In this study, we established a cisplatin-resistant (CR) bladder cancer cell line using an urothelial carcinoma cell lines (UM-UC-3 and J82). We screened for potential targets in CR cells and found that claspin (CLSPN) was overexpressed. CLSPN mRNA knockdown revealed that CLSPN had a role in cisplatin resistance in CR cells. In our previous study, we identified human leukocyte antigen (HLA)-A*02:01-restricted CLSPN peptide by HLA ligandome analysis. Thus, we generated a CLSPN peptide-specific cytotoxic T lymphocyte clone that recognized CR cells at a higher level than wild-type UM-UC-3 cells. These findings indicate that CLSPN is a driver of cisplatin resistance and CLSPN peptide-specific immunotherapy may be effective for cisplatin-resistant cases.

Unsupervised Mining of HLA-I Peptidomes Reveals New Binding Motifs and Potential False Positives in the Community Database.

Modern vaccine designs and studies of human leukocyte antigen (HLA)-mediated immune responses rely heavily on the knowledge of HLA allele-specific binding motifs and computational prediction of HLA-peptide binding affinity. Breakthroughs in HLA peptidomics have considerably expanded the databases of natural HLA ligands and enabled detailed characterizations of HLA-peptide binding specificity. However, cautions must be made when analyzing HLA peptidomics data because identified peptides may be contaminants in mass spectrometry or may weakly bind to the HLA molecules. Here, a hybrid de novo peptide sequencing approach was applied to large-scale mono-allelic HLA peptidomics datasets to uncover new ligands and refine current knowledge of HLA binding motifs. Up to 12-40% of the peptidomics data were low-binding affinity peptides with an arginine or a lysine at the C-terminus and likely to be tryptic peptide contaminants. Thousands of these peptides have been reported in a community database as legitimate ligands and might be erroneously used for training prediction models. Furthermore, unsupervised clustering of identified ligands revealed additional binding motifs for several HLA class I alleles and effectively isolated outliers that were experimentally confirmed to be false positives. Overall, our findings expanded the knowledge of HLA binding specificity and advocated for more rigorous interpretation of HLA peptidomics data that will ensure the high validity of community HLA ligandome databases.

Natural and cryptic peptides dominate the immunopeptidome of atypical teratoid rhabdoid tumors

BackgroundAtypical teratoid/rhabdoid tumors (AT/RT) are highly aggressive CNS tumors of infancy and early childhood. Hallmark is the surprisingly simple genome with inactivating mutations or deletions in the SMARCB1 gene as...

Acid Stripping after Infection Improves the Detection of Viral HLA Class I Natural Ligands Identified by Mass Spectrometry.

Identification of a natural human leukocyte antigen (HLA) ligandome is a key element to understand the cellular immune response. Advanced high throughput mass spectrometry analyses identify a relevant, but not complete, fraction of the many tens of thousands of self-peptides generated by antigen processing in live cells. In infected cells, in addition to this complex HLA ligandome, a minority of peptides from degradation of the few proteins encoded by the viral genome are also bound to HLA class I molecules. In this study, the standard immunopeptidomics strategy was modified to include the classical acid stripping treatment after virus infection to enrich the HLA ligandome in virus ligands. Complexes of HLA-B*27:05-bound peptide pools were isolated from vaccinia virus (VACV)-infected cells treated with acid stripping after virus infection. The HLA class I ligandome was identified using high throughput mass spectrometry analyses, yielding 37 and 51 natural peptides processed and presented untreated and after acid stripping treatment VACV-infected human cells, respectively. Most of these virus ligands were identified in both conditions, but exclusive VACV ligands detected by mass spectrometry detected on acid stripping treatment doubled the number of those identified in the untreated VACV-infected condition. Theoretical binding affinity prediction of the VACV HLA-B*27:05 ligands and acute antiviral T cell response characterization in the HLA transgenic mice model showed no differences between HLA ligands identified under the two conditions: untreated and under acid stripping condition. These findings indicated that acid stripping treatment could be useful to identify HLA class I ligands from virus-infected cells.

MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism

BackgroundAccurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within personalized T-cell based immunotherapy. Many recent prediction tools developed upon the deep learning algorithms and mass spectrometry data have indeed showed improvement on the average predicting power for class I HLA-peptide interaction. However, their prediction performances show great variability over individual HLA alleles and peptides with different lengths, which is particularly the case for HLA-C alleles due to the limited amount of experimental data. To meet the increasing demand for attaining the most accurate HLA-peptide binding prediction for individual patient in the real-world clinical studies, more advanced deep learning framework with higher prediction accuracy for HLA-C alleles and longer peptides is highly desirable.ResultsWe present a pan-allele HLA-peptide binding prediction framework—MATHLA which integrates bi-directional long short-term memory network and multiple head attention mechanism. This model achieves better prediction accuracy in both fivefold cross-validation test and independent test dataset. In addition, this model is superior over existing tools regarding to the prediction accuracy for longer ligand ranging from 11 to 15 amino acids. Moreover, our model also shows a significant improvement for HLA-C-peptide-binding prediction. By investigating multiple-head attention weight scores, we depicted possible interaction patterns between three HLA I supergroups and their cognate peptides.ConclusionOur method demonstrates the necessity of further development of deep learning algorithm in improving and interpreting HLA-peptide binding prediction in parallel to increasing the amount of high-quality HLA ligandome data.

The HLA Ligand Atlas: A novel immuno-oncology resource for T-cell antigen discovery.

3128 Background: The human leukocyte antigen (HLA) complex regulates the adaptive immune response by showcasing the intracellular and extracellular protein content to the immune system, which is the basis for T cell-dependent tumor rejection. Therefore, a comprehensive map of the entirety of both HLA class I- and class II-presented peptides from various benign tissues is a highly sought after resource, as it enables the definition of tumor-association on the immunologically pivotal level of the HLA ligandome. Methods: Human tissue samples were snap frozen post mortem during autopsy. The study was approved by the local IRB. HLA ligands were immunopurified and characterized using an Orbitrap Fusion Lumos mass spectrometer coupled to an Ultimate 3000 RSLC Nano UHPLC System. Data acquisition was performed as technical triplicates in data-dependent mode, and data were analyzed using the containerized, computational pipeline MHCquant. Results: In this work, we describe the HLA Ligand Atlas, a comprehensive collection of matched HLA class I and class II ligandomes from 29 non-malignant tissues and 13 human subjects (208 samples in total), covering 38 HLA class I, and 17 HLA*DRB alleles and comprising 48,381 HLA class I and 16,146 HLA class II peptides. Nearly 50% of HLA ligands have not been previously described. The HLA Ligand Atlas is publicly available as a raw data resource, but also in the form of a user-friendly web interface that allows users to quickly formulate complex queries against the data set. Both downloadable data and the query interface are available at www.hla-ligand-atlas.org. Conclusions: This data set provides a valuable tool for research in diverse fields such as systems biology, general immunology, autoimmune disease and organ transplantation. Most importantly, the HLA Ligand Atlas provides essential information for translational applications in immuno-oncology. The knowledge of HLA ligands from benign tissues strongly supports the informed design of proteogenomic HLA-dependent target discovery approaches.

IMMU-08. THE ANTIGENIC LANDSCAPE OF GLIOBLASTOMA - REFINING THE TARGETS FOR IMMUNOTHERAPY

Abstract We provide a comprehensive analysis of the antigenic landscape of glioblastoma using a multi-omics approach including ligandome mapping of the Human Leukocyte Antigen (HLA) ligandome, next generation sequencing (NGS) as well as an in-depth characterization of tumor-infiltrating lymphocytes (TIL) using mass cytometry and ultra-deep sequencing of the T-cell receptor (TCR). Tumor-exclusive HLA class I and class II ligands (immune precipitation and LC-MS/MS) of 24 isocitrate dehydrogenase 1 wild type glioblastoma samples and 10 autologous primary glioblastoma cell lines were defined in comparison to an HLA ligandome normal tissue reference database (n > 418). We found 11,496 glioblastoma exclusive HLA class I ligands (2,064 shared with cell lines; 3,754 on ≥ 2 glioblastoma samples). On the source protein level, 239 glioblastoma exclusive proteins were identified; among them 54 were also found in cell lines. For HLA class II ligands the analysis revealed 11,870 glioblastoma exclusive peptides (444 shared with cell lines; 3,420 on ≥ 2 glioblastoma samples) and 278 glioblastoma exclusive proteins; among which 18 were present also in cell lines. Moreover, whole-exome sequencing and whole RNA sequencing of 13 tumor samples was performed with the aim to predict neoantigens. On average 5,662 somatic missense effects were identified per patient (min: 4,258; max: 7,479). Candidate peptides are grouped into (i) in silico predicted neoepitopes, (ii) tumor-exclusivity on HLA, (iii) gene expression (e.g. cancer testis antigens). Top-ranking candidates from each group will be tested with regards to their immunogenicity in an autologous setting (TIL, peripheral blood mononuclear cells, patient derived tumor cells). Finally, the peptide and immunogenicity data is correlated with the immune phenotype of the TIL compartment as well as the TCR repertoire of the sample.

Proteogenomic discovery of cancer antigens: Neoantigens and beyond.

Host T cells infiltrate the cancer lesion and contribute to patient survival. T cells recognize antigen peptides displayed by the cancer cell human leukocyte antigen (HLA) system. Cancer antigens constitute an essential element of T-cell discrimination and play an indispensable role in anti-cancer responses. HLA ligandome analysis directly and comprehensively detects the peptides that are naturally presented by HLA of given cells, leading to discovery of cancer antigens. A proteogenomic approach, which combines conventional proteomics with genomic information, has further deciphered the landscape of the cancer HLA ligandome. Neoantigens that arise from somatic mutations are arguably the major type of peptides patient T cells recognize. Moreover, cancer cells present peptides derived from alleged noncoding regions, which also elicit T-cell responses thereby serving as cancer antigens. The diversity of newly discovered antigen sources implies that T cells are capable of sensing a variety of genomic aberrations in cancer.

Estimating the Contribution of Proteasomal Spliced Peptides to the HLA-I Ligandome*

Spliced peptides are short protein fragments spliced together in the proteasome by peptide bond formation. True estimation of the contribution of proteasome-spliced peptides (PSPs) to the global human leukocyte antigen (HLA) ligandome is critical. A recent study suggested that PSPs contribute up to 30% of the HLA ligandome. We performed a thorough reanalysis of the reported results using multiple computational tools and various validation steps and concluded that only a fraction of the proposed PSPs passes the quality filters. To better estimate the actual number of PSPs, we present an alternative workflow. We performed de novo sequencing of the HLA-peptide spectra and discarded all de novo sequences found in the UniProt database. We checked whether the remaining de novo sequences could match spliced peptides from human proteins. The spliced sequences were appended to the UniProt fasta file, which was searched by two search tools at a false discovery rate (FDR) of 1%. We find that 2-6% of the HLA ligandome could be explained as spliced protein fragments. The majority of these potential PSPs have good peptide-spectrum match properties and are predicted to bind the respective HLA molecules. However, it remains to be shown how many of these potential PSPs actually originate from proteasomal splicing events.

Mass Spectrometry-Based Immunopeptidome Analysis of Acute Myeloid Leukemia Cells Under Decitabine Treatment Delineates Induced Presentation of Cancer/Testis Antigens on HLA Class I Molecules

In recent years, therapeutic approaches for acute myeloid leukemia (AML) have been improved, however the disease is still characterized by high relapse rates and a poor overall survival mainly in elderly patients aged 60 years and older. The standard therapy for these AML patients involves hypomethylating agents (HMAs) such as decitabine. With this, treatment remission can be achieved in some patients, however effective post-remission therapies are still overdue. Recent data suggests that HMAs induce gene expression of various cancer/testis antigens (CTAs), which could lead to the presentation of cancer/testis antigen-derived peptides on human leukocyte antigen (HLA) molecules. These CTA-derived peptides might serve as prime targets for tailored T cell-based immunotherapy approaches, which could represent an effective post-remission combination therapy.Here we present a mass spectrometry-based study, which longitudinally maps the HLA-presented immunopeptidome and in particular cancer/testis antigens of AML cells under in vitro decitabine treatment. To analyze the impact of decitabine on the presentation of HLA ligands we treated the AML cell lines U937 and MONO-MAC-6 as well as primary AML cells (n = 1) with decitabine for 48 h (t48) and 72 h (t72) in vitro. Upon flow cytometry-based quantification of HLA class I and II surface expression levels, no significant changes of HLA surface molecule levels under decitabine treatment compared to untreated controls were observed. Implementing label-free quantitation mass spectrometry, we then quantitatively assessed HLA class I ligand presentation under decitabine treatment. Only minor effects of decitabine on the whole HLA class I-restricted peptidome were observed: We detected a significant upregulation of 2.6 ± 0.9% of HLA class I ligands (fold change (FC) ≥ 4, p ≤ 0.01) after 48 h of decitabine treatment, whereas 9.6 ± 5.7% of the ligands were altered in their abundance over time without treatment. At t72 similar proportions of decitabine modulation were observed with 4.2 ± 2.7% of up-regulated HLA ligands.A total of 69 HLA class I ligands derived from 31 different CTAs were identified by mass spectrometric analysis, 9 of these ligands were presented exclusively under decitabine treatment. Furthermore, we showed that decitabine exposure caused a significantly increased presentation of 7/69 CTA-derived HLA ligands at least at one time point in the cell lines and the primary AML cells (FC ≥ 4, p ≤ 0.01). From the CTA cyclin A1, two HLA class I-presented peptides were significantly upregulated in U937 cells at t48 (FC 79.0 and 8.2) and t72 (FC 14.1 and 12.4). In primary AML cells, two peptides derived from JARID1B and KIAA0100 were significantly upregulated at either t48 (FC 21.8) or t72 (FC 6.6). In addition, we screened our dataset for HLA ligands derived from previously described decitabine-regulated genes and identified a HLA class I-presented peptide from DAZL, which was significantly upregulated in U937 cells at t72 under decitabine treatment (FC 57.2).Taken together, our results demonstrate a modulatory effect of the hypomethylating agent decitabine on the HLA ligandome of AML cells, enhancing the presentation of CTA-derived peptides on HLA class I molecules. The latter will be further evaluated for their eligibility as targets for tailored peptide-based immunotherapeutic approaches in AML patients undergoing HMA treatment. DisclosuresSalih:Several patent applications: Patents & Royalties: e.g. EP3064507A1.

Mapping the tumour human leukocyte antigen (HLA) ligandome by mass spectrometry.

The entirety of human leukocyte antigen (HLA)-presented peptides is referred to as the HLA ligandome of a cell or tissue, in tumours often termed immunopeptidome. Mapping the tumour immunopeptidome by mass spectrometry (MS) comprehensively views the pathophysiologically relevant antigenic signature of human malignancies. MS is an unbiased approach stringently filtering the candidates to be tested as opposed to epitope prediction algorithms. In the setting of peptide-specific immunotherapies, MS-based strategies significantly diminish the risk of lacking clinical benefit, as they yield highly enriched amounts of truly presented peptides. Early immunopeptidomic efforts were severely limited by technical sensitivity and manual spectra interpretation. The technological progress with development of orbitrap mass analysers and enhanced chromatographic performance led to vast improvements in mass accuracy, sensitivity, resolution, and speed. Concomitantly, bioinformatic tools were developed to process MS data, integrate sequencing results, and deconvolute multi-allelic datasets. This enabled the immense advancement of tumour immunopeptidomics. Studying the HLA-presented peptide repertoire bears high potential for both answering basic scientific questions and translational application. Mapping the tumour HLA ligandome has started to significantly contribute to target identification for the design of peptide-specific cancer immunotherapies in clinical trials and compassionate need treatments. In contrast to prediction algorithms, rare HLA allotypes and HLA class II can be adequately addressed when choosing MS-guided target identification platforms. Herein, we review the identification of tumour HLA ligands focusing on sources, methods, bioinformatic data analysis, translational application, and provide an outlook on future developments.

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry

Immunoinformatics and epitope prediction in the age of genomic medicine.

Immunoinformatics involves the application of computational methods to immunological problems. Prediction of B- and T-cell epitopes has long been the focus of immunoinformatics, given the potential translational implications, and many tools have been developed. With the advent of next-generation sequencing (NGS) methods, an unprecedented wealth of information has become available that requires more-advanced immunoinformatics tools. Based on information from whole-genome sequencing, exome sequencing and RNA sequencing, it is possible to characterize with high accuracy an individual’s human leukocyte antigen (HLA) allotype (i.e., the individual set of HLA alleles of the patient), as well as changes arising in the HLA ligandome (the collection of peptides presented by the HLA) owing to genomic variation. This has allowed new opportunities for translational applications of epitope prediction, such as epitope-based design of prophylactic and therapeutic vaccines, and personalized cancer immunotherapies. Here, we review a wide range of immunoinformatics tools, with a focus on B- and T-cell epitope prediction. We also highlight fundamental differences in the underlying algorithms and discuss the various metrics employed to assess prediction quality, comparing their strengths and weaknesses. Finally, we discuss the new challenges and opportunities presented by high-throughput data-sets for the field of epitope prediction.

A combined approach of human leukocyte antigen ligandomics and immunogenicity analysis to improve peptide-based cancer immunotherapy.

The breakthrough development of immune checkpoint inhibitors as clinically effective novel therapies demonstrates the potential of cancer immunotherapy. The identification of suitable targets for specific immunotherapy, however, remains a challenging task. Most peptides previously used for vaccination in clinical trials were able to elicit strong immunological responses but failed with regard to clinical benefit. This might, at least partly, be caused by an inadequate peptide selection, usually derived from established tumor-associated antigens which are not necessarily presented as human leukocyte antigen (HLA) ligands. Recently, HLA ligandome analysis revealed cancer-associated peptides, which have been used in clinical trials showing encouraging impact on survival. To improve peptide-based cancer immunotherapy, our group established a combined approach of HLA ligandomics and immunogenicity analysis for the identification of vaccine peptides. This approach is based on the identification of naturally presented HLA ligands on tumor samples, the selection of tumor-associated/tumor-specific HLA ligands and their subsequent testing for immunogenicity in vitro. In this review, we want to present our pipeline for the identification of vaccine peptides, focusing on ovarian cancer, and want to discuss differences to other approaches. Furthermore, we want to give a short outlook of a potential multi-peptide vaccination trial using the novel identified peptides.

Mapping the HLA ligandome landscape of acute myeloid leukemia: a targeted approach toward peptide-based immunotherapy.

Identification of physiologically relevant peptide vaccine targets calls for the direct analysis of the entirety of naturally presented human leukocyte antigen (HLA) ligands, termed the HLA ligandome. In this study, we implemented this direct approach using immunoprecipitation and mass spectrometry to define acute myeloid leukemia (AML)-associated peptide vaccine targets. Mapping the HLA class I ligandomes of 15 AML patients and 35 healthy controls, more than 25 000 different naturally presented HLA ligands were identified. Target prioritization based on AML exclusivity and high presentation frequency in the AML cohort identified a panel of 132 LiTAAs (ligandome-derived tumor-associated antigens), and 341 corresponding HLA ligands (LiTAPs (ligandome-derived tumor-associated peptides)) represented subset independently in >20% of AML patients. Functional characterization of LiTAPs by interferon-γ ELISPOT (Enzyme-Linked ImmunoSpot) and intracellular cytokine staining confirmed AML-specific CD8(+) T-cell recognition. Of note, our platform identified HLA ligands representing several established AML-associated antigens (e.g. NPM1, MAGED1, PRTN3, MPO, WT1), but found 80% of them to be also represented in healthy control samples. Mapping of HLA class II ligandomes provided additional CD4(+) T-cell epitopes and potentially synergistic embedded HLA ligands, allowing for complementation of a multipeptide vaccine for the immunotherapy of AML.

The Human Leukocyte Antigen–presented Ligandome of B Lymphocytes

Peptides presented by human leukocyte antigen (HLA) molecules on the cell surface play a crucial role in adaptive immunology, mediating the communication between T cells and antigen presenting cells. Knowledge of these peptides is of pivotal importance in fundamental studies of T cell action and in cellular immunotherapy and transplantation. In this paper we present the in-depth identification and relative quantification of 14,500 peptide ligands constituting the HLA ligandome of B cells. This large number of identified ligands provides general insight into the presented peptide repertoire and antigen presentation. Our uniquely large set of HLA ligands allowed us to characterize in detail the peptides constituting the ligandome in terms of relative abundance, peptide length distribution, physicochemical properties, binding affinity to the HLA molecule, and presence of post-translational modifications. The presented B-lymphocyte ligandome is shown to be a rich source of information by the presence of minor histocompatibility antigens, virus-derived epitopes, and post-translationally modified HLA ligands, and it can be a good starting point for solving a wealth of specific immunological questions. These HLA ligands can form the basis for reversed immunology approaches to identify T cell epitopes based not on in silico predictions but on the bona fide eluted HLA ligandome.

Identification of Novel Tumor-Associated Antigens for Chronic Lymphocytic Leukemia (CLL) Based On HLA Ligandome Analysis – New Targets for Peptide Based Immunotherapy

AbstractAbstract 4119Recurrent disease, selection for chemo resistant clones and inhibition of immune effector functions are limitations of chemotherapeutic treatment of cancer including CLL. This is even more since graft-versus-leukemia effects and remissions after donor lymphocyte infusions have been correlated to long-term CLL free survival. Clonotype analysis of such cases suggested clonally expanded CD8+ T cells that recognize tumor associated antigens (TAAs) presented on HLA (human leukocyte antigen) as mediators of the observed effects, thus making CLL an attractive target for peptide vaccine based immunotherapy. For this goal we established the approach of direct isolation and identification of naturally processed and presented HLA ligands from tissues of interest by affinity chromatography and mass spectrometry. Comparative, semi-quantitative analysis of the HLA ligandomes of malignant and benign samples provided the rationale for selection of potential targets. 42 CLL patients (ages 48–85; median 70 years) of different HLA types and disease stages (Binet A, 24 patients; Binet B, 11 patients; Binet C, 7 patients) were enrolled in this study. Furthermore we collected blood samples from 50 healthy volunteers. HLA class I ligands were isolated from CLL cells as well as benign B cells from healthy donors and unsorted healthy PBMC using a standard affinity chromatography protocol implementing the pan-HLA class I specific antibody W6/32. Liquid chromatography coupled mass spectrometry (LC-MS/MS) based peptide analysis was performed on an LTQ Orbitrap hybrid mass spectrometer followed by annotation of fragment spectra using the MASCOT search algorithm against the human proteome comprised in the SwissProt database. Spectral counting based analysis provided semi-quantitative information regarding the abundance of HLA ligands and their source proteins in the respective ligandomes. In addition, HLA quantification experiments on the cell surface of CLL cells and autologous healthy B cells were performed using a flow cytometric indirect immunofluorescence assay. For this interim analysis we completely analyzed the HLA ligandomes of 7 CLL patients and 10 healthy controls. In total, we identified more than 15.000 different HLA ligands representing more than 7300 different proteins. This comprised more than 6,500 different ligands from CLL cells representing a total of 4,149 source proteins. Comparative analysis of representation in the HLA ligandomes of CLL cells, healthy B cells and healthy PBMC identified 1741 different source proteins as being exclusively expressed in CLL. Semi-quantitative evaluation revealed 138 of these proteins as being highly expressed on CLL (e.g. SET proto-oncogene, Pim-1 Oncogene, Mucin 1). Flow cytomerty based quantification of surface HLA expression revealed similar amounts of HLA class I (p=0.23, unpaired t-test) and II (p=0.33, unpaired t-test) molecules on CLL cells and autologous benign B lymphocytes, with a trend to higher HLA expression on CLL cells.Taken together, the presented strategy enabled the identification of a vast array of both known and novel TAAs and their corresponding naturally processed and presented HLA ligands in CLL. It pinpointed highly overrepresented TAAs for further analysis and immunogenicity testing. Expansion of the dataset after analysis of all enrolled patients will provide an unprecedented in-depth characterization of the HLA ligandome of CLL for future immunotherapeutic approaches. Disclosures:No relevant conflicts of interest to declare.