Publisher Correction: Clonal analysis of Salmonella-specific effector T cells reveals serovar-specific and cross-reactive T cell responses.

In the version of this article initially published, the first affiliation lacked 'MRC'; the correct name of the institution is 'MRC Weatherall Institute of Molecular Medicine'. Two designations (SP110Y and ST110H) were incorrect in the legend to Fig. 6f,h,i. The correct text is as follows: for panel f, "...loaded with either the CdtB(105-125)SP110Y (DRB4*SP110Y) or the CdtB(105-125)ST110H (DRB4*ST110H) peptide variants..."; for panel h, "...decorated by the DRB4*SP110Y tetramer (lower-right quadrant), the DRB4*ST110H (upper-left quadrant)..."; and for panel i, "...stained ex vivo with DRB4*SP110Y, DRB4*ST110H...". In Fig. 8e, the final six residues (LTEAFF) of the sequence in the far right column of the third row of the table were missing; the correct sequence is 'CASSYRRTPPLTEAFF'. In the legend to Fig. 8d, a designation (HLyE) was incorrect; the correct text is as follows: "(HlyE?)." Portions of the Acknowledgements section were incorrect; the correct text is as follows: "This work was supported by the UK Medical Research Council (MRC) (MR/K021222/1) (G.N., M.A.G., A.S., V.C., A.J.P.),...the Oxford Biomedical Research Centre (A.J.P., V.C.),...and core funding from the Singapore Immunology Network (SIgN) (E.W.N.) and the SIgN immunomonitoring platform (E.W.N.)." Finally, a parenthetical element was phrased incorrectly in the final paragraph of the Methods subsection "T cell cloning and live fluorescence barcoding"; the correct phrasing is as follows: "...(which in all cases included HlyE, CdtB, Ty21a, Quailes, NVGH308, and LT2 strains and in volunteers T5 and T6 included PhoN)...". Also, in Figs. 3c and 4a, the right outlines of the plots were not visible; in the legend to Fig. 3, panel letter 'f' was not bold; and in Fig. 8f, 'ND' should be aligned directly beneath DRB4 in the key and 'ND' should be removed from the diagram at right, and the legend should be revised accordingly as follows: "...colors indicate the HLA class II restriction (gray indicates clones for which restriction was not determined (ND)). Clonotypes are grouped on the basis of pathogen selectivity (continuous line), protein specificity (dashed line) and epitope specificity; for ten HlyE-specific clones (pixilated squares), the epitope specificity was not determined...". The errors have been corrected in the HTML and PDF versions of the article.

Clonal analysis of Salmonella-specific effector T cells reveals serovar-specific and cross-reactive T cell responses.

To tackle the complexity of cross-reactive and pathogen-specific T cell responses against related Salmonella serovars, we used mass cytometry, unbiased single-cell cloning, live fluorescence barcoding, and T cell-receptor sequencing to reconstruct the Salmonella-specific repertoire of circulating effector CD4+ T cells, isolated from volunteers challenged with Salmonella enterica serovar Typhi (S. Typhi) or Salmonella Paratyphi A (S. Paratyphi). We describe the expansion of cross-reactive responses against distantly related Salmonella serovars and of clonotypes recognizing immunodominant antigens uniquely expressed by S. Typhi or S. Paratyphi A. In addition, single-amino acid variations in two immunodominant proteins, CdtB and PhoN, lead to the accumulation of T cells that do not cross-react against the different serovars, thus demonstrating how minor sequence variations in a complex microorganism shape the pathogen-specific T cell repertoire. Our results identify immune-dominant, serovar-specific, and cross-reactive T cell antigens, which should aid in the design of T cell-vaccination strategies against Salmonella.

Glycolysis inhibition affects proliferation and cytotoxicity of Vγ9Vδ2 T cells expanded for adoptive cell therapy.

BACKGROUND AIMS: Vγ9Vδ2 T cells are under investigation as alternative effector cells for adoptive cell therapy (ACT) in cancer. Despite promising in vitro results, anti-tumor efficacies in early clinical studies have been lower than expected, which could be ascribed to the complex interplay of tumor and immune cell metabolism competing for the same nutrients in the tumor microenvironment. METHODS: To contribute to the scarce knowledge regarding gamma delta T-cell metabolism, we investigated the metabolic phenotype of 25-day-expanded Vγ9Vδ2 T cells and how it is intertwined with functionality. RESULTS: We found that Vγ9Vδ2 T cells displayed a quiescent metabolism, utilizing both glycolysis and oxidative phosphorylation (OXPHOS) for energy production, as measured in Seahorse assays. Upon T-cell receptor activation, both pathways were upregulated, and inhibition with metabolic inhibitors showed that Vγ9Vδ2 T cells were dependent on glycolysis and the pentose phosphate pathway for proliferation. The dependency on glucose for proliferation was confirmed in glucose-free conditions. Cytotoxicity against malignant melanoma was reduced by glycolysis inhibition but not OXPHOS inhibition. CONCLUSIONS: These findings lay the groundwork for further studies on manipulation of Vγ9Vδ2 T-cell metabolism for improved ACT outcome.

JAK2V617F drives gut microbiota differences in patients with myeloproliferative neoplasms.

BACKGROUND: Essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (MF) are myeloproliferative neoplasms (MPN). Inflammation is involved in the initiation, progression, and symptomology of the diseases. The gut microbiota impacts the immune system, infection control, and steady-state hematopoiesis. METHODS: We analyzed the gut microbiota of 227 MPN patients and healthy controls (HCs) using next-generation sequencing. We expanded our previous results in PV and ET patients with additional PV, pre-MF, and MF patients which allowed us to compare MPN patients collectively, MPN sub-diagnoses, and MPN mutations (separately and combined) vs. HCs (N = 42) and compare within MPN sub-diagnoses and MPN mutation. RESULTS: MPN patients had a higher observed richness (median, 245 [range, 49-659]) compared with HCs (191.5 [range, 111-300; p = .003]) and a lower relative abundance of taxa within the Firmicutes phylum; for example, Faecalibacterium (6% vs. 14%, p < .001). The microbiota of CALR-positive patients (N = 30) resembled that of HCs more than that of patients with JAK2V617F (N = 177). In JAK2V617F-positive patients, only minor differences in the gut microbiota were observed between MPN sub-diagnoses, illustrating the importance of this mutation. CONCLUSION: The gut microbiota in MPN patients differs from HCs and is driven by JAK2V617F, whereas the gut microbiota in CALR patients resembles HCs more.

Expression and splicing mediate distinct biological signals.

BACKGROUND: Through alternative splicing, most human genes produce multiple isoforms in a cell-, tissue-, and disease-specific manner. Numerous studies show that alternative splicing is essential for development, diseases, and their treatments. Despite these important examples, the extent and biological relevance of splicing are currently unknown. RESULTS: To solve this problem, we developed pairedGSEA and used it to profile transcriptional changes in 100 representative RNA-seq datasets. Our systematic analysis demonstrates that changes in splicing, on average, contribute to 48.1% of the biological signal in expression analyses. Gene-set enrichment analysis furthermore indicates that expression and splicing both convey shared and distinct biological signals. CONCLUSIONS: These findings establish alternative splicing as a major regulator of the human condition and suggest that most contemporary RNA-seq studies likely miss out on critical biological insights. We anticipate our results will contribute to the transition from a gene-centric to an isoform-centric research paradigm.

TANTIGEN 2.0: a knowledge base of tumor T cell antigens and epitopes.

We previously developed TANTIGEN, a comprehensive online database cataloging more than 1000 T cell epitopes and HLA ligands from 292 tumor antigens. In TANTIGEN 2.0, we significantly expanded coverage in both immune response targets (T cell epitopes and HLA ligands) and tumor antigens. It catalogs 4,296 antigen variants from 403 unique tumor antigens and more than 1500 T cell epitopes and HLA ligands. We also included neoantigens, a class of tumor antigens generated through mutations resulting in new amino acid sequences in tumor antigens. TANTIGEN 2.0 contains validated TCR sequences specific for cognate T cell epitopes and tumor antigen gene/mRNA/protein expression information in major human cancers extracted by Human Pathology Atlas. TANTIGEN 2.0 is a rich data resource for tumor antigens and their associated epitopes and neoepitopes. It hosts a set of tailored data analytics tools tightly integrated with the data to form meaningful analysis workflows. It is freely available at http://projects.met-hilab.org/tadb .

BioReader: a text mining tool for performing classification of biomedical literature.

BACKGROUND: Scientific data and research results are being published at an unprecedented rate. Many database curators and researchers utilize data and information from the primary literature to populate databases, form hypotheses, or as the basis for analyses or validation of results. These efforts largely rely on manual literature surveys for collection of these data, and while querying the vast amounts of literature using keywords is enabled by repositories such as PubMed, filtering relevant articles from such query results can be a non-trivial and highly time consuming task. RESULTS: We here present a tool that enables users to perform classification of scientific literature by text mining-based classification of article abstracts. BioReader (Biomedical Research Article Distiller) is trained by uploading article corpora for two training categories - e.g. one positive and one negative for content of interest - as well as one corpus of abstracts to be classified and/or a search string to query PubMed for articles. The corpora are submitted as lists of PubMed IDs and the abstracts are automatically downloaded from PubMed, preprocessed, and the unclassified corpus is classified using the best performing classification algorithm out of ten implemented algorithms. CONCLUSION: BioReader supports data and information collection by implementing text mining-based classification of primary biomedical literature in a web interface, thus enabling curators and researchers to take advantage of the vast amounts of data and information in the published literature. BioReader outperforms existing tools with similar functionalities and expands the features used for mining literature in database curation efforts. The tool is freely available as a web service at http://www.cbs.dtu.dk/services/BioReader.

Molecular profiling of short-term and long-term surviving patients identifies CD34 mRNA level as prognostic for glioblastoma survival.

Despite extensive treatment, overall survival (OS) for glioblastoma (GBM) remains poor. A small proportion of patients present long survival over 3 years, but the underlying molecular background separating these long-term survivors (LTS) from short-term survivors (STS) are insufficiently understood. Accordingly, study aim was to identify independent prognostic biomarkers for survival. Study cohort consisted of 93 primary GBM patients treated with radiation-, chemo- and bevacizumab therapy, among which 14 STS (OS ≤ 12 months) and 6 LTS (OS ≥ 36 months) were identified, all confirmed being IDH wild-type. RNA expression levels in diagnostic tumor specimen for 792 genes were analyzed by NanoString technology. While no differences were found with regard to GBM subtype between LTS versus STS, comparative analysis of individual genes identified 14 significantly differently expressed candidate genes. Univariate analysis in the whole patient cohort found that 12 of these were significantly associated with OS, of which increased IFNG, CXCL9, LGALS4, CD34 and decreased MGMT levels remained significant associated with prolonged OS in multivariate analysis correcting for known prognostic variables. Validation analyses in an independent dataset from the AVAglio study confirmed CD34 as significant in comparative analysis between STS and LTS patients and as an independent prognostic factor. Analysis of this dataset further supported CD34 expression to be associated with improved bevacizumab efficacy, while CD34 immunohistochemistry indicated variation in CD34 expression to result primarily from varying tumor vascularization. Collectively, CD34 expression candidates as a prognostic biomarker in GBM able to identify survival outliers and could also be predictive for efficacy of bevacizumab.

TANTIGEN: a comprehensive database of tumor T cell antigens.

Tumor T cell antigens are both diagnostically and therapeutically valuable molecules. A large number of new peptides are examined as potential tumor epitopes each year, yet there is no infrastructure for storing and accessing the results of these experiments. We have retroactively cataloged more than 1000 tumor peptides from 368 different proteins, and implemented a web-accessible infrastructure for storing and accessing these experimental results. All peptides in TANTIGEN are labeled as one of the four categories: (1) peptides measured in vitro to bind the HLA, but not reported to elicit either in vivo or in vitro T cell response, (2) peptides found to bind the HLA and to elicit an in vitro T cell response, (3) peptides shown to elicit in vivo tumor rejection, and (4) peptides processed and naturally presented as defined by physical detection. In addition to T cell response, we also annotate peptides that are naturally processed HLA binders, e.g., peptides eluted from HLA in mass spectrometry studies. TANTIGEN provides a rich data resource for tumor-associated epitope and neoepitope discovery studies and is freely available at http://cvc.dfci.harvard.edu/tantigen/ or http://projects.met-hilab.org/tadb (mirror).

Transcriptional changes induced by bevacizumab combination therapy in responding and non-responding recurrent glioblastoma patients.

BACKGROUND: Bevacizumab combined with chemotherapy produces clinical durable response in 25-30% of recurrent glioblastoma patients. This group of patients has shown improved survival and quality of life. The aim of this study was to investigate changes in gene expression associated with response and resistance to bevacizumab combination therapy. METHODS: Recurrent glioblastoma patients who had biomarker-accessible tumor tissue surgically removed both before bevacizumab treatment and at time of progression were included. Patients were grouped into responders (n = 7) and non-responders (n = 14). Gene expression profiling of formalin-fixed paraffin-embedded tumor tissue was performed using RNA-sequencing. RESULTS: By comparing pretreatment samples of responders with those of non-responders no significant difference was observed. In a paired comparison analysis of pre- and posttreatment samples of non-responders 1 gene was significantly differentially expressed. In responders, this approach revealed 256 significantly differentially expressed genes (72 down- and 184 up-regulated genes at the time of progression). Genes differentially expressed in responders revealed a shift towards a more proneural and less mesenchymal phenotype at the time of progression. CONCLUSIONS: Bevacizumab combination treatment demonstrated a significant impact on the transcriptional changes in responders; but only minimal changes in non-responders. This suggests that non-responding glioblastomas progress chaotically without following distinct gene expression changes while responding tumors adaptively respond or progress by means of the same transcriptional changes. In conclusion, we hypothesize that the identified gene expression changes of responding tumors are associated to bevacizumab response or resistance mechanisms.

Bioinformatics for cancer immunotherapy target discovery.

The mechanisms of immune response to cancer have been studied extensively and great effort has been invested into harnessing the therapeutic potential of the immune system. Immunotherapies have seen significant advances in the past 20 years, but the full potential of protective and therapeutic cancer immunotherapies has yet to be fulfilled. The insufficient efficacy of existing treatments can be attributed to a number of biological and technical issues. In this review, we detail the current limitations of immunotherapy target selection and design, and review computational methods to streamline therapy target discovery in a bioinformatics analysis pipeline. We describe specialized bioinformatics tools and databases for three main bottlenecks in immunotherapy target discovery: the cataloging of potentially antigenic proteins, the identification of potential HLA binders, and the selection epitopes and co-targets for single-epitope and multi-epitope strategies. We provide examples of application to the well-known tumor antigen HER2 and suggest bioinformatics methods to ameliorate therapy resistance and ensure efficient and lasting control of tumors.

Building flexible and robust analysis frameworks for molecular subtyping of cancers.

Molecular subtyping is essential to infer tumor aggressiveness and predict prognosis. In practice, tumor profiling requires in-depth knowledge of bioinformatics tools involved in the processing and analysis of the generated data. Additionally, data incompatibility (e.g., microarray versus RNA sequencing data) and technical and uncharacterized biological variance between training and test data can pose challenges in classifying individual samples. In this article, we provide a roadmap for implementing bioinformatics frameworks for molecular profiling of human cancers in a clinical diagnostic setting. We describe a framework for integrating several methods for quality control, normalization, batch correction, classification and reporting, and develop a use case of the framework in breast cancer.

A multi-omics systems vaccinology resource to develop and test computational models of immunity.

Systems vaccinology studies have identified factors affecting individual vaccine responses, but comparing these findings is challenging due to varying study designs. To address this lack of reproducibility, we established a community resource for comparing Bordetella pertussis booster responses and to host annual contests for predicting patients' vaccination outcomes. We report here on our experiences with the "dry-run" prediction contest. We found that, among 20+ models adopted from the literature, the most successful model predicting vaccination outcome was based on age alone. This confirms our concerns about the reproducibility of conclusions between different vaccinology studies. Further, we found that, for newly trained models, handling of baseline information on the target variables was crucial. Overall, multiple co-inertia analysis gave the best results of the tested modeling approaches. Our goal is to engage community in these prediction challenges by making data and models available and opening a public contest in August 2024.

Coordinated Immune Cell Networks in the Bone Marrow Microenvironment Define the Graft versus Leukemia Response with Adoptive Cellular Therapy.

Understanding how intra-tumoral immune populations coordinate to generate anti-tumor responses following therapy can guide precise treatment prioritization. We performed systematic dissection of an established adoptive cellular therapy, donor lymphocyte infusion (DLI), by analyzing 348,905 single-cell transcriptomes from 74 longitudinal bone-marrow samples of 25 patients with relapsed myeloid leukemia; a subset was evaluated by protein-based spatial analysis. In acute myelogenous leukemia (AML) responders, diverse immune cell types within the bone-marrow microenvironment (BME) were predicted to interact with a clonally expanded population of ZNF683 + GZMB + CD8+ cytotoxic T lymphocytes (CTLs) which demonstrated in vitro specificity for autologous leukemia. This population, originating predominantly from the DLI product, expanded concurrently with NK and B cells. AML nonresponder BME revealed a paucity of crosstalk and elevated TIGIT expression in CD8+ CTLs. Our study highlights recipient BME differences as a key determinant of effective anti-leukemia response and opens new opportunities to modulate cell-based leukemia-directed therapy.

Putting computational models of immunity to the test - an invited challenge to predict B. pertussis vaccination outcomes.

Systems vaccinology studies have been used to build computational models that predict individual vaccine responses and identify the factors contributing to differences in outcome. Comparing such models is challenging due to variability in study designs. To address this, we established a community resource to compare models predicting B. pertussis booster responses and generate experimental data for the explicit purpose of model evaluation. We here describe our second computational prediction challenge using this resource, where we benchmarked 49 algorithms from 53 scientists. We found that the most successful models stood out in their handling of nonlinearities, reducing large feature sets to representative subsets, and advanced data preprocessing. In contrast, we found that models adopted from literature that were developed to predict vaccine antibody responses in other settings performed poorly, reinforcing the need for purpose-built models. Overall, this demonstrates the value of purpose-generated datasets for rigorous and open model evaluations to identify features that improve the reliability and applicability of computational models in vaccine response prediction.

PHUSER (Primer Help for USER): a novel tool for USER fusion primer design.

Uracil-Specific Exision Reagent (USER) fusion is a recently developed technique that allows for assembly of multiple DNA fragments in a few simple steps. However, designing primers for USER fusion is both tedious and time consuming. Here, we present the Primer Help for USER (PHUSER) software, a novel tool for designing primers specifically for USER fusion and USER cloning applications. We also present proof-of-concept experimental validation of its functionality. PHUSER offers quick and easy design of PCR optimized primers ensuring directionally correct fusion of fragments into a plasmid containing a customizable USER cassette. Designing primers using PHUSER ensures that the primers have similar annealing temperature (T(m)), which is essential for efficient PCR. PHUSER also avoids identical overhangs, thereby ensuring correct order of assembly of DNA fragments. All possible primers are individually analysed in terms of GC content, presence of GC clamp at 3'-end, the risk of primer dimer formation, the risk of intra-primer complementarity (secondary structures) and the presence of polyN stretches. Furthermore, PHUSER offers the option to insert linkers between DNA fragments, as well as highly flexible cassette options. PHUSER is publicly available at http://www.cbs.dtu.dk/services/phuser/.

An insulin hypersecretion phenotype precedes pancreatic ß cell failure in MODY3 patient-specific cells.

MODY3 is a monogenic hereditary form of diabetes caused by mutations in the transcription factor HNF1A. The patients progressively develop hyperglycemia due to perturbed insulin secretion, but the pathogenesis is unknown. Using patient-specific hiPSCs, we recapitulate the insulin secretion sensitivity to the membrane depolarizing agent sulfonylurea commonly observed in MODY3 patients. Unexpectedly, MODY3 patient-specific HNF1A+/R272C ß cells hypersecrete insulin both in vitro and in vivo after transplantation into mice. Consistently, we identified a trend of increased birth weight in human HNF1A mutation carriers compared with healthy siblings. Reduced expression of potassium channels, specifically the KATP channel, in MODY3 ß cells, increased calcium signaling, and rescue of the insulin hypersecretion phenotype by pharmacological targeting ATP-sensitive potassium channels or low-voltage-activated calcium channels suggest that more efficient membrane depolarization underlies the hypersecretion of insulin in MODY3 ß cells. Our findings identify a pathogenic mechanism leading to ß cell failure in MODY3.