ImmunoDataAnalyzer: a bioinformatics pipeline for processing barcoded and UMI tagged immunological NGS data

Background Next-generation sequencing (NGS) is nowadays the most used high-throughput technology for DNA sequencing. Among others NGS enables the in-depth analysis of immune repertoires. Research in the field of T cell receptor (TCR) and immunoglobulin (IG) repertoires aids in understanding immunological diseases. A main objective is the analysis of the V(D)J recombination defining the structure and specificity of the immune repertoire. Accurate processing, evaluation and visualization of immune repertoire NGS data is important for better understanding immune responses and immunological behavior. Results ImmunoDataAnalyzer (IMDA) is a pipeline we have developed for automatizing the analysis of immunological NGS data. IMDA unites the functionality from carefully selected immune repertoire analysis software tools and covers the whole spectrum from initial quality control up to the comparison of multiple immune repertoires. It provides methods for automated pre-processing of barcoded and UMI tagged immune repertoire NGS data, facilitates the assembly of clonotypes and calculates key figures for describing the immune repertoire. These include commonly used clonality and diversity measures, as well as indicators for V(D)J gene segment usage and between sample similarity. IMDA reports all relevant information in a compact summary containing visualizations, calculations, and sample details, all of which serve for a more detailed overview. IMDA further generates an output file including key figures for all samples, designed to serve as input for machine learning frameworks to find models for differentiating between specific traits of samples. Conclusions IMDA constructs TCR and IG repertoire data from raw NGS reads and facilitates descriptive data analysis and comparison of immune repertoires. The IMDA workflow focus on quality control and ease of use for non-computer scientists. The provided output directly facilitates the interpretation of input data and includes information about clonality, diversity, clonotype overlap as well as similarity, and V(D)J gene segment usage. IMDA further supports the detection of sample swaps and cross-sample contamination that potentially occurred during sample preparation. In summary, IMDA reduces the effort usually required for immune repertoire data analysis by providing an automated workflow for processing raw NGS data into immune repertoires and subsequent analysis. The implementation is open-source and available on https://bioinformatics.fh-hagenberg.at/immunoanalyzer/.

IMMU-17. SYSTEMIC IMMUNOSUPPRESSION OF CD4+ T HELPER CELLS IN GLIOMA

Cancer is a systemic disease. Due to the exceedingly rare occurrence of metastasis of cerebral glioma, systemic alterations have, however, not been considered to play a major role in disease progression of glioma. CD4+ T helper (TH) cells orchestrate the adaptive immune response in an antigen-specific, cytokine mediated manner. The aim of our study was to investigate how far cerebral glioma impacts the systemic CD4+ immune repertoire. We therefore analyzed the peripheral blood CD4+ TH cell phenotype and cytokine production in 100 patients with IDHwt, 30 IDHmut and 16 IDHmut 1p19q co-deleted gliomas in comparison with age-matched healthy donors (HD). We found a significant skewing of the peripheral phenotype in IDHwt glioma patients, showing a TH1 expansion and reduced numbers of T follicular helper cells (TFH), TH1* and mucosa associated invariant T (MAIT) cells, while TH2 and TH17 percentages remained stable compared to IDHmut and HD. Interestingly, although TH1 cells were dominant in IDHwt patients, intracellular cytokine staining showed a distinct reduction of IFNg and TNFa production after in vitro stimulation, while IL-4 was significantly increased compared to HD. No alterations between all groups were observed in IL-2, IL-10 or IL-17 production. Profiling of metabolic surface markers further revealed three distinct groups of CD4+ T cells which are altered in IDHwt patients, indicating a metabolic shift in the CD4+ repertoire compared to HD. Taken together, our results show a CD4+ TH cell type specific skewing of the peripheral immune repertoire in patients with IDHwt gliomas. Our data highlights the importance of considering malignant glioma as a systemic disease that fundamentally alters the immune repertoire in affected patients.

Comprehensive B-Cell Immune Repertoire Analysis of Anti-NMDAR Encephalitis and Anti-LGI1 Encephalitis

Anti-N-methyl-D-aspartate receptor encephalitis (anti-NMDARE) and anti-leucine-rich glioma-inactivated 1 encephalitis (anti-LGI1E) are the two most common types of antibody-mediated autoimmune encephalitis. We performed a comprehensive analysis of the B-cell immune repertoire in patients with anti-NMDARE (n = 7) and anti-LGI1E (n = 10) and healthy controls (n = 4). The results revealed the presence of many common clones between patients with these two types of autoimmune encephalitis, which were mostly class-switched. Additionally, many differences were found among the anti-NMDARE, anti-LGI1E, and healthy control groups, including the diversity of the B-cell immune repertoire and gene usage preference. These findings suggest that the same adaptive immune responses occur in patients with anti-NMDARE and anti-LGI1E, which deserves further exploration.

MotifBoost: k-mer based data-efficient immune repertoire classification method

The repertoire of T cell receptors encodes various types of immunological information. Machine learning is indispensable for decoding such information from repertoire datasets measured by next-generation sequencing. In particular, the classification of repertoires is the most basic task, which is relevant for a variety of scientific and clinical problems. Supported by the recent appearance of large datasets, efficient but data-expensive methods have been proposed. However, it is unclear whether they can work efficiently when the available sample size is severely restricted as in practical situations. In this study, we demonstrate that the their performances are impaired catastrophically below critical sample sizes. To overcome this, we propose MotifBoost, which exploits the information of short motifs of TCRs. MotifBoost can perform the classification as efficiently as a deep learning method on large datasets while providing more stable and reliable results on small datasets. We also clarify that the robustness of MotifBoost can be attributed to the efficiency of motifs as representation features of repertoires. Finally, by comparing predictions of these methods, we show that the whole sequence identity and sequence motifs encode partially different information and that a combination of such complementary information is necessary for further development of repertoire analysis.

P16.03 Cerebral gliom alters the peripheral CD4+ T helper cell phenotype

BACKGROUND Cancer is a systemic disease. Due to the exceedingly rare occurrence of metastasis of cerebral glioma, systemic alterations have, however, not been considered to play a major role in disease progression of glioma. CD4+ T helper (TH) cells orchestrate the adaptive immune response in an antigen-specific, cytokine mediated manner. The aim of our study was to investigate how far cerebral glioma impacts the systemic CD4+ immune repertoire. MATERIAL AND METHODS We performed flow-cytometry analysis of the peripheral blood CD4+ TH cell phenotype and cytokine production in 100 patients with IDHwt, 30 IDHmut and 16 IDHmut 1p19q co-deleted gliomas in comparison with age-matched healthy donors (HD). Data was analyzed using a Fortessa LSR and Diva software. Multiparameter analyses were performed using UMAP and SpadeVizR trees. The study was approved by the ethics committee (PV4904). RESULTS We found a significant skewing of the peripheral immunophenotype in IDHwt glioma patients, showing a TH1 expansion and reduced numbers of T follicular helper cells (TFH), TH1* and mucosa associated invariant T (MAIT) cells (p<0.001), while TH2 and TH17 percentages remained stable compared to IDHmut and HD. Although TH1 cells were dominant in IDHwt patients (p<0.01), intracellular cytokine staining showed a reduction of IFNγ and TNFα production after in vitro stimulation, while IL-4 was significantly increased compared to HD (p<0.05). No alterations between all groups were observed in IL-2, IL-10 or IL-17 production. Profiling of metabolic surface markers further revealed increased expression of GLUT1 on CD4+ T cells in IDHwt patients, indicating an activated CD4+ repertoire compared to HD. CONCLUSION Taken together, our results show a CD4+ TH cell type specific skewing of the peripheral immune repertoire in patients with IDHwt gliomas. Our data highlights the importance of considering malignant glioma as a disease with profound systemic effects fundamentally altering the immune repertoire in affected patients.

Biomarkers and Immune Repertoire Metrics Identified by Peripheral Blood Transcriptomic Sequencing Reveal the Pathogenesis of COVID-19

The coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a global crisis; however, our current understanding of the host immune response to SARS-CoV-2 infection remains limited. Herein, we performed RNA sequencing using peripheral blood from acute and convalescent patients and interrogated the dynamic changes of adaptive immune response to SARS-CoV-2 infection over time. Our results revealed numerous alterations in these cohorts in terms of gene expression profiles and the features of immune repertoire. Moreover, a machine learning method was developed and resulted in the identification of five independent biomarkers and a collection of biomarkers that could accurately differentiate and predict the development of COVID-19. Interestingly, the increased expression of one of these biomarkers, UCHL1, a molecule related to nervous system damage, was associated with the clustering of severe symptoms. Importantly, analyses on immune repertoire metrics revealed the distinct kinetics of T-cell and B-cell responses to SARS-CoV-2 infection, with B-cell response plateaued in the acute phase and declined thereafter, whereas T-cell response can be maintained for up to 6 months post-infection onset and T-cell clonality was positively correlated with the serum level of anti-SARS-CoV-2 IgG. Together, the significantly altered genes or biomarkers, as well as the abnormally high levels of B-cell response in acute infection, may contribute to the pathogenesis of COVID-19 through mediating inflammation and immune responses, whereas prolonged T-cell response in the convalescents might help these patients in preventing reinfection. Thus, our findings could provide insight into the underlying molecular mechanism of host immune response to COVID-19 and facilitate the development of novel therapeutic strategies and effective vaccines.