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We present the results of the human genomic small variant calling benchmarking initiative of the German Research Foundation (DFG) funded Next Generation Sequencing Competence Network (NGS-CN) and the German Human Genome-Phenome Archive (GHGA). In this effort, we developed NCBench, a continuous benchmarking platform for the evaluation of small genomic variant callsets in terms of recall, precision, and false positive/negative error patterns. NCBench is implemented as a continuously re-evaluated open-source repository. We show that it is possible to entirely rely on public free infrastructure (Github, Github Actions, Zenodo) in combination with established open-source tools. NCBench is agnostic of the used dataset and can evaluate an arbitrary number of given callsets, while reporting the results in a visual and interactive way. We used NCBench to evaluate over 40 callsets generated by various variant calling pipelines available in the participating groups that were run on three exome datasets from different enrichment kits and at different coverages. While all pipelines achieve high overall quality, subtle systematic differences between callers and datasets exist and are made apparent by NCBench.These insights are useful to improve existing pipelines and develop new workflows. NCBench is meant to be open for the contribution of any given callset. Most importantly, for authors, it will enable the omission of repeated re-implementation of paper-specific variant calling benchmarks for the publication of new tools or pipelines, while readers will benefit from being able to (continuously) observe the performance of tools and pipelines at the time of reading instead of at the time of writing.

Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data with sequencing errors and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls, including a mixture of bulk and single-cell sequencing datasets. Using this dataset, we extended the convergence findings to 20 additional subjects, highlighting the applicability of nf-core/airrflow to validate findings in small in-house cohorts with reanalysis of large publicly available AIRR datasets.

IntroductionB cells are acknowledged as crucial players in the pathogenesis of multiple sclerosis (MS). Several disease modifying drugs including cladribine have been shown to exert differential effects on peripheral blood B cell subsets. However, little is known regarding functional changes within the peripheral B cell populations. In this study, we obtained a detailed picture of B cell repertoire changes under cladribine treatment on a combined immunoglobulin (Ig) transcriptome and proteome level.MethodsWe performed next-generation sequencing of Ig heavy chain (IGH) transcripts and Ig mass spectrometry in cladribine-treated patients with relapsing-remitting multiple sclerosis (n = 8) at baseline and after 6 and 12 months of treatment in order to generate Ig transcriptome and Ig peptide libraries. Ig peptides were overlapped with the corresponding IGH transcriptome in order to analyze B cell clones on a combined transcriptome and proteome level.ResultsThe analysis of peripheral blood B cell percentages pointed towards a significant decrease of memory B cells and an increase of naive B cells following cladribine therapy. While basic IGH repertoire parameters (e.g. variable heavy chain family usage and Ig subclasses) were only slightly affected by cladribine treatment, a significantly decreased number of clones and significantly lower diversity in the memory subset was noticeable at 6 months following treatment which was sustained at 12 months. When looking at B-cell clones comprising sequences from the different time-points, clones spanning between all three time-points were significantly more frequent than clones including sequences from two time-points. Furthermore, Ig proteome analyses showed that Ig transcriptome specific peptides could mostly be equally aligned to all three time-points pointing towards a proportion of B-cell clones that are maintained during treatment.DiscussionOur findings suggest that peripheral B cell related treatment effects of cladribine tablets might be exerted through a reduction of possibly disease relevant clones in the memory B cell subset without disrupting the overall clonal composition of B cells. Our results -at least partially- might explain the relatively mild side effects regarding infections and the sustained immune response after vaccinations during treatment. However, exact disease driving B cell subsets and their effects remain unknown and should be addressed in future studies.

Abstract Background As technical developments in omics and biomedical imaging increase the throughput of data generation in life sciences, the need for information systems capable of managing heterogeneous digital assets is increasing. In particular, systems supporting the findability, accessibility, interoperability, and reusability (FAIR) principles of scientific data management. Results We propose a Service Oriented Architecture approach for integrated management and analysis of multi-omics and biomedical imaging data. Our architecture introduces an image management system into a FAIR-supporting, web-based platform for omics data management. Interoperable metadata models and middleware components implement the required data management operations. The resulting architecture allows for FAIR management of omics and imaging data, facilitating metadata queries from software applications. The applicability of the proposed architecture is demonstrated using two technical proofs of concept and a use case, aimed at molecular plant biology and clinical liver cancer research, which integrate various imaging and omics modalities. Conclusions We describe a data management architecture for integrated, FAIR-supporting management of omics and biomedical imaging data, and exemplify its applicability for basic biology research and clinical studies. We anticipate that FAIR data management systems for multi-modal data repositories will play a pivotal role in data-driven research, including studies which leverage advanced machine learning methods, as the joint analysis of omics and imaging data, in conjunction with phenotypic metadata, becomes not only desirable but necessary to derive novel insights into biological processes.

Abstract Membranolytic anticancer peptides represent a potential strategy in the fight against cancer. However, our understanding of the underlying structure-activity relationships and the mechanisms driving their cell selectivity is still limited. We developed a computational approach as a step towards the rational design of potent and selective anticancer peptides. This machine learning model distinguishes between peptides with and without anticancer activity. This classifier was experimentally validated by synthesizing and testing a selection of 12 computationally generated peptides. In total, 83% of these predictions were correct. We then utilized an evolutionary molecular design algorithm to improve the peptide selectivity for cancer cells. This simulated molecular evolution process led to a five-fold selectivity increase with regard to human dermal microvascular endothelial cells and more than ten-fold improvement towards human erythrocytes. The results of the present study advocate for the applicability of machine learning models and evolutionary algorithms to design and optimize novel synthetic anticancer peptides with reduced hemolytic liability and increased cell-type selectivity.

Double negative (DN) (CD19+CD20lowCD27-IgD-) B cells are expanded in patients with autoimmune and infectious diseases; however their role in the humoral immune response remains unclear. Using systematic flow cytometric analyses of peripheral blood B cell subsets, we observed an inflated DN B cell population in patients with variety of active inflammatory conditions: myasthenia gravis, Guillain-Barré syndrome, neuromyelitis optica spectrum disorder, meningitis/encephalitis, and rheumatic disorders. Furthermore, we were able to induce DN B cells in healthy subjects following vaccination against influenza and tick borne encephalitis virus. Transcriptome analysis revealed a gene expression profile in DN B cells that clustered with naïve B cells, memory B cells, and plasmablasts. Immunoglobulin VH transcriptome sequencing and analysis of recombinant antibodies revealed clonal expansion of DN B cells that were targeted against the vaccine antigen. Our study suggests that DN B cells are expanded in multiple inflammatory neurologic diseases and represent an inducible B cell population that responds to antigenic stimulation, possibly through an extra-follicular maturation pathway.

During the last few decades, the role of B cells has been well established and redefined in neuro-inflammatory diseases, including multiple sclerosis and autoantibody-associated diseases. In particular, B cell maturation and trafficking across the blood–brain barrier (BBB) has recently been deciphered with the development of next-generation sequencing (NGS) approaches, which allow the assessment of representative cerebrospinal fluid (CSF) and peripheral blood B cell repertoires. In this review, we perform literature research focusing on NGS studies that allow further insights into B cell pathophysiology during neuro-inflammation. Besides the analysis of CSF B cells, the paralleled assessment of peripheral blood B cell repertoire provides deep insights into not only the CSF compartment, but also in B cell trafficking patterns across the BBB. In multiple sclerosis, CSF-specific B cell maturation, in combination with a bidirectional exchange of B cells across the BBB, is consistently detectable. These data suggest that B cells most likely encounter antigen(s) within the CSF and migrate across the BBB, with further maturation also taking place in the periphery. Autoantibody-mediated diseases, such as neuromyelitis optica spectrum disorder and LGI1 / NMDAR encephalitis, also show features of a CSF-specific B cell maturation and clonal connectivity with peripheral blood. In conclusion, these data suggest an intense exchange of B cells across the BBB, possibly feeding autoimmune circuits. Further developments in sequencing technologies will help to dissect the exact pathophysiologic mechanisms of B cells during neuro-inflammation.