Scalable and efficient DNA sequencing analysis on different compute infrastructures aiding variant discovery

DNA variation analysis has become indispensable in many aspects of modern biomedicine, most prominently in the comparison of normal and tumor samples. Thousands of samples are collected in local sequencing efforts and public databases requiring highly scalable, portable, and automated workflows for streamlined processing. Here, we present nf-core/sarek 3, a well-established, comprehensive variant calling and annotation pipeline for germline and somatic samples. It is suitable for any genome with a known reference. We present a full rewrite of the original pipeline showing a significant reduction of storage requirements by using the CRAM format and runtime by increasing intra-sample parallelization. Both are leading to a 70% cost reduction in commercial clouds enabling users to do large-scale and cross-platform data analysis while keeping costs and CO2 emissions low. The code is available at https://nf-co.re/sarek.

How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis

Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

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. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Detailed documentation and example results are available on the nf-core website at (https://nf-co.re/airrflow).

mlf-core: a framework for deterministic machine learning

MOTIVATION

Machine learning has shown extensive growth in recent years and is now routinely applied to sensitive areas. To allow appropriate verification of predictive models before deployment, models must be deterministic. Solely fixing all random seeds is not sufficient for deterministic machine learning, as major machine learning libraries default to the usage of non-deterministic algorithms based on atomic operations.

RESULTS

Various machine learning libraries released deterministic counterparts to the non-deterministic algorithms. We evaluated the effect of these algorithms on determinism and runtime. Based on these results, we formulated a set of requirements for deterministic machine learning and developed a new software solution, the mlf-core ecosystem, which aids machine learning projects to meet and keep these requirements. We applied mlf-core to develop deterministic models in various biomedical fields including a single cell autoencoder with TensorFlow, a PyTorch-based U-Net model for liver-tumor segmentation in CT scans, and a liver cancer classifier based on gene expression profiles with XGBoost.

AVAILABILITY

The complete data together with the implementations of the mlf-core ecosystem and use case models are available at https://github.com/mlf-core.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Clinical outcome of biomarker-guided therapies in adult patients with tumors of the nervous system

Background

The clinical utility of molecular profiling and targeted therapies for neuro-oncology patients outside of clinical trials is not established. We aimed at investigating feasibility and clinical utility of molecular profiling and targeted therapy in adult patients with advanced tumors in the nervous system within a prospective observational study.

Methods

molecular tumor board (MTB)@ZPM (NCT03503149) is a prospective observational precision medicine study for patients with advanced tumors. After inclusion of patients, we performed comprehensive molecular profiling, formulated ranked biomarker-guided therapy recommendations based on consensus by the MTB, and collected prospective clinical outcome data.

Results

Here, we present initial data of 661 adult patients with tumors of the nervous system enrolled by December 31, 2021. Of these, 408 patients were presented at the MTB. Molecular-instructed therapy recommendations could be made in 380/408 (93.1%) cases and were prioritized by evidence levels. Therapies were initiated in 86/380 (22.6%) cases until data cutoff. We observed a progression-free survival ratio >1.3 in 31.3% of patients.

Conclusions

Our study supports the clinical utility of biomarker-guided therapies for neuro-oncology patients and indicates clinical benefit in a subset of patients. Our data might inform future clinical trials, translational studies, and even clinical care.

Cladribine treatment specifically affects peripheral blood memory B cell clones and clonal expansion in multiple sclerosis patients

Introduction

B 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.

Methods

We 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.

Results

The 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.

Discussion

Our 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.

nf-core/mag: a best-practice pipeline for metagenome hybrid assembly and binning

The analysis of shotgun metagenomic data provides valuable insights into microbial communities, while allowing resolution at individual genome level. In absence of complete reference genomes, this requires the reconstruction of metagenome assembled genomes (MAGs) from sequencing reads. We present the nf-core/mag pipeline for metagenome assembly, binning and taxonomic classification. It can optionally combine short and long reads to increase assembly continuity and utilize sample-wise group-information for co-assembly and genome binning. The pipeline is easy to install-all dependencies are provided within containers-portable and reproducible. It is written in Nextflow and developed as part of the nf-core initiative for best-practice pipeline development. All codes are hosted on GitHub under the nf-core organization https://github.com/nf-core/mag and released under the MIT license.

Downregulation of TGR5 (GPBAR1) in biliary epithelial cells contributes to the pathogenesis of sclerosing cholangitis

BACKGROUND & AIMS

Primary sclerosing cholangitis (PSC) is characterized by chronic inflammation and progressive fibrosis of the biliary tree. The bile acid receptor TGR5 (GPBAR1) is found on biliary epithelial cells (BECs), where it promotes secretion, proliferation and tight junction integrity. Thus, we speculated that changes in TGR5-expression in BECs may contribute to PSC pathogenesis.

METHODS

TGR5-expression and -localization were analyzed in PSC livers and liver tissue, isolated bile ducts and BECs from Abcb4^-/-, Abcb4^-/-/Tgr5^Tg and ursodeoxycholic acid (UDCA)- or 24-norursodeoxycholic acid (norUDCA)-fed Abcb4^-/- mice. The effects of IL8/IL8 homologues on TGR5 mRNA and protein levels were studied. BEC gene expression was analyzed by single-cell transcriptomics (scRNA-seq) from distinct mouse models.

RESULTS

TGR5 mRNA expression and immunofluorescence staining intensity were reduced in BECs of PSC and Abcb4^-/- livers, in Abcb4^-/- extrahepatic bile ducts, but not in intrahepatic macrophages. No changes in TGR5 BEC fluorescence intensity were detected in liver tissue of other liver diseases, including primary biliary cholangitis. Incubation of BECs with IL8/IL8 homologues, but not with other cytokines, reduced TGR5 mRNA and protein levels. BECs from Abcb4^-/- mice had lower levels of phosphorylated Erk and higher expression levels of Icam1, Vcam1 and Tgfβ2. Overexpression of Tgr5 abolished the activated inflammatory phenotype characteristic of Abcb4^-/- BECs. NorUDCA-feeding restored TGR5-expression levels in BECs in Abcb4^-/- livers.

CONCLUSIONS

Reduced TGR5 levels in BECs from patients with PSC and Abcb4^-/- mice promote development of a reactive BEC phenotype, aggravate biliary injury and thus contribute to the pathogenesis of sclerosing cholangitis. Restoration of biliary TGR5-expression levels represents a previously unknown mechanism of action of norUDCA.

LAY SUMMARY

Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease-associated with progressive inflammation of the bile duct, leading to fibrosis and end-stage liver disease. Bile acid (BA) toxicity may contribute to the development and disease progression of PSC. TGR5 is a membrane-bound receptor for BAs, which is found on bile ducts and protects bile ducts from BA toxicity. In this study, we show that TGR5 levels were reduced in bile ducts from PSC livers and in bile ducts from a genetic mouse model of PSC. Our investigations indicate that lower levels of TGR5 in bile ducts may contribute to PSC development and progression. Furthermore, treatment with norUDCA, a drug currently being tested in a phase III trial for PSC, restored TGR5 levels in biliary epithelial cells.

Specific Induction of Double Negative B Cells During Protective and Pathogenic Immune Responses

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.

Morphing of Amphipathic Helices to Explore the Activity and Selectivity of Membranolytic Antimicrobial Peptides

Naturally occurring membranolytic antimicrobial peptides (AMPs) are rarely cell-type selective and highly potent at the same time. Template-based peptide design can be used to generate AMPs with improved properties de novo. Following this approach, 18 linear peptides were obtained by computationally morphing the natural AMP Aurein 2.2d2 GLFDIVKKVVGALG into the synthetic model AMP KLLKLLKKLLKLLK. Eleven of the 18 chimeric designs inhibited the growth of Staphylococcus aureus, and six peptides were tested and found to be active against one resistant pathogenic strain or more. One of the peptides was broadly active against bacterial and fungal pathogens without exhibiting toxicity to certain human cell lines. Solution nuclear magnetic resonance and molecular dynamics simulation suggested an oblique-oriented membrane insertion mechanism of this helical de novo peptide. Temperature-resolved circular dichroism spectroscopy pointed to conformational flexibility as an essential feature of cell-type selective AMPs.

Abstract 5728: Liver progenitor cells induces fibrosis via RAGE signaling upon liver injury

Liver fibrosis is characterized by hepatic stellate cell activation and extracellular matrix deposition upon persistent injury and inflammation, which can impair hepatic function and its ability to regenerate. The origin and physiological role of facultative liver progenitor cells (LPCs) have been a controversial issue as it was found to be a major player to regenerate the damaged liver, but it is also associated with fibrosis, disease progression or tumor initiation.

The receptor for advanced glycation end products (RAGE) signaling axis is often associated with chronic inflammation-associated tissue damage and plays an essential role in modulating the tumor microenvironment. Our previous data suggested that RAGE mediates LPC expansion, onset of liver fibrosis and HCC formation (Pusterla et al. Hepatology. 2013.) Hence, in this study, we seek to delineate the functional role and underlying mechanism of RAGE activity in LPC activation in response to inflammation-associated liver injury.

R26TomHnf1β-CreER transgenic mice were crossed with Rage flox/flox (Ragefl/fl) mice to generate tamoxifen-inducible LPC-specific RAGE knockout mice (RAGEΔLPC). They were exposed to a choline-deficient ethionine-supplemented (CDE) diet for three weeks to induce liver damage. Ablation of RAGE in LPCs strongly impairs LPC expanding capacities in CDE diet-treated mice. Strikingly, this is accompanied by reduction of activated hepatic stellate cells and bridging fibrosis. This demonstrated that RAGE signaling in LPCs is a mediator of liver fibrosis in vivo.

Necroinflammation is known to be associated with liver fibrosis. To investigate the role of RAGE in LPCs in the context of necroinflammation in vitro, primary LPCs were isolated from CDE-treated Ragefl/fl C57BL/6 mouse. Wildtype and knockout Rage cell lines were established. LPCs were stimulated with supernatants from necrotic hepatocytes followed by whole transcriptome sequencing to identify downstream targets of RAGE-dependent pathways. Stress response, inflammatory and pro-fibrotic pathways were enriched in LPCs upon treatment with necrotic medium. Most interestingly, signaling pathways that regulate organ size, tissue homeostasis and cell survival were found to be RAGE-dependent. Moreover, clusters of stem cell renewal-related genes were deregulated upon ablation of RAGE. In line with the whole transcriptome profile, we demonstrated that ablation of RAGE attenuates LPCs organoid-forming ability, implying that RAGE regulates stemness properties of LPCs.

Our recent results demonstrated that RAGE is required for LPCs activation and proliferation, as well as the crosstalk with stellate cells in supporting fibrogenesis. Taken together, our data uncovers a potential mechanistic insight on the role of RAGE in LPCs in association with fibrosis upon chronic liver injury.

Citation Format: Wai Ling Macrina Lam, Gisela Gabernet, Tanja Poth, Aurora De Ponti, Anna Saborowski, Sven Nahnsen, Doris Schneller, Peter Angel. Liver progenitor cells induces fibrosis via RAGE signaling upon liver injury [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 5728.

In silico design and optimization of selective membranolytic anticancer peptides

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.

De novo design of anticancer peptides by ensemble artificial neural networks

Membranolytic anticancer peptides (ACPs) are drawing increasing attention as potential future therapeutics against cancer, due to their ability to hinder the development of cellular resistance and their potential to overcome common hurdles of chemotherapy, e.g., side effects and cytotoxicity. In this work, we present an ensemble machine learning model to design potent ACPs. Four counter-propagation artificial neural-networks were trained to identify peptides that kill breast and/or lung cancer cells. For prospective application of the ensemble model, we selected 14 peptides from a total of 1000 de novo designs, for synthesis and testing in vitro on breast cancer (MCF7) and lung cancer (A549) cell lines. Six de novo designs showed anticancer activity in vitro, five of which against both MCF7 and A549 cell lines. The novel active peptides populate uncharted regions of ACP sequence space.

Characterisation of anticancer peptides at the single-cell level

The development of efficacious anticancer therapeutics is difficult due to the heterogeneity of the cellular response to chemotherapy. Anticancer peptides (ACPs) are promising drug candidates that have been shown to be active against a range of cancer cells. However, few ACP studies focus on tumour single-cell heterogeneities. In order to address this need, we developed a microfluidic device and an imaging procedure that enable the capture, monitoring, and analysis of several hundred single cells for the study of drug response. MCF-7 human breast adenocarcinoma cells were captured in hydrodynamic traps and isolated in individual microchambers of less than 100 pL volume. With pneumatic valves, different sets of microchambers were actuated to expose the cells to various drugs. Here, the effect of three membranolytic ACPs - melittin, aurein 1.2 and aurein 2.2 - was investigated by monitoring the efflux of calcein from single MCF-7 cells. The loss of membrane integrity was observed with two different strategies that allow either focusing on one cell for mechanistic studies or parallel analysis of hundreds of individual cells. In general, the device is applicable to the analysis of the effect of various drugs on a large number of different cell types. The platform will enable us in the future to determine the origin of heterogeneous responses on pharmacological substances like ACPs within cell populations by combining it with other on-chip analytical methods.

Hybrid Network Model for "Deep Learning" of Chemical Data: Application to Antimicrobial Peptides

We present a "deep" network architecture for chemical data analysis and classification together with a prospective proof-of-concept application. The model features a self-organizing map (SOM) as the input layer of a feedforward neural network. The SOM converts molecular descriptors to a two-dimensional image for further processing. We implemented lateral neuron inhibition for contrast enhancement. The model achieved improved classification accuracy and predictive robustness compared to feedforward network classifiers lacking the SOM layer. By nonlinear dimensionality reduction the networks extracted meaningful chemical features from the data and outperformed linear principal component analysis (PCA). The learning machine was trained on the sequence-length independent recognition of antibacterial peptides and correctly predicted the killing activity of a synthetic test peptide against Staphylococcus aureus in an in vitro experiment.

Membranolytic anticancer peptides

Understanding the structure–activity relationships and mechanisms of action of membranolytic anticancer peptides could help them advance to therapeutic success.

A simple two-step silane-based (bio-) receptor molecule immobilization without additional binding site passivation

Immobilizing (bio-) receptor molecules via 3-(triethoxysilyl)propylsuccinic anhydride makes subsequent binding site blocking dispensable, while maintaining receptor specificity for target analytes.

Apoptotic DNA degradation into oligonucleosomal fragments, but not apoptotic nuclear morphology, relies on a cytosolic pool of DFF40/CAD endonuclease

Apoptotic cell death is characterized by nuclear fragmentation and oligonucleosomal DNA degradation, mediated by the caspase-dependent specific activation of DFF40/CAD endonuclease. Here, we describe how, upon apoptotic stimuli, SK-N-AS human neuroblastoma-derived cells show apoptotic nuclear morphology without displaying concomitant internucleosomal DNA fragmentation. Cytotoxicity afforded after staurosporine treatment is comparable with that obtained in SH-SY5Y cells, which exhibit a complete apoptotic phenotype. SK-N-AS cell death is a caspase-dependent process that can be impaired by the pan-caspase inhibitor q-VD-OPh. The endogenous inhibitor of DFF40/CAD, ICAD, is correctly processed, and dff40/cad cDNA sequence does not reveal mutations altering its amino acid composition. Biochemical approaches show that both SH-SY5Y and SK-N-AS resting cells express comparable levels of DFF40/CAD. However, the endonuclease is poorly expressed in the cytosolic fraction of healthy SK-N-AS cells. Despite this differential subcellular distribution of DFF40/CAD, we find no differences in the subcellular localization of both pro-caspase-3 and ICAD between the analyzed cell lines. After staurosporine treatment, the preferential processing of ICAD in the cytosolic fraction allows the translocation of DFF40/CAD from this fraction to a chromatin-enriched one. Therefore, the low levels of cytosolic DFF40/CAD detected in SK-N-AS cells determine the absence of DNA laddering after staurosporine treatment. In these cells DFF40/CAD cytosolic levels can be restored by the overexpression of their own endonuclease, which is sufficient to make them proficient at degrading their chromatin into oligonucleosome-size fragments after staurosporine treatment. Altogether, the cytosolic levels of DFF40/CAD are determinants in achieving a complete apoptotic phenotype, including oligonucleosomal DNA degradation.