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.

Functionally-instructed modifiers of response to ATR inhibition in experimental glioma.

BACKGROUND: The DNA damage response (DDR) is a physiological network preventing malignant transformation, e.g. by halting cell cycle progression upon DNA damage detection and promoting DNA repair. Glioblastoma are incurable primary tumors of the nervous system and DDR dysregulation contributes to acquired treatment resistance. Therefore, DDR targeting is a promising therapeutic anti-glioma strategy. Here, we investigated Ataxia telangiectasia and Rad3 related (ATR) inhibition (ATRi) and functionally-instructed combination therapies involving ATRi in experimental glioma. METHODS: We used acute cytotoxicity to identify treatment efficacy as well as RNAseq and DigiWest protein profiling to characterize ATRi-induced modulations within the molecular network in glioma cells. Genome-wide CRISPR/Cas9 functional genomic screens and subsequent validation with functionally-instructed compounds and selected shRNA-based silencing were employed to discover and investigate molecular targets modifying response to ATRi in glioma cell lines in vitro, in primary cultures ex vivo and in zebrafish and murine models in vivo. RESULTS: ATRi monotherapy displays anti-glioma efficacy in vitro and ex vivo and modulates the molecular network. We discovered molecular targets by genome-wide CRISPR/Cas9 loss-of-function and activation screens that enhance therapeutic ATRi effects. We validated selected druggable targets by a customized drug library and functional assays in vitro, ex vivo and in vivo. CONCLUSION: In conclusion, our study leads to the identification of novel combination therapies involving ATRi that could inform future preclinical studies and early phase clinical trials.

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).

Chromosome 8p engineering reveals increased metastatic potential targetable by patient-specific synthetic lethality in liver cancer.

Large-scale chromosomal aberrations are prevalent in human cancer, but their function remains poorly understood. We established chromosome-engineered hepatocellular carcinoma cell lines using CRISPR-Cas9 genome editing. A 33-mega-base pair region on chromosome 8p (chr8p) was heterozygously deleted, mimicking a frequently observed chromosomal deletion. Using this isogenic model system, we delineated the functional consequences of chr8p loss and its impact on metastatic behavior and patient survival. We found that metastasis-associated genes on chr8p act in concert to induce an aggressive and invasive phenotype characteristic for chr8p-deleted tumors. Genome-wide CRISPR-Cas9 viability screening in isogenic chr8p-deleted cells served as a powerful tool to find previously unidentified synthetic lethal targets and vulnerabilities accompanying patient-specific chromosomal alterations. Using this target identification strategy, we showed that chr8p deletion sensitizes tumor cells to targeting of the reactive oxygen sanitizing enzyme Nudix hydrolase 17. Thus, chromosomal engineering allowed for the identification of novel synthetic lethalities specific to chr8p loss of heterozygosity.

Pangenome graph layout by Path-Guided Stochastic Gradient Descent.

Motivation: The increasing availability of complete genomes demands for models to study genomic variability within entire populations. Pangenome graphs capture the full genomic similarity and diversity between multiple genomes. In order to understand them, we need to see them. For visualization, we need a human readable graph layout: A graph embedding in low (e.g. two) dimensional depictions. Due to a pangenome graph's potential excessive size, this is a significant challenge. Results: In response, we introduce a novel graph layout algorithm: the Path-Guided Stochastic Gradient Descent (PG-SGD). PG-SGD uses the genomes, represented in the pangenome graph as paths, as an embedded positional system to sample genomic distances between pairs of nodes. This avoids the quadratic cost seen in previous versions of graph drawing by Stochastic Gradient Descent (SGD). We show that our implementation efficiently computes the low dimensional layouts of gigabase-scale pangenome graphs, unveiling their biological features. Availability: We integrated PG-SGD in ODGI which is released as free software under the MIT open source license. Source code is available at https://github.com/pangenome/odgi.

Building pangenome graphs.

Pangenome graphs can represent all variation between multiple genomes, but existing methods for constructing them are biased due to reference-guided approaches. In response, we have developed PanGenome Graph Builder (PGGB), a reference-free pipeline for constructing unbi-ased pangenome graphs. PGGB uses all-to-all whole-genome alignments and learned graph embeddings to build and iteratively refine a model in which we can identify variation, measure conservation, detect recombination events, and infer phylogenetic relationships.

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 nondeterministic algorithms based on atomic operations. RESULTS: Various machine learning libraries released deterministic counterparts to the nondeterministic 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 computed tomography scans, and a liver cancer classifier based on gene expression profiles with XGBoost. AVAILABILITY AND IMPLEMENTATION: The complete data together with the implementations of the mlf-core ecosystem and use case models are available at https://github.com/mlf-core.

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.

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.

Decorin improves human pancreatic ß-cell function and regulates ECM expression in vitro.

Transplantation of islets of Langerhans is a promising alternative treatment strategy in severe cases of type 1 diabetes mellitus; however, the success rate is limited by the survival rate of the cells post-transplantation. Restoration of the native pancreatic niche during transplantation potentially can help to improve cell viability and function. Here, we assessed for the first time the regulatory role of the small leucine-rich proteoglycan decorin (DCN) in insulin secretion in human ß-cells, and its impact on pancreatic extracellular matrix (ECM) protein expression in vitro. In depth analyses utilizing next-generation sequencing as well as Raman microspectroscopy and Raman imaging identified pathways related to glucose metabolism to be upregulated in DCN-treated cells, including oxidative phosphorylation within the mitochondria as well as proteins and lipids of the endoplasmic reticulum. We further showed the effectiveness of DCN in a transplantation setting by treating collagen type 1-encapsulated ß-cell-containing pseudo-islets with DCN. Taken together, in this study, we demonstrate the potential of DCN to improve the function of insulin-secreting ß-cells while reducing the expression of ECM proteins affiliated with fibrotic capsule formation, making DCN a highly promising therapeutic agent for islet transplantation.

Multiomics surface receptor profiling of the NCI-60 tumor cell panel uncovers novel theranostics for cancer immunotherapy.

BACKGROUND: Immunotherapy with immune checkpoint inhibitors (ICI) has revolutionized cancer therapy. However, therapeutic targeting of inhibitory T cell receptors such as PD-1 not only initiates a broad immune response against tumors, but also causes severe adverse effects. An ideal future stratified immunotherapy would interfere with cancer-specific cell surface receptors only. METHODS: To identify such candidates, we profiled the surface receptors of the NCI-60 tumor cell panel via flow cytometry. The resulting surface receptor expression data were integrated into proteomic and transcriptomic NCI-60 datasets applying a sophisticated multiomics multiple co-inertia analysis (MCIA). This allowed us to identify surface profiles for skin, brain, colon, kidney, and bone marrow derived cell lines and cancer entity-specific cell surface receptor biomarkers for colon and renal cancer. RESULTS: For colon cancer, identified biomarkers are CD15, CD104, CD324, CD326, CD49f, and for renal cancer, CD24, CD26, CD106 (VCAM1), EGFR, SSEA-3 (B3GALT5), SSEA-4 (TMCC1), TIM1 (HAVCR1), and TRA-1-60R (PODXL). Further data mining revealed that CD106 (VCAM1) in particular is a promising novel immunotherapeutic target for the treatment of renal cancer. CONCLUSION: Altogether, our innovative multiomics analysis of the NCI-60 panel represents a highly valuable resource for uncovering surface receptors that could be further exploited for diagnostic and therapeutic purposes in the context of cancer immunotherapy.

ODGI: understanding pangenome graphs.

MOTIVATION: Pangenome graphs provide a complete representation of the mutual alignment of collections of genomes. These models offer the opportunity to study the entire genomic diversity of a population, including structurally complex regions. Nevertheless, analyzing hundreds of gigabase-scale genomes using pangenome graphs is difficult as it is not well-supported by existing tools. Hence, fast and versatile software is required to ask advanced questions to such data in an efficient way. RESULTS: We wrote Optimized Dynamic Genome/Graph Implementation (ODGI), a novel suite of tools that implements scalable algorithms and has an efficient in-memory representation of DNA pangenome graphs in the form of variation graphs. ODGI supports pre-built graphs in the Graphical Fragment Assembly format. ODGI includes tools for detecting complex regions, extracting pangenomic loci, removing artifacts, exploratory analysis, manipulation, validation and visualization. Its fast parallel execution facilitates routine pangenomic tasks, as well as pipelines that can quickly answer complex biological questions of gigabase-scale pangenome graphs. AVAILABILITY AND IMPLEMENTATION: ODGI is published as free software under the MIT open source license. Source code can be downloaded from https://github.com/pangenome/odgi and documentation is available at https://odgi.readthedocs.io. ODGI can be installed via Bioconda https://bioconda.github.io/recipes/odgi/README.html or GNU Guix https://github.com/pangenome/odgi/blob/master/guix.scm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

A data management infrastructure for the integration of imaging and omics data in life sciences.

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.

Comprehensive Analysis of Human Cytomegalovirus- and HIV-Mediated Plasma Membrane Remodeling in Macrophages.

The plasma membrane (PM) must be overcome by viruses during entry and release. Furthermore, the PM represents the cellular communication compartment and the immune system interface. Hence, viruses have evolved sophisticated strategies to remodel the PM, for instance to avoid immune sensing and clearance of infected cells. We performed a comprehensive analysis of cell surface dysregulation by two human-pathogenic viruses, human cytomegalovirus (HCMV) and human immunodeficiency virus type 1 (HIV-1), in primary macrophages, which are classical antigen-presenting cells and orchestrators of the immune system. Scanning ion conductance microscopy revealed a loss of roughness and an overall smooth phenotype of HCMV-infected macrophages, in contrast to HIV-1 infection. This phenotype was also evident on the molecular level. When we screened for cell surface receptors modulated by HCMV, 42 of 332 receptors tested were up- or downregulated, whereas HIV-1 affected only 7 receptors. In particular CD164, CD84, and CD180 were targeted by HCMV. Mechanistically, HCMV induced transcriptional silencing of these receptors in an interferon (IFN)-independent manner, and expression was reduced not only by lab-adapted HCMV but also by clinical HCMV isolates. Altogether, our plasma membrane profiling of human macrophages provides clues to understand how viruses evade the immune system and identified novel cell surface receptors targeted by HCMV. IMPORTANCE The PM is a key component that viruses have to cope with. It is a barrier for infection and egress and is critically involved in antiviral immune signaling. We hence asked the question how two immunomodulatory viruses, HIV-1 and HCMV, dysregulate this compartment in infected macrophages, relevant in vivo targets of both viruses. We employed a contact-free microscopic technique to image the PM of infected cells and performed a phenotypic flow cytometry-based screen to identify receptor modulations on a molecular level. Our results show that HIV-1 and HCMV differentially manipulate the PM of macrophages. While HIV-1-mediated changes are relatively subtle, HCMV induces major alterations of the PM. We identify novel immune receptors manipulated by HCMV and define mechanisms of how HCMV interferes with receptor expression. Altogether, our study reveals differential strategies of how two human-pathogenic viruses manipulate infected cells and identifies potential novel pathways of HCMV immune evasion.

Absence of Non-Canonical, Inhibitory MYD88 Splice Variants in B Cell Lymphomas Correlates With Sustained NF-κB Signaling.

Gain-of-function mutations of the TLR adaptor and oncoprotein MyD88 drive B cell lymphomagenesis via sustained NF-κB activation. In myeloid cells, both short and sustained TLR activation and NF-κB activation lead to the induction of inhibitory MYD88 splice variants that restrain prolonged NF-κB activation. We therefore sought to investigate whether such a negative feedback loop exists in B cells. Analyzing MYD88 splice variants in normal B cells and different primary B cell malignancies, we observed that MYD88 splice variants in transformed B cells are dominated by the canonical, strongly NF-κB-activating isoform of MYD88 and contain at least three novel, so far uncharacterized signaling-competent splice isoforms. Sustained TLR stimulation in B cells unexpectedly reinforces splicing of NF-κB-promoting, canonical isoforms rather than the 'MyD88s', a negative regulatory isoform reported to be typically induced by TLRs in myeloid cells. This suggests that an essential negative feedback loop restricting TLR signaling in myeloid cells at the level of alternative splicing, is missing in B cells when they undergo proliferation, rendering B cells vulnerable to sustained NF-κB activation and eventual lymphomagenesis. Our results uncover MYD88 alternative splicing as an unappreciated promoter of B cell lymphomagenesis and provide a rationale why oncogenic MYD88 mutations are exclusively found in B cells.

Sustainable data analysis with Snakemake.

Data analysis often entails a multitude of heterogeneous steps, from the application of various command line tools to the usage of scripting languages like R or Python for the generation of plots and tables. It is widely recognized that data analyses should ideally be conducted in a reproducible way. Reproducibility enables technical validation and regeneration of results on the original or even new data. However, reproducibility alone is by no means sufficient to deliver an analysis that is of lasting impact (i.e., sustainable) for the field, or even just one research group. We postulate that it is equally important to ensure adaptability and transparency. The former describes the ability to modify the analysis to answer extended or slightly different research questions. The latter describes the ability to understand the analysis in order to judge whether it is not only technically, but methodologically valid. Here, we analyze the properties needed for a data analysis to become reproducible, adaptable, and transparent. We show how the popular workflow management system Snakemake can be used to guarantee this, and how it enables an ergonomic, combined, unified representation of all steps involved in data analysis, ranging from raw data processing, to quality control and fine-grained, interactive exploration and plotting of final results.

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-/-/Tgr5Tg 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 Tgfß2. 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.

Transcriptome Profiling Identifies TIGIT as a Marker of T-Cell Exhaustion in Liver Cancer.

BACKGROUND AND AIMS: Programmed death 1 (PD-1) checkpoint inhibition has shown promising results in patients with hepatocellular carcinoma, inducing objective responses in approximately 20% of treated patients. The roles of other coinhibitory molecules and their individual contributions to T-cell dysfunction in liver cancer, however, remain largely elusive. APPROACH AND RESULTS: We performed a comprehensive mRNA profiling of cluster of differentiation 8 (CD8) T cells in a murine model of autochthonous liver cancer by comparing the transcriptome of naive, functional effector, and exhausted, tumor-specific CD8 T cells. Subsequently, we functionally validated the role of identified genes in T-cell exhaustion. Our results reveal a unique transcriptome signature of exhausted T cells and demonstrate that up-regulation of the inhibitory immune receptor T-cell immunoreceptor with immunoglobulin and immunoreceptor tyrosine-based inhibitor motif domains (TIGIT) represents a hallmark in the process of T-cell exhaustion in liver cancer. Compared to PD-1, expression of TIGIT more reliably identified exhausted CD8 T cells at different stages of their differentiation. In combination with PD-1 inhibition, targeting of TIGIT with antagonistic antibodies resulted in synergistic inhibition of liver cancer growth in immunocompetent mice. Finally, we demonstrate expression of TIGIT on tumor-infiltrating CD8 T cells in tissue samples of patients with hepatocellular carcinoma and intrahepatic cholangiocarcinoma and identify two subsets of patients based on differential expression of TIGIT on tumor-specific T cells. CONCLUSIONS: Our transcriptome analysis provides a valuable resource for the identification of key pathways involved in T-cell exhaustion in patients with liver cancer and identifies TIGIT as a potential target in checkpoint combination therapies.