A liver immune rheostat regulates CD8 T cell immunity in chronic HBV infection.

Chronic hepatitis B virus (HBV) infection affects 300 million patients worldwide1,2, in whom virus-specific CD8 T cells by still ill-defined mechanisms lose their function and cannot eliminate HBV-infected hepatocytes3-7. Here we demonstrate that a liver immune rheostat renders virus-specific CD8 T cells refractory to activation and leads to their loss of effector functions. In preclinical models of persistent infection with hepatotropic viruses such as HBV, dysfunctional virus-specific CXCR6+ CD8 T cells accumulated in the liver and, as a characteristic hallmark, showed enhanced transcriptional activity of cAMP-responsive element modulator (CREM) distinct from T cell exhaustion. In patients with chronic hepatitis B, circulating and intrahepatic HBV-specific CXCR6+ CD8 T cells with enhanced CREM expression and transcriptional activity were detected at a frequency of 12-22% of HBV-specific CD8 T cells. Knocking out the inhibitory CREM/ICER isoform in T cells, however, failed to rescue T cell immunity. This indicates that CREM activity was a consequence, rather than the cause, of loss in T cell function, further supported by the observation of enhanced phosphorylation of protein kinase A (PKA) which is upstream of CREM. Indeed, we found that enhanced cAMP-PKA-signalling from increased T cell adenylyl cyclase activity augmented CREM activity and curbed T cell activation and effector function in persistent hepatic infection. Mechanistically, CD8 T cells recognizing their antigen on hepatocytes established close and extensive contact with liver sinusoidal endothelial cells, thereby enhancing adenylyl cyclase-cAMP-PKA signalling in T cells. In these hepatic CD8 T cells, which recognize their antigen on hepatocytes, phosphorylation of key signalling kinases of the T cell receptor signalling pathway was impaired, which rendered them refractory to activation. Thus, close contact with liver sinusoidal endothelial cells curbs the activation and effector function of HBV-specific CD8 T cells that target hepatocytes expressing viral antigens by means of the adenylyl cyclase-cAMP-PKA axis in an immune rheostat-like fashion.

A bottom-up approach identifies the antipsychotic and antineoplastic trifluoperazine and the ribose derivative deoxytubercidin as novel microglial phagocytosis inhibitors.

Phagocytosis is an indispensable function of microglia, the brain professional phagocytes. Microglia is particularly efficient phagocytosing cells that undergo programmed cell death (apoptosis) in physiological conditions. However, mounting evidence suggests microglial phagocytosis dysfunction in multiple brain disorders. These observations prompted us to search for phagocytosis modulators (enhancers or inhibitors) with therapeutic potential. We used a bottom-up strategy that consisted on the identification of phagocytosis modulators using phenotypic high throughput screenings (HTSs) in cell culture and validation in organotypic cultures and in vivo. We performed two complementary HTS campagnes: at Achucarro, we used primary cultures of mouse microglia and compounds of the Prestwick Chemical Library; at Roche, we used human iPSC derived macrophage-like cells and a proprietary chemo-genomic library with 2200 compounds with known mechanism-of-action. Next, we validated the more robust compounds using hippocampal organotypic cultures and identified two phagocytosis inhibitors: trifluoperazine, a dopaminergic and adrenergic antagonist used as an antipsychotic and antineoplastic; and deoxytubercidin, a ribose derivative. Finally, we tested whether these compounds were able to modulate phagocytosis of apoptotic newborn cells in the adult hippocampal neurogenic niche in vivo by administering them into the mouse hippocampus using osmotic minipumps. We confirmed that both trifluoperazine and deoxytubercidin have anti-phagocytic activity in vivo, and validated our bottom-up strategy to identify novel phagocytosis modulators. These results show that chemical libraries with annotated mechanism of action are an starting point for the pharmacological modulation of microglia in drug discovery projects aiming at the therapeutic manipulation of phagocytosis in brain diseases.

Discovery of a first-in-class orally available HBV cccDNA inhibitor.

BACKGROUND & AIMS: The persistence of covalently closed circular DNA (cccDNA) in infected hepatocytes is the major barrier preventing viral eradication with existing therapies in patients with chronic hepatitis B. Therapeutic agents that can eliminate cccDNA are urgently needed to achieve viral eradication and thus HBV cure. METHODS: A phenotypic assay with HBV-infected primary human hepatocytes (PHHs) was employed to screen for novel cccDNA inhibitors. A HBVcircle mouse model and a uPA-SCID (urokinase-type plasminogen activator-severe combined immunodeficiency) humanized liver mouse model were used to evaluate the anti-HBV efficacy of the discovered cccDNA inhibitors. RESULTS: Potent and dose-dependent reductions in extracellular HBV DNA, HBsAg, and HBeAg levels were achieved upon the initiation of ccc_R08 treatment two days after the HBV infection of PHHs. More importantly, the level of cccDNA was specifically reduced by ccc_R08, while it did not obviously affect mitochondrial DNA. Additionally, ccc_R08 showed no significant cytotoxicity in PHHs or in multiple proliferating cell lines. The twice daily oral administration of ccc_R08 to HBVcircle model mice, which contained surrogate cccDNA molecules, significantly decreased the serum levels of HBV DNA and antigens, and these effects were sustained during the off-treatment follow-up period. Moreover, at the end of follow-up, the levels of surrogate cccDNA molecules in the livers of ccc_R08-treated HBVcircle mice were reduced to below the lower limit of quantification. CONCLUSIONS: We have discovered a small-molecule cccDNA inhibitor that reduces HBV cccDNA levels. cccDNA inhibitors potentially represent a new approach to completely cure patients chronically infected with HBV. IMPACT AND IMPLICATIONS: Covalently closed circular DNA (cccDNA) persistence in HBV-infected hepatocytes is the root cause of chronic hepatitis B. We discovered a novel small-molecule cccDNA inhibitor that can specifically reduce cccDNA levels in HBV-infected hepatocytes. This type of molecule could offer a new approach to completely cure patients chronically infected with HBV.

Inducers of the endothelial cell barrier identified through chemogenomic screening in genome-edited hPSC-endothelial cells.

The blood-retina barrier and blood-brain barrier (BRB/BBB) are selective and semipermeable and are critical for supporting and protecting central nervous system (CNS)-resident cells. Endothelial cells (ECs) within the BRB/BBB are tightly coupled, express high levels of Claudin-5 (CLDN5), a junctional protein that stabilizes ECs, and are important for proper neuronal function. To identify novel CLDN5 regulators (and ultimately EC stabilizers), we generated a CLDN5-P2A-GFP stable cell line from human pluripotent stem cells (hPSCs), directed their differentiation to ECs (CLDN5-GFP hPSC-ECs), and performed flow cytometry-based chemogenomic library screening to measure GFP expression as a surrogate reporter of barrier integrity. Using this approach, we identified 62 unique compounds that activated CLDN5-GFP. Among them were TGF-ß pathway inhibitors, including RepSox. When applied to hPSC-ECs, primary brain ECs, and retinal ECs, RepSox strongly elevated barrier resistance (transendothelial electrical resistance), reduced paracellular permeability (fluorescein isothiocyanate-dextran), and prevented vascular endothelial growth factor A (VEGFA)-induced barrier breakdown in vitro. RepSox also altered vascular patterning in the mouse retina during development when delivered exogenously. To determine the mechanism of action of RepSox, we performed kinome-, transcriptome-, and proteome-profiling and discovered that RepSox inhibited TGF-ß, VEGFA, and inflammatory gene networks. In addition, RepSox not only activated vascular-stabilizing and barrier-establishing Notch and Wnt pathways, but also induced expression of important tight junctions and transporters. Taken together, our data suggest that inhibiting multiple pathways by selected individual small molecules, such as RepSox, may be an effective strategy for the development of better BRB/BBB models and novel EC barrier-inducing therapeutics.

Ten simple rules for doing a postdoc in pharma.

Postdoctoral programs in the pharmaceutical and life science industry offer opportunities for personal and professional development, if you know why to join, what to expect, and how to prepare.

Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology.

MOTIVATION: The composition and density of immune cells in the tumor microenvironment (TME) profoundly influence tumor progression and success of anti-cancer therapies. Flow cytometry, immunohistochemistry staining or single-cell sequencing are often unavailable such that we rely on computational methods to estimate the immune-cell composition from bulk RNA-sequencing (RNA-seq) data. Various methods have been proposed recently, yet their capabilities and limitations have not been evaluated systematically. A general guideline leading the research community through cell type deconvolution is missing. RESULTS: We developed a systematic approach for benchmarking such computational methods and assessed the accuracy of tools at estimating nine different immune- and stromal cells from bulk RNA-seq samples. We used a single-cell RNA-seq dataset of ∼11 000 cells from the TME to simulate bulk samples of known cell type proportions, and validated the results using independent, publicly available gold-standard estimates. This allowed us to analyze and condense the results of more than a hundred thousand predictions to provide an exhaustive evaluation across seven computational methods over nine cell types and ∼1800 samples from five simulated and real-world datasets. We demonstrate that computational deconvolution performs at high accuracy for well-defined cell-type signatures and propose how fuzzy cell-type signatures can be improved. We suggest that future efforts should be dedicated to refining cell population definitions and finding reliable signatures. AVAILABILITY AND IMPLEMENTATION: A snakemake pipeline to reproduce the benchmark is available at https://github.com/grst/immune_deconvolution_benchmark. An R package allows the community to perform integrated deconvolution using different methods (https://grst.github.io/immunedeconv). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Large-Scale Production of Human iPSC-Derived Macrophages for Drug Screening.

Tissue-resident macrophages are key players in inflammatory processes, and their activation and functionality are crucial in health and disease. Numerous diseases are associated with alterations in homeostasis or dysregulation of the innate immune system, including allergic reactions, autoimmune diseases, and cancer. Macrophages are a prime target for drug discovery due to their major regulatory role in health and disease. Currently, the main sources of macrophages used for therapeutic compound screening are primary cells isolated from blood or tissue or immortalized or neoplastic cell lines (e.g., THP-1). Here, we describe an improved method to employ induced pluripotent stem cells (iPSCs) for the high-yield, large-scale production of cells resembling tissue-resident macrophages. For this, iPSC-derived macrophage-like cells are thoroughly characterized to confirm their cell identity and thus their suitability for drug screening purposes. These iPSC-derived macrophages show strong cellular identity with primary macrophages and recapitulate key functional characteristics, including cytokine release, phagocytosis, and chemotaxis. Furthermore, we demonstrate that genetic modifications can be readily introduced at the macrophage-like progenitor stage in order to interrogate drug target-relevant pathways. In summary, this novel method overcomes previous shortcomings with primary and leukemic cells and facilitates large-scale production of genetically modified iPSC-derived macrophages for drug screening applications.

Modeling the Effects of Severe Metabolic Disease by Genome Editing of hPSC-Derived Endothelial Cells Reveals an Inflammatory Phenotype.

The kinase AKT2 (PKB) is an important mediator of insulin signaling, for which loss-of-function knockout (KO) mutants lead to early onset diabetes mellitus, and dominant active mutations lead to early development of obesity and endothelial cell (EC) dysfunction. To model EC dysfunction, we used edited human pluripotent stem cells (hPSCs) that carried either a homozygous deletion of AKT2 (AKT2 KO) or a dominant active mutation (AKT2 E17K), which, along with the parental wild type (WT), were differentiated into ECs. Profiling of EC lines indicated an increase in proinflammatory and a reduction in anti-inflammatory fatty acids, an increase in inflammatory chemokines in cell supernatants, increased expression of proinflammatory genes, and increased binding to the EC monolayer in a functional leukocyte adhesion assay for both AKT2 KO and AKT2 E17K. Collectively, these findings suggest that vascular endothelial inflammation that results from dysregulated insulin signaling (homeostasis) may contribute to coronary artery disease, and that either downregulation or upregulation of the insulin pathway may lead to inflammation of endothelial cells. This suggests that the standard of care for patients must be expanded from control of metabolic parameters to include control of inflammation, such that endothelial dysfunction and cardiovascular disorders can ultimately be prevented.

Correction to: Detect tissue heterogeneity in gene expression data with BioQC.

After the publication of this work [1], a mistake was noticed in the Eq. 1. Given an m × n expression matrix with m genes and samples of n tissues, the correct definition of the Gini index for gene i is.

A novel orally available small molecule that inhibits hepatitis B virus expression.

BACKGROUND & AIMS: The hallmarks of chronic HBV infection are a high viral load (HBV DNA) and even higher levels (>100-fold in excess of virions) of non-infectious membranous particles containing the tolerogenic viral S antigen (HBsAg). Currently, standard treatment effectively reduces viremia but only rarely results in a functional cure (defined as sustained HBsAg loss). There is an urgent need to identify novel therapies that reduce HBsAg levels and restore virus-specific immune responsiveness in patients. We report the discovery of a novel, potent and orally bioavailable small molecule inhibitor of HBV gene expression (RG7834). METHODS: RG7834 antiviral characteristics and selectivity against HBV were evaluated in HBV natural infection assays and in a urokinase-type plasminogen activator/severe combined immunodeficiency humanized mouse model of HBV infection, either alone or in combination with entecavir. RESULTS: Unlike nucleos(t)ide therapies, which reduce viremia but do not lead to an effective reduction in HBV antigen expression, RG7834 significantly reduced the levels of viral proteins (including HBsAg), as well as lowering viremia. Consistent with its proposed mechanism of action, time course RNA-seq analysis revealed a fast and selective reduction in HBV mRNAs in response to RG7834 treatment. Furthermore, oral treatment of HBV-infected humanized mice with RG7834 led to a mean HBsAg reduction of 1.09 log10 compared to entecavir, which had no significant effect on HBsAg levels. Combination of RG7834, entecavir and pegylated interferon α-2a led to significant reductions of both HBV DNA and HBsAg levels in humanized mice. CONCLUSION: We have identified a novel oral HBV viral gene expression inhibitor that blocks viral antigen and virion production, that is highly selective for HBV, and has a unique antiviral profile that is clearly differentiated from nucleos(t)ide analogues. LAY SUMMARY: We discovered a novel small molecule viral expression inhibitor that is highly selective for HBV and unlike current therapy inhibits the expression of viral proteins by specifically reducing HBV mRNAs. RG7834 can therefore potentially provide anti-HBV benefits and increase HBV cure rates, by direct reduction of viral agents needed to complete the viral life cycle, as well as a reduction of viral agents involved in evasion of the host immune responses.

Detect tissue heterogeneity in gene expression data with BioQC.

BACKGROUND: Gene expression data can be compromised by cells originating from other tissues than the target tissue of profiling. Failures in detecting such tissue heterogeneity have profound implications on data interpretation and reproducibility. A computational tool explicitly addressing the issue is warranted. RESULTS: We introduce BioQC, a R/Bioconductor software package to detect tissue heterogeneity in gene expression data. To this end BioQC implements a computationally efficient Wilcoxon-Mann-Whitney test and provides more than 150 signatures of tissue-enriched genes derived from large-scale transcriptomics studies. Simulation experiments show that BioQC is both fast and sensitive in detecting tissue heterogeneity. In a case study with whole-organ profiling data, BioQC predicted contamination events that are confirmed by quantitative RT-PCR. Applied to transcriptomics data of the Genotype-Tissue Expression (GTEx) project, BioQC reveals clustering of samples and suggests that some samples likely suffer from tissue heterogeneity. CONCLUSIONS: Our experience with gene expression data indicates a prevalence of tissue heterogeneity that often goes unnoticed. BioQC addresses the issue by integrating prior knowledge with a scalable algorithm. We propose BioQC as a first-line tool to ensure quality and reproducibility of gene expression data.

Pathway reporter genes define molecular phenotypes of human cells.

BACKGROUND: The phenotype of a living cell is determined by its pattern of active signaling networks, giving rise to a "molecular phenotype" associated with differential gene expression. Digital amplicon based RNA quantification by sequencing is a useful technology for molecular phenotyping as a novel tool to characterize the state of biological systems. RESULTS: We show here that the activity of signaling networks can be assessed based on a set of established key regulators and expression targets rather than the entire transcriptome. We compiled a panel of 917 human pathway reporter genes, representing 154 human signaling and metabolic networks for integrated knowledge- and data-driven understanding of biological processes. The reporter genes are significantly enriched for regulators and effectors covering a wide range of biological processes, and faithfully capture gene-level and pathway-level changes. We apply the approach to iPSC derived cardiomyocytes and primary human hepatocytes to describe changes in molecular phenotype during development or drug response. The reporter genes deliver an accurate pathway-centric view of the biological system under study, and identify known and novel modulation of signaling networks consistent with literature or experimental data. CONCLUSIONS: A panel of 917 pathway reporter genes is sufficient to describe changes in the molecular phenotype defined by 154 signaling cascades in various human cell types. AmpliSeq-RNA based digital transcript imaging enables simultaneous monitoring of the entire pathway reporter gene panel in up to 150 samples. We propose molecular phenotyping as a useful approach to understand diseases and drug action at the network level.

Combined DNA methylation and gene expression profiling in gastrointestinal stromal tumors reveals hypomethylation of SPP1 as an independent prognostic factor.

Gastrointestinal stromal tumors (GISTs) have distinct gene expression patterns according to localization, genotype and aggressiveness. DNA methylation at CpG dinucleotides is an important mechanism for regulation of gene expression. We performed targeted DNA methylation analysis of 1.505 CpG loci in 807 cancer-related genes in a cohort of 76 GISTs, combined with genome-wide mRNA expression analysis in 22 GISTs, to identify signatures associated with clinicopathological parameters and prognosis. Principal component analysis revealed distinct DNA methylation patterns associated with anatomical localization, genotype, mitotic counts and clinical follow-up. Methylation of a single CpG dinucleotide in the non-CpG island promoter of SPP1 was significantly correlated with shorter disease-free survival. Hypomethylation of this CpG was an independent prognostic parameter in a multivariate analysis compared to anatomical localization, genotype, tumor size and mitotic counts in a cohort of 141 GISTs with clinical follow-up. The epigenetic regulation of SPP1 was confirmed in vitro, and the functional impact of SPP1 protein on tumorigenesis-related signaling pathways was demonstrated. In summary, SPP1 promoter methylation is a novel and independent prognostic parameter in GISTs, and might be helpful in estimating the aggressiveness of GISTs from the intermediate-risk category.

Highly sensitive amplicon-based transcript quantification by semiconductor sequencing.

BACKGROUND: In clinical and basic research custom panels for transcript profiling are gaining importance because only project specific informative genes are interrogated. This approach reduces costs and complexity of data analysis and allows multiplexing of samples. Polymerase-chain-reaction (PCR) based TaqMan assays have high sensitivity but suffer from a limited dynamic range and sample throughput. Hence, there is a gap for a technology able to measure expression of large gene sets in multiple samples. RESULTS: We have adapted a commercially available mRNA quantification assay (AmpliSeq-RNA) that measures mRNA abundance based on the frequency of PCR amplicons determined by high-throughput semiconductor sequencing. This approach allows for parallel, accurate quantification of about 1000 transcripts in multiple samples covering a dynamic range of five orders of magnitude. Using samples derived from a well-characterized stem cell differentiation model, we obtained a good correlation (r = 0.78) of transcript levels measured by AmpliSeq-RNA and DNA-microarrays. A significant portion of low abundant transcripts escapes detection by microarrays due to limited sensitivity. Standard quantitative RNA sequencing of the same samples confirms expression of low abundant genes with an overall correlation coefficient of r = 0.87. Based on digital AmpliSeq-RNA imaging we show switches of signaling cascades at four time points during differentiation of stem cells into cardiomyocytes. CONCLUSIONS: The AmpliSeq-RNA technology adapted to high-throughput semiconductor sequencing allows robust transcript quantification based on amplicon frequency. Multiplexing of at least 900 parallel PCR reactions is feasible because sequencing-based quantification eliminates artefacts coming from off-target amplification. Using this approach, RNA quantification and detection of genetic variations can be performed in the same experiment.

Another string to your bow: machine learning prediction of the pharmacokinetic properties of small molecules.

INTRODUCTION: Prediction of pharmacokinetic (PK) properties is crucial for drug discovery and development. Machine-learning (ML) models, which use statistical pattern recognition to learn correlations between input features (such as chemical structures) and target variables (such as PK parameters), are being increasingly used for this purpose. To embed ML models for PK prediction into workflows and to guide future development, a solid understanding of their applicability, advantages, limitations, and synergies with other approaches is necessary. AREAS COVERED: This narrative review discusses the design and application of ML models to predict PK parameters of small molecules, especially in light of established approaches including in vitro-in vivo extrapolation (IVIVE) and physiologically based pharmacokinetic (PBPK) models. The authors illustrate scenarios in which the three approaches are used and emphasize how they enhance and complement each other. In particular, they highlight achievements, the state of the art and potentials of applying machine learning for PK prediction through a comphrehensive literature review. EXPERT OPINION: ML models, when carefully crafted, regularly updated, and appropriately used, empower users to prioritize molecules with favorable PK properties. Informed practitioners can leverage these models to improve the efficiency of drug discovery and development process.

splicekit: an integrative toolkit for splicing analysis from short-read RNA-seq.

Motivation: Analysis of alternative splicing using short-read RNA-seq data is a complex process that involves several steps: alignment of reads to the reference genome, identification of alternatively spliced features, motif discovery, analysis of RNA-protein binding near donor and acceptor splice sites, and exploratory data visualization. To the best of our knowledge, there is currently no integrative open-source software dedicated to this task. Results: Here, we introduce splicekit, a Python package that provides and integrates a set of existing and novel splicing analysis tools for conducting splicing analysis. Availability and implementation: The software splicekit is open-source and available at Github (https://github.com/bedapub/splicekit) and via the Python Package Index.

G-PLIP: Knowledge graph neural network for structure-free protein-ligand bioactivity prediction.

Protein-ligand interactions (PLIs) determine the efficacy and safety profiles of small molecule drugs. Existing methods rely on either structural information or resource-intensive computations to predict PLI, casting doubt on whether it is possible to perform structure-free PLI predictions at low computational cost. Here we show that a light-weight graph neural network (GNN), trained with quantitative PLIs of a small number of proteins and ligands, is able to predict the strength of unseen PLIs. The model has no direct access to structural information about the protein-ligand complexes. Instead, the predictive power is provided by encoding the entire chemical and proteomic space in a single heterogeneous graph, encapsulating primary protein sequence, gene expression, the protein-protein interaction network, and structural similarities between ligands. This novel approach performs competitively with, or better than, structure-aware models. Our results suggest that existing PLI prediction methods may be improved by incorporating representation learning techniques that embed biological and chemical knowledge.

Global microRNA level regulation of EGFR-driven cell-cycle protein network in breast cancer.

The EGFR-driven cell-cycle pathway has been extensively studied due to its pivotal role in breast cancer proliferation and pathogenesis. Although several studies reported regulation of individual pathway components by microRNAs (miRNAs), little is known about how miRNAs coordinate the EGFR protein network on a global miRNA (miRNome) level. Here, we combined a large-scale miRNA screening approach with a high-throughput proteomic readout and network-based data analysis to identify which miRNAs are involved, and to uncover potential regulatory patterns. Our results indicated that the regulation of proteins by miRNAs is dominated by the nucleotide matching mechanism between seed sequences of the miRNAs and 3'-UTR of target genes. Furthermore, the novel network-analysis methodology we developed implied the existence of consistent intrinsic regulatory patterns where miRNAs simultaneously co-regulate several proteins acting in the same functional module. Finally, our approach led us to identify and validate three miRNAs (miR-124, miR-147 and miR-193a-3p) as novel tumor suppressors that co-target EGFR-driven cell-cycle network proteins and inhibit cell-cycle progression and proliferation in breast cancer.

Causal inference in drug discovery and development.

To discover new drugs is to seek and to prove causality. As an emerging approach leveraging human knowledge and creativity, data, and machine intelligence, causal inference holds the promise of reducing cognitive bias and improving decision-making in drug discovery. Although it has been applied across the value chain, the concepts and practice of causal inference remain obscure to many practitioners. This article offers a nontechnical introduction to causal inference, reviews its recent applications, and discusses opportunities and challenges of adopting the causal language in drug discovery and development.