Benchmarking tools for transcription factor prioritization.

Spatiotemporal regulation of gene expression is controlled by transcription factor (TF) binding to regulatory elements, resulting in a plethora of cell types and cell states from the same genetic information. Due to the importance of regulatory elements, various sequencing methods have been developed to localise them in genomes, for example using ChIP-seq profiling of the histone mark H3K27ac that marks active regulatory regions. Moreover, multiple tools have been developed to predict TF binding to these regulatory elements based on DNA sequence. As altered gene expression is a hallmark of disease phenotypes, identifying TFs driving such gene expression programs is critical for the identification of novel drug targets. In this study, we curated 84 chromatin profiling experiments (H3K27ac ChIP-seq) where TFs were perturbed through e.g., genetic knockout or overexpression. We ran nine published tools to prioritize TFs using these real-world datasets and evaluated the performance of the methods in identifying the perturbed TFs. This allowed the nomination of three frontrunner tools, namely RcisTarget, MEIRLOP and monaLisa. Our analyses revealed opportunities and commonalities of tools that will help to guide further improvements and developments in the field.

Efficacy and safety of remibrutinib, a selective potent oral BTK inhibitor, in Sjögren's syndrome: results from a randomised, double-blind, placebo-controlled phase 2 trial.

OBJECTIVES: To evaluate the safety and efficacy of remibrutinib in patients with moderate-to-severe Sjögren's syndrome (SjS) in a phase 2 randomised, double-blind trial (NCT04035668; LOUiSSE (LOU064 in Sjögren's Syndrome) study). METHODS: Eligible patients fulfilling 2016 American College of Rheumatology/European League Against Rheumatism (EULAR) criteria for SjS, positive for anti-Ro/Sjögren's syndrome-related antigen A antibodies, with moderate-to-severe disease activity (EULAR Sjögren's Syndrome Disease Activity Index (ESSDAI) (based on weighted score) ≥ 5, EULAR Sjögren's Syndrome Patient Reported Index (ESSPRI) ≥ 5) received remibrutinib (100 mg) either one or two times a day, or placebo for the 24-week study treatment period. The primary endpoint was change from baseline in ESSDAI at week 24. Key secondary endpoints included change from baseline in ESSDAI over time, change from baseline in ESSPRI over time and safety of remibrutinib in SjS. Key exploratory endpoints included changes to the salivary flow rate, soluble biomarkers, blood transcriptomic and serum proteomic profiles. RESULTS: Remibrutinib significantly improved ESSDAI score in patients with SjS over 24 weeks compared with placebo (ΔESSDAI -2.86, p=0.003). No treatment effect was observed in ESSPRI score (ΔESSPRI 0.17, p=0.663). There was a trend towards improvement of unstimulated salivary flow with remibrutinib compared with placebo over 24 weeks. Remibrutinib had a favourable safety profile in patients with SjS over 24 weeks. Remibrutinib induced significant changes in gene expression in blood, and serum protein abundance compared with placebo. CONCLUSIONS: These data show preliminary efficacy and favourable safety of remibrutinib in a phase 2 trial for SjS.

Response to anti-IL17 therapy in inflammatory disease is not strongly impacted by genetic background.

Response to the anti-IL17 monoclonal antibody secukinumab is heterogeneous, and not all participants respond to treatment. Understanding whether this heterogeneity is driven by genetic variation is a key aim of pharmacogenetics and could influence precision medicine approaches in inflammatory diseases. Using changes in disease activity scores across 5,218 genotyped individuals from 19 clinical trials across four indications (psoriatic arthritis, psoriasis, ankylosing spondylitis, and rheumatoid arthritis), we tested whether genetics predicted response to secukinumab. We did not find any evidence of association between treatment response and common variants, imputed HLA alleles, polygenic risk scores of disease susceptibility, or cross-disease components of shared genetic risk. This suggests that anti-IL17 therapy is equally effective regardless of an individual's genetic background, a finding that has important implications for future genetic studies of biological therapy response in inflammatory diseases.

Remibrutinib (LOU064) inhibits neuroinflammation driven by B cells and myeloid cells in preclinical models of multiple sclerosis.

BACKGROUND: Bruton's tyrosine kinase (BTK) is a key signaling node in B cell receptor (BCR) and Fc receptor (FcR) signaling. BTK inhibitors (BTKi) are an emerging oral treatment option for patients suffering from multiple sclerosis (MS). Remibrutinib (LOU064) is a potent, highly selective covalent BTKi with a promising preclinical and clinical profile for MS and other autoimmune or autoallergic indications. METHODS: The efficacy and mechanism of action of remibrutinib was assessed in two different experimental autoimmune encephalomyelitis (EAE) mouse models for MS. The impact of remibrutinib on B cell-driven EAE pathology was determined after immunization with human myelin oligodendrocyte glycoprotein (HuMOG). The efficacy on myeloid cell and microglia driven neuroinflammation was determined in the RatMOG EAE. In addition, we assessed the relationship of efficacy to BTK occupancy in tissue, ex vivo T cell response, as well as single cell RNA-sequencing (scRNA-seq) in brain and spinal cord tissue. RESULTS: Remibrutinib inhibited B cell-dependent HuMOG EAE in dose-dependent manner and strongly reduced neurological symptoms. At the efficacious oral dose of 30 mg/kg, remibrutinib showed strong BTK occupancy in the peripheral immune organs and in the brain of EAE mice. Ex vivo MOG-specific T cell recall response was reduced, but not polyclonal T cell response, indicating absence of non-specific T cell inhibition. Remibrutinib also inhibited RatMOG EAE, suggesting that myeloid cell and microglia inhibition contribute to its efficacy in EAE. Remibrutinib did not reduce B cells, total Ig levels nor MOG-specific antibody response. In brain and spinal cord tissue a clear anti-inflammatory effect in microglia was detected by scRNA-seq. Finally, remibrutinib showed potent inhibition of in vitro immune complex-driven inflammatory response in human microglia. CONCLUSION: Remibrutinib inhibited EAE models by a two-pronged mechanism based on inhibition of pathogenic B cell autoreactivity, as well as direct anti-inflammatory effects in microglia. Remibrutinib showed efficacy in both models in absence of direct B cell depletion, broad T cell inhibition or reduction of total Ig levels. These findings support the view that remibrutinib may represent a novel treatment option for patients with MS.

Analysis of serum proteomics data identifies a quantitative association between beta-defensin 2 at baseline and clinical response to IL-17 blockade in psoriatic arthritis.

OBJECTIVES: Despite several effective targeted therapies, biomarkers that predict whether a patient with psoriatic arthritis (PsA) will respond to a particular treatment are currently lacking. METHODS: We analysed proteomics data from serum samples of nearly 2000 patients with PsA in placebo-controlled phase-III clinical trials of the interleukin-17 inhibitor secukinumab. To discover predictive biomarkers of clinical response, we used statistical learning with controlled feature selection. The top candidate was validated using an ELISA and was separately assessed in a trial of almost 800 patients with PsA treated with secukinumab or the tumour necrosis factor inhibitor adalimumab. RESULTS: Serum levels of beta-defensin 2 (BD-2) at baseline were found to be robustly associated with subsequent clinical response (eg, American College of Rheumatology definition of 20%, 50% and 70% improvement) to secukinumab, but not to placebo. This finding was validated in two independent clinical studies not used for discovery. Although BD-2 is known to be associated with psoriasis severity, the predictivity of BD-2 was independent of baseline Psoriasis Area and Severity Index. The association between BD-2 and response to secukinumab was observed as early as 4 weeks and maintained up to 52 weeks. BD-2 was also found to predict response to treatment with adalimumab. Unlike in PsA, BD-2 was not predictive of response to secukinumab in rheumatoid arthritis. CONCLUSIONS: In PsA, BD-2 at baseline is quantitatively associated with clinical response to secukinumab. Patients with high levels of BD-2 at baseline reach and sustain higher rates of clinical response after treatment with secukinumab.

Preclinical characterization of the Toll-like receptor 7/8 antagonist MHV370 for lupus therapy.

Genetic and in vivo evidence suggests that aberrant recognition of RNA-containing autoantigens by Toll-like receptors (TLRs) 7 and 8 drives autoimmune diseases. Here we report on the preclinical characterization of MHV370, a selective oral TLR7/8 inhibitor. In vitro, MHV370 inhibits TLR7/8-dependent production of cytokines in human and mouse cells, notably interferon-α, a clinically validated driver of autoimmune diseases. Moreover, MHV370 abrogates B cell, plasmacytoid dendritic cell, monocyte, and neutrophil responses downstream of TLR7/8. In vivo, prophylactic or therapeutic administration of MHV370 blocks secretion of TLR7 responses, including cytokine secretion, B cell activation, and gene expression of, e.g., interferon-stimulated genes. In the NZB/W F1 mouse model of lupus, MHV370 halts disease. Unlike hydroxychloroquine, MHV370 potently blocks interferon responses triggered by specific immune complexes from systemic lupus erythematosus patient sera, suggesting differentiation from clinical standard of care. These data support advancement of MHV370 to an ongoing phase 2 clinical trial.

Translational precision medicine: an industry perspective.

In the era of precision medicine, digital technologies and artificial intelligence, drug discovery and development face unprecedented opportunities for product and business model innovation, fundamentally changing the traditional approach of how drugs are discovered, developed and marketed. Critical to this transformation is the adoption of new technologies in the drug development process, catalyzing the transition from serendipity-driven to data-driven medicine. This paradigm shift comes with a need for both translation and precision, leading to a modern Translational Precision Medicine approach to drug discovery and development. Key components of Translational Precision Medicine are multi-omics profiling, digital biomarkers, model-based data integration, artificial intelligence, biomarker-guided trial designs and patient-centric companion diagnostics. In this review, we summarize and critically discuss the potential and challenges of Translational Precision Medicine from a cross-industry perspective.

Novel Insights Into Rheumatoid Arthritis Through Characterization of Concordant Changes in DNA Methylation and Gene Expression in Synovial Biopsies of Patients With Differing Numbers of Swollen Joints.

In this study, we sought to characterize synovial tissue obtained from individuals with arthralgia and disease-specific auto-antibodies and patients with established rheumatoid arthritis (RA), by applying an integrative multi-omics approach where we investigated differences at the level of DNA methylation and gene expression in relation to disease pathogenesis. We performed concurrent whole-genome bisulphite sequencing and RNA-Sequencing on synovial tissue obtained from the knee and ankle from 4 auto-antibody positive arthralgia patients and thirteen RA patients. Through multi-omics factor analysis we observed that the latent factor explaining the variance in gene expression and DNA methylation was associated with Swollen Joint Count 66 (SJC66), with patients with SJC66 of 9 or more displaying separation from the rest. Interrogating these observed differences revealed activation of the immune response as well as dysregulation of cell adhesion pathways at the level of both DNA methylation and gene expression. We observed differences for 59 genes in particular at the level of both transcript expression and DNA methylation. Our results highlight the utility of genome-wide multi-omics profiling of synovial samples for improved understanding of changes associated with disease spread in arthralgia and RA patients, and point to novel candidate targets for the treatment of the disease.

CARD10 cleavage by MALT1 restricts lung carcinoma growth in vivo.

CARD-CC complexes involving BCL10 and MALT1 are major cellular signaling hubs. They govern NF-κB activation through their scaffolding properties as well as MALT1 paracaspase function, which cleaves substrates involved in NF-κB regulation. In human lymphocytes, gain-of-function defects in this pathway lead to lymphoproliferative disorders. CARD10, the prototypical CARD-CC protein in non-hematopoietic cells, is overexpressed in several cancers and has been associated with poor prognosis. However, regulation of CARD10 remains poorly understood. Here, we identified CARD10 as the first MALT1 substrate in non-hematopoietic cells and showed that CARD10 cleavage by MALT1 at R587 dampens its capacity to activate NF-κB. Preventing CARD10 cleavage in the lung tumor A549 cell line increased basal levels of IL-6 and extracellular matrix components in vitro, and led to increased tumor growth in a mouse xenograft model, suggesting that CARD10 cleavage by MALT1 might be a built-in mechanism controlling tumorigenicity.

Latent Factor Modeling of scRNA-Seq Data Uncovers Dysregulated Pathways in Autoimmune Disease Patients.

Latent factor modeling applied to single-cell RNA sequencing (scRNA-seq) data is a useful approach to discover gene signatures. However, it is often unclear what methods are best suited for specific tasks and how latent factors should be interpreted. Here, we compare four state-of-the-art methods and propose an approach to assign derived latent factors to pathway activities and specific cell subsets. By applying this framework to scRNA-seq datasets from biopsies of patients with rheumatoid arthritis and systemic lupus erythematosus, we discover disease-relevant gene signatures in specific cellular subsets. In rheumatoid arthritis, we identify an inflammatory OSMR signaling signature active in a subset of synovial fibroblasts and an efferocytic signature in a subset of synovial monocytes. Overall, we provide insights into latent factors models for the analysis of scRNA-seq data, develop a framework to identify cell subtypes in a phenotype-driven way, and use it to identify novel pathways dysregulated in rheumatoid arthritis.

Whole-Genome DNA Methylation Profiling of CD14+ Monocytes Reveals Disease Status and Activity Differences in Crohn's Disease Patients.

Crohn's disease (CD) is a multifactorial incurable chronic disorder. Current medical treatment seeks to induce and maintain a state of remission. During episodes of inflammation, monocytes infiltrate the inflamed mucosa whereupon they differentiate into macrophages with a pro-inflammatory phenotype. Here, we sought to characterize the circulating monocytes by profiling their DNA methylome and relate it to the level of CD activity. We gathered an all-female age-matched cohort of 16 CD patients and 7 non-CD volunteers. CD patients were further subdivided into 8 CD patients with active disease (CD-active) and 8 CD patients in remission (CD-remissive) as determined by the physician global assessment. We identified 15 and 12 differentially methylated genes (DMGs) when comparing CD with non-CD and CD-active with CD-remissive, respectively. Differential methylation was predominantly found in the promoter regions of inflammatory genes. Comparing our observations with gene expression data on classical (CD14++CD16-), non-classical (CD14+CD16++) and intermediate (CD14++CD16+) monocytes indicated that while 7 DMGs were differentially expressed across the 3 subsets, the remaining DMGs could not immediately be associated with differences in known populations. We conclude that CD activity is associated with differences in DNA methylation at the promoter region of inflammation-associated genes.

The midbody interactome reveals unexpected roles for PP1 phosphatases in cytokinesis.

The midbody is an organelle assembled at the intercellular bridge between the two daughter cells at the end of mitosis. It controls the final separation of the daughter cells and has been involved in cell fate, polarity, tissue organization, and cilium and lumen formation. Here, we report the characterization of the intricate midbody protein-protein interaction network (interactome), which identifies many previously unknown interactions and provides an extremely valuable resource for dissecting the multiple roles of the midbody. Initial analysis of this interactome revealed that PP1ß-MYPT1 phosphatase regulates microtubule dynamics in late cytokinesis and de-phosphorylates the kinesin component MKLP1/KIF23 of the centralspindlin complex. This de-phosphorylation antagonizes Aurora B kinase to modify the functions and interactions of centralspindlin in late cytokinesis. Our findings expand the repertoire of PP1 functions during mitosis and indicate that spatiotemporal changes in the distribution of kinases and counteracting phosphatases finely tune the activity of cytokinesis proteins.

Meta-Analysis of in vitro-Differentiated Macrophages Identifies Transcriptomic Signatures That Classify Disease Macrophages in vivo.

Macrophages are heterogeneous leukocytes regulated in a tissue- and disease-specific context. While in vitro macrophage models have been used to study diseases empirically, a systematic analysis of the transcriptome thereof is lacking. Here, we acquired gene expression data from eight commonly-used in vitro macrophage models to perform a meta-analysis. Specifically, we obtained gene expression data from unstimulated macrophages (M0) and macrophages stimulated with lipopolysaccharides (LPS) for 2-4 h (M-LPSearly), LPS for 24 h (M-LPSlate), LPS and interferon-γ (M-LPS+IFNγ), IFNγ (M-IFNγ), interleukin-4 (M-IL4), interleukin-10 (M-IL10), and dexamethasone (M-dex). Our meta-analysis identified consistently differentially expressed genes that have been implicated in inflammatory and metabolic processes. In addition, we built macIDR, a robust classifier capable of distinguishing macrophage activation states with high accuracy (>0.95). We classified in vivo macrophages with macIDR to define their tissue- and disease-specific characteristics. We demonstrate that alveolar macrophages display high resemblance to IL10 activation, but show a drop in IFNγ signature in chronic obstructive pulmonary disease patients. Adipose tissue-derived macrophages were classified as unstimulated macrophages, but acquired LPS-activation features in diabetic-obese patients. Rheumatoid arthritis synovial macrophages exhibit characteristics of IL10- or IFNγ-stimulation. Altogether, we defined consensus transcriptional profiles for the eight in vitro macrophage activation states, built a classification model, and demonstrated the utility of the latter for in vivo macrophages.

A distinct epigenetic profile distinguishes stenotic from non-inflamed fibroblasts in the ileal mucosa of Crohn's disease patients.

BACKGROUND: The chronic remitting and relapsing intestinal inflammation characteristic of Crohn's disease frequently leads to fibrosis and subsequent stenosis of the inflamed region. Approximately a third of all Crohn's disease patients require resection at some stage in their disease course. As the pathogenesis of Crohn's disease associated fibrosis is largely unknown, a strong necessity exists to better understand the pathophysiology thereof. METHODS: In this study, we investigated changes of the DNA methylome and transcriptome of ileum-derived fibroblasts associated to the occurrence of Crohn's disease associated fibrosis. Eighteen samples were included in a DNA methylation array and twenty-one samples were used for RNA sequencing. RESULTS: Most differentially methylated regions and differentially expressed genes were observed when comparing stenotic with non-inflamed samples. By contrast, few differences were observed when comparing Crohn's disease with non-Crohn's disease, or inflamed with non-inflamed tissue. Integrative methylation and gene expression analyses revealed dysregulation of genes associated to the PRKACA and E2F1 network, which is involved in cell cycle progression, angiogenesis, epithelial to mesenchymal transition, and bile metabolism. CONCLUSION: Our research provides evidence that the methylome and the transcriptome are systematically dysregulated in stenosis-associated fibroblasts.

Connecting genetics and gene expression data for target prioritisation and drug repositioning.

Developing new drugs continues to be a highly inefficient and costly business. By repurposing an existing compound for a different indication, drug repositioning offers an attractive alternative to traditional drug discovery. Most of these approaches work by matching transcriptional disease signatures to anti-correlated gene expression profiles of drug perturbations. Genome-wide association studies (GWASs) are of great interest to researchers in the pharmaceutical industry because drug programmes with supporting genetic evidence are more likely to successfully progress through the drug discovery pipeline. Here, we present a systematic approach to generate drug repositioning hypothesis based on disease genetics by mining public repositories of GWAS data and drug transcriptomic profiles. We find that genes genetically associated with a certain disease are more likely to be differentially expressed in the same disease (p-value = 1.54e-17 and AUC = 0.75) and that, in existing drug - disease combinations, genes significantly up- or down-regulated after drug treatment are enriched for genes genetically associated with that disease (p-value = 1.1e-79 and AUC = 0.64). Finally, we use this framework to generate and rank novel GWAS-driven drug repositioning predictions.

Opportunities and obstacles for deep learning in biology and medicine.

Deep learning describes a class of machine learning algorithms that are capable of combining raw inputs into layers of intermediate features. These algorithms have recently shown impressive results across a variety of domains. Biology and medicine are data-rich disciplines, but the data are complex and often ill-understood. Hence, deep learning techniques may be particularly well suited to solve problems of these fields. We examine applications of deep learning to a variety of biomedical problems-patient classification, fundamental biological processes and treatment of patients-and discuss whether deep learning will be able to transform these tasks or if the biomedical sphere poses unique challenges. Following from an extensive literature review, we find that deep learning has yet to revolutionize biomedicine or definitively resolve any of the most pressing challenges in the field, but promising advances have been made on the prior state of the art. Even though improvements over previous baselines have been modest in general, the recent progress indicates that deep learning methods will provide valuable means for speeding up or aiding human investigation. Though progress has been made linking a specific neural network's prediction to input features, understanding how users should interpret these models to make testable hypotheses about the system under study remains an open challenge. Furthermore, the limited amount of labelled data for training presents problems in some domains, as do legal and privacy constraints on work with sensitive health records. Nonetheless, we foresee deep learning enabling changes at both bench and bedside with the potential to transform several areas of biology and medicine.

Using regulatory genomics data to interpret the function of disease variants and prioritise genes from expression studies.

The identification of therapeutic targets is a critical step in the research and developement of new drugs, with several drug discovery programmes failing because of a weak linkage between target and disease. Genome-wide association studies and large-scale gene expression experiments are providing insights into the biology of several common diseases, but the complexity of transcriptional regulation mechanisms often limits our understanding of how genetic variation can influence changes in gene expression. Several initiatives in the field of regulatory genomics are aiming to close this gap by systematically identifying and cataloguing regulatory elements such as promoters and enhacers across different tissues and cell types. In this Bioconductor workflow, we will explore how different types of regulatory genomic data can be used for the functional interpretation of disease-associated variants and for the prioritisation of gene lists from gene expression experiments.

In silico prediction of novel therapeutic targets using gene-disease association data.

BACKGROUND: Target identification and validation is a pressing challenge in the pharmaceutical industry, with many of the programmes that fail for efficacy reasons showing poor association between the drug target and the disease. Computational prediction of successful targets could have a considerable impact on attrition rates in the drug discovery pipeline by significantly reducing the initial search space. Here, we explore whether gene-disease association data from the Open Targets platform is sufficient to predict therapeutic targets that are actively being pursued by pharmaceutical companies or are already on the market. METHODS: To test our hypothesis, we train four different classifiers (a random forest, a support vector machine, a neural network and a gradient boosting machine) on partially labelled data and evaluate their performance using nested cross-validation and testing on an independent set. We then select the best performing model and use it to make predictions on more than 15,000 genes. Finally, we validate our predictions by mining the scientific literature for proposed therapeutic targets. RESULTS: We observe that the data types with the best predictive power are animal models showing a disease-relevant phenotype, differential expression in diseased tissue and genetic association with the disease under investigation. On a test set, the neural network classifier achieves over 71% accuracy with an AUC of 0.76 when predicting therapeutic targets in a semi-supervised learning setting. We use this model to gain insights into current and failed programmes and to predict 1431 novel targets, of which a highly significant proportion has been independently proposed in the literature. CONCLUSIONS: Our in silico approach shows that data linking genes and diseases is sufficient to predict novel therapeutic targets effectively and confirms that this type of evidence is essential for formulating or strengthening hypotheses in the target discovery process. Ultimately, more rapid and automated target prioritisation holds the potential to reduce both the costs and the development times associated with bringing new medicines to patients.

STOPGAP: a database for systematic target opportunity assessment by genetic association predictions.

SUMMARY: We developed the STOPGAP (Systematic Target OPportunity assessment by Genetic Association Predictions) database, an extensive catalog of human genetic associations mapped to effector gene candidates. STOPGAP draws on a variety of publicly available GWAS associations, linkage disequilibrium (LD) measures, functional genomic and variant annotation sources. Algorithms were developed to merge the association data, partition associations into non-overlapping LD clusters, map variants to genes and produce a variant-to-gene score used to rank the relative confidence among potential effector genes. This database can be used for a multitude of investigations into the genes and genetic mechanisms underlying inter-individual variation in human traits, as well as supporting drug discovery applications. AVAILABILITY AND IMPLEMENTATION: Shell, R, Perl and Python scripts and STOPGAP R data files (version 2.5.1 at publication) are available at https://github.com/StatGenPRD/STOPGAP . Some of the most useful STOPGAP fields can be queried through an R Shiny web application at http://stopgapwebapp.com . CONTACT: matthew.r.nelson@gsk.com. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.