Consensus gene modules strategy identifies candidate blood-based biomarkers for primary Sjögren's disease.

Primary Sjögren disease (pSD) is an autoimmune disease characterized by lymphoid infiltration of exocrine glands leading to dryness of the mucosal surfaces and by the production of autoantibodies. The pathophysiology of pSD remains elusive and no treatment with demonstrated efficacy is available yet. To better understand the biology underlying pSD heterogeneity, we aimed at identifying Consensus gene Modules (CMs) that summarize the high-dimensional transcriptomic data of whole blood samples in pSD patients. We performed unsupervised gene classification on four data sets and identified thirteen CMs. We annotated and interpreted each of these CMs as corresponding to cell type abundances or biological functions by using gene set enrichment analyses and transcriptomic profiles of sorted blood cell subsets. Correlation with independently measured cell type abundances by flow cytometry confirmed these annotations. We used these CMs to reconcile previously proposed patient stratifications of pSD. Importantly, we showed that the expression of modules representing lymphocytes and erythrocytes before treatment initiation is associated with response to hydroxychloroquine and leflunomide combination therapy in a clinical trial. These consensus modules will help the identification and translation of blood-based predictive biomarkers for the treatment of pSD.

High-content multimodal analysis supports the IL-7/IL-7 receptor axis as a relevant therapeutic target in primary Sjögren's syndrome.

OBJECTIVE: While the involvement of IL-7/IL-7R axis in pSS has been described in relation to T cells, little is known about the contribution of this pathway in relationship with other immune cells, and its implication in autoimmunity. Using high-content multiomics data, we aimed at characterizing IL-7R expressing cells and the involvement of IL-7/IL-7R pathway in pSS pathophysiology. METHODS: An IL-7 signature established using RNA-sequencing of human PBMCs incubated with IL-7 was applied to 304 pSS patients, and on RNA-Seq datasets from tissue biopsies. High-content immunophenotyping using flow and imaging mass cytometry was developed to characterize peripheral and in situ IL-7R expression. RESULTS: We identified a blood 4-gene IL-7 module (IKZF4, KIAA0040, PGAP1 and SOS1) associated with anti-SSA/Ro positiveness in patients as well as disease activity, and a tissue 5-gene IL-7 module (IL7R, PCED1B, TNFSF8, ADAM19, MYBL1) associated with infiltration severity. We confirmed expression of IL-7R on T cells subsets, and further observed upregulation of IL-7R on double-negative (DN) B cells, and especially DN2 B cells. IL-7R expression was increased in pSS compared to sicca patients with variations seen according to the degree of infiltration. When expressed, IL-7R was mainly found on epithelial cells, CD4+ and CD8+ T cells, switched memory B cells, DN B cells and M1 macrophages. CONCLUSION: This exhaustive characterization of the IL-7/IL-7R pathway in pSS pathophysiology established that two IL-7 gene modules discriminate pSS patients with a high IL-7 axis involvement. Their use could guide the implementation of an anti-IL-7R targeted therapy in a precision medicine approach.

Antibody-mediated neutralization of galectin-3 as a strategy for the treatment of systemic sclerosis.

Systemic sclerosis (SSc) is an autoimmune, inflammatory and fibrotic disease with limited treatment options. Developing new therapies is therefore crucial to address patient needs. To this end, we focused on galectin-3 (Gal-3), a lectin known to be associated with several pathological processes seen in SSc. Using RNA sequencing of whole-blood samples in a cross-sectional cohort of 249 patients with SSc, Gal-3 and its interactants defined a strong transcriptomic fingerprint associated with disease severity, pulmonary and cardiac malfunctions, neutrophilia and lymphopenia. We developed new Gal-3 neutralizing monoclonal antibodies (mAb), which were then evaluated in a mouse model of hypochlorous acid (HOCl)-induced SSc. We show that two of these antibodies, D11 and E07, reduced pathological skin thickening, lung and skin collagen deposition, pulmonary macrophage content, and plasma interleukin-5 and -6 levels. Moreover, E07 changed the transcriptional profiles of HOCl-treated mice, resulting in a gene expression pattern that resembled that of control mice. Similarly, pathological pathways engaged in patients with SSc were counteracted by E07 in mice. Collectively, these findings demonstrate the translational potential of Gal-3 blockade as a therapeutic option for SSc.

Autoimmune epithelitis in primary Sjögren's syndrome.

Primary Sjögren's syndrome (pSS) is characterized by an autoimmune epithelitis associated with chronic inflammation of the exocrine glands. Alterations of extra-glandular functions in pSS is associated with lymphocytic infiltrates that invade the epithelial structures of affected organs. Within epithelial tissue, the expression of class II major histocompatibility complexes and costimulatory molecules by epithelial cells acting as non-professional antigen presenting cells, leads to the activation of T and B lymphocytes through multiple cellular crosstalk pathways. Although the pathogenetic mechanisms underlying pSS have not yet been elucidated, it is accepted that glandular epithelial cells are central regulators of the local autoimmune response.

Industrializing AI-powered drug discovery: lessons learned from the Patrimony computing platform.

INTRODUCTION: As a mid-size international pharmaceutical company, we initiated 4 years ago the launch of a dedicated high-throughput computing platform supporting drug discovery. The platform named 'Patrimony' was built up on the initial predicate to capitalize on our proprietary data while leveraging public data sources in order to foster a Computational Precision Medicine approach with the power of artificial intelligence. AREAS COVERED: Specifically, Patrimony is designed to identify novel therapeutic target candidates. With several successful use cases in immuno-inflammatory diseases, and current ongoing extension to applications to oncology and neurology, we document how this industrial computational platform has had a transformational impact on our R&D, making it more competitive, as well time and cost effective through a model-based educated selection of therapeutic targets and drug candidates. EXPERT OPINION: We report our achievements, but also our challenges in implementing data access and governance processes, building up hardware and user interfaces, and acculturing scientists to use predictive models to inform decisions.

Model-based computational precision medicine to develop combination therapies for autoimmune diseases.

INTRODUCTION: The complex pathophysiology of autoimmune diseases (AIDs) is being progressively deciphered, providing evidence for a multiplicity of pro-inflammatory pathways underlying heterogeneous clinical phenotypes and disease evolution. AREAS COVERED: Treatment strategies involving drug combinations are emerging as a preferred option to achieve remission in a vast majority of patients affected by systemic AIDs. The design of appropriate drug combinations can benefit from AID modeling following a comprehensive multi-omics molecular profiling of patients combined with Artificial Intelligence (AI)-powered computational analyses. Such disease models support patient stratification in homogeneous subgroups, shed light on dysregulated pro-inflammatory pathways and yield hypotheses regarding potential therapeutic targets and candidate biomarkers to stratify and monitor patients during treatment. AID models inform the rational design of combination therapies interfering with independent pro-inflammatory pathways related to either one of five prominent immune compartments contributing to the pathophysiology of AIDs, i.e. pro-inflammatory signals originating from tissues, innate immune mechanisms, T lymphocyte activation, autoantibodies and B cell activation, as well as soluble mediators involved in immune cross-talk. EXPERT OPINION: The optimal management of AIDs in the future will rely upon rationally designed combination therapies, as a modality of a model-based Computational Precision Medicine taking into account the patients' biological and clinical specificities.

Network-based repurposing identifies anti-alarmins as drug candidates to control severe lung inflammation in COVID-19.

While establishing worldwide collective immunity with anti SARS-CoV-2 vaccines, COVID-19 remains a major health issue with dramatic ensuing economic consequences. In the transition, repurposing existing drugs remains the fastest cost-effective approach to alleviate the burden on health services, most particularly by reducing the incidence of the acute respiratory distress syndrome associated with severe COVID-19. We undertook a computational repurposing approach to identify candidate therapeutic drugs to control progression towards severe airways inflammation during COVID-19. Molecular profiling data were obtained from public sources regarding SARS-CoV-2 infected epithelial or endothelial cells, immune dysregulations associated with severe COVID-19 and lung inflammation induced by other respiratory viruses. From these data, we generated a protein-protein interactome modeling the evolution of lung inflammation during COVID-19 from inception to an established cytokine release syndrome. This predictive model assembling severe COVID-19-related proteins supports a role for known contributors to the cytokine storm such as IL1ß, IL6, TNFα, JAK2, but also less prominent actors such as IL17, IL23 and C5a. Importantly our analysis points out to alarmins such as TSLP, IL33, members of the S100 family and their receptors (ST2, RAGE) as targets of major therapeutic interest. By evaluating the network-based distances between severe COVID-19-related proteins and known drug targets, network computing identified drugs which could be repurposed to prevent or slow down progression towards severe airways inflammation. This analysis confirmed the interest of dexamethasone, JAK2 inhibitors, estrogens and further identified various drugs either available or in development interacting with the aforementioned targets. We most particularly recommend considering various inhibitors of alarmins or their receptors, currently receiving little attention in this indication, as candidate treatments for severe COVID-19.

A new molecular classification to drive precision treatment strategies in primary Sjögren's syndrome.

There is currently no approved treatment for primary Sjögren's syndrome, a disease that primarily affects adult women. The difficulty in developing effective therapies is -in part- because of the heterogeneity in the clinical manifestation and pathophysiology of the disease. Finding common molecular signatures among patient subgroups could improve our understanding of disease etiology, and facilitate the development of targeted therapeutics. Here, we report, in a cross-sectional cohort, a molecular classification scheme for Sjögren's syndrome patients based on the multi-omic profiling of whole blood samples from a European cohort of over 300 patients, and a similar number of age and gender-matched healthy volunteers. Using transcriptomic, genomic, epigenetic, cytokine expression and flow cytometry data, combined with clinical parameters, we identify four groups of patients with distinct patterns of immune dysregulation. The biomarkers we identify can be used by machine learning classifiers to sort future patients into subgroups, allowing the re-evaluation of response to treatments in clinical trials.

S95021, a novel selective and pan-neutralizing anti interferon alpha (IFN-α) monoclonal antibody as a candidate treatment for selected autoimmune rheumatic diseases.

Increased interferon-α (IFN-α) production is a critical component in the pathophysiology of systemic lupus erythematosus (SLE) and other rheumatic autoimmune diseases. Herein, we report the characterization of S95021, a fully human IgG1 anti-IFN-α monoclonal antibody (mAb) as a novel therapeutic candidate for targeted patient populations. S95021 was expressed in CHOZN GS-/- cells, purified by chromatography and characterized by using electrophoresis, size exclusion chromatography and liquid chromatography-mass spectrometry. High purity S95021 was obtained as a monomeric entity comprising different charge variants mainly due to N-glycosylation. Surface plasmon resonance kinetics experiments showed strong association rates with all IFN-α subtypes and estimated KDs below picomolar values. Pan-IFN-α-binding properties were confirmed by immunoprecipitation assays and neutralization capacity with reporter HEK-Blue IFN-α/ß cells. S95021 was IFN-α-selective and exhibited superior potency and broader neutralization profile when compared with the benchmark anti-IFN-α mAbs rontalizumab and sifalimumab. STAT-1 phosphorylation and the type I IFN gene signature induced in human peripheral blood mononuclear cells by recombinant IFN-α subtypes or plasmas from selected autoimmune patients were efficiently reduced by S95021 in a dose-dependent manner. Together, our results show that S95021 is a new potent, selective and pan IFN-α-neutralizing mAb. It is currently further evaluated as a valid therapeutic candidate in selected autoimmune diseases in which the IFN-α pro-inflammatory pathway is dysregulated.

Cherenkov luminescence imaging is a fast and relevant preclinical tool to assess tumour hypoxia in vivo.

PURPOSE: Molecular imaging techniques visualise biomarkers for both drug development and personalised medicine. In this field, Cherenkov luminescence imaging (CLI) seems to be very attractive by allowing imaging with clinical PET radiotracers with high-throughput capabilities. In this context, we developed a fast CLI method to detect tumour hypoxia with 18F-fluoromisonidazole (FMISO) for drug development purposes. METHODS: Colon cancer model was induced in mice by subcutaneous injection of 1 × 106 CT-26 cells. FMISO was injected, and simultaneous PET-blood oxygen level dependent (BOLD)-MRI followed by CLI were performed along with immunohistochemistry staining with pimonidazole. RESULTS: There was a significant correlation between FMISO PET and CLI tumour uptakes, consistent with the BOLD-MRI mapping. Tumour-to-background ratio was significantly higher for CLI compared with PET and MRI. Immunohistochemistry confirmed tumour hypoxia. The imaging workflow with CLI was about eight times faster than the PET-MRI procedure. CONCLUSION: CLI is a fast and relevant tool to assess tumour hypoxia. This approach could be particularly interesting for hypoxia-targeting drug development.