Chronic inflammatory diseases such as rheumatoid arthritis, inflammatory bowel disease, spondyloarthritis (SpA) and psoriasis cause significant morbidity and are a substantial burden for the affected individuals and the society. An important obstacle to early diagnosis and the development of more specific and effective therapies is the very limited understanding of the pathogenesis of these diseases. In the past years genome-wide association studies have identified many genes that were not known to be involved in pathogenesis, and have linked several genes in immune pathways to inflammatory diseases, indicating that the immune system plays an essential role in the pathogenesis of these diseases. The current challenge is to correlate these genetic variants with the effector mechanisms implicated in pathogenesis, to allow translation of the genetic data into relevant diagnostics and innovative treatment strategies. To meet this challenge, we have designed a clinical study that examines the immune signaling pathways, the transcriptional networks and the genotype in the same SpA patient, in order to establish a link between genetic variation, cellular phenotype and function, and pathology. This approach will advance our understanding of the pathogenic mechanisms, and identify novel and relevant diagnostic tools, therapeutic targets and biomarkers. The long-term outcome of this strategy will be the rational design of specific therapies tailored to the genotype of the patient.

Anti-TNF therapy has revolutionized treatment of many chronic inflammatory diseases, including rheumatoid arthritis, Crohn’s disease and spondyloarthritis (SpA). However, clinical efficacy of TNF-inhibitors (TNFi) is limited by a high rate of non-responsiveness (30-40%) both in SpA and other diseases, exposing a substantial fraction of patients to important side-effects without any clinical benefit. Despite the extensive use of TNFi since many years, it is still not possible to determine which patients will respond to TNFi before treatment initiation. In this study, we have tested the hypothesis that the functional analysis of immune responses may not only improve our understanding of the molecular mechanisms of TNF-blocker activity, but also identify correlates of therapeutic responses in SpA patients. To facilitate the potential translation of our findings into a clinical setting, we have used standardized whole-blood stimulation assays (“TruCulture” assays, Duffy et al., Immunity 2014), and have minimized sources of pre-analytical variability, implementing a highly sensitive and robust pipeline to assess immune functions in patients. To investigate the concept that the immune status of a patient will define their response to TNFi treatment, we have used machine-learning algorithms to identify, in whole-blood stimulation assays, immunological transcripts that correlate with clinical efficacy of TNFi. Our results obtained with a cohort of 67 SpA patients demonstrate that high expression, before treatment initiation, of molecules associated with leukocyte invasion/migration and inflammatory processes predisposes to favorable outcome of anti-TNF therapy, while high-level expression of cytotoxic molecules was associated with poor therapeutic responses to TNF-blockers. These findings may suggest that SpA patients whose immune response is characterized by strong, NF-kB-mediated inflammation are more likely to benefit from TNFi treatment than patients with an active T/NK-cell component. Unfortunately our manuscript describing these results has been rejected by Nature Medicine. However, in her letter the editor mentioned, “Should future experimental data allow you to demonstrate that the identified gene signatures predict response to treatment and outperform previously reported approaches in an independent cohort, we would be happy to look at a new submission…”. We have recruited additional SpA patients over the summer and we are currently in the process of performing the gene expression analysis. The goal of this bioinformatic analysis will be to identify transcripts in stimulated immune cells that predict therapeutic outcome in a training set of patients using machine-learning algorithms and validate the findings in a replication cohort.

Mitochondria are double-membrane bound organelles that are essential in every tissue of the body. They are metabolic hubs and signalling platforms that are deeply integrated into cellular homeostasis. The functions of mitochondria are intimately linked to their form, which is regulated by a balance of membrane fusion and fission: dynamin-like GTPases OPA1 and MFN1/2 perform membrane fusion and DRP1 regulates membrane fission. Mutations in mitochondrial genes cause a pleiotropic spectrum of clinical disorders whose underlying genetic, morphological and biochemical defects can be easily studied in skin fibroblasts generated from patient biopsies. The morphology of mitochondria is inextricably linked to its many essential functions in the cell and we are interested in understanding the relationship between mitochondrial shape changes and metabolism in the context of acquired and inborn human diseases. Balanced fusion and fission events shape mitochondria to meet metabolic demands and to ensure removal of damaged organelles. Mitochondrial fragmentation occurs in response to nutrient excess and cellular dysfunction and has been observed in mitochondrial genetic diseases and is thought to play an important role in the development of disease. The physiological relevance of mitochondrial morphology and the mechanisms that regulate mitochondrial dynamics are incomplete and so we have set out to find ways to rebalance mitochondrial dynamics in genetic diseases. We recently developed imaging and informatics pipelines to allow for the automated, rapid, reliable quantification of mitochondrial morphology in human fibroblasts. We applied this new technology in the context of genome-wide siRNA screens in immortalized fibroblasts from an OPA1 patient with dominant optic atrophy and control fibroblasts to identify candidate genes able to reverse the mitochondrial fragmentation phenotype associated with mitochondrial dysfunction in patient cells. We have performed the same genome-wide screen in healthy immortalized control fibroblasts. Together, these studies will help us identify lists of genetic modifiers and therapeutic targets that can be investigated further using cell biology and biochemical tools in the lab.

Our hypothesis is that gut microbiota could define predictive markers of response and tolerance to biologics. A. Preliminary results: gut bacteria predicting response to TNF blockers. A proof-of-concept study has been performed on 58 patients who were recruited according to the following criteria: active disease despite NSAIDs intake; no history of inflammatory bowel disease; no antibiotics intake within 3 months prior recruitment. Bacterial 16S rRNA gene sequencing region was performed on stools samples before and after TNF-blocker treatment. Diversity metrics and custom LefSe were used to explore the relationship between the composition of the intestinal microbiota and the efficacy of TNF-blockers. A lower alpha diversity at baseline was unexpectedly associated with better treatment response, HLA-B27 genotype and smoking behavior. Meanwhile, beta diversity was associated with smoking behavior and HLA-B27 genotype before and after treatment. Beta diversity at baseline was associated with the BASDAI index after treatment, and the response to the treatment. These results indicate a potential regulatory role for the gut microbiota on the underlying mechanisms involved in the response to TNF-blockers. Moreover, a LefSe-like approach identified 6 bacterial species as potential biomarkers for the treatment response, despite the absence of global changes (beta diversity) in the microbiota composition following a 3-month TNF-blockers intake. B. Current project: ITS2 fungal rDNA sequencing analyses In order to establish a causal link between host-microbe interactions and clinical efficacy of anti-TNF, we expect the following research endpoints from our experimental and translation approach (cohorts/clinical trials): 1. Defining the impact of anti-TNF on fungal microbiota and on the relative representation of fungal/bacterial components 2. Defining correlations between gut fungal composition and clinical outcome, with the aim of identifying stool microbial fingerprint of durable responses and/or primary resistance to anti-TNFα in SpA patients. The analyses will be performed on the same 58 SpA patients that were previously analyzed for their 16S bacterial component. These patients perfectly described regarding their disease characteristics, demographics, ongoing treatments, response to anti-TNF after a 3-month treatment period.

HPV RNA-Seq, a novel and unique molecular test that allows detection of pre-cancerous lesions of the cervix from cervical samples.