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Background Myotonic dystrophy type 1 (DM1) is an incurable multisystem disease caused by a CTG-repeat expansion in the DM1 protein kinase (DMPK) gene. The OPTIMISTIC clinical trial demonstrated positive and heterogenous effects of cognitive behavioral therapy (CBT) on the capacity for activity and social participations in DM1 patients. Through a process of reverse engineering, this study aims to identify druggable molecular biomarkers associated with the clinical improvement in the OPTIMISTIC cohort. Methods Based on full blood samples collected during OPTIMISTIC, we performed paired mRNA sequencing for 27 patients before and after the CBT intervention. Linear mixed effect models were used to identify biomarkers associated with the disease-causing CTG expansion and the mean clinical improvement across all clinical outcome measures. Results We identified 608 genes for which their expression was significantly associated with the CTG-repeat expansion, as well as 1176 genes significantly associated with the average clinical response towards the intervention. Remarkably, all 97 genes associated with both returned to more normal levels in patients who benefited the most from CBT. This main finding has been replicated based on an external dataset of mRNA data of DM1 patients and controls, singling these genes out as candidate biomarkers for therapy response. Among these candidate genes were DNAJB12, HDAC5, and TRIM8, each belonging to a protein family that is being studied in the context of neurological disorders or muscular dystrophies. Across the different gene sets, gene pathway enrichment analysis revealed disease-relevant impaired signaling in, among others, insulin-, metabolism-, and immune-related pathways. Furthermore, evidence for shared dysregulations with another neuromuscular disease, Duchenne muscular dystrophy, was found, suggesting a partial overlap in blood-based gene dysregulation. Conclusions DM1-relevant disease signatures can be identified on a molecular level in peripheral blood, opening new avenues for drug discovery and therapy efficacy assessments.

DIABLO sPLSDA model results. (DOCX 18966 kb)

Myotonic dystrophy type 1 (DM1) is the second most common muscular dystrophy and most prevalent adult form. A microsatellite expansion comprised of CTG repetitions in the dystrophia myotonica protein kinase (DMPK) gene, DM1 is characterized by muscle weakness, wasting, and myotonia. The expanded nucleotide sequence of the DMPK mRNA results in the misregulation of important RNA-binding proteins (RNABPs), Muscleblind-like 1 (MBNL1) in particular. MBNL1 becomes trapped in myonuclei within the repeating CUG transcript, which reduces the RNABPs ability to process newly synthesized mRNAs that are important for the maintenance of healthy muscle function. Recent studies with DM1 participants demonstrate that exercise is safe, enjoyable, and elicits benefits in muscle strength and function. However, the molecular mechanisms of exercise adaptation in DM1 are largely unknown. Understanding the cellular processes that drive exercise-induced remodelling may assist in the discovery of effective lifestyle interventions to mitigate DM1. In this thesis, three groups of mice were utilized: i) sedentary DM1 animals (SED-DM1), ii) DM1 mice who volitionally exercised daily on a home cage running wheel (EX-DM1), and iii) sedentary healthy, wild-type mice (WT). EX-DM1 animals ran 5.6 km/day during the 7-week experimental time course, a volume of volitional physical activity that is lower than that observed in WT animals. Post-exercise functional tests demonstrated that chronic exercise significantly improved motor performance, muscle strength and endurance. Electromyography revealed that chronic exercise mitigated myotonia. At the cellular and molecular levels, we found that chronic physical activity attenuated RNA toxicity, liberated MBNL1 from myonuclear sequestration, and selectively normalized the spliceopathy of bridging integrator 1 and muscle-specific chloride channel mRNAs. Collectively, our data indicate that chronic exercise improves DM1 at the molecular, cellular and physiological levels. Thesis Master of Science (MSc) Myotonic dystrophy type 1 (DM1) is the second most common muscular dystrophy and most prevalent adult form. Muscle weakness, wasting, and myotonia most prominently characterize DM1. A microsatellite repeat expansion mutation in the dystrophia myotonica protein kinase gene, which results in RNA toxicity and dysregulation of mRNA processing, are the root causes of the disorder. Recent studies with DM1 participants demonstrate that exercise is safe, enjoyable, and elicits benefits in muscle strength and function. However, the molecular mechanisms of exercise adaptation in DM1 are largely unknown. Understanding the cellular processes that drive exercise-induced remodelling may assist in the discovery of effective lifestyle interventions to mitigate DM1. In this thesis, three groups of mice were utilized: i) sedentary DM1 animals (SED-DM1), ii) DM1 mice who volitionally exercised daily on a home cage running wheel for 7 weeks (EX-DM1), and iii) sedentary healthy, wild-type mice (WT). Post-exercise functional tests demonstrated that chronic exercise significantly improved motor performance, muscle strength and endurance, as well as reduced myotonia. At the cellular level, we found that chronic physical activity attenuated RNA toxicity and improved mRNA processing. Our data indicate that physical activity improves DM1 at the molecular and physiological levels and lays the foundation for future work to optimize the exercise prescription.

Background: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. Methods: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. Results: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. Conclusions: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.

Table S7. Estimated accuracy and standard deviation of the RFE procedure. Table S8. Accuracy and Kappa values of the Random Forest models in the training set. Table S9. Performances values for the Random Forest model in the testing set. Figure S11. Relative importance of the top 20 predictors building the final model of the RF. The importance axis is scaled, with the mRNA expression of CD3D scaled to 100% and the methylation state of POLA2 to 0% (not shown). (DOCX 18 kb)

BackgroundAsthma is a chronic inflammatory disease involving diverse cells and mediators whose interconnectivity and relationships to asthma severity are unclear.ObjectiveWe performed a comprehensive assessment of TH17 cells, regulatory T cells, mucosal-associated invariant T (MAIT) cells, other T-cell subsets, and granulocyte mediators in asthmatic patients.MethodsSixty patients with mild-to-severe asthma and 24 control subjects underwent detailed clinical assessment and provided induced sputum, endobronchial biopsy, bronchoalveolar lavage, and blood samples. Adaptive and invariant T-cell subsets, cytokines, mast cells, and basophil mediators were analyzed.ResultsSignificant heterogeneity of T-cell phenotypes was observed, with levels of IL-13–secreting T cells and type 2 cytokines increased at some, but not all, asthma severities. TH17 cells and ??-17 cells, proposed drivers of neutrophilic inflammation, were not strongly associated with asthma, even in severe neutrophilic forms. MAIT cell frequencies were strikingly reduced in both blood and lung tissue in relation to corticosteroid therapy and vitamin D levels, especially in patients with severe asthma in whom bronchoalveolar lavage regulatory T-cell numbers were also reduced. Bayesian network analysis identified complex relationships between pathobiologic and clinical parameters. Topological data analysis identified 6 novel clusters that are associated with diverse underlying disease mechanisms, with increased mast cell mediator levels in patients with severe asthma both in its atopic (type 2 cytokine–high) and nonatopic forms.ConclusionThe evidence for a role for TH17 cells in patients with severe asthma is limited. Severe asthma is associated with a striking deficiency of MAIT cells and high mast cell mediator levels. This study provides proof of concept for disease mechanistic networks in asthmatic patients with clusters that could inform the development of new therapies.

BACKGROUND: Multilevel data integration is becoming a major area of research in systems biology. Within this area, multi-'omics datasets on complex diseases are becoming more readily available and there is a need to set standards and good practices for integrated analysis of biological, clinical and environmental data. We present a framework to plan and generate single and multi-'omics signatures of disease states. METHODS: The framework is divided into four major steps: dataset subsetting, feature filtering, 'omics-based clustering and biomarker identification. RESULTS: We illustrate the usefulness of this framework by identifying potential patient clusters based on integrated multi-'omics signatures in a publicly available ovarian cystadenocarcinoma dataset. The analysis generated a higher number of stable and clinically relevant clusters than previously reported, and enabled the generation of predictive models of patient outcomes. CONCLUSIONS: This framework will help health researchers plan and perform multi-'omics big data analyses to generate hypotheses and make sense of their rich, diverse and ever growing datasets, to enable implementation of translational P4 medicine.