Modeling Functional Modules Using Statistical and Machine Learning Methods

Understanding the aspects of the cell functionality that account for disease or drug action mechanisms is the main challenge for precision medicine. In spite of the increasing availability of genomic and transcriptomic data, there is still a gap between the detection of perturbations in gene expression and the understanding of their contribution to the molecular mechanisms that ultimately account for the phenotype studied. Over the last decade, different computational and mathematical models have been proposed for pathway analysis. However, they are not taking into account the dynamic mechanisms contained by pathways as represented in their layout and the interactions between genes and proteins. In this thesis, I present two slightly different mathematical models to integrate human transcriptomic data with prior knowledge of signalling and metabolic pathways to estimate the Mechanistic Pathway Activities (MPAs). MPAs are continuous and individual level values that can be used with machine learning and statistical methods to determine biomarkers for the early diagnosis and subtype classification of the diseases, and also to suggest potential therapeutic targets for individualized therapeutic interventions. The overall objective is, developing new and advanced systems biology approaches to propose functional hypotheses that help us to understand and interpret the complex mechanism of the diseases. These mechanisms are crucial for robust personalized drug treatments and predict clinical outcomes. First, I contributed to the development of a method which is designed to extract elementary sub-pathways from a signalling pathway and to estimate their activity. Second, this algorithm adapted to metabolic modules and it is implemented as a webtool. Third, the method used to reveal a pan-cancer metabolic landscape. In this study, I analyzed the metabolic module profile of 25 different cancer types and the method is also validated using different computational and experimental approaches. Each method developed in this thesis was benchmarked against the existing similar methods, evaluated for their sensitivity and specificity, experimentally validated when it is possible and used to predict clinical outcomes of different cancer types. The research described in this thesis and the results obtained were published in different systems biology and cancer-related peer-reviewed journals and also in national newspapers

Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape

BIO2014-57291-R and SAF2017-88908-R from the Spanish Ministry of Economy and Competitivenessgrant PI15/00854 from the FIS“Plataforma de Recursos Biomoleculares y Bioinformáticos” PT17/0009/0006 from the ISCIII, cofunded with European Regional Development FundsFP7-PEOPLE-2012-ITN MLPM2012EU H2020-INFRADEV-1-2015-1 ELIXIR-EXCELERAT

Mechanistic Models of Signaling Pathways Reveal the Drug Action Mechanisms behind Gender-Specific Gene Expression for Cancer Treatments

This article belongs to the Special Issue Targeting Signal Transduction Pathways and Non-coding RNAs as Potential Therapy in Cancer and Aging.Despite the existence of differences in gene expression across numerous genes between males and females having been known for a long time, these have been mostly ignored in many studies, including drug development and its therapeutic use. In fact, the consequences of such differences over the disease mechanisms or the drug action mechanisms are completely unknown. Here we applied mechanistic mathematical models of signaling activity to reveal the ultimate functional consequences that gender-specific gene expression activities have over cell functionality and fate. Moreover, we also used the mechanistic modeling framework to simulate the drug interventions and unravel how drug action mechanisms are affected by gender-specific differential gene expression. Interestingly, some cancers have many biological processes significantly affected by these gender-specific differences (e.g., bladder or head and neck carcinomas), while others (e.g., glioblastoma or rectum cancer) are almost insensitive to them. We found that many of these gender-specific differences affect cancer-specific pathways or in physiological signaling pathways, also involved in cancer origin and development. Finally, mechanistic models have the potential to be used for finding alternative therapeutic interventions on the pathways targeted by the drug, which lead to similar results compensating the downstream consequences of gender-specific differences in gene expression.This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and “Plataforma de Recursos Biomoleculares y Bioinformáticos” PT17/0009/0006 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network "Machine Learning Frontiers in Precision Medicine" (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559)

Models of cell signaling uncover molecular mechanisms of high-risk neuroblastoma and predict disease outcome

Spanish Ministry of Economy and Competitiveness grant BIO2014–57291-RSpanish Ministry of Economy and Competitiveness grant SAF2017–88908-R“Plataforma de Recursos Biomoleculares y Bioinformáticos” PT13/0001/0007EU H2020-INFRADEV-1-2015-1 ELIXIR-EXCELERATE (ref. 676559)EU FP7-People ITN Marie Curie Project (ref 316861)

High throughput estimation of functional cell activities reveals disease mechanisms and predicts relevant clinical outcomes

This work is supported by grants BIO2014- 57291-R from the Spanish Ministry of Economy and Competitiveness and “Plataforma de Recursos Biomoleculares y Bioinformáticos” PT13/0001/0007 from the ISCIII, both co-funded with European Regional Development Funds (ERDF); PROMETEOII/2014/025 from the Generalitat Valenciana (GVA-FEDER); Fundació la Marató TV3 (ref. 20133134); and EU H2020- INFRADEV-1-2015-1 ELIXIR-EXCELERATE (ref. 676559) and EU FP7-People ITN Marie Curie Project (ref 316861)

Role of synovial fibroblast subsets across synovial pathotypes in rheumatoid arthritis: a deconvolution analysis

OBJECTIVES: To integrate published single-cell RNA sequencing (scRNA-seq) data and assess the contribution of synovial fibroblast (SF) subsets to synovial pathotypes and respective clinical characteristics in treatment-naïve early arthritis. METHODS: In this in silico study, we integrated scRNA-seq data from published studies with additional unpublished in-house data. Standard Seurat, Harmony and Liger workflow was performed for integration and differential gene expression analysis. We estimated single cell type proportions in bulk RNA-seq data (deconvolution) from synovial tissue from 87 treatment-naïve early arthritis patients in the Pathobiology of Early Arthritis Cohort using MuSiC. SF proportions across synovial pathotypes (fibroid, lymphoid and myeloid) and relationship of disease activity measurements across different synovial pathotypes were assessed. RESULTS: We identified four SF clusters with respective marker genes: PRG4(+) SF (CD55, MMP3, PRG4, THY1(neg)); CXCL12(+) SF (CXCL12, CCL2, ADAMTS1, THY1(low)); POSTN(+) SF (POSTN, collagen genes, THY1); CXCL14(+) SF (CXCL14, C3, CD34, ASPN, THY1) that correspond to lining (PRG4(+) SF) and sublining (CXCL12(+) SF, POSTN(+) + and CXCL14(+) SF) SF subsets. CXCL12(+) SF and POSTN(+) + were most prominent in the fibroid while PRG4(+) SF appeared highest in the myeloid pathotype. Corresponding, lining assessed by histology (assessed by Krenn-Score) was thicker in the myeloid, but also in the lymphoid pathotype + the fibroid pathotype. PRG4(+) SF correlated positively with disease severity parameters in the fibroid, POSTN(+) SF in the lymphoid pathotype whereas CXCL14(+) SF showed negative association with disease severity in all pathotypes. CONCLUSION: This study shows a so far unexplored association between distinct synovial pathologies and SF subtypes defined by scRNA-seq. The knowledge of the diverse interplay of SF with immune cells will advance opportunities for tailored targeted treatments

Axl and MerTK regulate synovial inflammation and are modulated by IL-6 inhibition in rheumatoid arthritis.

The TAM tyrosine kinases, Axl and MerTK, play an important role in rheumatoid arthritis (RA). Here, using a unique synovial tissue bioresource of patients with RA matched for disease stage and treatment exposure, we assessed how Axl and MerTK relate to synovial histopathology and disease activity, and their topographical expression and longitudinal modulation by targeted treatments. We show that in treatment-naive patients, high AXL levels are associated with pauci-immune histology and low disease activity and inversely correlate with the expression levels of pro-inflammatory genes. We define the location of Axl/MerTK in rheumatoid synovium using immunohistochemistry/fluorescence and digital spatial profiling and show that Axl is preferentially expressed in the lining layer. Moreover, its ectodomain, released in the synovial fluid, is associated with synovial histopathology. We also show that Toll-like-receptor 4-stimulated synovial fibroblasts from patients with RA modulate MerTK shedding by macrophages. Lastly, Axl/MerTK synovial expression is influenced by disease stage and therapeutic intervention, notably by IL-6 inhibition. These findings suggest that Axl/MerTK are a dynamic axis modulated by synovial cellular features, disease stage and treatment

Taxonomic variations in the gut microbiome of gout patients with and without tophi might have a functional impact on urate metabolism

Objective: To evaluate the taxonomic composition of the gut microbiome in gout patients with and without tophi formation, and predict bacterial functions that might have an impact on urate metabolism. Methods: Hypervariable V3–V4 regions of the bacterial 16S rRNA gene from fecal samples of gout patients with and without tophi (n=33 and n=25, respectively) were sequenced and compared to fecal samples from 53 healthy controls. We explored predictive functional profles using bioinformatics in order to identify diferences in taxonomy and metabolic pathways. Results: We identifed a microbiome characterized by the lowest richness and a higher abundance of Phascolarctobacterium, Bacteroides, Akkermansia, and Ruminococcus_gnavus_group genera in patients with gout without tophi when compared to controls. The Proteobacteria phylum and the Escherichia-Shigella genus were more abundant in patients with tophaceous gout than in controls. Fold change analysis detected nine genera enriched in healthy controls compared to gout groups (Bifdobacterium, Butyricicoccus, Oscillobacter, Ruminococcaceae_UCG_010, Lachnospiraceae_ND2007_group, Haemophilus, Ruminococcus_1, Clostridium_sensu_stricto_1, and Ruminococcaceae_ UGC_013). We found that the core microbiota of both gout groups shared Bacteroides caccae, Bacteroides stercoris ATCC 43183, and Bacteroides coprocola DSM 17136. These bacteria might perform functions linked to one-carbon metabo‑ lism, nucleotide binding, amino acid biosynthesis, and purine biosynthesis. Finally, we observed diferences in key bacterial enzymes involved in urate synthesis, degradation, and elimination. Conclusion: Our fndings revealed that taxonomic variations in the gut microbiome of gout patients with and with‑ out tophi might have a functional impact on urate metabolism. Keywords: Gout, Gut microbiota, Uric acid metabolis