The Dark side of Obesity: Multi-omics analysis of the dysmetabolic morbidities spectrum

Obesity is one of the most prevalent clinical conditions worldwide and is associated with a wide spectrum of dysmetabolic comorbidities. Complex cardio-metabolic disease cohorts, such as obesity cohorts are characterised by population heterogeneity, multiple underlying diseases status and different comorbidities’ treatment regiments. The systematic collection of multiple types of clinical and biological data from such cohorts and the data-analysis in an integrative manner is a challenging task due to the variables’ dimensionality and the lack of standardised know-how of post-processing.The main resource of this thesis has been the BARIA cohort, a detailed collection over time of multiple omics and demographic data from participants in bariatric surgery. BARIA datasets included plasma metabolites, RNA from hepatic, jejunal, mesenteric and subcutaneous adipose tissues and gut microbial metagenome, besides biometric data. The work presented in this thesis included the development of a systems biology integrative framework based on BARIA that (i) utilised unsupervised machine learning algorithms, self-organizing maps in particular, and multi-omics integrative frameworks, the DIABLO library, in order to stratify the BARIA heterogeneous obesity cohort and predict the bariatric surgery’s outcome. The thesis covered how BARIA can be the onset for (ii) studying molecular mechanisms related to type 2 diabetes (T2D) and G-protein coupled receptors (GPCRs) and for identifying a minimal set of biomarkers for obesity’s comorbidities such as (iii) non-alcoholic fatty liver disease (NAFL) and (iv) gallstones formation after bariatric surgery.The results indicated that the metabotypes comprising a bariatric surgery cohort exhibited a concrete metabolic status and different responses over time after the bariatric surgery. It has been demonstrated how obesity and T2D associated metabolites, such as 3-hydroxydecanoate, can increase inflammatory responses via GPCRs molecular activation and signalling. Last but not least, minimal sets of both evasive and non-evasive multi-omic discriminatory biomarkers for obesity’s dysmetabolic morbidities (NAFLD and gallstones after bariatric surgery) were obtained. Taking into consideration all the findings, this thesis presented how data-driven approaches can be used for studying in-depth heterogeneous cohorts, hereby facilitating early diagnosis and enabling potential preventive actions

Multi-Omic Biomarkers for Patient Stratification in Sjogren's Syndrome—A Review of the Literature

Sjögren's syndrome (SS) is a heterogeneous autoimmune rheumatic disease (ARD) characterised by dryness due to the chronic lymphocytic infiltration of the exocrine glands. Patients can also present other extra glandular manifestations, such as arthritis, anaemia and fatigue or various types of organ involvement. Due to its heterogenicity, along with the lack of effective treatments, the diagnosis and management of this disease is challenging. The objective of this review is to summarize recent multi-omic publications aiming to identify biomarkers in tears, saliva and peripheral blood from SS patients that could be relevant for their better stratification aiming at improved treatment selection and hopefully better outcomes. We highlight the relevance of pro-inflammatory cytokines and interferon (IFN) as biomarkers identified in higher concentrations in serum, saliva and tears. Transcriptomic studies confirmed the upregulation of IFN and interleukin signalling in patients with SS, whereas immunophenotyping studies have shown dysregulation in the immune cell population frequencies, specifically CD4+and C8+T activated cells, and their correlations with clinical parameters, such as disease activity scores. Lastly, we discussed emerging findings derived from different omic technologies which can provide integrated knowledge about SS pathogenesis and facilitate personalised medicine approaches leading to better patient outcomes in the future

A Framework for Multi-Omic Prediction of Treatment Response to Biologic Therapy for Psoriasis.

Biologic therapies have shown high efficacy in psoriasis, but individual response varies and is poorly understood. To inform biomarker discovery in the Psoriasis Stratification to Optimise Relevant Therapy (i.e., PSORT) study, we evaluated a comprehensive array of omics platforms across three time points and multiple tissues in a pilot investigation of 10 patients with severe psoriasis, treated with the tumor necrosis factor (TNF) inhibitor, etanercept. We used RNA sequencing to analyze mRNA and small RNA transcriptome in blood, lesional and nonlesional skin, and the SOMAscan platform to investigate the serum proteome. Using an integrative systems biology approach, we identified signals of treatment response in genes and pathways associated with TNF signaling, psoriasis pathology, and the major histocompatibility complex region. We found association between clinical response and TNF-regulated genes in blood and skin. Using a combination of differential expression testing, upstream regulator analysis, clustering techniques, and predictive modeling, we show that baseline samples are indicative of patient response to biologic therapies, including signals in blood, which have traditionally been considered unreliable for inference in dermatology. In conclusion, our pilot study provides both an analytical framework and empirical basis to estimate power for larger studies, specifically the ongoing PSORT study, which we show as powered for biomarker discovery and patient stratification

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes

COVID-19; Cuantificación fluorométrica; PiruvatoCOVID-19; Quantificació fluoromètrica; PiruvatCOVID-19; Fluorometric quantification; PyruvateBackground: Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness. Methods: We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays. Results: Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p<0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p<0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p<0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p<0.001) as an early criterion to accurately classify patients with critical outcomes. Conclusion: Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.This work has been developed in the framework of the COVIDOMICS’ project supported by Direcció General de Recerca i Innovació en Salut (DGRIS), Departament de Salut, Generalitat de Catalunya (PoC-6-17 and PoC1-5). The research was also funded by the Programa de Suport als Grups de Recerca AGAUR (2017SGR948), the SPANISH AIDS Research Network [RD16/0025/0006]-ISCIII-FEDER (Spain) and the Centro de Investigación Biomédica en Red de Enfermedades Infecciosas-ISCIII [CB21/13/00020], Madrid, Spain. LR is supported by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CD20/00105” through the program “Contratos Sara Borrell”. FV is supported by grants from the Programa de Intensificación de Investigadores (INT20/00031)-ISCIII and by “Premi a la Trajectòria Investigadora dels Hospitals de l’ICS 2018”. AR is supported by a grant from IISPV through the project “2019/IISPV/05” (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores 2019” and by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CP19/00146” through the Miguel Servet Program. This study was also supported by grants SAF2015–65019-R and RTI2018–093919-B-100 (to SF-V.) funded by MCIN/AEI and by “ERFD A way of making Europe”; PI19/01337 to FV, PI20/00095 to VC.-M, PI20/00326 to AR and PI20/00338 to JV funded by ISCIII, cofinanced by the European Regional Development Fund (ERDF), and from Fundación Bancaria Caixa d’Estalvis i Pensions de Barcelona (HR20-00051 to SF-V). The Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM) (CB07708/0012) is an initiative of the Instituto de Salud Carlos III. SF-V acknowledges support from the Miguel Servet tenure-track program (CP10/00438 and CPII16/00008) from the Fondo de Investigación Sanitaria, cofinanced by the ERDF. VC-M acknowledges support from the Ramón y Cajal program (RYC2019-026490-I) from the Spanish Ministry of Science and Innovation, cofinanced by the ERDF. The work was also supported by Consejeria de Salud y Familia (COVID-0005-2020), Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades Junta de Andalucia (CV20-85418to ER-M) and Instituto de Salud Carlos III (ISCIII) under grant agreement CP19/00159 to AGV “a way to make Europe”. ER-M was supported by the Spanish Research Council (CSIC). The funders have no roles in study design, data collection, data analysis, interpretation or the writing of this research

Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence

Patients with end-stage kidney disease (ESKD) are at high risk of severe COVID-19. Here, we perform longitudinal blood sampling of ESKD haemodialysis patients with COVID-19, collecting samples pre-infection, serially during infection, and after clinical recovery. Using plasma proteomics, and RNA-sequencing and flow cytometry of immune cells, we identify transcriptomic and proteomic signatures of COVID-19 severity, and find distinct temporal molecular profiles in patients with severe disease. Supervised learning reveals that the plasma proteome is a superior indicator of clinical severity than the PBMC transcriptome. We show that a decreasing trajectory of plasma LRRC15, a proposed co-receptor for SARS-CoV-2, is associated with a more severe clinical course. We observe that two months after the acute infection, patients still display dysregulated gene expression related to vascular, platelet and coagulation pathways, including PF4 (platelet factor 4), which may explain the prolonged thrombotic risk following COVID-19

“Bridging the Gap” Everything that Could Have Been Avoided If We Had Applied Gender Medicine, Pharmacogenetics and Personalized Medicine in the Gender-Omics and Sex-Omics Era

Gender medicine is the first step of personalized medicine and patient-centred care, an essential development to achieve the standard goal of a holistic approach to patients and diseases. By addressing the interrelation and integration of biological markers (i.e., sex) with indicators of psychological/cultural behaviour (i.e., gender), gender medicine represents the crucial assumption for achieving the personalized health-care required in the third millennium. However, ‘sex’ and ‘gender’ are often misused as synonyms, leading to frequent misunderstandings in those who are not deeply involved in the field. Overall, we have to face the evidence that biological, genetic, epigenetic, psycho-social, cultural, and environmental factors mutually interact in defining sex/gender differences, and at the same time in establishing potential unwanted sex/gender disparities. Prioritizing the role of sex/gender in physiological and pathological processes is crucial in terms of efficient prevention, clinical signs’ identification, prognosis definition, and therapy optimization. In this regard, the omics-approach has become a powerful tool to identify sex/genderspecific disease markers, with potential benefits also in terms of socio-psychological wellbeing for each individual, and cost-effectiveness for National Healthcare systems. “Being a male or being a female” is indeed important from a health point of view and it is no longer possible to avoid “sex and gender lens” when approaching patients. Accordingly, personalized healthcare must be based on evidence from targeted research studies aimed at understanding how sex and gender influence health across the entire life span. The rapid development of genetic tools in the molecular medicine approaches and their impact in healthcare is an example of highly specialized applications that have moved from specialists to primary care providers (e.g., pharmacogenetic and pharmacogenomic applications in routine medical practice). Gender medicine needs to follow the same path and become an established medical approach. To face the genetic, molecular and pharmacological bases of the existing sex/gender gap by means of omics approaches will pave the way to the discovery and identification of novel drug-targets/therapeutic protocols, personalized laboratory tests and diagnostic procedures (sex/gender-omics). In this scenario, the aim of the present review is not to simply resume the state-of-the-art in the field, rather an opportunity to gain insights into gender medicine, spanning from molecular up to social and psychological stances. The description and critical discussion of some key selected multidisciplinary topics considered as paradigmatic of sex/gender differences and sex/gender inequalities will allow to draft and design strategies useful to fill the existing gap and move forward

Circulating pyruvate is a potent prognostic marker for critical COVID-19 outcomes

Background: Coronavirus-19 (COVID-19) disease is driven by an unchecked immune response to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus which alters host mitochondrial-associated mechanisms. Compromised mitochondrial health results in abnormal reprogramming of glucose metabolism, which can disrupt extracellular signalling. We hypothesized that examining mitochondrial energy-related signalling metabolites implicated in host immune response to SARS-CoV-2 infection would provide potential biomarkers for predicting the risk of severe COVID-19 illness. Methods: We used a semi-targeted serum metabolomics approach in 273 patients with different severity grades of COVID-19 recruited at the acute phase of the infection to determine the relative abundance of tricarboxylic acid (Krebs) cycle-related metabolites with known extracellular signaling properties (pyruvate, lactate, succinate and α-ketoglutarate). Abundance levels of energy-related metabolites were evaluated in a validation cohort (n=398) using quantitative fluorimetric assays. Results: Increased levels of four energy-related metabolites (pyruvate, lactate, a-ketoglutarate and succinate) were found in critically ill COVID-19 patients using semi-targeted and targeted approaches (p<0.05). The combined strategy proposed herein enabled us to establish that circulating pyruvate levels (p<0.001) together with body mass index (p=0.025), C-reactive protein (p=0.039), D-Dimer (p<0.001) and creatinine (p=0.043) levels, are independent predictors of critical COVID-19. Furthermore, classification and regression tree (CART) analysis provided a cut-off value of pyruvate in serum (24.54 µM; p<0.001) as an early criterion to accurately classify patients with critical outcomes. Conclusion: Our findings support the link between COVID-19 pathogenesis and immunometabolic dysregulation, and show that fluorometric quantification of circulating pyruvate is a cost-effective clinical decision support tool to improve patient stratification and prognosis prediction.This work has been developed in the framework of the COVIDOMICS’ project supported by Direcció General de Recerca i Innovació en Salut (DGRIS), Departament de Salut, Generalitat de Catalunya (PoC-6-17 and PoC1-5). The research was also funded by the Programa de Suport als Grups de Recerca AGAUR (2017SGR948), the SPANISH AIDS Research Network [RD16/0025/0006]-ISCIII-FEDER (Spain) and the Centro de Investigación Biomédica en Red de Enfermedades Infecciosas-ISCIII [CB21/13/00020], Madrid, Spain. LR is supported by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CD20/00105” through the program “Contratos Sara Borrell”. FV is supported by grants from the Programa de Intensificación de Investigadores (INT20/00031)-ISCIII and by “Premi a la Trajectòria Investigadora dels Hospitals de l’ICS 2018”. AR is supported by a grant from IISPV through the project “2019/IISPV/05” (Boosting Young Talent), by GeSIDA through the “III Premio para Jóvenes Investigadores 2019” and by the Instituto de Salud Carlos III (ISCIII) under grant agreement “CP19/00146” through the Miguel Servet Program. This study was also supported by grants SAF2015–65019-R and RTI2018–093919-B-100 (to SF-V.) funded by MCIN/AEI and by “ERFD A way of making Europe”; PI19/01337 to FV, PI20/00095 to VC.-M, PI20/00326 to AR and PI20/00338 to JV funded by ISCIII, cofinanced by the European Regional Development Fund (ERDF), and from Fundación Bancaria Caixa d’Estalvis i Pensions de Barcelona (HR20-00051 to SF-V). The Spanish Biomedical Research Center in Diabetes and Associated Metabolic Disorders (CIBERDEM) (CB07708/0012) is an initiative of the Instituto de Salud Carlos III. SF-V acknowledges support from the Miguel Servet tenure-track program (CP10/00438 and CPII16/00008) from the Fondo de Investigación Sanitaria, cofinanced by the ERDF. VC-M acknowledges support from the Ramón y Cajal program (RYC2019-026490-I) from the Spanish Ministry of Science and Innovation, cofinanced by the ERDF. The work was also supported by Consejeria de Salud y Familia (COVID-0005-2020), Consejeria de Transformacion Economica, Industria, Conocimiento y Universidades Junta de Andalucia (CV20-85418to ER-M) and Instituto de Salud Carlos III (ISCIII) under grant agreement CP19/00159 to AGV “a way to make Europe”. ER-M was supported by the Spanish Research Council (CSIC). The funders have no roles in study design, data collection, data analysis, interpretation or the writing of this research.Peer reviewe

The Omics of Obesity

Obesity is a complex multi‐faceted disease affecting billions of people worldwide. Traditionally, obesity was thought to be a consequence of having access to energy dense food and busy lifestyles that do not factor in sufficient physical activity. Although diet and exercise play a major role in obesity development, these are not the only contributors. It is widely accepted that genetic and epigenetic factors also play a major role in obesity development and these in turn affect the lipidome, metabolome and proteome. With new technological advances, it is now possible to delve into these specific areas to further understand the mechanisms involved in obesity development. These technologies are collectively termed “omics” technologies, and this chapter will summarise the recent advances in obesity and metabolism research and describe new technologies that have been used to identify mechanisms that play a major role in the development of obesity. In particular, we will examine the different omics platforms that are available and have been used to study obesity. Collectively, these studies will be fundamental in identifying new and effective treatment strategies