Molecular phenomics and metagenomics of hepatic steatosis in non-diabetic obese women

The role of molecular signals from the microbiome and their coordinated interactions with those from the host in hepatic steatosis – notably in obese patients and as risk factors for insulin resistance and atherosclerosis – needs to be understood. We reveal molecular networks linking gut microbiome and host phenome to hepatic steatosis in a cohort of non diabetic obese women. Steatotic patients had low microbial gene richness and increased genetic potential for processing of dietary lipids and endotoxin biosynthesis (notably from Proteobacteria), hepatic inflammation and dysregulation of aromatic and branched-chain amino acid (AAA and BCAA) metabolism. We demonstrated that faecal microbiota transplants and chronic treatment with phenylacetic acid (PAA), a microbial product of AAA metabolism, successfully trigger steatosis and BCAA metabolism. Molecular phenomic signatures were predictive (AUC = 87%) and consistent with the gut microbiome making an impact on the steatosis phenome (>75% shared variation) and, therefore, actionable via microbiome-based therapies

Multimodal Data Fusion and Quantitative Analysis for Medical Applications

Medical big data is not only enormous in its size, but also heterogeneous and complex in its data structure, which makes conventional systems or algorithms difficult to process. These heterogeneous medical data include imaging data (e.g., Positron Emission Tomography (PET), Computerized Tomography (CT), Magnetic Resonance Imaging (MRI)), and non-imaging data (e.g., laboratory biomarkers, electronic medical records, and hand-written doctor notes). Multimodal data fusion is an emerging vital field to address this urgent challenge, aiming to process and analyze the complex, diverse and heterogeneous multimodal data. The fusion algorithms bring great potential in medical data analysis, by 1) taking advantage of complementary information from different sources (such as functional-structural complementarity of PET/CT images) and 2) exploiting consensus information that reflects the intrinsic essence (such as the genetic essence underlying medical imaging and clinical symptoms). Thus, multimodal data fusion benefits a wide range of quantitative medical applications, including personalized patient care, more optimal medical operation plan, and preventive public health. Though there has been extensive research on computational approaches for multimodal fusion, there are three major challenges of multimodal data fusion in quantitative medical applications, which are summarized as feature-level fusion, information-level fusion and knowledge-level fusion: • Feature-level fusion. The first challenge is to mine multimodal biomarkers from high-dimensional small-sample multimodal medical datasets, which hinders the effective discovery of informative multimodal biomarkers. Specifically, efficient dimension reduction algorithms are required to alleviate "curse of dimensionality" problem and address the criteria for discovering interpretable, relevant, non-redundant and generalizable multimodal biomarkers. • Information-level fusion. The second challenge is to exploit and interpret inter-modal and intra-modal information for precise clinical decisions. Although radiomics and multi-branch deep learning have been used for implicit information fusion guided with supervision of the labels, there is a lack of methods to explicitly explore inter-modal relationships in medical applications. Unsupervised multimodal learning is able to mine inter-modal relationship as well as reduce the usage of labor-intensive data and explore potential undiscovered biomarkers; however, mining discriminative information without label supervision is an upcoming challenge. Furthermore, the interpretation of complex non-linear cross-modal associations, especially in deep multimodal learning, is another critical challenge in information-level fusion, which hinders the exploration of multimodal interaction in disease mechanism. • Knowledge-level fusion. The third challenge is quantitative knowledge distillation from multi-focus regions on medical imaging. Although characterizing imaging features from single lesions using either feature engineering or deep learning methods have been investigated in recent years, both methods neglect the importance of inter-region spatial relationships. Thus, a topological profiling tool for multi-focus regions is in high demand, which is yet missing in current feature engineering and deep learning methods. Furthermore, incorporating domain knowledge with distilled knowledge from multi-focus regions is another challenge in knowledge-level fusion. To address the three challenges in multimodal data fusion, this thesis provides a multi-level fusion framework for multimodal biomarker mining, multimodal deep learning, and knowledge distillation from multi-focus regions. Specifically, our major contributions in this thesis include: • To address the challenges in feature-level fusion, we propose an Integrative Multimodal Biomarker Mining framework to select interpretable, relevant, non-redundant and generalizable multimodal biomarkers from high-dimensional small-sample imaging and non-imaging data for diagnostic and prognostic applications. The feature selection criteria including representativeness, robustness, discriminability, and non-redundancy are exploited by consensus clustering, Wilcoxon filter, sequential forward selection, and correlation analysis, respectively. SHapley Additive exPlanations (SHAP) method and nomogram are employed to further enhance feature interpretability in machine learning models. • To address the challenges in information-level fusion, we propose an Interpretable Deep Correlational Fusion framework, based on canonical correlation analysis (CCA) for 1) cohesive multimodal fusion of medical imaging and non-imaging data, and 2) interpretation of complex non-linear cross-modal associations. Specifically, two novel loss functions are proposed to optimize the discovery of informative multimodal representations in both supervised and unsupervised deep learning, by jointly learning inter-modal consensus and intra-modal discriminative information. An interpretation module is proposed to decipher the complex non-linear cross-modal association by leveraging interpretation methods in both deep learning and multimodal consensus learning. • To address the challenges in knowledge-level fusion, we proposed a Dynamic Topological Analysis framework, based on persistent homology, for knowledge distillation from inter-connected multi-focus regions in medical imaging and incorporation of domain knowledge. Different from conventional feature engineering and deep learning, our DTA framework is able to explicitly quantify inter-region topological relationships, including global-level geometric structure and community-level clusters. K-simplex Community Graph is proposed to construct the dynamic community graph for representing community-level multi-scale graph structure. The constructed dynamic graph is subsequently tracked with a novel Decomposed Persistence algorithm. Domain knowledge is incorporated into the Adaptive Community Profile, summarizing the tracked multi-scale community topology with additional customizable clinically important factors

A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes

Early diagnosis remains a significant challenge for many neurological disorders, especially for rare disorders where studying large cohorts is not possible. A novel solution that investigators have undertaken is combining advanced machine learning algorithms with resting-state functional Magnetic Resonance Imaging to unveil hidden pathological brain connectome patterns to uncover diagnostic and prognostic biomarkers. Recently, state-of-the-art deep learning techniques are outperforming traditional machine learning methods and are hailed as a milestone for artificial intelligence. However, whole brain classification that combines brain connectome with deep learning has been hindered by insufficient training samples. Inspired by the transfer learning strategy employed in computer vision, we exploited previously collected resting-state functional MRI data for healthy subjects from existing databases and transferred this knowledge for new disease classification tasks. We developed a deep transfer learning neural network (DTL-NN) framework for enhancing the classification of whole brain functional connectivity patterns. Briefly, we trained a stacked sparse autoencoder (SSAE) prototype to learn healthy functional connectivity patterns in an offline learning environment. Then, the SSAE prototype was transferred to a DTL-NN model for a new classification task. To test the validity of our framework, we collected resting-state functional MRI data from the Autism Brain Imaging Data Exchange (ABIDE) repository. Using autism spectrum disorder (ASD) classification as a target task, we compared the performance of our DTL-NN approach with a traditional deep neural network and support vector machine models across four ABIDE data sites that enrolled at least 60 subjects. As compared to traditional models, our DTL-NN approach achieved an improved performance in accuracy, sensitivity, specificity and area under receiver operating characteristic curve. These findings suggest that DTL-NN approaches could enhance disease classification for neurological conditions, where accumulating large neuroimaging datasets has been challenging

Multi-omics biomarkers of metabolic homeostasis of risk factors associated to non-communicable diseases

Les malalties no transmissibles, com l'obesitat, la síndrome metabòlica, les malalties cardiovasculars, el càncer i les malalties neurodegeneratives, es consideren malalties multifactorials. Per aquesta raó, s'ha proposat que l'aparició d’aquestes malalties es deu a un desequilibri de processos globals. El seguiment d'aquests processos obre la porta a la possibilitat de modular-los i, per tant, prevenir-los mitjançant el disseny d'intervencions/tractaments personalitzats més precisos. No obstant això, els biomarcadors actuals no tenen la capacitat d'avaluar les alteracions primerenques que podrien conduir al desenvolupament de la malaltia, la qual cosa posa de manifest la necessitat de definir nous biomarcadors. Per tant, en el present treball es presenta una signatura metabòlica característica de processos específics obtinguda mitjançant l'ús de tecnologies òmiques: disfunció de carbohidrats, hiperlipèmia, hipertensió i dysbiosis intestinal, com a representatius de l'estrès metabòlic; l'estrès inflamatori; l'estrès oxidatiu i l'estrès psicològic. Per això, s'han desenvolupat diferents models animals i s'ha avaluat el perfil metabòlic dels diferents factors de risc d'interès en plasma i orina. Els resultats indiquen que els lípids i els intermediaris del cicle del TCA són els metabòlits més prometedors del perfil metabòlic. En tots els factors de risc, els diacilglicerols (DG) són els biomarcadors lipídics amb major impacte: en concret, el DG 36:4 i el DG 34:2 vinculen els factors de risc amb el metabolisme de l'àcid araquidònic. En inflamació, estrès oxidatiu i psicològic, l'altre protagonista és el cicle del TCA a causa del seu paper clau en el mitocondri amb l'alfa-cetoglutarat com l'intermediari més prometedor. En conseqüència, el perfil metabòlic presentat és una eina potencial per al seguiment dels factors de risc i podria obrir una finestra per a orientar l'aparició de malalties i intentar prevenir-les i tractar-les.Las enfermedades no transmisibles, como la obesidad, el síndrome metabólico, las enfermedades cardiovasculares, el cáncer y las enfermedades neurodegenerativas, se consideran enfermedades multifactoriales. Por esta razón, se ha propuesto que la aparición de estas enfermedades se debe a un desequilibrio de procesos globales. El seguimiento de estos procesos abre la puerta a la posibilidad de modularlos y, por lo tanto, prevenirlos mediante el diseño de intervenciones/tratamientos personalizados más precisos. Sin embargo, los biomarcadores actuales no tienen la capacidad de evaluar las alteraciones tempranas que podrían conducir al desarrollo de la enfermedad, lo que pone de manifiesto la necesidad de definir nuevos biomarcadores. Por lo tanto, en el presente trabajo se presenta una firma metabólica característica de procesos específicos obtenida mediante el uso de tecnologías ómicas: disfunción de carbohidratos, hiperlipidemia, hipertensión y disbiosis intestinal, como representativos del estrés metabólico; el estrés inflamatorio; el estrés oxidativo y el estrés psicológico. Para ello se han desarrollado diferentes modelos animales y se ha evaluado el perfil metabólico de los diferentes factores de riesgo de interés en plasma y orina. Los resultados indicaron que los lípidos y los intermediarios del ciclo del TCA son los metabolitos más prometedores del perfil metabólico. En todos los factores de riesgo, los diacilgliceroles (DG) son el biomarcador lipídico con mayor impacto: en concreto, el DG 36:4 y el DG 34:2 vinculan los factores de riesgo con el metabolismo del ácido araquidónico. En inflamación, estrés oxidativo y psicológico, el otro protagonista es el ciclo del TCA debido a su papel clave en la mitocondria con el alfa-cetoglutarato como el intermediario más prometedor. En consecuencia, el perfil metabólico presentado es una herramienta potencial para el seguimiento de los factores de riesgo y podría abrir una ventana para orientar la aparición de enfermedades e intentar prevenirlas y tratarlas.Non-communicable diseases, such as obesity, metabolic syndrome, cardiovascular diseases, cancer and neurodegenerative diseases, are considered multifactorial diseases. For this reason, it has been proposed that the occurrence of these diseases is due to an imbalance of overarching processes (i.e., metabolic, inflammatory, oxidative, and psychological stress). Monitoring these overarching processes opens the door to the possibility of modulating them, and thus preventing the occurrence of different process through the design of more precise personalised interventions or treatments. However, current biomarkers of disease cannot assess early alterations that could lead to the development of disease, highlighting the need to define new biomarkers. Thus, the present work presents a characteristic metabolic signature for the detection of specific processes using omic technologies: carbohydrate dysfunction, hyperlipidaemia, hypertension and intestinal dysbiosis, as representative of metabolic stress; inflammatory stress; oxidative stress and psychological stress. For this purpose, different animal models have been developed and the metabolic profile in plasma and urine has been evaluated in the different risk factors of interest. The results indicated that lipids and TCA cycle intermediates are the most promising metabolites of the metabolic profile. In all the risk factors, diacylglycerols (DG) are the lipidic biomarker with the greatest impact on metabolic profiles: specifically, DG 36:4 and DG 34:2 linking risk factors to arachidonic acid metabolism. In inflammation, oxidative and psychological stress, the other protagonist is the TCA cycle due to its multiple roles in mitochondrial metabolism: being alpha-ketoglutarate one of the most promising intermediate. In consequence, the presented metabolic profile is a potential tool for the monitoring of risk factors and could open a window to target the onset of diseases and try to prevent and treat them

Alzheimer's disease

In this Seminar, we highlight the main developments in the field of Alzheimer's disease. The most recent data indicate that, by 2050, the prevalence of dementia will double in Europe and triple worldwide, and that estimate is 3 times higher when based on a biological (rather than clinical) definition of Alzheimer's disease. The earliest phase of Alzheimer's disease (cellular phase) happens in parallel with accumulating amyloid β, inducing the spread of tau pathology. The risk of Alzheimer's disease is 60-80% dependent on heritable factors, with more than 40 Alzheimer's disease-associated genetic risk loci already identified, of which the APOE alleles have the strongest association with the disease. Novel biomarkers include PET scans and plasma assays for amyloid β and phosphorylated tau, which show great promise for clinical and research use. Multidomain lifestyle-based prevention trials suggest cognitive benefits in participants with increased risk of dementia. Lifestyle factors do not directly affect Alzheimer's disease pathology, but can still contribute to a positive outcome in individuals with Alzheimer's disease. Promising pharmacological treatments are poised at advanced stages of clinical trials and include anti-amyloid β, anti-tau, and anti-inflammatory strategies

Personalized Medicine in Epidemics

This reprint contains 11 chapters on a wide range of subjects related to the impacts of different types of epidemics on our ability to practice personalized medicine. Together, these chapters provide a broad overview with many different examples of epidemics. The personalization of medicine is present both despite and because of epidemics. Many more examples are possible, but this reprint offers a primary overview emphasizing the widely spread relevance of the topic

Biochemical and molecular characterisation of the dyslipidaemia in Portugal

Tese de doutoramento, Biologia (Biologia de Sistemas), Universidade de Lisboa, Faculdade de Ciências, 2018Dyslipidaemia is one of the major modifiable independent risk factors for cardiovascular disease (CVD), with both genetic and environmental determinants. Although genetic risk factors are considered as non modifiable, their CVD-associated risk can be prevented if early identified. The correct and early identification of dyslipidaemia is important for a better patient management and could definitely contribute to CVD prevention. This thesis intended the most complete characterisation of the dyslipidaemia in the Portuguese population, both biochemically and molecularly. Reference values based on population-specific percentiles for lipid and lipoprotein biomarkers were provided for the first time in the Portuguese population, namely total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), apolipoprotein A1 (apoA1), apolipoprotein B (apoB), small, dense LDL-C (sdLDL-C), lipoprotein(a) [Lp(a)], as well apoB/apoA1 and sdLDL-C/LDL-C ratios, and non-HDL-C and remnant cholesterol. To our knowledge, the sdLDL-C percentiles were the first to be established in an European population. The percentiles were estimated through a rigorous methodology and compared with other population percentiles by a very visual and feasible method, showing relevant differences. These newly determined reference values for lipid biomarkers were then used to characterise the dyslipidaemia in our population, and can now be used in the clinic for a better patient care and management. More than cholesterol per se, our study highlighted apoB and sdLDL-C as important biomarkers to be used in dyslipidaemia evaluation. Individuals presenting extreme phenotypes were further investigated to assess possible monogenic causes, and three individuals were found to have familial hypercholesterolemia (FH), the most common genetic dyslipidaemia and one of the most common disorders that confer an increased cardiovascular risk. Finally, in an attempt to explore the causes for the FH phenotype, a polygenic risk score was validated for the first time in the Portuguese population. A total of 289 index cases were identified with monogenic FH and other causes for their dyslipidaemia, and also 100 were identified with polygenic hypercholesterolaemia, representing 53.21% of the cohort. From the monogenic causes, 91.35% have a mutation in LDLR, 4.84% in APOB, 1.04% in PCSK9 and 2.08% had mutations in phenocopies genes (LIPA, APOE, ALB), suggesting that all those monogenic and polygenic causes should be always investigated for a better patient identification. This study provided the most complete characterisation of the dyslipidaemia in the Portuguese population, and important evidences for dyslipidaemia evaluation has been produced. The results obtained have application, not only for Portugal or a south European populations, but also might have an worldwide utility for the dyslipidaemia assessment. Together, the results obtained provide useful information on an important cardiovascular risk factor and should help to tackle and identify at risk situations that need urgent measures.Fundação para a Ciência e a Tecnologia (SFRH/BD/52494/2014