IntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our... Show moreIntroductionThe Alzheimer's Disease Research Summits of 2012 and 2015 incorporated experts from academia, industry, and nonprofit organizations to develop new research directions to transform our understanding of Alzheimer's disease (AD) and propel the development of critically needed therapies. In response to their recommendations, big data at multiple levels are being generated and integrated to study network failures in disease. We used metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to cerebrospinal fluid pathology markers, imaging features, and cognitive performance.MethodsFasting serum samples from the Alzheimer's Disease Neuroimaging Initiative (199 control, 356 mild cognitive impairment, and 175 AD participants) were analyzed using the AbsoluteIDQ-p180 kit. Performance was validated in blinded replicates, and values were medication adjusted.Results Multivariable-adjusted analyses showed that sphingomyelins and ether-containing phosphatidylcholines were altered in preclinical biomarker-defined AD stages, whereas acylcarnitines and several amines, including the branched-chain amino acid valine and α-aminoadipic acid, changed in symptomatic stages. Several of the analytes showed consistent associations in the Rotterdam, Erasmus Rucphen Family, and Indiana Memory and Aging Studies. Partial correlation networks constructed for Aβ1–42, tau, imaging, and cognitive changes provided initial biochemical insights for disease-related processes. Coexpression networks interconnected key metabolic effectors of disease.DiscussionMetabolomics identified key disease-related metabolic changes and disease-progression-related changes. Defining metabolic changes during AD disease trajectory and its relationship to clinical phenotypes provides a powerful roadmap for drug and biomarker discovery. Show less
The work described in this thesis presents part of a framework that can be used to extract detailed disease biological information from peripheral tissue. This framework is based on the... Show moreThe work described in this thesis presents part of a framework that can be used to extract detailed disease biological information from peripheral tissue. This framework is based on the central dogma of biology “DNA to RNA to protein” and on a systems biology approach that aims to produce synergetic data whose disease pathological, prognostic and predictive value is greater than the sum of the individual experiment results. HD patients are often characterized by a multifaceted clinical profile, consisting of several symptoms and variable disease progression rates. Therefore, a systems approach such as the one described above is expected to be the most effective in identifying potential treatments and predictive biomarkers that will be most informative for the different patient subpopulations. Show less
This thesis demonstrates the application of bioinformatics to investigate the mechanisms that are implicated in Huntington’s Disease (HD). HD is an inherited neurodegenerative disorder and although... Show moreThis thesis demonstrates the application of bioinformatics to investigate the mechanisms that are implicated in Huntington’s Disease (HD). HD is an inherited neurodegenerative disorder and although the cause of the disease is known since 1993 we are still lacking a cure or treatment that can effectively treat the symptoms of HD. In order to tackle such a complicated case study, we followed a multidisciplinary approach to exploit the expertise and knowledge of people with diverse scientific background (chapter 2). This blend of disciplines facilitates constant collaboration between bioinformaticians, wet lab technicians, biologists, computer engineers and data scientists. A collaborative eScience model is proposed as a way to combine state-of-the-art computation analysis and laboratory work (chapter 3). At the same time, we explored methods to preserve the results, materials and methods involved in the experiment to increase the reproducibility and reusability of our research (chapter 4). In chapter 5 we identified disease signatures in blood that are functionally similar to signatures in brain. These are proposed as candidate biomarkers to be used as a monitoring tool for the state of the disease in brain, but also as a means to determine whether a treatment is successful or not. Show less
