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Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases.

ABSTRACT: Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

SUBMITTER: La Cognata V

PROVIDER: S-EPMC8125201 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases.

La Cognata Valentina V Morello Giovanna G Cavallaro Sebastiano S

International journal of molecular sciences 20210501 9

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease ...[more]

PMID: 34062930

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Project description:While the central common feature of the neurodegenerative diseases (NDs) is the accumulation of misfolded proteins, they share other pathogenic mechanisms. However, we miss an explanation for the onset of the NDs. The mechanisms through which genetic mutations, present from conception are expressed only after several decades of life are unknown. We aim to find clues on the complexity of the disease onset trigger of the different NDs expressed in the number of steps of factors related to a disease. We collected brain autopsies on diseased patients with NDs, and found a dynamic increase of the ND multimorbidity with the advance of age. Together with the observation that the NDs accumulate multiple misfolded proteins, and the same misfolded proteins are involved in more than one ND, motivated us to propose a model for a genealogical tree of the NDs. To collect the dynamic data needed to build the tree, we used a Big-data approach that searched automatically epidemiological datasets for age-stratified incidence of NDs. Based on meta-analysis of over 400 datasets, we developed an algorithm that checks whether a ND follows a multistep model, finds the number of steps necessary for the onset of each ND, finds the number of common steps with other NDs and the number of specific steps of each ND, and builds with these findings a parsimony tree of the genealogy of the NDs. The tree discloses three types of NDs: the stem NDs with less than 3 steps; the trunk NDs with 5 to 6 steps; and the crown NDs with more than 7 steps. The tree provides a comprehensive understanding of the relationship across the different NDs, as well as a mathematical framework for dynamic adjustment of the genealogical tree of the NDs with the appearance of new epidemiological studies and the addition of new NDs to the model, thus setting the basis for the search for the identity and order of these steps. Understanding the complexity, or number of steps, of factors related to disease onset trigger is important prior deciding to study single factors for a multiple steps disease.

Project description:Immune mediated inflammatory diseases (IMIDs) are a heterogeneous group of debilitating, multifactorial and unrelated conditions featured by a dysregulated immune response leading to destructive chronic inflammation. The immune dysregulation can affect various organ systems: gut (e.g., inflammatory bowel disease), joints (e.g., rheumatoid arthritis), skin (e.g., psoriasis, atopic dermatitis), resulting in significant morbidity, reduced quality of life, increased risk for comorbidities, and premature death. As there are no reliable disease progression and therapy response biomarkers currently available, it is very hard to predict how the disease will develop and which treatments will be effective in a given patient. In addition, a considerable proportion of patients do not respond sufficiently to the treatment. ImmUniverse is a large collaborative consortium of 27 partners funded by the Innovative Medicine Initiative (IMI), which is sponsored by the European Union (Horizon 2020) and in-kind contributions of participating pharmaceutical companies within the European Federation of Pharmaceutical Industries and Associations (EFPIA). ImmUniverse aims to advance our understanding of the molecular mechanisms underlying two immune-mediated diseases, ulcerative colitis (UC) and atopic dermatitis (AD), by pursuing an integrative multi-omics approach. As a consequence of the heterogeneity among IMIDs patients, a comprehensive, evidence-based identification of novel biomarkers is necessary to enable appropriate patient stratification that would account for the inter-individual differences in disease severity, drug efficacy, side effects or prognosis. This would guide clinicians in the management of patients and represent a major step towards personalized medicine. ImmUniverse will combine the existing and novel advanced technologies, including multi-omics, to characterize both the tissue microenvironment and blood. This comprehensive, systems biology-oriented approach will allow for identification and validation of tissue and circulating biomarker signatures as well as mechanistic principles, which will provide information about disease severity and future disease progression. This truly makes the ImmUniverse Consortium an unparalleled approach.

Project description:BackgroundObservational studies have shown that the correlation between neurodegenerative diseases and colorectal cancer (CRC) remains controversial. Therefore, this study aimed to verify the causal association between these two diseases.MethodsMendelian randomization (MR) analysis was used to assess the causal relationships between five major neurodegenerative diseases and CRC. Multivariable MR (MVMR) analysis was conducted to assess the direct causal effect of neurodegenerative diseases on CRC. Colocalization and pathway enrichment analyses were conducted to further elucidate our results. Sensitivity analysis was conducted to assess the robustness of the results.ResultsGenetically predicted Alzheimer's disease (AD) nominally increased CRC risk (OR = 1.0620, 95%CI = 1.0127-1.1136, P = 0.013). There was no causal effect of genetically predicted CRC on neurodegenerative diseases. Furthermore, we demonstrated that genetically predicted AD marginally increased colon cancer risk (OR = 1.1621, 95%CI = 1.0267-1.3153, P = 0.017). Genetically predicted Lewy body dementia (LBD) had a significant causal effect on the increasing risk of colon cancer (IVW OR = 1.1779, 95%CI = 1.0694-1.2975, P = 0.001). MVMR indicated that effect of AD on colon cancer was driven by LBD, type 2 diabetes, body mass index, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, triglyceride, total cholesterol (TC), processed meat consumption, smoking, alcohol consumption, and educational attainment, whereas the effect of LBD on colon cancer was only influenced by TC. Colocalization and pathway enrichment analysis suggested that LBD and colon cancer possibly shared causal variants (nearby gene APOE), and ERBB4 signaling and lipid metabolism may mediate the causal association between LBD and colon cancer. Sensitivity analysis confirmed the reliability of our findings.ConclusionsOur study demonstrated that genetic vulnerabilities to AD nominally increased the overall risk of CRC and colon cancer. Genetically predicted LBD indicated an elevated risk of colon cancer, potentially linked to ERBB4 signaling and lipid metabolism.

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Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases.

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Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases.

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