Dataset Information

MiR-33 in cardiometabolic diseases: lessons learned from novel animal models and approaches.

ABSTRACT: miRNAs have emerged as critical regulators of nearly all biologic processes and important therapeutic targets for numerous diseases. However, despite the tremendous progress that has been made in this field, many misconceptions remain among much of the broader scientific community about the manner in which miRNAs function. In this review, we focus on miR-33, one of the most extensively studied miRNAs, as an example, to highlight many of the advances that have been made in the miRNA field and the hurdles that must be cleared to promote the development of miRNA-based therapies. We discuss how the generation of novel animal models and newly developed experimental techniques helped to elucidate the specialized roles of miR-33 within different tissues and begin to define the specific mechanisms by which miR-33 contributes to cardiometabolic diseases including obesity and atherosclerosis. This review will summarize what is known about miR-33 and highlight common obstacles in the miRNA field and then describe recent advances and approaches that have allowed researchers to provide a more complete picture of the specific functions of this miRNA.

SUBMITTER: Price NL

PROVIDER: S-EPMC8103095 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature

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miR-33 in cardiometabolic diseases: lessons learned from novel animal models and approaches.

Price Nathan L NL Goedeke Leigh L Suárez Yajaira Y Fernández-Hernando Carlos C

EMBO molecular medicine 20210503 5

miRNAs have emerged as critical regulators of nearly all biologic processes and important therapeutic targets for numerous diseases. However, despite the tremendous progress that has been made in this field, many misconceptions remain among much of the broader scientific community about the manner in which miRNAs function. In this review, we focus on miR-33, one of the most extensively studied miRNAs, as an example, to highlight many of the advances that have been made in the miRNA field and the ...[more]

PMID: 33938628

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Project description:BackgroundGiven the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases.MethodsIn an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM.ResultsWe have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs.DiscussionThis work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM.ConclusionThe customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

Dataset Information

MiR-33 in cardiometabolic diseases: lessons learned from novel animal models and approaches.

Publications

miR-33 in cardiometabolic diseases: lessons learned from novel animal models and approaches.

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