MicroRNAs as Clinical Biomarkers and Therapeutic Tools in Perioperative Medicine

Over the past decade, evolutionarily conserved, noncoding small RNAs-so-called microRNAs (miRNAs)-have emerged as important regulators of virtually all cellular processes. miRNAs influence gene expression by binding to the 3'-untranslated region of protein-coding RNA, leading to its degradation and translational repression. In medicine, miRNAs have been revealed as novel, highly promising biomarkers and as attractive tools and targets for novel therapeutic approaches. miRNAs are currently entering the field of perioperative medicine, and they may open up new perspectives in anesthesia, critical care, and pain medicine. In this review, we provide an overview of the biology of miRNAs and their potential role in human disease. We highlight current paradigms of miRNA-mediated effects in perioperative medicine and provide a survey of miRNA biomarkers in the field known so far. Finally, we provide a perspective on miRNA-based therapeutic opportunities and perspectives. (Anesth Analg 2018;126: 670-81

MiRNA Bioinformatik als Werkzeug medizinischer Forschung – Etablierung eines Datenbank- und Zielvorhersagesystems zur Bearbeitung von miRNA-bezogenen Fragestellungen

Mit Beginn der Ära der Hochdurchsatz-Sequenzierung und Transkriptions-messungen ist das Angebot an genetischer Information in den letzten Jahren exponentiell gestiegen. Die Entdeckung der miRNAs als regulative Elemente mit weitreichendem Einfluss hat dabei eine Schlüsselrolle in der Erforschung von diagnostischen Möglichkeiten, pathogenetischen Prozessen und therapeutischen Konzepten vieler Krankheiten eingenommen. Mit der stetig wachsenden Informationsvielfalt steigt allerdings auch die Komplexität der Informationsverarbeitung und -aufbereitung. In der vorliegenden Arbeit wurde daher eine miRNA Datenbank konzipiert und evaluiert, die verfügbare Informationen handhabbar macht und bei der Generierung von Hypothesen hilft. Um Aussagen über die biologische Bedeutung von miRNAs treffen zu können, werden miRNA-mRNA-Interaktions-Vorhersagealgorithmen benutzt und so mögliche Ziel-mRNAs identifiziert. Aufgrund der beschriebenen Limitationen (Kapitel 2) wurde in dieser Arbeit ein Konsensusverfahren zur Ziel-Vorhersage etabliert und validiert, das das Prediction Agreement als Maß der Konfidenz einer Interaktion nutzt. Exemplarisch wurde dieses Verfahren eingesetzt, um vier miRNAs im Kontext der Apoptose-Signalkaskade zu beleuchten. Die Gene von zwei dieser vier miRNAs befinden sich in Introns proteinkodierender Gene (Host-Gene). Mithilfe der erstellten Datenbank ließen sich Charakteristika von Host-Genen extrahieren, die denen der Ziel-Gene ähneln. Die Summe der Beobachtungen erlaubt die Spekulation, dass die bislang biologisch wenig charakterisierten Host-Gene potentiell in funktionellem Zusammenhang zu den Ziel-Genen der miRNAs stehen. Am Beispiel von bei Sepsis differentiell exprimierten miRNAs konnte in der vorliegenden Arbeit gezeigt werden, wie durch die Entwicklung einer bioinformatischen Datenbank schwer handhabbare Datenmengen und –strukturen genutzt werden können, um die Entwicklung klinisch relevanter Hypothesen zu leiten. Die Möglichkeiten eines solchen Systems sind allerdings nicht ausgeschöpft. Je nach Fragestellung können weitere Daten integriert (Informationen über Promotor-Bereiche, Sequenzen, Protein-Protein-Interaktionen, weitere miRNA-/mRNA-Expressionsmessungen) und direkt analysiert werden. Mit zunehmendem Fortschritt biologischer Forschung und Methodik wird auch die informationsverarbeitende Methodik einen immer größeren Stellenwert einnehmen und der Bedarf an Datenbanksystemen und Konzepten zur strukturierten Analyse und Eingrenzung der Informationsvielfalt wird stetig steigen

Silencing the host : the role of intronic microRNAs

Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 62-68).Fifteen years ago lin-4 was reported to be the first endogenous small non-coding, but interfering RNA structure involved in developmental timing in C. elegans. First thought not, or only rarely, to occur in mammals, microRNAs are now among the major players in up-to-date genomic research. The mature molecules are ~22 nucleotides in length and, by targeting predominantly the 3' UTR of mRNAs, lead to translational repression or degradation of the target message, hence controlling important cellular mechanisms, including division, differentiation and death. This key role makes them excellent targets for cancer research. In fact they have been shown to have a major impact on cancer development in many cases. However, miRNAs are not a homogeneous class and can be sub classified into intragenic and intergenic, depending on their genomic position. Whereas intergenic miRNAs are expected to be independent transcriptional units, intragenic miRNAs are commonly believed to be regulated through their host gene. Despite of the growing knowledge on how miRNAs integrate into cellular regulatory networks, our current knowledge about the specific role of intragenic miRNAs is rather limited. In this work we integrated current miRNA knowledge bases, ranging from miRNA sequence and genomic localization information to target prediction, with biochemical pathway information and publicly available expression data to investigate functional properties of intragenic miRNAs and their relationship to their host genes. To the best of our knowledge, we are the first to show in a large-scale analysis that intragenic miRNAs seem to act as negative feedback regulators on multiple levels. We furthermore investigated the impact of this model on the potential role of intronic miRNAs in cancer pathogenesis.by Ludwig Christian Giuseppe Hinske.S.M

Evaluation of a Large-Scale Biomedical Data Annotation Initiative

Background: This study describes a large-scale manual re-annotation of data samples in the Gene Expression Omnibus (GEO), using variables and values derived from the National Cancer Institute thesaurus. A framework is described for creating an annotation scheme for various diseases that is flexible, comprehensive, and scalable. The annotation structure is evaluated by measuring coverage and agreement between annotators. Results: There were 12,500 samples annotated with approximately 30 variables, in each of six disease categories – breast cancer, colon cancer, inflammatory bowel disease (IBD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), and Type 1 diabetes mellitus (DM). The annotators provided excellent variable coverage, with known values for over 98% of three critical variables: disease state, tissue, and sample type. There was 89% strict inter-annotator agreement and 92% agreement when using semantic and partial similarity measures. Conclusion: We show that it is possible to perform manual re-annotation of a large repository in a reliable manner

Clinical Knowledge Platform (CKP): a collaborative ecosystem to share interoperable clinical forms, viewers, and order sets with various EMRs

International audienceA large number of Electronic Medical Records (EMR) are currently available with a variety of features and architectures. Existing studies and frameworks presented some solutions to overcome the problem of specification and application of clinical guidelines toward the automation of their use at the point of care. However, they could not yet support thoroughly the dynamic use of medical knowledge in EMRs according to the clinical contexts and provide local application of international recommendations. This study presents the development of the Clinical Knowledge Platform (CKP): a collaborative interoperable environment to create, use, and share sets of information elements that we entitled Clinical Use Contexts (CUCs). A CUC could include medical forms, patient dashboards, and order sets that are usable in various EMRs. For this purpose, we have identified and developed three basic requirements: an interoperable, inter-mapped dictionary of concepts leaning on standard terminologies, the possibility to define relevant clinical contexts, and an interface for collaborative content production via communities of professionals. Community members work together to create and/or modify, CUCs based on different clinical contexts. These CUCs will then be uploaded to be used in clinical applications in various EMRs. With this method, each CUC is, on the one hand, specific to a clinical context and on the other hand, could be adapted to the local practice conditions and constraints. Once a CUC has been developed, it could be shared with other potential users that can consume it directly or modify it according to their needs