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Tidsskrift For Den Norske Laegeforening... Feb 2013
Topics: Communicable Diseases, Emerging; Disease; Norway; Terminology as Topic
PubMed: 23423212
DOI: 10.4045/tidsskr.13.0066 -
Human Molecular Genetics Oct 2015Genetic variants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), in the non-coding regions of the human genome can play an important role... (Review)
Review
Genetic variants, including single-nucleotide variants (SNVs) and copy number variants (CNVs), in the non-coding regions of the human genome can play an important role in human traits and complex diseases. Most of the genome-wide association study (GWAS) signals map to non-coding regions and potentially point to non-coding variants, whereas their functional interpretation is challenging. In this review, we discuss the human non-coding variants and their contributions to human diseases in the following four parts. (i) Functional annotations of non-coding SNPs mapped by GWAS: we discuss recent progress revealing some of the molecular mechanisms for GWAS signals affecting gene function. (ii) Technical progress in interpretation of non-coding variants: we briefly describe some of the technologies for functional annotations of non-coding variants, including the methods for genome-wide mapping of chromatin interaction, computational tools for functional predictions and the new genome editing technologies useful for dissecting potential functional consequences of non-coding variants. (iii) Non-coding CNVs in human diseases: we review our emerging understanding the role of non-coding CNVs in human disease. (iv) Compound inheritance of large genomic deletions and non-coding variants: compound inheritance at a locus consisting of coding variants plus non-coding ones is described.
Topics: Animals; DNA Copy Number Variations; Disease; Genetic Variation; Genome-Wide Association Study; Humans; Polymorphism, Single Nucleotide; Regulatory Sequences, Nucleic Acid
PubMed: 26152199
DOI: 10.1093/hmg/ddv259 -
Cell Death & Disease May 2021Circular RNAs (circRNAs) are a class of endogenous RNAs characterized by a covalent loop structure. In comparison to other types of RNAs, the abundance of circRNAs is... (Review)
Review
Circular RNAs (circRNAs) are a class of endogenous RNAs characterized by a covalent loop structure. In comparison to other types of RNAs, the abundance of circRNAs is relatively low but due to the circular configuration, their stability is very high. In addition, circRNAs display high degree of tissue specificity. The sponging activity of circRNAs toward microRNAs is the best-described mode of action of circRNAs. However, the ability of circRNAs to bind with specific proteins, as well as to encode short proteins, propose alternative functions. This review introduces the biogenesis of circRNAs and summarizes the roles played by circRNAs in human diseases. These include examples of their functional roles in several organ-specific cancers, such as head and neck and breast and lung cancers. In addition, we review potential functions of circRNAs in diabetes, cardiovascular, and neurodegenerative diseases. Recently, a growing number of studies have demonstrated involvement of circRNAs in a wide spectrum of signaling molecular pathways, but at the same time many different and controversial views on circRNAs role and function are emerging. We conclude by offering cellular homeostasis generated by networks comprising circular RNAs, other non-coding RNAs and RNA-binding proteins. Accordingly, it is predictable that circRNAs, due to their highly stable nature and remarkable tissue specificity, will emerge as reliable biomarkers of disease course and treatment efficacy.
Topics: Biomarkers, Tumor; Disease; Humans; RNA, Circular
PubMed: 33976116
DOI: 10.1038/s41419-021-03743-3 -
Ciencia & Saude Coletiva 2008Health or disease is shaped for all individuals by interactions between their genes and environment. Exactly how the environment changes gene expression and how this can... (Review)
Review
Health or disease is shaped for all individuals by interactions between their genes and environment. Exactly how the environment changes gene expression and how this can lead to disease are being explored in a fruitful new approach to environmental health research, representative studies of which are reviewed here. We searched Web of Science and references of relevant publications to understand the diversity of gene regulatory mechanisms affected by environmental exposures with disease implications. Pharmaceuticals, pesticides, air pollutants, industrial chemicals, heavy metals, hormones, nutrition, and behavior can change gene expression through a broad array of gene regulatory mechanisms. Furthermore, chemically induced changes in gene regulation are associated with serious and complex human diseases, including cancer, diabetes and obesity, infertility, respiratory diseases, allergies, and neurodegenerative disorders such as Parkinson and Alzheimer diseases. The reviewed studies indicate that genetic predisposition for disease is best predicted in the context of environmental exposures. And the genetic mechanisms investigated in these studies offer new avenues for risk assessment research. Finally, we are likely to witness dramatic improvements in human health, and reductions in medical costs, if environmental pollution is decreased.
Topics: DNA Methylation; Disease; Environmental Exposure; Gene Expression Regulation; Humans
PubMed: 18813540
DOI: 10.1590/s1413-81232008000100030 -
Journal of Environmental and Public... 2012Throughout the continuum of medical and scientific history, repeated evidence has confirmed that the main etiological determinants of disease are nutritional deficiency,... (Review)
Review
Throughout the continuum of medical and scientific history, repeated evidence has confirmed that the main etiological determinants of disease are nutritional deficiency, toxicant exposures, genetic predisposition, infectious agents, and psychological dysfunction. Contemporary conventional medicine generally operates within a genetic predestination paradigm, attributing most chronic and degenerative illness to genomic factors, while incorporating pathogens and psychological disorder in specific situations. Toxicity and deficiency states often receive insufficient attention as common source causes of chronic disease in the developed world. Recent scientific evidence in health disciplines including molecular medicine, epigenetics, and environmental health sciences, however, reveal ineluctable evidence that deficiency and toxicity states feature prominently as common etiological determinants of contemporary ill-health. Incorporating evidence from historical and emerging science, it is evident that a reevaluation of conventional wisdom on the current construct of disease origins should be considered and that new knowledge should receive expeditious translation into clinical strategies for disease management and health promotion. An analysis of almost any scientific problem leads automatically to a study of its history.--Ernst Mayr.
Topics: Chronic Disease; Deficiency Diseases; Disease; Disease Management; Genomics; Hazardous Substances; Health Promotion; History, 15th Century; History, 16th Century; History, 17th Century; History, 19th Century; History, 20th Century; History, 21st Century; History, Ancient; History, Medieval; Humans
PubMed: 22262979
DOI: 10.1155/2012/605137 -
Bioengineered Jun 2022miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of... (Review)
Review
miRNA is a small endogenous RNA and an important regulator of gene expression. miR-4443 is abnormally expressed in 12 diseases including cancer. The expression of miR-4443 is regulated by 3 upstream factors. miR-4443 has 12 downstream target genes. miR-4443 inhibits the expression of its target genes, thereby affecting the migration, proliferation, and invasion of pathological cells. miR-4443 participates in 4 signaling pathways and plays a role in the occurrence and development of several diseases. In addition, miR-4443 can also promote resistance to multiple drugs. Here, this article summarizes the aberrant expression of miR-4443 and its pathogenic molecular mechanisms in human diseases, which provides clues and directions for the follow-up research of miR-4443.
Topics: Humans; MicroRNAs; Disease; Neoplasms; Gene Expression Regulation
PubMed: 36250718
DOI: 10.1080/21655979.2022.2109807 -
Scientific Reports Jan 2017Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational...
Diseases are developed by abnormal behavior of genes in biological events such as gene regulation, mutation, phosphorylation, and epigenetics and post-translational modification. Many studies of text mining attempted to identify the relationship between gene and disease by mining the literature, but they did not consider the biological events in which genes show abnormal behaviour in response to diseases. In this study, we propose to identify disease-related genes that are involved in the development of disease through biological events from Medline abstracts. We identified associations between 13,054 genes and 4,494 disease types, which cover more disease-related genes than manually curated databases for all disease types (e.g., Online Mendelian Inheritance in Man) and also than those for specific diseases (e.g., Alzheimer's disease and hypertension). We show that the text mining findings are reliable, as per the PubMed scale, in that the disease-disease relationships inferred from the literature-wide findings are similar to those inferred from manually curated databases in a well-known study. In addition, literature-wide distribution of biological events across disease types reveals different characteristics of disease types.
Topics: Data Mining; Disease; Genetic Association Studies; Humans; MEDLINE
PubMed: 28054646
DOI: 10.1038/srep40154 -
Journal of Biomedical Semantics Jun 2017Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on...
BACKGROUND
Medical ontologies are expected to contribute to the effective use of medical information resources that store considerable amount of data. In this study, we focused on disease ontology because the complicated mechanisms of diseases are related to concepts across various medical domains. The authors developed a River Flow Model (RFM) of diseases, which captures diseases as the causal chains of abnormal states. It represents causes of diseases, disease progression, and downstream consequences of diseases, which is compliant with the intuition of medical experts. In this paper, we discuss a fact repository for causal chains of disease based on the disease ontology. It could be a valuable knowledge base for advanced medical information systems.
METHODS
We developed the fact repository for causal chains of diseases based on our disease ontology and abnormality ontology. This section summarizes these two ontologies. It is developed as linked data so that information scientists can access it using SPARQL queries through an Resource Description Framework (RDF) model for causal chain of diseases.
RESULTS
We designed the RDF model as an implementation of the RFM for the fact repository based on the ontological definitions of the RFM. 1554 diseases and 7080 abnormal states in six major clinical areas, which are extracted from the disease ontology, are published as linked data (RDF) with SPARQL endpoint (accessible API). Furthermore, the authors developed Disease Compass, a navigation system for disease knowledge. Disease Compass can browse the causal chains of a disease and obtain related information, including abnormal states, through two web services that provide general information from linked data, such as DBpedia, and 3D anatomical images.
CONCLUSIONS
Disease Compass can provide a complete picture of disease-associated processes in such a way that fits with a clinician's understanding of diseases. Therefore, it supports user exploration of disease knowledge with access to pertinent information from a variety of sources.
Topics: Biological Ontologies; Computer Graphics; Disease; Humans; Semantic Web
PubMed: 28629436
DOI: 10.1186/s13326-017-0132-2 -
Biological Research For Nursing Mar 2018For precision health care to be successful, an in-depth understanding of the biological mechanisms for symptom development and severity is essential. Omics-based... (Review)
Review
For precision health care to be successful, an in-depth understanding of the biological mechanisms for symptom development and severity is essential. Omics-based research approaches facilitate identification of the biological underpinnings of symptoms. We reviewed literature for omics-based approaches and exemplar symptoms (sleep disruption, cognitive impairment, fatigue, gastrointestinal [GI] distress, and pain) to identify genes associated with the symptom or symptoms across disease processes. The review yielded 27 genes associated with more than one symptom. ABCB1 (MDR1), APOE, BDNF, CNR1, COMT, DAT1 (SLC6A3), DRD4, ESR1, HLA-DRB1, IL10, IL1B, IL6, LTA, PTGS2 (COX-2), SLC6A4, and TNF were associated with cognitive impairment and pain, which had the most genes in common. COMT and TNF were related to all symptoms except sleep disruption. IL1B was associated with all symptoms except cognitive impairment. IL10, IL1A, IL1B, IL1RN, IL6, and IL8 (CXCL8) were linked with all the exemplar symptoms in various combinations. ABCB1 (MDR1) and SLC6A4 were associated with cognitive impairment, GI distress, and pain. IL10 and IL6 were linked to cognitive impairment, fatigue, and pain. APOE and BDNF were associated with sleep disruption, cognitive impairment, and pain. The 27 genes were associated with canonical pathways including immune, inflammatory, and cell signaling. The pathway analysis generated a 15-gene model from the 27 as well as 3 networks, which incorporated new candidate genes. The findings support the hypothesis of overlapping biological underpinnings across the exemplar symptoms. Candidate genes may be targeted in future omics research to identify mechanisms of co-occurring symptoms for potential precision treatments.
Topics: Biological Phenomena; Disease; Female; Humans; Syndrome; Virulence
PubMed: 29325450
DOI: 10.1177/1099800417751069 -
PLoS Computational Biology Jul 2021miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely...
miRNAs belong to small non-coding RNAs that are related to a number of complicated biological processes. Considerable studies have suggested that miRNAs are closely associated with many human diseases. In this study, we proposed a computational model based on Similarity Constrained Matrix Factorization for miRNA-Disease Association Prediction (SCMFMDA). In order to effectively combine different disease and miRNA similarity data, we applied similarity network fusion algorithm to obtain integrated disease similarity (composed of disease functional similarity, disease semantic similarity and disease Gaussian interaction profile kernel similarity) and integrated miRNA similarity (composed of miRNA functional similarity, miRNA sequence similarity and miRNA Gaussian interaction profile kernel similarity). In addition, the L2 regularization terms and similarity constraint terms were added to traditional Nonnegative Matrix Factorization algorithm to predict disease-related miRNAs. SCMFMDA achieved AUCs of 0.9675 and 0.9447 based on global Leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, the case studies on two common human diseases were also implemented to demonstrate the prediction accuracy of SCMFMDA. The out of top 50 predicted miRNAs confirmed by experimental reports that indicated SCMFMDA was effective for prediction of relationship between miRNAs and diseases.
Topics: Algorithms; Computational Biology; Disease; Humans; MicroRNAs; Models, Statistical
PubMed: 34252084
DOI: 10.1371/journal.pcbi.1009165