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Translational Research : the Journal of... Nov 2017Lipidomics is a rapidly developing field of study that focuses on the identification and quantitation of various lipid species in the lipidome. Lipidomics has now... (Review)
Review
Lipidomics is a rapidly developing field of study that focuses on the identification and quantitation of various lipid species in the lipidome. Lipidomics has now emerged in the forefront of scientific research due to the importance of lipids in metabolism, cancer, and disease. Using both targeted and untargeted mass spectrometry as a tool for analysis, progress in the field has rapidly progressed in the last decade. Having the ability to assess these small molecules in vivo has led to better understanding of several lipid-driven mechanisms and the identification of lipid-based biomarkers in neurodegenerative disease, cancer, sepsis, wound healing, and pre-eclampsia. Biomarker identification and mechanistic understanding of specific lipid pathways linked to a disease's pathologies can form the foundation in the development of novel therapeutics in hopes of curing human disease.
Topics: Biomarkers; Disease; Humans; Lipids; Mass Spectrometry; Metabolomics; Translational Research, Biomedical
PubMed: 28668521
DOI: 10.1016/j.trsl.2017.06.006 -
Autophagy Oct 2019Although best understood as a degradative pathway, recent evidence demonstrates pronounced involvement of the macroautophagic/autophagic molecular machinery in cellular... (Review)
Review
Although best understood as a degradative pathway, recent evidence demonstrates pronounced involvement of the macroautophagic/autophagic molecular machinery in cellular secretion. With either overexpression or inhibition of autophagy mediators, dramatic alterations in the cellular secretory profile occur. This affects secretion of a plethora of factors ranging from cytokines, to granule contents, and even viral particles. Encompassing a wide range of secreted factors, autophagy-dependent secretion is implicated in diseases ranging from cancer to neurodegeneration. With a growing body of evidence shedding light onto the molecular mediators, this review delineates the molecular machinery involved in selective targeting of the autophagosome for either degradation or secretion. In addition, we summarize the current understanding of factors and cargo secreted through this unconventional route, and describe the implications of this pathway in both health and disease. : BECN1, beclin 1; CAF, cancer associated fibroblast; CUPS, compartment for unconventional protein secretion; CXCL, C-X-C motif chemokine ligand; ER, endoplasmic reticulum; FGF2, fibroblast growth factor 2; HMGB1, high mobility group box 1; IDE, insulin degrading enzyme; IL, Interleukin; MAP1LC3/LC3, microtubule associated protein 1 light chain 3; MAPS, misfolding associated protein secretion; MEF, mouse embryonic fibroblast; MTORC1, MTOR complex I; PtdIns, phosphatidyl inositol; SEC22B, SEC22 homolog B, vesicle trafficking protein (gene/pseudogene); SFV, Semliki forest virus; SNCA, synuclein alpha; SQSTM1, sequestosome 1; STX, Syntaxin; TASCC, TOR-associated spatial coupling compartment; TGFB, transforming growth factor beta; TRIM16, tripartite motif containing 16; UPS, unconventional protein secretion; VWF, von Willebrand factor.
Topics: Animals; Autophagy; Biological Transport; Disease; Humans; Mice; Proteins; Secretory Pathway; Secretory Vesicles
PubMed: 30894055
DOI: 10.1080/15548627.2019.1596479 -
Nature Communications Jan 2022Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum...
Circulating proteins can be used to diagnose and predict disease-related outcomes. A deep serum proteome survey recently revealed close associations between serum protein networks and common disease. In the current study, 54,469 low-frequency and common exome-array variants were compared to 4782 protein measurements in the serum of 5343 individuals from the AGES Reykjavik cohort. This analysis identifies a large number of serum proteins with genetic signatures overlapping those of many diseases. More specifically, using a study-wide significance threshold, we find that 2021 independent exome array variants are associated with serum levels of 1942 proteins. These variants reside in genetic loci shared by hundreds of complex disease traits, highlighting serum proteins' emerging role as biomarkers and potential causative agents of a wide range of diseases.
Topics: Aged; Blood Proteins; Disease; Exome; Female; Genetic Predisposition to Disease; Genotype; Humans; Iceland; Male; Polymorphism, Single Nucleotide; Proteome
PubMed: 35079000
DOI: 10.1038/s41467-022-28081-6 -
Clinical & Translational Oncology :... Jun 2021Exosomes, the nanoscale phospholipid bilayer vesicles, enriched in selected proteins, nucleic acids and lipids, which they participated in a variety of biological... (Review)
Review
Exosomes, the nanoscale phospholipid bilayer vesicles, enriched in selected proteins, nucleic acids and lipids, which they participated in a variety of biological processes in the body, including physiology and pathology. CircRNAs (circular RNAs) are a class of single-stranded closed molecules with tissue development specific expression patterns that have crucial regulatory functions in various diseases. Non-coding RNAs (such as microRNAs and long non‑coding RNAs) in exosomes have also been shown to play an important regulatory role in humans. However, little research has focused on exosomal circRNAs. Recently, CircRNAs have been identified to be enriched and stably expressed in exosomes. In this review, we summarize the biogenesis and biological functions of exosomes and circRNA, and further revealed the potential role of exosome-derived circRNA in different diseases. Besides, we propose its use as a diagnostic marker and therapeutic punctuation for diseases, especially in cancer.
Topics: Disease; Exosomes; Humans; Neoplasms; RNA, Circular
PubMed: 32935262
DOI: 10.1007/s12094-020-02485-6 -
Nefrologia 2022
Topics: Genetic Variation; Humans; Disease
PubMed: 36925326
DOI: 10.1016/j.nefroe.2023.02.002 -
Biomedicine & Pharmacotherapy =... Jul 2021Long non-coding RNAs (lncRNAs) represent a group of ncRNAs with more than 200 nucleotides. These RNAs can specifically regulate gene expression at both the... (Review)
Review
Long non-coding RNAs (lncRNAs) represent a group of ncRNAs with more than 200 nucleotides. These RNAs can specifically regulate gene expression at both the transcriptional and the post-transcriptional levels, and increasing evidence indicates that they play vital roles in a variety of disease-related cellular processes. The lncRNA GAS8 antisense RNA 1 (GAS8-AS1, also known as C16orf3) is located in the second intron of GAS8 and has been reported to be both abnormally expressed in several diseases and closely correlated with many clinical characteristics. GAS8-AS1 has been shown to affect many biological functions, including cell proliferation, migration, invasiveness, and autophagy using several signaling pathways. In this review, we have summarized current studies on GAS8-AS1 roles in disease and discuss its potential clinical utility. GAS8-AS1 may be a promising biomarker for both diagnoses and prognoses, and a novel target for many disease therapies.
Topics: Animals; Biomarkers; Diagnosis; Disease; Humans; Prognosis; RNA, Long Noncoding
PubMed: 33838502
DOI: 10.1016/j.biopha.2021.111572 -
Trends in Genetics : TIG Dec 2020Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in... (Review)
Review
Single-cell multimodal omics (scMulti-omics) technologies have made it possible to trace cellular lineages during differentiation and to identify new cell types in heterogeneous cell populations. The derived information is especially promising for computing cell-type-specific biological networks encoded in complex diseases and improving our understanding of the underlying gene regulatory mechanisms. The integration of these networks could, therefore, give rise to a heterogeneous regulatory landscape (HRL) in support of disease diagnosis and drug therapeutics. In this review, we provide an overview of this field and pay particular attention to how diverse biological networks can be inferred in a specific cell type based on integrative methods. Then, we discuss how HRL can advance our understanding of regulatory mechanisms underlying complex diseases and aid in the prediction of prognosis and therapeutic responses. Finally, we outline challenges and future trends that will be central to bringing the field of HRL in complex diseases forward.
Topics: Animals; Computational Biology; Disease; Gene Regulatory Networks; Humans; Single-Cell Analysis
PubMed: 32868128
DOI: 10.1016/j.tig.2020.08.004 -
Nucleic Acids Research Jan 2019The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification...
The Human Disease Ontology (DO) (http://www.disease-ontology.org), database has undergone significant expansion in the past three years. The DO disease classification includes specific formal semantic rules to express meaningful disease models and has expanded from a single asserted classification to include multiple-inferred mechanistic disease classifications, thus providing novel perspectives on related diseases. Expansion of disease terms, alternative anatomy, cell type and genetic disease classifications and workflow automation highlight the updates for the DO since 2015. The enhanced breadth and depth of the DO's knowledgebase has expanded the DO's utility for exploring the multi-etiology of human disease, thus improving the capture and communication of health-related data across biomedical databases, bioinformatics tools, genomic and cancer resources and demonstrated by a 6.6× growth in DO's user community since 2015. The DO's continual integration of human disease knowledge, evidenced by the more than 200 SVN/GitHub releases/revisions, since previously reported in our DO 2015 NAR paper, includes the addition of 2650 new disease terms, a 30% increase of textual definitions, and an expanding suite of disease classification hierarchies constructed through defined logical axioms.
Topics: Biological Ontologies; Databases, Factual; Disease; Humans; Workflow
PubMed: 30407550
DOI: 10.1093/nar/gky1032 -
BMC Systems Biology Jul 2015Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by...
BACKGROUND
Understanding the underlying molecular mechanisms in human diseases is important for diagnosis and treatment of complex conditions and has traditionally been done by establishing associations between disorder-genes and their associated diseases. This kind of network analysis usually includes only the interaction of molecular components and shared genes. The present study offers a network and association analysis under a bioinformatics frame involving the integration of HUGO Gene Nomenclature Committee approved gene symbols, KEGG metabolic pathways and ICD-10-CM codes for the analysis of human diseases based on the level of inclusion and hypergeometric enrichment between genes and metabolic pathways shared by the different human disorders.
METHODS
The present study offers the integration of HGNC approved gene symbols, KEGG metabolic pathways andICD-10-CM codes for the analysis of associations based on the level of inclusion and hypergeometricenrichment between genes and metabolic pathways shared by different diseases.
RESULTS
880 unique ICD-10-CM codes were mapped to the 4315 OMIM phenotypes and 3083 genes with phenotype-causing mutation. From this, a total of 705 ICD-10-CM codes were linked to 1587 genes with phenotype-causing mutations and 801 KEGG pathways creating a tripartite network composed by 15,455 code-gene-pathway interactions. These associations were further used for an inclusion analysis between diseases along with gene-disease predictions based on a hypergeometric enrichment methodology.
CONCLUSIONS
The results demonstrate that even though a large number of genes and metabolic pathways are shared between diseases of the same categories, inclusion levels between these genes and pathways are directional and independent of the disease classification. However, the gene-disease-pathway associations can be used for prediction of new gene-disease interactions that will be useful in drug discovery and therapeutic applications.
Topics: Computational Biology; Disease; Humans; Metabolic Networks and Pathways
PubMed: 26168918
DOI: 10.1186/s12918-015-0184-9 -
PloS One Feb 2011An analysis of NIH funding in 1996 found that the strongest predictor of funding, disability-adjusted life-years (DALYs), explained only 39% of the variance in funding....
BACKGROUND
An analysis of NIH funding in 1996 found that the strongest predictor of funding, disability-adjusted life-years (DALYs), explained only 39% of the variance in funding. In 1998, Congress requested that the Institute of Medicine (IOM) evaluate priority-setting criteria for NIH funding; the IOM recommended greater consideration of disease burden. We examined whether the association between current burden and funding has changed since that time.
METHODS
We analyzed public data on 2006 NIH funding for 29 common conditions. Measures of US disease burden in 2004 were obtained from the World Health Organization's Global Burden of Disease study and national databases. We assessed the relationship between disease burden and NIH funding dollars in univariate and multivariable log-linear models that evaluated all measures of disease burden. Sensitivity analyses examined associations with future US burden, current and future measures of world disease burden, and a newly standardized NIH accounting method.
RESULTS
In univariate and multivariable analyses, disease-specific NIH funding levels increased with burden of disease measured in DALYs (p = 0.001), which accounted for 33% of funding level variation. No other factor predicted funding in multivariable models. Conditions receiving the most funding greater than expected based on disease burden were AIDS ($2474 M), diabetes mellitus ($390 M), and perinatal conditions ($297 M). Depression ($719 M), injuries ($691 M), and chronic obstructive pulmonary disease ($613 M) were the most underfunded. Results were similar using estimates of future US burden, current and future world disease burden, and alternate NIH accounting methods.
CONCLUSIONS
Current levels of NIH disease-specific research funding correlate modestly with US disease burden, and correlation has not improved in the last decade.
Topics: Cost of Illness; Cross-Sectional Studies; Disease; Epidemiology; Female; Humans; International Classification of Diseases; Male; National Institutes of Health (U.S.); Quality-Adjusted Life Years; Research; Research Support as Topic; United States
PubMed: 21383981
DOI: 10.1371/journal.pone.0016837