Your browser doesn't support javascript.
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 81
Filtrar
Mais filtros










Base de dados
Intervalo de ano de publicação
1.
Curr Opin Neurol ; 33(2): 249-254, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32073441

RESUMO

PURPOSE OF REVIEW: With the advancement of computational approaches and abundance of biomedical data, a broad range of neurodegenerative disease models have been developed. In this review, we argue that computational models can be both relevant and useful in neurodegenerative disease research and although the current established models have limitations in clinical practice, artificial intelligence has the potential to overcome deficiencies encountered by these models, which in turn can improve our understanding of disease. RECENT FINDINGS: In recent years, diverse computational approaches have been used to shed light on different aspects of neurodegenerative disease models. For example, linear and nonlinear mixed models, self-modeling regression, differential equation models, and event-based models have been applied to provide a better understanding of disease progression patterns and biomarker trajectories. Additionally, the Cox-regression technique, Bayesian network models, and deep-learning-based approaches have been used to predict the probability of future incidence of disease, whereas nonnegative matrix factorization, nonhierarchical cluster analysis, hierarchical agglomerative clustering, and deep-learning-based approaches have been employed to stratify patients based on their disease subtypes. Furthermore, the interpretation of neurodegenerative disease data is possible through knowledge-based models which use prior knowledge to complement data-driven analyses. These knowledge-based models can include pathway-centric approaches to establish pathways perturbed in a given condition, as well as disease-specific knowledge maps, which elucidate the mechanisms involved in a given disease. Collectively, these established models have revealed high granular details and insights into neurodegenerative disease models. SUMMARY: In conjunction with increasingly advanced computational approaches, a wide spectrum of neurodegenerative disease models, which can be broadly categorized into data-driven and knowledge-driven, have been developed. We review the state of the art data and knowledge-driven models and discuss the necessary steps which are vital to bring them into clinical application.

2.
Front Genet ; 10: 1203, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31824580

RESUMO

Pathway-centric approaches are widely used to interpret and contextualize -omics data. However, databases contain different representations of the same biological pathway, which may lead to different results of statistical enrichment analysis and predictive models in the context of precision medicine. We have performed an in-depth benchmarking of the impact of pathway database choice on statistical enrichment analysis and predictive modeling. We analyzed five cancer datasets using three major pathway databases and developed an approach to merge several databases into a single integrative one: MPath. Our results show that equivalent pathways from different databases yield disparate results in statistical enrichment analysis. Moreover, we observed a significant dataset-dependent impact on the performance of machine learning models on different prediction tasks. In some cases, MPath significantly improved prediction performance and also reduced the variance of prediction performances. Furthermore, MPath yielded more consistent and biologically plausible results in statistical enrichment analyses. In summary, this benchmarking study demonstrates that pathway database choice can influence the results of statistical enrichment analysis and predictive modeling. Therefore, we recommend the use of multiple pathway databases or integrative ones.

3.
Gigascience ; 8(11)2019 11 01.
Artigo em Inglês | MEDLINE | ID: mdl-31730697

RESUMO

BACKGROUND: Precision medicine requires a stratification of patients by disease presentation that is sufficiently informative to allow for selecting treatments on a per-patient basis. For many diseases, such as neurological disorders, this stratification problem translates into a complex problem of clustering multivariate and relatively short time series because (i) these diseases are multifactorial and not well described by single clinical outcome variables and (ii) disease progression needs to be monitored over time. Additionally, clinical data often additionally are hindered by the presence of many missing values, further complicating any clustering attempts. FINDINGS: The problem of clustering multivariate short time series with many missing values is generally not well addressed in the literature. In this work, we propose a deep learning-based method to address this issue, variational deep embedding with recurrence (VaDER). VaDER relies on a Gaussian mixture variational autoencoder framework, which is further extended to (i) model multivariate time series and (ii) directly deal with missing values. We validated VaDER by accurately recovering clusters from simulated and benchmark data with known ground truth clustering, while varying the degree of missingness. We then used VaDER to successfully stratify patients with Alzheimer disease and patients with Parkinson disease into subgroups characterized by clinically divergent disease progression profiles. Additional analyses demonstrated that these clinical differences reflected known underlying aspects of Alzheimer disease and Parkinson disease. CONCLUSIONS: We believe our results show that VaDER can be of great value for future efforts in patient stratification, and multivariate time-series clustering in general.

4.
Sci Rep ; 9(1): 16543, 2019 Nov 12.
Artigo em Inglês | MEDLINE | ID: mdl-31719623

RESUMO

Scientific evidence suggests that α-synuclein and tau have prion-like properties and that prion-like spreading and seeding of misfolded protein aggregates constitutes a central mechanism for neurodegeneration. Heparan sulfate proteoglycans (HSPGs) in the plasma membrane support this process by attaching misfolded protein fibrils. Despite of intense studies, contribution of specific HSPGs to seeding and spreading of α-synuclein and tau has not been explored yet. Here we report that members of the syndecan family of HSPGs mediate cellular uptake of α-synuclein and tau fibrils via a lipid-raft dependent and clathrin-independent endocytic route. Among syndecans, the neuron predominant syndecan-3 exhibits the highest affinity for both α-synuclein and tau. Syndecan-mediated internalization of α-synuclein and tau depends heavily on conformation as uptake via syndecans start to dominate once fibrils are formed. Overexpression of syndecans, on the other hand, reduces cellular uptake of monomeric α-synuclein and tau, yet exerts a fibril forming effect on both proteins. Data obtained from syndecan overexpressing cellular models presents syndecans, especially the neuron predominant syndecan-3, as important mediators of seeding and spreading of α-synuclein and tau and reveal how syndecans contribute to fundamental molecular events of α-synuclein and tau pathology.

5.
BMC Bioinformatics ; 20(1): 494, 2019 Oct 11.
Artigo em Inglês | MEDLINE | ID: mdl-31604427

RESUMO

BACKGROUND: Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. RESULTS: Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. CONCLUSIONS: This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.


Assuntos
Algoritmos , Encéfalo/metabolismo , Doenças Neurodegenerativas/metabolismo , Biologia de Sistemas/métodos , Doença de Alzheimer/genética , Doença de Alzheimer/metabolismo , Regulação da Expressão Gênica , Humanos , Redes e Vias Metabólicas , Mitocôndrias/metabolismo , Mitocôndrias/fisiologia , Doenças Neurodegenerativas/genética , Doenças Neurodegenerativas/fisiopatologia , Doença de Parkinson/genética , Doença de Parkinson/metabolismo , Doença de Parkinson/fisiopatologia , Mapas de Interação de Proteínas , Transdução de Sinais
6.
Database (Oxford) ; 20192019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-31260040

RESUMO

The PTSD Biomarker Database (PTSDDB) is a database that provides a landscape view of physiological markers being studied as putative biomarkers in the current post-traumatic stress disorder (PTSD) literature to enable researchers to explore and compare findings quickly. The PTSDDB currently contains over 900 biomarkers and their relevant information from 109 original articles published from 1997 to 2017. Further, the curated content stored in this database is complemented by a web application consisting of multiple interactive visualizations that enable the investigation of biomarker knowledge in PTSD (e.g. clinical study metadata, biomarker findings, experimental methods, etc.) by compiling results from biomarker studies to visualize the level of evidence for single biomarkers and across functional categories. This resource is the first attempt, to the best of our knowledge, to capture and organize biomarker and metadata in the area of PTSD for storage in a comprehensive database that may, in turn, facilitate future analysis and research in the field.


Assuntos
Bases de Dados Factuais , Metadados , Transtornos de Estresse Pós-Traumáticos , Biomarcadores , Humanos
7.
Database (Oxford) ; 20192019 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-31225582

RESUMO

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.


Assuntos
Mineração de Dados , MEDLINE , Reconhecimento Automatizado de Padrão , Semântica
8.
BMC Bioinformatics ; 20(1): 243, 2019 May 15.
Artigo em Inglês | MEDLINE | ID: mdl-31092193

RESUMO

BACKGROUND: The complexity of representing biological systems is compounded by an ever-expanding body of knowledge emerging from multi-omics experiments. A number of pathway databases have facilitated pathway-centric approaches that assist in the interpretation of molecular signatures yielded by these experiments. However, the lack of interoperability between pathway databases has hindered the ability to harmonize these resources and to exploit their consolidated knowledge. Such a unification of pathway knowledge is imperative in enhancing the comprehension and modeling of biological abstractions. RESULTS: Here, we present PathMe, a Python package that transforms pathway knowledge from three major pathway databases into a unified abstraction using Biological Expression Language as the pivotal, integrative schema. PathMe is complemented by a novel web application (freely available at https://pathme.scai.fraunhofer.de/ ) which allows users to comprehensively explore pathway crosstalk and compare areas of consensus and discrepancies. CONCLUSIONS: This work has harmonized three major pathway databases and transformed them into a unified schema in order to gain a holistic picture of pathway knowledge. We demonstrate the utility of the PathMe framework in: i) integrating pathway landscapes at the database level, ii) comparing the degree of consensus at the pathway level, and iii) exploring pathway crosstalk and investigating consensus at the molecular level.


Assuntos
Transdução de Sinais , Software , Biologia Computacional , Bases de Dados como Assunto , Bases de Dados Factuais , Humanos , Serina-Treonina Quinases TOR/metabolismo
10.
Sci Rep ; 9(1): 1393, 2019 Feb 04.
Artigo em Inglês | MEDLINE | ID: mdl-30718543

RESUMO

Intraneuronal accumulation of amyloid-ß(1-42) (Aß1-42) is one of the earliest signs of Alzheimer's disease (AD). Cell surface heparan sulfate proteoglycans (HSPGs) have profound influence on the cellular uptake of Aß1-42 by mediating its attachment and subsequent internalization into the cells. Colocalization of amyloid plaques with members of the syndecan family of HSPGs, along with the increased expression of syndecan-3 and -4 have already been reported in postmortem AD brains. Considering the growing evidence on the involvement of syndecans in the pathogenesis of AD, we analyzed the contribution of syndecans to cellular uptake and fibrillation of Aß1-42. Among syndecans, the neuron specific syndecan-3 isoform increased cellular uptake of Aß1-42 the most. Kinetics of Aß1-42 uptake also proved to be fairly different among SDC family members: syndecan-3 increased Aß1-42 uptake from the earliest time points, while other syndecans facilitated Aß1-42 internalization at a slower pace. Internalized Aß1-42 colocalized with syndecans and flotillins, highlighting the role of lipid-rafts in syndecan-mediated uptake. Syndecan-3 and 4 also triggered fibrillation of Aß1-42, further emphasizing the pathophysiological relevance of syndecans in plaque formation. Overall our data highlight syndecans, especially the neuron-specific syndecan-3 isoform, as important players in amyloid pathology and show that syndecans, regardless of cell type, facilitate key molecular events in neurodegeneration.

11.
NPJ Syst Biol Appl ; 5: 3, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30564458

RESUMO

Although pathways are widely used for the analysis and representation of biological systems, their lack of clear boundaries, their dispersion across numerous databases, and the lack of interoperability impedes the evaluation of the coverage, agreements, and discrepancies between them. Here, we present ComPath, an ecosystem that supports curation of pathway mappings between databases and fosters the exploration of pathway knowledge through several novel visualizations. We have curated mappings between three of the major pathway databases and present a case study focusing on Parkinson's disease that illustrates how ComPath can generate new biological insights by identifying pathway modules, clusters, and cross-talks with these mappings. The ComPath source code and resources are available at https://github.com/ComPath and the web application can be accessed at https://compath.scai.fraunhofer.de/.


Assuntos
Bases de Dados Factuais , Genômica/métodos , Metabolômica/métodos , Software , Mineração de Dados/métodos , Humanos , Doença de Parkinson/genética , Doença de Parkinson/metabolismo
12.
Database (Oxford) ; 20182018 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-30576488

RESUMO

The rapid accumulation of knowledge in the field of systems and networks biology during recent years requires complex, but user-friendly and accessible web applications that allow from visualization to complex algorithmic analysis. While several web applications exist with various focuses on creation, revision, curation, storage, integration, collaboration, exploration, visualization and analysis, many of these services remain disjoint and have yet to be packaged into a cohesive environment.Here, we present BEL Commons: an integrative knowledge discovery environment for networks encoded in the Biological Expression Language (BEL). Users can upload files in BEL to be parsed, validated, compiled and stored with fine granular permissions. After, users can summarize, explore and optionally shared their networks with the scientific community. We have implemented a query builder wizard to help users find the relevant portions of increasingly large and complex networks and a visualization interface that allows them to explore their resulting networks. Finally, we have included a dedicated analytical service for performing data-driven analysis of knowledge networks to support hypothesis generation.


Assuntos
Biologia Computacional/métodos , Bases de Dados Genéticas , Software , Algoritmos , Animais , Redes Reguladoras de Genes , Internet , Redes e Vias Metabólicas , Transdução de Sinais
13.
ACS Omega ; 3(10): 12330-12340, 2018 Oct 31.
Artigo em Inglês | MEDLINE | ID: mdl-30411002

RESUMO

The study of protein conformations using molecular dynamics (MD) simulations has been in place for decades. A major contribution to the structural stability and native conformation of a protein is made by the primary sequence and disulfide bonds formed during the folding process. Here, we investigated µ-conotoxins GIIIA, KIIIA, PIIIA, SIIIA, and SmIIIA as model peptides possessing three disulfide bonds. Their NMR structures were used for MD simulations in a novel approach studying the conformations between the folded and the unfolded states by systematically breaking the distinct disulfide bonds and monitoring the conformational stability of the peptides. As an outcome, the use of a combination of the existing knowledge and results from the simulations to classify the studied peptides within the extreme models of disulfide folding pathways, namely the bovine pancreatic trypsin inhibitor pathway and the hirudin pathway, is demonstrated. Recommendations for the design and synthesis of cysteine-rich peptides with a reduced number of disulfide bonds conclude the study.

14.
Sci Rep ; 8(1): 11173, 2018 07 24.
Artigo em Inglês | MEDLINE | ID: mdl-30042519

RESUMO

Alzheimer's Disease (AD) is among the most frequent neuro-degenerative diseases. Early diagnosis is essential for successful disease management and chance to attenuate symptoms by disease modifying drugs. In the past, a number of cerebrospinal fluid (CSF), plasma and neuro-imaging based biomarkers have been proposed. Still, in current clinical practice, AD diagnosis cannot be made until the patient shows clear signs of cognitive decline, which can partially be attributed to the multi-factorial nature of AD. In this work, we integrated genotype information, neuro-imaging as well as clinical data (including neuro-psychological measures) from ~900 normal and mild cognitively impaired (MCI) individuals and developed a highly accurate machine learning model to predict the time until AD is diagnosed. We performed an in-depth investigation of the relevant baseline characteristics that contributed to the AD risk prediction. More specifically, we used Bayesian Networks to uncover the interplay across biological scales between neuro-psychological assessment scores, single genetic variants, pathways and neuro-imaging related features. Together with information extracted from the literature, this allowed us to partially reconstruct biological mechanisms that could play a role in the conversion of normal/MCI into AD pathology. This in turn may open the door to novel therapeutic options in the future.


Assuntos
Doença de Alzheimer/diagnóstico , Disfunção Cognitiva/diagnóstico por imagem , Disfunção Cognitiva/genética , Modelos Teóricos , Neuroimagem/métodos , Junções Aderentes/fisiologia , Idoso , Doença de Alzheimer/etiologia , Autofagia/fisiologia , Teorema de Bayes , Barreira Hematoencefálica/fisiopatologia , Encéfalo/fisiopatologia , Disfunção Cognitiva/complicações , Disfunção Cognitiva/patologia , Bases de Dados Genéticas , Diagnóstico Precoce , Humanos , Insulina/metabolismo , Estimativa de Kaplan-Meier , Células Matadoras Naturais/metabolismo , Aprendizado de Máquina , Polimorfismo de Nucleotídeo Único , Prognóstico , Modelos de Riscos Proporcionais , Risco
15.
Database (Oxford) ; 20182018 01 01.
Artigo em Inglês | MEDLINE | ID: mdl-29873705

RESUMO

Cross-sectional epidemiological studies have shown that the incidence of several nervous system diseases is more frequent in epilepsy patients than in the general population. Some comorbidities [e.g. Alzheimer's disease (AD) and Parkinson's disease] are also risk factors for the development of seizures; suggesting they may share pathophysiological mechanisms with epilepsy. A literature-based approach was used to identify gene overlap between epilepsy and its comorbidities as a proxy for a shared genetic basis for disease, or genetic pleiotropy, as a first effort to identify shared mechanisms. While the results identified neurological disorders as the group of diseases with the highest gene overlap, this analysis was insufficient for identifying putative common mechanisms shared across epilepsy and its comorbidities. This motivated the use of a dedicated literature mining and knowledge assembly approach in which a cause-and-effect model of epilepsy was captured with Biological Expression Language. After enriching the knowledge assembly with information surrounding epilepsy, its risk factors, its comorbidities, and anti-epileptic drugs, a novel comparative mechanism enrichment approach was used to propose several downstream effectors (including the GABA receptor, GABAergic pathways, etc.) that could explain the therapeutic effects carbamazepine in both the contexts of epilepsy and AD. We have made the Epilepsy Knowledge Assembly available at https://www.scai.fraunhofer.de/content/dam/scai/de/downloads/bioinformatik/epilepsy.bel and queryable through NeuroMMSig at http://neurommsig.scai.fraunhofer.de. The source code used for analysis and tutorials for reproduction are available on GitHub at https://github.com/cthoyt/epicom.


Assuntos
Doença de Alzheimer , Bases de Dados Genéticas , Epilepsia , Doença de Parkinson , Doença de Alzheimer/tratamento farmacológico , Doença de Alzheimer/epidemiologia , Doença de Alzheimer/genética , Doença de Alzheimer/metabolismo , Carbamazepina/uso terapêutico , Comorbidade , Mineração de Dados , Bases de Dados Bibliográficas , Epilepsia/tratamento farmacológico , Epilepsia/epidemiologia , Epilepsia/genética , Epilepsia/metabolismo , Humanos , Doença de Parkinson/tratamento farmacológico , Doença de Parkinson/epidemiologia , Doença de Parkinson/genética , Doença de Parkinson/metabolismo
16.
Bioinformatics ; 34(13): 2316-2318, 2018 07 01.
Artigo em Inglês | MEDLINE | ID: mdl-29949955

RESUMO

Summary: While cause-and-effect knowledge assembly models encoded in Biological Expression Language are able to support generation of mechanistic hypotheses, they are static and limited in their ability to encode temporality. Here, we present BEL2ABM, a software for producing continuous, dynamic, executable agent-based models from BEL templates. Availability and implementation: The tool has been developed in Java and NetLogo. Code, data and documentation are available under the Apache 2.0 License at https://github.com/pybel/bel2abm. Supplementary information: Supplementary data are available at Bioinformatics online.


Assuntos
Linguagem , Software , Documentação , Humanos , Modelos Biológicos
17.
Amino Acids ; 50(3-4): 383-395, 2018 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-29249020

RESUMO

Breast cancer epidemic in the early twenty-first century results in around two million new cases and half-a-million of the disease-related deaths registered annually worldwide. A particularly dramatic situation is attributed to some specific patient subgroups such as the triple-negative breast cancer (TNBC). TNBC is a particularly aggressive type of breast cancer lacking clear diagnostic approach and targeted therapies. Consequently, more than 50% of the TNBC patients die of the metastatic BC within the first 6 months of the diagnosis. In the current study we have hypothesised that multi-omic approach utilising blood samples may lead to discovery of a unique molecular signature of the TNBC subtype. The results achieved demonstrate, indeed, multi-omics as highly promising approach that could be of great clinical utility for development of predictive diagnosis, targeted prevention and treatments tailored to the person-overall advancing the management of the TNBC.


Assuntos
Biomarcadores Tumorais/genética , Proteínas de Neoplasias/genética , Neoplasias/genética , Neoplasias de Mama Triplo Negativas/genética , Adulto , Biomarcadores Tumorais/sangue , Eletroforese em Gel Bidimensional , Feminino , Humanos , Pessoa de Meia-Idade , Metástase Neoplásica , Neoplasias/sangue , Neoplasias/patologia , Medicina de Precisão , Proteômica , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz , Neoplasias de Mama Triplo Negativas/sangue , Neoplasias de Mama Triplo Negativas/classificação , Neoplasias de Mama Triplo Negativas/patologia
19.
J Alzheimers Dis ; 60(2): 721-731, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-28922161

RESUMO

BACKGROUND: Various studies suggest a comorbid association between Alzheimer's disease (AD) and type 2 diabetes mellitus (T2DM) indicating that there could be shared underlying pathophysiological mechanisms. OBJECTIVE: This study aims to systematically model relevant knowledge at the molecular level to find a mechanistic rationale explaining the existing comorbid association between AD and T2DM. METHOD: We have used a knowledge-based modeling approach to build two network models for AD and T2DM using Biological Expression Language (BEL), which is capable of capturing and representing causal and correlative relationships at both molecular and clinical levels from various knowledge resources. RESULTS: Using comparative analysis, we have identified several putative "shared pathways". We demonstrate, at a mechanistic level, how the insulin signaling pathway is related to other significant AD pathways such as the neurotrophin signaling pathway, PI3K/AKT signaling, MTOR signaling, and MAPK signaling and how these pathways do cross-talk with each other both in AD and T2DM. In addition, we present a mechanistic hypothesis that explains both favorable and adverse effects of the anti-diabetic drug metformin in AD. CONCLUSION: The two computable models introduced here provide a powerful framework to identify plausible mechanistic links shared between AD and T2DM and thereby identify targeted pathways for new therapeutics. Our approach can also be used to provide mechanistic answers to the question of why some T2DM treatments seem to increase the risk of AD.


Assuntos
Doença de Alzheimer/epidemiologia , Diabetes Mellitus Tipo 2/epidemiologia , Diabetes Mellitus Tipo 2/fisiopatologia , Insulina/metabolismo , Transdução de Sinais/fisiologia , Doença de Alzheimer/induzido quimicamente , Doença de Alzheimer/metabolismo , Comorbidade , Simulação por Computador , Diabetes Mellitus Tipo 2/tratamento farmacológico , Diabetes Mellitus Tipo 2/metabolismo , Feminino , Humanos , Hipoglicemiantes/efeitos adversos , Masculino , Redes e Vias Metabólicas , Metformina/efeitos adversos , Modelos Biológicos
20.
Nucleic Acids Res ; 45(16): 9290-9301, 2017 Sep 19.
Artigo em Inglês | MEDLINE | ID: mdl-28934507

RESUMO

With this study, we provide a comprehensive reference dataset of detailed miRNA expression profiles from seven types of human peripheral blood cells (NK cells, B lymphocytes, cytotoxic T lymphocytes, T helper cells, monocytes, neutrophils and erythrocytes), serum, exosomes and whole blood. The peripheral blood cells from buffy coats were typed and sorted using FACS/MACS. The overall dataset was generated from 450 small RNA libraries using high-throughput sequencing. By employing a comprehensive bioinformatics and statistical analysis, we show that 3' trimming modifications as well as composition of 3' added non-templated nucleotides are distributed in a lineage-specific manner-the closer the hematopoietic progenitors are, the higher their similarities in sequence variation of the 3' end. Furthermore, we define the blood cell-specific miRNA and isomiR expression patterns and identify novel cell type specific miRNA candidates. The study provides the most comprehensive contribution to date towards a complete miRNA catalogue of human peripheral blood, which can be used as a reference for future studies. The dataset has been deposited in GEO and also can be explored interactively following this link: http://134.245.63.235/ikmb-tools/bloodmiRs.


Assuntos
Células Sanguíneas/metabolismo , MicroRNAs/sangue , Linhagem da Célula , Eritrócitos/metabolismo , Exossomos/metabolismo , Humanos , Linfócitos/metabolismo , MicroRNAs/química , Células Mieloides/metabolismo , Análise de Sequência de RNA , Transcriptoma
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA