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1.
Sheng Wu Gong Cheng Xue Bao ; 35(10): 1901-1913, 2019 Oct 25.
Artigo em Chinês | MEDLINE | ID: mdl-31668037

RESUMO

To quickly and efficiently understand the intracellular metabolic characteristics of industrial microorganisms, and to find potential metabolic engineering targets, genome-scale metabolic network models (GSMMs) as a systems biology tool, are attracting more and more attention. We review here the 20-year history of metabolic network model, analyze the research status and development of GSMMs, summarize the methods for model construction and analysis, and emphasize the applications of metabolic network model for analyzing intracellular metabolic activity of microorganisms from cellular phenotypes, and metabolic engineering. Furthermore, we indicate future development trend of metabolic network model.


Assuntos
Microbiologia Industrial , Engenharia Metabólica , Redes e Vias Metabólicas , Modelos Biológicos , Biologia de Sistemas , Redes e Vias Metabólicas/genética
2.
Sheng Wu Gong Cheng Xue Bao ; 35(10): 1955-1973, 2019 Oct 25.
Artigo em Chinês | MEDLINE | ID: mdl-31668041

RESUMO

In industrial biotechnology, microbial cell factories utilize renewable resources to produce energy, materials and chemicals. Industrial biotechnology plays an increasingly important role in solving the resource, energy and environmental problems. Systems biology has shed new light on industrial biotechnology, deepening our understanding of industrial microbial cell factories and their bioprocess from "Black-box" to "White-box". Systems-wide profiling of genome, transcriptome, proteome, metabolome, and fluxome has proven valuable to better unveil network operation and regulation on the genome scale. System biology has been successfully applied to create microbial cell factories for numerous products and derive attractive industrial processes, which has constantly expedited the development of industrial biotechnology. This review focused on the recent advance and applications of omics and trans-omics in industrial biotechnology, including genomics, transcriptomics, proteomics, metabolomics, fluxomics and genome scale modeling, and so on. Furthermore, this review also discussed the potential and promise of systems biology in industrial biotechnology.


Assuntos
Biotecnologia , Microbiologia Industrial , Biologia de Sistemas , Genômica , Engenharia Metabólica , Metabolômica
3.
An Acad Bras Cienc ; 91(3): e20180424, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31553364

RESUMO

Abstract: Cardiovascular diseases (CVDs) are leading causes of death in the world, owing to noticeable incidence and mortality. Traditional Chinese Medicine (TCM) SINI Decoction (SND) is used to prevent and treat CVDs, which has attracted extensive attention for its moderate and little side effects. However, the involved molecular mechanisms are exceedingly complicated and remain unclear. Systems pharmacology, as a novel approach that integrates systems biology and pharmacology plays a significant role in investigating the molecular mechanism of TCM. In systems pharmacology approach, we use to systematically uncover the mechanisms of action in Chinese medicinal formula SND as an effective treatment for CVDs, which mainly includes:1) molecular database building; 2) ADME evaluation; 3) target-fishing 4) network construction and analysis. The results show that 78 underlying valid ingredients and their corresponding 71 direct targets of SND were obtained. And SND take part in cardiomyocyte protection, blood pressure regulation, and lipid regulation module in treatment of CVDs by cooperative way. Systems pharmacology as an emerging field that investigates the molecular mechanisms of TCM through pharmacokinetic evaluation target prediction, and pathway analysis, which will facilitate the development of traditional Chinese herbs in modern medicine.


Assuntos
Doenças Cardiovasculares/tratamento farmacológico , Medicamentos de Ervas Chinesas/química , Medicina Tradicional Chinesa , Redes Neurais (Computação) , Biologia de Sistemas/métodos , Humanos , Modelos Biológicos
4.
BMC Bioinformatics ; 20(1): 449, 2019 Sep 02.
Artigo em Inglês | MEDLINE | ID: mdl-31477006

RESUMO

BACKGROUND: Hodgkin Lymphoma (HL) is a type of aggressive malignancy in lymphoma that has high incidence in young adults and elderly patients. Identification of reliable diagnostic markers and efficient therapeutic targets are especially important for the diagnosis and treatment of HL. Although many HL-related molecules have been identified, our understanding on the molecular mechanisms underlying the disease is still far from complete due to its complex and heterogeneous characteristics. In such situation, exploring the molecular mechanisms underlying HL via systems biology approaches provides a promising option. In this study, we try to elucidate the molecular mechanisms related to the disease and identify potential pharmaceutical targets from a network-based perspective. RESULTS: We constructed a series of network models. Based on the analysis of these networks, we attempted to identify the biomarkers and elucidate the molecular mechanisms underlying HL. Initially, we built three different but related protein networks, i.e., background network, HL-basic network and HL-specific network. By analyzing these three networks, we investigated the connection characteristic of the HL-related proteins. Subsequently, we explored the miRNA regulation on HL-specific network and analyzed three kinds of simple regulation patterns, i.e., co-regulation of protein pairs, as well as the direct and indirect regulation of triple proteins. Finally, we constructed a simplified protein network combined with the regulation of miRNAs on proteins to better understand the relation between HL-related proteins and miRNAs. CONCLUSIONS: We find that the HL-related proteins are more likely to connect with each other compared to other proteins. Moreover, the HL-specific network can be further divided into five sub-networks and 49 proteins as the backbone of HL-specific network make up and connect these 5 sub-networks. Thus, they may be closely associated with HL. In addition, we find that the co-regulation of protein pairs is the main regulatory pattern of miRNAs on the protein network in the HL-specific network. According to the regulation of miRNA on protein network, we have identified 5 core miRNAs as the potential biomarkers for diagnostic of HL. Finally, several protein pathways have been identified to closely associated with HL, which provides deep insights into underlying mechanism of HL.


Assuntos
Biomarcadores Tumorais/genética , Biomarcadores Tumorais/metabolismo , Regulação Neoplásica da Expressão Gênica , Redes Reguladoras de Genes , Doença de Hodgkin/patologia , MicroRNAs/genética , Mapas de Interação de Proteínas , Perfilação da Expressão Gênica , Doença de Hodgkin/genética , Doença de Hodgkin/metabolismo , Humanos , Biologia de Sistemas
5.
Life Sci ; 235: 116820, 2019 Oct 15.
Artigo em Inglês | MEDLINE | ID: mdl-31476308

RESUMO

AIMS: Osteoporosis (OP) is a systemic metabolic bone disease characterized by bone mass decrease and microstructural degradation, which may increase the risk of bone fracture and leading to high morbidity. Dipsaci Radix (DR), one typical traditional Chinese medicine (TCM), which has been applied in the treatment of OP with good therapeutic effects and few side effects. However, the underlying molecular mechanisms of DR to treat OP have not been fully elucidated. In this study, we aim to dissect the molecular mechanism of DR in the treatment of OP. MATERIALS AND METHODS: A systems pharmacology approach was employed to comprehensively dissect the action mechanisms of DR for the treatment of OP. KEY FINDINGS: 10 compounds were screened out as the potential active ingredients with excellent biological activity based on in silico ADME (absorption, distribution, metabolism and excretion) prediction model. Then, 36 key protein targets of 6 compounds were identified by systems drug targeting model (SysDT) and they were involved in several biological processes, such as osteoclast differentiation, osteoblast differentiation and anti-inflammation. The target-pathway network indicated that targets are mainly mapped in multiple signaling pathways, i.e., MAPK, Tumor necrosis factor α (TNF-α), NF-κb and Toll-like receptor pathways. The in vitro results indicated that the compounds ursolic acid and beta-sitosterol effectively inhibited the osteoclast differentiation. SIGNIFICANCE: These results systematically dissected that DR exhibits the therapeutic effects of OP by the regulation of immune system-related pathways, which provide novel perspective to drug development of OP.


Assuntos
Bases de Dados de Produtos Farmacêuticos , Dipsacaceae/química , Medicamentos de Ervas Chinesas/farmacologia , Redes Reguladoras de Genes , Redes e Vias Metabólicas , Osteoporose/tratamento farmacológico , Biologia de Sistemas/métodos , Células CACO-2 , Humanos , Osteoporose/genética , Osteoporose/metabolismo , Transdução de Sinais
7.
Chimia (Aarau) ; 73(7): 540-548, 2019 Aug 21.
Artigo em Inglês | MEDLINE | ID: mdl-31431214

RESUMO

Predicting how a system behaves under changing conditions is an essential component of science and engineering. The ability to make accurate predictions about the system indicates that it is well understood and provides the opportunity to simulate the response to conditions that would be empirically difficult or impossible to test. In the life sciences, the term systems biology was introduced to articulate the notion that the molecular and phenotypic response of a cell or organism to perturbations is the result of interplay of a multitude of molecules. The ability to predict the behavior of such complex molecular systems remains challenging and inevitably requires the involvement of different types of models and data that support them. In this article, we discuss a range of data-driven models that have proven particularly useful for predicting the behavior of biological systems at different levels of complexity and the matching data generation methods that support them. We specifically focus on predictions based on protein or proteome data generated by mass spectrometry. We describe three case studies that represent frequently encountered situations in systems biology.


Assuntos
Proteoma , Biologia de Sistemas , Espectrometria de Massas
8.
Pestic Biochem Physiol ; 158: 88-100, 2019 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-31378365

RESUMO

Methyl halide group of pesticides are being used widely in past decades as fumigant but due to their hazardous effect, these pesticides are not sold directly. They are volatile and gaseous in nature and may easily come in the contact of trophosphere and stratosphere. In troposphere, they are harmful to the living beings; nevertheless, in stratosphere they react with ozone and degrade the ozone layers. In this study, we have investigated the in-silico pathways of methyl halide and its toxic effect on living systems like pest, humans and environment. Till date, limited studies provide the understanding of degradation of methyl halide and its effect on the environment. This leads to availability of scanty information for overall bio-magnifications of methyl halides at molecular and cellular level. The model developed in the present study explains how a volatile toxic compound not only affects living systems on earth but also on environmental layers. Hub nodes were also evaluated by investigating the developed model topologically. Methyl transferase system is identified as promising enzyme in response to degradation of methyl halides.


Assuntos
Bactérias/metabolismo , Cloreto de Metila/metabolismo , Biodegradação Ambiental , Biologia de Sistemas
9.
Adv Exp Med Biol ; 1140: 143-154, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31347046

RESUMO

Understanding multicellular organism development from a molecular perspective is no small feat, yet this level of comprehension affords clinician-scientists the ability to identify root causes and mechanisms of congenital diseases. Inarguably, the maturation of molecular biology tools has significantly contributed to the identification of genetic loci that underlie normal and aberrant developmental programs. In combination with cell biology approaches, these tools have begun to elucidate the spatiotemporal expression and function of developmentally-regulated proteins. The emergence of quantitative mass spectrometry (MS) for biological applications has accelerated the pace at which these proteins can be functionally characterized, driving the construction of an increasingly detailed systems biology picture of developmental processes. Here, we review the quantitative MS-based proteomic technologies that have contributed significantly to understanding the role of proteome regulation in developmental processes. We provide a brief overview of these methodologies, focusing on their ability to provide precise and accurate proteome measurements. We then highlight the use of discovery-based and targeted mass spectrometry approaches in model systems to study cellular differentiation states, tissue phenotypes, and spatiotemporal subcellular organization. We also discuss the current application and future perspectives of MS proteomics to study PTM coordination and the role of protein complexes during development.


Assuntos
Biologia do Desenvolvimento , Espectrometria de Massas , Proteômica , Proteoma , Biologia de Sistemas
10.
BMC Bioinformatics ; 20(1): 385, 2019 Jul 09.
Artigo em Inglês | MEDLINE | ID: mdl-31288758

RESUMO

BACKGROUND: In cancer research, robustness of a complex biochemical network is one of the most relevant properties to investigate for the development of novel targeted therapies. In cancer systems biology, biological networks are typically modeled through Ordinary Differential Equation (ODE) models. Hence, robustness analysis consists in quantifying how much the temporal behavior of a specific node is influenced by the perturbation of model parameters. The Conditional Robustness Algorithm (CRA) is a valuable methodology to perform robustness analysis on a selected output variable, representative of the proliferation activity of cancer disease. RESULTS: Here we introduce our new freely downloadable software, the CRA Toolbox. The CRA Toolbox is an Object-Oriented MATLAB package which implements the features of CRA for ODE models. It offers the users the ability to import a mathematical model in Systems Biology Markup Language (SBML), to perturb the model parameter space and to choose the reference node for the robustness analysis. The CRA Toolbox allows the users to visualize and save all the generated results through a user-friendly Graphical User Interface (GUI). The CRA Toolbox has a modular and flexible architecture since it is designed according to some engineering design patterns. This tool has been successfully applied in three nonlinear ODE models: the Prostate-specific Pten-/- mouse model, the Pulse Generator Network and the EGFR-IGF1R pathway. CONCLUSIONS: The CRA Toolbox for MATLAB is an open-source tool implementing the CRA to perform conditional robustness analysis. With its unique set of functions, the CRA Toolbox is a remarkable software for the topological study of biological networks. The source and example code and the corresponding documentation are freely available at the web site: http://gitlab.ict4life.com/SysBiOThe/CRA-Matlab .


Assuntos
Algoritmos , Modelos Biológicos , Neoplasias/metabolismo , Software , Biologia de Sistemas/métodos , Animais , Simulação por Computador , Modelos Animais de Doenças , Receptores ErbB/metabolismo , Humanos , Cinética , Masculino , Camundongos , Especificidade de Órgãos , PTEN Fosfo-Hidrolase/deficiência , PTEN Fosfo-Hidrolase/metabolismo , Próstata/metabolismo , Receptor IGF Tipo 1/metabolismo , Transdução de Sinais
11.
BMC Bioinformatics ; 20(1): 395, 2019 Jul 16.
Artigo em Inglês | MEDLINE | ID: mdl-31311516

RESUMO

BACKGROUND: Ordinary differential equation systems are frequently utilized to model biological systems and to infer knowledge about underlying properties. For instance, the development of drugs requires the knowledge to which extent malign cells differ from healthy ones to provide a specific treatment with least side effects. As these cell-type specific properties may stem from any part of biochemical cell processes, systematic quantitative approaches are necessary to identify the relevant potential drug targets. An ℓ1 regularization for the maximum likelihood parameter estimation proved to be successful, but falsely predicted cell-type dependent behaviour had to be corrected manually by using a Profile Likelihood approach. RESULTS: The choice of extended ℓ1 penalty functions significantly decreased the number of falsely detected cell-type specific parameters. Thus, the total accuracy of the prediction could be increased. This was tested on a realistic dynamical benchmark model used for the DREAM6 challenge. Among Elastic Net, Adaptive Lasso and a non-convex ℓq penalty, the latter one showed the best predictions whilst also requiring least computation time. All extended methods include a hyper-parameter in the regularization function. For an Erythropoietin (EPO) induced signalling pathway, the extended methods ℓq and Adaptive Lasso revealed an unpublished alternative parsimonious model when varying the respective hyper-parameters. CONCLUSIONS: Using ℓq or Adaptive Lasso with an a-priori choice for the hyper-parameter can lead to a more specific and accurate result than ℓ1. Scanning different hyper-parameters can yield additional pieces of information about the system.


Assuntos
Modelos Biológicos , Eritropoetina/metabolismo , Humanos , Janus Quinase 2/metabolismo , Funções Verossimilhança , Fator de Transcrição STAT5/metabolismo , Transdução de Sinais , Biologia de Sistemas/métodos
12.
Nat Biotechnol ; 37(7): 810-818, 2019 07.
Artigo em Inglês | MEDLINE | ID: mdl-31267104

RESUMO

A major challenge for stem cell engineering is achieving a holistic understanding of the molecular networks and biological processes governing cell differentiation. To address this challenge, we describe a computational approach that combines gene expression analysis, previous knowledge from proteomic pathway informatics and cell signaling models to delineate key transitional states of differentiating cells at high resolution. Our network models connect sparse gene signatures with corresponding, yet disparate, biological processes to uncover molecular mechanisms governing cell fate transitions. This approach builds on our earlier CellNet and recent trajectory-defining algorithms, as illustrated by our analysis of hematopoietic specification along the erythroid lineage, which reveals a role for the EGF receptor family member, ErbB4, as an important mediator of blood development. We experimentally validate this prediction and perturb the pathway to improve erythroid maturation from human pluripotent stem cells. These results exploit an integrative systems perspective to identify new regulatory processes and nodes useful in cell engineering.


Assuntos
Engenharia Celular , Células-Tronco Hematopoéticas/metabolismo , Células-Tronco Pluripotentes Induzidas/fisiologia , Biologia de Sistemas/métodos , Algoritmos , Animais , Antígenos CD34/genética , Antígenos CD34/metabolismo , Diferenciação Celular , Linhagem da Célula , Proliferação de Células , Biologia Computacional/métodos , Eritrócitos , Eritropoese , Citometria de Fluxo , Regulação da Expressão Gênica , Redes Reguladoras de Genes , Humanos , Camundongos , Receptor ErbB-4/metabolismo , Transdução de Sinais , Peixe-Zebra
14.
Methods Mol Biol ; 2018: 213-231, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31228159

RESUMO

One of the most fruitful resources for systems genetic studies of nonhuman mammals is a panel of inbred strains that exhibits significant genetic diversity between strains but genetic stability (isogenicity) within strains. These characteristics allow for fine mapping of complex phenotypes (QTLs) and provide statistical power to identify loci which contribute nominally to the phenotype. This type of resource also allows the planning and performance of investigations using the same genetic backgrounds over several generations of the test animals. Often, rats are preferred over mice for physiologic and behavioral studies because of their larger size and more distinguishable anatomy (particularly for their central nervous system). The Hybrid Rat Diversity Panel (HRDP) is a panel of inbred rat strains, which combines two recombinant inbred panels (the HXB/BXH, 30 strains; the LEXF/FXLE, 34 strains and 35 more strains of inbred rats which were selected for genetic diversity, based on their fully sequenced genomes and/or thorough genotyping). The genetic diversity and statistical power of this panel for mapping studies rivals or surpasses currently available panels in mouse. The genetic stability of this panel makes it particularly suitable for collection of high-throughput omics data as relevant technology becomes available for engaging in truly integrative systems biology. The PhenoGen website ( http://phenogen.org ) is the repository for the initial transcriptome data, making the raw data, the processed data, and the analysis results, e.g., organ-specific protein coding and noncoding transcripts, isoform analysis, expression quantitative trait loci, and co-expression networks, available to the research public. The data sets and tools being developed will complement current efforts to analyze the human transcriptome and its genetic controls (the Genotype-Tissue Expression Project (GTEx)) and allow for dissection of genetic networks that predispose to particular phenotypes and gene-by-environment interactions that are difficult or even impossible to study in humans. The HRDP is an essential population for exploring truly integrative systems genetics.


Assuntos
Variação Genética , Ratos Endogâmicos/genética , Biologia de Sistemas/métodos , Animais , Quimera/genética , Redes Reguladoras de Genes , Humanos , Modelos Animais , Locos de Características Quantitativas , Ratos , Software , Sequenciamento Completo do Genoma
15.
Nat Commun ; 10(1): 2472, 2019 06 06.
Artigo em Inglês | MEDLINE | ID: mdl-31171781

RESUMO

The evolution of microbial and viral organisms often generates clonal interference, a mode of competition between genetic clades within a population. Here we show how interference impacts systems biology by constraining genetic and phenotypic complexity. Our analysis uses biophysically grounded evolutionary models for molecular phenotypes, such as fold stability and enzymatic activity of genes. We find a generic mode of phenotypic interference that couples the function of individual genes and the population's global evolutionary dynamics. Biological implications of phenotypic interference include rapid collateral system degradation in adaptation experiments and long-term selection against genome complexity: each additional gene carries a cost proportional to the total number of genes. Recombination above a threshold rate can eliminate this cost, which establishes a universal, biophysically grounded scenario for the evolution of sex. In a broader context, our analysis suggests that the systems biology of microbes is strongly intertwined with their mode of evolution.


Assuntos
Bactérias/genética , Evolução Biológica , Dobramento de Proteína , Estabilidade Proteica , Vírus/genética , Bactérias/metabolismo , Evolução Molecular , Aptidão Genética , Fenótipo , Recombinação Genética , Seleção Genética , Biologia de Sistemas , Vírus/metabolismo
17.
Comput Biol Chem ; 80: 409-418, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-31128452

RESUMO

Reverse engineering of biochemical networks remains an important open challenge in computational systems biology. The goal of model inference is to, based on time-series gene expression data, obtain the sparse topological structure and parameters that quantitatively understand and reproduce the dynamics of biological systems. In this paper, we propose a multi-objective approach for the inference of S-System structures for Gene Regulatory Networks (GRNs) based on Pareto dominance and Pareto optimality theoretical concepts instead of the conventional single-objective evaluation of Mean Squared Error (MSE). Our motivation is that, using a multi-objective formulation for the GRN, it is possible to optimize the sparse topology of a given GRN as well as the kinetic order and rate constant parameters in a decoupled S-System, yet avoiding the use of additional penalty weights. A flexible and robust Multi-Objective Cellular Evolutionary Algorithm is adapted to perform the tasks of parameter learning and network topology inference for the proposed approach. The resulting software, called MONET, is evaluated on real-based academic and synthetic time-series of gene expression taken from the DREAM3 challenge and the IRMA in vivo datasets. The ability to reproduce biological behavior and robustness to noise is assessed and compared. The results obtained are competitive and indicate that the proposed approach offers advantages over previously used methods. In addition, MONET is able to provide experts with a set of trade-off solutions involving GRNs with different typologies and MSEs.


Assuntos
Algoritmos , Redes Reguladoras de Genes , Biologia de Sistemas/métodos , Escherichia coli/genética , Galactose/metabolismo , Glucose/metabolismo , Modelos Genéticos , Saccharomyces cerevisiae/genética , Saccharomyces cerevisiae/metabolismo
18.
PLoS Comput Biol ; 15(5): e1007036, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31083653

RESUMO

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.


Assuntos
Redes e Vias Metabólicas , Modelos Biológicos , Biologia Computacional , Bases de Dados Factuais , Bases de Dados Genéticas , Escherichia coli/genética , Escherichia coli/metabolismo , Expressão Gênica , Genoma Bacteriano , Cinética , Metabolômica , Biologia de Sistemas , Termodinâmica
19.
Nat Genet ; 51(5): 793-803, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31043756

RESUMO

Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study (GWAS) including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P < 1 × 10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (P < 5 × 10-8) in the discovery GWAS were not genome-wide significant in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis, 30 loci were genome-wide significant, including 20 newly identified loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene sets, including regulation of insulin secretion and endocannabinoid signaling. Bipolar I disorder is strongly genetically correlated with schizophrenia, driven by psychosis, whereas bipolar II disorder is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential biological mechanisms for bipolar disorder.


Assuntos
Transtorno Bipolar/genética , Loci Gênicos , Transtorno Bipolar/classificação , Estudos de Casos e Controles , Transtorno Depressivo Maior/genética , Feminino , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Humanos , Masculino , Polimorfismo de Nucleotídeo Único , Transtornos Psicóticos/genética , Esquizofrenia/genética , Biologia de Sistemas
20.
Nat Commun ; 10(1): 2236, 2019 05 20.
Artigo em Inglês | MEDLINE | ID: mdl-31110181

RESUMO

Genome-wide association studies (GWAS) have identified more than 50,000 unique associations with common human traits. While this represents a substantial step forward, establishing the biology underlying these associations has proven extremely difficult. Even determining which cell types and which particular gene(s) are relevant continues to be a challenge. Here, we conduct a cell-specific pathway analysis of the latest GWAS in multiple sclerosis (MS), which had analyzed a total of 47,351 cases and 68,284 healthy controls and found more than 200 non-MHC genome-wide associations. Our analysis identifies pan immune cell as well as cell-specific susceptibility genes in T cells, B cells and monocytes. Finally, genotype-level data from 2,370 patients and 412 controls is used to compute intra-individual and cell-specific susceptibility pathways that offer a biological interpretation of the individual genetic risk to MS. This approach could be adopted in any other complex trait for which genome-wide data is available.


Assuntos
Regulação da Expressão Gênica , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Esclerose Múltipla/genética , Biologia de Sistemas/métodos , Genes Reguladores/genética , Genótipo , Humanos , Polimorfismo de Nucleotídeo Único
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