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1.
Genome Med ; 13(1): 134, 2021 Aug 27.
Artigo em Inglês | MEDLINE | ID: mdl-34446072

RESUMO

BACKGROUND: Metagenome sampling bias for geographical location and lifestyle is partially responsible for the incomplete catalog of reference genomes of gut microbial species. Thus, genome assembly from currently under-represented populations may effectively expand the reference gut microbiome and improve taxonomic and functional profiling. METHODS: We assembled genomes using public whole-metagenomic shotgun sequencing (WMS) data for 110 and 645 fecal samples from India and Japan, respectively. In addition, we assembled genomes from newly generated WMS data for 90 fecal samples collected from Korea. Expecting genome assembly for low-abundance species may require a much deeper sequencing than that usually employed, so we performed ultra-deep WMS (> 30 Gbp or > 100 million read pairs) for the fecal samples from Korea. We consequently assembled 29,082 prokaryotic genomes from 845 fecal metagenomes for the three under-represented Asian countries and combined them with the Unified Human Gastrointestinal Genome (UHGG) to generate an expanded catalog, the Human Reference Gut Microbiome (HRGM). RESULTS: HRGM contains 232,098 non-redundant genomes for 5414 representative prokaryotic species including 780 that are novel, > 103 million unique proteins, and > 274 million single-nucleotide variants. This is an over 10% increase from the UHGG. The new 780 species were enriched for the Bacteroidaceae family, including species associated with high-fiber and seaweed-rich diets. Single-nucleotide variant density was positively associated with the speciation rate of gut commensals. We found that ultra-deep sequencing facilitated the assembly of genomes for low-abundance taxa, and deep sequencing (e.g., > 20 million read pairs) may be needed for the profiling of low-abundance taxa. Importantly, the HRGM significantly improved the taxonomic and functional classification of sequencing reads from fecal samples. Finally, analysis of human self-antigen homologs on the HRGM species genomes suggested that bacterial taxa with high cross-reactivity potential may contribute more to the pathogenesis of gut microbiome-associated diseases than those with low cross-reactivity potential by promoting inflammatory condition. CONCLUSIONS: By including gut metagenomes from previously under-represented Asian countries, Korea, India, and Japan, we developed a substantially expanded microbiome catalog, HRGM. Information of the microbial genomes and coding genes is publicly available ( www.mbiomenet.org/HRGM/ ). HRGM will facilitate the identification and functional analysis of disease-associated gut microbiota.

2.
Nat Commun ; 12(1): 4194, 2021 07 07.
Artigo em Inglês | MEDLINE | ID: mdl-34234144

RESUMO

Photomorphogenesis, light-mediated development, is an essential feature of all terrestrial plants. While chloroplast development and brassinosteroid (BR) signaling are known players in photomorphogenesis, proteins that regulate both pathways have yet to be identified. Here we report that DE-ETIOLATION IN THE DARK AND YELLOWING IN THE LIGHT (DAY), a membrane protein containing DnaJ-like domain, plays a dual-role in photomorphogenesis by stabilizing the BR receptor, BRI1, as well as a key enzyme in chlorophyll biosynthesis, POR. DAY localizes to both the endomembrane and chloroplasts via its first transmembrane domain and chloroplast transit peptide, respectively, and interacts with BRI1 and POR in their respective subcellular compartments. Using genetic analysis, we show that DAY acts independently on BR signaling and chlorophyll biogenesis. Collectively, this work uncovers DAY as a factor that simultaneously regulates BR signaling and chloroplast development, revealing a key regulator of photomorphogenesis that acts across cell compartments.


Assuntos
Proteínas de Arabidopsis/metabolismo , Proteínas de Choque Térmico HSP40/metabolismo , Proteínas de Membrana/metabolismo , Morfogênese/fisiologia , Proteínas Quinases/metabolismo , Arabidopsis/genética , Arabidopsis/crescimento & desenvolvimento , Arabidopsis/metabolismo , Proteínas de Arabidopsis/genética , Brassinosteroides/metabolismo , Clorofila/biossíntese , Cloroplastos/metabolismo , Estiolamento/fisiologia , Regulação da Expressão Gênica de Plantas/fisiologia , Técnicas de Silenciamento de Genes , Proteínas de Choque Térmico HSP40/genética , Proteínas de Choque Térmico HSP40/isolamento & purificação , Luz , Proteínas de Membrana/genética , Proteínas de Membrana/isolamento & purificação , Proteínas Associadas aos Microtúbulos/genética , Proteínas Associadas aos Microtúbulos/metabolismo , Morfogênese/efeitos da radiação , Mutação , Plantas Geneticamente Modificadas , Proteínas Quinases/genética , RNA-Seq , Proteínas Recombinantes/genética , Proteínas Recombinantes/isolamento & purificação , Proteínas Recombinantes/metabolismo , Plântula/crescimento & desenvolvimento , Transdução de Sinais/fisiologia
3.
Food Chem ; 360: 129740, 2021 Oct 30.
Artigo em Inglês | MEDLINE | ID: mdl-34023715

RESUMO

An enzyme-addition method to pretreat fried fruit and vegetable chips for acrylamide analysis is reported, followed by determination of the acrylamide contents in 36 marketed fruit and vegetable chip products using LC-MS/MS. To improve the extraction process, the FDA method was modified. Specifically, digestive enzymes were added, overcoming the clogging of filters (or SPE cartridges) after extraction of vegetable chips using water. Diastase was added to extract high-starch products, including potato chips. Recoveries of 90.3-105.5% acrylamide were obtained at the spiking levels of 25-500 µg/kg. LOD and LOQ were similar between the method with (4.5 and 13.7 µg/kg) and without diastase addition (4.4 and 13.2 µg/kg). Okra chip with high mucin content was extracted after adding pepsin. This method provided a recovery of 99.8-102.2%, LOD of 6.0 µg/kg, and LOQ of 18.1 µg/kg. Both methods could be used for analyzing acrylamide, with critical method parameters satisfying European Union regulations.


Assuntos
Acrilamida/química , Frutas/química , Verduras/química , Acrilamida/metabolismo , Cromatografia Líquida de Alta Pressão , Frutas/metabolismo , Espectrometria de Massas em Tandem , Verduras/metabolismo
4.
Methods Mol Biol ; 2200: 187-210, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33175379

RESUMO

Genome-wide association studies (GWAS) have proven effective at identifying genetic variants and genes that are associated with phenotypes in humans, animals, and plants. Since most phenotypes of plant species are complex traits regulated by many genes and their functional interactions, GWAS are increasing in popularity for genetic dissections of plant phenotypes. For the reference plant, Arabidopsis thaliana, detailed information on genetic variations became available with the completion of the 1001 Genomes Project, enabling highly resolved association mapping between chromosomal loci and complex traits. Improvements have been made in the statistical analysis methods for testing the significance of genotype-to-phenotype associations, thereby substantially reducing the confounding effects of population structures. Furthermore, there have been large efforts toward post-GWAS augmentation of signals via integration with other types of information to overcome the limited statistical power of GWAS. This chapter describes the stepwise procedure of GWAS in Arabidopsis, focusing on data analysis processes including preprocessing of genotype and phenotype data, statistical analysis to identify phenotype-associated chromosomal loci, identification of phenotype-associated genes based on the phenotype-associated loci, and finally network-based augmentation of GWAS signals to identify additional candidate genes for the phenotype.


Assuntos
Arabidopsis , Estudos de Associação Genética , Variação Genética , Característica Quantitativa Herdável , Arabidopsis/genética , Arabidopsis/metabolismo , Estudo de Associação Genômica Ampla
5.
Exp Mol Med ; 52(11): 1798-1808, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-33244151

RESUMO

Understanding cellular heterogeneity is the holy grail of biology and medicine. Cells harboring identical genomes show a wide variety of behaviors in multicellular organisms. Genetic circuits underlying cell-type identities will facilitate the understanding of the regulatory programs for differentiation and maintenance of distinct cellular states. Such a cell-type-specific gene network can be inferred from coregulatory patterns across individual cells. Conventional methods of transcriptome profiling using tissue samples provide only average signals of diverse cell types. Therefore, reconstructing gene regulatory networks for a particular cell type is not feasible with tissue-based transcriptome data. Recently, single-cell omics technology has emerged and enabled the capture of the transcriptomic landscape of every individual cell. Although single-cell gene expression studies have already opened up new avenues, network biology using single-cell transcriptome data will further accelerate our understanding of cellular heterogeneity. In this review, we provide an overview of single-cell network biology and summarize recent progress in method development for network inference from single-cell RNA sequencing (scRNA-seq) data. Then, we describe how cell-type-specific gene networks can be utilized to study regulatory programs specific to disease-associated cell types and cellular states. Moreover, with scRNA data, modeling personal or patient-specific gene networks is feasible. Therefore, we also introduce potential applications of single-cell network biology for precision medicine. We envision a rapid paradigm shift toward single-cell network analysis for systems biology in the near future.

6.
Exp Mol Med ; 52(9): 1550-1563, 2020 09.
Artigo em Inglês | MEDLINE | ID: mdl-32879421

RESUMO

Although approved programmed cell death protein (PD)-1 inhibitors show durable responses, clinical benefits to these agents are only seen in one-third of patients in most cancer types. Therefore, strategies for improving the response to PD-1 inhibitor for treating various cancers including non-small cell lung cancer (NSCLC) are urgently needed. Compared with genome and transcriptome, tumor DNA methylome in anti-PD-1 response was relatively unexplored. We compared the pre-treatment methylation status of cis-regulatory elements between responders and non-responders to treatment with nivolumab or pembrolizumab using the Infinium Methylation EPIC Array, which can profile ~850,000 CpG sites, including ~350,000 CpG sites located in enhancer regions. Then, we analyzed differentially methylated regions overlapping promoters (pDMRs) or enhancers (eDMRs) between responders and non-responders to PD-1 inhibitors. We identified 1007 pDMRs and 607 eDMRs associated with the anti-PD-1 response. We also identified 1109 and 1173 target genes putatively regulated by these pDMRs and eDMRs, respectively. We found that eDMRs contribute to the epigenetic regulation of the anti-PD-1 response more than pDMRs. Hypomethylated pDMRs of Cytohesin 1 Interacting Protein (CYTIP) and TNF superfamily member 8 (TNFSF8) were more predictive than programmed cell death protein ligand 1 (PD-L1) expression for anti-PD-1 response and progression-free survival (PFS) and overall survival (OS) in a validation cohort, suggesting their potential as predictive biomarkers for anti-PD-1 immunotherapy. The catalog of promoters and enhancers differentially methylated between responders and non-responders to PD-1 inhibitors presented herein will guide the development of biomarkers and therapeutic strategies for improving anti-PD-1 immunotherapy in NSCLC.

7.
Cancer Immunol Res ; 8(11): 1393-1406, 2020 11.
Artigo em Inglês | MEDLINE | ID: mdl-32878747

RESUMO

Regulatory T cells (Treg) are enriched in the tumor microenvironment (TME) and suppress antitumor immunity; however, the molecular mechanism underlying the accumulation of Tregs in the TME is poorly understood. In various tumor models, tumor-infiltrating Tregs were highly enriched in the TME and had significantly higher expression of immune checkpoint molecules. To characterize tumor-infiltrating Tregs, we performed bulk RNA sequencing (RNA-seq) and found that proliferation-related genes, immune suppression-related genes, and cytokine/chemokine receptor genes were upregulated in tumor-infiltrating Tregs compared with tumor-infiltrating CD4+Foxp3- conventional T cells or splenic Tregs from the same tumor-bearing mice. Single-cell RNA-seq and T-cell receptor sequencing also revealed active proliferation of tumor infiltrating Tregs by clonal expansion. One of these genes, ST2, an IL33 receptor, was identified as a potential factor driving Treg accumulation in the TME. Indeed, IL33-directed ST2 signaling induced the preferential proliferation of tumor-infiltrating Tregs and enhanced tumor progression, whereas genetic deletion of ST2 in Tregs limited their TME accumulation and delayed tumor growth. These data demonstrated the IL33/ST2 axis in Tregs as one of the critical pathways for the preferential accumulation of Tregs in the TME and suggests that the IL33/ST2 axis may be a potential therapeutic target for cancer immunotherapy.

8.
Comput Struct Biotechnol J ; 18: 1429-1439, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32637041

RESUMO

Most genetic variations associated with human complex traits are located in non-coding genomic regions. Therefore, understanding the genotype-to-phenotype axis requires a comprehensive catalog of functional non-coding genomic elements, most of which are involved in epigenetic regulation of gene expression. Genome-wide maps of open chromatin regions can facilitate functional analysis of cis- and trans-regulatory elements via their connections with trait-associated sequence variants. Currently, Assay for Transposase Accessible Chromatin with high-throughput sequencing (ATAC-seq) is considered the most accessible and cost-effective strategy for genome-wide profiling of chromatin accessibility. Single-cell ATAC-seq (scATAC-seq) technology has also been developed to study cell type-specific chromatin accessibility in tissue samples containing a heterogeneous cellular population. However, due to the intrinsic nature of scATAC-seq data, which are highly noisy and sparse, accurate extraction of biological signals and devising effective biological hypothesis are difficult. To overcome such limitations in scATAC-seq data analysis, new methods and software tools have been developed over the past few years. Nevertheless, there is no consensus for the best practice of scATAC-seq data analysis yet. In this review, we discuss scATAC-seq technology and data analysis methods, ranging from preprocessing to downstream analysis, along with an up-to-date list of published studies that involved the application of this method. We expect this review will provide a guideline for successful data generation and analysis methods using appropriate software tools and databases for the study of chromatin accessibility at single-cell resolution.

9.
Front Plant Sci ; 11: 98, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32133024

RESUMO

Cultivated barley (Hordeum vulgare L.) is one of the most produced cereal crops worldwide after maize, bread wheat, and rice. Barley is an important crop species not only as a food source, but also in plant genetics because it harbors numerous stress response alleles in its genome that can be exploited for crop engineering. However, the functional annotation of its genome is relatively poor compared with other major crops. Moreover, bioinformatics tools for system-wide analyses of omics data from barley are not yet available. We have thus developed BarleyNet, a co-functional network of 26,145 barley genes, along with a web server for network-based predictions (http://www.inetbio.org/barleynet). We demonstrated that BarleyNet's prediction of biological processes is more accurate than that of an existing barley gene network. We implemented three complementary network-based algorithms for prioritizing genes or functional concepts to study genetic components of complex traits such as environmental stress responses: (i) a pathway-centric search for candidate genes of pathways or complex traits; (ii) a gene-centric search to infer novel functional concepts for genes; and (iii) a context-centric search for novel genes associated with stress response. We demonstrated the usefulness of these network analysis tools in the study of stress response using proteomics and transcriptomics data from barley leaves and roots upon drought or heat stresses. These results suggest that BarleyNet will facilitate our understanding of the underlying genetic components of complex traits in barley.

10.
Genome Med ; 12(1): 22, 2020 02 28.
Artigo em Inglês | MEDLINE | ID: mdl-32111241

RESUMO

BACKGROUND: T cells exhibit heterogeneous functional states in the tumor microenvironment. Immune checkpoint inhibitors (ICIs) can reinvigorate only the stem cell-like progenitor exhausted T cells, which suggests that inhibiting the exhaustion progress will improve the efficacy of immunotherapy. Thus, regulatory factors promoting T cell exhaustion could serve as potential targets for delaying the process and improving ICI efficacy. METHODS: We analyzed the single-cell transcriptome data derived from human melanoma and non-small cell lung cancer (NSCLC) samples and classified the tumor-infiltrating (TI) CD8+ T cell population based on PDCD1 (PD-1) levels, i.e., PDCD1-high and PDCD1-low cells. Additionally, we identified differentially expressed genes as candidate factors regulating intra-tumoral T cell exhaustion. The co-expression of candidate genes with immune checkpoint (IC) molecules in the TI CD8+ T cells was confirmed by single-cell trajectory and flow cytometry analyses. The loss-of-function effect of the candidate regulator was examined by a cell-based knockdown assay. The clinical effect of the candidate regulator was evaluated based on the overall survival and anti-PD-1 responses. RESULTS: We retrieved many known factors for regulating T cell exhaustion among the differentially expressed genes between PDCD1-high and PDCD1-low subsets of the TI CD8+ T cells in human melanoma and NSCLC. TOX was the only transcription factor (TF) predicted in both tumor types. TOX levels tend to increase as CD8+ T cells become more exhausted. Flow cytometry analysis revealed a correlation between TOX expression and severity of intra-tumoral T cell exhaustion. TOX knockdown in the human TI CD8+ T cells resulted in downregulation of PD-1, TIM-3, TIGIT, and CTLA-4, which suggests that TOX promotes intra-tumoral T cell exhaustion by upregulating IC proteins in cancer. Finally, the TOX level in the TI T cells was found to be highly predictive of overall survival and anti-PD-1 efficacy in melanoma and NSCLC. CONCLUSIONS: We predicted the regulatory factors involved in T cell exhaustion using single-cell transcriptome profiles of human TI lymphocytes. TOX promoted intra-tumoral CD8+ T cell exhaustion via upregulation of IC molecules. This suggested that TOX inhibition can potentially impede T cell exhaustion and improve ICI efficacy. Additionally, TOX expression in the TI T cells can be used for patient stratification during anti-tumor treatments, including anti-PD-1 immunotherapy.


Assuntos
Carcinoma Pulmonar de Células não Pequenas/genética , Proteínas de Grupo de Alta Mobilidade/genética , Neoplasias Pulmonares/genética , Linfócitos do Interstício Tumoral/imunologia , Melanoma/genética , Transcriptoma , Animais , Carcinoma Pulmonar de Células não Pequenas/tratamento farmacológico , Feminino , Proteínas de Grupo de Alta Mobilidade/metabolismo , Humanos , Inibidores de Checkpoint Imunológico/uso terapêutico , Neoplasias Pulmonares/tratamento farmacológico , Melanoma/tratamento farmacológico , Camundongos , Camundongos Endogâmicos C57BL , Receptor de Morte Celular Programada 1/antagonistas & inibidores , Receptor de Morte Celular Programada 1/genética , Receptor de Morte Celular Programada 1/metabolismo , RNA-Seq , Análise de Célula Única , Células Tumorais Cultivadas
11.
Methods Mol Biol ; 2074: 35-44, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31583628

RESUMO

Proteins are major functional molecules that physically and functionally interact to carry out cellular processes. The physical interactions are generally mediated by domain-level interactions. Thus, novel protein-protein interactions can be predicted using various computational methods based on domain-domain interactions, using resolved structures of protein complexes. Functional protein interactions can be inferred based on shared domains between proteins, since proteins involved in the same biological processes tend to harbor common domains. We recently developed a method of inferring functional interactions between proteins using associations between their domain compositions, which can be represented as domain profiles. Since the method requires only protein domain annotations, it can be easily applied to any species with a sequenced genome. Here, we describe in detail the method of generating domain profiles for proteins and measuring the association between them to infer functional interactions between proteins. We also demonstrate that domain profile association can be used to successfully construct a large-scale functional network of human proteins.


Assuntos
Biologia Computacional/métodos , Mapeamento de Interação de Proteínas/métodos , Proteínas/química , Animais , Humanos
12.
Bioinformatics ; 36(2): 546-551, 2020 01 15.
Artigo em Inglês | MEDLINE | ID: mdl-31373613

RESUMO

MOTIVATION: The immune system has diverse types of cells that are differentiated or activated via various signaling pathways and transcriptional regulation upon challenging conditions. Immunophenotyping by flow and mass cytometry are the major approaches for identifying key signaling molecules and transcription factors directing the transition between the functional states of immune cells. However, few proteins can be evaluated by flow cytometry in a single experiment, preventing researchers from obtaining a comprehensive picture of the molecular programs involved in immune cell differentiation. Recent advances in single-cell RNA sequencing (scRNA-seq) have enabled unbiased genome-wide quantification of gene expression in individual cells on a large scale, providing a new and versatile analytical pipeline for studying immune cell differentiation. RESULTS: We present VirtualCytometry, a web-based computational pipeline for evaluating immune cell differentiation by exploiting cell-to-cell variation in gene expression with scRNA-seq data. Differentiating cells often show a continuous spectrum of cellular states rather than distinct populations. VirtualCytometry enables the identification of cellular subsets for different functional states of differentiation based on the expression of marker genes. Case studies have highlighted the usefulness of this subset analysis strategy for discovering signaling molecules and transcription factors for human T-cell exhaustion, a state of T-cell dysfunction, in tumor and mouse dendritic cells activated by pathogens. With more than 226 scRNA-seq datasets precompiled from public repositories covering diverse mouse and human immune cell types in normal and disease tissues, VirtualCytometry is a useful resource for the molecular dissection of immune cell differentiation. AVAILABILITY AND IMPLEMENTATION: www.grnpedia.org/cytometry.


Assuntos
RNA , Software , Animais , Diferenciação Celular , Perfilação da Expressão Gênica , Humanos , Camundongos , Análise de Sequência de RNA , Análise de Célula Única
13.
Bioinformatics ; 36(5): 1584-1589, 2020 03 01.
Artigo em Inglês | MEDLINE | ID: mdl-31599923

RESUMO

MOTIVATION: Owing to advanced DNA sequencing and genome assembly technology, the number of species with sequenced genomes is rapidly increasing. The aim of the recently launched Earth BioGenome Project is to sequence genomes of all eukaryotic species on Earth over the next 10 years, making it feasible to obtain genomic blueprints of the majority of animal and plant species by this time. Genetic models of the sequenced species will later be subject to functional annotation, and a comprehensive molecular network should facilitate functional analysis of individual genes and pathways. However, network databases are lagging behind genome sequencing projects as even the largest network database provides gene networks for less than 10% of sequenced eukaryotic genomes, and the knowledge gap between genomes and interactomes continues to widen. RESULTS: We present BiomeNet, a database of 95 scored networks comprising over 8 million co-functional links, which can build and analyze gene networks for any species with the sequenced genome. BiomeNet transfers functional interactions between orthologous proteins from source networks to the target species within minutes and automatically constructs gene networks with the quality comparable to that of existing networks. BiomeNet enables assembly of the first-in-species gene networks not available through other databases, which are highly predictive of diverse biological processes and can also provide network analysis by extracting subnetworks for individual biological processes and network-based gene prioritizations. These data indicate that BiomeNet could enhance the benefits of decoding the genomes of various species, thus improving our understanding of the Earth' biodiversity. AVAILABILITY AND IMPLEMENTATION: The BiomeNet is freely available at http://kobic.re.kr/biomenet/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.


Assuntos
Bases de Dados Genéticas , Genoma , Animais , Redes Reguladoras de Genes , Genômica , Análise de Sequência de DNA
14.
Mol Cells ; 42(8): 579-588, 2019 Aug 31.
Artigo em Inglês | MEDLINE | ID: mdl-31307154

RESUMO

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.


Assuntos
Regulação da Expressão Gênica , Redes Reguladoras de Genes , Área Sob a Curva , Budesonida/farmacologia , Budesonida/uso terapêutico , Neoplasias Colorretais/tratamento farmacológico , Neoplasias Colorretais/genética , Bases de Dados Genéticas , Sistemas de Liberação de Medicamentos , Regulação da Expressão Gênica/efeitos dos fármacos , Redes Reguladoras de Genes/efeitos dos fármacos , Humanos , Internet , Fenótipo , Curva ROC
15.
mSystems ; 4(4)2019 May 21.
Artigo em Inglês | MEDLINE | ID: mdl-31117026

RESUMO

Global increases in the use of carbapenems have resulted in several strains of Gram-negative bacteria acquiring carbapenem resistance, thereby limiting treatment options. Klebsiella pneumoniae is a common carbapenem-resistant pathogenic bacterium that is widely studied to identify novel antibiotic resistance mechanisms and drug targets. Antibiotic-resistant clinical isolates generally harbor many genetic alterations, and the identification of responsible mutations would provide insights into the molecular mechanisms of antibiotic resistance. We propose a method to prioritize mutated genes responsible for antibiotic resistance on the basis of expression changes in their local subnetworks, hypothesizing that mutated genes that show significant expression changes among the corresponding functionally associated genes are more likely to be involved in the carbapenem resistance. For network-based gene prioritization, we developed KlebNet (www.inetbio.org/klebnet), a genome-scale cofunctional network of K. pneumoniae genes. Using KlebNet, we reconstructed the functional modules for carbapenem resistance and virulence and identified the functional association between antibiotic resistance and virulence. Using complementation assays with the top candidate genes, we were able to validate a novel gene that negatively regulated carbapenem resistance and four novel genes that positively regulated virulence in Galleria mellonella larvae. Therefore, our study demonstrated the feasibility of network-based identification of genes required for antibiotic resistance and virulence of human-pathogenic bacteria.IMPORTANCE Klebsiella pneumoniae is a major bacterial pathogen that causes pneumonia and urinary tract infections in human. K. pneumoniae infections are treated with carbapenem, but carbapenem-resistant K. pneumoniae has been spreading worldwide. We are able to identify antimicrobial-resistant genes among mutated genes of the antibiotic-resistant clinical isolates. However, they usually harbor many mutated genes, including those that cause weak or neutral functional effects. Therefore, we need to prioritize the mutated genes to identify the more likely candidates for the follow-up functional analysis. For this study, we present a functional network of K. pneumoniae genes and propose a network-based method of prioritizing the mutated genes of the resistant clinical isolates. We also reconstructed the network-based functional modules for carbapenem resistance and virulence and retrieved the functional association between antibiotic resistance and virulence. This study demonstrated the feasibility of network-based analysis of clinical genomics data for the study of K. pneumoniae infection.

16.
PLoS Comput Biol ; 15(5): e1007052, 2019 05.
Artigo em Inglês | MEDLINE | ID: mdl-31075101

RESUMO

Protein domains are basic functional units of proteins. Many protein domains are pervasive among diverse biological processes, yet some are associated with specific pathways. Human complex diseases are generally viewed as pathway-level disorders. Therefore, we hypothesized that pathway-specific domains could be highly informative for human diseases. To test the hypothesis, we developed a network-based scoring scheme to quantify specificity of domain-pathway associations. We first generated domain profiles for human proteins, then constructed a co-pathway protein network based on the associations between domain profiles. Based on the score, we classified human protein domains into pathway-specific domains (PSDs) and non-specific domains (NSDs). We found that PSDs contained more pathogenic variants than NSDs. PSDs were also enriched for disease-associated mutations that disrupt protein-protein interactions (PPIs) and tend to have a moderate number of domain interactions. These results suggest that mutations in PSDs are likely to disrupt within-pathway PPIs, resulting in functional failure of pathways. Finally, we demonstrated the prediction capacity of PSDs for disease-associated genes with experimental validations in zebrafish. Taken together, the network-based quantitative method of modeling domain-pathway associations presented herein suggested underlying mechanisms of how protein domains associated with specific pathways influence mutational impacts on diseases via perturbations in within-pathway PPIs, and provided a novel genomic feature for interpreting genetic variants to facilitate the discovery of human disease genes.


Assuntos
Doença/etiologia , Domínios Proteicos , Mapas de Interação de Proteínas , Animais , Animais Geneticamente Modificados , Biologia Computacional , Doença da Artéria Coronariana/etiologia , Doença da Artéria Coronariana/genética , Doença da Artéria Coronariana/metabolismo , Doença/genética , Predisposição Genética para Doença , Variação Genética , Estudo de Associação Genômica Ampla , Humanos , Modelos Animais , Modelos Biológicos , Mutação , Polimorfismo de Nucleotídeo Único , Domínios Proteicos/genética , Mapeamento de Interação de Proteínas , Mapas de Interação de Proteínas/genética , Peixe-Zebra/genética
17.
Plant J ; 99(3): 571-582, 2019 08.
Artigo em Inglês | MEDLINE | ID: mdl-31006149

RESUMO

Maize (Zea mays) has multiple uses in human food, animal fodder, starch and sweetener production and as a biofuel, and is accordingly the most extensively cultivated cereal worldwide. To enhance maize production, genetic factors underlying important agricultural traits, including stress tolerance and flowering, have been explored through forward and reverse genetics approaches. Co-functional gene networks are systems biology resources useful in identifying trait-associated genes in plants by prioritizing candidate genes. Here, we present MaizeNet (http://www.inetbio.org/maizenet/), a genome-scale co-functional network of Z. mays genes, and a companion web server for network-assisted systems genetics. We describe the validation of MaizeNet network quality and its ability to functionally predict molecular pathways and complex traits in maize. Furthermore, we demonstrate that MaizeNet-based prioritization of candidate genes can facilitate the identification of cell wall biosynthesis genes and detect network communities associated with flowering-time candidate genes derived from genome-wide association studies. The demonstrated gene prioritization and subnetwork analysis can be conducted by simply submitting maize gene models based on the commonly used B73 RefGen_v3 and the latest B73 RefGen_v4 reference genomes on the MaizeNet web server. MaizeNet-based network-assisted systems genetics will substantially accelerate the discovery of trait-associated genes for crop improvement.


Assuntos
Biologia Computacional/métodos , Produtos Agrícolas/genética , Redes Reguladoras de Genes , Genes de Plantas/genética , Estudo de Associação Genômica Ampla/métodos , Zea mays/genética , Produtos Agrícolas/crescimento & desenvolvimento , Perfilação da Expressão Gênica , Regulação da Expressão Gênica no Desenvolvimento , Regulação da Expressão Gênica de Plantas , Internet , Fenótipo , Zea mays/crescimento & desenvolvimento
18.
Mol Cells ; 42(2): 166-174, 2019 Feb 28.
Artigo em Inglês | MEDLINE | ID: mdl-30759970

RESUMO

Bacterial species in the genus Xanthomonas infect virtually all crop plants. Although many genes involved in Xanthomonas virulence have been identified through molecular and cellular studies, the elucidation of virulence-associated regulatory circuits is still far from complete. Functional gene networks have proven useful in generating hypotheses for genetic factors of biological processes in various species. Here, we present a genome-scale co-functional network of Xanthomonas oryze pv. oryzae (Xoo) genes, XooNet (www.inetbio.org/xoonet/), constructed by integrating heterogeneous types of genomics data derived from Xoo and other bacterial species. XooNet contains 106,000 functional links, which cover approximately 83% of the coding genome. XooNet is highly predictive for diverse biological processes in Xoo and can accurately reconstruct cellular pathways regulated by two-component signaling transduction systems (TCS). XooNet will be a useful in silico research platform for genetic dissection of virulence pathways in Xoo.


Assuntos
Redes Reguladoras de Genes , Genes Bacterianos , Transdução de Sinais , Xanthomonas/genética , Regulação Bacteriana da Expressão Gênica , Internet , Transdução de Sinais/genética , Xanthomonas/imunologia
19.
Nucleic Acids Res ; 47(D1): D573-D580, 2019 01 08.
Artigo em Inglês | MEDLINE | ID: mdl-30418591

RESUMO

Human gene networks have proven useful in many aspects of disease research, with numerous network-based strategies developed for generating hypotheses about gene-disease-drug associations. The ability to predict and organize genes most relevant to a specific disease has proven especially important. We previously developed a human functional gene network, HumanNet, by integrating diverse types of omics data using Bayesian statistics framework and demonstrated its ability to retrieve disease genes. Here, we present HumanNet v2 (http://www.inetbio.org/humannet), a database of human gene networks, which was updated by incorporating new data types, extending data sources and improving network inference algorithms. HumanNet now comprises a hierarchy of human gene networks, allowing for more flexible incorporation of network information into studies. HumanNet performs well in ranking disease-linked gene sets with minimal literature-dependent biases. We observe that incorporating model organisms' protein-protein interactions does not markedly improve disease gene predictions, suggesting that many of the disease gene associations are now captured directly in human-derived datasets. With an improved interactive user interface for disease network analysis, we expect HumanNet will be a useful resource for network medicine.


Assuntos
Bases de Dados Genéticas , Redes Reguladoras de Genes , Algoritmos , Doença/genética , Humanos , Interface Usuário-Computador
20.
Methods Mol Biol ; 1907: 37-50, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30542989

RESUMO

Identifying genes that are capable of inducing tumorigenesis has been a major challenge in cancer research. In many cases, such genes frequently show somatic mutations in tumor samples; thus various computational methods for predicting cancer genes have been developed based on "significantly mutated genes." However, this approach is intrinsically limited by the fact that there are many cancer genes infrequently mutated in cancer genomes. Therefore, we recently developed MUFFINN (Mutations For Functional Impact on Network Neighbors), a method for cancer gene prediction based not only on mutation occurrences in each gene but also those of neighbors in functional networks. This enables the identification of cancer genes with infrequent mutation occurrence. We demonstrated that MUFFINN could retrieve known cancer genes more efficiently than gene-based methods and predicted cancer genes with low mutation occurrences in tumor samples. Users can freely access a web server ( http://www.inetbio.org/muffinn ) and run predictions with either public or private data of cancer somatic mutations. For given information of mutation occurrence profiles, the MUFFINN server returns lists of candidate cancer genes by four distinct predictions with different combinations between gene networks and scoring algorithms. Stand-alone software is also available, which allows MUFFINN to be run on local machines with a custom gene network. Here, we present an overall guideline for using the MUFFINN web server and stand-alone software for the discovery of novel cancer genes.


Assuntos
Biologia Computacional/métodos , Análise Mutacional de DNA/métodos , Redes Reguladoras de Genes , Sequenciamento de Nucleotídeos em Larga Escala/métodos , Mutação , Proteínas de Neoplasias/genética , Neoplasias/genética , Algoritmos , Perfilação da Expressão Gênica , Humanos , Neoplasias/diagnóstico , Transdução de Sinais , Software
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