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1.
Epigenomics ; 11(13): 1487-1500, 2019 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-31536415

RESUMO

Aim: Cigarette smoking influences DNA methylation genome wide, in newborns from pregnancy exposure and in adults from personal smoking. Whether a unique methylation signature exists for in utero exposure in newborns is unknown. Materials & methods: We separately meta-analyzed newborn blood DNA methylation (assessed using Illumina450k Beadchip), in relation to sustained maternal smoking during pregnancy (9 cohorts, 5648 newborns, 897 exposed) and adult blood methylation and personal smoking (16 cohorts, 15907 participants, 2433 current smokers). Results & conclusion: Comparing meta-analyses, we identified numerous signatures specific to newborns along with many shared between newborns and adults. Unique smoking-associated genes in newborns were enriched in xenobiotic metabolism pathways. Our findings may provide insights into specific health impacts of prenatal exposure on offspring.

2.
J Allergy Clin Immunol ; 143(6): 2062-2074, 2019 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-30579849

RESUMO

BACKGROUND: Epigenetic mechanisms, including methylation, can contribute to childhood asthma. Identifying DNA methylation profiles in asthmatic patients can inform disease pathogenesis. OBJECTIVE: We sought to identify differential DNA methylation in newborns and children related to childhood asthma. METHODS: Within the Pregnancy And Childhood Epigenetics consortium, we performed epigenome-wide meta-analyses of school-age asthma in relation to CpG methylation (Illumina450K) in blood measured either in newborns, in prospective analyses, or cross-sectionally in school-aged children. We also identified differentially methylated regions. RESULTS: In newborns (8 cohorts, 668 cases), 9 CpGs (and 35 regions) were differentially methylated (epigenome-wide significance, false discovery rate < 0.05) in relation to asthma development. In a cross-sectional meta-analysis of asthma and methylation in children (9 cohorts, 631 cases), we identified 179 CpGs (false discovery rate < 0.05) and 36 differentially methylated regions. In replication studies of methylation in other tissues, most of the 179 CpGs discovered in blood replicated, despite smaller sample sizes, in studies of nasal respiratory epithelium or eosinophils. Pathway analyses highlighted enrichment for asthma-relevant immune processes and overlap in pathways enriched both in newborns and children. Gene expression correlated with methylation at most loci. Functional annotation supports a regulatory effect on gene expression at many asthma-associated CpGs. Several implicated genes are targets for approved or experimental drugs, including IL5RA and KCNH2. CONCLUSION: Novel loci differentially methylated in newborns represent potential biomarkers of risk of asthma by school age. Cross-sectional associations in children can reflect both risk for and effects of disease. Asthma-related differential methylation in blood in children was substantially replicated in eosinophils and respiratory epithelium.

3.
PLoS One ; 12(10): e0187287, 2017.
Artigo em Inglês | MEDLINE | ID: mdl-29088275

RESUMO

Recent developments in high throughput genomic assays have opened up the possibility of testing hundreds and thousands of genes simultaneously. However, adhering to the regular statistical assumptions regarding the null distributions of test statistics in such large-scale multiple testing frameworks has the potential of leading to incorrect significance testing results and biased inference. This problem gets worse when one combines results from different independent genomic experiments with a possibility of ending up with gross false discoveries of significant genes. In this article, we develop a meta-analysis method of combining p-values from different independent experiments involving large-scale multiple testing frameworks, through empirical adjustments of the individual test statistics and p-values. Even though, it is based on various existing ideas, this specific combination is novel and potentially useful. Through simulation studies and real genomic datasets we show that our method outperforms the standard meta-analysis approach of significance testing in terms of accurately identifying the truly significant set of genes.


Assuntos
Genômica , Metanálise como Assunto , Estatística como Assunto , Interpretação Estatística de Dados , Pesquisa Empírica , Expressão Gênica , Genes/genética , Genômica/métodos , Humanos , Neoplasias Pulmonares/genética , Modelos Estatísticos , Análise de Sequência com Séries de Oligonucleotídeos
4.
BMC Bioinformatics ; 18(1): 79, 2017 Feb 02.
Artigo em Inglês | MEDLINE | ID: mdl-28148240

RESUMO

BACKGROUND: Transcription factors are known to play key roles in carcinogenesis and therefore, are gaining popularity as potential therapeutic targets in drug development. A 'master regulator' transcription factor often appears to control most of the regulatory activities of the other transcription factors and the associated genes. This 'master regulator' transcription factor is at the top of the hierarchy of the transcriptomic regulation. Therefore, it is important to identify and target the master regulator transcription factor for proper understanding of the associated disease process and identifying the best therapeutic option. METHODS: We present a novel two-step computational approach for identification of master regulator transcription factor in a genome. At the first step of our method we test whether there exists any master regulator transcription factor in the system. We evaluate the concordance of two ranked lists of transcription factors using a statistical measure. In case the concordance measure is statistically significant, we conclude that there is a master regulator. At the second step, our method identifies the master regulator transcription factor, if there exists one. RESULTS: In the simulation scenario, our method performs reasonably well in validating the existence of a master regulator when the number of subjects in each treatment group is reasonably large. In application to two real datasets, our method ensures the existence of master regulators and identifies biologically meaningful master regulators. An R code for implementing our method in a sample test data can be found in http://www.somnathdatta.org/software . CONCLUSION: We have developed a screening method of identifying the 'master regulator' transcription factor just using only the gene expression data. Understanding the regulatory structure and finding the master regulator help narrowing the search space for identifying biomarkers for complex diseases such as cancer. In addition to identifying the master regulator our method provides an overview of the regulatory structure of the transcription factors which control the global gene expression profiles and consequently the cell functioning.


Assuntos
Perfilação da Expressão Gênica , Fatores de Transcrição/metabolismo , Interpretação Estatística de Dados , Redes Reguladoras de Genes , Humanos
5.
Biol Direct ; 11(1): 65, 2016 12 20.
Artigo em Inglês | MEDLINE | ID: mdl-27993151

RESUMO

BACKGROUND: It is believed that all cancers occur due to the mutation or change in one or more genes. In order to investigate the significance of the biological pathways which are interrupted by these genetic mutations, we pursue an integrated analysis using multiple cancer datasets released by the International Cancer Genome Consortium (ICGC). This dataset consists of expression profiles for genes/proteins of patients receiving treatment, for three types of cancer - Head and Neck Squamous Cell Carcinoma (HNSC), Lung Adenocarcinoma (LUAD) and Kidney Renal Clear Cell Carcinoma (KIRC). We consider pathway analysis to identify all the biological pathways which are active among the patients and investigate the roles of the significant pathways using a differential network analysis of the protein expression datasets for the three cancers separately. We then integrate the pathway based results of all the three cancers which provide a more comprehensive picture of the three cancers. RESULTS: From our analysis of the protein expression data, overall, RAS and PI3K signaling pathways appear to play the most significant roles in the three cancers - Head and Neck Squamous Cell Carcinoma (HNSC), Lung Adenocarcinoma (LUAD) and Kidney Renal Clear Cell Carcinoma (KIRC). CONCLUSION: This analysis suggests that the RAS and PI3K signaling pathways are the two most important pathways in all the three cancers and should be investigated further for their potential roles in cancers. REVIEWERS: This article was reviewed by Joaquin Dopazo and Samiran Ghosh.


Assuntos
Adenocarcinoma/genética , Carcinoma de Células Renais/genética , Carcinoma de Células Escamosas/genética , Neoplasias de Cabeça e Pescoço/genética , Neoplasias Renais/genética , Neoplasias Pulmonares/genética , Transdução de Sinais , Transcriptoma , Adenocarcinoma de Pulmão , Humanos , Proteoma , Carcinoma de Células Escamosas de Cabeça e Pescoço
6.
Brief Bioinform ; 17(2): 262-9, 2016 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-26141827

RESUMO

MOTIVATION: Many approaches have been proposed for the protein identification problem based on tandem mass spectrometry (MS/MS) data. In these experiments, proteins are digested into peptides and the resulting peptide mixture is subjected to mass spectrometry. Some interesting putative peptide features (peaks) are selected from the mass spectra. Following that, the precursor ions undergo fragmentation and are analyzed by MS/MS. The process of identification of peptides from the mass spectra and the constituent proteins in the sample is called protein identification from MS/MS data. There are many two-step protein identification procedures, reviewed in the literature, which first attempt to identify the peptides in a separate process and then use these results to infer the proteins. However, in recent years, there have been attempts to provide a one-step solution to protein identification, which simultaneously identifies the proteins and the peptides in the sample. RESULTS: In this review, we briefly introduce the most popular two-step protein identification procedure, PeptideProphet coupled with ProteinProphet. Following that, we describe the difficulties with two-step procedures and review some recently introduced one-step protein/peptide identification procedures that do not suffer from these issues. The focus of this review is on one-step procedures that are based on statistical likelihood-based models, but some discussion of other one-step procedures is also included. We report comparative performances of one-step and two-step methods, which support the overall superiorities of one-step procedures. We also cover some recent efforts to improve protein identification by incorporating other molecular data along with MS/MS data.


Assuntos
Algoritmos , Bases de Dados de Proteínas , Mapeamento de Peptídeos/métodos , Proteínas/análise , Proteínas/química , Espectrometria de Massas em Tandem/métodos , Sequência de Aminoácidos , Mineração de Dados/métodos , Dados de Sequência Molecular , Reprodutibilidade dos Testes , Sensibilidade e Especificidade , Integração de Sistemas
7.
Bioinformation ; 10(10): 647-51, 2014.
Artigo em Inglês | MEDLINE | ID: mdl-25489174

RESUMO

Various methods to determine the connectivity scores between groups of proteins associated with lung adenocarcinoma are examined. Proteins act together to perform a wide range of functions within biological processes. Hence, identification of key proteins and their interactions within protein networks can provide invaluable information on disease mechanisms. Differential network analysis provides a means of identifying differences in the interactions among proteins between two networks. We use connectivity scores based on the method of partial least squares to quantify the strength of the interactions between each pair of proteins. These scores are then used to perform permutation-based statistical tests. This examines if there are significant differences between the network connectivity scores for individual proteins or classes of proteins. The expression data from a study on lung adenocarcinoma is used in this study. Connectivity scores are computed for a group of 109 subjects who were in the complete remission and as well as for a group of 51 subjects whose cancer had progressed. The distributions of the connectivity scores are similar for the two networks yet subtle but statistically significant differences have been identified and their impact discussed.

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