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1.
PLoS One ; 15(4): e0232046, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32352996

RESUMO

Advancements in the field of synthetic biology have been possible due to the development of genetic tools that are able to regulate gene expression. However, the current toolbox of gene regulatory tools for eukaryotic systems have been outpaced by those developed for simple, single-celled systems. Here, we engineered a set of gene regulatory tools by combining self-cleaving ribozymes with various upstream competing sequences that were designed to disrupt ribozyme self-cleavage. As a proof-of-concept, we were able to modulate GFP expression in mammalian cells, and then showed the feasibility of these tools in Drosophila embryos. For each system, the fold-reduction of gene expression was influenced by the location of the self-cleaving ribozyme/upstream competing sequence (i.e. 5' vs. 3' untranslated region) and the competing sequence used. Together, this work provides a set of genetic tools that can be used to tune gene expression across various eukaryotic systems.


Assuntos
Engenharia Genética/métodos , RNA Catalítico/fisiologia , Biologia Sintética/métodos , Animais , Drosophila/genética , Eucariotos/genética , Eucariotos/metabolismo , Células Eucarióticas/metabolismo , Expressão Gênica/genética , Expressão Gênica/fisiologia , Regulação da Expressão Gênica/genética , Regulação da Expressão Gênica/fisiologia , Conformação de Ácido Nucleico , Estudo de Prova de Conceito , RNA Catalítico/genética , RNA Mensageiro/metabolismo
2.
J Natl Cancer Inst ; 112(10): 1003-1012, 2020 Oct 01.
Artigo em Inglês | MEDLINE | ID: mdl-31917448

RESUMO

BACKGROUND: Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. METHODS: To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74-421 samples). RESULTS: We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate < .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. CONCLUSIONS: By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.

3.
Nat Commun ; 9(1): 556, 2018 02 08.
Artigo em Inglês | MEDLINE | ID: mdl-29422604

RESUMO

In 2020, 146,063 deaths due to pancreatic cancer are estimated to occur in Europe and the United States combined. To identify common susceptibility alleles, we performed the largest pancreatic cancer GWAS to date, including 9040 patients and 12,496 controls of European ancestry from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4). Here, we find significant evidence of a novel association at rs78417682 (7p12/TNS3, P = 4.35 × 10-8). Replication of 10 promising signals in up to 2737 patients and 4752 controls from the PANcreatic Disease ReseArch (PANDoRA) consortium yields new genome-wide significant loci: rs13303010 at 1p36.33 (NOC2L, P = 8.36 × 10-14), rs2941471 at 8q21.11 (HNF4G, P = 6.60 × 10-10), rs4795218 at 17q12 (HNF1B, P = 1.32 × 10-8), and rs1517037 at 18q21.32 (GRP, P = 3.28 × 10-8). rs78417682 is not statistically significantly associated with pancreatic cancer in PANDoRA. Expression quantitative trait locus analysis in three independent pancreatic data sets provides molecular support of NOC2L as a pancreatic cancer susceptibility gene.


Assuntos
Carcinoma Ductal Pancreático/genética , Neoplasias Pancreáticas/genética , Bases de Dados Genéticas , Predisposição Genética para Doença , Estudo de Associação Genômica Ampla , Fator 1-beta Nuclear de Hepatócito/genética , Fator 4 Nuclear de Hepatócito/genética , Humanos , Peptídeos e Proteínas de Sinalização Intracelular , Polimorfismo de Nucleotídeo Único , Proteínas/genética , Proteínas Repressoras/genética , Tensinas/genética
4.
Gut ; 67(3): 521-533, 2018 03.
Artigo em Inglês | MEDLINE | ID: mdl-28634199

RESUMO

OBJECTIVE: To elucidate the genetic architecture of gene expression in pancreatic tissues. DESIGN: We performed expression quantitative trait locus (eQTL) analysis in histologically normal pancreatic tissue samples (n=95) using RNA sequencing and the corresponding 1000 genomes imputed germline genotypes. Data from pancreatic tumour-derived tissue samples (n=115) from The Cancer Genome Atlas were included for comparison. RESULTS: We identified 38 615 cis-eQTLs (in 484 genes) in histologically normal tissues and 39 713 cis-eQTL (in 237 genes) in tumour-derived tissues (false discovery rate <0.1), with the strongest effects seen near transcriptional start sites. Approximately 23% and 42% of genes with significant cis-eQTLs appeared to be specific for tumour-derived and normal-derived tissues, respectively. Significant enrichment of cis-eQTL variants was noted in non-coding regulatory regions, in particular for pancreatic tissues (1.53-fold to 3.12-fold, p≤0.0001), indicating tissue-specific functional relevance. A common pancreatic cancer risk locus on 9q34.2 (rs687289) was associated with ABO expression in histologically normal (p=5.8×10-8) and tumour-derived (p=8.3×10-5) tissues. The high linkage disequilibrium between this variant and the O blood group generating deletion variant in ABO (exon 6) suggested that nonsense-mediated decay (NMD) of the 'O' mRNA might explain this finding. However, knockdown of crucial NMD regulators did not influence decay of the ABO 'O' mRNA, indicating that a gene regulatory element influenced by pancreatic cancer risk alleles may underlie the eQTL. CONCLUSIONS: We have identified cis-eQTLs representing potential functional regulatory variants in the pancreas and generated a rich data set for further studies on gene expression and its regulation in pancreatic tissues.


Assuntos
Sistema ABO de Grupos Sanguíneos/genética , Expressão Gênica , Pâncreas , Neoplasias Pancreáticas/genética , Locos de Características Quantitativas , RNA Neoplásico/análise , Transcriptoma , Alelos , Cromossomos Humanos Par 9 , Estudo de Associação Genômica Ampla , Genótipo , Humanos , Degradação do RNAm Mediada por Códon sem Sentido , Polimorfismo de Nucleotídeo Único , Sequências Reguladoras de Ácido Nucleico , Análise de Sequência de RNA
5.
BMC Syst Biol ; 10(1): 85, 2016 08 31.
Artigo em Inglês | MEDLINE | ID: mdl-27576572

RESUMO

BACKGROUND: A complex network of gene interactions controls gene regulation throughout development and the life of the organisms. Insights can be made into these processes by studying the functional interactions (or "motifs") which make up these networks. RESULTS: We sought to understand the functionality of one of these network motifs, negative feedback, in a multi-cellular system. This was accomplished using a synthetic network expressed in the Drosophila melanogaster embryo using the yeast proteins Gal4 (a transcriptional activator) and Gal80 (an inhibitor of Gal4 activity). This network is able to produce an attenuation or shuttling phenotype depending on the Gal80/Gal4 ratio. This shuttling behavior was validated by expressing Gal3, which inhibits Gal80, to produce a localized increase in free Gal4 and therefore signaling. Mathematical modeling was used to demonstrate the capacity for negative feedback to produce these varying outputs. CONCLUSIONS: The capacity of a network motif to exhibit different phenotypes due to minor changes to the network in multi-cellular systems was shown. This work demonstrates the importance of studying network motifs in multi-cellular systems.


Assuntos
Biologia Computacional , Drosophila melanogaster/embriologia , Drosophila melanogaster/genética , Embrião não Mamífero/metabolismo , Retroalimentação Fisiológica , Redes Reguladoras de Genes , Proteínas de Saccharomyces cerevisiae/genética , Animais , Expressão Gênica , Modelos Genéticos
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