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Identifying MicroRNA and Gene Expression Networks Using Graph Communities

Tsinghua Science and Technology 2016年第2期21卷 176-195页

作者： Benika Hall andrew quitadamo Xinghua Shi Department of Bioinformatics University of North Carolina at CharlotteCharlotteNC 28213USA.

Integrative network analysis is powerful in helping understand the underlying mechanisms of genetic and epigenetic perturbations for disease studies. Although it becomes clear that microRNAs, one type of epigenetic factors, have direct effect on target genes, it is unclear how microRNAs perturb downstream genetic neighborhood. Hence, we propose a network community approach to integrate microRNA and gene expression profiles, to construct an integrative genetic network perturbed by microRNAs. We apply this approach to an ovarian cancer dataset from The Cancer Genome Atlas project to identify the fluctuation of microRNA expression and its effects on gene expression. First, we perform expression quantitative loci analysis between microRNA and gene expression profiles via both a classical regression framework and a sparse learning model. Then, we apply the spin glass community detection algorithm to find genetic neighborhoods of the microRNAs and their associated genes. Finally, we construct an integrated network between microRNA and gene expression based on their community structure. Various disease related microRNAs and genes, particularly related to ovarian cancer, are identified in this network. Such an integrative network allows us to investigate the genetic neighborhood affected by microRNA expression that may lead to disease manifestation and progression.

关键词： network integration graph community detection spin glass algorithm microRNA networks

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Methods for Population-Based eQTL Analysis in Human Genetics

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Tsinghua Science and Technology 2014年第6期19卷 624-634页

作者： Lu Tian andrew quitadamo Frederick Lin Xinghua Shi Department of Bioinformatics and Genomics College of Computing and Informatics University of North Carolina at Charlotte NC 28223 USA

Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci（e QTL） analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide e QTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, e QTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for e QTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing e QTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.

关键词： expression Quantitative Trait Loci(e QTL) analysis confounding factors sparse learning models Lasso

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Comparison of a monoclonal antigen stool test (Hp StAR) with the ^(13)C-urea breath test in monitoring Helicobacter pylori eradication therapy

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World Journal of Gastroenterology 2005年第37期11卷 5878-5881页

作者： Francesco Perri Michele quitadamo Rosalba Ricciardi Ada Piepoli Rosa Cotugno Annamaria Gentile Alberto Pilotto Angelo Andriulli Department of Internal Medicine Gastroenterology Unit "Casa Sollievo della Sofferenza" Hospital IRCCS San Giovanni Rotondo Italy Department of Internal Medicine Geriatrics Unit "Casa Sollievo della Sofferenza" Hospital IRCCS San Giovanni Rotondo Italy

AIM： To evaluate the agreement between a mAb-based stool test （HP STAR） and the urea breath test （UBT） in monitoring （H pylon） infection after eradication therapy. METHODS： Patients with discordant results on UBT and Hp STAR underwent endoscopy with biopsies for rapid urease test, culture, and histology to confirm H pylori status. RESULTS： Among 250 patients （50±14 years）, 240 （96.0%） had concordant UBT and Hp STAR tests with a significant correlation between DOB and A values （R = 0.87; P〈0.0001）. The remaining 10 （4.0%） patients had discordant tests （positive Hp STAR and negative UBT） with the Hp STAR inaccurate in five cases （false positive） and UBT inaccurate in the other five cases （false negative）. The “maximal expected” sensitivity, specificity, ＋PV, -PV, ＋LR, and -LR were 91%, 100%, 100%, 97.4%, ∞, and 8.2 respectively, for the UBT, and 100%, 97.4%, 91%, 100%, 38.8, and 0, respectively, for the Hp STAR. Overall accuracy for both tests was 98%. CONCLUSION： Both the UBT and the Hp StAR are equally accurate in monitoring H pylori infection. Nowadays, the choice of the “best” non-invasive H pylori test in the post-treatment setting should be done not only in terms of diagnostic accuracy but also in view of cost and local facilities.

关键词： Diagnosis Helicobacter pylor Stool antigen test Urea breath test

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Lack of association between UGT1A7,UGT1A9,ARP,SPINK1 and CFTR gene polymorphisms and pancreatic cancer in Italian patients

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World Journal of Gastroenterology 2006年第39期12卷 6343-6348页

作者： Ada Piepoli Annamaria Gentile Maria Rosa Valvano Daniela Barana Cristina Oliani Rosa Cotugno Michele quitadamo Angelo Andriulli Francesco Perri Unit and Research Laboratory of Gastroenterology "Casa Sollievo della Sofferenza" Hospital IRCCS San Giovanni Rotondo Italy Cristina Oliani Department of Medical Oncology Hospital and University of Verona Verona Italy Oncology Unit ULSS5 Ovest Vicentino Verona Italy

AIM： To investigate simultaneously UGT1A7, UGT1A9, ARP, SPINK and CFTR genes to verify whether genetic polymorphisms predispose to the development of pancreatic cancer （PC）.METHODS： Genomic DNA of 61 pancreatic cancer patients and 105 healthy controls （HC） were analyzed. UGT1A7 genotyping was determined by PCR-RFLP analysis. Specific PCR and sequencing were used to analyze genetic variants of UGT1A9, ARP, SPINK1 and CFTR genes.RESULTS： Four different alleles （^＊1：WT; ＂2： N129K and R131K; ＂3： N129K, R131K, and W208R; and ＂4： W208R） in UGT1A7 and three different alleles （^＊1：WT;^＊4： Y242X; and ^＊5： D256N） in UGT1A9 were detected. All UGT1A polymorphisms were observed at similar frequency in PC patients and HC. Seven different alleles in ARP were found in PC patients and HC at similar frequency. The SPINK1 mutations N34S and P55S occurred in five PC patients with a prevalence （4.1%） not significantly different from that observed （2.0%） in HC. The only CFTR AFS08 mutation was recognized in three PC patients with a prevalence （4.9%） similar to HC.CONCLUSION： UGT1A7, UGT1A9, ARP, SPINK1 and CFTR gene polymorphisms are not associated with PC in Italian patients.

关键词： Pancreatic cancer Genetic polymorphisms Risk factors Sporadic

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