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Gene expression profile of cervical and skin tissues from human papillomavirus type 16 E6 transgenic mice
D Mendoza-Villanueva, J Diaz-Chavez, L Uribe-Figueroa, C Rangel-Escare?o, A Hidalgo-Miranda, S March-Mifsut, G Jimenez-Sanchez, PF Lambert, P Gariglio
BMC Cancer , 2008, DOI: 10.1186/1471-2407-8-347
Abstract: We evaluated the expression profile of 14,000 genes in skin or cervix from young K14E6 transgenic mice compared with nontransgenic. To identify differentially expressed genes a linear model was implemented using R and the LIMMA package. Two criteria were used to select the set of relevant genes. First a set of genes with a Log-odds ≥ 3 were selected. Then, a hierarchical search of genes was based on Log Fold Changes.Microarray analysis identified a total of 676 and 1154 genes that were significantly up and down-regulated, respectively, in skin from K14E6 transgenic mice. On the other hand, in the cervix from K14E6 transgenic mice we found that only 97 and 252 genes were significantly up and down-regulated, respectively. One of the most affected processes in the skin from K14E6 transgenic mice was the cell cycle. We also found that skin from transgenic mice showed down-regulation of pro-apoptotic genes and genes related to the immune response. In the cervix of K14E6 transgenic mice, we could not find affected any gene related to the cell cycle and apoptosis pathways but did observe alterations in the expression of immune response genes. Pathways such as angiogenesis, cell junction and epidermis development, also were altered in their gene expression profiles in both tissues.Expression of the HPV16 E6 oncoprotein in our model alters expression of genes that fell into several functional groups providing insights into pathways by which E6 deregulate cell cycle progression, apoptosis, the host resistance to infection and immune function, providing new opportunities for early diagnostic markers and therapeutic drug targets.Cancer development usually takes several decades to arise, and follows a progressive histopathological pattern that involves acquisition of multiple genetic changes to the cancer cell. Human papillomaviruses (HPVs) are small DNA tumor viruses that cause benign tumors in human skin. A subset of anogenital HPVs, the high-risk HPVs (HR-HPVs), is associated
Microarray profile of differentially expressed genes in a monkey model of allergic asthma
Jun Zou, Simon Young, Feng Zhu, Ferdous Gheyas, Susan Skeans, Yuntao Wan, Luquan Wang, Wei Ding, Motasim Billah, Terri McClanahan, Robert L Coffman, Robert Egan, Shelby Umland
Genome Biology , 2002, DOI: 10.1186/gb-2002-3-5-research0020
Abstract: Of the approximately 40,000 cDNAs represented on the microarray, expression levels of 169 changed by more than 2.5-fold in at least one of the pairwise probe comparisons; these cDNAs encoded 149 genes, of which two thirds are known genes. The largest number of regulated genes was observed 4 h after challenge. Confirmation of differential expression in the original tissue was obtained for 95% of a set of these genes using real-time PCR. Cluster analysis revealed at least five groups of genes with unique expression patterns. One cluster contained genes for several chemokine mediators including eotaxin, PARC, MCP-1 and MCP-3. Genes involved in tissue remodeling and antioxidant responses were also identified as regulated by antigen and IL-4 or by antigen only.This study provides a large-scale profile of gene expression in the primate lung following allergen or IL-4 challenge. It shows that microarrays, with real-time PCR, are a powerful tool for identifying and validating differentially expressed genes in a disease model.Asthma is one of the most serious allergic diseases. Characteristic features of atopic asthma are circulating specific IgE antibodies, positive skin tests to common allergens, and infiltration of the bronchial mucosa with eosinophils and Th2 cells. The resulting pulmonary inflammation leads to bronchoconstriction, airway hyper-responsiveness, and ultimately to airway remodeling [1]. Many cellular mediators, including cytokines and chemokines, are involved in asthma; Th2-type cytokines such as interleukin-4 (IL-4), IL-5, IL-9 and IL-13 may contribute to pathophysiological changes in asthma [2]. The complexity of asthma originates from the interaction of an unknown number of genes with environmental factors [3]. Studies of twins have shown that concordance rates for asthma are significantly higher in monozygotic twins than in dizygotic twins, and that the heritability of asthma may be as high as 75% [4]. Linkage analysis of asthma within families has reve
The Long Noncoding RNA Expression Profile of Hepatocellular Carcinoma Identified by Microarray Analysis  [PDF]
Juanjuan Zhu, Shanshan Liu, Fuqiang Ye, Yuan Shen, Yi Tie, Jie Zhu, Yinghua Jin, Xiaofei Zheng, Yongge Wu, Hanjiang Fu
PLOS ONE , 2014, DOI: 10.1371/journal.pone.0101707
Abstract: Background Thousands of long noncoding RNAs (lncRNAs) have been reported in mammalian genomes. These RNAs represent an important subset of pervasive genes involved in a broad range of biological functions. Aberrant expression of lncRNAs is associated with many types of cancers. Here, in order to explore the potential lncRNAs involved in hepatocellular carcinoma (HCC) oncogenesis, we performed lncRNA gene expression profile analysis in 3 pairs of human HCC and adjacent non-tumor (NT) tissues by microarray. Methodology Differentially expressed lncRNAs and mRNAs were detected by human lncRNA microarray containing 33,045 lncRNAs and 30,215 coding transcripts. Bioinformatic analyses (gene ontology, pathway and network analysis) were applied for further study of these differentially expressed mRNAs. By qRT-PCR analysis in nineteen pairs of HCC and adjacent normal tissues, we found that eight lncRNAs were aberrantly expressed in HCC compared with adjacent NT tissues, which is consistent with microarray data. Conclusions We identified 214 lncRNAs and 338 mRNAs abnormally expressed in all three HCC tissues (Fold Change ≥2.0, P<0.05 and FDR <0.05) with the genome-wide lncRNAs and mRNAs expression profile analysis. The lncRNA-mRNA co-expression network was constructed, which may be used for predicting target genes of lncRNAs. Furthermore, we demonstrated for the first time that BC017743, ENST00000395084, NR_026591, NR_015378 and NR_024284 were up-regulated, whereas NR_027151, AK056988 and uc003yqb.1 were down-regulated in nineteen pairs of HCC samples compared with adjacent NT samples. Expression of seven lncRNAs was significantly correlated to their nearby coding genes. In conclusion, our results indicated that the lncRNA expression profile in HCC was significantly changed, and we identified a series of new hepatocarcinoma associated lncRNAs. These results provide important insights about the lncRNAs in HCC pathogenesis.
A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility
Paul Roepman, Arenda Schuurman, Leonie JMJ Delahaye, Anke T Witteveen, Arno N Floore, Annuska M Glas
BMC Medical Genomics , 2009, DOI: 10.1186/1755-8794-2-52
Abstract: In this study we investigated whether we could use transcriptional activity of a specific set of genes instead of histopathological review to identify samples with sufficient tumour cell content. Genome-wide gene expression measurements were used to develop a transcriptional gene profile that could accurately assess a sample's tumour cell percentage.Supervised analysis across 165 breast tumour samples resulted in the identification of a set of 13 genes which expression correlated with presence of tumour cells. The developed gene profile showed a high performance (AUC 0.92) for identification of samples that are suitable for microarray diagnostics. Validation on 238 additional breast tumour samples indicated a robust performance for correct classification with an overall accuracy of 91 percent and a kappa score of 0.63 (95%CI 0.47–0.73).The developed 13-gene profile provides an objective tool for assessment whether a breast cancer sample contains sufficient tumour cells for microarray diagnostics. It will improve the efficiency and throughput for diagnostic gene expression profiling as it no longer requires histopathological analysis for initial tumour percentage scoring. Such profile will also be very use useful for assessment of tumour cell percentage in biopsies where conventional histopathology is difficult, such as fine needle aspirates.Microarray diagnostics of tumour specimens is based on gene expression measurement of a specific set of predictive or prognostic genes. Bulk tumour samples that consist of tumour cells admixed with surrounding stromal tissue are commonly used for microarray analysis. Although tumour stroma likely plays an important role in tumour development and metastasis [1-3], gene expression profiles are typically generated using tissue that contains sufficient amount of tumour cells, not stroma. Most prognostic gene profiles were originally identified using samples containing at least 50% tumour cells and are, therefore, likely based on gene
Evaluation of time profile reconstruction from complex two-color microarray designs
Ana C Fierro, Raphael Thuret, Kristof Engelen, Gilles Bernot, Kathleen Marchal, Nicolas Pollet
BMC Bioinformatics , 2008, DOI: 10.1186/1471-2105-9-1
Abstract: On average the correlation between the estimated ratios was high, and all methods agreed with each other in predicting the same profile, especially for genes of which the expression profile showed a large variance across the different time points. Assessing the similarity in profile shape, it appears that, the more similar the underlying principles of the methods (model and input data), the more similar their results. Methods with a dye effect seemed more robust against array failure. The influence of a different normalization was not drastic and independent of the method used.Including a dye effect such as in the methods lmbr_dye, anovaFix and anovaMix compensates for residual dye related inconsistencies in the data and renders the results more robust against array failure. Including random effects requires more parameters to be estimated and is only advised when a design is used with a sufficient number of replicates. Because of this, we believe lmbr_dye, anovaFix and anovaMix are most appropriate for practical use.Microarray experiments have become an important tool for biological studies, allowing the quantification of thousands of mRNA levels simultaneously. They are being customarily applied in current molecular biology practice.In contrast to the Affymetrix based technology, for the two-channel microarray technology assays, mRNA extracted from two conditions is hybridised simultaneously on a given microarray. Which conditions to pair on the same array is a non trivial issue and relates to the choice of the "microarray design". The most intuitively interpretable and frequently used design is the "reference design" in which a single, fixed reference condition is chosen against which all conditions are compared. Alternatively, other designs have been proposed (e.g. a loop design). From a theoretical point of view, these alternative designs usually offer, at the same cost, more balanced measurements in the number of replicates per condition than a common referenc
Maximum predictive power of the microarray-based models for clinical outcomes is limited by correlation between endpoint and gene expression profile
Zhao Chen,Shi Leming,Tong Weida,Shaughnessy John D
BMC Genomics , 2011, DOI: 10.1186/1471-2164-12-s5-s3
Abstract: Background Microarray data have been used for gene signature selection to predict clinical outcomes. Many studies have attempted to identify factors that affect models' performance with only little success. Fine-tuning of model parameters and optimizing each step of the modeling process often results in over-fitting problems without improving performance. Results We propose a quantitative measurement, termed consistency degree, to detect the correlation between disease endpoint and gene expression profile. Different endpoints were shown to have different consistency degrees to gene expression profiles. The validity of this measurement to estimate the consistency was tested with significance at a p-value less than 2.2e-16 for all of the studied endpoints. According to the consistency degree score, overall survival milestone outcome of multiple myeloma was proposed to extend from 730 days to 1561 days, which is more consistent with gene expression profile. Conclusion For various clinical endpoints, the maximum predictive powers of different microarray-based models are limited by the correlation between endpoint and gene expression profile of disease samples as indicated by the consistency degree score. In addition, previous defined clinical outcomes can also be reassessed and refined more coherent according to related disease gene expression profile. Our findings point to an entirely new direction for assessing the microarray-based predictive models and provide important information to gene signature based clinical applications.
Skin and Soft Tissue Infections: Bacteriological Profile and Antibiotic Resistance Pattern of Isolates  [PDF]
P Sah,R Khanal,S Upadhaya
Journal of Universal College of Medical Sciences , 2013, DOI: 10.3126/jucms.v1i3.8759
Abstract: INTRODUCTION: Emergence and spread of antibiotic resistance in organisms causing skin and soft tissue infections (SSTIs) is posing a great therapeutic challenge. This study aimed to determine bacteriology of SSTIs and study antibiotic resistance among the isolates.
Metabolic Changes in Skin Caused by Scd1 Deficiency: A Focus on Retinol Metabolism  [PDF]
Matthew T. Flowers,Chad M. Paton,Sheila M. O'Byrne,Kevin Schiesser,John A. Dawson,William S. Blaner,Christina Kendziorski,James M. Ntambi
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0019734
Abstract: We previously reported that mice with skin-specific deletion of stearoyl-CoA desaturase-1 (Scd1) recapitulated the skin phenotype and hypermetabolism observed in mice with a whole-body deletion of Scd1. In this study, we first performed a diet-induced obesity experiment at thermoneutral temperature (33°C) and found that skin-specific Scd1 knockout (SKO) mice still remain resistant to obesity. To elucidate the metabolic changes in the skin that contribute to the obesity resistance and skin phenotype, we performed microarray analysis of skin gene expression in male SKO and control mice fed a standard rodent diet. We identified an extraordinary number of differentially expressed genes that support the previously documented histological observations of sebaceous gland hypoplasia, inflammation and epidermal hyperplasia in SKO mice. Additionally, transcript levels were reduced in skin of SKO mice for genes involved in fatty acid synthesis, elongation and desaturation, which may be attributed to decreased abundance of key transcription factors including SREBP1c, ChREBP and LXRα. Conversely, genes involved in cholesterol synthesis were increased, suggesting an imbalance between skin fatty acid and cholesterol synthesis. Unexpectedly, we observed a robust elevation in skin retinol, retinoic acid and retinoic acid-induced genes in SKO mice. Furthermore, SEB-1 sebocytes treated with retinol and SCD inhibitor also display an elevation in retinoic acid-induced genes. These results highlight the importance of monounsaturated fatty acid synthesis for maintaining retinol homeostasis and point to disturbed retinol metabolism as a novel contributor to the Scd1 deficiency-induced skin phenotype.
Cytokine profile in Montenegro skin test of patients with localized cutaneous and mucocutaneous leishmaniasis
Nogueira, Marcia Ferraz;Goto, Hiro;Sotto, Mírian Nacagami;Cucé, Luiz Carlos;
Revista do Instituto de Medicina Tropical de S?o Paulo , 2008, DOI: 10.1590/S0036-46652008000600004
Abstract: american tegumentary leishmaniasis presents as two major clinical forms: localized cutaneous leishmaniasis (lcl) and mucocutaneous leishmaniasis (mcl). the immune response in leishmaniasis is efficiently evaluated by the response to leishmania antigen through the montenegro skin test (mst). both lcl and mcl present positive response to mst, indicating that the patients present cell-mediated immunity against the parasite - leishmania. in spite of the presence of immunity in mcl, this is not sufficient to stop disease progression and prevent resistance to treatment. in this study we demonstrated interleukin (il) 2, 4, 5 and interferon (ifn) gamma expression in biopsies of mst of ten patients with american tegumentary leishmaniasis. the obtained results were compared between lcl (n = 5) and mcl (n = 5) patients. the mst of mcl patients displayed a higher expression of il-2, il-4 and il-5, in comparison to lcl. there was no significant difference in ifn-gamma expression between groups. the obtained results suggest the role of il-4 and il-5 in the maintenance of the immunopathogenic mechanism of the destructive lesions that characterize mcl.
Discovering time-lagged rules from microarray data using gene profile classifiers
Cristian A Gallo, Jessica A Carballido, Ignacio Ponzoni
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-123
Abstract: This paper aims at reconstructing gene regulatory networks from multiple genome-wide microarray time series datasets. In this sense, a new model-free algorithm called GRNCOP2 (Gene Regulatory Network inference by Combinatorial OPtimization 2), which is a significant evolution of the GRNCOP algorithm, was developed using combinatorial optimization of gene profile classifiers. The method is capable of inferring potential time-delay relationships with any span of time between genes from various time series datasets given as input. The proposed algorithm was applied to time series data composed of twenty yeast genes that are highly relevant for the cell-cycle study, and the results were compared against several related approaches. The outcomes have shown that GRNCOP2 outperforms the contrasted methods in terms of the proposed metrics, and that the results are consistent with previous biological knowledge. Additionally, a genome-wide study on multiple publicly available time series data was performed. In this case, the experimentation has exhibited the soundness and scalability of the new method which inferred highly-related statistically-significant gene associations.A novel method for inferring time-delayed gene regulatory networks from genome-wide time series datasets is proposed in this paper. The method was carefully validated with several publicly available data sets. The results have demonstrated that the algorithm constitutes a usable model-free approach capable of predicting meaningful relationships between genes, revealing the time-trends of gene regulation.The genome encodes thousands of genes whose products enable cell survival and numerous cellular functions. The amount and the temporal pattern in which these products appear in the cell are crucial to the processes of life. Gene Regulatory Networks (GRNs) govern the levels of these gene products. A GRN is the collection of molecular species and their interactions, which together control gene product abundanc
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