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  • Gene Expression Profiling
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  • 1
    Language: English
    In: PLOS ONE, 9/22/2017, Vol.12(9), p.e0184795
    Description: The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches.
    Keywords: Algorithms ; Gene Regulatory Networks ; Gene Expression Profiling -- Methods;
    ISSN: PLOS ONE
    E-ISSN: 1932-6203
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  • 2
    In: PLoS ONE, 2014, Vol.9(3)
    Description: Background Although microarrays are analysis tools in biomedical research, they are known to yield noisy output that usually requires experimental confirmation. To tackle this problem, many studies have developed rules for optimizing probe design and devised complex statistical tools to analyze the output. However, less emphasis has been placed on systematically identifying the noise component as part of the experimental procedure. One source of noise is the variance in probe binding, which can be assessed by replicating array probes. The second source is poor probe performance, which can be assessed by calibrating the array based on a dilution series of target molecules. Using model experiments for copy number variation and gene expression measurements, we investigate here a revised design for microarray experiments that addresses both of these sources of variance. Results Two custom arrays were used to evaluate the revised design: one based on 25 mer probes from an Affymetrix design and the other based on 60 mer probes from an Agilent design. To assess experimental variance in probe binding, all probes were replicated ten times. To assess probe performance, the probes were calibrated using a dilution series of target molecules and the signal response was fitted to an adsorption model. We found that significant variance of the signal could be controlled by averaging across probes and removing probes that are nonresponsive or poorly responsive in the calibration experiment. Taking this into account, one can obtain a more reliable signal with the added option of obtaining absolute rather than relative measurements. Conclusion The assessment of technical variance within the experiments, combined with the calibration of probes allows to remove poorly responding probes and yields more reliable signals for the remaining ones. Once an array is properly calibrated, absolute quantification of signals becomes straight forward, alleviating the need for normalization and reference hybridizations.
    Keywords: Research Article ; Biology ; Chemistry ; Computer Science
    E-ISSN: 1932-6203
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  • 3
    In: PLoS ONE, 2015, Vol.10(9)
    Description: Inferring the gene regulatory network (GRN) is crucial to understanding the working of the cell. Many computational methods attempt to infer the GRN from time series expression data, instead of through expensive and time-consuming experiments. However, existing methods make the convenient but unrealistic assumption of causal sufficiency , i.e. all the relevant factors in the causal network have been observed and there are no unobserved common cause. In principle, in the real world, it is impossible to be certain that all relevant factors or common causes have been observed, because some factors may not have been conceived of, and therefore are impossible to measure. In view of this, we have developed a novel algorithm named HCC-CLINDE to infer an GRN from time series data allowing the presence of hidden common cause(s). We assume there is a sparse causal graph (possibly with cycles) of interest, where the variables are continuous and each causal link has a delay (possibly more than one time step). A small but unknown number of variables are not observed. Each unobserved variable has only observed variables as children and parents, with at least two children, and the children are not linked to each other. Since it is difficult to obtain very long time series, our algorithm is also capable of utilizing multiple short time series, which is more realistic. To our knowledge, our algorithm is far less restrictive than previous works. We have performed extensive experiments using synthetic data on GRNs of size up to 100, with up to 10 hidden nodes. The results show that our algorithm can adequately recover the true causal GRN and is robust to slight deviation from Gaussian distribution in the error terms. We have also demonstrated the potential of our algorithm on small YEASTRACT subnetworks using limited real data.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 4
    In: PLoS ONE, 2015, Vol.10(9)
    Description: Background Gene expression analysis is an essential part of biological and medical investigations. Quantitative real-time PCR (qPCR) is characterized with excellent sensitivity, dynamic range, reproducibility and is still regarded to be the gold standard for quantifying transcripts abundance. Parallelization of qPCR such as by microfluidic Taqman Fluidigm Biomark Platform enables evaluation of multiple transcripts in samples treated under various conditions. Despite advanced technologies, correct evaluation of the measurements remains challenging. Most widely used methods for evaluating or calculating gene expression data include geNorm and ΔΔ C t , respectively. They rely on one or several stable reference genes (RGs) for normalization, thus potentially causing biased results. We therefore applied multivariable regression with a tailored error model to overcome the necessity of stable RGs. Results We developed a RG independent data normalization approach based on a tailored l inear e rror m odel for parallel qPCR data, called LEMming. It uses the assumption that the mean C t values within samples of similarly treated groups are equal. Performance of LEMming was evaluated in three data sets with different stability patterns of RGs and compared to the results of geNorm normalization. Data set 1 showed that both methods gave similar results if stable RGs are available. Data set 2 included RGs which are stable according to geNorm criteria, but became differentially expressed in normalized data evaluated by a t-test. geNorm -normalized data showed an effect of a shifted mean per gene per condition whereas LEMming-normalized data did not. Comparing the decrease of standard deviation from raw data to geNorm and to LEMming, the latter was superior. In data set 3 according to geNorm calculated average expression stability and pairwise variation, stable RGs were available, but t-tests of raw data contradicted this. Normalization with RGs resulted in distorted data contradicting literature, while LEMming normalized data did not. Conclusions If RGs are coexpressed but are not independent of the experimental conditions the stability criteria based on inter- and intragroup variation fail. The linear error model developed, LEMming, overcomes the dependency of using RGs for parallel qPCR measurements, besides resolving biases of both technical and biological nature in qPCR. However, to distinguish systematic errors per treated group from a global treatment effect an additional measurement is needed. Quantification of total cDNA content per sample helps to identify systematic errors.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 5
    In: PLoS ONE, 2014, Vol.9(6)
    Description: Using published dairy cattle liver transcriptomics dataset along with novel blood biomarkers of liver function, metabolism, and inflammation we have attempted an integrative systems biology approach applying the classical functional enrichment analysis using DAVID, a newly-developed Dynamic Impact Approach (DIA), and an upstream gene network analysis using Ingenuity Pathway Analysis (IPA). Transcriptome data was generated from experiments evaluating the impact of prepartal plane of energy intake [overfed (OF) or restricted (RE)] on liver of dairy cows during the peripartal period. Blood biomarkers uncovered that RE vs. OF led to greater prepartal liver distress accompanied by a low-grade inflammation and larger proteolysis (i.e., higher haptoglobin, bilirubin, and creatinine). Post-partum the greater bilirubinaemia and lipid accumulation in OF vs. RE indicated a large degree of liver distress. The re-analysis of microarray data revealed that expression of 〉4,000 genes was affected by diet × time. The bioinformatics analysis indicated that RE vs. OF cows had a liver with a greater lipid and amino acid catabolic capacity both pre- and post-partum while OF vs. RE cows had a greater activation of pathways/functions related to triglyceride synthesis. Furthermore, RE vs. OF cows had a larger (or higher capacity to cope with) ER stress likely associated with greater protein synthesis/processing, and a higher activation of inflammatory-related functions. Liver in OF vs. RE cows had a larger cell proliferation and cell-to-cell communication likely as a response to the greater lipid accumulation. Analysis of upstream regulators indicated a pivotal role of several lipid-related transcription factors (e.g., PPARs, SREBPs, and NFE2L2) in priming the liver of RE cows to better face the early postpartal metabolic and inflammatory challenges. An all-encompassing dynamic model was proposed based on the findings.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Research And Analysis Methods
    E-ISSN: 1932-6203
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  • 6
    Language: English
    In: 2013, Vol.8(9), p.e76623
    Description: Oligodendroglial tumors form a distinct subgroup of gliomas, characterized by a better response to treatment and prolonged overall survival. Most oligodendrogliomas and also some oligoastrocytomas are characterized by a unique and typical unbalanced translocation, der(1,19), resulting in a 1p/19q co-deletion. Candidate tumor suppressor genes targeted by these losses, CIC on 19q13.2 and FUBP1 on 1p31.1, were only recently discovered. We analyzed 17 oligodendrogliomas and oligoastrocytomas by applying a comprehensive approach consisting of RNA expression analysis, DNA sequencing of CIC , FUBP1 , IDH1/2 , and array CGH. We confirmed three different genetic subtypes in our samples: i) the “oligodendroglial” subtype with 1p/19q co-deletion in twelve out of 17 tumors; ii) the “astrocytic” subtype in three tumors; iii) the “other” subtype in two tumors. All twelve tumors with the 1p/19q co-deletion carried the most common IDH1 R132H mutation. In seven of these tumors, we found protein-disrupting point mutations in the remaining allele of CIC , four of which are novel. One of these tumors also had a deleterious mutation in FUBP1 . Only by integrating RNA expression and array CGH data, were we able to discover an exon-spanning homozygous microdeletion within the remaining allele of CIC in an additional tumor with 1p/19q co-deletion. Therefore we propose that the mutation rate might be underestimated when looking at sequence variants alone. In conclusion, the high frequency and the spectrum of CIC mutations in our 1p/19q-codeleted tumor cohort support the hypothesis that CIC acts as a tumor suppressor in these tumors, whereas FUBP1 might play only a minor role.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 7
    Language: English
    In: PLoS ONE, 01 January 2012, Vol.7(12), p.e52555
    Description: miRNA cluster miR-17-92 is known as oncomir-1 due to its potent oncogenic function. miR-17-92 is a polycistronic cluster that encodes 6 miRNAs, and can both facilitate and inhibit cell proliferation. Known targets of miRNAs encoded by this cluster are largely regulators of cell cycle progression...
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 8
    In: PLoS ONE, 2014, Vol.9(3)
    Description: Compelling evidence suggests that the early interaction between porcine circovirus 2 (PCV-2) and the innate immune system is the key event in the pathogenesis of Post-Weaning Multisystemic Wasting Syndrome (PMWS). Furthermore, PCV2 has been detected in bone-marrow samples, potentially enabling an easy spread and reservoir for the virus. To assess the gene-expression differences induced by an in-vitro PCV2b infection in different three different myeloid innate immune cell subsets generated from the same animal, we used the Agilent Porcine Gene Expression Microarray (V2). Alveolar macrophages (AMØs), monocyte-derived dendritic cells (MoDCs) and bone-marrow cells (BMCs) were generated from each animal, and challenged with a UK-isolate of a PCV2 genotype b-strain at a MOI of 0.5. Remarkably, analysis showed a highly distinct and cell-type dependent response to PCV2b challenge. Overall, MoDCs showed the most marked response to PCV2b challenge in vitro and revealed a key role for TNF in the interaction with PCV2b, whereas only few genes were affected in BMCs and AMØs. These observations were further supported by an enrichment of genes in the downstream NF-κB Signalling pathway as well as an up regulation of genes with pro-apoptotic functions post-challenge. PCV2b challenge increases the expression of a large number of immune-related and pro-apoptotic genes mainly in MoDC, which possibly explain the increased inflammation, granulomatous inflammation and lymphocyte depletion seen in PMWS-affected pigs.
    Keywords: Research Article ; Veterinary Science
    E-ISSN: 1932-6203
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  • 9
    In: PLoS ONE, 2017, Vol.12(1)
    Description: Objectives Lung cancer in Xuanwei (LCXW), China, is known throughout the world for its distinctive characteristics, but little is known about its pathogenesis. The purpose of this study was to screen potential novel “driver genes” in LCXW. Methods Genome-wide DNA copy number alterations (CNAs) were detected by array-based comparative genomic hybridization and differentially expressed genes (DEGs) by gene expression microarrays in 8 paired LCXW and non-cancerous lung tissues. Candidate driver genes were screened by integrated analysis of CNAs and DEGs. The candidate genes were further validated by real-time quantitative polymerase chain reaction. Results Large numbers of CNAs and DEGs were detected, respectively. Some of the most frequently occurring CNAs included gains at 5p15.33-p15.32, 5p15.1-p14.3, and 5p14.3-p14.2 and losses at 11q24.3, 21q21.1, 21q22.12-q22.13, and 21q22.2. Integrated analysis of CNAs and DEGs identified 24 candidate genes with frequent copy number gains and concordant upregulation, which were considered potential oncogenes, including CREB3L4 , TRIP13 , and CCNE2 . In addition, the analysis identified 19 candidate genes with a negative association between copy number change and expression change, considered potential tumor suppressor genes, including AHRR , NKD2 , and KLF10 . One of the most studied oncogenes, MYC , may not play a carcinogenic role in LCXW. Conclusions This integrated analysis of CNAs and DEGs identified several potential novel LCXW-related genes, laying an important foundation for further research on the pathogenesis of LCXW and identification of novel biomarkers or therapeutic targets.
    Keywords: Research Article ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Biology And Life Sciences ; Engineering And Technology ; Physical Sciences ; Physical Sciences ; Medicine And Health Sciences
    E-ISSN: 1932-6203
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  • 10
    In: PLoS ONE, 2016, Vol.11(6)
    Description: Technical variation plays an important role in microarray-based gene expression studies, and batch effects explain a large proportion of this noise. It is therefore mandatory to eliminate technical variation while maintaining biological variability. Several strategies have been proposed for the removal of batch effects, although they have not been evaluated in large-scale longitudinal gene expression data. In this study, we aimed at identifying a suitable method for batch effect removal in a large study of microarray-based longitudinal gene expression. Monocytic gene expression was measured in 1092 participants of the Gutenberg Health Study at baseline and 5-year follow up. Replicates of selected samples were measured at both time points to identify technical variability. Deming regression, Passing-Bablok regression, linear mixed models, non-linear models as well as ReplicateRUV and ComBat were applied to eliminate batch effects between replicates. In a second step, quantile normalization prior to batch effect correction was performed for each method. Technical variation between batches was evaluated by principal component analysis. Associations between body mass index and transcriptomes were calculated before and after batch removal. Results from association analyses were compared to evaluate maintenance of biological variability. Quantile normalization, separately performed in each batch, combined with ComBat successfully reduced batch effects and maintained biological variability. ReplicateRUV performed perfectly in the replicate data subset of the study, but failed when applied to all samples. All other methods did not substantially reduce batch effects in the replicate data subset. Quantile normalization plus ComBat appears to be a valuable approach for batch correction in longitudinal gene expression data.
    Keywords: Research Article ; Biology And Life Sciences ; Biology And Life Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Research And Analysis Methods ; Research And Analysis Methods ; Research And Analysis Methods ; Research And Analysis Methods ; Physical Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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