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  • Gene Expression
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  • 1
    In: PLoS ONE, 2013, Vol.8(7)
    Description: Perturbation experiments for example using RNA interference (RNAi) offer an attractive way to elucidate gene function in a high throughput fashion. The placement of hit genes in their functional context and the inference of underlying networks from such data, however, are challenging tasks. One of the problems in network inference is the exponential number of possible network topologies for a given number of genes. Here, we introduce a novel mathematical approach to address this question. We formulate network inference as a linear optimization problem, which can be solved efficiently even for large-scale systems. We use simulated data to evaluate our approach, and show improved performance in particular on larger networks over state-of-the art methods. We achieve increased sensitivity and specificity, as well as a significant reduction in computing time. Furthermore, we show superior performance on noisy data. We then apply our approach to study the intracellular signaling of human primary nave CD4 + T-cells, as well as ErbB signaling in trastuzumab resistant breast cancer cells. In both cases, our approach recovers known interactions and points to additional relevant processes. In ErbB signaling, our results predict an important role of negative and positive feedback in controlling the cell cycle progression.
    Keywords: Research Article ; Biology ; Computer Science
    E-ISSN: 1932-6203
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  • 2
    In: PLoS ONE, 2015, Vol.10(3)
    Description: A single transcription factor may interact with a multitude of targets on the genome, some of which are at gene promoters, others being part of DNA repeat elements. Being sequestered at binding sites, protein molecules can be prevented from partaking in other pathways, specifically, from regulating the expression of the very gene that encodes them. Acting as decoys at the expense of the autoregulatory loop, the binding sites can have a profound impact on protein abundance—on its mean as well as on its cell-to-cell variability. In order to quantify this impact, we study in this paper a mathematical model for pulsatile expression of a transcription factor that autoregulates its expression and interacts with decoys. We determine the exact stationary distribution for protein abundance at the single-cell level, showing that in the case of non-cooperative positive autoregulation, the distribution can be bimodal, possessing a basal expression mode and a distinct, up-regulated, mode. Bimodal protein distributions are more feasible if the rate of degradation is the same irrespective of whether protein is bound or not. Contrastingly, the presence of decoy binding sites which protect the protein from degradation reduces the availability of the bimodal scenario.
    Keywords: Research Article
    E-ISSN: 1932-6203
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  • 3
    Language: English
    In: PLoS ONE, 2012, Vol.7(5), p.e35077
    Description: Network inference deals with the reconstruction of biological networks from experimental data. A variety of different reverse engineering techniques are available; they differ in the underlying assumptions and mathematical models used. One common problem for all approaches stems from the complexity of the task, due to the combinatorial explosion of different network topologies for increasing network size. To handle this problem, constraints are frequently used, for example on the node degree, number of edges, or constraints on regulation functions between network components. We propose to exploit topological considerations in the inference of gene regulatory networks. Such systems are often controlled by a small number of hub genes, while most other genes have only limited influence on the network's dynamic. We model gene regulation using a Bayesian network with discrete, Boolean nodes. A hierarchical prior is employed to identify hub genes. The first layer of the prior is used to regularize weights on edges emanating from one specific node. A second prior on hyperparameters controls the magnitude of the former regularization for different nodes. The net effect is that central nodes tend to form in reconstructed networks. Network reconstruction is then performed by maximization of or sampling from the posterior distribution. We evaluate our approach on simulated and real experimental data, indicating that we can reconstruct main regulatory interactions from the data. We furthermore compare our approach to other state-of-the art methods, showing superior performance in identifying hubs. Using a large publicly available dataset of over 800 cell cycle regulated genes, we are able to identify several main hub genes. Our method may thus provide a valuable tool to identify interesting candidate genes for further study. Furthermore, the approach presented may stimulate further developments in regularization methods for network reconstruction from data.
    Keywords: Research Article ; Biology ; Genetics And Genomics ; Computational Biology
    E-ISSN: 1932-6203
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  • 4
    In: PLoS ONE, 2014, Vol.9(5)
    Description: To obtain predictive genes with lower redundancy and better interpretability, a hybrid gene selection method encoding prior information is proposed in this paper. To begin with, the prior information referred to as gene-to-class sensitivity (GCS) of all genes from microarray data is exploited by a single hidden layered feedforward neural network (SLFN). Then, to select more representative and lower redundant genes, all genes are grouped into some clusters by K-means method, and some low sensitive genes are filtered out according to their GCS values. Finally, a modified binary particle swarm optimization (BPSO) encoding the GCS information is proposed to perform further gene selection from the remainder genes. For considering the GCS information, the proposed method selects those genes highly correlated to sample classes. Thus, the low redundant gene subsets obtained by the proposed method also contribute to improve classification accuracy on microarray data. The experiments results on some open microarray data verify the effectiveness and efficiency of the proposed approach.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences ; Research And Analysis Methods
    E-ISSN: 1932-6203
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  • 5
    In: PLoS ONE, 2018, Vol.13(10)
    Description: Single-cell RNA sequencing (scRNA-seq) is an emerging technology for profiling the gene expression of thousands of cells at the single cell resolution. Currently, the labeling of cells in an scRNA-seq dataset is performed by manually characterizing clusters of cells or by fluorescence-activated cell sorting (FACS). Both methods have inherent drawbacks: The first depends on the clustering algorithm used and the knowledge and arbitrary decisions of the annotator, and the second involves an experimental step in addition to the sequencing and cannot be incorporated into the higher throughput scRNA-seq methods. We therefore suggest a different approach for cell labeling, namely, classifying cells from scRNA-seq datasets by using a model transferred from different (previously labeled) datasets. This approach can complement existing methods, and–in some cases–even replace them. Such a transfer-learning framework requires selecting informative features and training a classifier. The specific implementation for the framework that we propose, designated ''CaSTLe–classification of single cells by transfer learning,'' is based on a robust feature engineering workflow and an XGBoost classification model built on these features. Evaluation of CaSTLe against two benchmark feature-selection and classification methods showed that it outperformed the benchmark methods in most cases and yielded satisfactory classification accuracy in a consistent manner. CaSTLe has the additional advantage of being parallelizable and well suited to large datasets. We showed that it was possible to classify cell types using transfer learning, even when the databases contained a very small number of genes, and our study thus indicates the potential applicability of this approach for analysis of scRNA-seq datasets.
    Keywords: Research Article ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Biology And Life Sciences ; Medicine And Health Sciences ; Computer And Information Sciences ; Engineering And Technology ; Biology And Life Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Computer And Information Sciences
    E-ISSN: 1932-6203
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  • 6
    In: PLoS ONE, 2014, Vol.9(4)
    Description: Inferring gene regulatory networks (GRNs) is a major issue in systems biology, which explicitly characterizes regulatory processes in the cell. The Path Consistency Algorithm based on Conditional Mutual Information (PCA-CMI) is a well-known method in this field. In this study, we introduce a new algorithm (IPCA-CMI) and apply it to a number of gene expression data sets in order to evaluate the accuracy of the algorithm to infer GRNs. The IPCA-CMI can be categorized as a hybrid method, using the PCA-CMI and Hill-Climbing algorithm (based on MIT score). The conditional dependence between variables is determined by the conditional mutual information test which can take into account both linear and nonlinear genes relations. IPCA-CMI uses a score and search method and defines a selected set of variables which is adjacent to one of or Y . This set is used to determine the dependency between X and Y . This method is compared with the method of evaluating dependency by PCA-CMI in which the set of variables adjacent to both X and Y , is selected. The merits of the IPCA-CMI are evaluated by applying this algorithm to the DREAM3 Challenge data sets with n variables and n samples ( ) and to experimental data from Escherichia coil containing 9 variables and 9 samples. Results indicate that applying the IPCA-CMI improves the precision of learning the structure of the GRNs in comparison with that of the PCA-CMI.
    Keywords: Research Article ; Biology And Life Sciences ; Computer And Information Sciences ; Physical Sciences
    E-ISSN: 1932-6203
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  • 7
    In: PLoS ONE, 2017, Vol.12(10)
    Description: High-throughput gene expression data are often obtained from pure or complex (heterogeneous) biological samples. In the latter case, data obtained are a mixture of different cell types and the heterogeneity imposes some difficulties in the analysis of such data. In order to make conclusions on gene expresssion data obtained from heterogeneous samples, methods such as microdissection and flow cytometry have been employed to physically separate the constituting cell types. However, these manual approaches are time consuming when measuring the responses of multiple cell types simultaneously. In addition, exposed samples, on many occasions, end up being contaminated with external perturbations and this may result in an altered yield of molecular content. In this paper, we model the heterogeneous gene expression data using a Bayesian framework, treating the cell type proportions and the cell-type specific expressions as the parameters of the model. Specifically, we present a novel sequential Monte Carlo (SMC) sampler for estimating the model parameters by approximating their posterior distributions with a set of weighted samples. The SMC framework is a robust and efficient approach where we construct a sequence of artificial target (posterior) distributions on spaces of increasing dimensions which admit the distributions of interest as marginals. The proposed algorithm is evaluated on simulated datasets and publicly available real datasets, including Affymetrix oligonucleotide arrays and national center for biotechnology information (NCBI) gene expression omnibus (GEO), with varying number of cell types. The results obtained on all datasets show a superior performance with an improved accuracy in the estimation of cell type proportions and the cell-type specific expressions, and in addition, more accurate identification of differentially expressed genes when compared to other widely known methods for blind decomposition of heterogeneous gene expression data such as Dsection and the nonnegative matrix factorization (NMF) algorithms. MATLAB implementation of the proposed SMC algorithm is available to download at https://github.com/moyanre/smcgenedeconv.git .
    Keywords: Research Article ; Biology And Life Sciences ; Physical Sciences ; Research And Analysis Methods ; Biology And Life Sciences ; Medicine And Health Sciences ; Science Policy ; Physical Sciences ; Physical Sciences ; Physical Sciences ; Computer And Information Sciences ; Engineering And Technology
    E-ISSN: 1932-6203
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  • 8
    Language: English
    In: PLoS ONE, 01 January 2015, Vol.10(7), p.e0134529
    Description: Apolipoprotein E (ApoE), an exchangeable apolipoprotein, is necessary for production of infectious Hepatitis C virus (HCV) particles. However, ApoE is not the only liver-expressed apolipoprotein and the role of other apolipoproteins for production of infectious HCV progeny is incompletely defined. Therefore, we quantified mRNA expression of human apolipoproteins in primary human hepatocytes. Subsequently, cDNAs encoding apolipoproteins were expressed in 293T/miR-122 cells to explore if they complement HCV virus production in cells that are non-permissive due to limiting endogenous levels of human apolipoproteins. Primary human hepatocytes expressed high mRNA levels of ApoA1, A2, C1, C3, E, and H. ApoA4, A5, B, D, F, J, L1, L2, L3, L4, L6, M, and O were expressed at intermediate levels, and C2, C4, and L5 were not detected. All members of the ApoA and ApoC family of lipoproteins complemented HCV virus production in HCV transfected 293T/miR-122 cells, albeit with significantly lower efficacy compared with ApoE. In contrast, ApoD expression did not support production of infectious HCV. Specific infectivity of released particles complemented with ApoA family members was significantly lower compared with ApoE. Moreover, the ratio of extracellular to intracellular infectious virus was significantly higher for ApoE compared to ApoA2 and ApoC3. Since apolipoproteins complementing HCV virus production share amphipathic alpha helices as common structural features we altered the two alpha helices of ApoC1. Helix breaking mutations in both ApoC1 helices impaired virus assembly highlighting a critical role of alpha helices in apolipoproteins supporting HCV assembly. In summary, various liver expressed apolipoproteins with amphipathic alpha helices complement HCV virus production in human non liver cells. Differences in the efficiency of virus assembly, the specific infectivity of released particles, and the ratio between extracellular and intracellular infectivity point to distinct characteristics of these apolipoproteins that influence HCV assembly and cell entry. This will guide future research to precisely pinpoint how apolipoproteins function during virus assembly and cell entry.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 9
    Language: English
    In: PLoS ONE, 01 January 2015, Vol.10(1), p.e0116029
    Description: Changes in the airway microbiome may be important in the pathophysiology of chronic lung disease in patients with cystic fibrosis. However, little is known about the microbiome in early cystic fibrosis lung disease and the relationship between the microbiomes from different niches in the upper and lower airways. Therefore, in this cross-sectional study, we examined the relationship between the microbiome in the upper (nose and throat) and lower (sputum) airways from children with cystic fibrosis using next generation sequencing. Our results demonstrate a significant difference in both α and β-diversity between the nose and the two other sampling sites. The nasal microbiome was characterized by a polymicrobial community while the throat and sputum communities were less diverse and dominated by a few operational taxonomic units. Moreover, sputum and throat microbiomes were closely related especially in patients with clinically stable lung disease. There was a high inter-individual variability in sputum samples primarily due to a decrease in evenness linked to increased abundance of potential respiratory pathogens such as Pseudomonas aeruginosa. Patients with chronic Pseudomonas aeruginosa infection exhibited a less diverse sputum microbiome. A high concordance was found between pediatric and adult sputum microbiomes except that Burkholderia was only observed in the adult cohort. These results indicate that an adult-like lower airways microbiome is established early in life and that throat swabs may be a good surrogate in clinically stable children with cystic fibrosis without chronic Pseudomonas aeruginosa infection in whom sputum sampling is often not feasible.
    Keywords: Sciences (General)
    E-ISSN: 1932-6203
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  • 10
    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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