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Glass, Kimberly

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Glass

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Kimberly

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Glass, Kimberly
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Now showing 1 - 10 of 15
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    Haploinsufficiency of Hedgehog interacting protein causes increased emphysema induced by cigarette smoke through network rewiring
    (BioMed Central, 2015) Lao, Taotao; Glass, Kimberly; Qiu, Weiliang; Polverino, Francesca; Gupta, Kushagra; Morrow, Jarrett; Mancini, John Dominic; Vuong, Linh; Perrella, Mark; Hersh, Craig; Owen, Caroline; Quackenbush, John; Yuan, Guo-Cheng; Silverman, Edwin; Zhou, Xiaobo
    Background: The HHIP gene, encoding Hedgehog interacting protein, has been implicated in chronic obstructive pulmonary disease (COPD) by genome-wide association studies (GWAS), and our subsequent studies identified a functional upstream genetic variant that decreased HHIP transcription. However, little is known about how HHIP contributes to COPD pathogenesis. Methods: We exposed Hhip haploinsufficient mice (Hhip+/-) to cigarette smoke (CS) for 6 months to model the biological consequences caused by CS in human COPD risk-allele carriers at the HHIP locus. Gene expression profiling in murine lungs was performed followed by an integrative network inference analysis, PANDA (Passing Attributes between Networks for Data Assimilation) analysis. Results: We detected more severe airspace enlargement in Hhip+/- mice vs. wild-type littermates (Hhip+/+) exposed to CS. Gene expression profiling in murine lungs suggested enhanced lymphocyte activation pathways in CS-exposed Hhip+/- vs. Hhip+/+ mice, which was supported by increased numbers of lymphoid aggregates and enhanced activation of CD8+ T cells after CS-exposure in the lungs of Hhip+/-mice compared to Hhip+/+ mice. Mechanistically, results from PANDA network analysis suggested a rewired and dampened Klf4 signaling network in Hhip+/- mice after CS exposure. Conclusions: In summary, HHIP haploinsufficiency exaggerated CS-induced airspace enlargement, which models CS-induced emphysema in human smokers carrying COPD risk alleles at the HHIP locus. Network modeling suggested rewired lymphocyte activation signaling circuits in the HHIP haploinsufficiency state. Electronic supplementary material The online version of this article (doi:10.1186/s13073-015-0137-3) contains supplementary material, which is available to authorized users.
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    Regulatory network changes between cell lines and their tissues of origin
    (BioMed Central, 2017) Lopes-Ramos, Camila; Paulson, Joseph; Chen, Cho-Yi; Kuijjer, Marieke; Fagny, Maud; Platig, John; Sonawane, Abhijeet; Demeo, Dawn; Quackenbush, John; Glass, Kimberly
    Background: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin. Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE. Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues. Electronic supplementary material The online version of this article (10.1186/s12864-017-4111-x) contains supplementary material, which is available to authorized users.
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    Estimating gene regulatory networks with pandaR
    (Oxford University Press, 2017) Schlauch, Daniel; Paulson, Joseph; Young, Albert; Glass, Kimberly; Quackenbush, John
    Abstract PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that begins with a model of transcription factor–target gene interactions and uses message passing to update the network model given available transcriptomic and protein–protein interaction data. PANDA is used to estimate networks for each experimental group and the network models are then compared between groups to explore transcriptional processes that distinguish the groups. We present pandaR (bioconductor.org/packages/pandaR), a Bioconductor package that implements PANDA and provides a framework for exploratory data analysis on gene regulatory networks. Contact: johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR.
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    Combinatorial Recruitment of CREB, C/EBPβ and c-Jun Determines Activation of Promoters upon Keratinocyte Differentiation
    (Public Library of Science, 2013) Rozenberg, Julian M.; Bhattacharya, Paramita; Chatterjee, Raghunath; Glass, Kimberly; Vinson, Charles
    Background: Transcription factors CREB, C/EBPβ and Jun regulate genes involved in keratinocyte proliferation and differentiation. We questioned if specific combinations of CREB, C/EBPβ and c-Jun bound to promoters correlate with RNA polymerase II binding, mRNA transcript levels and methylation of promoters in proliferating and differentiating keratinocytes. Results: Induction of mRNA and RNA polymerase II by differentiation is highest when promoters are bound by C/EBP β alone, C/EBPβ together with c-Jun, or by CREB, C/EBPβ and c-Jun, although in this case CREB binds with low affinity. In contrast, RNA polymerase II binding and mRNA levels change the least upon differentiation when promoters are bound by CREB either alone or in combination with C/EBPβ or c-Jun. Notably, promoters bound by CREB have relatively high levels of RNA polymerase II binding irrespective of differentiation. Inhibition of C/EBPβ or c-Jun preferentially represses mRNA when gene promoters are bound by corresponding transcription factors and not CREB. Methylated promoters have relatively low CREB binding and, accordingly, those which are bound by C/EBPβ are induced by differentiation irrespective of CREB. Composite “Half and Half” consensus motifs and co localizing consensus DNA binding motifs are overrepresented in promoters bound by the combination of corresponding transcription factors. Conclusion: Correlational and functional data describes combinatorial mechanisms regulating the activation of promoters. Colocalization of C/EBPβ and c-Jun on promoters without strong CREB binding determines high probability of activation upon keratinocyte differentiation.
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    Annotation Enrichment Analysis: An Alternative Method for Evaluating the Functional Properties of Gene Sets
    (Nature Publishing Group, 2014) Glass, Kimberly; Girvan, Michelle
    Gene annotation databases (compendiums maintained by the scientific community that describe the biological functions performed by individual genes) are commonly used to evaluate the functional properties of experimentally derived gene sets. Overlap statistics, such as Fishers Exact test (FET), are often employed to assess these associations, but don't account for non-uniformity in the number of genes annotated to individual functions or the number of functions associated with individual genes. We find FET is strongly biased toward over-estimating overlap significance if a gene set has an unusually high number of annotations. To correct for these biases, we develop Annotation Enrichment Analysis (AEA), which properly accounts for the non-uniformity of annotations. We show that AEA is able to identify biologically meaningful functional enrichments that are obscured by numerous false-positive enrichment scores in FET, and we therefore suggest it be used to more accurately assess the biological properties of gene sets.
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    Passing Messages between Biological Networks to Refine Predicted Interactions
    (Public Library of Science, 2013) Glass, Kimberly; Huttenhower, Curtis; Quackenbush, John; Yuan, Guo-Cheng
    Regulatory network reconstruction is a fundamental problem in computational biology. There are significant limitations to such reconstruction using individual datasets, and increasingly people attempt to construct networks using multiple, independent datasets obtained from complementary sources, but methods for this integration are lacking. We developed PANDA (Passing Attributes between Networks for Data Assimilation), a message-passing model using multiple sources of information to predict regulatory relationships, and used it to integrate protein-protein interaction, gene expression, and sequence motif data to reconstruct genome-wide, condition-specific regulatory networks in yeast as a model. The resulting networks were not only more accurate than those produced using individual data sets and other existing methods, but they also captured information regarding specific biological mechanisms and pathways that were missed using other methodologies. PANDA is scalable to higher eukaryotes, applicable to specific tissue or cell type data and conceptually generalizable to include a variety of regulatory, interaction, expression, and other genome-scale data. An implementation of the PANDA algorithm is available at www.sourceforge.net/projects/panda-net.
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    Sexually-dimorphic targeting of functionally-related genes in COPD
    (BioMed Central, 2014) Glass, Kimberly; Quackenbush, John; Silverman, Edwin; Celli, Bartolome; Rennard, Stephen I; Yuan, Guo-Cheng; Demeo, Dawn
    Background: There is growing evidence that many diseases develop, progress, and respond to therapy differently in men and women. This variability may manifest as a result of sex-specific structures in gene regulatory networks that influence how those networks operate. However, there are few methods to identify and characterize differences in network structure, slowing progress in understanding mechanisms driving sexual dimorphism. Results: Here we apply an integrative network inference method, PANDA (Passing Attributes between Networks for Data Assimilation), to model sex-specific networks in blood and sputum samples from subjects with Chronic Obstructive Pulmonary Disease (COPD). We used a jack-knifing approach to build an ensemble of likely networks for each sex. By adapting statistical methods to compare these network ensembles, we were able to identify strong differential-targeting patterns associated with functionally-related sets of genes, including those involved in mitochondrial function and energy metabolism. Network analysis also identified several potential sex- and disease-specific transcriptional regulators of these pathways. Conclusions: Network analysis yielded insight into potential mechanisms driving sexual dimorphism in COPD that were not evident from gene expression analysis alone. We believe our ensemble approach to network analysis provides a principled way to capture sex-specific regulatory relationships and could be applied to identify differences in gene regulatory patterns in a wide variety of diseases and contexts. Electronic supplementary material The online version of this article (doi:10.1186/s12918-014-0118-y) contains supplementary material, which is available to authorized users.
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    Multi-scale chromatin state annotation using a hierarchical hidden Markov model
    (Nature Publishing Group, 2017) Marco, Eugenio; Meuleman, Wouter; Huang, Jialiang; Glass, Kimberly; Pinello, Luca; Wang, Jianrong; Kellis, Manolis; Yuan, Guo-Cheng
    Chromatin-state analysis is widely applied in the studies of development and diseases. However, existing methods operate at a single length scale, and therefore cannot distinguish large domains from isolated elements of the same type. To overcome this limitation, we present a hierarchical hidden Markov model, diHMM, to systematically annotate chromatin states at multiple length scales. We apply diHMM to analyse a public ChIP-seq data set. diHMM not only accurately captures nucleosome-level information, but identifies domain-level states that vary in nucleosome-level state composition, spatial distribution and functionality. The domain-level states recapitulate known patterns such as super-enhancers, bivalent promoters and Polycomb repressed regions, and identify additional patterns whose biological functions are not yet characterized. By integrating chromatin-state information with gene expression and Hi-C data, we identify context-dependent functions of nucleosome-level states. Thus, diHMM provides a powerful tool for investigating the role of higher-order chromatin structure in gene regulation.
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    Biomarker correlation network in colorectal carcinoma by tumor anatomic location
    (BioMed Central, 2017) Nakashima, Reiko; Glass, Kimberly; Mima, Kosuke; Hamada, Tsuyoshi; Nowak, Jonathan; Qian, Zhi Rong; Kraft, Phillip; Giovannucci, Edward; Fuchs, Charles; Chan, Andrew; Quackenbush, John; Ogino, Shuji; Onnela, Jukka-Pekka
    Background: Colorectal carcinoma evolves through a multitude of molecular events including somatic mutations, epigenetic alterations, and aberrant protein expression, influenced by host immune reactions. One way to interrogate the complex carcinogenic process and interactions between aberrant events is to model a biomarker correlation network. Such a network analysis integrates multidimensional tumor biomarker data to identify key molecular events and pathways that are central to an underlying biological process. Due to embryological, physiological, and microbial differences, proximal and distal colorectal cancers have distinct sets of molecular pathological signatures. Given these differences, we hypothesized that a biomarker correlation network might vary by tumor location. Results: We performed network analyses of 54 biomarkers, including major mutational events, microsatellite instability (MSI), epigenetic features, protein expression status, and immune reactions using data from 1380 colorectal cancer cases: 690 cases with proximal colon cancer and 690 cases with distal colorectal cancer matched by age and sex. Edges were defined by statistically significant correlations between biomarkers using Spearman correlation analyses. We found that the proximal colon cancer network formed a denser network (total number of edges, n = 173) than the distal colorectal cancer network (n = 95) (P < 0.0001 in permutation tests). The value of the average clustering coefficient was 0.50 in the proximal colon cancer network and 0.30 in the distal colorectal cancer network, indicating the greater clustering tendency of the proximal colon cancer network. In particular, MSI was a key hub, highly connected with other biomarkers in proximal colon cancer, but not in distal colorectal cancer. Among patients with non-MSI-high cancer, BRAF mutation status emerged as a distinct marker with higher connectivity in the network of proximal colon cancer, but not in distal colorectal cancer. Conclusion: In proximal colon cancer, tumor biomarkers tended to be correlated with each other, and MSI and BRAF mutation functioned as key molecular characteristics during the carcinogenesis. Our findings highlight the importance of considering multiple correlated pathways for therapeutic targets especially in proximal colon cancer. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1718-5) contains supplementary material, which is available to authorized users.
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    PyPanda: a Python package for gene regulatory network reconstruction
    (Oxford University Press, 2016) van IJzendoorn, David G.P.; Glass, Kimberly; Quackenbush, John; Kuijjer, Marieke
    Summary: PANDA (Passing Attributes between Networks for Data Assimilation) is a gene regulatory network inference method that uses message-passing to integrate multiple sources of ‘omics data. PANDA was originally coded in C ++. In this application note we describe PyPanda, the Python version of PANDA. PyPanda runs considerably faster than the C ++ version and includes additional features for network analysis. Availability and implementation: The open source PyPanda Python package is freely available at http://github.com/davidvi/pypanda. Contact: mkuijjer@jimmy.harvard.edu or d.g.p.van_ijzendoorn@lumc.nl