Analysis of Gene Sets Based on the Underlying Regulatory Network

Networks are often used to represent the interactions among genes and proteins. These interactions are known to play an important role in vital cell functions and should be included in the analysis of genes that are differentially expressed. Methods of gene set analysis take advantage of external biological information and analyze a priori defined sets of genes. These methods can potentially preserve the correlation among genes; however, they do not directly incorporate the information about the gene network. In this paper, we propose a latent variable model that directly incorporates the network information. We then use the theory of mixed linear models to present a general inference framework for the problem of testing the significance of subnetworks. Several possible test procedures are introduced and a network based method for testing the changes in expression levels of genes as well as the structure of the network is presented. The performance of the proposed method is compared with methods of gene set analysis using both simulation studies, as well as real data on genes related to the galactose utilization pathway in yeast.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/78147/1/cmb.2008.0081.pd

CoPub update: CoPub 5.0 a text mining system to answer biological questions

In this article, we present CoPub 5.0, a publicly available text mining system, which uses Medline abstracts to calculate robust statistics for keyword co-occurrences. CoPub was initially developed for the analysis of microarray data, but we broadened the scope by implementing new technology and new thesauri. In CoPub 5.0, we integrated existing CoPub technology with new features, and provided a new advanced interface, which can be used to answer a variety of biological questions. CoPub 5.0 allows searching for keywords of interest and its relations to curated thesauri and provides highlighting and sorting mechanisms, using its statistics, to retrieve the most important abstracts in which the terms co-occur. It also provides a way to search for indirect relations between genes, drugs, pathways and diseases, following an ABC principle, in which A and C have no direct connection but are connected via shared B intermediates. With CoPub 5.0, it is possible to create, annotate and analyze networks using the layout and highlight options of Cytoscape web, allowing for literature based systems biology. Finally, operations of the CoPub 5.0 Web service enable to implement the CoPub technology in bioinformatics workflows. CoPub 5.0 can be accessed through the CoPub portal http://www.copub.org

A human MAP kinase interactome.

Mitogen-activated protein kinase (MAPK) pathways form the backbone of signal transduction in the mammalian cell. Here we applied a systematic experimental and computational approach to map 2,269 interactions between human MAPK-related proteins and other cellular machinery and to assemble these data into functional modules. Multiple lines of evidence including conservation with yeast supported a core network of 641 interactions. Using small interfering RNA knockdowns, we observed that approximately one-third of MAPK-interacting proteins modulated MAPK-mediated signaling. We uncovered the Na-H exchanger NHE1 as a potential MAPK scaffold, found links between HSP90 chaperones and MAPK pathways and identified MUC12 as the human analog to the yeast signaling mucin Msb2. This study makes available a large resource of MAPK interactions and clone libraries, and it illustrates a methodology for probing signaling networks based on functional refinement of experimentally derived protein-interaction maps

Intertemporal excess burden, bequest motives, and the budget deficit

The author aims to empirically determine the significant factors that affect the levels of budget deficits of central governments across time and across countries. He empirically tests two prominent theories of budget deficits-the Barro (1979) tax-smoothing approach, and the still-untested theory of negative bequest motives advocated by Cukierman and Meltzer (1989). The author uses econometric techniques including fixed-effects (both country and time) panel regressions spanning 87 countries over the period 1975 to 1992, and the Griliches treatment of missing data. The author finds relatively stronger statistical support for the tax-smoothing approach among developing countries but not in industrial countries. The existence of empirical evidence supporting the theory of negative bequest motives is indeterminate. The author also conducted post-regression analyses to assess the proportion of observed differences in budget deficits the factors were actually able to explain. These reveal that both theories are generally weak in accounting for inter-temporal changes in budget deficit shares for both industrial and developing countries. The theories performed significantly better in accounting for cross-section differences. The author has many contributions to the literature. First, he analyzes the question of what determines the size of central government budget deficits using cross-country time series data leading into the 1990s. Second, he provides empirical tests of the still-untested Cukierman-Meltzer (1989) negative bequest motive theory of budget deficits. By using the panel data, the author attempts to determine the factors that influence not only the inter-temporal differences in budget deficits but also those factors that lead to cross-country differences. Last but not least, he provides some preliminary evidence that poverty reduction is necessary for long-term government budget deficit reduction.Public Sector Economics&Finance,Environmental Economics&Policies,Economic Theory&Research,Banks&Banking Reform,Municipal Financial Management,Public Sector Economics&Finance,Economic Theory&Research,Economic Stabilization,Banks&Banking Reform,National Governance

Combinatorial CRISPR-Cas9 screens for de novo mapping of genetic interactions.

We developed a systematic approach to map human genetic networks by combinatorial CRISPR-Cas9 perturbations coupled to robust analysis of growth kinetics. We targeted all pairs of 73 cancer genes with dual guide RNAs in three cell lines, comprising 141,912 tests of interaction. Numerous therapeutically relevant interactions were identified, and these patterns replicated with combinatorial drugs at 75% precision. From these results, we anticipate that cellular context will be critical to synthetic-lethal therapies

Bridging topological and functional information in protein interaction networks by short loops profiling

Protein-protein interaction networks (PPINs) have been employed to identify potential novel interconnections between proteins as well as crucial cellular functions. In this study we identify fundamental principles of PPIN topologies by analysing network motifs of short loops, which are small cyclic interactions of between 3 and 6 proteins. We compared 30 PPINs with corresponding randomised null models and examined the occurrence of common biological functions in loops extracted from a cross-validated high-confidence dataset of 622 human protein complexes. We demonstrate that loops are an intrinsic feature of PPINs and that specific cell functions are predominantly performed by loops of different lengths. Topologically, we find that loops are strongly related to the accuracy of PPINs and define a core of interactions with high resilience. The identification of this core and the analysis of loop composition are promising tools to assess PPIN quality and to uncover possible biases from experimental detection methods. More than 96% of loops share at least one biological function, with enrichment of cellular functions related to mRNA metabolic processing and the cell cycle. Our analyses suggest that these motifs can be used in the design of targeted experiments for functional phenotype detection.This research was supported by the Biotechnology and Biological Sciences Research Council (BB/H018409/1 to AP, ACCC and FF, and BB/J016284/1 to NSBT) and by the Leukaemia & Lymphoma Research (to NSBT and FF). SSC is funded by a Leukaemia & Lymphoma Research Gordon Piller PhD Studentship

An Integrative -omics Approach to Identify Functional Sub-Networks in Human Colorectal Cancer

Emerging evidence indicates that gene products implicated in human cancers often cluster together in “hot spots” in protein-protein interaction (PPI) networks. Additionally, small sub-networks within PPI networks that demonstrate synergistic differential expression with respect to tumorigenic phenotypes were recently shown to be more accurate classifiers of disease progression when compared to single targets identified by traditional approaches. However, many of these studies rely exclusively on mRNA expression data, a useful but limited measure of cellular activity. Proteomic profiling experiments provide information at the post-translational level, yet they generally screen only a limited fraction of the proteome. Here, we demonstrate that integration of these complementary data sources with a “proteomics-first” approach can enhance the discovery of candidate sub-networks in cancer that are well-suited for mechanistic validation in disease. We propose that small changes in the mRNA expression of multiple genes in the neighborhood of a protein-hub can be synergistically associated with significant changes in the activity of that protein and its network neighbors. Further, we hypothesize that proteomic targets with significant fold change between phenotype and control may be used to “seed” a search for small PPI sub-networks that are functionally associated with these targets. To test this hypothesis, we select proteomic targets having significant expression changes in human colorectal cancer (CRC) from two independent 2-D gel-based screens. Then, we use random walk based models of network crosstalk and develop novel reference models to identify sub-networks that are statistically significant in terms of their functional association with these proteomic targets. Subsequently, using an information-theoretic measure, we evaluate synergistic changes in the activity of identified sub-networks based on genome-wide screens of mRNA expression in CRC. Cross-classification experiments to predict disease class show excellent performance using only a few sub-networks, underwriting the strength of the proposed approach in discovering relevant and reproducible sub-networks

Multiple Routes to Oncogenesis Are Promoted by the Human Papillomavirus-Host Protein Network.

We have mapped a global network of virus-host protein interactions by purification of the complete set of human papillomavirus (HPV) proteins in multiple cell lines followed by mass spectrometry analysis. Integration of this map with tumor genome atlases shows that the virus targets human proteins frequently mutated in HPV- but not HPV+ cancers, providing a unique opportunity to identify novel oncogenic events phenocopied by HPV infection. For example, we find that the NRF2 transcriptional pathway, which protects against oxidative stress, is activated by interaction of the NRF2 regulator KEAP1 with the viral protein E1. We also demonstrate that the L2 HPV protein physically interacts with the RNF20/40 histone ubiquitination complex and promotes tumor cell invasion in an RNF20/40-dependent manner. This combined proteomic and genetic approach provides a systematic means to study the cellular mechanisms hijacked by virally induced cancers.Significance: In this study, we created a protein-protein interaction network between HPV and human proteins. An integrative analysis of this network and 800 tumor mutation profiles identifies multiple oncogenesis pathways promoted by HPV interactions that phenocopy recurrent mutations in cancer, yielding an expanded definition of HPV oncogenic roles. Cancer Discov; 8(11); 1474-89. ©2018 AACR.This article is highlighted in the In This Issue feature, p. 1333

Diverse interventions that extend mouse lifespan suppress shared age-associated epigenetic changes at critical gene regulatory regions

Background: Age-associated epigenetic changes are implicated in aging. Notably, age-associated DNA methylation changes comprise a so-called aging “clock”, a robust biomarker of aging. However, while genetic, dietary and drug interventions can extend lifespan, their impact on the epigenome is uncharacterised. To fill this knowledge gap, we defined age-associated DNA methylation changes at the whole-genome, single-nucleotide level in mouse liver and tested the impact of longevity-promoting interventions, specifically the Ames dwarf Prop1 df/df mutation, calorie restriction and rapamycin. Results: In wild-type mice fed an unsupplemented ad libitum diet, age-associated hypomethylation was enriched at super-enhancers in highly expressed genes critical for liver function. Genes harbouring hypomethylated enhancers were enriched for genes that change expression with age. Hypermethylation was enriched at CpG islands marked with bivalent activating and repressing histone modifications and resembled hypermethylation in liver cancer. Age-associated methylation changes are suppressed in Ames dwarf and calorie restricted mice and more selectively and less specifically in rapamycin treated mice. Conclusions: Age-associated hypo- and hypermethylation events occur at distinct regulatory features of the genome. Distinct longevity-promoting interventions, specifically genetic, dietary and drug interventions, suppress some age-associated methylation changes, consistent with the idea that these interventions exert their beneficial effects, in part, by modulation of the epigenome. This study is a foundation to understand the epigenetic contribution to healthy aging and longevity and the molecular basis of the DNA methylation clock