Differential enrichment of regulatory motifs in the composite network of protein-protein and gene regulatory interactions

Hegde, Shubhada R.; Pal, Khushbu; Mande, Shekhar C.

doi:10.1186/1752-0509-8-26

Cited by 6 publications

(4 citation statements)

References 26 publications

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“…We hypothesized that the risk associated genes act as upstream regulators for the genes directly involved in cancer development. Many genes in the human genome are regulated by multiple genes with an average gene in the human genome having $40 upstream regulators (17)(18)(19). Thus, it is plausible that genes directly involved in cancer development may have upstream regulators among GWAS-detected cancer risk-associated genes.…”

Section: Discussionmentioning

confidence: 99%

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway

Gorlova¹,

Demidenko²,

Amos³

et al. 2017

Human Molecular Genetics

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Genome-wide association studies (GWASs) identified over 500 single nucleotide polymorphisms (SNPs) influencing cancer risk. It is logical to expect the cancer-associated genes to cluster in pathways directly involved in carcinogenesis, e.g. cell cycle. Nevertheless, analyses of the GWAS-detected cancer risk genes usually show no or weak enrichment by known cancer genes.We hypothesized that GWAS-detected cancer risk-associated genes function as upstream regulators of the genes directly involved in carcinogenesis. We have analyzed four common cancers: breast, colon, lung, and prostate. To identify downstream targets of GWAS-detected cancer risk genes we used MedScan, which is a text mining tool offered by PathwayStudio. We also used data on protein/protein interactions reported by BioGRID database. Among all identified targets we have selected common downstream targets. A gene was considered a common downstream target if it was a downstream target for at least three GWAS-detected genes for a given cancer type. Common downstream targets were identified separately for each cancer type. We found that common downstream targets for all four cancer types were enriched by cell cycle genes, more specifically, the genes involved in G1/S transition. Common downstream targets for bipolar disorder, Crohn's disease, and type 2 diabetes did not show G1/S transition enrichment.The results of this analysis suggest that many cancer risk genes function as upstream regulators of the genes directly involved in G1/S transition and exert their risk effects by reducing threshold for G1/S transition, elevating the background level of cell proliferation and cancer risk.

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Section: Discussionmentioning

confidence: 99%

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway

Gorlova¹,

Demidenko²,

Amos³

et al. 2017

Human Molecular Genetics

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“…gene or protein expression, protein‐protein/DNA interactions, epigenetic states – 67, 70, 73. In particular, methods for differential gene regulatory network analysis have focused not only on detecting differences in individual regulatory interactions, but also in network motifs and regulatory modules across different cell types or cellular conditions 67, 75. More recently, differential network analysis methods, which rely on single‐cell expression data, have also been implemented.…”

Section: Subpopulation‐specific Gene Regulatory Network Can Be Infermentioning

confidence: 99%

“…More realistic integrative network models will allow the identification of the network stability determinants 4, 72, 75, 102, 103 sensing and transducing niche's signals, and how these signals trigger differentiation in specific PSCs subpopulations (Fig. 1).…”

Section: Modeling Heterogeneity In the Pluripotent State Will Be Essementioning

confidence: 99%

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

Angarica

Sol

2016

BioEssays

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Pluripotency can be considered a functional characteristic of pluripotent stem cells (PSCs) populations and their niches, rather than a property of individual cells. In this view, individual cells within the population independently adopt a variety of different expression states, maintained by different signaling, transcriptional, and epigenetics regulatory networks. In this review, we propose that generation of integrative network models from single cell data will be essential for getting a better understanding of the regulation of self‐renewal and differentiation. In particular, we suggest that the identification of network stability determinants in these integrative models will provide important insights into the mechanisms mediating the transduction of signals from the niche, and how these signals can trigger differentiation. In this regard, the differential use of these stability determinants in subpopulation‐specific regulatory networks would mediate differentiation into different cell fates. We suggest that this approach could offer a promising avenue for the development of novel strategies for increasing the efficiency and fidelity of differentiation, which could have a strong impact on regenerative medicine.

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“…The main drawback of the methods based on structural or sequential features is that they only focus on the local variation of the protein itself. For this reason, cellular interactions of proteins have been investigated, as in [29], for solving some particular problems. In [30], the combination of traditional sequential features of the amino acid, such as PSSM, and different networks, such as KEGG enrichment scores of the protein neighbors in STRING network [31], were studied.…”

Section: Introductionmentioning

confidence: 99%

An empirical study on the matrix-based protein representations and their combination with sequence-based approaches

2012

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Many domains have a stake in the development of reliable systems for automatic protein classification. Of particular interest in recent studies of automatic protein classification is the exploration of new methods for extracting features from a protein that enhance classification for specific problems. These methods have proven very useful in one or two domains, but they have failed to generalize well across several domains (i.e. classification problems). In this paper, we evaluate several feature extraction approaches for representing proteins with the aim of sequence-based protein classification. Several protein representations are evaluated, those starting from: the position specific scoring matrix (PSSM) of the proteins; the amino-acid sequence; a matrix representation of the protein, of dimension (length of the protein) ×20, obtained using the substitution matrices for representing each amino-acid as a vector. A valuable result is that a texture descriptor can be extracted from the PSSM protein representation which improves the performance of standard descriptors based on the PSSM representation. Experimentally, we develop our systems by comparing several protein descriptors on nine different datasets. Each descriptor is used to train a support vector machine (SVM) or an ensemble of SVM. Although different stand-alone descriptors work well on some datasets (but not on others), we have discovered that fusion among classifiers trained using different descriptors obtains a good performance across all the tested datasets. Matlab code/Datasets used in the proposed paper are available at http://www.bias.csr.unibo.it\nanni\PSSM.rar.

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Differential enrichment of regulatory motifs in the composite network of protein-protein and gene regulatory interactions

Cited by 6 publications

References 26 publications

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway

Downstream targets of GWAS-detected genes for breast, lung, and prostate and colon cancer converge to G1/S transition pathway

Modeling heterogeneity in the pluripotent state: A promising strategy for improving the efficiency and fidelity of stem cell differentiation

An empirical study on the matrix-based protein representations and their combination with sequence-based approaches

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