20,686 research outputs found
A sparse regulatory network of copy-number driven expression reveals putative breast cancer oncogenes
The influence of DNA cis-regulatory elements on a gene's expression has been
intensively studied. However, little is known about expressions driven by
trans-acting DNA hotspots. DNA hotspots harboring copy number aberrations are
recognized to be important in cancer as they influence multiple genes on a
global scale. The challenge in detecting trans-effects is mainly due to the
computational difficulty in detecting weak and sparse trans-acting signals
amidst co-occuring passenger events. We propose an integrative approach to
learn a sparse interaction network of DNA copy-number regions with their
downstream targets in a breast cancer dataset. Information from this network
helps distinguish copy-number driven from copy-number independent expression
changes on a global scale. Our result further delineates cis- and trans-effects
in a breast cancer dataset, for which important oncogenes such as ESR1 and
ERBB2 appear to be highly copy-number dependent. Further, our model is shown to
be efficient and in terms of goodness of fit no worse than other state-of the
art predictors and network reconstruction models using both simulated and real
data.Comment: Accepted at IEEE International Conference on Bioinformatics &
Biomedicine (BIBM 2010
HOXD8 exerts a tumor-suppressing role in colorectal cancer as an apoptotic inducer
Homeobox (HOX) genes are conserved transcription factors which determine the anterior-posterior body axis patterning. HOXD8 is a member of HOX genes deregulated in several tumors such as lung carcinoma, neuroblastoma, glioma and colorectal cancer (CRC) in a context-dependent manner. In CRC, HOXD8 is downregulated in cancer tissues and metastatic foci as compared to normal tissues. Whether HOXD8 acts as a tumor suppressor of malignant progression and metastasis is still unclear. Also, the underlying mechanism of its function including the downstream targets is totally unknown. Here, we clarified the lower expression of HOXD8 in clinical colorectal cancer vs. normal colon tissues. Also, we showed that stable expression of HOXD8 in colorectal cancer cells significantly reduced the cell proliferation, anchorage-independent growth and invasion. Further, using The Cancer Genome Atlas (TCGA), we identified the genes associated with HOXD8 in order to demonstrate its function as a suppressor or a promoter of colorectal carcinoma. Among inversely related genes, apoptotic inhibitors like STK38 kinase and MYC were shown to be negatively associated with HOXD8. We demonstrated the ability of HOXD8 to upregulate executioner caspases 6 & 7 and cleaved PARP, thus inducing the apoptotic events in colorectal cancer cells
A multi-species functional embedding integrating sequence and network structure
A key challenge to transferring knowledge between species is that different species have fundamentally different genetic architectures. Initial computational approaches to transfer knowledge across species have relied on measures of heredity such as genetic homology, but these approaches suffer from limitations. First, only a small subset of genes have homologs, limiting the amount of knowledge that can be transferred, and second, genes change or repurpose functions, complicating the transfer of knowledge. Many approaches address this problem by expanding the notion of homology by leveraging high-throughput genomic and proteomic measurements, such as through network alignment. In this work, we take a new approach to transferring knowledge across species by expanding the notion of homology through explicit measures of functional similarity between proteins in different species. Specifically, our kernel-based method, HANDL (Homology Assessment across Networks using Diffusion and Landmarks), integrates sequence and network structure to create a functional embedding in which proteins from different species are embedded in the same vector space. We show that inner products in this space and the vectors themselves capture functional similarity across species, and are useful for a variety of functional tasks. We perform the first whole-genome method for predicting phenologs, generating many that were previously identified, but also predicting new phenologs supported from the biological literature. We also demonstrate the HANDL embedding captures pairwise gene function, in that gene pairs with synthetic lethal interactions are significantly separated in HANDL space, and the direction of separation is conserved across species. Software for the HANDL algorithm is available at http://bit.ly/lrgr-handl.Published versio
A network inference method for large-scale unsupervised identification of novel drug-drug interactions
Characterizing interactions between drugs is important to avoid potentially
harmful combinations, to reduce off-target effects of treatments and to fight
antibiotic resistant pathogens, among others. Here we present a network
inference algorithm to predict uncharacterized drug-drug interactions. Our
algorithm takes, as its only input, sets of previously reported interactions,
and does not require any pharmacological or biochemical information about the
drugs, their targets or their mechanisms of action. Because the models we use
are abstract, our approach can deal with adverse interactions,
synergistic/antagonistic/suppressing interactions, or any other type of drug
interaction. We show that our method is able to accurately predict
interactions, both in exhaustive pairwise interaction data between small sets
of drugs, and in large-scale databases. We also demonstrate that our algorithm
can be used efficiently to discover interactions of new drugs as part of the
drug discovery process
Network-based approaches to explore complex biological systems towards network medicine
Network medicine relies on different types of networks: from the molecular level of protein–protein interactions to gene regulatory network and correlation studies of gene expression. Among network approaches based on the analysis of the topological properties of protein–protein interaction (PPI) networks, we discuss the widespread DIAMOnD (disease module detection) algorithm. Starting from the assumption that PPI networks can be viewed as maps where diseases can be identified with localized perturbation within a specific neighborhood (i.e., disease modules), DIAMOnD performs a systematic analysis of the human PPI network to uncover new disease-associated genes by exploiting the connectivity significance instead of connection density. The past few years have witnessed the increasing interest in understanding the molecular mechanism of post-transcriptional regulation with a special emphasis on non-coding RNAs since they are emerging as key regulators of many cellular processes in both physiological and pathological states. Recent findings show that coding genes are not the only targets that microRNAs interact with. In fact, there is a pool of different RNAs—including long non-coding RNAs (lncRNAs) —competing with each other to attract microRNAs for interactions, thus acting as competing endogenous RNAs (ceRNAs). The framework of regulatory networks provides a powerful tool to gather new insights into ceRNA regulatory mechanisms. Here, we describe a data-driven model recently developed to explore the lncRNA-associated ceRNA activity in breast invasive carcinoma. On the other hand, a very promising example of the co-expression network is the one implemented by the software SWIM (switch miner), which combines topological properties of correlation networks with gene expression data in order to identify a small pool of genes—called switch genes—critically associated with drastic changes in cell phenotype. Here, we describe SWIM tool along with its applications to cancer research and compare its predictions with DIAMOnD disease genes
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