Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Generic, network schema agnostic sparse tensor factorization for single-pass clustering of heterogeneous information networks

Heterogeneous information networks (e.g. bibliographic networks and social media networks) that consist of multiple interconnected objects are ubiquitous. Clustering analysis is an effective method to understand the semantic information and interpretable structure of the heterogeneous information networks, and it has attracted the attention of many researchers in recent years. However, most studies assume that heterogeneous information networks usually follow some simple schemas, such as bi-typed networks or star network schema, and they can only cluster one type of object in the network each time. In this paper, a novel clustering framework is proposed based on sparse tensor factorization for heterogeneous information networks, which can cluster multiple types of objects simultaneously in a single pass without any network schema information. The types of objects and the relations between them in the heterogeneous information networks are modeled as a sparse tensor. The clustering issue is modeled as an optimization problem, which is similar to the well-known Tucker decomposition. Then, an Alternating Least Squares (ALS) algorithm and a feasible initialization method are proposed to solve the optimization problem. Based on the tensor factorization, we simultaneously partition different types of objects into different clusters. The experimental results on both synthetic and real-world datasets have demonstrated that our proposed clustering framework, STFClus, can model heterogeneous information networks efficiently and can outperform state-of-the-art clustering algorithms as a generally applicable single-pass clustering method for heterogeneous network which is network schema agnostic

Recovering Protein-Protein and Domain-Domain Interactions from Aggregation of IP-MS Proteomics of Coregulator Complexes

Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/

Gene Expression Profiling Reveals New Aspects of PIK3CA Mutation in ERalpha-Positive Breast Cancer: Major Implication of the Wnt Signaling Pathway

BACKGROUND: The PI3K/AKT pathway plays a pivotal role in breast cancer development and maintenance. PIK3CA, encoding the PI3K catalytic subunit, is the oncogene exhibiting a high frequency of gain-of-function mutations leading to PI3K/AKT pathway activation in breast cancer. PIK3CA mutations have been observed in 30% to 40% of ERα-positive breast tumors. However the physiopathological role of PIK3CA mutations in breast tumorigenesis remains largely unclear. METHODOLOGY/PRINCIPAL FINDINGS: To identify relevant downstream target genes and signaling activated by aberrant PI3K/AKT pathway in breast tumors, we first analyzed gene expression with a pangenomic oligonucleotide microarray in a series of 43 ERα-positive tumors with and without PIK3CA mutations. Genes of interest were then investigated in 249 ERα-positive breast tumors by real-time quantitative RT-PCR. A robust collection of 19 genes was found to be differently expressed in PIK3CA-mutated tumors. PIK3CA mutations were associated with over-expression of several genes involved in the Wnt signaling pathway (WNT5A, TCF7L2, MSX2, TNFRSF11B), regulation of gene transcription (SEC14L2, MSX2, TFAP2B, NRIP3) and metal ion binding (CYP4Z1, CYP4Z2P, SLC40A1, LTF, LIMCH1). CONCLUSION/SIGNIFICANCE: This new gene set should help to understand the behavior of PIK3CA-mutated cancers and detailed knowledge of Wnt signaling activation could lead to novel therapeutic strategies

Multiway modeling and analysis in stem cell systems biology

<p>Abstract</p> <p>Background</p> <p>Systems biology refers to multidisciplinary approaches designed to uncover emergent properties of biological systems. Stem cells are an attractive target for this analysis, due to their broad therapeutic potential. A central theme of systems biology is the use of computational modeling to reconstruct complex systems from a wealth of reductionist, molecular data (e.g., gene/protein expression, signal transduction activity, metabolic activity, etc.). A number of deterministic, probabilistic, and statistical learning models are used to understand sophisticated cellular behaviors such as protein expression during cellular differentiation and the activity of signaling networks. However, many of these models are bimodal i.e., they only consider row-column relationships. In contrast, multiway modeling techniques (also known as tensor models) can analyze multimodal data, which capture much more information about complex behaviors such as cell differentiation. In particular, tensors can be very powerful tools for modeling the dynamic activity of biological networks over time. Here, we review the application of systems biology to stem cells and illustrate application of tensor analysis to model collagen-induced osteogenic differentiation of human mesenchymal stem cells.</p> <p>Results</p> <p>We applied Tucker1, Tucker3, and Parallel Factor Analysis (PARAFAC) models to identify protein/gene expression patterns during extracellular matrix-induced osteogenic differentiation of human mesenchymal stem cells. In one case, we organized our data into a tensor of type protein/gene locus link × gene ontology category × osteogenic stimulant, and found that our cells expressed two distinct, stimulus-dependent sets of functionally related genes as they underwent osteogenic differentiation. In a second case, we organized DNA microarray data in a three-way tensor of gene IDs × osteogenic stimulus × replicates, and found that application of tensile strain to a collagen I substrate accelerated the osteogenic differentiation induced by a static collagen I substrate.</p> <p>Conclusion</p> <p>Our results suggest gene- and protein-level models whereby stem cells undergo transdifferentiation to osteoblasts, and lay the foundation for mechanistic, hypothesis-driven studies. Our analysis methods are applicable to a wide range of stem cell differentiation models.</p

Building Disease-Specific Drug-Protein Connectivity Maps from Molecular Interaction Networks and PubMed Abstracts

The recently proposed concept of molecular connectivity maps enables researchers to integrate experimental measurements of genes, proteins, metabolites, and drug compounds under similar biological conditions. The study of these maps provides opportunities for future toxicogenomics and drug discovery applications. We developed a computational framework to build disease-specific drug-protein connectivity maps. We integrated gene/protein and drug connectivity information based on protein interaction networks and literature mining, without requiring gene expression profile information derived from drug perturbation experiments on disease samples. We described the development and application of this computational framework using Alzheimer's Disease (AD) as a primary example in three steps. First, molecular interaction networks were incorporated to reduce bias and improve relevance of AD seed proteins. Second, PubMed abstracts were used to retrieve enriched drug terms that are indirectly associated with AD through molecular mechanistic studies. Third and lastly, a comprehensive AD connectivity map was created by relating enriched drugs and related proteins in literature. We showed that this molecular connectivity map development approach outperformed both curated drug target databases and conventional information retrieval systems. Our initial explorations of the AD connectivity map yielded a new hypothesis that diltiazem and quinidine may be investigated as candidate drugs for AD treatment. Molecular connectivity maps derived computationally can help study molecular signature differences between different classes of drugs in specific disease contexts. To achieve overall good data coverage and quality, a series of statistical methods have been developed to overcome high levels of data noise in biological networks and literature mining results. Further development of computational molecular connectivity maps to cover major disease areas will likely set up a new model for drug development, in which therapeutic/toxicological profiles of candidate drugs can be checked computationally before costly clinical trials begin

Transcriptional responses of Burkholderia cenocepacia to polymyxin B in isogenic strains with diverse polymyxin B resistance phenotypes

<p>Abstract</p> <p>Background</p> <p><it>Burkholderia cenocepacia </it>is a Gram-negative opportunistic pathogen displaying high resistance to antimicrobial peptides and polymyxins. We identified mechanisms of resistance by analyzing transcriptional changes to polymyxin B treatment in three isogenic <it>B. cenocepacia </it>strains with diverse polymyxin B resistance phenotypes: the polymyxin B-resistant parental strain K56-2, a polymyxin B-sensitive K56-2 mutant strain with heptoseless lipopolysaccharide (LPS) (RSF34), and a derivative of RSF34 (RSF34 4000B) isolated through multiple rounds of selection in polymyxin B that despite having a heptoseless LPS is highly polymyxin B-resistant.</p> <p>Results</p> <p>A heptoseless LPS mutant of <it>B. cenocepacia </it>was passaged through multiple rounds of selection to regain high levels of polymyxin B-resistance. This process resulted in various phenotypic changes in the isolate that could contribute to polymyxin B resistance and are consistent with LPS-independent changes in the outer membrane. The transcriptional response of three <it>B. cenocepacia </it>strains to subinhibitory concentrations of polymyxin B was analyzed using microarray analysis and validated by quantitative Real Time-PCR. There were numerous baseline changes in expression between the three strains in the absence of polymyxin B. In both K56-2 and RSF34, similar transcriptional changes upon treatment with polymyxin B were found and included upregulation of various genes that may be involved in polymyxin B resistance and downregulation of genes required for the synthesis and operation of flagella. This last result was validated phenotypically as both swimming and swarming motility were impaired in the presence of polymyxin B. RSF34 4000B had altered the expression in a larger number of genes upon treatment with polymyxin B than either K56-2 or RSF34, but the relative fold-changes in expression were lower.</p> <p>Conclusions</p> <p>It is possible to generate polymyxin B-resistant isolates from polymyxin B-sensitive mutant strains of <it>B. cenocepacia</it>, likely due to the multifactorial nature of polymyxin B resistance of this bacterium. Microarray analysis showed that <it>B. cenocepacia </it>mounts multiple transcriptional responses following exposure to polymyxin B. Polymyxin B-regulated genes identified in this study may be required for polymyxin B resistance, which must be tested experimentally. Exposure to polymyxin B also decreases expression of flagellar genes resulting in reduced swimming and swarming motility.</p

Functional phosphoproteomic analysis reveals cold-shock domain protein A to be a Bcr-Abl effector-regulating proliferation and transformation in chronic myeloid leukemia

One proposed strategy to suppress the proliferation of imatinib-resistant cells in chronic myeloid leukemia (CML) is to inhibit key proteins downstream of Bcr-Abl. The PI3K/Akt pathway is activated by Bcr-Abl and is specifically required for the growth of CML cells. To identify targets of this pathway, we undertook a proteomic screen and identified several proteins that differentially bind 14-3-3, dependent on Bcr-Abl kinase activity. An siRNA screen of candidates selected by bioinformatics analysis reveals cold-shock domain protein A (CSDA), shown previously to regulate cell cycle progression in epithelial cells, to be a positive regulator of proliferation in a CML cell line. We show that Akt can phosphorylate the serine 134 residue of CSDA but, downstream of Bcr-Abl activity, this modification is mediated through the activation of MEK/p90 ribosomal S6 kinase (RSK) signaling. Inhibition of RSK, similarly to treatment with imatinib, blocked proliferation specifically in Bcr-Abl-positive leukemia cell lines, as well as cells from CML patients. Furthermore, these primary CML cells showed an increase in CSDA phosphorylation. Expression of a CSDA phospho-deficient mutant resulted in the decrease of Bcr-Abl-dependent transformation in Rat1 cells. Our results support a model whereby phosphorylation of CSDA downstream of Bcr-Abl enhances proliferation in CML cells to drive leukemogenesis