A Scalable Method for Molecular Network Reconstruction Identifies Properties of Targets and Mutations in Acute Myeloid Leukemia

Abstract

A key aim of systems biology is the reconstruction of molecular networks. We do not yet, however, have networks that integrate information from all datasets available for a particular clinical condition. This is in part due to the limited scalability, in terms of required computational time and power, of existing algorithms. Network reconstruction methods should also be scalable in the sense of allowing scientists from different backgrounds to efficiently integrate additional data. We present a network model of acute myeloid leukemia (AML). In the current version (AML 2.1), we have used gene expression data (both microarray and RNA-seq) from 5 different studies comprising a total of 771 AML samples and a protein-protein interactions dataset. Our scalable network reconstruction method is in part based on the well-known property of gene expression correlation among interacting molecules. The difficulty of distinguishing between direct and indirect interactions is addressed by optimizing the coefficient of variation of gene expression, using a validated goldstandard dataset of direct interactions. Computational time is much reduced compared to other network reconstruction methods. A key feature is the study of the reproducibility of interactions found in independent clinical datasets. An analysis of the most significant clusters, and of the network properties (intraset efficiency, degree, betweenness centrality, and PageRank) of common AML mutations demonstrated the biological significance of the network. A statistical analysis of the response of blast cells from 11 AML patients to a library of kinase inhibitors provided an experimental validation of the network. A combination of network and experimental data identified CDK1, CDK2, CDK4, and CDK6 and other kinases as potential therapeutic targets in AML.

Evolutionary and Topological Properties of Genes and Community Structures in Human Gene Regulatory Networks

“…One gene regulatory network used in this analysis, “AML 2.3”, is a partially directed, weighted acute myeloid leukemia (AML) GRN [ 14 ]. This network was chosen primarily for its quality.…”

“…In terms of network topology, we show that genes and DAVID groups which are old and cold tend to be central, and those which are young and hot tend to be peripheral. We demonstrate this with traditional single-node centrality measures as well as two new network measures, the set efficiency [ 14 ] and the interset efficiency, which quantify the mean distance between all nodes within a single set and between two sets, respectively (see Methods ). We find that PageRank [ 17 ], a finite-range centrality measure, shows stronger biological significance than degree (a local measure) and betweenness centrality (a global measure), and that the set efficiency and interset efficiency correlate strongly with the evolutionary histories of individual genes and DAVID groups.…”

“…Here, our goal is to establish quantitative relationships between the evolutionary history of genes and their topological properties in an acute myeloid leukemia GRN, AML 2.3 [ 14 ], as well as for a general human GRN, HumanNet [ 15 ]. In contrast to the earlier studies above, we go beyond an analysis based on single-node centrality and pairwise measures by studying the connection between topology and evolution from the point of view of network community structures.…”

Piperlongumine induces apoptosis and autophagy in leukemic cells through targeting the PI3K/Akt/mTOR and p38 signaling pathways

“…Leukemia is a kind of malignant tumor of the hemopoietic system posing a great threat to human health ( 1 ). The mortality rate ranks sixth in males and eighth in females among all malignant tumors in China in all age groups, and first among children and adults under 35 years of age ( 2 ). There is a higher number of patients with acute myeloid leukemia (AML) than with acute lymphoblastic leukemia (ALL) (AML:ALL=2.67:1.00); AML is mostly reported in young adults (76.2%) and has a negative effect on health ( 3 ).…”

Cell cycle time series gene expression data encoded as cyclic attractors in Hopfield systems

“…20 However, analysis of the sheer variety and vast quantities of data these techniques 21 produce requires the development of new mathematical tools. Inference and topological 22 analysis of gene regulatory networks has garnered much attention as a method for 23 distilling meaningful information from large datasets [28][29][30][31][32][33][34]. But because life is a 24 non-equilibrium phenomenon that can only be truly understood at the dynamical level, 25 there is a growing need to develop new methods for analyzing time series data.…”