Cis-regulatory module detection using constraint programming

We propose a method for finding CRMs in a set of co-regulated genes. Each CRM consists of a set of binding sites of transcription factors. We wish to find CRMs involving the same transcription factors in multiple sequences. Finding such a combination of transcription factors is inherently a combinatorial problem. We solve this problem by combining the principles of itemset mining and constraint programming. The constraints involve the putative binding sites of transcription factors, the number of sequences in which they co-occur and the proximity of the binding sites. Genomic background sequences are used to assess the significance of the modules. We experimentally validate our approach and compare it with state-of-the-art techniques

Analysis of Functional Genomic Signals Using the XOR Gate

Modeling gene regulatory networks requires recognition of active transcriptional sites in the genome. For this reason, we present a novel approach for inferring active transcriptional regulatory modules in a genome using an established systems model of bit encoded DNA sequences. Our analysis showed variations in several properties between random and functional sequences. Cross correlation within random and functional groups uncovered a wave pattern associated with functional sequences. Using the exclusive-OR (XOR) logic gate, we formulated a scheme to threshold signals that may correlate to putative active transcriptional modules from a population of random genomic fragments. It is our intent to use this as a basis for identifying novel regulatory sites in the genome

A systems-level framework for drug discovery identifies Csf1R as an anti-epileptic drug target

The identification of drug targets is highly challenging, particularly for diseases of the brain. To address this problem, we developed and experimentally validated a general computational framework for drug target discovery that combines gene regulatory information with causal reasoning (“Causal Reasoning Analytical Framework for Target discovery”—CRAFT). Using a systems genetics approach and starting from gene expression data from the target tissue, CRAFT provides a predictive framework for identifying cell membrane receptors with a direction-specified influence over disease-related gene expression profiles. As proof of concept, we applied CRAFT to epilepsy and predicted the tyrosine kinase receptor Csf1R as a potential therapeutic target. The predicted effect of Csf1R blockade in attenuating epilepsy seizures was validated in three pre-clinical models of epilepsy. These results highlight CRAFT as a systems-level framework for target discovery and suggest Csf1R blockade as a novel therapeutic strategy in epilepsy. CRAFT is applicable to disease settings other than epilepsy

Creating New β-Globin-Expressing Lentiviral Vectors by High-Resolution Mapping of Locus Control Region Enhancer Sequences.

Hematopoietic stem cell gene therapy is a promising approach for treating disorders of the hematopoietic system. Identifying combinations of cis-regulatory elements that do not impede packaging or transduction efficiency when included in lentiviral vectors has proven challenging. In this study, we deploy LV-MPRA (lentiviral vector-based, massively parallel reporter assay), an approach that simultaneously analyzes thousands of synthetic DNA fragments in parallel to identify sequence-intrinsic and lineage-specific enhancer function at near-base-pair resolution. We demonstrate the power of LV-MPRA in elucidating the boundaries of previously unknown intrinsic enhancer sequences of the human β-globin locus control region. Our approach facilitated the rapid assembly of novel therapeutic βAS3-globin lentiviral vectors harboring strong lineage-specific recombinant control elements capable of correcting a mouse model of sickle cell disease. LV-MPRA can be used to map any genomic locus for enhancer activity and facilitates the rapid development of therapeutic vectors for treating disorders of the hematopoietic system or other specific tissues and cell types

On Weight Matrix and Free Energy Models for Sequence Motif Detection

The problem of motif detection can be formulated as the construction of a discriminant function to separate sequences of a specific pattern from background. In computational biology, motif detection is used to predict DNA binding sites of a transcription factor (TF), mostly based on the weight matrix (WM) model or the Gibbs free energy (FE) model. However, despite the wide applications, theoretical analysis of these two models and their predictions is still lacking. We derive asymptotic error rates of prediction procedures based on these models under different data generation assumptions. This allows a theoretical comparison between the WM-based and the FE-based predictions in terms of asymptotic efficiency. Applications of the theoretical results are demonstrated with empirical studies on ChIP-seq data and protein binding microarray data. We find that, irrespective of underlying data generation mechanisms, the FE approach shows higher or comparable predictive power relative to the WM approach when the number of observed binding sites used for constructing a discriminant decision is not too small.Comment: 23 pages, 1 figure and 4 table

Gene regulatory network analysis predicts cooperating transcription factor regulons required for FLT3-ITD+ AML growth

Acute myeloid leukemia (AML) is a heterogeneous disease caused by different mutations. Previously, we showed that each mutational subtype develops its specific gene regulatory network (GRN) with transcription factors interacting within multiple gene modules, many of which are transcription factor genes themselves. Here, we hypothesize that highly connected nodes within such networks comprise crucial regulators of AML maintenance. We test this hypothesis using FLT3-ITD-mutated AML as a model and conduct an shRNA drop-out screen informed by this analysis. We show that AML-specific GRNs predict crucial regulatory modules required for AML growth. Furthermore, our work shows that all modules are highly connected and regulate each other. The careful multi-omic analysis of the role of one (RUNX1) module by shRNA and chemical inhibition shows that this transcription factor and its target genes stabilize the GRN of FLT3-ITD+ AML and that its removal leads to GRN collapse and cell death.</p

Identifying therapeutic targets in glioma using integrated network analysis

Gliomas are the most common brain tumours in adult population with rapid progression and poor prognosis. Survival among the patients diagnosed with the most aggressive histopathological subtype of gliomas, the glioblastoma, is a mere 12.6 months given the current standard of care. While glioblastomas mostly occur in people over 60, the lower-grade gliomas afflict themselves upon individuals in their third and fourth decades of life. Collectively, the gliomas are one of the major causes of cancer-related death in individuals under fortyin the UK. Over the past twenty years, little has changed in the standard of glioma treatment and the disease has remained incurable. This study focuses on identifying potential therapeutic targets in gliomasusing systems-level approaches and large-scale data integration.I used publicly available transcriptomic data to identify gene co-expression networks associated with the progression of IDH1-mutant 1p/19q euploid astrocytomas from grade II to grade III and high-lighted hub-genes of these networks, which could be targeted to modulate their biological function. I also studied the changes in co-expression patterns between grade II and grade III gliomas and identified a cluster of genes with differential co-expression in different disease states (module M2). By data integration and adaptation of reverse-engineering methods, I elucidated master regulators of the module M2. I then sought to counteract the regulatory activity by using drug-induced gene expression dataset to find compounds inducing gene expression in the opposite direction of the disease signature. I proposed resveratrol as a potentially disease modifying compound, which when administered to patients with a low-grade disease could potentially delay glioma progression.Finally, I appliedanensemble-learning algorithm on a large-scale loss-of-function viability screen in cancer cell-lines with different genetic backgrounds to identify gene dependencies associated with chromosomal copy-number losses common intheglioblastomas. I propose five novel target predictions to be validated in future experiments.Open acces

Discovery and development of Seliciclib. How systems biology approaches can lead to better drug performance

Seliciclib (R-Roscovitine) was identified as an inhibitor of CDKs and has undergone drug development and clinical testing as an anticancer agent. In this review, the authors describe the discovery of Seliciclib and give a brief summary of the biology of the CDKs Seliciclib inhibits. An overview of the published in vitro and in vivo work supporting the development as an anti-cancer agent, from in vitro experiments to animal model studies ending with a summary of the clinical trial results and trials underway is presented. In addition some potential non-oncology applications are explored and the potential mode of action of Seliciclib in these areas is described. Finally the authors argue that optimisation of the therapeutic effects of kinase inhibitors such as Seliciclib could be enhanced using a systems biology approach involving mathematical modelling of the molecular pathways regulating cell growth and division