Anatoly Yambartsev

Gene network reconstruction reveals cell cycle and antiviral genes as major drivers of cervical cancer

Although human papillomavirus (HPV) was identified as an etiological factor in cervical cancer, the key human gene drivers of this disease remain unknown. Here we apply an unbiased approach integrating gene expression and chromosomal aberration data. In an independent group of patients, we reconstruct and validate a gene regulatory meta-network, and identify cell cycle and antiviral genes that constitute two major sub-networks up-regulated in tumour samples. These genes are located within the same regions as chromosomal amplifications, most frequently on 3q. We propose a model in which selected chromosomal gains drive activation of antiviral genes contributing to episomal virus elimination, which synergizes with cell cycle dysregulation. These findings may help to explain the paradox of episomal HPV decline in women with invasive cancer who were previously unable to clear the virus.

New approach reveals CD28 and IFNG gene interaction in the susceptibility to cervical cancer

Cervical cancer is a complex disease with multiple environmental and genetic determinants. In this study, we sought an association between polymorphisms in immune response genes and cervical cancer using both single-locus and multi-locus analysis approaches. A total of 14 single nucleotide polymorphisms (SNPs) distributed in CD28, CTLA4, ICOS, PDCD1, FAS, TNFA, IL6, IFNG, TGFB1 and IL10 genes were determined in patients and healthy individuals from three independent case/control sets. The first two sets comprised White individuals (one group with 82 cases and 85 controls, the other with 83 cases and 85 controls) and the third was constituted by non-white individuals (64 cases and 75 controls). The multi-locus analysis revealed higher frequencies in cancer patients of three three-genotype combinations [CD28+17(TT)/IFNG+874(AA)/TNFA-308(GG), CD28+17(TT)/IFN+847(AA)/PDCD1+7785(CT), and CD28 +17(TT)/IFNG+874(AA)/ICOS+1564(TT)] (P < 0.01, Monte Carlo simulation). We hypothesized that this two-genotype [CD28(TT) and IFNG(AA)] combination could have a major contribution to the observed association. To address this question, we analyzed the frequency of the CD28(TT), IFNG(AA) genotype combination in the three groups combined, and observed its increase in patients (P = 0.0011 by Fishers exact test). The contribution of a third polymorphism did not reach statistical significance (P = 0.1). Further analysis suggested that gene-gene interaction between CD28 and IFNG might contribute to susceptibility to cervical cancer. Our results showed an epistatic effect between CD28 and IFNG genes in susceptibility to cervical cancer, a finding that might be relevant for a better understanding of the disease pathogenesis. In addition, the novel analytical approach herein proposed might be useful for increasing the statistical power of future genome-wide multi-locus studies.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview.

BackgroundDAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes.ResultsEach significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools.ConclusionsDAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.

Reverse enGENEering of Regulatory Networks from Big Data: A Roadmap for Biologists

Omics technologies enable unbiased investigation of biological systems through massively parallel sequence acquisition or molecular measurements, bringing the life sciences into the era of Big Data. A central challenge posed by such omics datasets is how to transform these data into biological knowledge, for example, how to use these data to answer questions such as: Which functional pathways are involved in cell differentiation? Which genes should we target to stop cancer? Network analysis is a powerful and general approach to solve this problem consisting of two fundamental stages, network reconstruction, and network interrogation. Here we provide an overview of network analysis including a step-by-step guide on how to perform and use this approach to investigate a biological question. In this guide, we also include the software packages that we and others employ for each of the steps of a network analysis workflow.

A Mermin–Wagner theorem on Lorentzian triangulations with quantum spins

We consider infinite random casual Lorentzian triangulations emerging in quantum gravity for critical values of parameters. With each vertex of the triangulation we associate a Hilbert space representing a bosonic particle moving in accordance with standard laws of Quantum Mechanics. The particles interact via two-body potentials decaying with the graph distance. A Mermin--Wagner type theorem is proven for infinite-volume reduced density matrices related to solutions to DLR equations in the Feynman--Kac (FK) representation.

Bounds on the critical line via transfer matrix methods for an Ising model coupled to causal dynamical triangulations

We introduce a transfer matrix formalism for the (annealed) Ising model coupled to two-dimensional causal dynamical triangulations. Using the Krein-Rutman theory of positivity preserving operators we study several properties of the emerging transfer matrix. In particular, we determine regions in the quadrant of parameters β, μ > 0 where the infinite-volume free energy converges, yielding results on the convergence and asymptotic properties of the partition function and the Gibbs measure.

Phase Transition in Ferromagnetic Ising Model with a Cell-Board External Field

We show the presence of a first-order phase transition for a ferromagnetic Ising model on

Differentially correlated genes in co-expression networks control phenotype transitions

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