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.

BackgroundGene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis.ResultsWe propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.ConclusionsThus, our study provides a new robust approach for improving reconstruction of covariation networks.ReviewersThis article was reviewed by Eugene Koonin, Sergei Maslov, Daniel Yasumasa Takahashi.Electronic supplementary materialThe online version of this article (doi:10.1186/s13062-016-0155-0) contains supplementary material, which is available to authorized users.

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.

The purpose of this single center retrospective study was to investigate the relationship between HLA and ABO polymorphisms and COVID‐19 susceptibility and severity in kidney transplant recipients. It included 720 recipients who had COVID‐19 and 1680 controls composed by recipients in follow‐up who did not contact the transplantation center for COVID‐19 symptoms, up to the moment of their inclusion in the study. HLA‐A, ‐B, and ‐DRB1 allele groups and ABO frequencies were compared between recipients with COVID‐19 (all cases, or separately mild/moderate and severe disease) and controls. The HLA association study was conducted in two case–control series and only associations that showed a p‐value <0.05 in both series were considered. No HLA association regarding COVID‐19 occurrence or severity met this criterion. Homozygosity at HLA‐A locus was associated with COVID‐19 susceptibility (odds ratio 1.4) but not severity. Blood groups A and O were associated with susceptibility and resistance to COVID‐19, respectively. COVID‐19 severity was associated only with older age and cardiac disease, in a multivariate analysis. We conclude that an influence of HLA on COVID‐19 susceptibility is supported by the association with homozygosity at HLA‐A locus but that there is no evidence for a role of any particular HLA‐A, ‐B, or ‐DRB1 polymorphism. Thus, we suggest that what matters is the overall capability of an individual's HLA molecules to present SARS‐CoV‐2 peptides to T cells, a factor that might have a great influence on the breadth of the immune response.

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Background: Gene covariation networks are commonly used to study biological processes. The inference of gene covariation networks from observational data can be challenging, especially considering the large number of players involved and the small number of biological replicates available for analysis. Results: We propose a new statistical method for estimating the number of erroneous edges in reconstructed networks that strongly enhances commonly used inference approaches. This method is based on a special relationship between sign of correlation (positive/negative) and directionality (up/down) of gene regulation, and allows for the identification and removal of approximately half of all erroneous edges. Using the mathematical model of Bayesian networks and positive correlation inequalities we establish a mathematical foundation for our method. Analyzing existing biological datasets, we find a strong correlation between the results of our method and false discovery rate (FDR). Furthermore, simulation analysis demonstrates that our method provides a more accurate estimate of network error than FDR.