A comparative study of covariance selection models for the inference of gene regulatory networks.

MOTIVATION: The inference, or 'reverse-engineering', of gene regulatory networks from expression data and the description of the complex dependency structures among genes are open issues in modern molecular biology. RESULTS: In this paper we compared three regularized methods of covariance selection for the inference of gene regulatory networks, developed to circumvent the problems raising when the number of observations n is smaller than the number of genes p. The examined approaches provided three alternative estimates of the inverse covariance matrix: (a) the 'PINV' method is based on the Moore-Penrose pseudoinverse, (b) the 'RCM' method performs correlation between regression residuals and (c) 'ℓ(2C)' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ℓ(2C) outperformed the other two methods having the most predictive partial correlation estimates and the highest values of sensitivity to infer conditional dependencies between genes even when a few number of observations was available. The application of this method for inferring gene networks of the isoprenoid biosynthesis pathways in Arabidopsis thaliana allowed to enlighten a negative partial correlation coefficient between the two hubs in the two isoprenoid pathways and, more importantly, provided an evidence of cross-talk between genes in the plastidial and the cytosolic pathways. When applied to gene expression data relative to a signature of HRAS oncogene in human cell cultures, the method revealed 9 genes (p-value<0.0005) directly interacting with HRAS, sharing the same Ras-responsive binding site for the transcription factor RREB1. This result suggests that the transcriptional activation of these genes is mediated by a common transcription factor downstream of Ras signaling. AVAILABILITY: Software implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip.

Activated innate immunity and the involvement of CX3CR1-fractalkine in promoting hematuria in patients with IgA nephropathy.

A hallmark of immunoglobulin A nephropathy (IgAN) is episodes of gross hematuria coinciding with mucosal infections that can represent the disease-triggering event. Here we performed a whole genomic screen of IgAN patients during gross hematuria to clarify the link between mucosal antigens and glomerular hematuria. Modulated genes showed a clear involvement of the intracellular interferon signaling, antigen-presenting pathway, and the immunoproteasome. The mRNA and protein level of the chemokine receptor characterizing cytotoxic effector lymphocytes, CX3CR1, was upregulated. In vitro antigenic stimulation of peripheral blood mononuclear cells from IgAN patients, healthy blood donors, and other nephropathies with microscopic hematuria showed that only in IgAN patients was CX3CR1 enhanced in a dose-dependent manner. A significantly higher amount of glomerular and urinary fractalkine, the only ligand of CX3CR1, was also found in IgAN patients with recurrent episodes of gross hematuria compared with other patients with microscopic or no hematuria. This suggests a predisposition for cytotoxic cell extravasation only in patients with recurrent gross hematuria. Thus, we found a defect in antigen handling in peripheral blood mononuclear cells of IgAN patients with a specific increase of CX3CR1. This constitutive upregulation of glomerular and urinary fractalkine suggests an involvement of the CX3CR1-fractalkine axis in the exacerbation of gross hematuria.