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

Display Omitted Three different models for inferring gene networks from microarray data are proposed.The most sensitive approach is selected by an exhaustive simulation study.The method reveals a cross-talk between the isoprenoid biosynthesis pathways in Arabidopsis thaliana.The method highlights 9 genes in HRAS signature regulated by the transcription factor RREB1. MotivationThe 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. ResultsIn 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) '?2C' method maximizes a properly regularized log-likelihood function. Our extensive simulation studies showed that ?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. AvailabilitySoftware implementing the methods in the form of Matlab scripts are available at: http://users.ba.cnr.it/issia/iesina18/CovSelModelsCodes.zip

Guidelines for the use and interpretation of assays for monitoring autophagy (3rd edition)

In 2008 we published the first set of guidelines for standardizing research in autophagy. Since then, research on this topic has continued to accelerate, and many new scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Accordingly, it is important to update these guidelines for monitoring autophagy in different organisms. Various reviews have described the range of assays that have been used for this purpose. Nevertheless, there continues to be confusion regarding acceptable methods to measure autophagy, especially in multicellular eukaryotes. For example, a key point that needs to be emphasized is that there is a difference between measurements that monitor the numbers or volume of autophagic elements (e.g., autophagosomes or autolysosomes) at any stage of the autophagic process versus those that measure fl ux through the autophagy pathway (i.e., the complete process including the amount and rate of cargo sequestered and degraded). In particular, a block in macroautophagy that results in autophagosome accumulation must be differentiated from stimuli that increase autophagic activity, defi ned as increased autophagy induction coupled with increased delivery to, and degradation within, lysosomes (inmost higher eukaryotes and some protists such as Dictyostelium ) or the vacuole (in plants and fungi). In other words, it is especially important that investigators new to the fi eld understand that the appearance of more autophagosomes does not necessarily equate with more autophagy. In fact, in many cases, autophagosomes accumulate because of a block in trafficking to lysosomes without a concomitant change in autophagosome biogenesis, whereas an increase in autolysosomes may reflect a reduction in degradative activity. It is worth emphasizing here that lysosomal digestion is a stage of autophagy and evaluating its competence is a crucial part of the evaluation of autophagic flux, or complete autophagy. Here, we present a set of guidelines for the selection and interpretation of methods for use by investigators who aim to examine macroautophagy and related processes, as well as for reviewers who need to provide realistic and reasonable critiques of papers that are focused on these processes. These guidelines are not meant to be a formulaic set of rules, because the appropriate assays depend in part on the question being asked and the system being used. In addition, we emphasize that no individual assay is guaranteed to be the most appropriate one in every situation, and we strongly recommend the use of multiple assays to monitor autophagy. Along these lines, because of the potential for pleiotropic effects due to blocking autophagy through genetic manipulation it is imperative to delete or knock down more than one autophagy-related gene. In addition, some individual Atg proteins, or groups of proteins, are involved in other cellular pathways so not all Atg proteins can be used as a specific marker for an autophagic process. In these guidelines, we consider these various methods of assessing autophagy and what information can, or cannot, be obtained from them. Finally, by discussing the merits and limits of particular autophagy assays, we hope to encourage technical innovation in the field

Loss of Connectivity in Cancer Co-Expression Networks

<div><p>Differential gene expression profiling studies have lead to the identification of several disease biomarkers. However, the oncogenic alterations in coding regions can modify the gene functions without affecting their own expression profiles. Moreover, post-translational modifications can modify the activity of the coded protein without altering the expression levels of the coding gene, but eliciting variations to the expression levels of the regulated genes. These considerations motivate the study of the rewiring of networks co-expressed genes as a consequence of the aforementioned alterations in order to complement the informative content of differential expression. We analyzed 339 mRNAomes of five distinct cancer types to find single genes that presented co-expression patterns strongly differentiated between normal and tumor phenotypes. Our analysis of differentially connected genes indicates the loss of connectivity as a common topological trait of cancer networks, and unveils novel candidate cancer genes. Moreover, our integrated approach that combines the differential expression together with the differential connectivity improves the classic enrichment pathway analysis providing novel insights on putative cancer gene biosystems not still fully investigated.</p></div

Comparison between pathway enrichment studies for differential expression and differential connectivity.

<p>(<b>A-E</b>) Benjamini–Hochberg False Discovery Rate (FDR) as a function of the p-value. Red color refers to the pathways enriched of differentially expressed genes (DE). Blu color refers to the pathways enriched of differentially expressed genes or differentially connected ones (DEC). (<b>F</b>) Reactome pathways of Immune System. Enrichment meta-analysis p-values across the tissues for ``Reactome Immune System'', its first and second sub-pathways. The histogram in the inset shows the tissue-specific enrichment p-values of ``Reactome Immune System''. (<b>G-H</b>) Core set pathway enrichment analysis. The numbers of core set pathways found as significant at 0.01 level (<b>G</b>) and in the top-ranked positions (<b>H</b>) are displayed on the bars. In the inset it is reported the p-value associated to the relative merit of DEC measure with respect to DE obtained by a permutation test.</p

Comparison between differential expression and differential connectivity.

<p>(<b>A-E</b>) (Upper panel) Gene differential connectivity p-value as a function of degree ratio . (Lower panel) Gene differential expression p-value as a function of degree ratio . Each point represents a gene and the trend line is the least-square line. (<b>F</b>) Correlations between the differential expression p-value and the gain of connections. P-values on the bars refer to right-tail tests for the positive correlations, and left-tail tests for the negative correlations. (<b>G</b>) Correlations between differential expression p-values and differential connection p-values.</p

Cancer tissues are characterized by loss of connectivity.

<p>(<b>A-E</b>) Cumulative distribution functions of the gene degree. (<b>F</b>) Boxplots of the gene degrees for the five tissues in the two conditions. Red color refers to the cancer phenotype. Blue color refers to the normal phenotype. The median degree in cancer is lower than in normal conditions.</p

Transcriptional Analysis of Acinetobacter sp. neg1 Capable of Degrading Ochratoxin A

Ochratoxin A (OTA) is a nephrotoxic and potentially carcinogenic mycotoxin produced by several species of Aspergillus and Penicillium, contaminating grapes, wine and a variety of food products. We recently isolated from OTA contaminated soil vineyard a novel free-living strain of Acinetobacter sp. neg1, ITEM 17016, able to degrade OTA into the non-toxic catabolic product ochratoxin a. Biochemical studies suggested that the degradation reaction proceeds via peptide bond hydrolysis with phenylalanine (Phe) release. In order to identify genes responsible for OTA degradation we performed a differential gene expression analysis of ITEM 17016 grown in the presence or absence of the toxin. Among the differentially expressed genes, six peptidases up-regulated at 6 h were identified. The degrading activity of the carboxypeptidase PJ_1540 was confirmed in vitro in a heterologous system. The enrichment analysis for Gene Ontology terms confirmed that OTA degradation proceeds through peptidase activities and revealed the over-representation of pathways related to Phe catabolism. These results indicate that Phe may represent an energy source for this Acinetobacter sp. neg1 strain and that OTA degrading reaction triggers the modulation of further catabolic activities