Gerardo Coello

How to identify essential genes from molecular networks

BackgroundThe prediction of essential genes from molecular networks is a way to test the understanding of essentiality in the context of what is known about the network. However, the current knowledge on molecular network structures is incomplete yet, and consequently the strategies aimed to predict essential genes are prone to uncertain predictions. We propose that simultaneously evaluating different network structures and different algorithms representing gene essentiality (centrality measures) may identify essential genes in networks in a reliable fashion.ResultsBy simultaneously analyzing 16 different centrality measures on 18 different reconstructed metabolic networks for Saccharomyces cerevisiae, we show that no single centrality measure identifies essential genes from these networks in a statistically significant way; however, the combination of at least 2 centrality measures achieves a reliable prediction of most but not all of the essential genes. No improvement is achieved in the prediction of essential genes when 3 or 4 centrality measures were combined.ConclusionThe method reported here describes a reliable procedure to predict essential genes from molecular networks. Our results show that essential genes may be predicted only by combining centrality measures, revealing the complex nature of the function of essential genes.

Gene expression analysis during dengue virus infection in HepG2 cells reveals virus control of innate immune response.

OBJECTIVES Liver damage occurs during Dengue Virus infection and constitutes a characteristic of severe forms of the disease. The present study was focused on the modulation of gene expression in a human hepatic cell lineage, HepG2, in response to Dengue Virus infection. METHODS The global gene expression changes in HepG2 cells after 6, 24 and 48h of infection with Dengue Virus were investigated using a new tool of microarray data analysis and real-time PCR. RESULTS HepG2 cells infected with Dengue Virus showed alterations in several signaling pathways involved in innate immune response. The analysis of pattern recognition pathways genes demonstrated that TLR3, TLR8, RIG-I and MDA5 mRNAs were up-regulated during Dengue Virus infection along with an increase in the expression of the type I interferon, IFN-beta and pro-inflammatory cytokines IL-6, IL-8 and RANTES genes. CONCLUSIONS Our results suggest that innate immune pathways are involved in the recognition of Dengue Virus by HepG2 cells. These observations may contribute to the understanding of the inflammatory responses induced by Dengue Virus-hepatocytes interaction during dengue diseases.

ARACNe-based inference, using curated microarray data, of Arabidopsis thaliana root transcriptional regulatory networks

BackgroundUncovering the complex transcriptional regulatory networks (TRNs) that underlie plant and animal development remains a challenge. However, a vast amount of data from public microarray experiments is available, which can be subject to inference algorithms in order to recover reliable TRN architectures.ResultsIn this study we present a simple bioinformatics methodology that uses public, carefully curated microarray data and the mutual information algorithm ARACNe in order to obtain a database of transcriptional interactions. We used data from Arabidopsis thaliana root samples to show that the transcriptional regulatory networks derived from this database successfully recover previously identified root transcriptional modules and to propose new transcription factors for the SHORT ROOT/SCARECROW and PLETHORA pathways. We further show that these networks are a powerful tool to integrate and analyze high-throughput expression data, as exemplified by our analysis of a SHORT ROOT induction time-course microarray dataset, and are a reliable source for the prediction of novel root gene functions. In particular, we used our database to predict novel genes involved in root secondary cell-wall synthesis and identified the MADS-box TF XAL1/AGL12 as an unexpected participant in this process.ConclusionsThis study demonstrates that network inference using carefully curated microarray data yields reliable TRN architectures. In contrast to previous efforts to obtain root TRNs, that have focused on particular functional modules or tissues, our root transcriptional interactions provide an overview of the transcriptional pathways present in Arabidopsis thaliana roots and will likely yield a plethora of novel hypotheses to be tested experimentally.

Lack of Genetic Variation in Lacandonia schismatica (Lacandoniaceae: Triuridales) in Its Only Known Locality

Lacandonia schismatica has only one known population in a small patch of tropical rain forest. Using electrophoretic techniques, we assessed its genetic variation in eight loci; no variation was found. We discuss this result and its implications for the conservation prospects of this threatened species

YAAM: Yeast Amino Acid Modifications Database

Abstract Proteins are dynamic molecules that regulate a myriad of cellular functions; these functions may be regulated by protein post-translational modifications (PTMs) that mediate the activity, localization and interaction partners of proteins. Thus, understanding the meaning of a single PTM or the combination of several of them is essential to unravel the mechanisms of protein regulation. Yeast Amino Acid Modification (YAAM) (http://yaam.ifc.unam.mx) is a comprehensive database that contains information from 121 921 residues of proteins, which are post-translationally modified in the yeast model Saccharomyces cerevisiae. All the PTMs contained in YAAM have been confirmed experimentally. YAAM database maps PTM residues in a 3D canvas for 680 proteins with a known 3D structure. The structure can be visualized and manipulated using the most common web browsers without the need for any additional plugin. The aim of our database is to retrieve and organize data about the location of modified amino acids providing information in a concise but comprehensive and user-friendly way, enabling users to find relevant information on PTMs. Given that PTMs influence almost all aspects of the biology of both healthy and diseased cells, identifying and understanding PTMs is critical in the study of molecular and cell biology. YAAM allows users to perform multiple searches, up to three modifications at the same residue, giving the possibility to explore possible regulatory mechanism for some proteins. Using YAAM search engine, we found three different PTMs of lysine residues involved in protein translation. This suggests an important regulatory mechanism for protein translation that needs to be further studied. Database URL: http://yaam.ifc.unam.mx/