FiRES: A computational method for the de novo identification of internal structure similarity in proteins.

Internal structure similarity in proteins can be observed at the domain and subdomain levels. From an evolutionary perspective, structurally similar elements may arise divergently by gene duplication and fusion events but may also be the product of convergent evolution under physicochemical constraints. The characterization of proteins that contain repeated structural elements has implications for many fields of protein science including protein domain evolution, structure classification, structure prediction, and protein engineering. FiRES (Find Repeated Elements in Structure) is an algorithm that relies on a topology-independent structure alignment method to identify repeating elements in protein structure. FiRES was tested against two hand curated databases of protein repeats: MALIDUP, for very divergent duplicated domains; and RepeatsDB for short tandem repeats. The performance of FiRES was compared to that of lalign, RADAR, HHrepID, CE-symm, ReUPred, and Swelfe. FiRES was the method that most accurately detected proteins either with duplicated domains (accuracy = 0.86) or with multiple repeated units (accuracy = 0.92). FiRES is a new methodology for the discovery of proteins containing structurally similar elements. The FiRES web server is publicly available at http://fires.ifc.unam.mx. The scripts, results, and benchmarks from this study can be downloaded from https://github.com/Claualvarez/fires.

YAAM: Yeast Amino Acid Modifications Database.

Database URL: http://yaam.ifc.unam.mx/.

IreA Controls Endoplasmic Reticulum Stress-Induced Autophagy and Survival through Homeostasis Recovery.

The unfolded protein response (UPR) is an adaptive pathway that restores cellular homeostasis after endoplasmic reticulum (ER) stress. The ER-resident kinase/RNase Ire1 is the only UPR sensor conserved during evolution. Autophagy, a lysosomal degradative pathway, also contributes to the recovery of cell homeostasis after ER stress, but the interplay between these two pathways is still poorly understood. We describe the Dictyostelium discoideum ER stress response and characterize its single bona fide Ire1 orthologue, IreA. We found that tunicamycin (TN) triggers a gene-expression reprogramming that increases the protein folding capacity of the ER and alleviates ER protein load. Further, IreA is required for cell survival after TN-induced ER stress and is responsible for nearly 40% of the transcriptional changes induced by TN. The response of Dictyostelium cells to ER stress involves the combined activation of an IreA-dependent gene expression program and the autophagy pathway. These two pathways are independently activated in response to ER stress but, interestingly, autophagy requires IreA at a later stage for proper autophagosome formation. We propose that unresolved ER stress in cells lacking IreA causes structural alterations of the ER, leading to a late-stage blockade of autophagy clearance. This unexpected functional link may critically affect eukaryotic cell survival under ER stress.

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

BACKGROUND: Uncovering 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. RESULTS: In 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. CONCLUSIONS: This 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.

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.

How to identify essential genes from molecular networks?

BACKGROUND: The 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. RESULTS: By 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. CONCLUSION: The 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.

Kluyveromyces lactis sexual pheromones. Gene structures and cellular responses to alpha-factor.

The Kluyveromyces lactis genes for sexual pheromones have been analyzed. The alpha-factor gene encodes a predicted polypeptide of 187 amino acid residues containing four tridecapeptide repeats (WSWITLRPGQPIF). A nucleotide blast search of the entire K. lactis genome sequence allowed the identification of the nonannotated putative a-pheromone gene that encodes a predicted protein of 33 residues containing one copy of the dodecapeptide a-factor (WIIPGFVWVPQC). The role of the K. lactis structural genes KlMFalpha1 and KlMFA1 in mating has been investigated by the construction of disruption mutations that totally eliminate gene functions. Mutants of both alleles showed sex-dependent sterility, indicating that these are single-copy genes and essential for mating. MATalpha, Klsst2 mutants, which, by analogy to Saccharomyces cerevisiae, are defective in Galpha-GTPase activity, showed increased sensitivity to synthetic alpha-factor and increased capacity to mate. Additionally, Klbar1 mutants (putatively defective in alpha-pheromone proteolysis) showed delay in mating but sensitivity to alpha-pheromone. From these results, it can be deduced that the K. lactis MATa cell produces the homolog of the S. cerevisiaealpha-pheromone, whereas the MATalpha cell produces the a-pheromone.

The pheromone response pathway of Kluyveromyces lactis.

The mating pheromone response pathway in Saccharomyces cerevisiae is one of the best understood signalling pathways in eukaryotes. Comparison of this system with pathways in other fungal species has generated surprises and insights. Cloning and targetted disruption of genes encoding components of the pheromone response pathway has allowed the attribution of specific functions to these signal transduction components. In this review we describe current knowledge of the Kluyveromyces lactis mating system, and compare it with the well-understood S. cerevisiae pathway, emphasizing the similarities and differences in the heterotrimeric G protein activity. This mating pathway is controlled positively by both the Galpha and the Gbeta subunits of the heterotrimeric G protein.