Clustering of cognate proteins among distinct proteomes derived from multiple links to a single seed sequence

<p>Abstract</p> <p>Background</p> <p>Modern proteomes evolved by modification of pre-existing ones. It is extremely important to comparative biology that related proteins be identified as members of the same cognate group, since a characterized putative homolog could be used to find clues about the function of uncharacterized proteins from the same group. Typically, databases of related proteins focus on those from completely-sequenced genomes. Unfortunately, relatively few organisms have had their genomes fully sequenced; accordingly, many proteins are ignored by the currently available databases of cognate proteins, despite the high amount of important genes that are functionally described only for these incomplete proteomes.</p> <p>Results</p> <p>We have developed a method to cluster cognate proteins from multiple organisms beginning with only one sequence, through connectivity saturation with that Seed sequence. We show that the generated clusters are in agreement with some other approaches based on full genome comparison.</p> <p>Conclusion</p> <p>The method produced results that are as reliable as those produced by conventional clustering approaches. Generating clusters based only on individual proteins of interest is less time consuming than generating clusters for whole proteomes. </p

A multilayer network approach for guiding drug repositioning in neglected diseases

Drug development for neglected diseases has been historically hampered due to lack of market incentives. The advent of public domain resources containing chemical information from high throughput screenings is changing the landscape of drug discovery for these diseases. In this work we took advantage of data from extensively studied organisms like human, mouse, E. coli and yeast, among others, to develop a novel integrative network model to prioritize and identify candidate drug targets in neglected pathogen proteomes, and bioactive drug-like molecules. We modeled genomic (proteins) and chemical (bioactive compounds) data as a multilayer weighted network graph that takes advantage of bioactivity data across 221 species, chemical similarities between 1.7 105 compounds and several functional relations among 1.67 105 proteins. These relations comprised orthology, sharing of protein domains, and shared participation in defined biochemical pathways. We showcase the application of this network graph to the problem of prioritization of new candidate targets, based on the information available in the graph for known compound-target associations. We validated this strategy by performing a cross validation procedure for known mouse and Trypanosoma cruzi targets and showed that our approach outperforms classic alignment-based approaches. Moreover, our model provides additional flexibility as two different network definitions could be considered, finding in both cases qualitatively different but sensible candidate targets. We also showcase the application of the network to suggest targets for orphan compounds that are active against Plasmodium falciparum in high-throughput screens. In this case our approach provided a reduced prioritization list of target proteins for the query molecules and showed the ability to propose new testable hypotheses for each compound. Moreover, we found that some predictions highlighted by our network model were supported by independent experimental validations as found post-facto in the literature.Fil: Berenstein, Ariel José. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Magariños, María Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); ArgentinaFil: Chernomoretz, Ariel. Fundación Instituto Leloir; Argentina. Universidad de Buenos Aires. Facultad de Ingeniería. Departamento de Física; ArgentinaFil: Fernandez Aguero, Maria Jose. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús). Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas. Instituto de Investigaciones Biotecnológicas "Dr. Raúl Alfonsín" (sede Chascomús); Argentin

Filling the gap between biology and computer science

This editorial introduces BioData Mining, a new journal which publishes research articles related to advances in computational methods and techniques for the extraction of useful knowledge from heterogeneous biological data. We outline the aims and scope of the journal, introduce the publishing model and describe the open peer review policy, which fosters interaction within the research community

Suppression and triggering of Arabidopsis immunity by Albugo species

Albugo species are obligate biotrophic phytopathogens. Like other biotrophs, they are anticipated to secrete effectors that can suppress or trigger plant defenses; the nature of Albugo effectors is currently unknown. Sequencing of A. laibachii isolate Nc14 (AlNc14) genome reveals 13032 genes encoded in a ~37 Mb genome. We analyze the effector complement of AlNc14 and find known effector classes but also classes unique to A. laibachii. Experiments reveal that CHXCs are a novel class of effectors that suppress host defense. We functionally characterize two predicted AlNc14 effectors in detail; CHXC1 a potential core effector conserved in other oomycete species, and SSP6, a fast-evolving effector specific to A. laibachii. CHXC1 encodes a nuclear localized HECT E3 ligase homolog, which suppresses host defenses dependent on cys651. We find 7 variants of SSP6 that are under diversifying selection. Two highly expressed variants SSP6-2c and SSP6-A are plasma membrane localized when expressed in planta. Interestingly, SSP6-2c but not SSP6-A, is able to enhance growth of P. infestans race blue 13 and suppress flg22-dependent ROS production. In Arabidopsis cells we find SSP6-2c localizes around AlNc14 haustoria. We propose that AlNc14 secretes the effectors SSP6-2c and CHXC1 into the plant cell to suppress defense and promote infection. Current methods to screen for virulence of effector candidates predominantly rely on measuring growth of bacterial pathogens. Quantitative assessment of resistance and susceptibility to eukaryotic pathogens is more difficult. We develop a semi-automated high-throughput system for assaying Hpa growth. We investigate the genetic basis of resistance to Albugo in Arabidopsis. We find that resistance to AlNc14 is linked to RAC1 and RAC3 in Ksk-1. In contrast, resistance to A. candida Nc2 (AcNc2) is linked to WRR4 in Col-0, Col-5 and Ksk-1. A second dominant locus, WRR5a/b in Col-5 also confers resistance to AlNc2. Thus, different R-genes and presumably different effectors govern resistance to AlNc14 and AcNc2.

Current trends in the bioinformatic sequence analysis of metabolic pathways in prokaryotes

The study of metabolic pathways is becoming increasingly important to exploit an integrated, systems-level approach for optimizing a desired cellular property or phenotype. In this context, the integration of genomics data with genetic, metabolic and regulatory models is essential because the systematic design of artificial, biological systems requires the identification of robust building blocks like gene promoters, metabolic pathways or genetic circuits taken from natural organisms, and manipulated to develop ad hoc features. Computational tools allowing precise descriptions of natural pathways might thus allow improving the performance of artificial routes. In this review, we introduce the most recent bioinformatics tools enabling detailed characterizations of metabolic pathways in bacteria from different perspectives

Preimplantation development regulatory pathway construction through a text-mining approach

BACKGROUND: The integration of sequencing and gene interaction data and subsequent generation of pathways and networks contained in databases such as KEGG Pathway is essential for the comprehension of complex biological processes. We noticed the absence of a chart or pathway describing the well-studied preimplantation development stages; furthermore, not all genes involved in the process have entries in KEGG Orthology, important information for knowledge application with relation to other organisms. RESULTS: In this work we sought to develop the regulatory pathway for the preimplantation development stage using text-mining tools such as Medline Ranker and PESCADOR to reveal biointeractions among the genes involved in this process. The genes present in the resulting pathway were also used as seeds for software developed by our group called SeedServer to create clusters of homologous genes. These homologues allowed the determination of the last common ancestor for each gene and revealed that the preimplantation development pathway consists of a conserved ancient core of genes with the addition of modern elements. CONCLUSIONS: The generation of regulatory pathways through text-mining tools allows the integration of data generated by several studies for a more complete visualization of complex biological processes. Using the genes in this pathway as "seeds" for the generation of clusters of homologues, the pathway can be visualized for other organisms. The clustering of homologous genes together with determination of the ancestry leads to a better understanding of the evolution of such process

The natural history of the WRKY–GCM1 zinc fingers and the relationship between transcription factors and transposons

WRKY and GCM1 are metal chelating DNA-binding domains (DBD) which share a four stranded fold. Using sensitive sequence searches, we show that this WRKY–GCM1 fold is also shared by the FLYWCH Zn-finger domain and the DBDs of two classes of Mutator-like element (MULE) transposases. We present evidence that they share a stabilizing core, which suggests a possible origin from a BED finger-like intermediate that was in turn ultimately derived from a C2H2 Zn-finger domain. Through a systematic study of the phyletic pattern, we show that this WRKY–GCM1 superfamily is a widespread eukaryote-specific group of transcription factors (TFs). We identified several new members across diverse eukaryotic lineages, including potential TFs in animals, fungi and Entamoeba. By integrating sequence, structure, gene expression and transcriptional network data, we present evidence that at least two major global regulators belonging to this superfamily in Saccharomyces cerevisiae (Rcs1p and Aft2p) have evolved from transposons, and attained the status of transcription regulatory hubs in recent course of ascomycete yeast evolution. In plants, we show that the lineage-specific expansion of WRKY–GCM1 domain proteins acquired functional diversity mainly through expression divergence rather than by protein sequence divergence. We also use the WRKY–GCM1 superfamily as an example to illustrate the importance of transposons in the emergence of new TFs in different lineages

A proteomic and cytological characterisation of the buff-tailed bumblebee (Bombus terrestris) fat body and haemolymph -An immune perspective

Bees, including solitary and social, native and managed, are vital insect pollinators that provide essential ecosystem services. Bombus terrestris (Linnaeus) is a widespread and important bumblebee pollinator of wild and cultivated crops and although found commonly across Europe, is available commercially to supplement pollination requirements. Due to their activity, B. terrestris workers encounter a variety of diseases which in addition to habitat loss and agrichemical use, are key factors in global bumblebee declines. The profound economic and environmental consequences of this decline warrant a detailed investigation of the molecular and cellular aspects of bumblebee health. The principal components of the B. terrestris immune system, the fat body (FB) and haemolymph were characterised here using proteomic and cytological methodologies. The FB proteome is highly enriched in metabolic, detoxification and proteostasis processes whereas the haemolymph is enriched in cellular transport and immunity. At a cellular level the FB was shown to predominantly comprise adipocytes and oenocytes, while spherulocytes, oenocytoids and plasmatocytes were the most frequently found haemocytes in bumblebee haemolymph. The FB and haemolymph were also investigated under various stresses and contexts. In general, typical immune responses to microbial challenge were observed although immune signatures were lower than expected. Although specific responses to Gram-positive and Gram-negative bacteria and fungi were observed, a broad and conserved response to microbial challenge was found. The major responses in both the haemolymph and FB, however involved energy metabolism, protein processing and detoxification which provides insight into the mechanisms that support and regulate the immune response in bumblebees. Worryingly, pesticide exposure had a significant effect on the FB proteome and its ability to mount an immune response. Overall these results provide novel insights into molecular aspects of bee health and highlight the importance of nutrition and the risks posed by pesticides use on our important pollinator species