Balázs Ligeti

Census of solo LuxR genes in prokaryotic genomes

luxR genes encode transcriptional regulators that control acyl homoserine lactone-based quorum sensing (AHL QS) in Gram negative bacteria. On the bacterial chromosome, luxR genes are usually found next or near to a luxI gene encoding the AHL signal synthase. Recently, a number of luxR genes were described that have no luxI genes in their vicinity on the chromosome. These so-called solo luxR genes may either respond to internal AHL signals produced by a non-adjacent luxI in the chromosome, or can respond to exogenous signals. Here we present a survey of solo luxR genes found in complete and draft bacterial genomes in the NCBI databases using HMMs. We found that 2698 of the 3550 luxR genes found are solos, which is an unexpectedly high number even if some of the hits may be false positives. We also found that solo LuxR sequences form distinct clusters that are different from the clusters of LuxR sequences that are part of the known luxR-luxI topological arrangements. We also found a number of cases that we termed twin luxR topologies, in which two adjacent luxR genes were in tandem or divergent orientation. Many of the luxR solo clusters were devoid of the sequence motifs characteristic of AHL binding LuxR proteins so there is room to speculate that the solos may be involved in sensing hitherto unknown signals. It was noted that only some of the LuxR clades are rich in conserved cysteine residues. Molecular modeling suggests that some of the cysteines may be involved in disulfide formation, which makes us speculate that some LuxR proteins, including some of the solos may be involved in redox regulation.

JBioWH: an open-source Java framework for bioinformatics data integration

The Java BioWareHouse (JBioWH) project is an open-source platform-independent programming framework that allows a user to build his/her own integrated database from the most popular data sources. JBioWH can be used for intensive querying of multiple data sources and the creation of streamlined task-specific data sets on local PCs. JBioWH is based on a MySQL relational database scheme and includes JAVA API parser functions for retrieving data from 20 public databases (e.g. NCBI, KEGG, etc.). It also includes a client desktop application for (non-programmer) users to query data. In addition, JBioWH can be tailored for use in specific circumstances, including the handling of massive queries for high-throughput analyses or CPU intensive calculations. The framework is provided with complete documentation and application examples and it can be downloaded from the Project Web site at http://code.google.com/p/jbiowh. A MySQL server is available for demonstration purposes at hydrax.icgeb.trieste.it:3307. Database URL: http://code.google.com/p/jbiowh

A Network-Based Target Overlap Score for Characterizing Drug Combinations: High Correlation with Cancer Clinical Trial Results.

Drug combinations are highly efficient in systemic treatment of complex multigene diseases such as cancer, diabetes, arthritis and hypertension. Most currently used combinations were found in empirical ways, which limits the speed of discovery for new and more effective combinations. Therefore, there is a substantial need for efficient and fast computational methods. Here, we present a principle that is based on the assumption that perturbations generated by multiple pharmaceutical agents propagate through an interaction network and can cause unexpected amplification at targets not immediately affected by the original drugs. In order to capture this phenomenon, we introduce a novel Target Overlap Score (TOS) that is defined for two pharmaceutical agents as the number of jointly perturbed targets divided by the number of all targets potentially affected by the two agents. We show that this measure is correlated with the known effects of beneficial and deleterious drug combinations taken from the DCDB, TTD and Drugs.com databases. We demonstrate the utility of TOS by correlating the score to the outcome of recent clinical trials evaluating trastuzumab, an effective anticancer agent utilized in combination with anthracycline- and taxane- based systemic chemotherapy in HER2-receptor (erb-b2 receptor tyrosine kinase 2) positive breast cancer.

Fast and sensitive alignment of microbial whole genome sequencing reads to large sequence datasets on a desktop PC: application to metagenomic datasets and pathogen identification.

Next generation sequencing (NGS) of metagenomic samples is becoming a standard approach to detect individual species or pathogenic strains of microorganisms. Computer programs used in the NGS community have to balance between speed and sensitivity and as a result, species or strain level identification is often inaccurate and low abundance pathogens can sometimes be missed. We have developed Taxoner, an open source, taxon assignment pipeline that includes a fast aligner (e.g. Bowtie2) and a comprehensive DNA sequence database. We tested the program on simulated datasets as well as experimental data from Illumina, IonTorrent, and Roche 454 sequencing platforms. We found that Taxoner performs as well as, and often better than BLAST, but requires two orders of magnitude less running time meaning that it can be run on desktop or laptop computers. Taxoner is slower than the approaches that use small marker databases but is more sensitive due the comprehensive reference database. In addition, it can be easily tuned to specific applications using small tailored databases. When applied to metagenomic datasets, Taxoner can provide a functional summary of the genes mapped and can provide strain level identification. Taxoner is written in C for Linux operating systems. The code and documentation are available for research applications at http://code.google.com/p/taxoner.

CX, DPX, and PCW: Web Servers for the Visualization of Interior and Protruding Regions of Protein Structures in 3D and 1D.

The CX and DPX web-based servers at http://pongor.itk.ppke.hu/bioinfoservices are dedicated to the analysis of protein 3D structures submitted by the users as Protein Data Bank (PDB) files. CX computes an atomic protrusion index, cx that makes it possible to highlight the protruding atoms within a protein 3D structure. DPX calculates a depth index, dpx for buried atoms, and allows one to visualize the distribution of buried residues. CX and DPX visualize 3D structures colored according to the calculated indices and return PDB files that can be visualized using standard programs. A combined server site, the Protein Core Workbench allows visualization of dpx, cx, solvent-accessible area as well as the number of atomic contacts as 3D plots and 1D sequence plots. Online visualization of the 3D structures and 1D sequence plots are available in all three servers. Mirror sites are available at http://hydra.icgeb.trieste.it/protein/ .

Predicting effective drug combinations via network propagation

Drug combinations are frequently used in treating complex diseases including cancer, diabetes, arthritis and hypertension. Most drug combinations were found in empirical ways so there is a need of efficient computational methods. Here we present a novel method based on network analysis which estimates the efficacy of drug combinations from a perturbation analysis performed on a protein-protein association network. The results suggest that those drugs are likely to form effective combinations that perturb a large number of proteins in common, even if the original targets are found in seemingly unrelated pathways.

Multiple polyvalency provided by intrinsically disordered segments is a key feature of postsynaptic scaffold proteins

The human postsynaptic density is an elaborate network comprising thousands of proteins, playing a vital role in the molecular events of learning and the formation of memory. Despite our growing knowledge of specific proteins and their interactions, atomic-level details of their full three-dimensional structure and their rearrangements are mostly elusive. Advancements in structural bioinformatics enabled us to depict the characteristic features of proteins involved in different processes aiding neurotransmission. We show that postsynaptic protein-protein interactions are mediated through the delicate balance of intrinsically disordered regions and folded domains, and this duality is also imprinted in the amino acid sequence. We introduce Diversity of Potential Interactions (DPI), a structure and regulation based descriptor to assess the diversity of interactions. Our approach reveals that the postsynaptic proteome has its own characteristic features and these properties reliably discriminate them from other proteins of the human proteome. Our results suggest that postsynaptic proteins are especially susceptible to forming diverse interactions with each other, which might be key in the reorganization of the PSD in molecular processes related to learning and memory.The postsynaptic density, a key regulator of the molecular events of learning and memory is composed of an elaborate network of interacting proteins capable of dynamic reorganization. Despite our growing knowledge on specific proteins and their interactions, atomic-level details of its full three-dimensional structure and its rearrangements are still largely elusive. In this work we addressed the extent and possible role of intrinsic disorder in postsynaptic proteins in a detailed in silico analysis. Using a strict consensus of predicted intrinsic disorder and a number of other protein sets as controls, we show that postsynaptic proteins are particularly enriched in disordered segments. Although the number of interacting partner proteins is not exceptionally large, the estimated diversity of the combinations of putative complexes is high in postsynaptic proteins.

Differential signal sensitivities can contribute to the stability of multispecies bacterial communities

BackgroundBacterial species present in multispecies microbial communities often react to the same chemical signal but at vastly different concentrations. The existence of different response thresholds with respect to the same signal molecule has been well documented in quorum sensing which is one of the best studied inter-cellular signalling mechanisms in bacteria. The biological significance of this phenomenon is still poorly understood, and cannot be easily studied in nature or in laboratory models. The aim of this study is to establish the role of differential signal response thresholds in stabilizing microbial communities.ResultsWe tested binary competition scenarios using an agent-based model in which competing bacteria had different response levels with respect to signals, cooperation factors or both, respectively. While in previous scenarios fitter species outcompete slower growing competitors, we found that stable equilibria could form if the fitter species responded to a higher chemical concentration level than the slower growing competitor. We also found that species secreting antibiotic could form a stable community with other competing species if antibiotic production started at higher response thresholds.ConclusionsMicrobial communities in nature rely on the stable coexistence of species that necessarily differ in their fitness. We found that differential response thresholds provide a simple and elegant way for keeping slower growing species within the community. High response thresholds can be considered as self-restraint of the fitter species that allows metabolically useful but slower growing species to remain within a community, and thereby the metabolic repertoire of the community will be maintained.ReviewersThis article was reviewed by Michael Gromiha, Sebastian Maurer-Stroh, István Simon and L. Aravind.

Propagation on molecular interaction networks: Prediction of effective drug combinations and biomarkers in cancer treatment

BACKGROUND Biomedical sciences use a variety of data sources on drug molecules, genes, proteins, diseases and scientific publications etc. This system can be best pictured as a giant data-network linked together by physical, functional, logical and similarity relationships. A new hypothesis or discovery can be considered as a new link that can be deduced from the existing connections. For instance, interactions of two pharmacons - if not already known - represent a testable novel hypothesis. Such implicit effects are especially important in complex diseases such as cancer. METHODS The method we applied was to test whether novel drug combinations or novel biomarkers can be predicted from a network of existing oncological databases. We start from the hypothesis that novel, implicit links can be discovered between the network neighborhoods of data items. RESULTS We showed that the overlap of network neighborhoods is strongly correlated with the pairwise interaction strength of two pharmacons used in cancer therapy, and it is also well correlated with clinical data. In a second case study we employed this strategy to the discovery of novel biomarkers based on text analysis. In 2012 we prioritized 10 potential biomarkers for ovarian cancers, 2 of which were in fact described as such in the subsequent years. CONCLUSION The strategy seems to hold promises for prioritizing new drug combinations or new biomarkers for experimental testing. Its use is naturally limited by the sparsity and the quality of experimental data, however both of these aspects are expected to improve given the development of current databases.

Biomedical Hypothesis Generation by Text Mining and Gene Prioritization

Text mining methods can facilitate the generation of biomedical hypotheses by suggesting novel associations between diseases and genes. Previously, we developed a rare-term model called RaJoLink (Petric et al, J. Biomed. Inform. 42(2): 219-227, 2009) in which hypotheses are formulated on the basis of terms rarely associated with a target domain. Since many current medical hypotheses are formulated in terms of molecular entities and molecular mechanisms, here we extend the methodology to proteins and genes, using a standardized vocabulary as well as a gene/protein network model. The proposed enhanced RaJoLink rare-term model combines text mining and gene prioritization approaches. Its utility is illustrated by finding known as well as potential gene-disease associations in ovarian cancer using MEDLINE abstracts and the STRING database.