The Modular Organization of Protein Interactions in Escherichia coli

ToxoNet: A high confidence map of protein-protein interactions in Toxoplasma gondii

The apicomplexan intracellular parasite Toxoplasma gondii is a major food borne pathogen that is highly prevalent in the global population. The majority of the T. gondii proteome remains uncharacterized and the organization of proteins into complexes is unclear. To overcome this knowledge gap, we used a biochemical fractionation strategy to predict interactions by correlation profiling. To overcome the deficit of high-quality training data in non-model organisms, we complemented a supervised machine learning strategy, with an unsupervised approach, based on similarity network fusion. The resulting combined high confidence network, ToxoNet, comprises 2,063 interactions connecting 652 proteins. Clustering identifies 93 protein complexes. We identified clusters enriched in mitochondrial machinery that include previously uncharacterized proteins that likely represent novel adaptations to oxidative phosphorylation. Furthermore, complexes enriched in proteins localized to secretory organelles and the inner membrane complex, predict additional novel components representing novel targets for detailed functional characterization. We present ToxoNet as a publicly available resource with the expectation that it will help drive future hypotheses within the research community.

Commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus and intestinal bacteria in healthy and B-cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with Tritrichomonas musculus functional changes, which were accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single-cell transcriptomics identified distinct Tritrichomonas musculus life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable data sets to drive future mechanistic studies.

The commensal protist Tritrichomonas musculus exhibits a dynamic life cycle that induces extensive remodeling of the gut microbiota

Commensal protists and gut bacterial communities exhibit complex relationships, mediated at least in part through host immunity. To improve our understanding of this tripartite interplay, we investigated community and functional dynamics between the murine protist Tritrichomonas musculus ( T. mu ) and intestinal bacteria in healthy and B cell-deficient mice. We identified dramatic, protist-driven remodeling of resident microbiome growth and activities, in parallel with T. mu functional changes, accelerated in the absence of B cells. Metatranscriptomic data revealed nutrient-based competition between bacteria and the protist. Single cell transcriptomics identified distinct T. mu life stages, providing new evidence for trichomonad sexual replication and the formation of pseudocysts. Unique cell states were validated in situ through microscopy and flow cytometry. Our results reveal complex microbial dynamics during the establishment of a commensal protist in the gut, and provide valuable datasets to drive future mechanistic studies.

Systematic profiling of the chicken gut microbiome reveals dietary supplementation with antibiotics alters expression of multiple microbial pathways with minimal impact on community structure

BACKGROUND

The emergence of antimicrobial resistance is a major threat to global health and has placed pressure on the livestock industry to eliminate the use of antibiotic growth promotants (AGPs) as feed additives. To mitigate their removal, efficacious alternatives are required. AGPs are thought to operate through modulating the gut microbiome to limit opportunities for colonization by pathogens, increase nutrient utilization, and reduce inflammation. However, little is known concerning the underlying mechanisms. Previous studies investigating the effects of AGPs on the poultry gut microbiome have largely focused on 16S rDNA surveys based on a single gastrointestinal (GI) site, diet, and/or timepoint, resulting in an inconsistent view of their impact on community composition.

METHODS

In this study, we perform a systematic investigation of both the composition and function of the chicken gut microbiome, in response to AGPs. Birds were raised under two different diets and AGP treatments, and 16S rDNA surveys applied to six GI sites sampled at three key timepoints of the poultry life cycle. Functional investigations were performed through metatranscriptomics analyses and metabolomics.

RESULTS

Our study reveals a more nuanced view of the impact of AGPs, dependent on age of bird, diet, and intestinal site sampled. Although AGPs have a limited impact on taxonomic abundances, they do appear to redefine influential taxa that may promote the exclusion of other taxa. Microbiome expression profiles further reveal a complex landscape in both the expression and taxonomic representation of multiple pathways including cell wall biogenesis, antimicrobial resistance, and several involved in energy, amino acid, and nucleotide metabolism. Many AGP-induced changes in metabolic enzyme expression likely serve to redirect metabolic flux with the potential to regulate bacterial growth or produce metabolites that impact the host.

CONCLUSIONS

As alternative feed additives are developed to mimic the action of AGPs, our study highlights the need to ensure such alternatives result in functional changes that are consistent with site-, age-, and diet-associated taxa. The genes and pathways identified in this study are therefore expected to drive future studies, applying tools such as community-based metabolic modeling, focusing on the mechanistic impact of different dietary regimes on the microbiome. Consequently, the data generated in this study will be crucial for the development of next-generation feed additives targeting gut health and poultry production. Video Abstract.

Auxotrophic and prototrophic conditional genetic networks reveal the rewiring of transcription factors in Escherichia coli

Bacterial transcription factors (TFs) are widely studied in Escherichia coli. Yet it remains unclear how individual genes in the underlying pathways of TF machinery operate together during environmental challenge. Here, we address this by applying an unbiased, quantitative synthetic genetic interaction (GI) approach to measure pairwise GIs among all TF genes in E. coli under auxotrophic (rich medium) and prototrophic (minimal medium) static growth conditions. The resulting static and differential GI networks reveal condition-dependent GIs, widespread changes among TF genes in metabolism, and new roles for uncharacterized TFs (yjdC, yneJ, ydiP) as regulators of cell division, putrescine utilization pathway, and cold shock adaptation. Pan-bacterial conservation suggests TF genes with GIs are co-conserved in evolution. Together, our results illuminate the global organization of E. coli TFs, and remodeling of genetic backup systems for TFs under environmental change, which is essential for controlling the bacterial transcriptional regulatory circuits.

The bacterium E. coli has around 300 transcriptional factors, but the functions of many of them, and the interactions between their respective regulatory networks, are unclear. Here, the authors study genetic interactions among all transcription factor genes in E. coli, revealing condition-dependent interactions and roles for uncharacterized transcription factors.

Computationally Reconstructed Interactome of Bradyrhizobium diazoefficiens USDA110 Reveals Novel Functional Modules and Protein Hubs for Symbiotic Nitrogen Fixation

Symbiotic nitrogen fixation is an important part of the nitrogen biogeochemical cycles and the main nitrogen source of the biosphere. As a classical model system for symbiotic nitrogen fixation, rhizobium-legume systems have been studied elaborately for decades. Details about the molecular mechanisms of the communication and coordination between rhizobia and host plants is becoming clearer. For more systematic insights, there is an increasing demand for new studies integrating multiomics information. Here, we present a comprehensive computational framework integrating the reconstructed protein interactome of B. diazoefficiens USDA110 with its transcriptome and proteome data to study the complex protein-protein interaction (PPI) network involved in the symbiosis system. We reconstructed the interactome of B. diazoefficiens USDA110 by computational approaches. Based on the comparison of interactomes between B. diazoefficiens USDA110 and other rhizobia, we inferred that the slow growth of B. diazoefficiens USDA110 may be due to the requirement of more protein modifications, and we further identified 36 conserved functional PPI modules. Integrated with transcriptome and proteome data, interactomes representing free-living cell and symbiotic nitrogen-fixing (SNF) bacteroid were obtained. Based on the SNF interactome, a core-sub-PPI-network for symbiotic nitrogen fixation was determined and nine novel functional modules and eleven key protein hubs playing key roles in symbiosis were identified. The reconstructed interactome of B. diazoefficiens USDA110 may serve as a valuable reference for studying the mechanism underlying the SNF system of rhizobia and legumes.

Identification of protein complexes and functional modules in E. coli PPI networks

Background

Escherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less.

Results

In this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes.

Conclusions

In this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.

Grasping frequent subgraph mining for bioinformatics applications

Searching for interesting common subgraphs in graph data is a well-studied problem in data mining. Subgraph mining techniques focus on the discovery of patterns in graphs that exhibit a specific network structure that is deemed interesting within these data sets. The definition of which subgraphs are interesting and which are not is highly dependent on the application. These techniques have seen numerous applications and are able to tackle a range of biological research questions, spanning from the detection of common substructures in sets of biomolecular compounds, to the discovery of network motifs in large-scale molecular interaction networks. Thus far, information about the bioinformatics application of subgraph mining remains scattered over heterogeneous literature. In this review, we provide an introduction to subgraph mining for life scientists. We give an overview of various subgraph mining algorithms from a bioinformatics perspective and present several of their potential biomedical applications.

Discovery of new RNA classes and global RNA-binding proteins

The identification of new RNA functions and the functional annotation of transcripts in genomes represent exciting yet challenging endeavours of modern biology. Crucial insights into the biological roles of RNA molecules can be gained from the identification of the proteins with which they form specific complexes. Modern interactome techniques permit to profile RNA-protein interactions in a genome-wide manner and identify new RNA classes associated with globally acting RNA-binding proteins. Applied to a variety of organisms, these methods are already revolutionising our understanding of RNA-mediated biological processes. Here, we focus on one such approach-Gradient sequencing or Grad-seq-which has recently guided the discovery of protein ProQ and its associated small RNAs as a new domain of post-transcriptional control in bacteria.

Vicus: Exploiting local structures to improve network-based analysis of biological data

Biological networks entail important topological features and patterns critical to understanding interactions within complicated biological systems. Despite a great progress in understanding their structure, much more can be done to improve our inference and network analysis. Spectral methods play a key role in many network-based applications. Fundamental to spectral methods is the Laplacian, a matrix that captures the global structure of the network. Unfortunately, the Laplacian does not take into account intricacies of the network’s local structure and is sensitive to noise in the network. These two properties are fundamental to biological networks and cannot be ignored. We propose an alternative matrix Vicus. The Vicus matrix captures the local neighborhood structure of the network and thus is more effective at modeling biological interactions. We demonstrate the advantages of Vicus in the context of spectral methods by extensive empirical benchmarking on tasks such as single cell dimensionality reduction, protein module discovery and ranking genes for cancer subtyping. Our experiments show that using Vicus, spectral methods result in more accurate and robust performance in all of these tasks.

Networks are a representation of choice for many problems in biology and medicine including protein interactions, metabolic pathways, evolutionary biology, cancer subtyping and disease modeling to name a few. The key to much of network analysis lies in the spectrum decomposition represented by eigenvectors of the network Laplacian. While possessing many desirable algebraic properties, Laplacian lacks the power to capture fine-grained structure of the underlying network. Our novel matrix, Vicus, introduced in this work, takes advantage of the local structure of the network while preserving algebraic properties of the Laplacian. We show that using Vicus in spectral methods leads to superior performance across fundamental biological tasks such as dimensionality reduction in single cell analysis, identifying genes for cancer subtyping and identifying protein modules in a PPI network. We postulate, that in tasks where it is important to take into account local network information, spectral-based methods should be using Vicus matrix in place of Laplacian.

INDEX: Incremental depth extension approach for protein-protein interaction networks alignment

High-throughput methods have provided us with a large amount of data pertaining to protein-protein interaction networks. The alignment of these networks enables us to better understand biological systems. Given the fact that the alignment of networks is computationally intractable, it is important to introduce a more efficient and accurate algorithm which finds as large as possible similar areas among networks. This paper proposes a new algorithm named INDEX for the global alignment of protein-protein interaction networks. INDEX has multiple phases. First, it computes topological and biological scores of proteins and creates the initial alignment based on the proposed matching score strategy. Using networks topologies and aligned proteins, it then selects a set of high scoring proteins in each phase and extends new alignments around them until final alignment is obtained. Proposing a new alignment strategy, detailed consideration of matching scores, and growth of the alignment core has led INDEX to obtain a larger common connected subgraph with a much greater number of edges compared with previous methods. Regarding other measures such as edge correctness, symmetric substructure score, and runtime, the proposed algorithm performed considerably better than existing popular methods. Our results show that INDEX can be a promising method for identifying functionally conserved interactions.

AVAILABILITY

The INDEX executable file is available at https://github.com/a-mir/index/.

ModuleAlign: module-based global alignment of protein-protein interaction networks

MOTIVATION

As an increasing amount of protein-protein interaction (PPI) data becomes available, their computational interpretation has become an important problem in bioinformatics. The alignment of PPI networks from different species provides valuable information about conserved subnetworks, evolutionary pathways and functional orthologs. Although several methods have been proposed for global network alignment, there is a pressing need for methods that produce more accurate alignments in terms of both topological and functional consistency.

RESULTS

In this work, we present a novel global network alignment algorithm, named ModuleAlign, which makes use of local topology information to define a module-based homology score. Based on a hierarchical clustering of functionally coherent proteins involved in the same module, ModuleAlign employs a novel iterative scheme to find the alignment between two networks. Evaluated on a diverse set of benchmarks, ModuleAlign outperforms state-of-the-art methods in producing functionally consistent alignments. By aligning Pathogen-Human PPI networks, ModuleAlign also detects a novel set of conserved human genes that pathogens preferentially target to cause pathogenesis.

AVAILABILITY

http://ttic.uchicago.edu/∼hashemifar/ModuleAlign.html

CONTACT

canzar@ttic.edu or j3xu.ttic.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Improving protein complex prediction by reconstructing a high-confidence protein-protein interaction network of Escherichia coli from different physical interaction data sources

BACKGROUND

Although different protein-protein physical interaction (PPI) datasets exist for Escherichia coli, no common methodology exists to integrate these datasets and extract reliable modules reflecting the existing biological process and protein complexes. Naïve Bayesian formula is the highly accepted method to integrate different PPI datasets into a single weighted PPI network, but detecting proper weights in such network is still a major problem.

RESULTS

In this paper, we proposed a new methodology to integrate various physical PPI datasets into a single weighted PPI network in a way that the detected modules in PPI network exhibit the highest similarity to available functional modules. We used the co-expression modules as functional modules, and we shown that direct functional modules detected from Gene Ontology terms could be used as an alternative dataset. After running this integrating methodology over six different physical PPI datasets, orthologous high-confidence interactions from a related organism and two AP-MS PPI datasets gained high weights in the integrated networks, while the weights for one AP-MS PPI dataset and two other datasets derived from public databases have converged to zero. The majority of detected modules shaped around one or few hub protein(s). Still, a large number of highly interacting protein modules were detected which are functionally relevant and are likely to construct protein complexes.

CONCLUSIONS

We provided a new high confidence protein complex prediction method supported by functional studies and literature mining.

Cell-free Determination of Binary Complexes That Comprise Extended Protein-Protein Interaction Networks of Yersinia pestis

Binary protein interactions form the basic building blocks of molecular networks and dynamic assemblies that control all cellular functions of bacteria. Although these protein interactions are a potential source of targets for the development of new antibiotics, few high-confidence data sets are available for the large proteomes of most pathogenic bacteria. We used a library of recombinant proteins from the plague bacterium Yersinia pestis to probe planar microarrays of immobilized proteins that represented ∼85% (3552 proteins) of the bacterial proteome, resulting in >77,000 experimentally determined binary interactions. Moderate (K_D ∼μm) to high-affinity (K_D ∼nm) interactions were characterized for >1600 binary complexes by surface plasmon resonance imaging of microarrayed proteins. Core binary interactions that were in common with other gram-negative bacteria were identified from the results of both microarray methods. Clustering of proteins within the interaction network by function revealed statistically enriched complexes and pathways involved in replication, biosynthesis, virulence, metabolism, and other diverse biological processes. The interaction pathways included many proteins with no previously known function. Further, a large assembly of proteins linked to transcription and translation were contained within highly interconnected subregions of the network. The two-tiered microarray approach used here is an innovative method for detecting binary interactions, and the resulting data will serve as a critical resource for the analysis of protein interaction networks that function within an important human pathogen.

Trifunctional cross-linker for mapping protein-protein interaction networks and comparing protein conformational states

To improve chemical cross-linking of proteins coupled with mass spectrometry (CXMS), we developed a lysine-targeted enrichable cross-linker containing a biotin tag for affinity purification, a chemical cleavage site to separate cross-linked peptides away from biotin after enrichment, and a spacer arm that can be labeled with stable isotopes for quantitation. By locating the flexible proteins on the surface of 70S ribosome, we show that this trifunctional cross-linker is effective at attaining structural information not easily attainable by crystallography and electron microscopy. From a crude Rrp46 immunoprecipitate, it helped identify two direct binding partners of Rrp46 and 15 protein-protein interactions (PPIs) among the co-immunoprecipitated exosome subunits. Applying it to E. coli and C. elegans lysates, we identified 3130 and 893 inter-linked lysine pairs, representing 677 and 121 PPIs. Using a quantitative CXMS workflow we demonstrate that it can reveal changes in the reactivity of lysine residues due to protein-nucleic acid interaction.

DOI:http://dx.doi.org/10.7554/eLife.12509.001

Proteins fold into structures that are determined by the order of the amino acids that they are built from. These structures enable the protein to carry out its role, which often involves interacting with other proteins. Chemical cross-linking coupled with mass spectrometry (CXMS) is a powerful method used to study protein structure and how proteins interact, with a benefit of stabilizing and capturing brief interactions.

CXMS uses a chemical compound called a linker that has two arms, each of which can bind specific amino acids in a protein or in multiple proteins. Only when the regions are close to each other can they be “cross-linked” in this way. After cross-linking, the proteins are cut into small pieces known as peptides. The cross-linked peptides are then separated from the non cross-linked ones and characterized.

Although CXMS is a popular method, there are aspects about it that limit its use. It does not work well on complex samples that contain lots of different proteins, as it is difficult to separate the cross-linked peptides from the overwhelming amounts of non cross-linked peptides. Also, although it can be used to detect changes in the shape of a protein, which are often crucial to the protein's role, the method has not been smoothed out.

Tan, Li et al. have now developed a new cross-linker called Leiker that addresses these limitations. Leiker cross-links the amino acid lysine to another lysine, and contains a molecular tag that allows cross-linked peptides to be efficiently purified away from non cross-linked peptides. As part of a streamlined workflow to detect changes in the shape of a protein, Leiker also contains a region that can be labeled.

Analysing a bacterial ribosome, which contains more than 50 proteins, showed that Leiker-based CXMS could detect many more protein interactions than previous studies had. These included interactions that changed too rapidly to be studied by other structural methods. Tan, Li et al. then applied Leiker-based CXMS to the entire contents of bacterial cells at different stages of growth, and identified a protein interaction that is only found in growing cells.

In future, Leiker will be useful for analyzing the structure of large protein complexes, probing changes in protein structure, and mapping the interactions between proteins in complex mixtures.

DOI:http://dx.doi.org/10.7554/eLife.12509.002

Prediction and characterization of protein-protein interaction network in Bacillus licheniformis WX-02

In this study, we constructed a protein-protein interaction (PPI) network of B. licheniformis strain WX-02 with interolog method and domain-based method, which contained 15,864 edges and 2,448 nodes. Although computationally predicted networks have relatively low coverage and high false-positive rate, our prediction was confirmed from three perspectives: local structural features, functional similarities and transcriptional correlations. Further analysis of the COG heat map showed that protein interactions in B. licheniformis WX-02 mainly occurred in the same functional categories. By incorporating the transcriptome data, we found that the topological properties of the PPI network were robust under normal and high salt conditions. In addition, 267 different protein complexes were identified and 117 poorly characterized proteins were annotated with certain functions based on the PPI network. Furthermore, the sub-network showed that a hub protein CcpA jointed directly or indirectly many proteins related to γ-PGA synthesis and regulation, such as PgsB, GltA, GltB, ProB, ProJ, YcgM and two signal transduction systems ComP-ComA and DegS-DegU. Thus, CcpA might play an important role in the regulation of γ-PGA synthesis. This study therefore will facilitate the understanding of the complex cellular behaviors and mechanisms of γ-PGA synthesis in B. licheniformis WX-02.

Metatranscriptomic analysis of diverse microbial communities reveals core metabolic pathways and microbiome-specific functionality

BACKGROUND

Metatranscriptomics is emerging as a powerful technology for the functional characterization of complex microbial communities (microbiomes). Use of unbiased RNA-sequencing can reveal both the taxonomic composition and active biochemical functions of a complex microbial community. However, the lack of established reference genomes, computational tools and pipelines make analysis and interpretation of these datasets challenging. Systematic studies that compare data across microbiomes are needed to demonstrate the ability of such pipelines to deliver biologically meaningful insights on microbiome function.

RESULTS

Here, we apply a standardized analytical pipeline to perform a comparative analysis of metatranscriptomic data from diverse microbial communities derived from mouse large intestine, cow rumen, kimchi culture, deep-sea thermal vent and permafrost. Sequence similarity searches allowed annotation of 19 to 76% of putative messenger RNA (mRNA) reads, with the highest frequency in the kimchi dataset due to its relatively low complexity and availability of closely related reference genomes. Metatranscriptomic datasets exhibited distinct taxonomic and functional signatures. From a metabolic perspective, we identified a common core of enzymes involved in amino acid, energy and nucleotide metabolism and also identified microbiome-specific pathways such as phosphonate metabolism (deep sea) and glycan degradation pathways (cow rumen). Integrating taxonomic and functional annotations within a novel visualization framework revealed the contribution of different taxa to metabolic pathways, allowing the identification of taxa that contribute unique functions.

CONCLUSIONS

The application of a single, standard pipeline confirms that the rich taxonomic and functional diversity observed across microbiomes is not simply an artefact of different analysis pipelines but instead reflects distinct environmental influences. At the same time, our findings show how microbiome complexity and availability of reference genomes can impact comprehensive annotation of metatranscriptomes. Consequently, beyond the application of standardized pipelines, additional caution must be taken when interpreting their output and performing downstream, microbiome-specific, analyses. The pipeline used in these analyses along with a tutorial has been made freely available for download from our project website: http://www.compsysbio.org/microbiome .

Conditional Epistatic Interaction Maps Reveal Global Functional Rewiring of Genome Integrity Pathways in Escherichia coli

As antibiotic resistance is increasingly becoming a public health concern, an improved understanding of the bacterial DNA damage response (DDR), which is commonly targeted by antibiotics, could be of tremendous therapeutic value. Although the genetic components of the bacterial DDR have been studied extensively in isolation, how the underlying biological pathways interact functionally remains unclear. Here, we address this by performing systematic, unbiased, quantitative synthetic genetic interaction (GI) screens and uncover widespread changes in the GI network of the entire genomic integrity apparatus of Escherichia coli under standard and DNA-damaging growth conditions. The GI patterns of untreated cultures implicated two previously uncharacterized proteins (YhbQ and YqgF) as nucleases, whereas reorganization of the GI network after DNA damage revealed DDR roles for both annotated and uncharacterized genes. Analyses of pan-bacterial conservation patterns suggest that DDR mechanisms and functional relationships are near universal, highlighting a modular and highly adaptive genomic stress response.

Identification of Candidate Adherent-Invasive E. coli Signature Transcripts by Genomic/Transcriptomic Analysis

Adherent-invasive Escherichia coli (AIEC) strains are detected more frequently within mucosal lesions of patients with Crohn’s disease (CD). The AIEC phenotype consists of adherence and invasion of intestinal epithelial cells and survival within macrophages of these bacteria in vitro. Our aim was to identify candidate transcripts that distinguish AIEC from non-invasive E. coli (NIEC) strains and might be useful for rapid and accurate identification of AIEC by culture-independent technology. We performed comparative RNA-Sequence (RNASeq) analysis using AIEC strain LF82 and NIEC strain HS during exponential and stationary growth. Differential expression analysis of coding sequences (CDS) homologous to both strains demonstrated 224 and 241 genes with increased and decreased expression, respectively, in LF82 relative to HS. Transition metal transport and siderophore metabolism related pathway genes were up-regulated, while glycogen metabolic and oxidation-reduction related pathway genes were down-regulated, in LF82. Chemotaxis related transcripts were up-regulated in LF82 during the exponential phase, but flagellum-dependent motility pathway genes were down-regulated in LF82 during the stationary phase. CDS that mapped only to the LF82 genome accounted for 747 genes. We applied an in silico subtractive genomics approach to identify CDS specific to AIEC by incorporating the genomes of 10 other previously phenotyped NIEC. From this analysis, 166 CDS mapped to the LF82 genome and lacked homology to any of the 11 human NIEC strains. We compared these CDS across 13 AIEC, but none were homologous in each. Four LF82 gene loci belonging to clustered regularly interspaced short palindromic repeats region (CRISPR)—CRISPR-associated (Cas) genes were identified in 4 to 6 AIEC and absent from all non-pathogenic bacteria. As previously reported, AIEC strains were enriched for pdu operon genes. One CDS, encoding an excisionase, was shared by 9 AIEC strains. Reverse transcription quantitative polymerase chain reaction assays for 6 genes were conducted on fecal and ileal RNA samples from 22 inflammatory bowel disease (IBD), and 32 patients without IBD (non-IBD). The expression of Cas loci was detected in a higher proportion of CD than non-IBD fecal and ileal RNA samples (p <0.05). These results support a comparative genomic/transcriptomic approach towards identifying candidate AIEC signature transcripts.

Systems-based analysis of the Sarcocystis neurona genome identifies pathways that contribute to a heteroxenous life cycle

Sarcocystis neurona is a member of the coccidia, a clade of single-celled parasites of medical and veterinary importance including Eimeria, Sarcocystis, Neospora, and Toxoplasma. Unlike Eimeria, a single-host enteric pathogen, Sarcocystis, Neospora, and Toxoplasma are two-host parasites that infect and produce infectious tissue cysts in a wide range of intermediate hosts. As a genus, Sarcocystis is one of the most successful protozoan parasites; all vertebrates, including birds, reptiles, fish, and mammals are hosts to at least one Sarcocystis species. Here we sequenced Sarcocystis neurona, the causal agent of fatal equine protozoal myeloencephalitis. The S. neurona genome is 127 Mbp, more than twice the size of other sequenced coccidian genomes. Comparative analyses identified conservation of the invasion machinery among the coccidia. However, many dense-granule and rhoptry kinase genes, responsible for altering host effector pathways in Toxoplasma and Neospora, are absent from S. neurona. Further, S. neurona has a divergent repertoire of SRS proteins, previously implicated in tissue cyst formation in Toxoplasma. Systems-based analyses identified a series of metabolic innovations, including the ability to exploit alternative sources of energy. Finally, we present an S. neurona model detailing conserved molecular innovations that promote the transition from a purely enteric lifestyle (Eimeria) to a heteroxenous parasite capable of infecting a wide range of intermediate hosts.

Sarcocystis neurona is a member of the coccidia, a clade of single-celled apicomplexan parasites responsible for major economic and health care burdens worldwide. A cousin of Plasmodium, Cryptosporidium, Theileria, and Eimeria, Sarcocystis is one of the most successful parasite genera; it is capable of infecting all vertebrates (fish, reptiles, birds, and mammals—including humans). The past decade has witnessed an increasing number of human outbreaks of clinical significance associated with acute sarcocystosis. Among Sarcocystis species, S. neurona has a wide host range and causes fatal encephalitis in horses, marine mammals, and several other mammals. To provide insights into the transition from a purely enteric parasite (e.g., Eimeria) to one that forms tissue cysts (Toxoplasma), we present the first genome sequence of S. neurona. Comparisons with other coccidian genomes highlight the molecular innovations that drive its distinct life cycle strategies.

Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.

Comparative Genomics and Evolutionary Modularity of Prokaryotes

The soaring number of high-quality genomic sequences has ushered in the era of post-genomic research where our understanding of organisms has dramatically shifted towards defining the function of genes within their larger biological contexts. As a result, novel high-throughput experimental technologies are being increasingly employed to uncover physical and functional associations of genes and proteins in complex biological processes. Through the construction and analysis of physical, genetic and metabolic networks generated for the model organisms, such as Escherichia coli, organizational principles of the genome have been deduced, such as modularity, which has important implications toward understanding prokaryotic evolution and adaptation to novel lifestyles.

HubAlign: an accurate and efficient method for global alignment of protein-protein interaction networks

MOTIVATION

High-throughput experimental techniques have produced a large amount of protein-protein interaction (PPI) data. The study of PPI networks, such as comparative analysis, shall benefit the understanding of life process and diseases at the molecular level. One way of comparative analysis is to align PPI networks to identify conserved or species-specific subnetwork motifs. A few methods have been developed for global PPI network alignment, but it still remains challenging in terms of both accuracy and efficiency.

RESULTS

This paper presents a novel global network alignment algorithm, denoted as HubAlign, that makes use of both network topology and sequence homology information, based upon the observation that topologically important proteins in a PPI network usually are much more conserved and thus, more likely to be aligned. HubAlign uses a minimum-degree heuristic algorithm to estimate the topological and functional importance of a protein from the global network topology information. Then HubAlign aligns topologically important proteins first and gradually extends the alignment to the whole network. Extensive tests indicate that HubAlign greatly outperforms several popular methods in terms of both accuracy and efficiency, especially in detecting functionally similar proteins.

AVAILABILITY

HubAlign is available freely for non-commercial purposes at http://ttic.uchicago.edu/∼hashemifar/software/HubAlign.zip.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A network-based approach to identify substrate classes of bacterial glycosyltransferases

Background

Bacterial interactions with the environment- and/or host largely depend on the bacterial glycome. The specificities of a bacterial glycome are largely determined by glycosyltransferases (GTs), the enzymes involved in transferring sugar moieties from an activated donor to a specific substrate. Of these GTs their coding regions, but mainly also their substrate specificity are still largely unannotated as most sequence-based annotation flows suffer from the lack of characterized sequence motifs that can aid in the prediction of the substrate specificity.

Results

In this work, we developed an analysis flow that uses sequence-based strategies to predict novel GTs, but also exploits a network-based approach to infer the putative substrate classes of these predicted GTs. Our analysis flow was benchmarked with the well-documented GT-repertoire of Campylobacter jejuni NCTC 11168 and applied to the probiotic model Lactobacillus rhamnosus GG to expand our insights in the glycosylation potential of this bacterium. In L. rhamnosus GG we could predict 48 GTs of which eight were not previously reported. For at least 20 of these GTs a substrate relation was inferred.

Conclusions

We confirmed through experimental validation our prediction of WelI acting upstream of WelE in the biosynthesis of exopolysaccharides. We further hypothesize to have identified in L. rhamnosus GG the yet undiscovered genes involved in the biosynthesis of glucose-rich glycans and novel GTs involved in the glycosylation of proteins. Interestingly, we also predict GTs with well-known functions in peptidoglycan synthesis to also play a role in protein glycosylation.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-349) contains supplementary material, which is available to authorized users.

Quantitative genome-wide genetic interaction screens reveal global epistatic relationships of protein complexes in Escherichia coli

Large-scale proteomic analyses in Escherichia coli have documented the composition and physical relationships of multiprotein complexes, but not their functional organization into biological pathways and processes. Conversely, genetic interaction (GI) screens can provide insights into the biological role(s) of individual gene and higher order associations. Combining the information from both approaches should elucidate how complexes and pathways intersect functionally at a systems level. However, such integrative analysis has been hindered due to the lack of relevant GI data. Here we present a systematic, unbiased, and quantitative synthetic genetic array screen in E. coli describing the genetic dependencies and functional cross-talk among over 600,000 digenic mutant combinations. Combining this epistasis information with putative functional modules derived from previous proteomic data and genomic context-based methods revealed unexpected associations, including new components required for the biogenesis of iron-sulphur and ribosome integrity, and the interplay between molecular chaperones and proteases. We find that functionally-linked genes co-conserved among γ-proteobacteria are far more likely to have correlated GI profiles than genes with divergent patterns of evolution. Overall, examining bacterial GIs in the context of protein complexes provides avenues for a deeper mechanistic understanding of core microbial systems.

Genome-wide genetic interaction (GI) screens have been performed in yeast, but no analogous large-scale studies have yet been reported for bacteria. Here, we have used E. coli synthetic genetic array (eSGA) technology developed by our group to quantitatively map GIs to reveal epistatic dependencies and functional cross-talk among ∼600,000 digenic mutant combinations. By combining this epistasis information with functional modules derived by our group's earlier efforts from proteomic and genomic context (GC)-based methods, we identify several unexpected pathway-level dependencies, functional links between protein complexes, and biological roles of uncharacterized bacterial gene products. As part of the study, two of our pathway predictions from GI screens were validated experimentally, where we confirmed the role of these new components in iron-sulphur biogenesis and ribosome integrity. We also extrapolated the epistatic connectivity diagram of E. coli to 233 distantly related γ-proteobacterial species lacking GI information, and identified co-conserved genes and functional modules important for bacterial pathogenesis. Overall, this study describes the first genome-scale map of GIs in gram-negative bacterium, and through integrative analysis with previously derived protein-protein and GC-based interaction networks presents a number of novel insights into the architecture of bacterial pathways that could not have been discerned through either network alone.

A Newtonian framework for community detection in undirected biological networks

Community detection is a key problem of interest in network analysis, with applications in a variety of domains such as biological networks, social network modeling, and communication pattern analysis. In this paper, we present a novel framework for community detection that is motivated by a physical system analogy. We model a network as a system of point masses, and drive the process of community detection, by leveraging the Newtonian interactions between the point masses. Our framework is designed to be generic and extensible relative to the model parameters that are most suited for the problem domain. We illustrate the applicability of our approach by applying the Newtonian Community Detection algorithm on protein-protein interaction networks of E. coli , C. elegans, and S. cerevisiae. We obtain results that are comparable in quality to those obtained from the Newman-Girvan algorithm, a widely employed divisive algorithm for community detection. We also present a detailed analysis of the structural properties of the communities produced by our proposed algorithm, together with a biological interpretation using E. coli protein network as a case study. A functional enrichment heat map is constructed with the Gene Ontology functional mapping, in addition to a pathway analysis for each community. The analysis illustrates that the proposed algorithm elicits communities that are not only meaningful from a topological standpoint, but also possess biological relevance. We believe that our algorithm has the potential to serve as a key computational tool for driving therapeutic applications involving targeted drug development for personalized care delivery.

Corbi: a new R package for biological network alignment and querying

In the last decade, plenty of biological networks are built from the large scale experimental data produced by the rapidly developing high-throughput techniques as well as literature and other sources. But the huge amount of network data have not been fully utilized due to the limited biological network analysis tools. As a basic and essential bioinformatics method, biological network alignment and querying have been applied in many fields such as predicting new protein-protein interactions (PPI). Although many algorithms were published, the network alignment and querying problems are not solved satisfactorily. In this paper, we extended CNetQ, a novel network querying method based on the conditional random fields model, to solve network alignment problem, by adopting an iterative bi-directional mapping strategy. The new method, called CNetA, was compared with other four methods on fifty simulated and three real PPI network alignment instances by using four structural and five biological measures. The computational experiments on the simulated data, which were generated from a biological network evolutionary model to validate the effectiveness of network alignment methods, show that CNetA gets the best accuracy in terms of both nodes and networks. For the real data, larger biological conserved subnetworks and larger connected subnetworks were identified, compared with the structural-dominated methods and the biological-dominated methods, respectively, which suggests that CNetA can better balances the biological and structural similarities. Further, CNetQ and CNetA have been implemented in a new R package Corbi (http://doc.aporc.org/wiki/Corbi), and freely accessible and easy used web services for CNetQ and CNetA have also been constructed based on the R package. The simulated and real datasets used in this paper are available for downloading at http://doc.aporc.org/wiki/CNetA/.

Genome-scale co-evolutionary inference identifies functions and clients of bacterial Hsp90

The molecular chaperone Hsp90 is essential in eukaryotes, in which it facilitates the folding of developmental regulators and signal transduction proteins known as Hsp90 clients. In contrast, Hsp90 is not essential in bacteria, and a broad characterization of its molecular and organismal function is lacking. To enable such characterization, we used a genome-scale phylogenetic analysis to identify genes that co-evolve with bacterial Hsp90. We find that genes whose gain and loss were coordinated with Hsp90 throughout bacterial evolution tended to function in flagellar assembly, chemotaxis, and bacterial secretion, suggesting that Hsp90 may aid assembly of protein complexes. To add to the limited set of known bacterial Hsp90 clients, we further developed a statistical method to predict putative clients. We validated our predictions by demonstrating that the flagellar protein FliN and the chemotaxis kinase CheA behaved as Hsp90 clients in Escherichia coli, confirming the predicted role of Hsp90 in chemotaxis and flagellar assembly. Furthermore, normal Hsp90 function is important for wild-type motility and/or chemotaxis in E. coli. This novel function of bacterial Hsp90 agreed with our subsequent finding that Hsp90 is associated with a preference for multiple habitats and may therefore face a complex selection regime. Taken together, our results reveal previously unknown functions of bacterial Hsp90 and open avenues for future experimental exploration by implicating Hsp90 in the assembly of membrane protein complexes and adaptation to novel environments.

PheNetic: network-based interpretation of unstructured gene lists in E. coli

At the present time, omics experiments are commonly used in wet lab practice to identify leads involved in interesting phenotypes. These omics experiments often result in unstructured gene lists, the interpretation of which in terms of pathways or the mode of action is challenging. To aid in the interpretation of such gene lists, we developed PheNetic, a decision theoretic method that exploits publicly available information, captured in a comprehensive interaction network to obtain a mechanistic view of the listed genes. PheNetic selects from an interaction network the sub-networks highlighted by these gene lists. We applied PheNetic to an Escherichia coli interaction network to reanalyse a previously published KO compendium, assessing gene expression of 27 E. coli knock-out mutants under mild acidic conditions. Being able to unveil previously described mechanisms involved in acid resistance demonstrated both the performance of our method and the added value of our integrated E. coli network. PheNetic is available at .

Graphlet-based measures are suitable for biological network comparison

MOTIVATION

Large amounts of biological network data exist for many species. Analogous to sequence comparison, network comparison aims to provide biological insight. Graphlet-based methods are proving to be useful in this respect. Recently some doubt has arisen concerning the applicability of graphlet-based measures to low edge density networks-in particular that the methods are 'unstable'-and further that no existing network model matches the structure found in real biological networks.

RESULTS

We demonstrate that it is the model networks themselves that are 'unstable' at low edge density and that graphlet-based measures correctly reflect this instability. Furthermore, while model network topology is unstable at low edge density, biological network topology is stable. In particular, one must distinguish between average density and local density. While model networks of low average edge densities also have low local edge density, that is not the case with protein-protein interaction (PPI) networks: real PPI networks have low average edge density, but high local edge densities, and hence, they (and thus graphlet-based measures) are stable on these networks. Finally, we use a recently devised non-parametric statistical test to demonstrate that PPI networks of many species are well-fit by several models not previously tested. In addition, we model several viral PPI networks for the first time and demonstrate an exceptionally good fit between the data and theoretical models.

Genome reduction promotes increase in protein functional complexity in bacteria

Obligate pathogenic and endosymbiotic bacteria typically experience gene loss due to functional redundancy, asexuality, and genetic drift. We hypothesize that reduced genomes increase their functional complexity through protein multitasking, in which many genes adopt new roles to counteract gene loss. Comparisons of interaction networks among six bacteria that have varied genome sizes (Mycoplasma pneumoniae, Treponema pallidum, Helicobacter pylori, Campylobacter jejuni, Synechocystis sp., and Mycobacterium tuberculosis) reveal that proteins in small genomes interact with proteins from a wider range of functions than do their orthologs in larger genomes. This suggests that surviving proteins form increasingly complex functional relationships to compensate for genes that are lost.

Global network alignment using multiscale spectral signatures

MOTIVATION

Protein interaction networks provide an important system-level view of biological processes. One of the fundamental problems in biological network analysis is the global alignment of a pair of networks, which puts the proteins of one network into correspondence with the proteins of another network in a manner that conserves their interactions while respecting other evidence of their homology. By providing a mapping between the networks of different species, alignments can be used to inform hypotheses about the functions of unannotated proteins, the existence of unobserved interactions, the evolutionary divergence between the two species and the evolution of complexes and pathways.

RESULTS

We introduce GHOST, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks. It combines a seed-and-extend global alignment phase with a local search procedure and exceeds state-of-the-art performance on several network alignment tasks. We show that the spectral signature used by GHOST is highly discriminative, whereas the alignments it produces are also robust to experimental noise. When compared with other recent approaches, we find that GHOST is able to recover larger and more biologically significant, shared subnetworks between species.

AVAILABILITY

An efficient and parallelized implementation of GHOST, released under the Apache 2.0 license, is available at http://cbcb.umd.edu/kingsford_group/ghost

CONTACT

rob@cs.umd.edu.

Modeling regulatory networks to understand plant development: small is beautiful

We now have unprecedented capability to generate large data sets on the myriad genes and molecular players that regulate plant development. Networks of interactions between systems components can be derived from that data in various ways and can be used to develop mathematical models of various degrees of sophistication. Here, we discuss why, in many cases, it is productive to focus on small networks. We provide a brief and accessible introduction to relevant mathematical and computational approaches to model regulatory networks and discuss examples of small network models that have helped generate new insights into plant biology (where small is beautiful), such as in circadian rhythms, hormone signaling, and tissue patterning. We conclude by outlining some of the key technical and modeling challenges for the future.

Convergent evolution of modularity in metabolic networks through different community structures

Background

It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures.

Results

In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony.

Conclusions

We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network communities that better correspond to functional categorizations.

Interplay of gene expression noise and ultrasensitive dynamics affects bacterial operon organization

Bacterial chromosomes are organized into polycistronic cotranscribed operons, but the evolutionary pressures maintaining them are unclear. We hypothesized that operons alter gene expression noise characteristics, resulting in selection for or against maintaining operons depending on network architecture. Mathematical models for 6 functional classes of network modules showed that three classes exhibited decreased noise and 3 exhibited increased noise with same-operon cotranscription of interacting proteins. Noise reduction was often associated with a decreased chance of reaching an ultrasensitive threshold. Stochastic simulations of the lac operon demonstrated that the predicted effects of transcriptional coupling hold for a complex network module. We employed bioinformatic analysis to find overrepresentation of noise-minimizing operon organization compared with randomized controls. Among constitutively expressed physically interacting protein pairs, higher coupling frequencies appeared at lower expression levels, where noise effects are expected to be dominant. Our results thereby suggest an important role for gene expression noise, in many cases interacting with an ultrasensitive switch, in maintaining or selecting for operons in bacterial chromosomes.

In some species, most notably bacteria, chromosomal genes are arranged into clusters called operons. In operons, the process of transcription is physically coupled: a single pass of the RNA polymerase enzyme reading that region of the chromosome simultaneously produces messenger RNA encoding multiple proteins. So far, we do not have a satisfying explanation for what evolutionary forces have maintained operons on bacterial chromosomes. We hypothesized that different types of interactions between operon-coded proteins affect how strongly operons are selected for between two genes. The proposed mechanism for this effect is that operons correlate gene expression noise, changing how it manifests in the post-translational network depending on the type of protein interaction. Mathematical models demonstrate that operons reduce noise for some types of interactions but not others. We found that operon-dependent noise reduction has an underlying dependence on surprisingly high sensitivity of the network to the ratio of proteins from each gene. Databases of genetic information show that E. coli has operons more frequently than random if operons reduce noise for the type of interaction various gene pairs have, but not otherwise. Our study thus provides an example of how the architecture of post-translational networks affects bacterial evolution.

Metagenomic annotation networks: construction and applications

The derivation and comparison of biological interaction networks are vital for understanding the functional capacity and hierarchical organization of integrated microbial communities. In the current work we present metagenomic annotation networks as a novel taxonomy-free approach for understanding the functional architecture of metagenomes. Specifically, metagenomic operon predictions are exploited to derive functional interactions that are translated and categorized according to their associated functional annotations. The result is a collection of discrete networks of weighted annotation linkages. These networks are subsequently examined for the occurrence of annotation modules that portray the functional and organizational characteristics of various microbial communities. A variety of network perspectives and annotation categories are applied to recover a diverse range of modules with different degrees of annotative cohesiveness. Applications to biocatalyst discovery and human health issues are discussed, as well as the limitations of the current implementation.

Prediction and analysis of the protein interactome in Pseudomonas aeruginosa to enable network-based drug target selection

Pseudomonas aeruginosa (PA) is a ubiquitous opportunistic pathogen that is capable of causing highly problematic, chronic infections in cystic fibrosis and chronic obstructive pulmonary disease patients. With the increased prevalence of multi-drug resistant PA, the conventional “one gene, one drug, one disease” paradigm is losing effectiveness. Network pharmacology, on the other hand, may hold the promise of discovering new drug targets to treat a variety of PA infections. However, given the urgent need for novel drug target discovery, a PA protein-protein interaction (PPI) network of high accuracy and coverage, has not yet been constructed. In this study, we predicted a genome-scale PPI network of PA by integrating various genomic features of PA proteins/genes by a machine learning-based approach. A total of 54,107 interactions covering 4,181 proteins in PA were predicted. A high-confidence network combining predicted high-confidence interactions, a reference set and verified interactions that consist of 3,343 proteins and 19,416 potential interactions was further assembled and analyzed. The predicted interactome network from this study is the first large-scale PPI network in PA with significant coverage and high accuracy. Subsequent analysis, including validations based on existing small-scale PPI data and the network structure comparison with other model organisms, shows the validity of the predicted PPI network. Potential drug targets were identified and prioritized based on their essentiality and topological importance in the high-confidence network. Host-pathogen protein interactions between human and PA were further extracted and analyzed. In addition, case studies were performed on protein interactions regarding anti-sigma factor MucA, negative periplasmic alginate regulator MucB, and the transcriptional regulator RhlR. A web server to access the predicted PPI dataset is available at http://research.cchmc.org/PPIdatabase/.

Generation and analysis of a mouse intestinal metatranscriptome through Illumina based RNA-sequencing

With the advent of high through-put sequencing (HTS), the emerging science of metagenomics is transforming our understanding of the relationships of microbial communities with their environments. While metagenomics aims to catalogue the genes present in a sample through assessing which genes are actively expressed, metatranscriptomics can provide a mechanistic understanding of community inter-relationships. To achieve these goals, several challenges need to be addressed from sample preparation to sequence processing, statistical analysis and functional annotation. Here we use an inbred non-obese diabetic (NOD) mouse model in which germ-free animals were colonized with a defined mixture of eight commensal bacteria, to explore methods of RNA extraction and to develop a pipeline for the generation and analysis of metatranscriptomic data. Applying the Illumina HTS platform, we sequenced 12 NOD cecal samples prepared using multiple RNA-extraction protocols. The absence of a complete set of reference genomes necessitated a peptide-based search strategy. Up to 16% of sequence reads could be matched to a known bacterial gene. Phylogenetic analysis of the mapped ORFs revealed a distribution consistent with ribosomal RNA, the majority from Bacteroides or Clostridium species. To place these HTS data within a systems context, we mapped the relative abundance of corresponding Escherichia coli homologs onto metabolic and protein-protein interaction networks. These maps identified bacterial processes with components that were well-represented in the datasets. In summary this study highlights the potential of exploiting the economy of HTS platforms for metatranscriptomics.

RelA protein stimulates the activity of RyhB small RNA by acting on RNA-binding protein Hfq

The conserved RNA-binding protein Hfq and its associated small regulatory RNAs (sRNAs) are increasingly recognized as the players of a large network of posttranscriptional control of gene expression in Gram-negative bacteria. The role of Hfq in this network is to facilitate base pairing between sRNAs and their trans-encoded target mRNAs. Although the number of known sRNA-mRNA interactions has grown steadily, cellular factors that influence Hfq, the mediator of these interactions, have remained unknown. We report that RelA, a protein long known as the central regulator of the bacterial-stringent response, acts on Hfq and thereby affects the physiological activity of RyhB sRNA as a regulator of iron homeostasis. RyhB requires RelA in vivo to arrest growth during iron depletion and to down-regulate a subset of its target mRNAs (fdoG, nuoA, and sodA), whereas the sodB and sdhC targets are barely affected by RelA. In vitro studies with recombinant proteins show that RelA enhances multimerization of Hfq monomers and stimulates Hfq binding of RyhB and other sRNAs. Hfq from polysomes extracted from wild-type cells binds RyhB in vitro, whereas Hfq from polysomes of a relA mutant strain shows no binding. We propose that, by increasing the level of the hexameric form of Hfq, RelA enables binding of RNAs whose affinity for Hfq is low. Our results suggest that, under specific conditions and/or environments, Hfq concentrations are limiting for RNA binding, which thereby provides an opportunity for cellular proteins such as RelA to impact sRNA-mediated responses by modulating the activity of Hfq.

The molecular diversity of adaptive convergence

To estimate the number and diversity of beneficial mutations, we experimentally evolved 115 populations of Escherichia coli to 42.2°C for 2000 generations and sequenced one genome from each population. We identified 1331 total mutations, affecting more than 600 different sites. Few mutations were shared among replicates, but a strong pattern of convergence emerged at the level of genes, operons, and functional complexes. Our experiment uncovered a set of primary functional targets of high temperature, but we estimate that many other beneficial mutations could contribute to similar adaptive outcomes. We inferred the pervasive presence of epistasis among beneficial mutations, which shaped adaptive trajectories into at least two distinct pathways involving mutations either in the RNA polymerase complex or the termination factor rho.

C-GRAAL: common-neighbors-based global GRAph ALignment of biological networks

Networks are an invaluable framework for modeling biological systems. Analyzing protein-protein interaction (PPI) networks can provide insight into underlying cellular processes. It is expected that comparison and alignment of biological networks will have a similar impact on our understanding of evolution, biological function, and disease as did sequence comparison and alignment. Here, we introduce a novel pairwise global alignment algorithm called Common-neighbors based GRAph ALigner (C-GRAAL) that uses heuristics for maximizing the number of aligned edges between two networks and is based solely on network topology. As such, it can be applied to any type of network, such as social, transportation, or electrical networks. We apply C-GRAAL to align PPI networks of eukaryotic and prokaryotic species, as well as inter-species PPI networks, and we demonstrate that the resulting alignments expose large connected and functionally topologically aligned regions. We use the resulting alignments to transfer biological knowledge across species, successfully validating many of the predictions. Moreover, we show that C-GRAAL can be used to align human-pathogen inter-species PPI networks and that it can identify patterns of pathogen interactions with host proteins solely from network topology.

Algorithms for systematic identification of small subgraphs

The ability to analyze large biological networks proves to be a computationally expensive task, but the information one can gain is worth the cost and effort. In cancer research for example, one is able to derive knowledge about putative drug targets by revealing the strengths and weaknesses inherent in a protein-protein interaction (PPI) network. Further, network analyses can be used to optimize high-throughput genetic and proteomic experiments. In addition, the study of biological networks is now an active part of molecular biology. In this chapter, we review techniques for studying biological networks in general but with a focus on PPI networks, including an example of a bacterial PPI network. After a brief introduction, we concentrate on methods based on the analysis of subnetworks, namely, graph motifs and graphlets.

Protein complex prediction with RNSC

Graph theory analysis of biological networks, such as protein-protein interactions (PPIs), gene regulatory, metabolic, etc., has identified a strong relationship between topology of these networks and the underlying cellular function and biological processes (Sharan et al. Mol Syst Biol 3:88, 2007). We focus on PPI networks, in which nodes correspond to proteins and edges represent interactions among the proteins. The size of these networks is ever growing, and thus efficient identification of various network motifs and dense sub-networks has become necessary. Predicting highly connected sub-graphs in a PPI network is important to biologists as it may help to identify biologically meaningful protein complexes, and with further integrative analysis may lead to identifying dynamic assembly of individual subunits in these complexes. In this chapter, we describe one method for predicting protein complexes in two steps. The first step is to partition the nodes of a PPI network (i.e. proteins) into highly connected groups or clusters using the Restricted Neighbourhood Search Clustering algorithm. This provides a set of clusters that represent candidate complexes. The second step of the method is to filter the candidate complexes based on three criteria: minimum cluster size, minimum interaction density, and minimum functional homogeneity, which reflects the extent to which the proteins of the candidate cluster operate in the same functional group. Candidate complexes passing all three criteria are then put forward as predicted protein complexes. The effectiveness of this method is investigated in the previous studies (King et al. Bioinformatics 20:3013-3020, 2004; Brohee and van Helden BMC Bioinformatics 7:488, 2006; and Moschopoulos et al. BMC Bioinformatics 10(Suppl 6):S11, 2009).

RegnANN: Reverse Engineering Gene Networks using Artificial Neural Networks

RegnANN is a novel method for reverse engineering gene networks based on an ensemble of multilayer perceptrons. The algorithm builds a regressor for each gene in the network, estimating its neighborhood independently. The overall network is obtained by joining all the neighborhoods. RegnANN makes no assumptions about the nature of the relationships between the variables, potentially capturing high-order and non linear dependencies between expression patterns. The evaluation focuses on synthetic data mimicking plausible submodules of larger networks and on biological data consisting of submodules of Escherichia coli. We consider Barabasi and Erdös-Rényi topologies together with two methods for data generation. We verify the effect of factors such as network size and amount of data to the accuracy of the inference algorithm. The accuracy scores obtained with RegnANN is methodically compared with the performance of three reference algorithms: ARACNE, CLR and KELLER. Our evaluation indicates that RegnANN compares favorably with the inference methods tested. The robustness of RegnANN, its ability to discover second order correlations and the agreement between results obtained with this new methods on both synthetic and biological data are promising and they stimulate its application to a wider range of problems.

Bacterial protein interaction networks: puzzle stones from solved complex structures add to a clearer picture

Global scale studies of protein-protein interaction (PPI) networks have considerably expanded our view of how proteins act in the cell. In particular, bacterial "interactome" surveys have revealed that proteins can sometimes interact with a large number of protein partners and connect different cellular processes. More targeted, pathway-orientated PPI studies have also helped to propose functions for unknown proteins based on the "guilty by association" principle. However, given the immense repertoire of PPIs generated and the variability of PPI networks, more studies are required to understand the role(s) of these interactions in the cell. With the availability of bioinformatic analysis tools, transcriptomics and co-expression experiments for a given interaction, interactomes are being deciphered. More recently, functional and structural studies have been derived from these PPI networks. In this review, we will give a number of examples of how combining functional and structural studies into PPI networks has contributed to understanding the functions of some of these interactions. We discuss how interactomes now represent a unique opportunity to determine the structures of bacterial protein complexes on a large scale by the integration of multiple technologies.