Protein interaction networks in bacteria

Global Insights into the Lysine Acetylome Reveal the Role of Lysine Acetylation in the Adaptation of Bacillus altitudinis to Salt Stress

Bacillus altitudinis is a well-known beneficial microorganism in plant rhizosphere, capable of enhancing plant growth and salt tolerance in saline soils. However, the mechanistic changes underlying salt tolerance in B. altitudinis at the level of post-translational modifications remain unclear. Here, diverse lysine modifications including acetylation, succinylation, crotonylation, and malonylation were determined in the B. altitudinis response to salt stress by immunodetection, and the acetylation level greatly increased under salt stress. The in-depth acetylome landscape showed that 1032 proteins in B. altitudinis were differentially acetylated under salt stress. These proteins were involved in many physiological aspects closely related to salt tolerance like energy generation and conversion, amino acid synthesis and transport, cell motility, signal transduction, secretion system, and repair system. Moreover, we also identified the differential acetylation of key enzymes involved in the major osmolyte biosynthesis and conversion and antioxidant defenses. Thiol peroxidase (TPX), a key protective antioxidant enzyme, had 3 upregulated acetylation sites (K7/139/157) under salt stress. Site-specific mutations demonstrated that K7/139/157 acetylation strongly regulated TPX function in scavenging intracellular ROS, thereby impacting bacterial growth under salt stress. To our knowledge, this is the first study showing that bacteria adaptation to salt stress occurs at the level of PTMs.

Centralities in simplicial complexes. Applications to protein interaction networks

Complex networks can be used to represent complex systems which originate in the real world. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplices of the complex. We extend the concept of node centrality to that of simplicial centrality and study several mathematical properties of degree, closeness, betweenness, eigenvector, Katz, and subgraph centrality for simplicial complexes. We study the degree distributions of these centralities at the different levels. We also compare and describe the differences between the centralities at the different levels. Using these centralities we study a method for detecting essential proteins in PPI networks of cells and explain the varying abilities of the centrality measures at the different levels in identifying these essential proteins.

Cutaneous Microflora from Geographically Isolated Groups of Bradysia agrestis, an Insect Vector of Diverse Plant Pathogens

Larvae of Bradysia agrestis, an insect vector that transports plant pathogens, were sampled from geographically isolated regions in Korea to identify their cutaneous fungal and bacterial flora. Sampled areas were chosen within the distribution range of B. agrestis; each site was more than 91 km apart to ensure geographical segregation. We isolated 76 microbial (fungi and bacteria) strains (site 1, 29; site 2, 29; site 3, 18 strains) that were identified on the basis of morphological differences. Species identification was molecularly confirmed by determination of universal fungal internal transcribed spacer and bacterial 16S rRNA gene sequences in comparison to sequences in the EzTaxon database and the NCBI GenBank database, and their phylogenetic relationships were determined. The fungal isolates belonged to 2 phyla, 5 classes, and 7 genera; bacterial species belonged to 23 genera and 32 species. Microbial diversity differed significantly among the geographical groups with respect to Margalef's richness (3.9, 3.6, and 4.5), Menhinick's index (2.65, 2.46, and 3.30), Simpson's index (0.06, 0.12, and 0.01), and Shannon's index (2.50, 2.17, and 2.58). Although the microbial genera distribution or diversity values clearly varied among geographical groups, common genera were identified in all groups, including the fungal genus Cladosporium, and the bacterial genera Bacillus and Rhodococcus. According to classic principles of co-evolutionary relationship, these genera might have a closer association with their host insect vector B. agrestis than other genera identified. Some cutaneous bacterial genera (e.g., Pseudomonas) displaying weak interdependency with insect vectors may be hazardous to agricultural environments via mechanical transmission via B. agrestis. This study provides comprehensive information regarding the cutaneous microflora of B. agrestis, which can help in the control of such pests for crop management.

Exploring the "Middle Earth" of network spectra via a Gaussian matrix function

We study a Gaussian matrix function of the adjacency matrix of artificial and real-world networks. We motivate the use of this function on the basis of a dynamical process modeled by the time-dependent Schrödinger equation with a squared Hamiltonian. In particular, we study the Gaussian Estrada index-an index characterizing the importance of eigenvalues close to zero. This index accounts for the information contained in the eigenvalues close to zero in the spectra of networks. Such a method is a generalization of the so-called "Folded Spectrum Method" used in quantum molecular sciences. Here, we obtain bounds for this index in simple graphs, proving that it reaches its maximum for star graphs followed by complete bipartite graphs. We also obtain formulas for the Estrada Gaussian index of Erdős-Rényi random graphs and for the Barabási-Albert graphs. We also show that in real-world networks, this index is related to the existence of important structural patterns, such as complete bipartite subgraphs (bicliques). Such bicliques appear naturally in many real-world networks as a consequence of the evolutionary processes giving rise to them. In general, the Gaussian matrix function of the adjacency matrix of networks characterizes important structural information not described in previously used matrix functions of graphs.

Communicability angles reveal critical edges for network consensus dynamics

We consider the question of determining how the topological structure influences a consensus dynamical processes taking place on a network. By considering a large data set of real-world networks we first determine that the removal of edges according to their communicability angle, an angle between position vectors of the nodes in an Euclidean communicability space, increases the average time of consensus by a factor of 5.68 in real-world networks. The edge betweenness centrality also identifies, in a smaller proportion, those critical edges for the consensus dynamics; i.e., its removal increases the time of consensus by a factor of 3.70. We justify theoretically these findings on the basis of the role played by the algebraic connectivity and the isoperimetric number of networks on the dynamical process studied and their connections with the properties mentioned before. Finally, we study the role played by global topological parameters of networks on the consensus dynamics. We determine that the network density and the average distance-sum, which is analogous of the node degree for shortest-path distances, account for more than 80% of the variance of the average time of consensus in the real-world networks studied.

Macromolecular networks and intelligence in microorganisms

Living organisms persist by virtue of complex interactions among many components organized into dynamic, environment-responsive networks that span multiple scales and dimensions. Biological networks constitute a type of information and communication technology (ICT): they receive information from the outside and inside of cells, integrate and interpret this information, and then activate a response. Biological networks enable molecules within cells, and even cells themselves, to communicate with each other and their environment. We have become accustomed to associating brain activity - particularly activity of the human brain - with a phenomenon we call "intelligence." Yet, four billion years of evolution could have selected networks with topologies and dynamics that confer traits analogous to this intelligence, even though they were outside the intercellular networks of the brain. Here, we explore how macromolecular networks in microbes confer intelligent characteristics, such as memory, anticipation, adaptation and reflection and we review current understanding of how network organization reflects the type of intelligence required for the environments in which they were selected. We propose that, if we were to leave terms such as "human" and "brain" out of the defining features of "intelligence," all forms of life - from microbes to humans - exhibit some or all characteristics consistent with "intelligence." We then review advances in genome-wide data production and analysis, especially in microbes, that provide a lens into microbial intelligence and propose how the insights derived from quantitatively characterizing biomolecular networks may enable synthetic biologists to create intelligent molecular networks for biotechnology, possibly generating new forms of intelligence, first in silico and then in vivo.

Protein interaction networks as starting points to identify novel antimicrobial drug targets

Novel classes of antimicrobials are needed to address the challenge of multidrug-resistant bacteria. Current bacterial drug targets mainly consist of specific proteins or subsets of proteins without regard for either how these targets are integrated in cellular networks or how they may interact with host proteins. However, proteins rarely act in isolation, and the majority of biological processes are dependent on interactions with other proteins. Consequently, protein-protein interaction (PPI) networks offer a realm of unexplored potential for next-generation drug targets. In this review, we argue that the architecture of bacterial or host-pathogen protein interactomes can provide invaluable insights for the identification of novel antibacterial drug targets.

An expanded protein-protein interaction network in Bacillus subtilis reveals a group of hubs: Exploration by an integrative approach

We have generated a protein-protein interaction network in Bacillus subtilis focused on several essential cellular processes such as cell division, cell responses to various stresses, the bacterial cytoskeleton, DNA replication and chromosome maintenance by careful application of the yeast two-hybrid approach. This network, composed of 793 interactions linking 287 proteins with an average connectivity of five interactions per protein, represents a valuable resource for future functional analyses. A striking feature of the network is a group of highly connected hubs (GoH) linking many different cellular processes. Most of the proteins of the GoH have unknown functions and are associated to the membrane. By the integration of available knowledge, in particular of transcriptome data sets, the GoH was decomposed into subgroups of party hubs corresponding to protein complexes or regulatory pathways expressed under different conditions. At a global level, the GoH might function as a very robust group of date hubs having partially redundant functions to integrate information from the different cellular pathways. Our analyses also provide a rational way to study the highly redundant functions of the GoH by a genetic approach.

Double-strand break repair in bacteria: a view from Bacillus subtilis

In all living organisms, the response to double-strand breaks (DSBs) is critical for the maintenance of chromosome integrity. Homologous recombination (HR), which utilizes a homologous template to prime DNA synthesis and to restore genetic information lost at the DNA break site, is a complex multistep response. In Bacillus subtilis, this response can be subdivided into five general acts: (1) recognition of the break site(s) and formation of a repair center (RC), which enables cells to commit to HR; (2) end-processing of the broken end(s) by different avenues to generate a 3'-tailed duplex and RecN-mediated DSB 'coordination'; (3) loading of RecA onto single-strand DNA at the RecN-induced RC and concomitant DNA strand exchange; (4) branch migration and resolution, or dissolution, of the recombination intermediates, and replication restart, followed by (5) disassembly of the recombination apparatus formed at the dynamic RC and segregation of sister chromosomes. When HR is impaired or an intact homologous template is not available, error-prone nonhomologous end-joining directly rejoins the two broken ends by ligation. In this review, we examine the functions that are known to contribute to DNA DSB repair in B. subtilis, and compare their properties with those of other bacterial phyla.

The C-terminal domain of the bacterial SSB protein acts as a DNA maintenance hub at active chromosome replication forks

We have investigated in vivo the role of the carboxy-terminal domain of the Bacillus subtilis Single-Stranded DNA Binding protein (SSB(Cter)) as a recruitment platform at active chromosomal forks for many proteins of the genome maintenance machineries. We probed this SSB(Cter) interactome using GFP fusions and by Tap-tag and biochemical analysis. It includes at least 12 proteins. The interactome was previously shown to include PriA, RecG, and RecQ and extended in this study by addition of DnaE, SbcC, RarA, RecJ, RecO, XseA, Ung, YpbB, and YrrC. Targeting of YpbB to active forks appears to depend on RecS, a RecQ paralogue, with which it forms a stable complex. Most of these SSB partners are conserved in bacteria, while others, such as the essential DNA polymerase DnaE, YrrC, and the YpbB/RecS complex, appear to be specific to B. subtilis. SSB(Cter) deletion has a moderate impact on B. subtilis cell growth. However, it markedly affects the efficiency of repair of damaged genomic DNA and arrested replication forks. ssbΔCter mutant cells appear deficient in RecA loading on ssDNA, explaining their inefficiency in triggering the SOS response upon exposure to genotoxic agents. Together, our findings show that the bacterial SSB(Cter) acts as a DNA maintenance hub at active chromosomal forks that secures their propagation along the genome.

Databases of protein-protein interactions and complexes

In the current understanding, translation of genomic sequences into proteins is the most important path for realization of genome information. In exercising their intended function, proteins work together through various forms of direct (physical) or indirect interaction mechanisms. For a variety of basic functions, many proteins form a large complex representing a molecular machine or a macromolecular super-structural building block. After several high-throughput techniques for detection of protein-protein interactions had matured, protein interaction data became available in a large scale and curated databases for protein-protein interactions (PPIs) are a new necessity for efficient research. Here, their scope, annotation quality, and retrieval tools are reviewed. In addition, attention is paid to portals that provide unified access to a variety of such databases with added annotation value.

Generalized walks-based centrality measures for complex biological networks

A strategy for zooming in and out the topological environment of a node in a complex network is developed. This approach is applied here to generalize the subgraph centrality of nodes in complex networks. In this case the zooming in strategy is based on the use of some known matrix functions which allow focusing locally on the environment of a node. When a zooming out strategy is applied new matrix functions are introduced, which give a more global picture of the topological surrounds of a node. These indices permit a modulation of the scales at which the environment of a node influences its centrality. We apply them to the study of 10 protein-protein interaction (PPI) networks. We illustrate the similarities and differences between the generalized subgraph centrality indices as well as among them and some classical centrality measures. We show here that the use of centrality indices based on the zooming in strategy identifies a larger number of essential proteins in the yeast PPI network than any of the other centrality measures studied.

Role of selection in the emergence of lineages and the evolution of virulence in Neisseria meningitidis

Neisseria meningitis is a human commensal bacterium that occasionally causes life-threatening disease. As with a number of other bacterial pathogens, meningococcal populations comprise distinct lineages, which persist over many decades and during global spread in the face of high rates of recombination. In addition, the propensity to cause invasive disease is associated with particular "hyperinvasive" lineages that coexist with less invasive lineages despite the fact that disease does not contribute to host-to-host transmission. Here, by combining a modeling approach with molecular epidemiological data from 1,108 meningococci isolated in the Czech Republic over 27 years, we show that interstrain competition, mediated by immune selection, can explain both the persistence of multiple discrete meningococcal lineages and the association of a subset of these with invasive disease. The model indicates that the combinations of allelic variants of housekeeping genes that define these lineages are associated with very small differences in transmission efficiency among hosts. These findings have general implications for the emergence of lineage structure and virulence in recombining bacterial populations.

A general system for studying protein-protein interactions in Gram-negative bacteria

One of the most promising methods for large-scale studies of protein interactions is isolation of an affinity-tagged protein with its in vivo interaction partners, followed by mass spectrometric identification of the copurified proteins. Previous studies have generated affinity-tagged proteins using genetic tools or cloning systems that are specific to a particular organism. To enable protein-protein interaction studies across a wider range of Gram-negative bacteria, we have developed a methodology based on expression of affinity-tagged "bait" proteins from a medium copy-number plasmid. This construct is based on a broad-host-range vector backbone (pBBR1MCS5). The vector has been modified to incorporate the Gateway DEST vector recombination region, to facilitate cloning and expression of fusion proteins bearing a variety of affinity, fluorescent, or other tags. We demonstrate this methodology by characterizing interactions among subunits of the DNA-dependent RNA polymerase complex in two metabolically versatile Gram-negative microbial species of environmental interest, Rhodopseudomonas palustris CGA010 and Shewanella oneidensis MR-1. Results compared favorably with those for both plasmid and chromosomally encoded affinity-tagged fusion proteins expressed in a model organism, Escherichia coli.

Towards the development of Bacillus subtilis as a cell factory for membrane proteins and protein complexes

Background

The Gram-positive bacterium Bacillus subtilis is an important producer of high quality industrial enzymes and a few eukaryotic proteins. Most of these proteins are secreted into the growth medium, but successful examples of cytoplasmic protein production are also known. Therefore, one may anticipate that the high protein production potential of B. subtilis can be exploited for protein complexes and membrane proteins to facilitate their functional and structural analysis. The high quality of proteins produced with B. subtilis results from the action of cellular quality control systems that efficiently remove misfolded or incompletely synthesized proteins. Paradoxically, cellular quality control systems also represent bottlenecks for the production of various heterologous proteins at significant concentrations.

Conclusion

While inactivation of quality control systems has the potential to improve protein production yields, this could be achieved at the expense of product quality. Mechanisms underlying degradation of secretory proteins are nowadays well understood and often controllable. It will therefore be a major challenge for future research to identify and modulate quality control systems of B. subtilis that limit the production of high quality protein complexes and membrane proteins, and to enhance those systems that facilitate assembly of these proteins.

Role of bacterial peptidase F inferred by statistical analysis and further experimental validation

Despite the quantity of high-throughput data available nowadays, the precise role of many proteins has not been elucidated. Available methods for classifying proteins and reconstructing metabolic networks are efficient for finding global categories, but do not answer the biologist's specific and targeted questions. Following Yamanishi et al. [Yamanishi, Y, Vert, JP, Nakaya, A, and Kaneisha, M (2003). "Extraction of correlated clusters from multiple genomic data by generalized kernel canonical correlation analysis." Bioinformatics 19, Suppl. 1, i323-i330] we used a kernel canonical correlation analysis (KCCA) to predict the role of the bacterial peptidase PepF. We integrated five existing data types: protein metabolic networks, microarray data, phylogenetic profiles, distances between proteins and incomplete two-dimensional-gel data (for which we propose a completion strategy), available for Lactococcus lactis to determine relationships between proteins. The predicted relationships were then used to guide our laboratory work which proved most of the predictions correct. PepF had previously been characterized as a zinc dependent endopeptidase [Nardi, M, Renault, P, and Monnet, V (1997). "Duplication of the pepF gene and shuffling of DNA fragments on the lactose plasmid of Lactococcus lactis." J. Bacteriol. 179, 4164-4171; Monnet, V, Nardi, M, Chopin, MC, and Gripon, JC (1994). "Biochemical and genetic characterization of PepF on oligoendopeptidase from Lactococcus lactis." J. Bio. Chem. 269, 32070-32076]. Analyzing a PepF mutant, we confirmed its participation in protein secretion through a strong relationship between the signal peptidase I and PepF predicted by the KCCA. The global nature of our approach made it possible to discover pleiotropic roles of the protein which had remained unknown using classical approaches.

Protein-protein interactions: analysis and prediction

Proteins represent the tools and appliances of the cell — they assemble into larger structural elements, catalyze the biochemical reactions of metabolism, transmit signals, move cargo across membrane boundaries and carry out many other tasks. For most of these functions proteins cannot act in isolation but require close cooperation with other proteins to accomplish their task. Often, this collaborative action implies physical interaction of the proteins involved. Accordingly, experimental detection, in silico prediction and computational analysis of protein-protein interactions (PPI) have attracted great attention in the quest for discovering functional links among proteins and deciphering the complex networks of the cell.

Systematic localisation of proteins fused to the green fluorescent protein in Bacillus subtilis: identification of new proteins at the DNA replication factory

Construction and microscopic imaging of protein fusions to green fluorescent protein (GFP) have revolutionised our understanding of bacterial structure and function. We have undertaken a systematic study of the localisation of over 100 Bacillus subtilis proteins, following the development of high-throughput construction and analysis procedures. We focused on proteins linked in various ways to the DNA replication machinery, as well as on proteins exemplifying a range of other cellular functions and structures. The results validate the approach as a way of obtaining systematic protein localisation information. They also provide a range of novel biological insights, particularly through the identification of a number of proteins not previously known to be associated with the DNA replication factory.

Selective protein-protein interactions driven by a phenylalanine interface

Highly specific protein-protein interfaces have been the subject of considerable study for their potential utility in disrupting or interrogating cellular signaling and control networks. We report that coiled-coil sequences decorated with phenylalanine core residues fold into stable alpha-helical bundles and that these self-sort from similar peptide assemblies with aliphatic core side chains. For self-assembled ensembles derived from 30-residue monomeric peptides, the DeltaG of specificity is -1.5 kcal/mol, comparable with earlier self-sorting coiled-coil systems. Intriguingly, although this interface is constructed from canonical amino acids, it does not appear to have been exploited in native proteins.

Interactome: gateway into systems biology

Protein-protein interactions are fundamental to all biological processes, and a comprehensive determination of all protein-protein interactions that can take place in an organism provides a framework for understanding biology as an integrated system. The availability of genome-scale sets of cloned open reading frames has facilitated systematic efforts at creating proteome-scale data sets of protein-protein interactions, which are represented as complex networks or 'interactome' maps. Protein-protein interaction mapping projects that follow stringent criteria, coupled with experimental validation in orthogonal systems, provide high-confidence data sets immanently useful for interrogating developmental and disease mechanisms at a system level as well as elucidating individual protein function and interactome network topology. Although far from complete, currently available maps provide insight into how biochemical properties of proteins and protein complexes are integrated into biological systems. Such maps are also a useful resource to predict the function(s) of thousands of genes.