Toward an understanding of microbial communities through analysis of communication networks

Bacteria receive signals from diverse members of their biotic environment. They sense their own species through the process of quorum sensing, which detects the density of bacterial cells and regulates functions such as bioluminescence, virulence, and competence. Bacteria also respond to the presence of other microorganisms and eukaryotic hosts. Most studies of microbial communication focus on signaling between the microbe and one other organism for empirical simplicity and because few experimental systems offer the opportunity to study communication among various types of organisms. But in the real biological world, microorganisms must carry on multiple molecular conversations simultaneously between diverse organisms, thereby constructing communication networks. We propose that biocontrol of plant disease, the process of suppressing disease through application of a microorganism, offers a model for the study of communication among multiple organisms. Successful biocontrol requires the sending and receiving of signals between the biocontrol agent and the pathogen, plant host, and microbial community surrounding the host. We are using Bacillus cereus, a biocontrol agent, and the organisms it must interact with, to dissect a communication network. This system offers an excellent starting point for study because its members are defined and well studied. An understanding of signaling in the B. cereus biocontrol system may provide a model for network communication among organisms that share a habitat and provide a new angle of analysis for understanding the interconnections that define communities. This revised version was published online in August 2006 with corrections to the Cover Date.     

Systematic analysis of genes involved in oral cancer metastasis to lymph nodes

Oral cancer remains a deadly disease worldwide. Lymph node metastasis and invasion is one of the causes of death from oral cancer. Elucidating the mechanism of oral cancer lymph node metastasis and identifying critical regulatory genes are important for the treatment of this disease. This study aimed to identify differentially expressed genes (gene signature) and pathways that contribute to oral cancer metastasis to lymph nodes. The GSE70604-associated study compared gene profiles in lymph nodes with metastasis of oral cancer to those of normal lymph nodes. The GSE2280-associated study compared gene profiles in primary tumor of oral cancer with lymph node metastasis to those in tumors without lymph node metastasis. There are 28 common differentially expressed genes (DEGs) showing consistent changes in both datasets in overlapping analysis. GO biological process and KEGG pathway analysis of these 28 DEGs identified the gene signature CCND1, JUN and SPP1, which are categorized as key regulatory genes involved in the focal adhesion pathway. Silencing expression of CCND1, JUN and SPP1 in the human oral cancer cell line OECM-1 confirmed that those genes play essential roles in oral cancer cell invasion. Analysis of clinical samples of oral cancer found a strong correlation of these genes with short survival, especially JUN expression associated with metastasis. Our study identified a unique gene signature – CCND1, JUN and SPP1 – which may be involved in oral cancer lymph node metastasis.     

Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain

Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis) proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br) compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05) difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA) with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20), or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9). These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse) yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.     

Gene products involved in metastasis of bladder cancer

Metastasis is usually responsible for mortality in patients suffering from muscle invasive bladder cancer. Whilst expression of a great number of genes and their protein products have been associated with metastasis and/or poor prognosis in bladder cancer, evidence that they actively drive the metastatic process, and hence make potentially good therapeutic targets, is often lacking. This is due to the limited number and application of effective animal models which reflect the pathogenesis of the human disease. In this review I will discuss the processes involved in metastasis, consider the established animal models of bladder cancer progression and metastasis, and review the evidence for a role of various gene products in this process. Consideration of clinical studies in conjunction with evidence from experimental animal models reveals that the tyrosine kinase receptor erbB1/EGFR, the calcium binding protein S100A4 and the the cell cycle arrest/apoptosis-inducing p53 protein are amongst the most promising targets for therapy against metastatic disease in patients with bladder cancer.     

Systematic proteomic analysis of human hepotacellular carcinoma cells reveals molecular pathways and networks involved in metastasis

Systematic proteomic studying of the mechanism of hepatocellular carcinoma (HCC) metastasis remains challenging. We performed comparative proteomic and pathway analysis of four human metastatic HCC cell lines to identify metastasis-associated proteins. These HCC cell lines had a similar genetic background but with an increasing potential of metastasis. Using a combination of two dimensional electrophoresis (2-DE) and MALDI-TOF mass spectrometry, a total of 125 proteins and their post-translational modification forms or isoforms were found to be differentially expressed in the cell lines. Among them, 29 were gradually up-regulated whereas 17 were down-regulated with increasing metastatic potential. Instead of a traditional single-gene readout, global bioinformatics analysis was carried out, which revealed that the glycolysis pathway was the most significantly enriched pathway. The heat shock proteins (HSPs) centered and NF-kappaB centered networks were also enriched in the result, which may imply the key function of inflaming on metastasis. Meanwhile, knockdown of HDGF, an up-regulated protein and a target of NF-kappaB, induced cell apoptosis in the metastatic HCC cells. This work provides a demonstration that a combination of bioinformatics and comparative proteomics can help in finding out potential biomarkers associated with HCC metastasis on the level of pathways.     

EVEREST: automatic identification and classification of protein domains in all protein sequences

Background  

Proteins are comprised of one or several building blocks, known as domains. Such domains can be classified into families according to their evolutionary origin. Whereas sequencing technologies have advanced immensely in recent years, there are no matching computational methodologies for large-scale determination of protein domains and their boundaries. We provide and rigorously evaluate a novel set of domain families that is automatically generated from sequence data. Our domain family identification process, called EVEREST (EVolutionary Ensembles of REcurrent SegmenTs), begins by constructing a library of protein segments that emerge in an all vs. all pairwise sequence comparison. It then proceeds to cluster these segments into putative domain families. The selection of the best putative families is done using machine learning techniques. A statistical model is then created for each of the chosen families. This procedure is then iterated: the aforementioned statistical models are used to scan all protein sequences, to recreate a library of segments and to cluster them again.     

Carbohydrate-mediated cell adhesion involved in hematogenous metastasis of cancer

The carbohydrate determinants, sialyl Lewis A and sialyl Lewis X, which are frequently expressed on human cancer cells, serve as ligands for a cell adhesion molecule of the selectin family, E-selectin, which is expressed on vascular endothelial cells. These carbohydrate determinants are involved in the adhesion of cancer cells to vascular endothelium and thus contribute to hematogenous metastasis of cancer. The initial adhesion mediated by these molecules triggers activation of integrin molecules through the action of several cytokines and leads to the extravasation of cancer cells. Cancer cells also produce humoral factors that facilitate E-selectin expression on endothelial cells. The degree of expression of the carbohydrate ligands at the surface of cancer cells is well correlated with the frequency of hematogenous metastasis and prognostic outcome of patients with cancers. The alteration of glycosyltransferase activities that leads to the enhanced expression of these carbohydrate ligands on cancer cell surface are currently being investigated. This revised version was published online in November 2006 with corrections to the Cover Date.     

Critical analysis of protein signaling networks involved in the regulation of plant secondary metabolism: focus on anthocyanins

Anthocyanin biosynthesis in Arabidopsis is a convenient and relatively simple model for investigating the basic principles of secondary metabolism regulation. In recent years, many publications have described links between anthocyanin biosynthesis and general defense reactions in plants as well as photomorphogenesis and hormonal signaling. These relationships are complex, and they cannot be understood intuitively. Upon observing the lacuna in the Arabidopsis interactome (an interaction map of the factors involved in the regulation of Arabidopsis secondary metabolism is not available), we attempted to connect various cellular processes that affect anthocyanin biosynthesis. In this review, we revealed the main signaling protein modules that regulate anthocyanin biosynthesis. To our knowledge, this is the first reconstruction of a network of proteins involved in plant secondary metabolism.     

NetworkBLAST: comparative analysis of protein networks

The identification of protein complexes is a fundamental challenge in interpreting protein-protein interaction data. Cross-species analysis allows coping with the high levels of noise that are typical to these data. The NetworkBLAST web-server provides a platform for identifying protein complexes in protein-protein interaction networks. It can analyze a single network or two networks from different species. In the latter case, NetworkBLAST outputs a set of putative complexes that are evolutionarily conserved across the two networks. AVAILABILITY: NetworkBLAST is available as web-server at: www.cs.tau.ac.il/~roded/networkblast.htm.     

Structural and functional analysis of Tn4430: identification of an integrase-like protein involved in the co-integrate-resolution process

The 4149-bp transposon Tn4430 from Bacillus thuringiensis is delineated by 38-bp inverted repeats and codes for a 113-kd protein that shares homology with the transposases (TnpA) of Tn3, Tn21 and Tn501. Through transpositional recombination, this protein generates the formation of co-integrates between both donor and target replicons, with duplication of Tn4430 molecules. These features are characteristic of transposons of the Tn3 family (class II elements). The second step of the transposition process, the co-integrate resolution, is mediated by a 32-kd protein. This protein (TnpI) displays regional similarities with site-specific recombinases of the integrase family, such as Int of bacteriophage lambda, Cre of bacteriophage P1 or TnpA and TnpB of the Tn554 transposon. Moreover, the 250-bp sequence upstream to the tnpI gene contains several structural features that are reminiscent of the attP attachment site of phage lambda. This unique association between the integrase-like TnpI recombinase and the TnpA transposase qualifies Tn4430 as a member of a new group within the class II mobile genetic elements.     

Genomic instability and cancer: networks involved in response to DNA damage

A new approach to cancer and new methods in examining rare human chromosome breakage syndromes have brought to light complex interactions between different pathways involved in damage response, cell cycle checkpoint control and DNA repair. The genes affected in these different syndromes are involved in networks of processes that respond to DNA damage and prevent chromosomal aberrations during the cell cycle. The genes involved include the ATM, ATR, FA-associated genes, NBS1 and the cancer susceptibility genes BRCA1 and BRCA2. Chromosomal instability is a common feature of many human cancers and most of the instability syndromes, characterized by sensitivity to different types of DNA damage, also show increased cancer susceptibility. Better understanding of these syndromes and their links with familial cancer provide new insight into associations between defects in DNA damage response, cell cycle control, DNA repair and cancer. Understanding the damage response repair networks that these studies are revealing will have important implications for the development of cancer management and treatment.     

The function of communities in protein interaction networks at multiple scales

Background  

If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network.     

Mutational analysis of the flagellar rotor protein FliN: identification of surfaces important for flagellar assembly and switching

FliN is a component of the flagellar switch complex in many bacterial species. The crystal structure is known for most of FliN, and a targeted cross-linking study (K. Paul and D. F. Blair, J. Bacteriol. 188:2502-2511, 2006) showed that it is organized in ring-shaped tetramers at the bottom of the basal body C ring. FliN is essential for flagellar assembly and direction switching, but its precise functions have not been defined. Here, we identify functionally important regions on FliN by systematic mutagenesis. Nonconservative mutations were made at positions sampling the surface of the protein, and the effects on flagellar assembly and function were measured. Flagellar assembly was disrupted by mutations in a conserved hydrophobic patch centered on the dimer twofold axis or by mutations on the surface that forms the dimer-dimer interface in the tetramer. The assembly defect in hydrophobic-patch mutants was partially rescued by overexpression of the flagellar export proteins FliH and FliI, and coprecipitation assays demonstrated a binding interaction between FliN and FliH that was weakened by mutations in the hydrophobic patch. Thus, FliN might contribute to export by providing binding sites for FliH or FliH-containing complexes. The region around the hydrophobic patch is also important for switching; certain mutations in or near the patch caused a smooth-swimming chemotaxis defect that in most cases could be partially rescued by overexpression of the clockwise-signaling protein CheY. The results indicate that FliN is more closely involved in switching than has been supposed, possibly contributing to the binding site for CheY on the switch.     

KF-finder: identification of key factors from host-microbial networks in cervical cancer

Background

The human body is colonized by a vast number of microbes. Microbiota can benefit many normal life processes, but can also cause many diseases by interfering the regular metabolism and immune system. Recent studies have demonstrated that the microbial community is closely associated with various types of cell carcinoma. The search for key factors, which also refer to cancer causing agents, can provide an important clue in understanding the regulatory mechanism of microbiota in uterine cervix cancer.

Results

In this paper, we investigated microbiota composition and gene expression data for 58 squamous and adenosquamous cell carcinoma. A host-microbial covariance network was constructed based on the 16s rRNA and gene expression data of the samples, which consists of 259 abundant microbes and 738 differentially expressed genes (DEGs). To search for risk factors from host-microbial networks, the method of bi-partite betweenness centrality (BpBC) was used to measure the risk of a given node to a certain biological process in hosts. A web-based tool KF-finder was developed, which can efficiently query and visualize the knowledge of microbiota and differentially expressed genes (DEGs) in the network.

Conclusions

Our results suggest that prevotellaceade, tissierellaceae and fusobacteriaceae are the most abundant microbes in cervical carcinoma, and the microbial community in cervical cancer is less diverse than that of any other boy sites in health. A set of key risk factors anaerococcus, hydrogenophilaceae, eubacterium, PSMB10, KCNIP1 and KRT13 have been identified, which are thought to be involved in the regulation of viral response, cell cycle and epithelial cell differentiation in cervical cancer. It can be concluded that permanent changes of microbiota composition could be a major force for chromosomal instability, which subsequently enables the effect of key risk factors in cancer. All our results described in this paper can be freely accessed from our website at http://www.nwpu-bioinformatics.com/KF-finder/.

     

A computational analysis of protein interactions in metabolic networks reveals novel enzyme pairs potentially involved in metabolic channeling

Protein-protein interactions are operative at almost every level of cell structure and function as, for example, formation of sub-cellular organelles, packaging of chromatin, muscle contraction, signal transduction, and regulation of gene expression. Public databases of reported protein-protein interactions comprise hundreds of thousands interactions, and this number is steadily growing. Elucidating the implications of protein-protein interactions for the regulation of the underlying cellular or extra-cellular reaction network remains a great challenge for computational biochemistry. In this work, we have undertaken a systematic and comprehensive computational analysis of reported enzyme-enzyme interactions in the metabolic networks of the model organisms Escherichia coli and Saccharomyces cerevisiae. We grouped all enzyme pairs according to the topological distance that the catalyzed reactions have in the metabolic network and performed a statistical analysis of reported enzyme-enzyme interactions within these groups. We found a higher frequency of reported enzyme-enzyme interactions within the group of enzymes catalyzing reactions that are adjacent in the network, i.e. sharing at least one metabolite. As some of these interacting enzymes have already been implicated in metabolic channeling our analysis may provide a useful screening for candidates of this phenomenon. To check for a possible regulatory role of interactions between enzymes catalyzing non-neighboring reactions, we determined potentially regulatory enzymes using connectivity in the network and absolute change of Gibbs free energy. Indeed a higher portion of reported interactions pertain to such potentially regulatory enzymes.     

Cluster analysis of an extensive human breast cancer cell line protein expression map database

In the current study, the protein expression maps (PEMs) of 26 breast cancer cell lines and three cell lines derived from normal breast or benign disease tissue were visualised by high resolution two-dimensional gel electrophoresis. Analysis of this data was performed with ChiClust and ChiMap, two analytical bioinformatics tools that are described here. These tools are designed to facilitate recognition of specific patterns shared by two or more (a series) PEMs. Both tools use PEMs that were matched by an image analysis program and locally written programs to create a match table that is saved in an object relational database. The ChiClust tool uses clustering and subclustering methods to extract statistically significant protein expression patterns from a large series of PEMs. The ChiMap tool calculates a differential value (either as percentage change or a fold change) and represents these graphically. All such differentials or just those identified using ChiClust can be submitted to ChiMap. These methods are not dependent on any particular commercial image analysis program, and the whole software package gives an integrated procedure for the comparison and analysis of a series of PEMs. The ChiClust tool was used here to order the breast cell lines into groups according to biological characteristics including morphology in vitro and tumour forming ability in vivo. ChiMap was then used to highlight eight major protein feature-changes detected between breast cancer cell lines that either do or do not proliferate in nude mice. Mass spectrometry was used to identify the proteins. The possible role of these proteins in cancer is discussed.     

Cluster analysis of time-dependent crystallographic data: Direct identification of time-independent structural intermediates

The initial output of a time-resolved macromolecular crystallography experiment is a time-dependent series of difference electron density maps that displays the time-dependent changes in underlying structure as a reaction progresses. The goal is to interpret such data in terms of a small number of crystallographically refinable, time-independent structures, each associated with a reaction intermediate; to establish the pathways and rate coefficients by which these intermediates interconvert; and thereby to elucidate a chemical kinetic mechanism. One strategy toward achieving this goal is to use cluster analysis, a statistical method that groups objects based on their similarity. If the difference electron density at a particular voxel in the time-dependent difference electron density (TDED) maps is sensitive to the presence of one and only one intermediate, then its temporal evolution will exactly parallel the concentration profile of that intermediate with time. The rationale is therefore to cluster voxels with respect to the shapes of their TDEDs, so that each group or cluster of voxels corresponds to one structural intermediate. Clusters of voxels whose TDEDs reflect the presence of two or more specific intermediates can also be identified. From such groupings one can then infer the number of intermediates, obtain their time-independent difference density characteristics, and refine the structure of each intermediate. We review the principles of cluster analysis and clustering algorithms in a crystallographic context, and describe the application of the method to simulated and experimental time-resolved crystallographic data for the photocycle of photoactive yellow protein.