Dynamic Changes in Protein Functional Linkage Networks Revealed by Integration with Gene Expression Data

Response of cells to changing environmental conditions is governed by the dynamics of intricate biomolecular interactions. It may be reasonable to assume, proteins being the dominant macromolecules that carry out routine cellular functions, that understanding the dynamics of protein∶protein interactions might yield useful insights into the cellular responses. The large-scale protein interaction data sets are, however, unable to capture the changes in the profile of protein∶protein interactions. In order to understand how these interactions change dynamically, we have constructed conditional protein linkages for Escherichia coli by integrating functional linkages and gene expression information. As a case study, we have chosen to analyze UV exposure in wild-type and SOS deficient E. coli at 20 minutes post irradiation. The conditional networks exhibit similar topological properties. Although the global topological properties of the networks are similar, many subtle local changes are observed, which are suggestive of the cellular response to the perturbations. Some such changes correspond to differences in the path lengths among the nodes of carbohydrate metabolism correlating with its loss in efficiency in the UV treated cells. Similarly, expression of hubs under unique conditions reflects the importance of these genes. Various centrality measures applied to the networks indicate increased importance for replication, repair, and other stress proteins for the cells under UV treatment, as anticipated. We thus propose a novel approach for studying an organism at the systems level by integrating genome-wide functional linkages and the gene expression data.     

Systematic chromosomal deletion of bacterial ribosomal protein genes

Detailed studies of ribosomal proteins (RPs), essential components of the protein biosynthetic machinery, have been hampered by the lack of readily accessible chromosomal deletions of the corresponding genes. Here, we report the systematic genomic deletion of 41 individual RP genes in Escherichia coli, which are not included in the Keio collection. Chromosomal copies of these genes were replaced by an antibiotic resistance gene in the presence of an inducible, easy-to-exchange plasmid-born allele. Using this knockout collection, we found nine RPs (L15, L21, L24, L27, L29, L30, L34, S9, and S17) nonessential for survival under induction conditions at various temperatures. Taken together with previous results, this analysis revealed that 22 of the 54 E. coli RP genes can be individually deleted from the genome. These strains also allow expression of truncated protein variants to probe the importance of RNA-protein interactions in functional sites of the ribosome. This set of strains should enhance in vivo studies of ribosome assembly/function and may ultimately allow systematic substitution of RPs with RNA.     

Inducible Suppression of Global Translation by Overuse of Rare Codons

Recently, artificial gene networks have been developed in synthetic biology to control gene expression and make organisms as controllable as robots. Here, I present an artificial posttranslational gene-silencing system based on the codon usage bias and low tRNA content corresponding to minor codons. I engineered the green fluorescent protein (GFP) gene to inhibit translation indirectly with the lowest-usage codons to monopolize various minor tRNAs (lgfp). The expression of lgfp interfered nonspecifically with the growth of Escherichia coli, Saccharomyces cerevisiae, human HeLa cervical cancer cells, MCF7 breast cancer cells, and HEK293 kidney cells, as well as phage and adenovirus expansion. Furthermore, insertion of lgfp downstream of a phage response promoter conferred phage resistance on E. coli. Such engineered gene silencers could act as components of biological networks capable of functioning with suitable promoters in E. coli, S. cerevisiae, and human cells to control gene expression. The results presented here show general suppressor artificial genes for live cells and viruses. This robust system provides a gene expression or cell growth control device for artificially synthesized gene networks.     

The organisational structure of protein networks: revisiting the centrality–lethality hypothesis

Protein networks, describing physical interactions as well as functional associations between proteins, have been unravelled for many organisms in the recent past. Databases such as the STRING provide excellent resources for the analysis of such networks. In this contribution, we revisit the organisation of protein networks, particularly the centrality–lethality hypothesis, which hypothesises that nodes with higher centrality in a network are more likely to produce lethal phenotypes on removal, compared to nodes with lower centrality. We consider the protein networks of a diverse set of 20 organisms, with essentiality information available in the Database of Essential Genes and assess the relationship between centrality measures and lethality. For each of these organisms, we obtained networks of high-confidence interactions from the STRING database, and computed network parameters such as degree, betweenness centrality, closeness centrality and pairwise disconnectivity indices. We observe that the networks considered here are predominantly disassortative. Further, we observe that essential nodes in a network have a significantly higher average degree and betweenness centrality, compared to the network average. Most previous studies have evaluated the centrality–lethality hypothesis for Saccharomyces cerevisiae and Escherichia coli; we here observe that the centrality–lethality hypothesis hold goods for a large number of organisms, with certain limitations. Betweenness centrality may also be a useful measure to identify essential nodes, but measures like closeness centrality and pairwise disconnectivity are not significantly higher for essential nodes.     

Artificial septal targeting of Bacillus subtilis cell division proteins in Escherichia coli: an interspecies approach to the study of protein-protein interactions in multiprotein complexes

Bacterial cell division is mediated by a set of proteins that assemble to form a large multiprotein complex called the divisome. Recent studies in Bacillus subtilis and Escherichia coli indicate that cell division proteins are involved in multiple cooperative binding interactions, thus presenting a technical challenge to the analysis of these interactions. We report here the use of an E. coli artificial septal targeting system for examining the interactions between the B. subtilis cell division proteins DivIB, FtsL, DivIC, and PBP 2B. This technique involves the fusion of one of the proteins (the “bait”) to ZapA, an E. coli protein targeted to mid-cell, and the fusion of a second potentially interacting partner (the “prey”) to green fluorescent protein (GFP). A positive interaction between two test proteins in E. coli leads to septal localization of the GFP fusion construct, which can be detected by fluorescence microscopy. Using this system, we present evidence for two sets of strong protein-protein interactions between B. subtilis divisomal proteins in E. coli, namely, DivIC with FtsL and DivIB with PBP 2B, that are independent of other B. subtilis cell division proteins and that do not disturb the cytokinesis process in the host cell. Our studies based on the coexpression of three or four of these B. subtilis cell division proteins suggest that interactions among these four proteins are not strong enough to allow the formation of a stable four-protein complex in E. coli in contrast to previous suggestions. Finally, our results demonstrate that E. coli artificial septal targeting is an efficient and alternative approach for detecting and characterizing stable protein-protein interactions within multiprotein complexes from other microorganisms. A salient feature of our approach is that it probably only detects the strongest interactions, thus giving an indication of whether some interactions suggested by other techniques may either be considerably weaker or due to false positives.     

Unraveling regulation and new components of human P-bodies through a protein interaction framework and experimental validation

The cellular factors involved in mRNA degradation and translation repression can aggregate into cytoplasmic domains known as GW bodies or mRNA processing bodies (P-bodies). However, current understanding of P-bodies, especially the regulatory aspect, remains relatively fragmentary. To provide a framework for studying the mechanisms and regulation of P-body formation, maintenance, and disassembly, we compiled a list of P-body proteins found in various species and further grouped both reported and predicted human P-body proteins according to their functions. By analyzing protein-protein interactions of human P-body components, we found that many P-body proteins form complex interaction networks with each other and with other cellular proteins that are not recognized as P-body components. The observation suggests that these other cellular proteins may play important roles in regulating P-body dynamics and functions. We further used siRNA-mediated gene knockdown and immunofluorescence microscopy to demonstrate the validity of our in silico analyses. Our combined approach identifies new P-body components and suggests that protein ubiquitination and protein phosphorylation involving 14-3-3 proteins may play critical roles for post-translational modifications of P-body components in regulating P-body dynamics. Our analyses provide not only a global view of human P-body components and their physical interactions but also a wealth of hypotheses to help guide future research on the regulation and function of human P-bodies.     

The Modular Organization of Protein Interactions in Escherichia coli

Escherichia coli serves as an excellent model for the study of fundamental cellular processes such as metabolism, signalling and gene expression. Understanding the function and organization of proteins within these processes is an important step towards a ‘systems’ view of E. coli. Integrating experimental and computational interaction data, we present a reliable network of 3,989 functional interactions between 1,941 E. coli proteins (∼45% of its proteome). These were combined with a recently generated set of 3,888 high-quality physical interactions between 918 proteins and clustered to reveal 316 discrete modules. In addition to known protein complexes (e.g., RNA and DNA polymerases), we identified modules that represent biochemical pathways (e.g., nitrate regulation and cell wall biosynthesis) as well as batteries of functionally and evolutionarily related processes. To aid the interpretation of modular relationships, several case examples are presented, including both well characterized and novel biochemical systems. Together these data provide a global view of the modular organization of the E. coli proteome and yield unique insights into structural and evolutionary relationships in bacterial networks.     

Augmenting Microarray Data with Literature-Based Knowledge to Enhance Gene Regulatory Network Inference

Gene regulatory networks are a crucial aspect of systems biology in describing molecular mechanisms of the cell. Various computational models rely on random gene selection to infer such networks from microarray data. While incorporation of prior knowledge into data analysis has been deemed important, in practice, it has generally been limited to referencing genes in probe sets and using curated knowledge bases. We investigate the impact of augmenting microarray data with semantic relations automatically extracted from the literature, with the view that relations encoding gene/protein interactions eliminate the need for random selection of components in non-exhaustive approaches, producing a more accurate model of cellular behavior. A genetic algorithm is then used to optimize the strength of interactions using microarray data and an artificial neural network fitness function. The result is a directed and weighted network providing the individual contribution of each gene to its target. For testing, we used invasive ductile carcinoma of the breast to query the literature and a microarray set containing gene expression changes in these cells over several time points. Our model demonstrates significantly better fitness than the state-of-the-art model, which relies on an initial random selection of genes. Comparison to the component pathways of the KEGG Pathways in Cancer map reveals that the resulting networks contain both known and novel relationships. The p53 pathway results were manually validated in the literature. 60% of non-KEGG relationships were supported (74% for highly weighted interactions). The method was then applied to yeast data and our model again outperformed the comparison model. Our results demonstrate the advantage of combining gene interactions extracted from the literature in the form of semantic relations with microarray analysis in generating contribution-weighted gene regulatory networks. This methodology can make a significant contribution to understanding the complex interactions involved in cellular behavior and molecular physiology.     

Development of Escherichia coli MG1655 strains to produce long chain fatty acids by engineering fatty acid synthesis (FAS) metabolism

The goal of this research was to develop recombinant Escherichia coli to improve fatty acid synthesis (FAS). Genes encoding acetyl-CoA carboxylase (accA, accB, accC), malonyl-CoA-[acyl-carrier-protein] transacylase (fabD), and acyl-acyl carrier protein thioesterase (EC 3.1.2.14 gene), which are all enzymes that catalyze key steps in the synthesis of fatty acids, were cloned and over-expressed in E. coli MG1655. The acetyl-CoA carboxylase (ACC) enzyme catalyzes the addition of CO₂ to acetyl-CoA to generate malonyl-CoA. The enzyme encoded by the fabD gene converts malonyl-CoA to malonyl-[acp], and the EC 3.1.2.14 gene converts fatty acyl-ACP chains to long chain fatty acids. All the genes except for the EC 3.1.2.14 gene were homologous to E. coli genes and were used to improve the enzymatic activities to over-express components of the FAS pathway through metabolic engineering. All recombinant E. coli MG1655 strains containing various gene combinations were developed using the pTrc99A expression vector. To observe changes in metabolism, the in vitro metabolites and fatty acids produced by the recombinants were analyzed. The fatty acids (C16) from recombinant strains were produced 1.23-2.41 times higher than that from the wild type.     

Evaluating the Effect of 3′-UTR Variants in DICER1 and DROSHA on Their Tissue-Specific Expression by miRNA Target Prediction

Untranslated gene regions (UTRs) play an important role in controlling gene expression. 3′-UTRs are primarily targeted by microRNA (miRNA) molecules that form complex gene regulatory networks. Cancer genomes are replete with non-coding mutations, many of which are connected to changes in tumor gene expression that accompany the development of cancer and are associated with resistance to therapy. Therefore, variants that occurred in 3′-UTR under cancer progression should be analysed to predict their phenotypic effect on gene expression, e.g., by evaluating their impact on miRNA target sites. Here, we analyze 3′-UTR variants in DICER1 and DROSHA genes in the context of myelodysplastic syndrome (MDS) development. The key features of this analysis include an assessment of both “canonical” and “non-canonical” types of mRNA-miRNA binding and tissue-specific profiling of miRNA interactions with wild-type and mutated genes. As a result, we obtained a list of DICER1 and DROSHA variants likely altering the miRNA sites and, therefore, potentially leading to the observed tissue-specific gene downregulation. All identified variants have low population frequency consistent with their potential association with pathology progression.     

Using effective subnetworks to predict selected properties of gene networks

Background

Difficulties associated with implementing gene therapy are caused by the complexity of the underlying regulatory networks. The forms of interactions between the hundreds of genes, proteins, and metabolites in these networks are not known very accurately. An alternative approach is to limit consideration to genes on the network. Steady state measurements of these influence networks can be obtained from DNA microarray experiments. However, since they contain a large number of nodes, the computation of influence networks requires a prohibitively large set of microarray experiments. Furthermore, error estimates of the network make verifiable predictions impossible.

Methodology/Principal Findings

Here, we propose an alternative approach. Rather than attempting to derive an accurate model of the network, we ask what questions can be addressed using lower dimensional, highly simplified models. More importantly, is it possible to use such robust features in applications? We first identify a small group of genes that can be used to affect changes in other nodes of the network. The reduced effective empirical subnetwork (EES) can be computed using steady state measurements on a small number of genetically perturbed systems. We show that the EES can be used to make predictions on expression profiles of other mutants, and to compute how to implement pre-specified changes in the steady state of the underlying biological process. These assertions are verified in a synthetic influence network. We also use previously published experimental data to compute the EES associated with an oxygen deprivation network of E.coli, and use it to predict gene expression levels on a double mutant. The predictions are significantly different from the experimental results for less than of genes.

Conclusions/Significance

The constraints imposed by gene expression levels of mutants can be used to address a selected set of questions about a gene network.