A proteome-wide screen of Campylobacter jejuni using protein microarrays identifies novel and conformational antigens

Campylobacter jejuni (C. jejuni) is a foodborne intestinal pathogen and major cause of gastroenteritis worldwide. C. jejuni proteins that are immunogenic have been sought for their potential use in the development of biomarkers, diagnostic assays, or subunit vaccines for humans or livestock. To identify new immunogenic C. jejuni proteins, we used a native protein microarray approach. A protein chip, with over 1400 individually purified GST-tagged C. jejuni proteins, representing over 86% of the proteome, was constructed to screen for antibody titers present in test sera raised against whole C. jejuni cells. Dual detection of GST signals was incorporated as a way of normalizing the variation of protein concentrations contributing to the antibody staining intensities. We detected strong signals to 102 C. jejuni antigens. In addition to antigens recognized by antiserum raised against C. jejuni, parallel experiments were conducted to identify antigens cross-reactive to antiserum raised against various serotypes of E. coli or Salmonella or to healthy human sera. This led to the identification of 34 antigens specifically recognized by the C. jejuni antiserum, only four of which were previously known. The chip approach also allowed identification of conformational antigens. We demonstrate in the case of Cj1621 that antigen signals are lost to denaturing conditions commonly used in other approaches to identify immunogens. Antigens identified in this study include those possessing sequence features indicative of cell surface localization, as well as those that do not. Together, our results indicate that the unbiased chip-based screen can help reveal the full repertoire of host antibodies against microbial proteomes.

High-throughput yeast two-hybrid screening

Charting the interactions among proteins is essential for understanding biological processes. While a number of complementary technologies for detecting protein interactions are available, the yeast two-hybrid system is one of the few that have been successfully scaled up. Two-hybrid screens have been used to construct extensive protein interaction maps for humans and several model organisms, and these maps have proven invaluable for studies on a variety of biological systems. These maps, however, have not come close to covering all proteins or interactions detectable by yeast two-hybrid. This is due in part to the difficulty of using library screening methods to sample all possible binary combinations of proteins. Ideally, every binary pair of proteins would be tested individually to ensure that every detectable interaction is identified. For organisms with large proteomes, however, this is not economically feasible and instead efficient pooling schemes must be implemented. The high-throughput two-hybrid screening methods presented here are designed to efficiently maximize coverage for selected sets of proteins or entire proteomes. We present two high-throughput screening protocols. Both methods are designed to identify interactors for any number of bait proteins expressed as DNA-binding domain (BD) fusions. The choice of which protocol to use depends largely on the nature of the available library of proteins fused to an activation domain (AD). The first protocol is appropriate for screening a library of AD clones, such as a cDNA library, a domain library, or a large pool of AD clones. By contrast, the second protocol is appropriate for screening a large array of individual sequence-verified AD clones. This protocol screens small pools of AD clones from the array in a two-phase scheme. Although the methods presented were developed using the LexA version of the yeast two-hybrid system, we include notes as appropriate to accommodate users of other versions.

A proteome-wide protein interaction map for Campylobacter jejuni

'Systematic identification of protein interactions for the bacterium Campylobacter jejuni using high-throughput yeast two-hybrid screens detected interactions for 80% of the organism's proteins.

Background

Data from large-scale protein interaction screens for humans and model eukaryotes have been invaluable for developing systems-level models of biological processes. Despite this value, only a limited amount of interaction data is available for prokaryotes. Here we report the systematic identification of protein interactions for the bacterium Campylobacter jejuni, a food-borne pathogen and a major cause of gastroenteritis worldwide.

Results

Using high-throughput yeast two-hybrid screens we detected and reproduced 11,687 interactions. The resulting interaction map includes 80% of the predicted C. jejuni NCTC11168 proteins and places a large number of poorly characterized proteins into networks that provide initial clues about their functions. We used the map to identify a number of conserved subnetworks by comparison to protein networks from Escherichia coli and Saccharomyces cerevisiae. We also demonstrate the value of the interactome data for mapping biological pathways by identifying the C. jejuni chemotaxis pathway. Finally, the interaction map also includes a large subnetwork of putative essential genes that may be used to identify potential new antimicrobial drug targets for C. jejuni and related organisms.

Conclusion

The C. jejuni protein interaction map is one of the most comprehensive yet determined for a free-living organism and nearly doubles the binary interactions available for the prokaryotic kingdom. This high level of coverage facilitates pathway mapping and function prediction for a large number of C. jejuni proteins as well as orthologous proteins from other organisms. The broad coverage also facilitates cross-species comparisons for the identification of evolutionarily conserved subnetworks of protein interactions.

Automated segmentation and classification of high throughput yeast assay spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.

Automated segmentation and classification of high throughput yeast assay spots

Several technologies for characterizing genes and proteins from humans and other organisms use yeast growth or color development as read outs. The yeast two-hybrid assay, for example, detects protein-protein interactions by measuring the growth of yeast on a specific solid medium, or the ability of the yeast to change color when grown on a medium containing a chromogenic substrate. Current systems for analyzing the results of these types of assays rely on subjective and inefficient scoring of growth or color by human experts. Here, an image analysis system is described for scoring yeast growth and color development in high throughput biological assays. The goal is to locate the spots and score them in color images of two types of plates named "X-Gal" and "growth assay" plates, with uniformly placed spots (cell areas) on each plate (both plates in one image). The scoring system relies on color for the X-Gal spots, and texture properties for the growth assay spots. A maximum likelihood projection-based segmentation is developed to automatically locate spots of yeast on each plate. Then color histogram and wavelet texture features are extracted for scoring using an optimal linear transformation. Finally, an artificial neural network is used to score the X-Gal and growth assay spots using the extracted features. The performance of the system is evaluated using spots of 60 images. After training the networks using training and validation sets, the system was assessed on the test set. The overall accuracies of 95.4% and 88.2% are achieved, respectively, for scoring the X-Gal and growth assay spots.

The protein network of bacterial motility

Motility is achieved in most bacterial species by the flagellar apparatus. It consists of dozens of different proteins with thousands of individual subunits. The published literature about bacterial chemotaxis and flagella documented 51 protein–protein interactions (PPIs) so far. We have screened whole genome two-hybrid arrays of Treponema pallidum and Campylobacter jejuni for PPIs involving known flagellar proteins and recovered 176 and 140 high-confidence interactions involving 110 and 133 proteins, respectively. To explore the biological relevance of these interactions, we tested an Escherichia coli gene deletion array for motility defects (using swarming assays) and found 159 gene deletion strains to have reduced or no motility. Comparing our interaction data with motility phenotypes from E. coli, Bacillus subtilis, and Helicobacter pylori, we found 23 hitherto uncharacterized proteins involved in motility. Integration of phylogenetic information with our interaction and phenotyping data reveals a conserved core of motility proteins, which appear to have recruited many additional species-specific components over time. Our interaction data also predict 18 110 interactions for 64 flagellated bacteria.

Yeast two-hybrid contributions to interactome mapping

Interactome mapping, the systematic identification of protein interactions within an organism, promises to facilitate systems-level studies of biological processes. Using in vitro technologies that measure specific protein interactions, static maps are being generated that include many of the protein networks that occur in vivo. Most of the binary protein interaction data currently available was generated by large-scale yeast two-hybrid screens. Recent efforts to map interactions in model organisms and in humans illustrate the promise and some of the limitations of the two-hybrid approach. Although these maps are incomplete and include false positives, they are proving useful as a framework around which to elaborate and model the in vivo interactome.

A highly conserved Sinorhizobium meliloti operon is induced microaerobically via the FixLJ system and by nitric oxide (NO) via NnrR

A previously generated collection of 11 Tn5-luxAB insertion mutants of Sinorhizobium meliloti harbouring lux reporter gene fusions induced under microaerobic (1% O2) conditions was further characterized and mapped on the sequenced S. meliloti genome. One highly induced gene fusion from this collection (loe-7) was found to be located in the intergenic region between sma1292, encoding a putative protease/collagenase, and a gene of unknown function (sma1294). The loe-7 fusion had been shown previously to be partially controlled by the oxygen sensor/regulator FixLJ system, but significant ( approximately 40%) Lux activity remained in a fixLJ mutant background. Therefore, a secondary Tn1721 mutagenesis of the loe-7 strain was carried out. Nine Tn1721 ('dark') insertions completely abolishing the Lux activity of the loe-7 fusion under microaerobic conditions were isolated. Surprisingly, five dark insertions mapped in denitrification genes [napA, napC, nirK--two insertions--and sma1245 encoding a NnrR-like transcriptional regulator controlling denitrification in response to nitric oxide (NO)]; Tn1721 insertions in the respiration genes fixG and fixP resulted in a reduced expression of the loe-7-lux fusion, and insertions in the regulatory genes fixJ and fixK1 resulted in low, but still detectable Lux activity. On the contrary, insertions in the norD or norQ genes resulted in constitutive Lux activity. In these mutant strains, NO would be expected to accumulate under microaerobic conditions. NO was found to be able to strongly induce the loe-7-luxAB fusion under microaerobic and aerobic conditions, but only in the presence of the functional nnrR-like gene (sma1245). These results suggest that NO, via the NnrR regulator, can serve as a signal molecule to induce the loe-7-luxAB fusion in concert with the FixLJ system.

A database and tool, IM Browser, for exploring and integrating emerging gene and protein interaction data for Drosophila

Background

Biological processes are mediated by networks of interacting genes and proteins. Efforts to map and understand these networks are resulting in the proliferation of interaction data derived from both experimental and computational techniques for a number of organisms. The volume of this data combined with the variety of specific forms it can take has created a need for comprehensive databases that include all of the available data sets, and for exploration tools to facilitate data integration and analysis. One powerful paradigm for the navigation and analysis of interaction data is an interaction graph or map that represents proteins or genes as nodes linked by interactions. Several programs have been developed for graphical representation and analysis of interaction data, yet there remains a need for alternative programs that can provide casual users with rapid easy access to many existing and emerging data sets.

Description

Here we describe a comprehensive database of Drosophila gene and protein interactions collected from a variety of sources, including low and high throughput screens, genetic interactions, and computational predictions. We also present a program for exploring multiple interaction data sets and for combining data from different sources. The program, referred to as the Interaction Map (IM) Browser, is a web-based application for searching and visualizing interaction data stored in a relational database system. Use of the application requires no downloads and minimal user configuration or training, thereby enabling rapid initial access to interaction data. IM Browser was designed to readily accommodate and integrate new types of interaction data as it becomes available. Moreover, all information associated with interaction measurements or predictions and the genes or proteins involved are accessible to the user. This allows combined searches and analyses based on either common or technique-specific attributes. The data can be visualized as an editable graph and all or part of the data can be downloaded for further analysis with other tools for specific applications. The database is available at

Conclusion

The Drosophila Interactions Database described here places a variety of disparate data into one easily accessible location. The database has a simple structure that maintains all relevant information about how each interaction was determined. The IM Browser provides easy, complete access to this database and could readily be used to publish other sets of interaction data. By providing access to all of the available information from a variety of data types, the program will also facilitate advanced computational analyses.

High-throughput cloning of Campylobacter jejuni ORfs by in vivo recombination in Escherichia coli

A rate-limiting and costly step in many proteomics analyses is the cloning of all of the ORFs for an organism into technique-specific vectors. Here, we describe the generation of a Campylobacter jejuni expression clone set using a high-throughput cloning approach based on recombination in E. coli. The approach uses native E. coli recombination functions and requires no in vitro enzymatic steps or special strains. Our results indicate that this approach is an efficient and economical alternative for high-throughput cloning.