J. Harry Caufield

The Yeast Two-Hybrid System: A Tool for Mapping Protein–Protein Interactions

Virtually all processes in living cells are dependent on protein-protein interactions (PPIs). Understanding PPI networks is thus essential for molecular biology and disease research. One powerful genetic system for mapping PPIs both at a small scale and in a high-throughput manner is the yeast two-hybrid (Y2H) screen. In Y2H screening, PPIs are detected through the activation of reporter genes responding to a reconstituted transcription factor. In this introduction, we describe library- and array-based Y2H methods and explain their basic theory. We also include the rationale behind different Y2H approaches and strategies for optimizing results.

Specific N‐terminal cleavage of ribosomal protein L27 in Staphylococcus aureus and related bacteria

Ribosomal protein L27 is a component of the eubacterial large ribosomal subunit that has been shown to play a critical role in substrate stabilization during protein synthesis. This function is mediated by the L27 N‐terminus, which protrudes into the peptidyl transferase center. In this report, we demonstrate that L27 in Staphylococcus aureus and other Firmicutes is encoded with an N‐terminal extension that is not present in most Gram‐negative organisms and is absent from mature ribosomes. We have identified a cysteine protease, conserved among bacteria containing the L27 N‐terminal extension, which performs post‐translational cleavage of L27. Ribosomal biology in eubacteria has largely been studied in the Gram‐negative bacterium Escherichia coli; our findings indicate that there are aspects of the basic biology of the ribosome in S. aureus and other related bacteria that differ substantially from that of the E. coli ribosome. This research lays the foundation for the development of new therapeutic approaches that target this novel pathway.

The Protein Interactome of Mycobacteriophage Giles Predicts Functions for Unknown Proteins

UNLABELLED Mycobacteriophages are viruses that infect mycobacterial hosts and are prevalent in the environment. Nearly 700 mycobacteriophage genomes have been completely sequenced, revealing considerable diversity and genetic novelty. Here, we have determined the protein complement of mycobacteriophage Giles by mass spectrometry and mapped its genome-wide protein interactome to help elucidate the roles of its 77 predicted proteins, 50% of which have no known function. About 22,000 individual yeast two-hybrid (Y2H) tests with four different Y2H vectors, followed by filtering and retest screens, resulted in 324 reproducible protein-protein interactions, including 171 (136 nonredundant) high-confidence interactions. The complete set of high-confidence interactions among Giles proteins reveals new mechanistic details and predicts functions for unknown proteins. The Giles interactome is the first for any mycobacteriophage and one of just five known phage interactomes so far. Our results will help in understanding mycobacteriophage biology and aid in development of new genetic and therapeutic tools to understand Mycobacterium tuberculosis. IMPORTANCE Mycobacterium tuberculosis causes over 9 million new cases of tuberculosis each year. Mycobacteriophages, viruses of mycobacterial hosts, hold considerable potential to understand phage diversity, evolution, and mycobacterial biology, aiding in the development of therapeutic tools to control mycobacterial infections. The mycobacteriophage Giles protein-protein interaction network allows us to predict functions for unknown proteins and shed light on major biological processes in phage biology. For example, Giles gp76, a protein of unknown function, is found to associate with phage packaging and maturation. The functions of mycobacteriophage-derived proteins may suggest novel therapeutic approaches for tuberculosis. Our ORFeome clone set of Giles proteins and the interactome data will be useful resources for phage interactomics.

Protein Complexes in Bacteria

Large-scale analyses of protein complexes have recently become available for Escherichia coli and Mycoplasma pneumoniae, yielding 443 and 116 heteromultimeric soluble protein complexes, respectively. We have coupled the results of these mass spectrometry-characterized protein complexes with the 285 “gold standard” protein complexes identified by EcoCyc. A comparison with databases of gene orthology, conservation, and essentiality identified proteins conserved or lost in complexes of other species. For instance, of 285 “gold standard” protein complexes in E. coli, less than 10% are fully conserved among a set of 7 distantly-related bacterial “model” species. Complex conservation follows one of three models: well-conserved complexes, complexes with a conserved core, and complexes with partial conservation but no conserved core. Expanding the comparison to 894 distinct bacterial genomes illustrates fractional conservation and the limits of co-conservation among components of protein complexes: just 14 out of 285 model protein complexes are perfectly conserved across 95% of the genomes used, yet we predict more than 180 may be partially conserved across at least half of the genomes. No clear relationship between gene essentiality and protein complex conservation is observed, as even poorly conserved complexes contain a significant number of essential proteins. Finally, we identify 183 complexes containing well-conserved components and uncharacterized proteins which will be interesting targets for future experimental studies.

Bacterial protein meta-interactomes predict cross-species interactions and protein function

BackgroundProtein-protein interactions (PPIs) can offer compelling evidence for protein function, especially when viewed in the context of proteome-wide interactomes. Bacteria have been popular subjects of interactome studies: more than six different bacterial species have been the subjects of comprehensive interactome studies while several more have had substantial segments of their proteomes screened for interactions. The protein interactomes of several bacterial species have been completed, including several from prominent human pathogens. The availability of interactome data has brought challenges, as these large data sets are difficult to compare across species, limiting their usefulness for broad studies of microbial genetics and evolution.ResultsIn this study, we use more than 52,000 unique protein-protein interactions (PPIs) across 349 different bacterial species and strains to determine their conservation across data sets and taxonomic groups. When proteins are collapsed into orthologous groups (OGs) the resulting meta-interactome still includes more than 43,000 interactions, about 14,000 of which involve proteins of unknown function. While conserved interactions provide support for protein function in their respective species data, we found only 429 PPIs (~1% of the available data) conserved in two or more species, rendering any cross-species interactome comparison immediately useful. The meta-interactome serves as a model for predicting interactions, protein functions, and even full interactome sizes for species with limited to no experimentally observed PPI, including Bacillus subtilis and Salmonella enterica which are predicted to have up to 18,000 and 31,000 PPIs, respectively.ConclusionsIn the course of this work, we have assembled cross-species interactome comparisons that will allow interactomics researchers to anticipate the structures of yet-unexplored microbial interactomes and to focus on well-conserved yet uncharacterized interactors for further study. Such conserved interactions should provide evidence for important but yet-uncharacterized aspects of bacterial physiology and may provide targets for anti-microbial therapies.

Phrase Mining of Textual Data to Analyze Extracellular Matrix Protein Patterns Across Cardiovascular Disease

Extracellular matrix (ECM) proteins have been shown to play important roles regulating multiple biological processes in an array of organ systems, including the cardiovascular system. Using a novel bioinformatics text-mining tool, we studied six categories of cardiovascular disease (CVD), namely, ischemic heart disease, cardiomyopathies, cerebrovascular accident, congenital heart disease, arrhythmias, and valve disease, anticipating novel ECM protein-disease and protein-protein relationships hidden within vast quantities of textual data. We conducted a phrase-mining analysis, delineating the relationships of 709 ECM proteins with the 6 groups of CVDs reported in 1,099,254 abstracts. The technology pipeline known as Context-Aware Semantic Online Analytical Processing was applied to semantically rank the association of proteins to each CVD and all six CVDs, performing analyses to quantify each protein-disease relationship. We performed principal component analysis and hierarchical clustering of the data, where each protein was visualized as a six-dimensional vector. We found that ECM proteins display variable degrees of association with the six CVDs; certain CVDs share groups of associated proteins, whereas others have divergent protein associations. We identified 82 ECM proteins sharing associations with all 6 CVDs. Our bioinformatics analysis ascribed distinct ECM pathways (via Reactome) from this subset of proteins, namely, insulin-like growth factor regulation and interleukin-4 and interleukin-13 signaling, suggesting their contribution to the pathogenesis of all six CVDs. Finally, we performed hierarchical clustering analysis and identified protein clusters predominantly associated with a targeted CVD; analyses of these proteins revealed unexpected insights underlying the key ECM-related molecular pathogenesis of each CVD, including virus assembly and release in arrhythmias. NEW & NOTEWORTHY The present study is the first application of a text-mining algorithm to characterize the relationships of 709 extracellular matrix-related proteins with 6 categories of cardiovascular disease described in 1,099,254 abstracts. Our analysis informed unexpected extracellular matrix functions, pathways, and molecular relationships implicated in the six cardiovascular diseases.

Uncovering Medical Insights from Vast Amounts of Biomedical Data in Clinical Case Reports

Clinical case reports (CCRs) have a time-honored tradition in serving as an important means of sharing clinical experiences on patients presenting with atypical disease phenotypes or receiving new therapies. However, the huge amount of accumulated case reports are isolated, unstructured, and heterogeneous clinical data, posing a great challenge to clinicians and researchers in mining relevant information through existing indexing tools. In this investigation, in order to render CCRs more findable, accessible, interoperable, and reusable (FAIR) by the biomedical community, we created a resource platform, including the construction of a test dataset consisting of 1000 CCRs spanning 14 disease phenotypes, a standardized metadata template and metrics, and a set of computational tools to automatically retrieve relevant medical information and to analyze all published PubMed clinical case reports with respect to trends in publication journals, citations impact, MeSH Terms, drug use, distributions of patient demographics, and relationships with other case reports and databases. Our standardized metadata template and CCR test dataset may be valuable resources to advance medical science and improve patient care for researchers who are using machine learning approaches with a high-quality dataset to train and validate their algorithms. In the future, our analytical tools may be applied towards other large clinical data sources as well.

Virus-host protein-protein interactions of mycobacteriophage Giles

Mycobacteriophage are viruses that infect mycobacteria. More than 1,400 mycobacteriophage genomes have been sequenced, coding for over one hundred thousand proteins of unknown functions. Here we investigate mycobacteriophage Giles-host protein-protein interactions (PPIs) using yeast two-hybrid screening (Y2H). A total of 25 reproducible PPIs were found for a selected set of 10 Giles proteins, including a putative virion assembly protein (gp17), the phage integrase (gp29), the endolysin (gp31), the phage repressor (gp47), and six proteins of unknown function (gp34, gp35, gp54, gp56, gp64, and gp65). We note that overexpression of the proteins is toxic to M. smegmatis, although whether this toxicity and the associated changes in cellular morphology are related to the putative interactions revealed in the Y2H screen is unclear.

Making the Right Choice: Critical Parameters of the Y2H Systems

Two-hybrid methods remain among the most preferred choices for detecting protein-protein interactions (PPIs) and much of the PPI data in databases have been produced using yeast two-hybrid (Y2H) screens. The Y2H methods are extensively used to detect PPIs because of their scalability and accessibility. Several variants of Y2H methods have been developed and used by different research groups, increasing the accessibility of these methods and their applications in detecting different types of PPIs. However, the availability of variations on the same core methodology emphasizes the need to have a systematic comparison of available Y2H methods in the context of their applicability, coverage and efficiency. In this chapter, we discuss the key parameters of Y2H methods, namely proteins of interest, vectors, libraries, screening strategies, data analysis, and provide a flowchart that should help to decide which Y2H strategy is most appropriate for a protein interaction screen.