Biological interactions occur on multiple length scales, ranging from molecular to population wide interactions. This work describes the study of two specific areas of biological interactions in microbial systems: intracellular protein-protein interactions and cell-to-cell interactions. The implementation of optical and atomic force microscopy and the methodologies developed during this study proved to be invaluable tools for investigating these systems. Identifying and characterizing protein interactions are fundamental steps toward understanding complex cellular networks. We have developed a unique methodology which combines an imaging-based protein interaction assay with a fluorescence recovery after photobleaching technique (FRAP). Protein interactions are readily detected by co-localization of two proteins of interest fused to green fluorescent protein (GFP) and DivIVA, a cell division protein from Bacillus subtilis. We demonstrate that the modified co-localization assay is sensitive enough to detect protein interactions over four orders of magnitude. FRAP data was analyzed using a combination of various image processing techniques and analytical models. This combined approach made it possible to estimate cell morphology parameters such as length, diameter, the effective laser probe volume, as well as to the mobile protein concentration in vivo, the number of bound molecules at the cellular poles, and the biophysical parameter koff. Cells not only utilize molecular interactions in the intracellular environment, but also express proteins, polysaccharides and other complex molecules to mediate interactions with the surrounding extracellular environment. In Azospirillum brasilense, cell surface properties, including exopolysaccharide production, are thought to play a direct role in promoting cell-to-cell interactions. Recently, the Che1 chemotaxis-like pathway from A. brasilense was shown to modulate flocculation, suggesting an associated modulation of cell surface properties. Using atomic force microscopy, distinct changes in the surface morphology of flocculating A. brasilense Che1 mutant strains were detected. Further analyses suggest that the extracellular matrix differs between the cheA1 and the cheY1 deletion mutants, despite similarity in the macroscopic floc structures. Collectively, these data indicate that disruption of the Che1 pathway is correlated with distinctive changes in the extracellular matrix, which likely result from changes in surface polysaccharides structure and/or composition.

Thesis (Ph. D.)--Ohio State University, 2006. / Title from first page of PDF file. Includes bibliographical references (p. 147-163).

Individual proteins, and small collections of proteins, have been extensively studied for at least two hundred years. Today, more than 350 genomes have been completely sequenced and the proteomes of these genomes have been at least partially mapped. The inventory of protein coding genes is the first step toward understanding the cellular machinery. Recent studies have generated a comprehensive data set for the physical interactions between the proteins of Saccharomyces cerevisiae, in addition to some less extensive proteome interaction maps of higher eukaryotes. Hence, it is now becoming feasible to investigate important questions regarding the evolution of protein-protein networks. For instance, what is the evolutionary relationship between proteins that interact, directly or indirectly? Do interacting proteins co-evolve? Are they often derived from each other? In order to perform such proteome-wide investigations, a top-down view is necessary. This is provided by network (or graph) theory. The proteins of the cell may be viewed as a community of individual molecules which together form a society of proteins (nodes), a network, where the proteins have various kinds of relationships (edges) to each other. There are several different types of protein networks, for instance the two networks studied here, namely metabolic networks and protein-protein interaction networks. The metabolic network is a representation of metabolism, which is defined as the sum of the reactions that take place inside the cell. These reactions often occur through the catalytic activity of enzymes, representing the nodes, connected to each other through substrate/product edges. The indirect interactions of metabolic enzymes are clearly different in nature from the direct physical interactions, which are fundamental to most biological processes, which constitute the edges in protein-protein interaction networks. This thesis describes three investigations into the evolution of metabolic and protein-protein interaction networks. We present a comparative study of the importance of retrograde evolution, the scenario that pathways assemble backward compared to the direction of the pathway, and patchwork evolution, where enzymes evolve from a broad to narrow substrate specificity. Shifting focus toward network topology, a suggested mechanism for the evolution of biological networks, preferential attachment, is investigated in the context of metabolism. Early in the investigation of biological networks it seemed clear that the networks often display a particular, 'scale-free', topology. This topology is characterized by many nodes with few interaction partners and a few nodes (hubs) with a large number of interaction partners. While the second paper describes the evidence for preferential attachment in metabolic networks, the final paper describes the characteristics of the hubs in the physical interaction network of S. cerevisiae.

Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.

Heme is the major iron source for the deadly human pathogen, Group A Streptococcus (GAS). During infection, GAS lyses host cells releasing hemoglobin and other hemoproteins. This dissertation aims to elucidate the general mechanism by which GAS obtains and utilizes heme as an iron source from the host hemoproteins. GAS encodes a heme relay system consisting of Shr, Shp and the SiaABC transporter. We specifically determine the role of Shr in the heme uptake process, by conducting a detailed functional characterization of its constituent domains. We also undertake to solve the long-standing mystery surrounding the catabolism of heme in streptococci. The studies presented herein established Shr as a prototype of a new family of NEAT-containing hemoproteins receptors. They demonstrate its importance in heme acquisition by GAS and provide a molecular model for heme scavenging and transfer by the protein. We show that Shr modulates heme uptake depending on heme availability by a mechanism where NEAT1 facilitates fast heme scavenging and delivery to Shp, whereas NEAT2 serves as a temporary storage for heme on the bacterial surface. Finally, we identified and characterized for the first time, a heme oxygenase (HO) in the Streptococcus genus which was named HupZ. Sequence comparison between HupZ and several HOs from different structural families indicates that this enzyme is unrelated to any of the previously characterized HOs. However, orthologs of the protein are found in other important pathogens. The structure and the catalytic mechanism of HupZ suggest that it is the representative of a new family of flavoenzymes capable of degrading heme using their reduced flavin cofactor as a source of electrons. Overall, this work contributes significant knowledge to the topic of heme utilization by pathogens and importantly, provides new direct evidence that associates flavins with heme metabolism in bacteria. Thus it sets a new direction in the field and lays the ground for future fundamental and applied discoveries.

Cancer is the main cause of death in economically developed countries and the second leading cause of death in developing countries. Along with today’s knowledge that more than two hundred different diseases lie in the category of this prognosis there is an urge for more detailed and case-specific treatments to replace the dramatic actions of available radiation- and chemotherapy, which in many cases do not make a difference between healthy and cancer cells. The transcription factor and onco-protein c-Myc has, after being extensively studied during the past decades, become a prognostic marker for almost all cancer forms known. Still, many questions remain regarding how c-Myc interacts with its many different target proteins involved in cell-cycle regulation, proliferation and apoptosis. Current cell biology states that one of the regulating proteins, hPin1, interacts with c-Myc in a phosphorylation-dependent manner which appears to direct the correct timing of c-Myc activation and degradation through the ubiquitin/proteasome-pathway. The critical phosphorylation sites, T58 and S62, are located in the Myc-Box-I (MBI) region, a highly conserved sequence strongly coupled to aggressive tumourigenesis by hotspot mutations. Interestingly, preliminary results in the Sunnerhagen group suggested that MBI alone did not bind hPin1, suggesting hPin1 targeting a site distal from the residues to be phosphorylated. In this thesis, results from Surface Plasmon Resonance (SPR) and Nuclear Magnetic Resonance (NMR) show that the docking WW-domain of hPin1 binds unphosphorylated c-Myc at a region distal from the phosphorylation site, including residues 13-34. Furthermore, SPR experiments revealed that hPin1 binds unphosphorylated c-Myc with apparently greater affinity and with much slower kinetics than phosphorylated c-Myc. Thus, hPin1 recognition and interaction with c-Myc appears not to be dependent on phosphorylation of c-Myc prior binding. The newly identified binding region of c-Myc, located N-terminal of MBI, may further increase the understanding of protein degradation control and c-Myc function. The studies presented in this thesis provide a brick in the puzzle of c-Myc and hPin1 coupled oncogenesis for further development of new therapeutic strategies.

The type II secretion system is used by many pathogenic and non-pathogenic bacteria for the extracellular secretion of enzymes and toxins. <i>Aeromonas hydrophila</i> is a Gram-negative pathogen that secretes proteins via the type II secretion system.<p> In the studies described here, a series of yeast two-hybrid assays was performed to identify protein-protein interactions in the type II secretion system of <i>A. hydrophila</i>. The periplasmic domains of ExeA and ExeB were assayed for interactions with the periplasmic domains of Exe A, B, C, D, K, L, M, and N. Interactions were observed for both ExeA and ExeB with the secretin ExeD in one orientation. In addition, a previously identified interaction between ExeC and ExeD was observed. In order to further examine and map these interactions, a series of eight two-codon insertion mutations in the amino terminal domain of ExeD was screened against the periplasmic domains of ExeA and ExeB. As a result, the interactions were verified and mapped to subdomains of the ExeD periplasmic domain. To positively identify the region of ExeD involved in the interactions with ExeA, B, C and D, deletion mutants of ExeD were constructed based on the two-codon insertion mutation mapping of subdomains of the ExeD periplasmic domain, and yeast two-hybrid assays were carried out. The results showed that a fragment of the periplasmic domain of ExeD, from amino acid residue 26 to 200 of ExeD, was involved in the interactions with ExeA, B and C. As an independent assay for interactions between ExeAB and the secretin, His-tagged derivatives of the periplasmic domains of ExeA and ExeB were constructed and co-purification on Ni-NTA agarose columns was used to test for interactions with untagged ExeD. These experiments confirmed the interaction between ExeA and ExeD, although there was background in the co-purification test.<p> These results provide support for the hypothesis that the ExeAB complex functions to organize the assembly of the secretin through interactions between both peptidoglycan and the secretin that result in its multimerization into the peptidoglycan and outer membrane layers of the envelope.

Metabolic networks are the collections of all cellular activities taking place in a living cell and all the relationships among biological elements of the cell including genes, proteins, enzymes, metabolites, and reactions. They provide a better understanding of cellular mechanisms and phenotypic characteristics of the studied organism. In order to reconstruct a metabolic network, interactions among genes and their molecular attributes along with their functions must be known. Using this information, proteins are distributed among pathways as sub-networks of a greater metabolic network. Proteins which carry out various steps of a biological process operate in same pathway.<p>The metabolic network of <i>Caenorhabditis elegans</i> was reconstructed based on current genomic information obtained from the KEGG database, and commonly found in SWISS-PROT and WormBase. Assuming proteins operating in a pathway are interacting proteins, currently available protein-protein interaction map of the studied organism was assembled. This map contains all known protein-protein interactions collected from various sources up to the time. Topology of the reconstructed network was briefly studied and the role of key enzymes in the interconnectivity of the network was analysed. The analysis showed that the shortest metabolic paths represent the most probable routes taken by the organism where endogenous sources of nutrient are available to the organism. Nonetheless, there are alternate paths to allow the organism to survive under extraneous variations. <p>Signature content information of proteins was utilized to reveal protein interactions upon a notion that when two proteins share signature(s) in their primary structures, the two proteins are more likely to interact. The signature content of proteins was used to measure the extent of similarity between pairs of proteins based on binary similarity score. Pairs of proteins with a binary similarity score greater than a threshold corresponding to confidence level 95% were predicted as interacting proteins. The reliability of predicted pairs was statistically analyzed. The sensitivity and specificity analysis showed that the proposed approach outperformed maximum likelihood estimation (MLE) approach with a 22% increase in area under curve of receiving operator characteristic (ROC) when they were applied to the same datasets. When proteins containing one and two known signatures were removed from the protein dataset, the area under curve (AUC) increased from 0.549 to 0.584 and 0.655, respectively. Increase in the AUC indicates that proteins with one or two known signatures do not provide sufficient information to predict robust protein-protein interactions. Moreover, it demonstrates that when proteins with more known signatures are used in signature profiling methods the overlap with experimental findings will increase resulting in higher true positive rate and eventually greater AUC. <p>Despite the accuracy of protein-protein interaction methods proposed here and elsewhere, they often predict true positive interactions along with numerous false positive interactions. A global algorithm was also proposed to reduce the number of false positive predicted protein interacting pairs. This algorithm relies on gene ontology (GO) annotations of proteins involved in predicted interactions. A dataset of experimentally confirmed protein pair interactions and their GO annotations was used as a training set to train keywords which were able to recover both their source interactions (training set) and predicted interactions in other datasets (test sets). These keywords along with the cellular component annotation of proteins were employed to set a pair of rules that were to be satisfied by any predicted pair of interacting proteins. When this algorithm was applied to four predicted datasets obtained using phylogenetic profiles, gene expression patterns, chance co-occurrence distribution coefficient, and maximum likelihood estimation for S. cerevisiae and <i>C. elegans</i>, the improvement in true positive fractions of the datasets was observed in a magnitude of 2-fold to 10-fold depending on the computational method used to create the dataset and the available information on the organism of interest. <p>The predicted protein-protein interactions were incorporated into the prior reconstructed metabolic network of <i>C. elegans</i>, resulting in 1024 new interactions among 94 metabolic pathways. In each of 1024 new interactions one unknown protein was interacting with a known partner found in the reconstructed metabolic network. Unknown proteins were characterized based on the involvement of their known partners. Based on the binary similarity scores, the function of an uncharacterized protein in an interacting pair was defined according to its known counterpart whose function was already specified. With the incorporation of new predicted interactions to the metabolic network, an expanded version of that network was resulted with 27% increase in the number of known proteins involved in metabolism. Connectivity of proteins in protein-protein interaction map changed from 42 to 34 due to the increase in the number of characterized proteins in the network.

Ryanodine receptor 1 (RyR1) is a homotetrameric calcium channel located in the sarcoplasmic reticulum (SR) of skeletal muscle. We employed metal affinity chromatography followed by liquid chromatography mass spectrometry from HEK-293 cells to purify affinity tagged cytosolic RyR1, with interacting proteins. In total, we identified 703 proteins with high confidence (>99%). Of the putative RyR1 interacting proteins, five candidates [calcium homeostasis endoplasmic reticulum protein (CHERP), ER-golgi intermediate compartment 53kDa protein (LMAN1), T-complex protein (TCP), phosphorylase b kinase (PHBK) and four and half LIM domains protein 1 (FHL1)], were selected for interaction studies. Immunofluorescence analysis showed that CHERP co-localizes with RyR1 in the SR of rat soleus muscle. Calcium transient assays in HEK293 cells over-expressing RyR1 with siRNA suppressed CHERP or FHL1, showed reduced calcium release via RyR1. In conclusion, we have identified RyR1 interacting proteins in CHERP and FHL1 which may represent novel regulatory mechanisms involved in excitation-contraction coupling.

Ryanodine receptor 1 (RyR1) is a homotetrameric calcium channel located in the sarcoplasmic reticulum (SR) of skeletal muscle. We employed metal affinity chromatography followed by liquid chromatography mass spectrometry from HEK-293 cells to purify affinity tagged cytosolic RyR1, with interacting proteins. In total, we identified 703 proteins with high confidence (>99%). Of the putative RyR1 interacting proteins, five candidates [calcium homeostasis endoplasmic reticulum protein (CHERP), ER-golgi intermediate compartment 53kDa protein (LMAN1), T-complex protein (TCP), phosphorylase b kinase (PHBK) and four and half LIM domains protein 1 (FHL1)], were selected for interaction studies. Immunofluorescence analysis showed that CHERP co-localizes with RyR1 in the SR of rat soleus muscle. Calcium transient assays in HEK293 cells over-expressing RyR1 with siRNA suppressed CHERP or FHL1, showed reduced calcium release via RyR1. In conclusion, we have identified RyR1 interacting proteins in CHERP and FHL1 which may represent novel regulatory mechanisms involved in excitation-contraction coupling.