In this study, we focused on interactions of structural proteins of mouse polyomavirus (MPyV) and BK virus (BKV) with the nuclear lamina. Our goal was to examine whether and how can the virus, hence viral structural proteins, interact with the nuclear lamina and how would these interactions affect its properties. We supposed, that the expression of viral proteins would induce disintegration of the structure of nuclear lamina, thus enabling nuclear egress of virions in the late phase of infection. Viral structural proteins were expressed transiently in cells transfected with an expression vector pMPyV LATE. In these cells, VP1 was localized in a likewise manner as it shows in infected cells - mostly in a perinuclear area. Concurrently, defects in staining of nuclear lamina were observed in these cells, similarly to infected cells. Also, another expression vector was used in our experiments, the pMPyV mut3 VP1 encoding for a mutated protein VP1. When transiently expressed in cells, the mutated VP1 protein showed mostly diffuse nuclear localization. However, we observed significant morphological deformations and defective staining of the nuclear lamina. These observations imply an important role of VP1 in mechanical and biochemical properties alterations of the nuclear lamina in transfected and...

Problem: Shigella is a gram-negative enteropathogen that, when passed through fecal particles from one host to the oral cavity of another host, causes an infectious disease known as shigellosis. One of the distinctive features of the infection by Shigella is its ability to bypass its host’s autophagic defenses. It does this through the use of a Type III secretion system, found in gram-negative pathogens like Shigella, which injects virulent proteins into the host cell. One of these proteins is IcsB; however, its exact function is not well understood. This study aims to better understand the role of this protein in the infection. Methods: A yeast two-hybrid screening test is used in this case to examine the interactions between variations of the protein IcsB, and a library of host proteins. Given IcsB’s high yeast toxicity and that resulted in the total absence of yeast colony formation, the first aim was to identify IcsB variants which expression would not prevent yeast growth. The second aim was to use the mutant with reduced cytotoxicity to perform a Y2H screen that will allow for the identification of candidate host proteins interacting with IcsB. Results: Two mutations of the IcsB protein grew in the Y2HG yeast strain, indicating a significant reduction in the protein’s toxicity. Of the cultures that reacted, high stringency and strong interaction was observed between four genes and IcsB proteins. Among the four identified clones that grew, three corresponded to the gene RNF2, while the last one corresponds to a non-coding sequence. Key control experiments revealed that the interaction of IcsB with RNF2 is likely false-positive. Thus, when screened full-length IcsB using new epithelial cells cDNAVI libraries, strong interaction was observed between three genes and our IcsB proteins. All the three genes DDX3X, FANCL, and SGT1 passed the false-positive interaction tests. It is interesting to notice that DDX3X and SGT1 interacted with catalytically active and inactive IcsB, suggesting that the interactions established between IcsB and prey proteins does not require the catalytic - C306A mutation and that IcsB most likely does not function as a protease against these two proteins. By contrast, FANCL bound catalytically inactive, but not catalytically active IcsB, suggesting it could be a substrate of IcsB. The literature provides some support for the putative role of DDX3X, FANCL, and SGT1 in regulating the vacuole escape of Shigella through IcsB action. Conclusion: The aim of this study was to determine the functional of IcsB in the vacuole escape of Shigella. This study successfully identified three candidates interacting partner proteins for IcsB. Key control experiments confirmed the interaction of IcsB with DDX3X, FANCL and SGT1. This study provides a basis for further research, with further study aimed at confirming these results during Shigella infection

A limited number of drug targets can be exploited by conventional drug-like compounds as the vast majority of disease-associated targets are involved in protein-protein interactions (PPI). PPI targets possess binding surfaces that lack a well-defined hydrophobic pocket amenable for binding to small drug-like compounds. A new class of therapeutics that has shown great potential at modulating PPI are macrocyclic peptides, particularly for their ability to bind to large and topologically complex protein surfaces as well as their potential to access intracellular targets. However, the efficiency of macrocyclic peptides at mediating PPIs and permeating cell membranes is conformation dependent. Here, I describe the role of peptide conformation on target recognition using three clinically relevant PPI targets: the Kelch like ECH Associated Protein-1 (KEAP1), (Chapter Two and Chapter Three); the RET receptor tyrosine kinase (Chapter Four); and β-catenin (Chapter Five). Guided by published X-ray crystal structures, peptides derived from PPI epitopes were designed and structurally constrained to mimic the conformation of the natural PPI recognition motif. In Chapter Two, I report the development of a cyclic heptapeptide derived from the transcription factor Nuclear Factor (Erythroid-derived 2)-Like 2 (Nrf2) with similar affinity for KEAP1 as native Nrf2 through conformational optimization of a linear Nrf2-derived heptapeptide. Efforts to improve the potency and physicochemical properties of the cyclic heptapeptide are discussed in Chapter Three. In Chapter Four, I describe the design of dimeric peptides as tool compounds to investigate the mechanism by which the interaction between glial cell-line derived neurotrophic factor family ligands (GFLs) and GPI-linked co-receptors, GFRα, induce RET signaling. These peptides were derived from the β-sheet regions of GFLs, GDNF and ART, that interact with GFRα1 and GFRα3, respectively. Peptide cyclization and the introduction of a β-turn promoting motif yielded GFL mimetic peptides with stronger affinity for GFRα. Lastly, Chapter Five focuses on exploring the scope of i, i+4 carbamate and amino-staples as a novel peptide stapling system to stabilize α-helical peptides. An axin-derived α-helical peptide that disrupts the β-catenin/TCF4 interaction was used as a model to determine the effect of peptide α-helical stabilization on binding affinity for β-catenin. / 2023-07-07T00:00:00Z

Signal transduction networks enable organisms to respond to environmental stimuli. Bacteria utilize two-component systems (TCSs) and phosphorelays as their primary means of signal transduction. Histidine kinase (HK) and response regulator (RR) proteins comprise these TCSs and phosphorelays. Previously, signal transduction within TCSs and phosphorelays was thought to only occur through a linear series of phosphotransfers between HKs and RRs. Recently multikinase networks have been shown to be involved in TCS and phosphorelay signal transmission. A multikinase network that includes the HKs RetS and GacS controls the switch between the motile invasive lifestyle and the sessile biofilm lifestyle of the opportunistic human pathogen Pseudomonas aeruginosa. GacS promotes the sessile biofilm lifestyle, while RetS promotes the motile invasive lifestyle via the inhibition of GacS. This inhibition occurs through three distinct mechanisms. Two of the mechanisms are dephosphorylating mechanisms and the third mechanism is a direct interaction between RetS and GacS which results in the inhibition of GacS autophosphorylation. This study examines the direct binding interaction between RetS and GacS using structural biology. We observed a heterodimeric RetS-GacS complex in which the canonical homodimerization interface was replaced with a heterodimeric interface. Heterodimerization between bacterial HKs is currently a novel observation, but it is likely that other HKs heterodimerize. The RetS-GacS direct interaction can serve as a model for HK-HK binding in multikinase networks. / Doctor of Philosophy / The way in which bacteria assess and respond to their environment is of great interest to microbiologists. Bacteria transmit environmental signals via protein interactions. Some of these interactions involve the transfer of phosphate groups, and some involve a direct binding interaction between proteins. We are investigating a direct binding interaction between two proteins, RetS and GacS. These proteins control whether Pseudomonas aeruginosa, an opportunistic pathogen of humans, causes an acute infection, which is characterized by motility and invasiveness, or a chronic infection, which is characterized by a sessile biofilm lifestyle, in a human host. Through the use of structural biology techniques we have visualized the three-dimensional structure of the complex between RetS and GacS. This complex has provided insight into the role of the RetS-GacS interaction in controlling the infection state of P. aeruginosa.

Proper control of gene expression is important for coordinating biological processes. Transcription factors (TFs) play a critical role in maintaining proper gene expression by binding their specific binding sites at cis-regulatory elements (CREs) and recruiting cofactors (COFs). Given their important roles in mediating gene expression, there is a need for methods that can profile TF-COF binding to DNA in a high throughput (HT) manner. In this thesis, I present two novel, array-based, HT methods – CASCADE (Customizable Approach to Survey Complex Assembly at DNA Elements) and CoRec (Cofactor Recruitment) – to profile the indirect recruitment of any COF in any cell type or cell state in a HT manner. CASCADE is a technique to profile the indirect recruitment of COFs to DNA via TFs in a stimulus-dependent manner. We demonstrate how CASCADE can be used to characterize COF recruitment at single nucleotide resolution to CREs in LPS-stimulated macrophages and demonstrate how it can be applied to characterize COF recruitment to ~1700 non-coding single nucleotide polymorphisms (ncSNPs). To understand genetic alterations that drive cancer, we apply CASCADE to cancer-associated non-coding variants (NCVs). Using our newly developed genetic burden test, we predict 2,555 driver NCVs and find that 765 candidate drivers altered transcriptional activity, 510 led to differential binding of TF-COF complexes, and that they primarily impact the binding to ETS factors. Lastly, I introduce the CoRec technique for profiling COF recruitment to a broad set of TF families. I show how CoRec can be used to develop cell type-specific TF-COF interaction “fingerprints” and reveal distinct COF recruitment preferences at single-nucleotide resolution. I integrate the findings from CoRec with genomic accessibility and gene expression data and demonstrate that COF-recruitment motifs captured with CoRec are enriched at promoters and significantly related to highly expressed genes. Taken together, the CASCADE and CoRec approaches are powerful methods to investigate TF-COF complex binding and their use will add to our understanding of CRE regulation, uncover biophysical mechanisms of NCVs, and characterizations of cell state-specific TF-COF complexes.

Immunoprecipitation coupled to mass spectrometry (IP-MS) methods are often used to identify protein-protein interactions (PPIs) in biological samples. While these approaches are prone to false-positive identifications through contamination and antibody non-specific binding, their results can be filtered by combining the use of negative controls and computational modelling. However, such filtering does not effectively detect false-positive interactions when IP-MS is performed on human plasma samples, given a higher propensity for non-specific interactions. Therein, proteins cannot be overexpressed or inhibited, and existing modelling algorithms are not adapted for execution without such controls. Hence, we introduce MAGPIE, a novel machine learning-based approach for identifying PPIs in human plasma using IP-MS, which leverages negative controls that include antibodies targeting proteins not known to be present in human plasma. Unsupervised learning algorithms are first applied to label-free MS quantification data to identify a set of high-quality negative controls that can be used for false- positive interaction modelling. MAGPIE then uses a logistic regression classifier to assess the reliability of PPIs detected in IP-MS experiments using antibodies targeting known plasma proteins. When applied to five IP-MS experiments, our algorithm identified 68 PPIs with an FDR of 20%. MAGPIE significantly outperformed a state-of-the-art PPI discovery tool, detecting three times more interactions at half the FDR. PPIs identified by MAGPIE are further supported by known or predicted interactions in the STRING PPI repository. Finally, our approach provides an unprecedented ability to detect human plasma PPIs, enabling a better understanding of biological processes in plasma.

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