O6-alkylguanine-DNA alkyltransferase as a new tool for functional proteomics

Recently the sequencing project of the human genome has been completed. The interest of the post-sequencing-era is shifting nowadays towards the investigation of the biological function and localization of the encoded proteins. Proteins are essential parts of all living organisms and participate in every process in the living cell. Despite their importance, the detailed function of the majority of all proteins remains still unknown. Therefore high-throughput methods are needed for the systematic examination of proteins. The first part of this thesis describes the development of a novel functional protein microarray, taking advantage of the highly selective reaction of the human O6-alkylguanine-DNA alkyltransferase (AGT) with O6-benzylguanine derivatives. By functionalization of otherwise bioinert glass surfaces with the substrate O6-benzylguanine, AGT fusion proteins can be selectively and covalently immobilized on a solid surface (Figure S1). Due to the high selectivity of the covalent immobilization the main advantage of this AGT based approach is that fusion proteins can be immobilized directly out of cell extracts without prior purification. By analyzing a set of literature known protein-protein interactions, protein-small molecule interactions and posttranslational modifications we demonstrate the feasibility of this approach (Chapter 3). Figure S1: Principle of an AGT-based protein microarray in which x can be any protein of interest Furthermore, the development of surface materials suitable for functional protein microarrays is described. The generated surfaces allow immobilization of proteins in defined orientation and density. Intrinsically bioinert polymer brushes were generated in collaboration with the Laboratory of Polymers at the EPFL. For the production of these bioinert brush-surfaces, surface-initiated atom transfer radical polymerization (SI-ATRP) was used (Chapter 4). Additionally we addressed the importance of the orientation of immobilized proteins on solid surfaces. The immobilization of fusion proteins with random orientation on aldehyde-coated glass slides was compared with immobilization in a defined orientation on O6-benzylguanine functionalyzed polymer brushes. Subsequently, retention of activity of the proteins upon immobilization on the two different surfaces was analyzed. We demonstrated that a defined orientation of immobilized proteins is advantageous over random immobilization due to the fact that proteins, immobilized using the AGT approach, are attached in a defined orientation with their active site facing the solution phase, whereas random immobilization of proteins can result in hidden active sites (Chapter 5). In the second part of this thesis the problem of insolubility of recombinant expressed proteins is addressed. We analyzed the protein expression of a set of proteins, which were prior determined to be insoluble when expressed in E. coli. The proteins were expressed as AGT fusion proteins either in E. coli or M. smegmatis, with the goal to identify a suitable host for soluble protein expression with high yield. The AGT approach was exploited to screen for expression of soluble proteins and for the determination of the expression yields. We showed that fusion to AGT does not influence the solubility of the expressed proteins and that the expression yield in E. coli does not significantly differ from the yield in M. smegmatis, where proteins were expressed with less degradation. However, we assessed E. coli. to be the more suitable host for multi-parallel protein expression due to its easy handling and fast growth (Chapter 6). The gained knowledge of this project was transferred to the investigation of proteins from Mycobacterium tuberculosis, which is presented in the last part of this thesis. A better understanding of M. tuberculosis, with the goal of unveiling and validating new therapeutic targets, is an imperative need to improve the control and treatment of tuberculosis disease. We focused in particular on the bacterial two component adaptation systems (TCS). Since these systems do not exist in mammals they present attractive candidates for new drug targets. In order to gain a better understanding of these adaptation systems we focused on one specific TCS, SenX/RegX, one of the first identified and well established TCS, which is believed to be implicated in virulence. A radioactive in vitro phosphorylation assay was developed, which allowed the identification of two new in vitro substrates for histidine kinase SenX, namely DevR and NarL. These results confirm the hypothesis that TCS might not only consist of two components, but that cross-talk between different TCS exist in order to adapt rapidly to environmental changes.

Computational methods and modeling of cellular function for the optimization of protein production and the study of cellular disease states

Joana Raquel Pinto Vieira

Systems biology is a multidisciplinary field that weaves together all the basic sciences through the use of computational and bioinformatics tools, to provide a more integrative view of the complex molecular interactions taking place within and among cells. The successes in the development and improvement of techniques for high throughput âomics has rapidly increased the amount of available data. The complexity of the underlying biological system it describes requires the development of tools to properly process it and analyze it. Computational models mathematically describe the systems interactions, allowing intrinsic properties of the data to emerge that would otherwise be overlooked. These models provide context to the data and are used to make predictions about the behavior of the system and to simulate a broader landscape of hypothesis, saving the time and cost of performing numerous experiments. However, the number of required parameters to mathematically formulate the system increases with the model complexity to integrate the available data. Thus, the development of estimation procedures and workflows to retrieve these values from literature and databases become crucial. In this work, we developed a set of computational models, analysis tools, and pipelines to support the study of two biological systems crucial to the cell survival: metabolism and protein synthesis. Metabolism is responsible for the production of most cell biomass, including proteins. Together, these two systems balance the renewal of protein in the cell, where metabolism provides the amino acids obtained from protein breakdown to the mRNA translation machinery. Deregulation of these systems is known to cause multiple disorders, such as neurodegenerative diseases and cancer. In the study of protein synthesis, we employ a combination of deterministic and stochastic modeling approaches to understand its intrinsic mechanistic properties and its rate-limiting steps. A better understanding of the system properties can have a profound impact on the development of drug targets and, in particular, in the optimization of heterologous protein production. Our studies revealed that more than one factor plays a role in the speed of translation: competition for tRNA resources and the type of cognate binding interaction between tRNA and the mRNA-ribosome complex. We also derived an equation that, given the knowledge about certain intracellular parameters pertaining to the host organism of interest, can assist in the design of transcripts for optimizing heterologous protein production. For the study of human metabolism, we established a pipeline to generate tissue-specific reduced metabolic models that can be used to study the metabolic reprogramming of different cancers and compare it with the metabolic phenotype of a healthy cell type. Despite being presented herein for human models, this pipeline is general and can be applied to the models of any organism. Starting from a human genome scale metabolic model, the pipeline improves compound annotation, identification, thermodynamics parameter retrieval, and facilitates data integration through the connection of several compound databases in a semi-automatized fashion. This work sets a standard for metabolic model assessment and curation and improves on existing tools to generate the first thermodynamically feasible reduced model of human metabolism, which is specifically tailored to the physiology and conditions under study.

EPFL2017

Proteomic identification of M. tuberculosis protein kinase substrates: PknB recruits GarA, a FHA domain-containing protein, through activation loop-mediated interactions

Stewart Cole, Pablo Fernandez

Genes for functional Ser/Thr protein kinases (STPKs) are ubiquitous in prokaryotic genomes, but little is known about their physiological substrates and their actual involvement in bacterial signal transduction pathways. We report here the identification of GarA (Rv1827), a Forkhead-associated (FHA) domain-containing protein, as a putative physiological substrate of PknB, an essential Ser/Thr protein kinase from Mycobacterium tuberculosis. Using a global proteomic approach, GarA was found to be the best detectable substrate of the PknB catalytic domain in non-denatured whole-cell protein extracts from M. tuberculosis and the saprophyte Mycobacterium smegmatis. Enzymological and binding studies of the recombinant proteins demonstrate that docking interactions between the activation loop of PknB and the C-terminal FHA domain of GarA are required to enable efficient phosphorylation at a single N-terminal threonine residue, Thr22, of the substrate. The predicted amino acid sequence of the garA gene, including both the N-terminal phosphorylation motif and the FHA domain, is strongly conserved in mycobacteria and other related actinomycetes, suggesting a functional role of GarA in putative STPK-mediated signal transduction pathways. The ensuing model of PknB-GarA interactions suggests a substrate recruitment mechanism that might apply to other mycobacterial kinases bearing multiple phosphorylation sites in their activation loops.

2005

Polymer Brushes as Substrates for Microarray Applications

Raphaël Barbey

The development of innovative bioanalytical technologies that enable the discovery of new therapeutics or the creation of systems for rapid, accurate and parallel analyses has gained an extraordinary interest in the biomedical sector in the last 10-15 years. Since proteins are the key-targets for such essential discoveries and developments, a particular emphasis has been placed on proteomics (study of proteins), including the investigation of protein structures, changes of protein expression upon treatment of cellular systems, the role of post-translational protein modifications on cell signaling events and the validation of selected proteins as disease-specific markers or pharmaceutical targets. These studies usually aim at using crude biological samples as the starting materials, which typically contain a large variety of proteins at concentrations covering a wide dynamic range and where the low abundant proteins are often the biologically most relevant. Therefore, the challenge is to create analytical tools which are able to cope with this large protein concentration range and which provide sufficiently high sensitivity to measure low abundant analytes in the presence of a high abundant protein matrix. The information that is expected from such technologies may soon exert a drastic change on the pace of medical research and considerably impact on the care of patients. As protein microarrays offer a powerful tool for the rapid, parallel analysis of complex samples and only require a minimal amount of reagents, they are currently one of the hot topics of life sciences, which strive to provide such analytical solutions. One of the current limiting factors in the field of protein microarrays for achieving the required analytical sensitivity is the amount of proteins that can be immobilized on the proteomic chip surface. Polymer brushes, which consist of an arrangement of polymer chains that are attached by one end to a substrate in a stretched, well-defined chain conformation, can increase the surface area available for protein binding, and thus act as a new category of 3D protein microarray substrates. With the advent of surface-initiated controlled polymerization techniques, extremely precise control over the thickness, composition, architecture and grafting density can be achieved, which allow to finely tune the properties of the polymer brush. In addition, these polymer chains may contain, natively or after specific post-polymerization reaction of their side chains, a high density of functional groups that can react with proteins. The different approaches available for the preparation of such surface-tethered polymer chains, the strategies allowing control over their architecture and properties as well as examples of applications related to protein binding are presented in Chapter 1. This Thesis describes the use of polymer brushes to create a new type of 3D proteomic chips, with the ultimate goal to improve the protein binding capacity compared to existing chip surface, without altering the accessibility of the detection reagents or increasing the intrinsic fluorescence of the background. The objective of this research work is to prepare polymer brush-based substrates that can outperform the sensitivity (i.e. increase the signal-to-noise ratio) that is obtained with a commercial microarray system, which is used as a benchmark in this Thesis. In this regard, functional polymer brushes containing epoxide groups that can react with the nucleophilic moieties of proteins have been chosen as promising candidates. In a preliminary set of experiments, the potential of glycidyl methacrylate-containing brushes to bind (bio)molecules via a post-polymerization modification reaction under mild conditions, i.e. at room temperature in aqueous medium, has been studied (Chapter 2). To this end, poly(glycidyl methacrylate) brushes, as well as several poly(glycidyl methacrylate)-co-poly(2-(diethylamino)ethyl methacrylate) copolymer brushes of different composition have been prepared by surface-initiated atom transfer radical (co)polymerization. The kinetics of the post-polymerization modification reaction of these (co)polymer brushes were followed using various model primary amines, as well as proteins. The introduction of tertiary amine groups, incorporated in the polymer brushes such as 2-(diethylamino)ethyl methacrylate units, was demonstrated not only to enhance the rate of the epoxide ring-opening reaction but also to induce higher protein binding capacities as compared to poly(glycidyl methacrylate) homopolymer brushes. The improved loading capacities of the (co)polymer brushes were evaluated in Chapter 3 under conditions that more closely resemble real microarray applications, such as the use of automated nanospotters to deposit proteins and fluorescence readers to detect protein immobilization. In this regard, commercially-available proteomic chips were coated with the epoxide-containing (co)polymer brushes and the spotting conditions as well as the proteins were varied. These studies revealed that the loading capacity depends on the pH and on the investigated protein, but also that the (co)polymer brush-coated substrates show far higher loading capacities than the standard, unmodified proteomic chips. This investigation also ascertained the supremacy of brushes containing 2-(diethylamino)ethyl methacrylate units compared to homopolymer brushes, as well as the superiority of thick (co)polymer brushes in comparison to their thinner equivalents. These (co)polymer brushes were subsequently used as platforms for real, although simplified, protein assays. The modified proteomic chips were spotted with unlabeled proteins that were further recognized with detection antibodies via either a one-step or a two-step approach. The clear benefits observed for copolymer brushes as compared to the existing technology under these realistic assay conditions nicely support the fact that the interior volume of the brush remains accessible for the detection reagents. In conclusion, the combination of the high loading capacity and the remaining accessibility for the detection partners make these (co)polymer brushes, and particularly the poly(glycidyl methacrylate)-co-poly(2-(diethylamino)ethyl methacrylate) brushes, excellent candidates for the improvement of the sensitivity in protein microarray applications.