Synthetic Biology Tools for Targeted Incorporation of Non-Canonical Amino Acids into Cellular Proteins
Notice bibliographique
Résumé
Proteins mediate many essential functions in cells, and methods to profile cellular proteins are of great interest for biological discovery. Whereas all of the cells in an organism share the same genome, the landscape of proteins (the proteome) varies between different cell types and over the lifetime of the organism. Rapid progress in mass spectrometers is enabling the detailed analysis of cellular proteomes. Whereas better instruments increase coverage, throughput, and measurement precision, new chemical reporters, metabolic tags, and synthetic biology techniques are required to enhance the specificity and spatiotemporal resolution of protein labeling and detection. This work introduces methods for cell-selective proteome analysis through the incorporation of non-canonical amino acids into newly synthesized proteins. Chapter I provides an overview of current technologies for translational profiling and proteomic analysis in cells. Strategies for the residue-specific incorporation of non-canonical amino acids and bioorthogonal non-canonical amino acid tagging are discussed. Chapter II introduces a new approach for the identification of secreted bacterial proteins from infected host cells using non-canonical amino acid labeling. This work demonstrates an application of cell-selective proteome labeling. Selectivity is achieved through controlled expression of a mutant aminoacyl tRNA synthetase (aaRS) enzyme that enables the metabolic incorporation of a non-canonical amino acid. Ideally, the activity of multiple genes should be used to genetically control the extent of proteome labeling in cells. This is useful because many cell states are characterized by the activity of multiple genes and identified based on the expression of several proteins. Therefore chapter III introduces a novel approach to control proteome labeling as a function of multiple promoters using a genetically encoded AND gate based on a bisected methionyl-tRNA synthetase, a class I aaRS. Cellular protein labeling occurs only upon activation of two different promoters that drive expression of the N- and C-terminal fragments of this bisected aaRS. The utility of this tool is demonstrated by the selective labeling of proteins in subpopulations of bacterial cells in a laminar-flow microfluidic channel. Chapter IV extends the cell-selective incorporation of non-canonical amino acids from bacterial systems to mammalian cells by introducing a mutant mammalian methionyl-tRNA synthetase for cell-targeted proteome labeling. This enzyme is genetically encoded and can be conditionally activated for time-resolved and cell-targeted proteome analysis in a variety of different mammalian cell types. Chapter V uses this enzyme for lineage-specific proteomic analysis of mouse embryonic stem cells during differentiation to cardiac and mesoderm lineages. This approach for lineage-specific protein labeling enables the unbiased and comprehensive analysis of proteomic changes that occur during stem cell differentiation and cell-fate commitment. Appendices A-G provide brief summaries of publications and research efforts during my PhD that are not directly related to this thesis. These publications are the result of a number of collaborations that I have been fortunate to be involved with during my graduate research. The technologies and methods introduced in this thesis provide versatile tools for the comprehensive and unbiased detection and identification of newly synthesized proteins in complex multicellular systems. Time-resolved, genetically encoded, and spatially defined non-canonical amino acid incorporation enables the identification of proteins involved in cell-cell interactions and the proteins made during specific cell states.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».