Identification and exploration of apoptotic and caspase proteolytic substrates.
Notice bibliographique
Résumé
Apoptosis, a type of programmed cell death, is a universal and essential cellular function. There are numerous homeostatic biological roles of apoptosis and many diseases have or cause mys- or dis- regulated apoptosis, most notably cancer’s escape from apoptotic signals. Apoptosis has many distinctive characteristics including membrane blebbing, chromatin condensation, and most importantly for these studies, the activation of a class of protease proteins called caspases. Caspases are cysteine aspartic proteases with a unique preference to cleave hundred of substrates after acidic residues, especially aspartate. These cleavage events can activate, modify, or inhibit the substrate’s function, which leads to the dismantling of the cell during apoptosis. This process is well conserved throughout metazoan evolution, with parallel pathways in coral, fish, flies, worms and mice. \tTo study apoptotic protease activity, the Wells Lab has developed a proteomic-based technique. This technique is an unbiased positive enrichment labeling mass spectrometry protocol. While the protocol was developed for studying caspase substrates during apoptosis in human cell culture, it is very versatile, allowing for use in almost any protein sample like perturbed cellular lysates, different species and primary samples. Chapter 1 covers the protocol specifics and uses, summarizing a decade’s worth of optimization and application.\tThe DegraBase is the compilation of 44 different experiments using the N-terminal labeling method to examine apoptotic proteolytic activity described in Chapter 2. While much of the experimentation work was completed before I started the project, I completed compilation and standardization of raw data, and worked with Emily Crawford on the analysis. This global analysis reveals there is a large increase in proteolytic activity after apoptotic induction, and caspases account for 25% of the newly created fragments. Within caspase substrates, there is no single biological process or sub-cellular location that is targeted, as caspases appear to cleave substrates throughout all the different pathways of the cell. Additionally, this database is also a good resource for endogenous proteolysis, including free methionines, and signal and transis peptide processing. \tAnalysis of the DegraBase also reveals evolutionary and biological discoveries. As the apoptotic pathway to activate caspases is highly conserved throughout metazoans, we wanted to investigate the conservation of caspase substrates. In Chapter 3, the comparison of caspase substrates in worm, fly, mouse and human reveals a hierarchal structure. My contribution includes the murine dataset and comparison analysis in collaboration with Emily Crawford. We found caspase cleavage is highly conserved at the pathway level, while individual targets and sites are not as well conserved the more distant the animals. \tThe unbiased and large size of the DegraBase also reveals broader caspase activity than previously described. Caspases had been assumed to have absolute specificity for aspartate and no activity for glutamate. However, with the large dataset, in Chapter 4, I reveal significant activity after glutamate and even potential activity after phosphoserine. This activity is verified through biochemical assays and x-ray crystallography, and expands the number of apoptotic caspase substrates by 15%.\tAs many chemotherapeutics induce apoptosis, apoptotic, especially caspase, proteolytic substrates may be good biomarkers of treatment efficacy. The development of mouse models and a positive control are discussed in Chapter 5. These models utilize the DegraBase and labeling technology to identify peptides enriched in the blood and tumor specific to treatment responding mice as potential biomarkers.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 ».