Investigation of Novel Gene Functions in Yeast Using Functional Genomics
Notice bibliographique
Résumé
Systems biology, a diverse and complex field, relies heavily on functional genomics and highthroughput methods to understand the intricacy of cellular and molecular interactions.Through large-scale, comprehensive genome screening experiments, we generate considerable amounts of data, referred to as "big data," that unravel the complex biochemical interactions within living cells.In this context, Saccharomyces cerevisiae, commonly referred to as baker's yeast serves as a pivotal model organism in scientific research.Its widespread utilization is attributed to the readily available high-throughput techniques, substantial genetic similarity to humans, ease of manipulation, and the wealth of genetic and biochemical resources at its disposal.Our research primarily targets the dissection of the intricate molecular machinery involved in the translation initiation pathway, specifically structured messenger RNAs(mRNAs) in Saccharomyces cerevisiae.Recognizing the centrality of translation to cellular functionality and its perturbation in disease conditions, we employ functional genomic strategies and high-throughput techniques to unearth previously unrecognized gene functions within this process.We examine around 5000 nonessential yeast genes for their effect on the translation of reporter genes, unmasking four previously unidentified gene functions: PEX11, RIM20, YRF1-6, and DBP7.These genes significantly modulate the translation of mRNAs that feature structured 5'UTRs.Further, we leverage a functional genomics computational platform to uncover novel genes implicated in the DNA damage pathway.Integrating protein-protein interactions (PPI), genetic interactions, and gene expression data, we identified three unknown gene functions, GAL7, YMR130W, and YHI9, which are associated with double-strand break repair via both homologous and non-homologous end-joining pathways.These genetic associations are further validated through experimental methodologies.Additionally, we aim to elucidate the inhibitory mechanisms of the SARS-CoV-2 NSP1 gene on translation pathway, proposing the involvement of several other proteins and translation initiation factors critical to viral translation.This research provides profound insights into the complexity of the translation initiation iii pathway, integral to cellular functionality and survival.The revelation of new gene functionalities expands our present understanding and paves the way for further exploration of translational perturbations in disease conditions, thus potentially aiding the development of innovative therapeutic strategies.The integration of large-scale functional genomics and computational strategies enhances our ability to simultaneously evaluate thousands of genes, enriching the depth of our investigations.Also, the investigation of SARS-CoV-2 NSP1 translation inhibitory mechanism using functional genomics approach may expose new targets for antiviral interventions.Given the genetic parallels between baker's yeast and humans, our discoveries could significantly influence human biology, informing future research in gene regulation and disease therapeutics.Golshani.Your guidance, expertise, and unwavering support throughout this journey have been invaluable.Your dedication to my academic growth and your commitment to pushing the boundaries of knowledge in our field have truly inspired me.I am deeply grateful and humbled by the opportunity to have worked under your esteemed mentorship.I appreciate your patience, expertise, and dedication in helping me navigate the challenges and complexities of my research.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| 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 source (Gemma direct ou Codex distillé), 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 ».