Investigation of Novel Gene Functions in Yeast Using Functional Genomics
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".