INTRODUCTION Genetic interactions occur when mutations in two or more genes combine to generate an unexpected phenotype. An extreme negative or synthetic lethal genetic interaction occurs when two mutations, neither lethal individually, combine to cause cell death. Conversely, positive genetic interactions occur when two mutations produce a phenotype that is less severe than expected. Genetic interactions identify functional relationships between genes and can be harnessed for biological discovery and therapeutic target identification. They may also explain a considerable component of the undiscovered genetics associated with human diseases. Here, we describe construction and analysis of a comprehensive genetic interaction network for a eukaryotic cell. RATIONALE Genome sequencing projects are providing an unprecedented view of genetic variation. However, our ability to interpret genetic information to predict inherited phenotypes remains limited, in large part due to the extensive buffering of genomes, making most individual eukaryotic genes dispensable for life. To explore the extent to which genetic interactions reveal cellular function and contribute to complex phenotypes, and to discover the general principles of genetic networks, we used automated yeast genetics to construct a global genetic interaction network. RESULTS We tested most of the ~6000 genes in the yeast Saccharomyces cerevisiae for all possible pairwise genetic interactions, identifying nearly 1 million interactions, including ~550,000 negative and ~350,000 positive interactions, spanning ~90% of all yeast genes. Essential genes were network hubs, displaying five times as many interactions as nonessential genes. The set of genetic interactions or the genetic interaction profile for a gene provides a quantitative measure of function, and a global network based on genetic interaction profile similarity revealed a hierarchy of modules reflecting the functional architecture of a cell. Negative interactions connected functionally related genes, mapped core bioprocesses, and identified pleiotropic genes, whereas positive interactions often mapped general regulatory connections associated with defects in cell cycle progression or cellular proteostasis. Importantly, the global network illustrates how coherent sets of negative or positive genetic interactions connect protein complex and pathways to map a functional wiring diagram of the cell. CONCLUSION A global genetic interaction network highlights the functional organization of a cell and provides a resource for predicting gene and pathway function. This network emphasizes the prevalence of genetic interactions and their potential to compound phenotypes associated with single mutations. Negative genetic interactions tend to connect functionally related genes and thus may be predicted using alternative functional information. Although less functionally informative, positive interactions may provide insights into general mechanisms of genetic suppression or resiliency. We anticipate that the ordered topology of the global genetic network, in which genetic interactions connect coherently within and between protein complexes and pathways, may be exploited to decipher genotype-to-phenotype relationships.

https://science.sciencemag.org/content/sci/353/6306/aaf1420.full.pdf

A global genetic interaction network maps a wiring diagram of cellular function

http://csmres.co.uk/cs.public.upd/article-downloads/Identifying-compound-efficacy-targets-in-phenotypic-drug-discovery.pdf

Identifying compound efficacy targets in phenotypic drug discovery

Members of the diazeniumdiolate class of natural compounds show potential for drug development because of their antifungal, antibacterial, antiviral, and antitumor activities. Yet, their biosynthesis has remained elusive to date. Here, we identify a gene cluster directing the biosynthesis of the diazeniumdiolate compound fragin in Burkholderia cenocepacia H111. We provide evidence that fragin is a metallophore and that metal chelation is the molecular basis of its antifungal activity. A subset of the fragin biosynthetic genes is involved in the synthesis of a previously undescribed cell-to-cell signal molecule, valdiazen. RNA-Seq analyses reveal that valdiazen controls fragin biosynthesis and affects the expression of more than 100 genes. Homologs of the valdiazen biosynthesis genes are found in various bacteria, suggesting that valdiazen-like compounds may constitute a new class of signal molecules. We use structural information, in silico prediction of enzymatic functions and biochemical data to propose a biosynthesis route for fragin and valdiazen.

/pdf/biosynthesis-of-fragin-is-controlled-by-a-novel-quorum-lgn2h8f6kx.pdf

Biosynthesis of fragin is controlled by a novel quorum sensing signal.

Cultivation of myxobacteria of the Nannocystis genus led to the isolation and structure elucidation of a class of novel cyclic lactone inhibitors of elongation factor 1. Whole genome sequence analysis and annotation enabled identification of the putative biosynthetic cluster and synthesis process. In biological assays the compounds displayed anti-fungal and cytotoxic activity. Combined genetic and proteomic approaches identified the eukaryotic translation elongation factor 1α (EF-1α) as the primary target for this compound class. Nannocystin A (1) displayed differential activity across various cancer cell lines and EEF1A1 expression levels appear to be the main differentiating factor. Biochemical and genetic evidence support an overlapping binding site of 1 with the anti-cancer compound didemnin B on EF-1α. This myxobacterial chemotype thus offers an interesting starting point for further investigations of the potential of therapeutics targeting elongation factor 1.

Nannocystin A: an Elongation Factor 1 Inhibitor from Myxobacteria with Differential Anti-Cancer Properties

Ebselen exerts antifungal activity by regulating glutathione (GSH) and reactive oxygen species (ROS) production in fungal cells.

High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Juerg Eichenberger

Author Tools

Chat about Author

Papers

High-resolution chemical dissection of a model eukaryote reveals targets, pathways and gene functions.