Subtopic Deep Dive

← Genetics, Aging, and Longevity in Model Organisms

RNA Interference Aging Screens
Research Guide

What is RNA Interference Aging Screens?

RNA Interference Aging Screens use RNAi libraries in C. elegans and Drosophila to perform genome-wide knockdowns identifying lifespan regulators and aging pathways.

These screens systematically silence genes to discover extenders and shorteners of lifespan, focusing on model organisms like nematodes and flies. Key pathways uncovered include insulin/IGF-1, TOR, and proteostasis networks (Labbadia and Morimoto, 2015; Hansen et al., 2008). Over 20 high-impact screens published since 2000 have validated hundreds of genetic interventions.

Curated Papers

Key Challenges

Why It Matters

RNAi aging screens in C. elegans identified the insulin signaling pathway via age-1 and daf-16 mutants, enabling dietary restriction mimics for lifespan extension (Paradis and Ruvkun, 1998). TOR pathway knockdowns extended chronological lifespan in yeast and worms, informing nutrient-sensing interventions (Powers et al., 2006; Hansen et al., 2008). Proteostasis regulators from these screens link protein homeostasis to aging diseases (Labbadia and Morimoto, 2015; Morimoto, 2008). Hits validate in mammals, accelerating longevity drug discovery.

Key Research Challenges

Off-target RNAi effects

RNAi screens produce false positives from unintended gene silencing, complicating hit validation. Multiple knockdowns and rescue experiments are required (Leung et al., 2008). Secondary assays in daf-2 mutants confirm specificity (Paradis and Ruvkun, 1998).

Lifespan assay variability

Environmental factors and bacterial contamination skew C. elegans lifespan measurements in high-throughput setups. Standardized protocols mitigate noise but reduce throughput (Hansen et al., 2008). Statistical power demands large replicates (Powers et al., 2006).

Pathway dissection complexity

Hits cluster in networks like TOR and insulin signaling, but epistasis analysis is labor-intensive. Genetic crosses and qPCR validate interactions (An and Blackwell, 2003). Proteostasis hits require chaperone induction assays (Morimoto, 2008).

Essential Papers

The Biology of Proteostasis in Aging and Disease

Johnathan Labbadia, Richard I. Morimoto · 2015 · Annual Review of Biochemistry · 1.4K citations

Loss of protein homeostasis (proteostasis) is a common feature of aging and disease that is characterized by the appearance of nonnative protein aggregates in various tissues. Protein aggregation i...

From discoveries in ageing research to therapeutics for healthy ageing

Judith Campisi, Pankaj Kapahi, Gordon J. Lithgow et al. · 2019 · Nature · 1.3K citations

The role of mitochondria in aging

Ana Bratić, Nils‐Göran Larsson · 2013 · Journal of Clinical Investigation · 1.0K citations

Over the last decade, accumulating evidence has suggested a causative link between mitochondrial dysfunction and major phenotypes associated with aging. Somatic mitochondrial DNA (mtDNA) mutations ...

Caenorhabditis elegans: An Emerging Model in Biomedical and Environmental Toxicology

Maxwell C. K. Leung, Phillip L. Williams, Alexandre Benedetto et al. · 2008 · Toxicological Sciences · 1.0K citations

The nematode Caenorhabditis elegans has emerged as an important animal model in various fields including neurobiology, developmental biology, and genetics. Characteristics of this animal model that...

Extension of chronological life span in yeast by decreased TOR pathway signaling

Ryan Powers, Matt Kaeberlein, Seth D. Caldwell et al. · 2006 · Genes & Development · 968 citations

Chronological life span (CLS) in Saccharomyces cerevisiae , defined as the time cells in a stationary phase culture remain viable, has been proposed as a model for the aging of post-mitotic tissues...

Proteotoxic stress and inducible chaperone networks in neurodegenerative disease and aging

Richard I. Morimoto · 2008 · Genes & Development · 868 citations

The long-term health of the cell is inextricably linked to protein quality control. Under optimal conditions this is accomplished by protein homeostasis, a highly complex network of molecular inter...

A Role for Autophagy in the Extension of Lifespan by Dietary Restriction in C. elegans

Malene Bredahl Hansen, Abha Chandra, Laura L. Mitic et al. · 2008 · PLoS Genetics · 755 citations

In many organisms, dietary restriction appears to extend lifespan, at least in part, by down-regulating the nutrient-sensor TOR (Target Of Rapamycin). TOR inhibition elicits autophagy, the large-sc...

Reading Guide

Foundational Papers

Start with Paradis and Ruvkun (1998) for insulin pathway discovery via daf mutants; Hansen et al. (2008) for TOR-autophagy lifespan links; Powers et al. (2006) for high-throughput CLS assays—these establish core methods and pathways.

Recent Advances

Labbadia and Morimoto (2015) reviews proteostasis from RNAi screens; Campisi et al. (2019) translates hits to therapeutics; Bratić and Larsson (2013) covers mitochondrial regulators.

Core Methods

RNAi via HT115 bacteria feeding; Kaplan-Meier lifespan stats; epistasis with daf-2/16 mutants; qPCR for knockdown efficiency (Leung et al., 2008; An and Blackwell, 2003).

How PapersFlow Helps You Research RNA Interference Aging Screens

Discover & Search

Research Agent uses searchPapers('RNAi lifespan screen C. elegans') to retrieve 50+ papers including Hansen et al. (2008), then citationGraph maps TOR pathway clusters from Powers et al. (2006). findSimilarPapers on Paradis and Ruvkun (1998) uncovers insulin signaling extensions; exaSearch drills into Drosophila RNAi datasets.

Analyze & Verify

Analysis Agent runs readPaperContent on Labbadia and Morimoto (2015) to extract proteostasis hits, then verifyResponse with CoVe cross-checks claims against 10 citing papers. runPythonAnalysis reanalyzes lifespan data from Hansen et al. (2008) using pandas for Kaplan-Meier survival curves; GRADE assigns A-level evidence to TOR-autophagy links.

Synthesize & Write

Synthesis Agent detects gaps in Drosophila RNAi screens versus C. elegans hits, flags contradictions in mitochondrial aging roles (Bratić and Larsson, 2013). Writing Agent applies latexEditText to draft methods, latexSyncCitations for 20-paper bibliography, latexCompile for figures; exportMermaid visualizes insulin-TOR pathway networks.

Use Cases

"Extract lifespan data from C. elegans RNAi screens and plot survival curves"

Research Agent → searchPapers → Analysis Agent → runPythonAnalysis(pandas survival analysis on Hansen et al. 2008 data) → matplotlib plots with p-values.

"Write LaTeX review of TOR pathway hits from RNAi screens"

Synthesis Agent → gap detection → Writing Agent → latexEditText(draft) → latexSyncCitations(Powers 2006, Hansen 2008) → latexCompile(PDF with pathway figure).

"Find GitHub repos analyzing C. elegans RNAi aging data"

Research Agent → paperExtractUrls(Leung 2008) → Code Discovery → paperFindGithubRepo → githubRepoInspect → runPythonAnalysis(reproduce screen statistics).

Automated Workflows

Deep Research workflow scans 50+ RNAi papers via searchPapers → citationGraph → structured report ranking hits by pathway (insulin, TOR). DeepScan applies 7-step CoVe to validate proteostasis claims from Labbadia and Morimoto (2015), with GRADE checkpoints. Theorizer generates hypotheses linking p38 MAPK (Troemel et al., 2006) to untested RNAi targets.

Try Doxa for RNA Interference Aging Screens Research

Frequently Asked Questions

What defines RNA Interference Aging Screens?

Genome-wide RNAi knockdowns in C. elegans or Drosophila measure lifespan changes to identify aging regulators like daf-16 and TOR components (Paradis and Ruvkun, 1998; Powers et al., 2006).

What methods are used in these screens?

Bacterial-feeding RNAi libraries silence genes; automated lifespan assays track worm viability; hits validated by qPCR and genetic epistasis (Hansen et al., 2008; Leung et al., 2008).

What are key papers?

Foundational: Hansen et al. (2008) on TOR-autophagy; Paradis and Ruvkun (1998) on insulin signaling (700+ citations each). Recent: Labbadia and Morimoto (2015) proteostasis review (1430 citations).

What open problems remain?

Translating fly/worm hits to mammals; resolving off-targets; integrating multi-omics with RNAi for pathway mechanisms (Morimoto, 2008; Bratić and Larsson, 2013).