Several hundred proteins become insoluble and aggregation-prone as a consequence of aging in Caenorhabditis elegans. The data indicate that these proteins influence disease-related protein aggregation and toxicity.

When unfolded proteins accumulate in the endoplasmic reticulum (ER), the unfolded protein response is activated. This ER stress response restores ER homeostasis by coordinating processes that decrease translation, degrade misfolded proteins, and increase the levels of ER-resident chaperones. Ribonuclease inositol-requiring protein-1 (IRE-1), an endoribonuclease that mediates unconventional splicing, and its target, the XBP-1 transcription factor, are key mediators of the unfolded protein response. In this study, we show that in Caenorhabditis elegans insulin/IGF-1 pathway mutants, IRE-1 and XBP-1 promote lifespan extension and enhance resistance to ER stress. We show that these effects are not achieved simply by increasing the level of spliced xbp-1 mRNA and expression of XBP-1's normal target genes. Instead, in insulin/IGF-1 pathway mutants, XBP-1 collaborates with DAF-16, a FOXO-transcription factor that is activated in these mutants, to enhance ER stress resistance and to activate new genes that promote longevity.aging | daf-2 | insulin signaling | unfolded protein response

When mitochondrial respiration or ubiquinone production is inhibited in Caenorhabditis elegans, behavioral rates are slowed and lifespan is extended. Here, we show that these perturbations increase the expression of cell-protective and metabolic genes and the abundance of mitochondrial DNA. This response is similar to the response triggered by inhibiting respiration in yeast and mammalian cells, termed the “retrograde response”. As in yeast, genes switched on in C. elegans mitochondrial mutants extend lifespan, suggesting an underlying evolutionary conservation of mechanism. Inhibition of fstr-1, a potential signaling gene that is up-regulated in clk-1 (ubiquinone-defective) mutants, and its close homolog fstr-2 prevents the expression of many retrograde-response genes and accelerates clk-1 behavioral and aging rates. Thus, clk-1 mutants live in “slow motion” because of a fstr-1/2–dependent pathway that responds to ubiquinone. Loss of fstr-1/2 does not suppress the phenotypes of all long-lived mitochondrial mutants. Thus, although different mitochondrial perturbations activate similar transcriptional and physiological responses, they do so in different ways.

Identification of co-expressed sets of genes (gene modules) is used widely for grouping functionally related genes during transcriptomic data analysis. An organism-wide atlas of high-quality gene modules would provide a powerful tool for unbiased detection of biological signals from gene expression data. Here, using a method based on independent component analysis we call DEXICA, we have defined and optimized 209 modules that broadly represent transcriptional wiring of the key experimental organism Caenorhabditis elegans. These modules represent responses to changes in the environment (e.g. starvation, exposure to xenobiotics), genes regulated by transcriptions factors (e.g. ATFS-1, DAF-16), genes specific to tissues (e.g. neurons, muscle), genes that change during development, and other complex transcriptional responses to genetic, environmental and temporal perturbations. Interrogation of these modules reveals processes that are activated in long-lived mutants in cases where traditional analyses of differentially expressed genes fail to do so. Additionally, we show that modules can inform the strength of the association between a gene and an annotation (e.g. GO term). Analysis of "module-weighted annotations" improves on several aspects of traditional annotation-enrichment tests and can aid in functional interpretation of poorly annotated genes. We provide an online interactive resource at http://genemodules.org/ in which users can find detailed information on each module, check genes for module-weighted annotations, and use both of these to analyze their own transcription data or gene sets of interest.

We present a new computational method for identifying regulated pathway components in transcript profiling (TP) experiments by evaluating transcriptional activity in the context of known biological pathways. We construct a graph representing thousands of protein functional relationships by integrating knowledge from public databases and review articles. We use the notion of distance in a graph to define pathway neighborhoods. The pathways perturbed in an experiment are then identified as the subgraph induced by the genes, referred to as activity centers, having significant density of transcriptional activity in their functional neighborhoods. We illustrate the predictive power of this approach by performing and analyzing an experiment of TP53 overexpression in NCI-H125 cells. The detected activity centers are in agreement with the known TP53 activation effects and our independent experimental results. We also apply the method to a serum starvation experiment using HEY cells and investigate the predicted activity of the transcription factor MYC. Finally, we discuss interesting properties of the activity center approach and its possible applications beyond the comparison of two experiments.