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Aaron J Schetter

Publications and source records attributed to Aaron J Schetter.

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Predictive models of molecular machines involved in Caenorhabditis elegans early embryogenesis.

Although numerous fundamental aspects of development have been uncovered through the study of individual genes and proteins, system-level models are still missing for most developmental processes. The first two cell divisions of Caenorhabditis elegans embryogenesis constitute an ideal test bed for a system-level approach. Early embryogenesis, including processes such as cell division and establishment of cellular polarity, is readily amenable to large-scale functional analysis. A first step toward a system-level understanding is to provide 'first-draft' models both of the molecular assemblies involved and of the functional connections between them. Here we show that such models can be derived from an integrated gene/protein network generated from three different types of functional relationship: protein interaction, expression profiling similarity and phenotypic profiling similarity, as estimated from detailed early embryonic RNA interference phenotypes systematically recorded for hundreds of early embryogenesis genes. The topology of the integrated network suggests that C. elegans early embryogenesis is achieved through coordination of a limited set of molecular machines. We assessed the overall predictive value of such molecular machine models by dynamic localization of ten previously uncharacterized proteins within the living embryo.

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New genes with roles in the C. elegans embryo revealed using RNAi of ovary-enriched ORFeome clones.

Several RNA interference (RNAi)-based functional genomic projects have been performed in Caenorhabditis elegans to identify genes required during embryogenesis. These studies have demonstrated that the ovary is enriched for transcripts essential for the first cell divisions. However, comparing RNAi results suggests that many genes involved in embryogenesis have yet to be identified, especially those eliciting partially penetrant phenotypes. To discover additional genes required for C. elegans embryonic development, we tested by RNAi 1123 ORFeome clones selected to represent ovary-enriched genes not associated with an embryonic phenotype. We discovered 155 new ovary-enriched genes with roles during embryogenesis, of which 69% show partial penetrance lethality. Time-lapse microscopy revealed specific phenotypes during early embryogenesis for genes giving rise to high penetrance lethality. Together with previous studies, we now have evidence that 1843 C. elegans genes have roles in embryogenesis, and that many more remain to be found. Using all available RNAi phenotypic data for the ovary-enriched genes, we re-examined the distribution of genes by chromosomal location, functional class, ovary enrichment, and conservation and found that trends are driven almost exclusively by genes eliciting high-penetrance phenotypes. Furthermore, we discovered a striking direct relationship between phylogenetic distribution and the penetrance level of embryonic lethality elicited by RNAi.

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Integrating interactome, phenome, and transcriptome mapping data for the C. elegans germline.

By integrating functional genomic and proteomic mapping approaches, biological hypotheses should be formulated with increasing levels of confidence. For example, yeast interactome and transcriptome data can be correlated in biologically meaningful ways. Here, we combine interactome mapping data generated for a multicellular organism with data from both large-scale phenotypic analysis ("phenome mapping") and transcriptome profiling. First, we generated a two-hybrid interactome map of the Caenorhabditis elegans germline by using 600 transcripts enriched in this tissue. We compared this map to a phenome map of the germline obtained by RNA interference (RNAi) and to a transcriptome map obtained by clustering worm genes across 553 expression profiling experiments. In this dataset, we find that essential proteins have a tendency to interact with each other, that pairs of genes encoding interacting proteins tend to exhibit similar expression profiles, and that, for approximately 24% of germline interactions, both partners show overlapping embryonic lethal or high incidence of males RNAi phenotypes and similar expression profiles. We propose that these interactions are most likely to be relevant to germline biology. Similar integration of interactome, phenome, and transcriptome data should be possible for other biological processes in the nematode and for other organisms, including humans.

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Gene clustering based on RNAi phenotypes of ovary-enriched genes in C. elegans.

Recently, a set of 766 genes that are enriched in the ovary as compared to the soma was identified by microarray analysis [1]. Here, we report a functional analysis of 98% of these genes by RNA interference (RNAi). Over half the genes tested showed at least one detectable phenotype, most commonly embryonic lethality, consistent with the expectation that ovary transcripts would be enriched for genes that are essential in basic cellular and developmental processes. We find that essential genes are more likely to be conserved and to be highly expressed in the ovary. We extend previous observations and find that fewer than the expected number of ovary-expressed essential genes are present on the X chromosome. We characterized early embryonic defects for 161 genes and used time-lapse microscopy to systematically describe the defects for each gene in terms of 47 RNAi-associated phenotypes. In this paper, we discuss the use of these data to group genes into "phenoclusters"; in the accompanying paper, we use these data as one component in the integration of different types of large-scale functional analyses. We find that phenoclusters correlate well with sequence-based functional predictions and thus may be useful in predicting functions of uncharacterized genes.

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