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the ribosome, proteasome, anaphase-promoting complex, and COPI coatomer, as
well as complexes involved in translation initiation, nucleocytoplasmic transport,
and cell polarity. They validated their predictions by testing localization of ten
function-unknown genes that were predicted to associate with these ''molecular
machines''. Their results suggest that early embryogenesis in C. elegans is regulated
by the coordination of a limited set of molecular machines, and provided hundreds of
new putative molecular components of these machines.
Integration of functional genomic datasets can also be used across species as well as
within. Because of functional conservation of orthologous genes and their genetic
interactions, integration of disparate datasets from multiple organisms can provide
stronger predictability for genetic interactions. An example illustrating the strength of
this strategy comes from a study aimed at acquiring a global view of genetic interac-
tions in C. elegans (
Zhong and Sternberg, 2006
). Zhong and Sternberg used a prob-
ability-based scoring system to integrate different datasets (interactome data, gene
expression data, phenotype data, and functional annotation data curated from the
literature) across three organisms (S. cerevisiae, C. elegans, D. melanogaster). They
then generated a genetic interaction network consisting of 2254 genes and 18,183
predicted interactions with probability values for interactions. As a part of their
experimental validation, they chose to verify the predicted interactions for two genes
that have been the subjects of a number of genetic screens, let-60, a worm Ras gene,
which plays a critical role in vulval induction, and itr-1, a worm 1,4,5-trisphosphate
(IP3) receptor gene, which regulates pharyngeal pumping. As individual disruption of
most of putative interacting genes caused no phenotype, double genetic perturbation
was used for interaction validation. These genes were depleted by RNAi one at a time
in the let-60 or itr-1 mutant backgrounds. Resulting enhancement or suppression of
the phenotypic defects caused by the let-60 or itr-1 mutation indicated that interac-
tions took place. Twelve of 49 predicted genes were confirmed to interact with let-60,
and two of six genes for itr-1. Importantly, these 14 verified genes were novel
modifiers that appeared to be missed in conventional screens, once again highlighting
the ability of functional genomic/systems approaches in effectively identifying new
genes and in particular functionally redundant genes or weak modifiers.
III. Ordering Genes into Pathways
Historically, the ordering of genes into pathways in C. elegans was accomplished
through genetic analysis [see review by
Huang and Sternberg (1995)
] with an
emphasis on arranging linear genetic pathways controlling developmental processes.
One realization over the past decade is that most pathways controlling biological
processes are not simply linear, but rather are highly regulated, buffered with
redundancy, and often branched with multiple feedback or feedforward mechanisms.
Modern pathway analysis involves the combination of genetic, biochemical, cell
biological, and functional genomic approaches. We outline here how these diverse
strategies are used to order genes into pathways that control biological processes.
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