Fabio Piano

Full-genome RNAi profiling of early embryogenesis in Caenorhabditis elegans

A key challenge of functional genomics today is to generate well-annotated data sets that can be interpreted across different platforms and technologies. Large-scale functional genomics data often fail to connect to standard experimental approaches of gene characterization in individual laboratories. Furthermore, a lack of universal annotation standards for phenotypic data sets makes it difficult to compare different screening approaches. Here we address this problem in a screen designed to identify all genes required for the first two rounds of cell division in the Caenorhabditis elegans embryo. We used RNA-mediated interference to target 98% of all genes predicted in the C. elegans genome in combination with differential interference contrast time-lapse microscopy. Through systematic annotation of the resulting movies, we developed a phenotypic profiling system, which shows high correlation with cellular processes and biochemical pathways, thus enabling us to predict new functions for previously uncharacterized genes.

Unlocking the secrets of the genome

Despite the successes of genomics, little is known about how genetic information produces complex organisms. A look at the crucial functional elements of fly and worm genomes could change that. The National Human Genome Research Institutes modENCODE project (the model organism ENCyclopedia Of DNA Elements) was set up in 2007 with the goal of identifying all the sequence-based functional elements in the genomes of two important experimental organisms, Caenorhabditis elegans and Drosophila melanogaster. Armed with modENCODE data, geneticists will be able to undertake the comprehensive molecular studies of regulatory networks that hold the key to how complex multicellular organisms arise from the list of instructions coded in the genome. In this issue, modENCODE team members outline their plan of campaign. Data from the project are to be made available on http://www.modencode.org and elsewhere as the work progresses.

A compendium of RNA-binding motifs for decoding gene regulation

RNA-binding proteins are key regulators of gene expression, yet only a small fraction have been functionally characterized. Here we report a systematic analysis of the RNA motifs recognized by RNA-binding proteins, encompassing 205 distinct genes from 24 diverse eukaryotes. The sequence specificities of RNA-binding proteins display deep evolutionary conservation, and the recognition preferences for a large fraction of metazoan RNA-binding proteins can thus be inferred from their RNA-binding domain sequence. The motifs that we identify in vitro correlate well with in vivo RNA-binding data. Moreover, we can associate them with distinct functional roles in diverse types of post-transcriptional regulation, enabling new insights into the functions of RNA-binding proteins both in normal physiology and in human disease. These data provide an unprecedented overview of RNA-binding proteins and their targets, and constitute an invaluable resource for determining post-transcriptional regulatory mechanisms in eukaryotes.

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.

RNAi analysis of genes expressed in the ovary of Caenorhabditis elegans

As a step towards comprehensive functional analysis of genomes, systematic gene knockout projects have been initiated in several organisms [1]. In metazoans like C. elegans, however, maternal contribution can mask the effects of gene knockouts on embryogenesis. RNA interference (RNAi) provides an alternative rapid approach to obtain loss-of-function information that can also reveal embryonic roles for the genes targeted [2,3]. We have used RNAi to analyze a random set of ovarian transcripts and have identified 81 genes with essential roles in embryogenesis. Surprisingly, none of them maps on the X chromosome. Of these 81 genes, 68 showed defects before the eight-cell stage and could be grouped into ten phenotypic classes. To archive and distribute these data we have developed a database system directly linked to the C. elegans database (Wormbase). We conclude that screening cDNA libraries by RNAi is an efficient way of obtaining in vivo function for a large group of genes. Furthermore, this approach is directly applicable to other organisms sensitive to RNAi and whose genomes have not yet been sequenced.

A Drosophila melanogaster homologue of Caenorhabditis elegans par-1 acts at an early step in embryonic-axis formation.

A Drosophila melanogaster homologue of Caenorhabditis elegans par-1 acts at an early step in embryonic-axis formation

Toward automatic phenotyping of developing embryos from videos

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.

A High-Resolution C. elegans Essential Gene Network Based on Phenotypic Profiling of a Complex Tissue

High-content screening for gene profiling has generally been limited to single cells. Here, we explore an alternative approach-profiling gene function by analyzing effects of gene knockdowns on the architecture of a complex tissue in a multicellular organism. We profile 554 essential C. elegans genes by imaging gonad architecture and scoring 94 phenotypic features. To generate a reference for evaluating methods for network construction, genes were manually partitioned into 102 phenotypic classes, predicting functions for uncharacterized genes across diverse cellular processes. Using this classification as a benchmark, we developed a robust computational method for constructing gene networks from high-content profiles based on a network context-dependent measure that ranks the significance of links between genes. Our analysis reveals that multi-parametric profiling in a complex tissue yields functional maps with a resolution similar to genetic interaction-based profiling in unicellular eukaryotes-pinpointing subunits of macromolecular complexes and components functioning in common cellular processes.

Pattern of ecological shifts in the diversification of Hawaiian Drosophila inferred from a molecular phylogeny

BACKGROUND The endemic Hawaiian drosophilids, a unique group that are remarkable for their diversity and rapid proliferation, provide a model for analysis of the process of insular speciation. Founder events and accompanying random drift, together with shifts in sexual selection, appear to explain the dramatic divergence in male morphology and mating behaviour among these flies, but these forces do not account for their spectacular ecological diversification into a wide array of breeding niches. Although recognized as contributing to the success of this group, the precise role of adaptive shifts has not been well defined. RESULTS To delineate the pattern of ecological diversification in the evolution of Hawaiian Drosophila, we generated a molecular phylogeny, using nucleotide sequences from the yolk protein gene Yp1, of 42 endemic Hawaiian and 5 continental species. By mapping ecological characters onto this phylogeny, we demonstrate that monophagy is the primitive condition, and that decaying leaves were the initial substrate for oviposition and larval development. Shifts to decaying stems, bark and tree fluxes followed in more derived species. By plotting female reproductive strategies, as reflected in ovarian developmental type, on the molecular tree, we also demonstrate a phylogenetic trend toward increasing fecundity. We find some statistical support for correlations between ecological shifts and shifts in female reproductive strategies. CONCLUSIONS Because of the short branches at the base of the phylogram, which lead to ecologically diverse lineages, we conclude that much of the adaptive radiation into alternate breeding substrates occurred rapidly, early in the groups evolution in Hawaii. Furthermore, we conclude that this ecological divergence and the correlated changes in ovarian patterns that adapt species to their ecological habitats were contributing factors in the major phyletic branching within the Hawaiian drosophilid fauna.

RNAiDB and PhenoBlast: web tools for genome‐wide phenotypic mapping projects

RNA interference (RNAi) is being used in large-scale genomic studies as a rapid way to obtain in vivo functional information associated with specific genes. How best to archive and mine the complex data derived from these studies provides a series of challenges associated with both the methods used to elicit the RNAi response and the functional data gathered. RNAiDB (RNAi Database; http://www. rnai.org) has been created for the archival, distribution and analysis of phenotypic data from large-scale RNAi analyses in Caenorhabditis elegans. The database contains a compendium of publicly available data and provides information on experimental methods and phenotypic results, including raw data in the form of images and streaming time-lapse movies. Phenotypic summaries together with graphical displays of RNAi to gene mappings allow quick intuitive comparison of results from different RNAi assays and visualization of the gene product(s) potentially inhibited by each RNAi experiment based on multiple sequence analysis methods. RNAiDB can be searched using combinatorial queries and using the novel tool PhenoBlast, which ranks genes according to their overall phenotypic similarity. RNAiDB could serve as a model database for distributing and navigating in vivo functional information from large-scale systematic phenotypic analyses in different organisms.