C. elegans mutant identification with a one-step whole-genome-sequencing and SNP mapping strategy.

Whole-genome sequencing (WGS) is becoming a fast and cost-effective method to pinpoint molecular lesions in mutagenized genetic model systems, such as Caenorhabditis elegans. As mutagenized strains contain a significant mutational load, it is often still necessary to map mutations to a chromosomal interval to elucidate which of the WGS-identified sequence variants is the phenotype-causing one. We describe here our experience in setting up and testing a simple strategy that incorporates a rapid SNP-based mapping step into the WGS procedure. In this strategy, a mutant retrieved from a genetic screen is crossed with a polymorphic C. elegans strain, individual F2 progeny from this cross is selected for the mutant phenotype, the progeny of these F2 animals are pooled and then whole-genome-sequenced. The density of polymorphic SNP markers is decreased in the region of the phenotype-causing sequence variant and therefore enables its identification in the WGS data. As a proof of principle, we use this strategy to identify the molecular lesion in a mutant strain that produces an excess of dopaminergic neurons. We find that the molecular lesion resides in the Pax-6/Eyeless ortholog vab-3. The strategy described here will further reduce the time between mutant isolation and identification of the molecular lesion.

Analysis of multiple ethyl methanesulfonate-mutagenized Caenorhabditis elegans strains by whole-genome sequencing.

Whole-genome sequencing (WGS) of organisms displaying a specific mutant phenotype is a powerful approach to identify the genetic determinants of a plethora of biological processes. We have previously validated the feasibility of this approach by identifying a point-mutated locus responsible for a specific phenotype, observed in an ethyl methanesulfonate (EMS)-mutagenized Caenorhabditis elegans strain. Here we describe the genome-wide mutational profile of 17 EMS-mutagenized genomes as assessed with a bioinformatic pipeline, called MAQGene. Surprisingly, we find that while outcrossing mutagenized strains does reduce the total number of mutations, a striking mutational load is still observed even in outcrossed strains. Such genetic complexity has to be taken into account when establishing a causative relationship between genotype and phenotype. Even though unintentional, the 17 sequenced strains described here provide a resource of allelic variants in almost 1000 genes, including 62 premature stop codons, which represent candidate knockout alleles that will be of further use for the C. elegans community to study gene function.

Cis-regulatory mutations in the Caenorhabditis elegans homeobox gene locus cog-1 affect neuronal development.

We apply here comparative genome hybridization as a novel tool to identify the molecular lesion in two Caenorhabditis elegans mutant strains that affect a neuronal cell fate decision. The phenotype of the mutant strains resembles those of the loss-of-function alleles of the cog-1 homeobox gene, an inducer of the fate of the gustatory neuron ASER. We find that both lesions map to the cis-regulatory control region of cog-1 and affect a phylogenetically conserved binding site for the C2H2 zinc-finger transcription factor CHE-1, a previously known regulator of cog-1 expression in ASER. Identification of this CHE-1-binding site as a critical regulator of cog-1 expression in the ASER in vivo represents one of the rare demonstrations of the in vivo relevance of an experimentally determined or predicted transcription-factor-binding site. Aside from the mutationally defined CHE-1-binding site, cog-1 contains a second, functional CHE-1-binding site, which in isolation is sufficient to drive reporter gene expression in the ASER but in an in vivo context is apparently insufficient for promoting appropriate ASER expression. The cis-regulatory control regions of other ASE-expressed genes also contain ASE motifs that can promote ASE neuron expression when isolated from their genomic context, but appear to depend on multiple ASE motifs in their normal genomic context. The multiplicity of cis-regulatory elements may ensure the robustness of gene expression.

Online tools for predicting integral membrane proteins.

We identify and describe a set of tools readily available for integral membrane protein prediction. These tools address two problems: finding potential transmembrane proteins in a pool of new sequences, and identifying their transmembrane regions. All methods involve comparing the query protein against one or more target models. In the simplest of these, the target "model" is another protein sequence, while the more elaborate methods group together the entire set of t ansmembrane helical or transmembrane beta-barrel proteins. In general, prediction accuracy either in identifying new integral membrane proteins or transmembrane regions of known integral membrane proteins depends strongly on how closely the query fits the model. Because of this, the best approach is an opportunistic one: submit the protein of interest to all methods and choose the results with the highest confidence scores.

Membrane protein prediction methods.

We survey computational approaches that tackle membrane protein structure and function prediction. While describing the main ideas that have led to the development of the most relevant and novel methods, we also discuss pitfalls, provide practical hints and highlight the challenges that remain. The methods covered include: sequence alignment, motif search, functional residue identification, transmembrane segment and protein topology predictions, homology and ab initio modeling. In general, predictions of functional and structural features of membrane proteins are improving, although progress is hampered by the limited amount of high-resolution experimental information available. While predictions of transmembrane segments and protein topology rank among the most accurate methods in computational biology, more attention and effort will be required in the future to ameliorate database search, homology and ab initio modeling.

PROFtmb: a web server for predicting bacterial transmembrane beta barrel proteins.

PROFtmb predicts transmembrane beta-barrel (TMB) proteins in Gram-negative bacteria. For each query protein, PROFtmb provides both a Z-value indicating that the protein actually contains a membrane barrel, and a four-state per-residue labeling of upward- and downward-facing strands, periplasmic hairpins and extracellular loops. While most users submit individual proteins known to contain TMBs, some groups submit entire proteomes to screen for potential TMBs. Response time is about 4 min for a 500-residue protein. PROFtmb is a profile-based Hidden Markov Model (HMM) with an architecture mirroring the structure of TMBs. The per-residue accuracy on the 8-fold cross-validated testing set is 86% while whole-protein discrimination accuracy was 70 at 60% coverage. The PROFtmb web server includes all source code, training data and whole-proteome predictions from 78 Gram-negative bacterial genomes and is available freely and without registration at http://rostlab.org/services/proftmb.

Predicting transmembrane beta-barrels in proteomes.

Very few methods address the problem of predicting beta-barrel membrane proteins directly from sequence. One reason is that only very few high-resolution structures for transmembrane beta-barrel (TMB) proteins have been determined thus far. Here we introduced the design, statistics and results of a novel profile-based hidden Markov model for the prediction and discrimination of TMBs. The method carefully attempts to avoid over-fitting the sparse experimental data. While our model training and scoring procedures were very similar to a recently published work, the architecture and structure-based labelling were significantly different. In particular, we introduced a new definition of beta- hairpin motifs, explicit state modelling of transmembrane strands, and a log-odds whole-protein discrimination score. The resulting method reached an overall four-state (up-, down-strand, periplasmic-, outer-loop) accuracy as high as 86%. Furthermore, accurately discriminated TMB from non-TMB proteins (45% coverage at 100% accuracy). This high precision enabled the application to 72 entirely sequenced Gram-negative bacteria. We found over 164 previously uncharacterized TMB proteins at high confidence. Database searches did not implicate any of these proteins with membranes. We challenge that the vast majority of our 164 predictions will eventually be verified experimentally. All proteome predictions and the PROFtmb prediction method are available at http://www.rostlab.org/ services/PROFtmb/.

CisOrtho: a program pipeline for genome-wide identification of transcription factor target genes using phylogenetic footprinting.

BACKGROUND: All known genomes code for a large number of transcription factors. It is important to develop methods that will reveal how these transcription factors act on a genome wide level, that is, through what target genes they exert their function. RESULTS: We describe here a program pipeline aimed at identifying transcription factor target genes in whole genomes. Starting from a consensus binding site, represented as a weight matrix, potential sites in a pre-filtered genome are identified and then further filtered by assessing conservation of the putative site in the genome of a related species, a process called phylogenetic footprinting. CisOrtho has been successfully used to identify targets for two homeodomain transcription factors in the genomes of the nematodes Caenorhabditis elegans and Caenorhabditis briggsae. CONCLUSIONS: CisOrtho will identify targets of other nematode transcription factors whose DNA binding specificity is known and can be easily adapted to search other genomes for transcription factor targets.