High-throughput Genetics Research Articles

Abstract 5560: A high-throughput cellular genetics approach to identifying genes associated with sorafenib response and toxicity

Abstract Background: Sorafenib is an oral multikinase inhibitor, approved for hepatocellular, renal and thyroid carcinomas, which decreases tumor angiogenesis and proliferation. The antitumor efficacy and toxicity profiles of sorafenib vary among patients. No predictive biomarkers of sorafenib activity exist to help guide clinicians. Novel pathways and targets of sorafenib activity remain to be identified. We aimed to identify novel genes associated with sorafenib activity by using an in vitro methodology based upon mouse genomics. Methods: We profiled primary mouse embryonic fibroblasts (MEFs) from 32 inbred strains for sorafenib cytotoxicity utilizing high content imaging and simultaneous evaluation of cell health parameters. The 32 strains have been genomically characterized previously (PMID: 21623374). MEF cells were treated with varying concentrations (0-300 µM) of sorafenib, incubated for 24 h or 72 h, and then fixed and stained. Nuclear staining was used to assess sorafenib cytotoxicity and establish our cell viability phenotype. Dose response curves were generated from data, and EC50 values for each strain were identified using a Brain-Cousens model. Genome-wide association mapping, using the SNPster algorithm, was performed on cell viability EC50 values to identify quantitative trait loci (QTLs) associated with sorafenib cytotoxicity. Approximately 277,000 single nucleotide polymorphisms were tested, and genomic loci with p-values < 3.5x10-5 were selected for additional analyses. Results: Interstrain EC50 variability among the 32 MEF strains was observed after 24 h (21-121 µM) and 72 h (17-32 µM) sorafenib incubations. We identified three total peaks associated with cell viability: two on chromosome 13 (23 Mb apart; p = 3.4x10-5 and = 1.6x10-5, respectively), and one on chromosome 4 (p = 2.2x10-5). From these three peaks, we have identified candidate genes that may underlie variability in sorafenib cytotoxicity. A total of 16 genes expressed in MEF cells at mRNA level are present in these QTLs. Of particular interest, we identified one locus that contains Nfyc, a gene that encodes the C subunit of the NF-Y transcription factor. This transcription factor complex is conserved between humans and mice. In humans, NF-Y regulates MYC signaling and DNA-dependent transcription of PDGFR-β (a primary target of sorafenib) (PMID: 12167641). Conclusions: Our innovative high-throughput cellular genetics approach has identified three regions with genetic loci potentially associated with sorafenib cytotoxicity. This approach is capable of identifying robust interstrain cellular differences in sorafenib activity. Functional validation of Nyfc and other promising candidates should be conducted. Citation Format: Daniel J. Crona, Oscar Suzuki, O. Joseph Trask, Amber Frick, Bethany Parks, Tim Wiltshire, Federico Innocenti. A high-throughput cellular genetics approach to identifying genes associated with sorafenib response and toxicity. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 5560. doi:10.1158/1538-7445.AM2014-5560

Abstract LB-319: Functionally validated shRNA libraries for large-scale RNAi screens

Abstract RNA interference (RNAi) has become an indispensable tool for loss-of-function genetics across eukaryotes. By enabling stable and reversible gene silencing, short hairpin RNA (shRNA) triggers of RNAi provide a means to study long term phenotypes, perform pool-based forward genetic screens and examine the consequences of temporary target inhibition in vivo. However, efficient implementation in vertebrate systems has been hindered by our incomplete understanding of the sequence requirements for effective shRNA biogenesis and target suppression. Consequently, single-gene studies still depend on laborious testing of many candidates and pooled shRNA screens contain non-functional sequences that make the interpretation of negative results inconclusive. To address this limitation, we recently developed a high-throughput “RNAi Sensor assay” that enables functional identification of effective shRNAs at large scale. We showed that this assay allows testing of tens of thousands of candidates in parallel and robustly identifies potent single-copy shRNAs targeting virtually any gene. Here we applied this technology to evaluate 20,000 preselected candidate shRNAs targeting over 330 disease-relevant mouse genes to generate a functionally validated shRNA library for potent single-copy target knockdown. Beyond their use in RNAi transgenic animals, such shRNA libraries will enable comprehensive pooled RNAi screening and open a promising avenue for high-throughput functional genetics in the post-genomic era. Citation Format: Vaishali Sridhar, Dan Yu Lai, Nishi Sinha, Prem K. Premsrirut, Christof Fellmann. Functionally validated shRNA libraries for large-scale RNAi screens. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr LB-319. doi:10.1158/1538-7445.AM2014-LB-319

A defined zebrafish line for high-throughput genetics and genomics: NHGRI-1.

Substantial intrastrain variation at the nucleotide level complicates molecular and genetic studies in zebrafish, such as the use of CRISPRs or morpholinos to inactivate genes. In the absence of robust inbred zebrafish lines, we generated NHGRI-1, a healthy and fecund strain derived from founder parents we sequenced to a depth of ∼50×. Within this strain, we have identified the majority of the genome that matches the reference sequence and documented most of the variants. This strain has utility for many reasons, but in particular it will be useful for any researcher who needs to know the exact sequence (with all variants) of a particular genomic region or who wants to be able to robustly map sequences back to a genome with all possible variants defined.

High-throughput identification of loss-of-function mutations for anti-interferon activity in the influenza A virus NS segment.

Viral proteins often display several functions which require multiple assays to dissect their genetic basis. Here, we describe a systematic approach to screen for loss-of-function mutations that confer a fitness disadvantage under a specified growth condition. Our methodology was achieved by genetically monitoring a mutant library under two growth conditions, with and without interferon, by deep sequencing. We employed a molecular tagging technique to distinguish true mutations from sequencing error. This approach enabled us to identify mutations that were negatively selected against, in addition to those that were positively selected for. Using this technique, we identified loss-of-function mutations in the influenza A virus NS segment that were sensitive to type I interferon in a high-throughput fashion. Mechanistic characterization further showed that a single substitution, D92Y, resulted in the inability of NS to inhibit RIG-I ubiquitination. The approach described in this study can be applied under any specified condition for any virus that can be genetically manipulated. Traditional genetics focuses on a single genotype-phenotype relationship, whereas high-throughput genetics permits phenotypic characterization of numerous mutants in parallel. High-throughput genetics often involves monitoring of a mutant library with deep sequencing. However, deep sequencing suffers from a high error rate (∼0.1 to 1%), which is usually higher than the occurrence frequency for individual point mutations within a mutant library. Therefore, only mutations that confer a fitness advantage can be identified with confidence due to an enrichment in the occurrence frequency. In contrast, it is impossible to identify deleterious mutations using most next-generation sequencing techniques. In this study, we have applied a molecular tagging technique to distinguish true mutations from sequencing errors. It enabled us to identify mutations that underwent negative selection, in addition to mutations that experienced positive selection. This study provides a proof of concept by screening for loss-of-function mutations on the influenza A virus NS segment that are involved in its anti-interferon activity.

Ecological Populations of Bacteria Act as Socially Cohesive Units of Antibiotic Production and Resistance

In animals and plants, social structure can reduce conflict within populations and bias aggression toward competing populations; however, for bacteria in the wild it remains unknown whether such population-level organization exists. Here, we show that environmental bacteria are organized into socially cohesive units in which antagonism occurs between rather than within ecologically defined populations. By screening approximately 35,000 possible mutual interactions among Vibrionaceae isolates from the ocean, we show that genotypic clusters known to have cohesive habitat association also act as units in terms of antibiotic production and resistance. Genetic analyses show that within populations, broad-range antibiotics are produced by few genotypes, whereas all others are resistant, suggesting cooperation between conspecifics. Natural antibiotics may thus mediate competition between populations rather than solely increase the success of individuals.

The broad‐leaf herbicide 2,4‐dichlorophenoxyacetic acid turns rice into a living trap for a major insect pest and a parasitic wasp

Synthetic chemical elicitors of plant defense have been touted as a powerful means for sustainable crop protection. Yet, they have never been successfully applied to control insect pests in the field. We developed a high-throughput chemical genetics screening system based on a herbivore-induced linalool synthase promoter fused to a β-glucuronidase (GUS) reporter construct to test synthetic compounds for their potential to induce rice defenses. We identified 2,4-dichlorophenoxyacetic acid (2,4-D), an auxin homolog and widely used herbicide in monocotyledonous crops, as a potent elicitor of rice defenses. Low doses of 2,4-D induced a strong defensive reaction upstream of the jasmonic acid and ethylene pathways, resulting in a marked increase in trypsin proteinase inhibitor activity and volatile production. Induced plants were more resistant to the striped stem borer Chilo suppressalis, but became highly attractive to the brown planthopper Nilaparvata lugens and its main egg parasitoid Anagrus nilaparvatae. In a field experiment, 2,4-D application turned rice plants into living traps for N. lugens by attracting parasitoids. Our findings demonstrate the potential of auxin homologs as defensive signals and show the potential of the herbicide to turn rice into a selective catch crop for an economically important pest.

A high-throughput cloning system for reverse genetics in Trypanosoma cruzi

BackgroundThe three trypanosomatids pathogenic to men, Trypanosoma cruzi, Trypanosoma brucei and Leishmania major, are etiological agents of Chagas disease, African sleeping sickness and cutaneous leishmaniasis, respectively. The complete sequencing of these trypanosomatid genomes represented a breakthrough in the understanding of these organisms. Genome sequencing is a step towards solving the parasite biology puzzle, as there are a high percentage of genes encoding proteins without functional annotation. Also, technical limitations in protein expression in heterologous systems reinforce the evident need for the development of a high-throughput reverse genetics platform. Ideally, such platform would lead to efficient cloning and compatibility with various approaches. Thus, we aimed to construct a highly efficient cloning platform compatible with plasmid vectors that are suitable for various approaches.ResultsWe constructed a platform with a flexible structure allowing the exchange of various elements, such as promoters, fusion tags, intergenic regions or resistance markers. This platform is based on Gateway® technology, to ensure a fast and efficient cloning system. We obtained plasmid vectors carrying genes for fluorescent proteins (green, cyan or yellow), and sequences for the c-myc epitope, and tandem affinity purification or polyhistidine tags. The vectors were verified by successful subcellular localization of two previously characterized proteins (TcRab7 and PAR 2) and a putative centrin. For the tandem affinity purification tag, the purification of two protein complexes (ribosome and proteasome) was performed.ConclusionsWe constructed plasmids with an efficient cloning system and suitable for use across various applications, such as protein localization and co-localization, protein partner identification and protein expression. This platform also allows vector customization, as the vectors were constructed to enable easy exchange of its elements. The development of this high-throughput platform is a step closer towards large-scale trypanosome applications and initiatives.

Applications of Signal Processing Techniques to Bioinformatics, Genomics, and Proteomics

The recent development of high-throughput molecular genetics technologies has brought a major impact to bioinformatics and systems biology. These technologies have made possible the measurement of the expression profiles of genes and proteins in a highly parallel and integrated fashion. The examination of the huge amounts of genomic and proteomic data holds the promise for understanding the complex interactions between genes and proteins, the functional processes of a cell, and the impact of various factors on a cell, and ultimately, for enabling the design of new technologies for intelligent management of diseases. This special issue focuses on modeling and processing of data arising in bioinformatics, genomics, and proteomics using signal processing methods. The importance of signal processing techniques is due to their important role in extracting, processing, and interpreting the information contained in genomic and proteomic data. It is our hope that signal processing methods will lead to new advances and insights in uncovering the structure, functioning and evolution of biological systems. The special issue consists of nine papers that span a wide range of problems and applications in bioinformatics, genomics, and proteomics such as design of compressive sensing microarrays, analysis of missing values in microarray data, and effect of imputation techniques on post genomic inference methods, RNA sequence alignment, detection of periodicity in genomic sequences and gene expression profiles, clustering and classification of gene and protein expression data, and intervention in probabilistic Boolean networks. Next, we will briefly introduce the papers reported in this special issue. W. Dai et al. analyze how to design a microarray that it is fit for compressive sensing and that captures also the biochemistry of probe-target DNA hybridization. Algorithms and design results are reported for determining probe sequences that satisfy the binding requirements and for evaluating the target concentrations. M. S. B. Sehgal et al. address the general problem of improving post genomic knowledge discovery procedures such as the selection of the most significant genes and inference of gene regulatory networks using missing microarray data imputation techniques. It is shown that instead of neglecting missing data, recycling microarray data via robust imputation techniques can yield substantial performance improvements in the subsequent post genomic discovery procedures. B.-J. Yoon developed a novel efficient and robust approach for fast and accurate structural alignment of RNAs, including pseudoknots. The proposed method turns out to accelerate the dynamic programming algorithm for family-specific models such as profile-csHMMs and CMs, and to be robust to small parameter changes that are present in the model used to predict the constraint. The paper by J. Epps explains in detail the origins of ambiguity in period estimation for symbolic sequences, and proposes a novel hybrid autocorrelation-IPDFT technique for periodicity characterization of sequences. W. Zhao et al. developed a novel algorithm for identification of genes involved in cyclic processes by combining gene expression analysis and prior knowledge. The proposed cyclic-genes detection algorithm is validated on data sets corresponding to Saccharomyces cerevisiae and Drosophila melanogaster, and shown to represent a valuable technique for unveiling pathways related to cyclic processes. T. J. Hestilow and Y. Huang propose a novel method for gene clustering using the shape information of gene expression profiles. The shape information which is represented in terms of normalized and time-scaled forward first-order differences is then exploited by a variational Bayes clustering approach and a non-Bayesian (Silhouette) cluster statistic, and shown to yield promising results in clustering time-series microarray data. The paper by W. Zhao et al. proposes a new clustering approach to combine the traditional clustering methods with power spectral analysis of time series gene expression measurements. Simulation results confirm that the proposed clustering approach provides superior performance relative to hierarchical, K-means, and self-organizing maps, and yields additional information about temporal regulated genetic processes, for example, cell-cycle. T. T. Vu and U. M. Braga-Neto address the important problem of assessing the effectiveness of bagging in the classification of small-sample genomic and proteomic data sets. Representative experimental results are presented and discussed. Finally, the paper by B. Faryabi et al. studies the effects on intervention performance in the context of probabilistic Boolean networks due to a reduction in the values of the model parameters.

Reverse Genetics for Functional Genomics of Phytopathogenic Fungi and Oomycetes

Sequencing of over 40 fungal and oomycete genomes has been completed. The next major challenge in modern fungal/oomycete biology is now to translate this plethora of genome sequence information into biological functions. Reverse genetics has emerged as a seminal tool for functional genomics investigations. Techniques utilized for reverse genetics like targeted gene disruption/replacement, gene silencing, insertional mutagenesis, and targeting induced local lesions in genomes will contribute greatly to the understanding of gene function of fungal and oomycete pathogens. This paper provides an overview on high-throughput reverse genetics approaches to decode fungal/oomycete genomes.

Target based Antibacterial Drug Discovery. Zero for the 20th Century

Armed with whole genome sequences of multiple pathogenic bacteria, large collections of synthetic small molecules and natural product extracts, increasingly sophisticated laboratory automation, high throughput genetics, long lists of essential genes, high throughput screening, crystal structures, virtual (in silico) screening and phalanxes of medicinal chemists not a single antibacterial compound, derived by target based drug discovery, has entered clinical development. Despite a considerable industrial (and to a lesser extent academic) investment the futility in discovering new antibiotics has been palpable, especially during a time of increasingly widespread multidrug resistant strains of bacteria mainly in hospitals but apparently now spreading into the community. These failures may be more apparent than real but two things are becoming clear: we do not really understand bacterial resistance, especially as the ecological problem it is; nor do we appreciate what properties a truly “valid” molecular target should possess.

Computational models of molecular self-organization in cellular environments

The cellular environment creates numerous obstacles to efficient chemistry, as molecular components must navigate through a complex, densely crowded, heterogeneous, and constantly changing landscape in order to function at the appropriate times and places. Such obstacles are especially challenging to self-organizing or self-assembling molecular systems, which often need to build large structures in confined environments and typically have high-order kinetics that should make them exquisitely sensitive to concentration gradients, stochastic noise, and other non-ideal reaction conditions. Yet cells nonetheless manage to maintain a finely tuned network of countless molecular assemblies constantly forming and dissolving with a robustness and efficiency generally beyond what human engineers currently can achieve under even carefully controlled conditions. Significant advances in high-throughput biochemistry and genetics have made it possible to identify many of the components and interactions of this network, but its scale and complexity will likely make it impossible to understand at a global, systems level without predictive computational models. It is thus necessary to develop a clear understanding of how the reality of cellular biochemistry differs from the ideal models classically assumed by simulation approaches and how simulation methods can be adapted to accurately reflect biochemistry in the cell, particularly for the self-organizing systems that are most sensitive to these factors. In this review, we present approaches that have been undertaken from the modeling perspective to address various ways in which self-organization in the cell differs from idealized models.

The need for macrolides in hospitalised community-acquired pneumonia: propensity analysis

The present study compared beta-lactam macrolide ("combination") therapy versus beta-lactam alone ("monotherapy") for hospitalised community-acquired pneumonia, using propensity scores to adjust for the differences between patients. A prospective multinational observational study was carried out. Baseline patient and infection characteristics were used to develop a propensity score for combination therapy. Patients were matched by the propensity score (three decimal point precision) and compared with 30-day mortality and hospital stay. The propensity score was used as a covariate in a logistic model for mortality. Patients treated with monotherapy (n = 169) were older (mean+/-sd age 70.6+/-17.3 versus 65.0+/-19.6 yrs), had a higher chronic diseases score and a different clinical presentation compared with patients treated with combination therapy (n = 282). Unadjusted mortality was significantly higher with monotherapy (37 (22%) out of 169 versus 21 (7%) out of 282). Only 27 patients in the monotherapy group could be matched to 27 patients in the combination group using the propensity score. The mortality in these groups was identical, with three (11%) demises each. The multivariable odds ratio for mortality associated with combination therapy, adjusted for the propensity score and the Pneumonia Severity Index, was 0.69 (95% confidence interval 0.32-1.48). The benefit of combination therapy versus monotherapy cannot be reliably assessed in observational studies, since the propensity to prescribe these regimens differs markedly.

Genome‐wide screening for gene function using RNAi in mammalian cells

Mammalian genome sequencing has identified numerous genes requiring functional annotation. The discovery that dsRNA can direct gene-specific silencing in both model organisms and mammalian cells through RNA interference (RNAi) has provided a platform for dissecting the function of independent genes. The generation of large-scale RNAi libraries targeting all predicted genes within mouse, rat and human cells, combined with the large number of cell-based assays, provides a unique opportunity to perform high-throughput genetics in these complex cell systems. Many different formats exist for the generation of genome-wide RNAi libraries for use in mammalian cells. Furthermore, the use of these libraries in either genetic screens or genetic selections allows for the identification of known and novel genes involved in complex cellular phenotypes and biological processes, some of which underpin human disease. In this review, we examine genome-wide RNAi libraries used in model organisms and mammalian cells and provide examples of how these information rich reagents can be used for determining gene function, discovering novel therapeutic targets and dissecting signalling pathways, cellular processes and complex phenotypes.

Inferring genetic regulatory logic from expression data

High-throughput molecular genetics methods allow the collection of data about the expression of genes at different time points and under different conditions. The challenge is to infer gene regulatory interactions from these data and to get an insight into the mechanisms of genetic regulation. We propose a model for genetic regulatory interactions, which has a biologically motivated Boolean logic semantics, but is of a probabilistic nature, and is hence able to confront noisy biological processes and data. We propose a method for learning the model from data based on the Bayesian approach and utilizing Gibbs sampling. We tested our method with previously published data of the Saccharomyces cerevisiae cell cycle and found relations between genes consistent with biological knowledge.

VEGF Trap as a Novel Antiangiogenic Treatment Currently in Clinical Trials for Cancer and Eye Diseases, and VelociGene(R)- based Discovery of the Next Generation of Angiogenesis Targets

The concept that tumors can be controlled by directly targeting their vascular supply has finally come of age, because clinical trials using a humanized monoclonal antibody that blocks VEGF have demonstrated exciting efficacy in cancer patients, as well as in vascular eye diseases that can lead to blindness. However, data suggest that these current regimens may not provide complete VEGF inhibition and, thus, that the maximum therapeutic potential of VEGF blockade has not yet been achieved. We describe the status of a very potent and high-affinity VEGF blocker, termed the VEGF Trap, that may provide the opportunity to maximize the potential of VEGF blockade in cancer as well as in vascular eye diseases. We also describe use of the VEGF Trap as a research tool, when coupled to high-throughput mouse genetics approaches such as VelociGene that can be exploited in strategies to discover and validate the next generation of angiogenesis targets.

Spatially and temporally controlled gene transfer by electroporation into adherent cells on plasmid DNA-loaded electrodes

Functional characterization of human genes is one of the most challenging tasks in current genomics. Owing to a large number of newly discovered genes, high-throughput methodologies are greatly needed to express in parallel each gene in living cells. To develop a method that allows efficient transfection of plasmids into adherent cells in spatial- and temporal-specific manners, we studied electric pulse-triggered gene transfer using a plasmid-loaded electrode. A plasmid was loaded on a gold electrode surface having an adsorbed layer of poly(ethyleneimine), and cells were then plated directly onto this modified surface. The plasmid was detached from the electrode by applying a short electric pulse and introduced into the cells cultured on the electrode, resulting in efficient gene expression, even in primary cultured cells. The location of transfected cells could be restricted within a small area on a micropatterned electrode, showing the versatility of the method for spatially controlled transfection. Plasmid transfection could also be performed in a temporally controlled manner without a marked loss of the efficiency when an electric pulse was applied within 3 days after cell plating. The method described here will provide an efficient means to transfer multiple genes, in parallel, into cultured mammalian cells for high-throughput reverse genetics research.

Naturally occurring genetic variation in Arabidopsis thaliana.

Currently, genetic variation is probably the most important basic resource for plant biology. In addition to the variation artificially generated by mutants in model plants, naturally occurring genetic variation is extensively found for most species, including Arabidopsis. In many cases, natural variation present among accessions is multigenic, which has historically hampered its analysis. However, the exploitation of this resource down to the molecular level has now become feasible, especially in model species like Arabidopsis, where several genes accounting for natural variation have already been identified. Dissecting this variation requires first a quantitative trait locus (QTL) analysis, which in Arabidopsis has proven very effective by using recombinant inbred lines (RILs). Second, identifying the particular gene and the nucleotide polymorphism underlying QTL is the major challenge, and is now feasible by combining high-throughput genetics and functional genomic strategies. The analysis of Arabidopsis natural genetic variation is providing unique knowledge from functional, ecological, and evolutionary perspectives. This is illustrated by reviewing current research in two different biological fields: flowering time and plant growth. The analysis of Arabidopsis natural variation for flowering time revealed the identity of several genes, some of which correspond to genes with previously unknown function. In addition, for many other traits such as those related to primary metabolism and plant growth, Arabidopsis QTL analyses are detecting loci with small effects that are not easily amenable by mutant approaches, and which might provide new insights into the networks of gene regulation.

The ERGO genome analysis and discovery system.

The ERGO (http://ergo.integratedgenomics.com/ERGO/) genome analysis and discovery suite is an integration of biological data from genomics, biochemistry, high-throughput expression profiling, genetics and peer-reviewed journals to achieve a comprehensive analysis of genes and genomes. Far beyond any conventional systems that facilitate functional assignments, ERGO combines pattern-based analysis with comparative genomics by visualizing genes within the context of regulation, expression profiling, phylogenetic clusters, fusion events, networked cellular pathways and chromosomal neighborhoods of other functionally related genes. The result of this multifaceted approach is to provide an extensively curated database of the largest available integration of genomes, with a vast collection of reconstructed cellular pathways spanning all domains of life. Although access to ERGO is provided only under subscription, it is already widely used by the academic community. The current version of the system integrates 500 genomes from all domains of life in various levels of completion, 403 of which are available for subscription.

Brachypodium distachyon. A New Model System for Functional Genomics in Grasses

A new model for grass functional genomics is described based on Brachypodium distachyon, which in the evolution of the Pooideae diverged just prior to the clade of "core pooid" genera that contain the majority of important temperate cereals and forage grasses. Diploid ecotypes of B. distachyon (2n = 10) have five easily distinguishable chromosomes that display high levels of chiasma formation at meiosis. The B. distachyon nuclear genome was indistinguishable in size from that of Arabidopsis, making it the simplest genome described in grasses to date. B. distachyon is a self-fertile, inbreeding annual with a life cycle of less than 4 months. These features, coupled with its small size (approximately 20 cm at maturity), lack of seed-head shatter, and undemanding growth requirements should make it amenable to high-throughput genetics and mutant screens. Immature embryos exhibited a high capacity for plant regeneration via somatic embryogenesis. Regenerated plants display very low levels of albinism and have normal fertility. A simple transformation system has been developed based on microprojectile bombardment of embryogenic callus and hygromycin selection. Selected B. distachyon ecotypes were resistant to all tested cereal-adapted Blumeria graminis species and cereal brown rusts (Puccinia reconditia). In contrast, different ecotypes displayed resistance or disease symptoms following challenge with the rice blast pathogen (Magnaporthe grisea) and wheat/barley yellow stripe rusts (Puccinia striformis). Despite its small stature, B. distachyon has large seeds that should prove useful for studies on grain filling. Such biological characteristics represent important traits for study in temperate cereals.

High-throughput reverse genetics: RNAi screens in Caenorhabditis elegans.

Two recent chromosome-wide screens for phenotypes caused by RNA-mediated interference (RNAi) in Caenorhabditis elegans have increased our understanding of essential genes in nematodes. These papers represent a major advance in functional genomics.