Systematic approaches to using the FOX hunting system to identify useful rice genes

Ectopic gene expression, or the gain-of-function approach, has the advantage that once the function of a gene is known the gene can be transferred to many different plants by transformation. We previously reported a method, called FOX hunting, that involves ectopic expression of Arabidopsis full-length cDNAs in Arabidopsis to systematically generate gain-of-function mutants. This technology is most beneficial for generating a heterologous gene resource for analysis of useful plant gene functions. As an initial model we generated more than 23 000 independent Arabidopsis transgenic lines that expressed rice fl-cDNAs (Rice FOX Arabidopsis lines). The short generation time and rapid and efficient transformation frequency of Arabidopsis enabled the functions of the rice genes to be analyzed rapidly. We screened rice FOX Arabidopsis lines for alterations in morphology, photosynthesis, element accumulation, pigment accumulation, hormone profiles, secondary metabolites, pathogen resistance, salt tolerance, UV signaling, high light tolerance, and heat stress tolerance. Some of the mutant phenotypes displayed by rice FOX Arabidopsis lines resulted from the expression of rice genes that had no homologs in Arabidopsis . This result demonstrated that rice fl-cDNAs could be used to introduce new gene functions in Arabidopsis. Furthermore, these findings showed that rice gene function could be analyzed by employing Arabidopsis as a heterologous host. This technology provides a framework for the analysis of plant gene function in a heterologous host and of plant improvement by using heterologous gene resources.     

Enzymes are enriched in bacterial essential genes

Essential genes, those indispensable for the survival of an organism, play a key role in the emerging field, synthetic biology. Characterization of functions encoded by essential genes not only has important practical implications, such as in identifying antibiotic drug targets, but can also enhance our understanding of basic biology, such as functions needed to support cellular life. Enzymes are critical for almost all cellular activities. However, essential genes have not been systematically examined from the aspect of enzymes and the chemical reactions that they catalyze. Here, by comprehensively analyzing essential genes in 14 bacterial genomes in which large-scale gene essentiality screens have been performed, we found that enzymes are enriched in essential genes. Essential enzymes have overrepresented ligases (especially those forming carbon-oxygen bonds and carbon-nitrogen bonds), nucleotidyltransferases and phosphotransferases, while have underrepresented oxidoreductases. Furthermore, essential enzymes tend to associate with more gene ontology domains. These results, from the aspect of chemical reactions, provide further insights into the understanding of functions needed to support natural cellular life, as well as synthetic cells, and provide additional parameters that can be integrated into gene essentiality prediction algorithms.     

Experimental approaches to identify non-coding RNAs

Cellular RNAs that do not function as messenger RNAs (mRNAs), transfer RNAs (tRNAs) or ribosomal RNAs (rRNAs) comprise a diverse class of molecules that are commonly referred to as non-protein-coding RNAs (ncRNAs). These molecules have been known for quite a while, but their importance was not fully appreciated until recent genome-wide searches discovered thousands of these molecules and their genes in a variety of model organisms. Some of these screens were based on biocomputational prediction of ncRNA candidates within entire genomes of model organisms. Alternatively, direct biochemical isolation of expressed ncRNAs from cells, tissues or entire organisms has been shown to be a powerful approach to identify ncRNAs both at the level of individual molecules and at a global scale. In this review, we will survey several such wet-lab strategies, i.e. direct sequencing of ncRNAs, shotgun cloning of small-sized ncRNAs (cDNA libraries), microarray analysis and genomic SELEX to identify novel ncRNAs, and discuss the advantages and limits of these approaches.     

Experimental approaches to identify genetic networks

Systems biology offers the promise of a fully integrated view of cellular physiology. To realize this potential requires the analysis of diverse genome-wide datasets and the incorporation of these analyses into integrated networks. In the past decade, the budding yeast Saccharomyces cerevisiae has provided the benchmark for the design of such large-scale experiments. Many of these experimental approaches have been adopted and adapted to study other systems, including worm, fly, fish and mammalian cultured cells, using an ingenious set of molecular tools.     

Computational approaches to identify leucine zippers.

The leucine zipper is a dimerization domain occurring mostly in regulatory and thus in many oncogenic proteins. The leucine repeat in the sequence has been traditionally used for identification, however with poor reliability. The coiled coil structure of a leucine zipper is required for dimerization and can be predicted with reasonable accuracy by existing algorithms. We exploit this fact for identification of leucine zippers from sequence alone. We present a program, 2ZIP, which combines a standard coiled coil prediction algorithm with an approximate search for the characteristic leucine repeat. No further information from homologues is required for prediction. This approach improves significantly over existing methods, especially in that the coiled coil prediction turns out to be highly informative and avoids large numbers of false positives. Many problems in predicting zippers or assessing prediction results stem from wrong sequence annotations in the database.     

Proteomic approaches to bacterial differentiation

Mass spectrometry-based proteomics has been used extensively to explore the proteomes of various organisms, and this technology is now being applied to the characterization of bacterial species. Predominantly, two methods emerge as leaders in this application. Intact protein profiling creates fingerprints of bacterial species which can be used for differentiation and tracking over time. Peptide-centric approaches, analyzed after enzymatic digestion, enable high-throughput proteome characterization in addition to species determination from the identification of peptides distinctive to a species. Highlighted herein is an application of a peptide-centric approach to the identification and quantitation of species-specific peptide identifiers using an in silico exploration and an experimental mass spectrometry-based method. The application to microbial communities is addressed with an in silico analysis of an artificial complex community of 25 microorganisms.     

Assessment of the utilization of the antisense RNA strategy to identify essential genes in heterologous bacteria

We employed an antisense RNA approach to identify essential genes common in both Gram-positive and Gram-negative bacteria by cloning a random library of Streptococcus mutans chromosomal DNA into an expression vector and transforming Escherichia coli. Twelve out of 27 E. coli transformants with growth defective phenotypes contained individual structural genes of S. mutans in the antisense orientation relative to the E. coli promoter. Thirty-three percent of these transformants (4/12) corresponded to the genes (gyrA, ileS, rplE and yihA orthologs) which are essential for bacterial viability.     

Functionality of essential genes drives gene strand-bias in bacterial genomes

Essential genes, indispensable genes for an organism’s survival, encode functions that are considered a foundation of life. Based on those experimentally determined for 10 bacteria, we find that essential genes are more preferentially situated at the leading strand than at the lagging strand, for all the 10 genomes studied, confirming previous findings based on either smaller datasets or putatively assigned ones by homology search. Furthermore, we find that rather than all essential genes, only those with the COG functional category of information storage and process (J, K and L), and subcategories D (cell cycle control), M (cell wall biogenesis), O (posttranslational modification), C (energy production and conversion), G (carbohydrate transport and metabolism), E (amino acid transport and metabolism) and F (nucleotide transport and metabolism) are preferentially situated at the leading strand. In contrast, the strand-bias for essential genes in other COG functional subcategories is not statistically significant. These results suggest that the remarkable strand-bias of the distribution of essential genes is mainly relevant to the aforementioned functionalities, which, therefore, likely play a key role in shaping the gene strand-bias in bacterial genomes.     

Genetic approaches to identify disease genes for birth defects with cleft lip/palate as a model

BACKGROUND: Understanding the etiology of birth defects is an important step toward developing improved treatment and preventive strategies. Most birth defects have an underlying genetic basis, ranging from single genes playing dominant or recessive roles in Mendelian disorders to a mixture of contributions from multiple genes and environmental triggers in complex traits. The purpose of this article is to provide an overview of genetic approaches to identifying disease genes for genetically complex birth defects. METHODS: A review of the literature describing successes and limitations for identifying disease genes for complex traits was conducted. RESULTS: Cleft lip and cleft palate are common congenital anomalies with significant medical, psychological, social, and economic ramifications. The Online Mendelian Inheritance in Man catalog (OMIM; http://www3.ncbi.nlm.nih.gov/Omim) lists more than 400 single-gene causes of clefts of the lip and/or palate. Genetic causes of clefting also include chromosomal rearrangements, genetic susceptibility to teratogenic exposures, and complex genetic contributions of multiple genes. CONCLUSIONS: Genetic causes of birth defects can be identified using an increasingly powerful combination of careful sample collection, molecular analytic methods, and statistical evaluations. We will describe a range of approaches to search for genetic factors of birth defects and use our own work with cleft lip and palate as a model.     

Identifying essential genes in bacterial metabolic networks with machine learning methods

Background  

Identifying essential genes in bacteria supports to identify potential drug targets and an understanding of minimal requirements for a synthetic cell. However, experimentally assaying the essentiality of their coding genes is resource intensive and not feasible for all bacterial organisms, in particular if they are infective.     

Intrinsic and extrinsic approaches for detecting genes in a bacterial genome.

The unannotated regions of the Escherichia coli genome DNA sequence from the EcoSeq6 database, totaling 1,278 'intergenic' sequences of the combined length of 359,279 basepairs, were analyzed using computer-assisted methods with the aim of identifying putative unknown genes. The proposed strategy for finding new genes includes two key elements: i) prediction of expressed open reading frames (ORFs) using the GeneMark method based on Markov chain models for coding and non-coding regions of Escherichia coli DNA, and ii) search for protein sequence similarities using programs based on the BLAST algorithm and programs for motif identification. A total of 354 putative expressed ORFs were predicted by GeneMark. Using the BLASTX and TBLASTN programs, it was shown that 208 ORFs located in the unannotated regions of the E. coli chromosome are significantly similar to other protein sequences. Identification of 182 ORFs as probable genes was supported by GeneMark and BLAST, comprising 51.4% of the GeneMark 'hits' and 87.5% of the BLAST 'hits'. 73 putative new genes, comprising 20.6% of the GeneMark predictions, belong to ancient conserved protein families that include both eubacterial and eukaryotic members. This value is close to the overall proportion of highly conserved sequences among eubacterial proteins, indicating that the majority of the putative expressed ORFs that are predicted by GeneMark, but have no significant BLAST hits, nevertheless are likely to be real genes. The majority of the putative genes identified by BLAST search have been described since the release of the EcoSeq6 database, but about 70 genes have not been detected so far. Among these new identifications are genes encoding proteins with a variety of predicted functions including dehydrogenases, kinases, several other metabolic enzymes, ATPases, rRNA methyltransferases, membrane proteins, and different types of regulatory proteins.