CiteULike: Tag gene

Plasmid-chromosome shuffling for non-deletion alleles in yeast

Zhiwei Huang — 2008-01-31T11:58:19-00:00

Nature Methods, Vol. 5, No. 2. (13 January 2008), pp. 167-169.

On the relation between promoter divergence and gene expression evolution

Itay Tirosh — 2008-01-15T16:39:46-00:00

Mol Syst Biol, Vol. 4 (15 January 2008)

Yeast life span extension by depletion of 60s ribosomal subunits is mediated by Gcn4.

KK Steffen — 2008-04-24T01:04:49-00:00

Cell, Vol. 133, No. 2. (18 April 2008), pp. 292-302.

In nearly every organism studied, reduced caloric intake extends life span. In yeast, span extension from dietary restriction is thought to be mediated by the highly conserved, nutrient-responsive target of rapamycin (TOR), protein kinase A (PKA), and Sch9 kinases. These kinases coordinately regulate various cellular processes including stress responses, protein turnover, cell growth, and ribosome biogenesis. Here we show that a specific reduction of 60S ribosomal subunit levels slows aging in yeast. Deletion of genes encoding 60S subunit proteins or processing factors or treatment with a small molecule, which all inhibit 60S subunit biogenesis, are each sufficient to significantly increase replicative life span. One mechanism by which reduced 60S subunit levels leads to life span extension is through induction of Gcn4, a nutrient-responsive transcription factor. Genetic epistasis analyses suggest that dietary restriction, reduced 60S subunit abundance, and Gcn4 activation extend yeast life span by similar mechanisms.

How does eukaryotic gene prediction work?

Michael Brent — 2007-08-09T00:39:25-00:00

Nature Biotechnology, Vol. 25, No. 8., pp. 883-885.

Evolution of chromosome organization driven by selection for reduced gene expression noise

Nizar Batada — 2007-07-27T23:29:57-00:00

Nature Genetics, Vol. 39, No. 8. (27 July 2007), pp. 945-949.

Genes overexpressed in different human solid cancers exhibit different tissue-specific expression profiles

Jacob Bock-Axelsen — 2007-08-09T03:00:55-00:00

PNAS, Vol. 104, No. 32. (7 August 2007), pp. 13122-13127.

We have analyzed gene expression in different normal human tissues and different types of solid cancers derived from these tissues. The cancers analyzed include brain (astrocytoma and glioblastoma), breast, colon, endometrium, kidney, liver, lung, ovary, prostate, skin, and thyroid cancers. Comparing gene expression in each normal tissue to 12 other normal tissues, we identified 4,917 tissue-selective genes that were selectively expressed in different normal tissues. We also identified 2,929 genes that are overexpressed at least 4-fold in the cancers compared with the normal tissue from which these cancers were derived. The overlap between these two gene groups identified 1,340 tissue-selective genes that are overexpressed in cancers. Different types of cancers, including different brain cancers arising from the same lineage, showed differences in the tissue-selective genes they overexpressed. Melanomas overexpressed the highest number of brain-selective genes and this may contribute to melanoma metastasis to the brain. Of all of the genes with tissue-selective expression, those selectively expressed in testis showed the highest frequency of genes that are overexpressed in at least two types of cancer. However, colon and prostate cancers did not overexpress any testis-selective gene. Nearly all of the genes with tissue-selective expression that are overexpressed in cancers showed selective expression in tissues different from the cancers' tissue of origin. Cancers aberrantly expressing such genes may acquire phenotypic alterations that contribute to cancer cell viability, growth, and metastasis. 10.1073/pnas.0705824104

Improvisation in evolution of genes and genomes: whose structure is it anyway?

Boris E Shakhnovich — 2008-05-22T09:43:34-00:00

Current opinion in structural biology (17 May 2008)

Significant progress has been made in recent years in a variety of seemingly unrelated fields such as sequencing, protein structure prediction, and high-throughput transcriptomics and metabolomics. At the same time, new microscopic models have been developed that made it possible to analyze the evolution of genes and genomes from first principles. The results from these efforts enable, for the first time, a comprehensive insight into the evolution of complex systems and organisms on all scales - from sequences to organisms and populations. Every newly sequenced genome uncovers new genes, families, and folds. Where do these new genes come from? How do gene duplication and subsequent divergence of sequence and structure affect the fitness of the organism? What role does regulation play in the evolution of proteins and folds? Emerging synergism between data and modeling provides first robust answers to these questions.

Orthologous Transcription Factors in Bacteria Have Different Functions and Regulate Different Genes

Morgan Price — 2007-09-07T19:49:10-00:00

PLoS Computational Biology, Vol. 3, No. 9. (1 September 2007), e175.

Transcription factors (TFs) form large paralogous gene families and have complex evolutionary histories. Here, we ask whether putative orthologs of TFs, from bidirectional best BLAST hits (BBHs), are evolutionary orthologs with conserved functions. We show that BBHs of TFs from distantly related bacteria are usually not evolutionary orthologs. Furthermore, the false orthologs usually respond to different signals and regulate distinct pathways, while the few BBHs that are evolutionary orthologs do have conserved functions. To test the conservation of regulatory interactions, we analyze expression patterns. We find that regulatory relationships between TFs and their regulated genes are usually not conserved for BBHs in Escherichia coli K12 and Bacillus subtilis. Even in the much more closely related bacteria Vibrio cholerae and Shewanella oneidensis MR-1, predicting regulation from E. coli BBHs has high error rates. Using gene–regulon correlations, we identify genes whose expression pattern differs between E. coli and S. oneidensis. Using literature searches and sequence analysis, we show that these changes in expression patterns reflect changes in gene regulation, even for evolutionary orthologs. We conclude that the evolution of bacterial regulation should be analyzed with phylogenetic trees, rather than BBHs, and that bacterial regulatory networks evolve more rapidly than previously thought.

Integration of Genome and Chromatin Structure with Gene Expression Profiles To Predict c-MYC Recognition Site Binding and Function.

Yili Chen — 2007-04-08T07:53:59-00:00

PLoS Comput Biol, Vol. 3, No. 4. (6 April 2007)

The MYC genes encode nuclear sequence specific-binding DNA-binding proteins that are pleiotropic regulators of cellular function, and the c-MYC proto-oncogene is deregulated and/or mutated in most human cancers. Experimental studies of MYC binding to the genome are not fully consistent. While many c-MYC recognition sites can be identified in c-MYC responsive genes, other motif matches-even experimentally confirmed sites-are associated with genes showing no c-MYC response. We have developed a computational model that integrates multiple sources of evidence to predict which genes will bind and be regulated by MYC in vivo. First, a Bayesian network classifier is used to predict those c-MYC recognition sites that are most likely to exhibit high-occupancy binding in chromatin immunoprecipitation studies. This classifier incorporates genomic sequence, experimentally determined genomic chromatin acetylation islands, and predicted methylation status from a computational model estimating the likelihood of genomic DNA methylation. We find that the predictions from this classifier are also applicable to other transcription factors, such as cAMP-response element-binding protein, whose binding sites are sensitive to DNA methylation. Second, the MYC binding probability is combined with the gene expression profile data from nine independent microarray datasets in multiple tissues. Finally, we may consider gene function annotations in Gene Ontology to predict the c-MYC targets. We assess the performance of our prediction results by comparing them with the c-myc targets identified in the biomedical literature. In total, we predict 460 likely c-MYC target genes in the human genome, of which 67 have been reported to be both bound and regulated by MYC, 68 are bound by MYC, and another 80 are MYC-regulated. The approach thus successfully identifies many known c-MYC targets and suggests many novel sites. Our findings suggest that to identify c-MYC genomic targets, integration of different data sources helps to improve the accuracy.

Network-based global inference of human disease genes

Xuebing Wu — 2008-05-06T20:36:46-00:00

Mol Syst Biol, Vol. 4 (6 May 2008)

Evolution of genes and genomes on the Drosophila phylogeny

2007-11-07T19:25:43-00:00

Nature, Vol. 450, No. 7167. (November 2007), pp. 203-218.

A Gene Wiki for Community Annotation of Gene Function

Jon Huss — 2008-07-08T15:23:32-00:00

PLoS Biology, Vol. 6, No. 7. (1 July 2008), e175.

Evolution: A gene is born

Tanita Casci — 2008-05-17T12:26:02-00:00

Nature Reviews Genetics, Vol. 9, No. 6., pp. 415-415.

A modular and extensible RNA-based gene-regulatory platform for engineering cellular function

Maung Win — 2007-08-25T02:58:08-00:00

PNAS (20 August 2007), 0703961104.

Edited by Arthur D. Riggs, Beckman Research Institute, City of Hope, Duarte, CA, and approved July 12, 2007 (received for review May 1, 2007)Engineered biological systems hold promise in addressing pressing human needs in chemical processing, energy production, materials construction, and maintenance and enhancement of human health and the environment. However, significant advancements in our ability to engineer biological systems have been limited by the foundational tools available for reporting on, responding to, and controlling intracellular components in living systems. Portable and scalable platforms are needed for the reliable construction of such communication and control systems across diverse organisms. We report an extensible RNA-based framework for engineering ligand-controlled gene-regulatory systems, called ribozyme switches, that exhibits tunable regulation, design modularity, and target specificity. These switch platforms contain a sensor domain, comprised of an aptamer sequence, and an actuator domain, comprised of a hammerhead ribozyme sequence. We examined two modes of standardized information transmission between these domains and demonstrate a mechanism that allows for the reliable and modular assembly of functioning synthetic RNA switches and regulation of ribozyme activity in response to various effectors. In addition to demonstrating examples of small molecule-responsive, in vivo functional, allosteric hammerhead ribozymes, this work describes a general approach for the construction of portable and scalable gene-regulatory systems. We demonstrate the versatility of the platform in implementing application-specific control systems for small molecule-mediated regulation of cell growth and noninvasive in vivo sensing of metabolite production. 10.1073/pnas.0703961104

Towards multidimensional genome annotation

Jennifer Reed — 2006-01-18T16:36:02-00:00

Nature Reviews Genetics, Vol. 7, No. 2., pp. 130-141.

Toward a Comprehensive Temperature-Sensitive Mutant Repository of the Essential Genes of Saccharomyces cerevisiae

Shay Ben-Aroya — 2008-06-02T06:16:08-00:00

Molecular Cell, Vol. 30, No. 2. (25 April 2008), pp. 248-258.

Summary The Saccharomyces cereivisiae gene deletion project revealed that approximately 20% of yeast genes are required for viability. The analysis of essential genes traditionally relies on conditional mutants, typically temperature-sensitive (ts) alleles. We developed a systematic approach (termed "diploid shuffle") useful for generating a ts allele for each essential gene in S. cerevisiae and for improved genetic manipulation of mutant alleles and gene constructs in general. Importantly, each ts allele resides at its normal genomic locus, flanked by specific cognate UPTAG and DNTAG bar codes. A subset of 250 ts mutants, including ts alleles for all uncharacterized essential genes and prioritized for genes with human counterparts, is now ready for distribution. The importance of this collection is demonstrated by biochemical and genetic screens that reveal essential genes involved in RNA processing and maintenance of chromosomal stability.

Molecular Engineering of Viral Gene Delivery Vehicles

David Schaffer — 2008-07-23T06:09:23-00:00

Annual Review of Biomedical Engineering, Vol. 10, No. 1. (2008), pp. 169-194.

Viruses can be engineered to efficiently deliver exogenous genes, but their natural gene delivery properties often fail to meet human therapeutic needs. Therefore, engineering viral vectors with new properties, including enhanced targeting abilities and resistance to immune responses, is a growing area of research. This review discusses protein engineering approaches to generate viral vectors with novel gene delivery capabilities. Rational design of viral vectors has yielded successful advances in vitro, and to an extent in vivo. However, there is often insufficient knowledge of viral structure-function relationships to reengineer existing functions or create new capabilities, such as virus-cell interactions, whose molecular basis is distributed throughout the primary sequence of the viral proteins. Therefore, high-throughput library and directed evolution methods offer alternative approaches to engineer viral vectors with desired properties. Parallel and integrated efforts in rational and library-based design promise to aid the translation of engineered viral vectors toward the clinic.

An Approach to Predict Transcription Factor DNA Binding Site Specificity Based upon Gene and Transcription Factor Functional Categorization

Ziliang Qian — 2007-08-15T02:41:51-00:00

Bioinformatics (10 July 2007), btm348.

Motivation: To understand transcription regulatory mechanisms, it is indispensable to investigate transcription factor (TF) DNA binding preferences. We noted that the generally acknowledged information of functional annotations of TFs as well as that of their target genes should provide useful hints in determining transcription factor DNA binding preferences. Results: In this contribution, we developed an integrative method based on the Nearest Neighbor Algorithm, to predict DNA binding preferences through integrating both the functional/structural information of transcription factors and the interaction between transcription factors and their targets. The accuracy of cross validation tests on the dataset consisting of 3430 positive samples and 7000 negative samples reaches 87.0% for 10-fold cross-validation and 87.9% for jackknife cross validation test, which is a much better result than that in our previous work (Qian, et al., 2006). The prediction result indicates that the improved method we developed could be a powerful approach to infer the transcription factor DNA preference in silico. 10.1093/bioinformatics/btm348

Programming gene expression with combinatorial promoters

Robert Cox — 2007-11-13T20:47:10-00:00

Mol Syst Biol, Vol. 3 (13 November 2007)

Natural history and evolutionary principles of gene duplication in fungi

Ilan Wapinski — 2007-09-05T18:18:33-00:00

Nature, Vol. 449, No. 7158., pp. 54-61.

Wasp Gene Expression Supports an Evolutionary Link Between Maternal Behavior and Eusociality

Amy Toth — 2007-10-11T01:16:02-00:00

Science (27 September 2007), 1146647.

The presence of workers that forgo reproduction and care for their siblings is a defining feature of eusociality and a major challenge for evolutionary theory. It has been proposed that worker behavior evolved from maternal care behavior. We explored this idea by studying gene expression in the primitively eusocial wasp Polistes metricus. Because little genomic information existed for this species, we used 454 sequencing to generate 391,157 brain cDNA reads, resulting in robust hits to 3,017 genes from the honey bee genome, from which we identified and assayed orthologs of 32 honey bee behaviorally-related genes. Wasp brain gene expression in workers was more similar to foundresses, which show maternal care, than to queens and gynes, which do not. Insulin-related genes were among the differentially regulated genes, suggesting that the evolution of eusociality involved major nutritional and reproductive pathways. 10.1126/science.1146647

Widespread Lateral Gene Transfer from Intracellular Bacteria to Multicellular Eukaryotes

Julie Hotopp — 2007-08-31T00:38:44-00:00

Science (30 August 2007), 1142490.

Although common among bacteria, lateral gene transferthe movement of genes between distantly related organismsis thought to occur only rarely between bacteria and multicellular eukaryotes. However, the presence of endosymbionts, such as Wolbachia pipientis, within some eukaryotic germlines may facilitate bacterial gene transfers to eukaryotic host genomes. We therefore examined host genomes for evidence of gene transfer events from Wolbachia bacteria to their hosts. We found and confirmed transfers into the genomes of 4 insect and 4 nematode species that range from nearly the entire Wolbachia genome (>1 megabase) to short (<500 base pairs) insertions. Potential Wolbachia to host transfers were also detected computationally in three additional sequenced insect genomes. We also show that some of these inserted Wolbachia genes are transcribed within eukaryotic cells lacking endosymbionts. Therefore, heritable lateral gene transfer occurs into eukaryotic hosts from their prokaryote symbionts, potentially providing a mechanism for acquisition of new genes and functions. 10.1126/science.1142490

Identifying clusters of functionally related genes in genomes.

Gangman Yi — 2007-02-04T20:41:06-00:00

Bioinformatics (19 January 2007)

MOTIVATION: An increasing body of literature shows that genomes of eukaryotes can contain clusters of functionally related genes. Most approaches to identify gene clusters utilize microarray data or metabolic pathway databases to find groups of genes on chromosomes that are linked by common attributes. A generalized method that can find gene clusters regardless of the mechanism of origin would provide researchers with an unbiased method for finding clusters and studying the evolutionary forces that give rise to them. RESULTS: We present an algorithm to identify gene clusters in eukaryotic genomes that utilizes functional categories defined in graph-based vocabularies such as the Gene Ontology (GO). Clusters identified in this manner need only have a common function and are not constrained by gene expression or other properties. We tested the algorithm by analyzing genomes of a representative set of species. We identified species-specific variation in percentage of clustered genes as well as in properties of gene clusters including size distribution and functional annotation. These properties may be diagnostic of the evolutionary forces that lead to the formation of gene clusters. AVAILABILITY: A software implementation of the algorithm and example output files are available at http://fcg.tamu.edu/C_Hunter/.

Protein subcellular relocalization: a new perspective on the origin of novel genes

Ashley Byun-Mckay — 2007-06-21T14:23:29-00:00

Trends in Ecology & Evolution, Vol. 22, No. 7. (July 2007), pp. 338-344.

Gene duplication is considered to be the most important evolutionary process for generating novel genes. However, the mechanisms involved in the evolution of such genetic innovations remain unclear. There is compelling evidence to suggest that changing the subcellular location of a protein can also alter its function, and that diversity in subcellular targeting within gene families is common. Here, we introduce the idea that protein subcellular relocalization might be an important evolutionary mechanism for the origins of new genes.

How gene order is influenced by the biophysics of transcription regulation

Grigory Kolesov — 2007-08-21T14:22:18-00:00

PNAS (20 August 2007), 0700672104.

Edited by Philip P. Green, University of Washington School of Medicine, Seattle, WA, and approved July 2, 2007 (received for review January 24, 2007)What are the forces that shape the structure of prokaryotic genomes: the order of genes, their proximity, and their orientation? Coregulation and coordinated horizontal gene transfer are believed to promote the proximity of functionally related genes and the formation of operons. However, forces that influence the structure of the genome beyond the level of a single operon remain unknown. Here, we show that the biophysical mechanism by which regulatory proteins search for their sites on DNA can impose constraints on genome structure. Using simulations, we demonstrate that rapid and reliable gene regulation requires that the transcription factor (TF) gene be close to the site on DNA the TF has to bind, thus promoting the colocalization of TF genes and their targets on the genome. We use parameters that have been measured in recent experiments to estimate the relevant length and times scales of this process and demonstrate that the search for a cognate site may be prohibitively slow if a TF has a low copy number and is not colocalized. We also analyze TFs and their sites in a number of bacterial genomes, confirm that they are colocalized significantly more often than expected, and show that this observation cannot be attributed to the pressure for coregulation or formation of selfish gene clusters, thus supporting the role of the biophysical constraint in shaping the structure of prokaryotic genomes. Our results demonstrate how spatial organization can influence timing and noise in gene expression. 10.1073/pnas.0700672104

Innovation and robustness in complex regulatory gene networks.

S Ciliberti — 2007-08-12T17:53:37-00:00

Proc Natl Acad Sci U S A (9 August 2007)

The history of life involves countless evolutionary innovations, a steady stream of ingenuity that has been flowing for more than 3 billion years. Very little is known about the principles of biological organization that allow such innovation. Here, we examine these principles for evolutionary innovation in gene expression patterns. To this end, we study a model for the transcriptional regulation networks that are at the heart of embryonic development. A genotype corresponds to a regulatory network of a given topology, and a phenotype corresponds to a steady-state gene expression pattern. Networks with the same phenotype form a connected graph in genotype space, where two networks are immediate neighbors if they differ by one regulatory interaction. We show that an evolutionary search on this graph can reach genotypes that are as different from each other as if they were chosen at random in genotype space, allowing evolutionary access to different kinds of innovation while staying close to a viable phenotype. Thus, although robustness to mutations may hinder innovation in the short term, we conclude that long-term innovation in gene expression patterns can only emerge in the presence of the robustness caused by connected genotype graphs.

High-Throughput In Vivo Analysis of Gene Expression in Caenorhabditis elegans

Rebecca Hunt-Newbury — 2007-09-17T09:13:22-00:00

PLoS Biology, Vol. 5, No. 9. (1 September 2007), e237.

Using DNA sequences 5′ to open reading frames, we have constructed green fluorescent protein (GFP) fusions and generated spatial and temporal tissue expression profiles for 1,886 specific genes in the nematode Caenorhabditis elegans. This effort encompasses about 10% of all genes identified in this organism. GFP-expressing wild-type animals were analyzed at each stage of development from embryo to adult. We have identified 5′ DNA regions regulating expression at all developmental stages and in 38 different cell and tissue types in this organism. Among the regulatory regions identified are sequences that regulate expression in all cells, in specific tissues, in combinations of tissues, and in single cells. Most of the genes we have examined in C. elegans have human orthologs. All the images and expression pattern data generated by this project are available at WormAtlas (http://gfpweb.aecom.yu.edu/index) and through WormBase (http://www.wormbase.org).

Constraint-based functional similarity of metabolic genes: going beyond network topology

Oleg Rokhlenko — 2007-09-17T08:39:05-00:00

Bioinformatics, Vol. 23, No. 16. (15 August 2007), pp. 2139-2146.

Motivation: Several recent studies attempted to establish measures for the similarity between genes that are based on the topological properties of metabolic networks. However, these approaches offer only a static description of the properties of interest and offer moderate (albeit significant) correlations with pertinent experimental data. Results: Using a constraint-based large-scale metabolic model, we present two effectively computable measures of functional gene similarity, one based on the response of the metabolic network to gene knockouts and the other based on the metabolic flux activity across a variety of growth media. We applied these measures to 750 genes comprising the metabolic network of the budding yeast. Comparing the in silico computed functional similarities to Gene Ontology (GO) annotations and gene expression data, we show that our computational method captures functional similarities between metabolic genes that go beyond those obtained by the topological analysis of metabolic networks alone, thus revealing dynamic characteristics of gene function. Interestingly, the measure based on the network response to different growth environments markedly outperforms the measure based on its response to gene knockouts, though both have some added synergistic value in depicting the functional relationships between metabolic genes. Contact: olegro@cs.technion.ac.il Supplementary information: Supplementary data are available at Bioinformatics online. 10.1093/bioinformatics/btm319

Superfamily Assignments for the Yeast Proteome through Integration of Structure Prediction with the Gene Ontology

Lars Malmström — 2007-04-19T14:09:12-00:00

PLoS Biology, Vol. 5, No. 4. (1 April 2007), e76.

Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.

Global Discriminative Learning for Higher-Accuracy Computational Gene Prediction

Axel Bernal — 2007-03-16T21:09:52-00:00

PLoS Computational Biology, Vol. 3, No. 3. (1 March 2007), e54.

Most ab initio gene predictors use a probabilistic sequence model, typically a hidden Markov model, to combine separately trained models of genomic signals and content. By combining separate models of relevant genomic features, such gene predictors can exploit small training sets and incomplete annotations, and can be trained fairly efficiently. However, that type of piecewise training does not optimize prediction accuracy and has difficulty in accounting for statistical dependencies among different parts of the gene model. With genomic information being created at an ever-increasing rate, it is worth investigating alternative approaches in which many different types of genomic evidence, with complex statistical dependencies, can be integrated by discriminative learning to maximize annotation accuracy. Among discriminative learning methods, large-margin classifiers have become prominent because of the success of support vector machines (SVM) in many classification tasks. We describe CRAIG, a new program for ab initio gene prediction based on a conditional random field model with semi-Markov structure that is trained with an online large-margin algorithm related to multiclass SVMs. Our experiments on benchmark vertebrate datasets and on regions from the ENCODE project show significant improvements in prediction accuracy over published gene predictors that use intrinsic features only, particularly at the gene level and on genes with long introns.

Potential Energy Landscape and Robustness of a Gene Regulatory Network: Toggle Switch

Keun-Young Kim — 2007-04-19T14:08:49-00:00

PLoS Computational Biology, Vol. 3, No. 3. (1 March 2007), e60.

Finding a multidimensional potential landscape is the key for addressing important global issues, such as the robustness of cellular networks. We have uncovered the underlying potential energy landscape of a simple gene regulatory network: a toggle switch. This was realized by explicitly constructing the steady state probability of the gene switch in the protein concentration space in the presence of the intrinsic statistical fluctuations due to the small number of proteins in the cell. We explored the global phase space for the system. We found that the protein synthesis rate and the unbinding rate of proteins to the gene were small relative to the protein degradation rate; the gene switch is monostable with only one stable basin of attraction. When both the protein synthesis rate and the unbinding rate of proteins to the gene are large compared with the protein degradation rate, two global basins of attraction emerge for a toggle switch. These basins correspond to the biologically stable functional states. The potential energy barrier between the two basins determines the time scale of conversion from one to the other. We found as the protein synthesis rate and protein unbinding rate to the gene relative to the protein degradation rate became larger, the potential energy barrier became larger. This also corresponded to systems with less noise or the fluctuations on the protein numbers. It leads to the robustness of the biological basins of the gene switches. The technique used here is general and can be applied to explore the potential energy landscape of the gene networks.

Estimating dynamic models for gene regulation networks

Jiguo Cao — 2008-06-03T03:00:21-00:00

Bioinformatics (27 May 2008), btn246.

Motivation: Transcription regulation is a fundamental process in biology, and it is important to model the dynamic behavior of gene regulation networks. Many approaches have been proposed to specify the network structure. However, finding the network connectivity is not sufficient to understand the network dynamics. Instead, one needs to model the regulation reactions, usually with a set of ordinary differential equations (ODEs). Because some of the parameters involved in these ODEs are unknown, their values need to be inferred from the observed data. Results: In this article, we introduce the generalized profiling method to estimate ODE parameters in a gene regulation network from microarray gene expression data which can be rather noisy. Because numerically solving ODEs is computationally expensive, we apply the penalized smoothing technique, a fast and stable computational method to approximate ODE solutions. The ODE solutions with our parameter estimates fit the data well. A goodness-of-fit test of dynamic models is developed to identify gene regulation networks. Contact: jca76@sfu.ca, hongyu.zhao@yale.edu 10.1093/bioinformatics/btn246

The gene and the genon concept: a functional and information-theoretic analysis.

K Scherrer — 2007-04-14T08:40:43-00:00

Mol Syst Biol, Vol. 3 (2007)

'Gene' has become a vague and ill-defined concept. To set the stage for mathematical analysis of gene storage and expression, we return to the original concept of the gene as a function encoded in the genome, basis of genetic analysis, that is a polypeptide or other functional product. The additional information needed to express a gene is contained within each mRNA as an ensemble of signals, added to or superimposed onto the coding sequence. To designate this programme, we introduce the term 'genon'. Individual genons are contained in the pre-mRNA forming a pre-genon. A genomic domain contains a proto-genon, with the signals of transcription activation in addition to the pre-genon in the transcripts. Some contain several mRNAs and hence genons, to be singled out by RNA processing and differential splicing. The programme in the genon in cis is implemented by corresponding factors of protein or RNA nature contained in the transgenon of the cell or organism. The gene, the cis programme contained in the individual domain and transcript, and the trans programme of factors, can be analysed by information theory.

Ribozymes, riboswitches and beyond: regulation of gene expression without proteins

Alexander Serganov — 2007-09-19T03:51:54-00:00

Nature Reviews Genetics, Vol. 8, No. 10. (11 September 2007), pp. 776-790.

The liver pharmacological and xenobiotic gene response repertoire

Georges Natsoulis — 2008-03-27T05:06:29-00:00

Mol Syst Biol, Vol. 4 (25 March 2008)

Functional Architecture and Evolution of Transcriptional Elements That Drive Gene Coexpression

Christopher Brown — 2007-09-14T17:12:44-00:00

Science, Vol. 317, No. 5844. (14 September 2007), pp. 1557-1560.

Transcriptional coexpression of interacting gene products is required for complex molecular processes; however, the function and evolution of cis-regulatory elements that orchestrate coexpression remain largely unexplored. We mutagenized 19 regulatory elements that drive coexpression of Ciona muscle genes and obtained quantitative estimates of the cis-regulatory activity of the 77 motifs that comprise these elements. We found that individual motif activity ranges broadly within and among elements, and among different instantiations of the same motif type. The activity of orthologous motifs is strongly constrained, although motif arrangement, type, and activity vary greatly among the elements of different co-regulated genes. Thus, the syntactical rules governing this regulatory function are flexible but become highly constrained evolutionarily once they are established in a particular element. 10.1126/science.1145893

Bayesian Orthogonal Least Squares (BOLS) algorithm for reverse engineering of gene regulatory networks

Chang Kim — 2007-07-13T16:19:01-00:00

BMC Bioinformatics, Vol. 8, No. 1. (2007)

BACKGROUND:A reverse engineering of gene regulatory network with large number of genes and limited number of experimental data points is a computationally challenging task. In particular, reverse engineering using linear systems is an underdetermined and ill conditioned problem, i.e. the amount of microarray data is limited and the solution is very sensitive to noise in the data. Therefore, the reverse engineering of gene regulatory networks with large number of genes and limited number of data points requires rigorous optimization algorithm.RESULTS:This study presents a novel algorithm for reverse engineering with linear systems. The proposed algorithm is a combination of the orthogonal least squares, second order derivative for network pruning, and Bayesian model comparison. In this study, the entire network is decomposed into a set of small networks that are defined as unit networks. The algorithm provides each unit network with P(D|Hi), which is used as confidence level. The unit network with higher P(D|Hi) has a higher confidence such that the unit network is correctly elucidated. Thus, the proposed algorithm is able to locate true positive interactions using P(D|Hi), which is a unique property of the proposed algorithm. The algorithm is evaluated with synthetic and Saccharomyces cerevisiae expression data using the dynamic Bayesian network. With synthetic data, it is shown that the performance of the algorithm depends on the number of genes, noise level, and the number of data points. With Yeast expression data, it is shown that there is remarkable number of known physical or genetic events among all interactions elucidated by the proposed algorithm. The performance of the algorithm is compared with Sparse Bayesian Learning algorithm using both synthetic and Saccharomyces cerevisiae expression data sets. The comparison experiments show that the algorithm produces sparser solutions with less false positives than Sparse Bayesian Learning algorithm.CONCLUSIONS:From our evaluation experiments, we draw the conclusion as follows: 1) Simulation results show that the algorithm can be used to elucidate gene regulatory networks using limited number of experimental data points. 2) Simulation results also show that the algorithm is able to handle the problem with noisy data. 3) The experiment with Yeast expression data shows that the proposed algorithm reliably elucidates known physical or genetic events. 4) The comparison experiments show that the algorithm more efficiently performs than Sparse Bayesian Learning algorithm with noisy and limited number of data.

Genome-wide prediction of matrix attachment regions that increase gene expression in mammalian cells

Pierre-Alain Girod — 2007-08-31T15:18:16-00:00

Nat Meth, Vol. 4, No. 9. (2007), pp. 747-753.

Parasites lead to evolution of robustness against gene loss in host signaling networks

Marcel Salathe — 2008-07-16T15:39:04-00:00

Mol Syst Biol, Vol. 4 (15 July 2008)

A Gene Regulatory Network Subcircuit Drives a Dynamic Pattern of Gene Expression

Joel Smith — 2007-11-05T14:33:29-00:00

Science, Vol. 318, No. 5851. (2 November 2007), pp. 794-797.

Early specification of endomesodermal territories in the sea urchin embryo depends on a moving torus of regulatory gene expression. We show how this dynamic patterning function is encoded in a gene regulatory network (GRN) subcircuit that includes the otx, wnt8, and blimp1 genes, the cis-regulatory control systems of which have all been experimentally defined. A cis-regulatory reconstruction experiment revealed that blimp1 autorepression accounts for progressive extinction of expression in the center of the torus, whereas its outward expansion follows reception of the Wnt8 ligand by adjacent cells. GRN circuitry thus controls not only static spatial assignment in development but also dynamic regulatory patterning. 10.1126/science.1146524

Lifespan Regulation by Evolutionarily Conserved Genes Essential for Viability.

Sean P Curran — 2007-04-08T22:05:16-00:00

PLoS Genet, Vol. 3, No. 4. (6 April 2007)

Evolutionarily conserved mechanisms that control aging are predicted to have prereproductive functions in order to be subject to natural selection. Genes that are essential for growth and development are highly conserved in evolution, but their role in longevity has not previously been assessed. We screened 2,700 genes essential for Caenorhabditis elegans development and identified 64 genes that extend lifespan when inactivated postdevelopmentally. These candidate lifespan regulators are highly conserved from yeast to humans. Classification of the candidate lifespan regulators into functional groups identified the expected insulin and metabolic pathways but also revealed enrichment for translation, RNA, and chromatin factors. Many of these essential gene inactivations extend lifespan as much as the strongest known regulators of aging. Early gene inactivations of these essential genes caused growth arrest at larval stages, and some of these arrested animals live much longer than wild-type adults. daf-16 is required for the enhanced survival of arrested larvae, suggesting that the increased longevity is a physiological response to the essential gene inactivation. These results suggest that insulin-signaling pathways play a role in regulation of aging at any stage in life.

A First-Principles Model of Early Evolution: Emergence of Gene Families, Species, and Preferred Protein Folds

Konstantin Zeldovich — 2007-07-13T18:27:36-00:00

PLoS Computational Biology, Vol. 3, No. 7. (1 July 2007), e139.

In this work we develop a microscopic physical model of early evolution where phenotype—organism life expectancy—is directly related to genotype—the stability of its proteins in their native conformations—which can be determined exactly in the model. Simulating the model on a computer, we consistently observe the “Big Bang” scenario whereby exponential population growth ensues as soon as favorable sequence–structure combinations (precursors of stable proteins) are discovered. Upon that, random diversity of the structural space abruptly collapses into a small set of preferred proteins. We observe that protein folds remain stable and abundant in the population at timescales much greater than mutation or organism lifetime, and the distribution of the lifetimes of dominant folds in a population approximately follows a power law. The separation of evolutionary timescales between discovery of new folds and generation of new sequences gives rise to emergence of protein families and superfamilies whose sizes are power-law distributed, closely matching the same distributions for real proteins. On the population level we observe emergence of species—subpopulations that carry similar genomes. Further, we present a simple theory that relates stability of evolving proteins to the sizes of emerging genomes. Together, these results provide a microscopic first-principles picture of how first-gene families developed in the course of early evolution.

Evolutionary plasticity of developmental gene regulatory network architecture

Veronica Hinman — 2007-11-28T06:17:29-00:00

Proceedings of the National Academy of Sciences (27 November 2007), 0709994104.

Sea stars and sea urchins evolved from a last common ancestor that lived at the end of the Cambrian, approximately half a billion years ago. In a previous comparative study of the gene regulatory networks (GRNs) that embody the genomic program for embryogenesis in these animals, we discovered an almost perfectly conserved five-gene network subcircuit required for endoderm specification. We show here that the GRN structure upstream and downstream of the conserved network kernel has, by contrast, diverged extensively. Mesoderm specification is accomplished quite differently; the DeltaNotch signaling system is used in radically distinct ways; and various regulatory genes have been coopted to different functions. The conservation of the conserved kernel is thus the more remarkable. The results indicate types of network linkage subject to evolutionary change. An emergent theme is that subcircuit design may be preserved even while the identity of genes performing given roles changes because of alteration in their cis-regulatory control systems. 10.1073/pnas.0709994104

A forest-based approach to identifying gene and gene gene interactions

Xiang Chen — 2007-11-29T08:30:32-00:00

Proceedings of the National Academy of Sciences (28 November 2007), 0709868104.

Multiple genes, gene-by-gene interactions, and gene-by-environment interactions are believed to underlie most complex diseases. However, such interactions are difficult to identify. Although there have been recent successes in identifying genetic variants for complex diseases, it still remains difficult to identify genegene and geneenvironment interactions. To overcome this difficulty, we propose a forest-based approach and a concept of variable importance. The proposed approach is demonstrated by simulation study for its validity and illustrated by a real data analysis for its use. Analyses of both real data and simulated data based on published genetic models show the effectiveness of our approach. For example, our analysis of a published data set on age-related macular degeneration (AMD) not only confirmed a known genetic variant (P value = 2E-6) for AMD, but also revealed an unreported haplotype surrounding single-nucleotide polymorphism (SNP) rs10272438 on chromosome 7 that was significantly associated with AMD (P value = 0.0024). These significance levels are obtained after the consideration for a large number of SNPs. Thus, the importance of this work is twofold: it proposes a powerful and flexible method to identify high-risk haplotypes and their interactions and reveals a potentially protective variant for AMD. 10.1073/pnas.0709868104

Regulation of the Expression of the Prostate-specific Antigen by Claudin-7

Zheng — 2007-06-05T03:29:19-00:00

Journal of Membrane Biology, Vol. 194, No. 3. (1 July 2003), pp. 187-197.

Claudins are a family of proteins involved in forming tight junctions between cells. Here we describe two forms of claudin-7 (CLDN-7), a full-length form of CLDN-7 with 211 amino-acid residues and a C-terminal truncated form with 158 amino-acid residues. These two forms of CLDN-7 are able to regulate the expression of a tissue-specific protein, the prostate-specific antigen (PSA), in the LNCaP prostate cancer cell line. We also found that the expression of CLDN-7 is responsive to androgen stimulation in the LNCaP cell line, suggesting that this protein is involved in the regulatory mechanism of androgen. Both forms of claudin-7 are expressed in human prostate, kidney and lung samples, and in most samples, the full-length form of claudin-7 was predominant. However, in some prostate samples from healthy individuals, the truncated form of claudin-7 is predominantly expressed. Our results demonstrated that unlike other claudins, CLDN-7 has both structural and regulatory functions, and the two forms of CLDN-7 may be related to cell differentiation in organ development.

SNPs3D: Candidate gene and SNP selection for association studies

Peng Yue — 2007-01-18T11:00:48-00:00

BMC Bioinformatics, Vol. 7, No. 1. (2006)

BACKGROUND:The relationship between disease susceptibility and genetic variation is complex, and many different types of data are relevant. We describe a web resource and database that provides and integrates as much information as possible on disease/gene relationships at the molecular level.DESCRIPTION:The resource http://www.SNPs3D.org has three primary modules. One module identifies which genes are candidates for involvement in a specified disease. A second module provides information about the relationships between sets of candidate genes. The third module analyzes the likely impact of non-synonymous SNPs on protein function. Disease/candidate gene relationships and gene-gene relationships are derived from the literature using simple but effective text profiling. SNP/protein function relationships are derived by two methods, one using principles of protein structure and stability, the other based on sequence conservation. Entries for each gene include a number of links to other data, such as expression profiles, pathway context, mouse knockout information and papers. Gene-gene interactions are presented in an interactive graphical interface, providing rapid access to the underlying information, as well as convenient navigation through the network. Use of the resource is illustrated with aspects of the inflammatory response and hypertension.CONCLUSION:The combination of SNP impact analysis, a knowledge based network of gene relationships and candidate genes, and access to a wide range of data and literature allow a user to quickly assimilate available information, and so develop models of gene-pathway-disease interaction.

Reconstruction of gene networks using Bayesian learning and manipulation experiments

Iosifina Pournara — 2006-04-08T19:20:37-00:00

Bioinformatics, Vol. 20, No. 17. (22 November 2004), pp. 2934-2942.

Motivation: The analysis of high-throughput experimental data, for example from microarray experiments, is currently seen as a promising way of finding regulatory relationships between genes. Bayesian networks have been suggested for learning gene regulatory networks from observational data. Not all causal relationships can be inferred from correlation data alone. Often several equivalent but different directed graphs explain the data equally well. Intervention experiments where genes are manipulated can help to narrow down the range of possible networks. Results: We describe an active learning algorithm that suggests an optimized sequence of intervention experiments. Simulation experiments show that our selection scheme is better than an unguided choice of interventions in learning the correct network and compares favorably in running time and results with methods based on value of information calculations. Availability: Algorithms are available from the authors on request. 10.1093/bioinformatics/bth337

Gene prediction: compare and CONTRAST

Paul Flicek — 2007-12-21T13:40:18-00:00

Genome Biology, Vol. 8, No. 12. (2007)

CONTRAST, a new gene-prediction algorithm that uses sophisticated machine-learning techniques, has pushed de novo prediction accuracy to new heights, and has significantly closed the gap between de novo and evidence-based methods for human genome annotation.

Gene structure prediction by linguistic methods

S Dong — 2007-04-02T02:26:40-00:00

(1994)

The higher-order structure of genes and other features of biological sequences can be described by means of formal grammars. These grammars can then be used by general-purpose parsers to detect and assemble such structures by means of syntactic pattern recognition. We describe a grammar and parser for eukaryotic protein-encoding genes, which by some measures is as effective as current connectionist and combinatorial algorithms in predicting gene structures for sequence database entries....

Simple one-week method to construct gene-targeting vectors: application to production of human knockout cell lines.

S Iiizumi — 2006-11-20T16:03:02-00:00

Biotechniques, Vol. 41, No. 3. (September 2006), pp. 311-316.

Targeted gene disruption is a powerful tool for studying gene function in cells and animals. In addition, this technology includes a potential to correct disease-causing mutations. However, constructing targeting vectors is a laborious step in the gene-targeting strategy, even apart from the low efficiency of homologous recombination in mammals. Here, we introduce a quick and simplified method to construct targeting vectors. This method is based on the commercially available MultiSite Gateway technology. The sole critical step is to design primers to PCR amplify genomic fragments for homologous DNA arms, after which neither ligation reaction nor extensive restriction mapping is necessary at all. The method therefore is readily applicable to embryonic stem (ES) cell studies as well as all organisms whose genome has been sequenced. Recently, we and others have shown that the human pre-B cell line Nalm-6 allows for high-efficiency gene targeting. The combination of the simplified vector construction system and the high-efficiency gene targeting in the Nalm-6 cell line has enabled rapid disruption of virtually any locus of the human genome within one month, and homozygous knockout clones lacking a human gene of interest can be created within 2-3 months. Thus, our system greatly facilitates reverse genetic studies of mammalian--particularly human--genes.