Cis-regulatory Motifs Research Articles

A systems biology approach to understanding atherosclerosis

Atherosclerosis, a chronic inflammatory disease of the vascular system, presents significant challenges to developing effective molecular diagnostics and novel therapies. A systems biology approach integrating data from large-scale measurements (e.g. transcriptomics, proteomics and genomics) is successfully contributing to deciphering regulatory networks underlying the response of many different cellular systems to perturbations. Such a network analysis strategy using pathway information and data from multiple measurement platforms, tissues and species is a promising approach to elucidate the mechanistic underpinnings of complex diseases. Here, we present our views on the contributions that a systems approach can bring to the study of atherosclerosis, propose ways to tackle the complexity of the disease in a systems manner and review recent systems-level studies of the disease.

Functional Characterization of Transcription Factor Motifs Using Cross-species Comparison across Large Evolutionary Distances

We address the problem of finding statistically significant associations between cis-regulatory motifs and functional gene sets, in order to understand the biological roles of transcription factors. We develop a computational framework for this task, whose features include a new statistical score for motif scanning, the use of different scores for predicting targets of different motifs, and new ways to deal with redundancies among significant motif–function associations. This framework is applied to the recently sequenced genome of the jewel wasp, Nasonia vitripennis, making use of the existing knowledge of motifs and gene annotations in another insect genome, that of the fruitfly. The framework uses cross-species comparison to improve the specificity of its predictions, and does so without relying upon non-coding sequence alignment. It is therefore well suited for comparative genomics across large evolutionary divergences, where existing alignment-based methods are not applicable. We also apply the framework to find motifs associated with socially regulated gene sets in the honeybee, Apis mellifera, using comparisons with Nasonia, a solitary species, to identify honeybee-specific associations.

Cloning, mapping and molecular characterization of porcine progesterone receptor membrane component 2 (PGRMC2) gene

Progesterone plays an important role in sow reproduction by stimulating classic genomic pathways via nuclear receptors and non-genomic pathways via membrane receptors such a progesterone receptor membrane component 2 (PGRMC2). In this work, we used radiation hybrid mapping to assign PGRMC2 to pig chromosome 8 and observed that this receptor has two transcripts in pigs. The full-length cDNA of the large transcript is 1858 bp long and contains a 669-bp open reading frame (ORF) encoding a protein of 223 amino acids. The shorter transcript encodes a protein of 170 amino acids. The porcine PGRMC2 gene consists of three exons 446 bp, 156 bp and 1259 bp in length. The promoter sequence is GC-rich and lacks a typical TATA box. Several putative cis-regulatory DNA motifs were identified in the 208-bp upstream genomic region. Five single nucleotide polymorphisms (SNPs) were detected in introns* and the 3' UTR. RT-PCR indicated that the PGRMC2 gene is expressed ubiquitously in all pig tissues examined.

A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions

AbstractTranscription is the central process of gene regulation. In higher eukaryotes, the transcription of a gene is usually regulated by multiple cis-regulatory regions (CRRs). In different tissues, different transcription factors bind to their cis-regulatory motifs in these CRRs to drive tissue-specific expression patterns of their target genes. By combining the genome-wide gene expression data with the genomic sequence data, we proposed multiple-instance scoring (MIS) method to predict the tissue-specific motifs and the corresponding CRRs. The method is mainly based on the assumption that only a subset of CRRs of the expressed gene should function in the studied tissue. By testing on the simulated datasets and the fly muscle dataset, MIS can identify true motifs when noise is high and shows higher specificity for predicting the tissue-specific functions of CRRs.

A multiple-instance scoring method to predict tissue-specific cis-regulatory motifs and regions

Transcription is the central process of gene regulation. In higher eukaryotes, the transcription of a gene is usually regulated by multiple cis-regulatory regions (CRRs). In different tissues, different transcription factors bind to their cis-regulatory motifs in these CRRs to drive tissue-specific expression patterns of their target genes. By combining the genome-wide gene expression data with the genomic sequence data, we proposed multiple-instance scoring (MIS) method to predict the tissue-specific motifs and the corresponding CRRs. The method is mainly based on the assumption that only a subset of CRRs of the expressed gene should function in the studied tissue. By testing on the simulated datasets and the fly muscle dataset, MIS can identify true motifs when noise is high and shows higher specificity for predicting the tissue-specific functions of CRRs.

Dissecting the fission yeast regulatory network reveals phase-specific control elements of its cell cycle

BackgroundFission yeast Schizosaccharomyces pombe and budding yeast Saccharomyces cerevisiae are among the original model organisms in the study of the cell-division cycle. Unlike budding yeast, no large-scale regulatory network has been constructed for fission yeast. It has only been partially characterized. As a result, important regulatory cascades in budding yeast have no known or complete counterpart in fission yeast.ResultsBy integrating genome-wide data from multiple time course cell cycle microarray experiments we reconstructed a gene regulatory network. Based on the network, we discovered in addition to previously known regulatory hubs in M phase, a new putative regulatory hub in the form of the HMG box transcription factor SPBC19G7.04. Further, we inferred periodic activities of several less known transcription factors over the course of the cell cycle, identified over 500 putative regulatory targets and detected many new phase-specific and conserved cis-regulatory motifs. In particular, we show that SPBC19G7.04 has highly significant periodic activity that peaks in early M phase, which is coordinated with the late G2 activity of the forkhead transcription factor fkh2. Finally, using an enhanced Bayesian algorithm to co-cluster the expression data, we obtained 31 clusters of co-regulated genes 1) which constitute regulatory modules from different phases of the cell cycle, 2) whose phase order is coherent across the 10 time course experiments, and 3) which lead to identification of phase-specific control elements at both the transcriptional and post-transcriptional levels in S. pombe. In particular, the ribosome biogenesis clusters expressed in G2 phase reveal new, highly conserved RNA motifs.ConclusionUsing a systems-level analysis of the phase-specific nature of the S. pombe cell cycle gene regulation, we have provided new testable evidence for post-transcriptional regulation in the G2 phase of the fission yeast cell cycle. Based on this comprehensive gene regulatory network, we demonstrated how one can generate and investigate plausible hypotheses on fission yeast cell cycle regulation which can potentially be explored experimentally.

Rapid Evolution of Sex Pheromone-Producing Enzyme Expression in Drosophila

Author SummaryMate selection is a complex process involving communication between potential partners on many levels, such as visual, aural, and olfactory cues. Many animals use chemical signals in the form of pheromones to communicate and correctly recognize individuals of the appropriate species and sex during reproduction. Evolutionary changes in the production of these chemicals have been suggested to contribute to speciation. Yet, the molecular mechanisms governing these transitions have seldom been addressed. Here, we show that expression of the gene desatF, which encodes an enzyme involved in the production of the Drosophila pheromones known as dienes, is highly variable and rapidly evolving across Drosophila species. Changes in desatF gene expression correlate with changes in sex- and species-specific production of dienes. Further, these changes in diene production can be explained by simple modifications in the regulatory regions of the desatF gene, providing a molecular level understanding of the evolution of pheromone production in Drosophila.

The proximal cis‐regulatory region of the RHD/RHCE promoter is 105 bp and contains a 55‐bp core devoid of known binding motifs but necessary for transcription

The RhD and RhCE polypeptides are erythroid-specific members of the RH gene family. Little is known about the promoter cis-regulatory proximal region responsible for transcription. The 1246-bp 5'-flanking regions of the RHD and RHCE promoter were amplified and ligated to a luciferase reporter vector and erythroid-specific transcription was evaluated in K562, HEL, U937, and HeLa cell lines. Deletion and substitution promoter-reporter constructs were generated to define the minimal cis-regulatory region. Deletion analysis in K562 cells revealed that the cis-regulatory region extended to a position between -78 and -120 relative to the site of initiation of transcription. Electrophoretic mobility shift assays confirmed binding motifs for Sp1, GATA-1, and E2F transcription factors. The use of 15-bp substitution mutagenesis showed that the minimal region required for transcription extended to -105 bp, and 6-bp substitution mutants from -35 to -90 identified a region necessary for transcription yet devoid of known cis-regulatory binding motifs. The proximal RHD/RHCE promoter regions contain cis-regulatory binding motifs and an internal sequence-dependent region that together regulate transcription. The results suggest that this region may be relevant in the weak expression of RhD.

Computational annotation of UTR cis-regulatory modules through Frequent Pattern Mining

BackgroundMany studies report about detection and functional characterization of cis-regulatory motifs in untranslated regions (UTRs) of mRNAs but little is known about the nature and functional role of their distribution. To address this issue we have developed a computational approach based on the use of data mining techniques. The idea is that of mining frequent combinations of translation regulatory motifs, since their significant co-occurrences could reveal functional relationships important for the post-transcriptional control of gene expression. The experimentation has been focused on targeted mitochondrial transcripts to elucidate the role of translational control in mitochondrial biogenesis and function.ResultsThe analysis is based on a two-stepped procedure using a sequential pattern mining algorithm. The first step searches for frequent patterns (FPs) of motifs without taking into account their spatial displacement. In the second step, frequent sequential patterns (FSPs) of spaced motifs are generated by taking into account the conservation of spacers between each ordered pair of co-occurring motifs. The algorithm makes no assumption on the relation among motifs and on the number of motifs involved in a pattern. Different FSPs can be found depending on different combinations of two parameters, i.e. the threshold of the minimum percentage of sequences supporting the pattern, and the granularity of spacer discretization. Results can be retrieved at the UTRminer web site: . The discovered FPs of motifs amount to 216 in the overall dataset and to 140 in the human subset. For each FP, the system provides information on the discovered FSPs, if any. A variety of search options help users in browsing the web resource. The list of sequence IDs supporting each pattern can be used for the retrieval of information from the UTRminer database.ConclusionComputational prediction of structural properties of regulatory sequences is not trivial. The presented data mining approach is able to overcome some limits observed in other competitive tools. Preliminary results on UTR sequences from nuclear transcripts targeting mitochondria are promising and lead us to be confident on the effectiveness of the approach for future developments.

Identification of gene co-regulatory modules and associated cis-elements involved in degenerative heart disease.

BackgroundCardiomyopathies, degenerative diseases of cardiac muscle, are among the leading causes of death in the developed world. Microarray studies of cardiomyopathies have identified up to several hundred genes that significantly alter their expression patterns as the disease progresses. However, the regulatory mechanisms driving these changes, in particular the networks of transcription factors involved, remain poorly understood. Our goals are (A) to identify modules of co-regulated genes that undergo similar changes in expression in various types of cardiomyopathies, and (B) to reveal the specific pattern of transcription factor binding sites, cis-elements, in the proximal promoter region of genes comprising such modules.MethodsWe analyzed 149 microarray samples from human hypertrophic and dilated cardiomyopathies of various etiologies. Hierarchical clustering and Gene Ontology annotations were applied to identify modules enriched in genes with highly correlated expression and a similar physiological function. To discover motifs that may underly changes in expression, we used the promoter regions for genes in three of the most interesting modules as input to motif discovery algorithms. The resulting motifs were used to construct a probabilistic model predictive of changes in expression across different cardiomyopathies.ResultsWe found that three modules with the highest degree of functional enrichment contain genes involved in myocardial contraction (n = 9), energy generation (n = 20), or protein translation (n = 20). Using motif discovery tools revealed that genes in the contractile module were found to contain a TATA-box followed by a CACC-box, and are depleted in other GC-rich motifs; whereas genes in the translation module contain a pyrimidine-rich initiator, Elk-1, SP-1, and a novel motif with a GCGC core. Using a naïve Bayes classifier revealed that patterns of motifs are statistically predictive of expression patterns, with odds ratios of 2.7 (contractile), 1.9 (energy generation), and 5.5 (protein translation).ConclusionWe identified patterns comprised of putative cis-regulatory motifs enriched in the upstream promoter sequence of genes that undergo similar changes in expression secondary to cardiomyopathies of various etiologies. Our analysis is a first step towards understanding transcription factor networks that are active in regulating gene expression during degenerative heart disease.

BRNI: Modular analysis of transcriptional regulatory programs

BackgroundTranscriptional responses often consist of regulatory modules – sets of genes with a shared expression pattern that are controlled by the same regulatory mechanisms. Previous methods allow dissecting regulatory modules from genomics data, such as expression profiles, protein-DNA binding, and promoter sequences. In cases where physical protein-DNA data are lacking, such methods are essential for the analysis of the underlying regulatory program.ResultsHere, we present a novel approach for the analysis of modular regulatory programs. Our method – Biochemical Regulatory Network Inference (BRNI) – is based on an algorithm that learns from expression data a biochemically-motivated regulatory program. It describes the expression profiles of gene modules consisting of hundreds of genes using a small number of regulators and affinity parameters. We developed an ensemble learning algorithm that ensures the robustness of the learned model. We then use the topology of the learned regulatory program to guide the discovery of a library of cis-regulatory motifs, and determined the motif compositions associated with each module.We test our method on the cell cycle regulatory program of the fission yeast. We discovered 16 coherent modules, covering diverse processes from cell division to metabolism and associated them with 18 learned regulatory elements, including both known cell-cycle regulatory elements (MCB, Ace2, PCB, ACCCT box) and novel ones, some of which are associated with G2 modules. We integrate the regulatory relations from the expression- and motif-based models into a single network, highlighting specific topologies that result in distinct dynamics of gene expression in the fission yeast cell cycle.ConclusionOur approach provides a biologically-driven, principled way for deconstructing a set of genes into meaningful transcriptional modules and identifying their associated cis-regulatory programs. Our analysis sheds light on the architecture and function of the regulatory network controlling the fission yeast cell cycle, and a similar approach can be applied to the regulatory underpinnings of other modular transcriptional responses.

Dual roles of the bZIP transcription factor PERIANTHIA in the control of floral architecture and homeotic gene expression

Flowers develop from floral meristems, which harbor stem cells that support the growth of floral organs. The MADS domain transcription factor AGAMOUS (AG) plays a central role in floral patterning and is required not only for the specification of the two reproductive organ types, but also for termination of stem cell fate. Using a highly conserved cis-regulatory motif as bait, we identified the bZIP transcription factor PERIANTHIA (PAN) as a direct regulator of AG in Arabidopsis. PAN and AG expression domains overlap, and mutations in either the PAN-binding site or PAN itself abolish the activity of a reporter devoid of redundant elements. Whereas under long-day conditions pan mutants have merely altered floral organ number, they display in addition typical AG loss-of-function phenotypes when grown under short days. Consistently, we found reduced AG RNA levels in these flowers. Finally, we show that PAN expression persists in ag mutant flowers, suggesting that PAN and AG are engaged in a negative-feedback loop, which might be mediated by the stem-cell-inducing transcription factor WUSCHEL (WUS).

Genome-wide de novo prediction of cis-regulatory binding sites in prokaryotes

Although cis-regulatory binding sites (CRBSs) are at least as important as the coding sequences in a genome, our general understanding of them in most sequenced genomes is very limited due to the lack of efficient and accurate experimental and computational methods for their characterization, which has largely hindered our understanding of many important biological processes. In this article, we describe a novel algorithm for genome-wide de novo prediction of CRBSs with high accuracy. We designed our algorithm to circumvent three identified difficulties for CRBS prediction using comparative genomics principles based on a new method for the selection of reference genomes, a new metric for measuring the similarity of CRBSs, and a new graph clustering procedure. When operon structures are correctly predicted, our algorithm can predict 81% of known individual binding sites belonging to 94% of known cis-regulatory motifs in the Escherichia coli K12 genome, while achieving high prediction specificity. Our algorithm has also achieved similar prediction accuracy in the Bacillus subtilis genome, suggesting that it is very robust, and thus can be applied to any other sequenced prokaryotic genome. When compared with the prior state-of-the-art algorithms, our algorithm outperforms them in both prediction sensitivity and specificity.

Discovering cis-Regulatory RNAs in Shewanella Genomes by Support Vector Machines

An increasing number of cis-regulatory RNA elements have been found to regulate gene expression post-transcriptionally in various biological processes in bacterial systems. Effective computational tools for large-scale identification of novel regulatory RNAs are strongly desired to facilitate our exploration of gene regulation mechanisms and regulatory networks. We present a new computational program named RSSVM (RNA Sampler+Support Vector Machine), which employs Support Vector Machines (SVMs) for efficient identification of functional RNA motifs from random RNA secondary structures. RSSVM uses a set of distinctive features to represent the common RNA secondary structure and structural alignment predicted by RNA Sampler, a tool for accurate common RNA secondary structure prediction, and is trained with functional RNAs from a variety of bacterial RNA motif/gene families covering a wide range of sequence identities. When tested on a large number of known and random RNA motifs, RSSVM shows a significantly higher sensitivity than other leading RNA identification programs while maintaining the same false positive rate. RSSVM performs particularly well on sets with low sequence identities. The combination of RNA Sampler and RSSVM provides a new, fast, and efficient pipeline for large-scale discovery of regulatory RNA motifs. We applied RSSVM to multiple Shewanella genomes and identified putative regulatory RNA motifs in the 5′ untranslated regions (UTRs) in S. oneidensis, an important bacterial organism with extraordinary respiratory and metal reducing abilities and great potential for bioremediation and alternative energy generation. From 1002 sets of 5′-UTRs of orthologous operons, we identified 166 putative regulatory RNA motifs, including 17 of the 19 known RNA motifs from Rfam, an additional 21 RNA motifs that are supported by literature evidence, 72 RNA motifs overlapping predicted transcription terminators or attenuators, and other candidate regulatory RNA motifs. Our study provides a list of promising novel regulatory RNA motifs potentially involved in post-transcriptional gene regulation. Combined with the previous cis-regulatory DNA motif study in S. oneidensis, this genome-wide discovery of cis-regulatory RNA motifs may offer more comprehensive views of gene regulation at a different level in this organism. The RSSVM software, predictions, and analysis results on Shewanella genomes are available at http://ural.wustl.edu/resources.html#RSSVM.

Regulation of brain gene expression in honey bees by brood pheromone

Pheromones are very important in animal communication. To learn more about the molecular basis of pheromone action, we studied the effects of a potent honey bee pheromone on brain gene expression. Brood pheromone (BP) caused changes in the expression of hundreds of genes in the bee brain in a manner consistent with its known effects on behavioral maturation. Brood pheromone exposure in young bees causes a delay in the transition from working in the hive to foraging, and we found that BP treatment tended to upregulate genes in the brain that are upregulated in bees specialized on brood care but downregulate genes that are upregulated in foragers. However, the effects of BP were age dependent; this pattern was reversed when older bees were tested, consistent with the stimulation of foraging by BP in older bees already competent to forage. These results support the idea that one way that pheromones influence behavior is by orchestrating large-scale changes in brain gene expression. We also found evidence for a relationship between cis and BP regulation of brain gene expression, with several cis-regulatory motifs statistically overrepresented in the promoter regions of genes regulated by BP. Transcription factors that target a few of these motifs have already been implicated in the regulation of bee behavior. Together these results demonstrate strong connections between pheromone effects, behavior, and regulation of brain gene expression.

Drosophila Proteome; Exome Re-sequencing; Sequencing in Nanopores

BioTechniquesVol. 46, No. 1 NIH Grant WatchOpen AccessDrosophila Proteome; Exome Re-sequencing; Sequencing in NanoporesK. Nybo† & B. Perry††K. Nybo††is assistant editor at BioTechniques.Search for more papers by this author & B. Perry††††is president of NIH Sales, Rockville, MD.Search for more papers by this authorPublished Online:25 Apr 2018https://doi.org/10.2144/000113054AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack Citations ShareShare onFacebookTwitterLinkedInReddit BioTechniques and NIH Sales reviewed grants made by the National Institutes of Health during 2008. Here are some highlights from among the largest R01 and R03 grants awarded last year.The Protein Complex Map of the Drosophila Proteome, $2,182,909(5R01HG003616-04, National Human Genome Research Institute)Spyridon Artavanis-Tsakonas (Harvard University Medical School, Boston, MA)Goal: To completely map protein interactions in Drosophila melanogaster. This will be accomplished by isolation of purified proteins that complex with tagged proteins and mass spectrometric analysis.Development and Implementation of Genome-wide Exome Re-sequencing for Medical Genetics, $1,784,943(1R01HL095045-01, National Heart, Lung, and Blood Institute)Stacey Gabriel (Massachusetts Institute of Technology, Cambridge, MA)Goal: To optimize and validate a cost-effective approach for whole-exon sequencing that will be used to sequence hundreds of samples at production scale. Next-generation sequencing technology will be employed. Data acquired from this project will facilitate broad, unbiased surveys of DNA-based variation in disease at a large enough scale to disclose statistically significant correlations.Electronic Sequencing in Nanopores, $1,685,766(2R01HG003703-04 and 5R01HG003703-03, National Human Genome Research Institute)Daniel Branton and Jene Golovchenko (Harvard University, Cambridge, MA)Goal: To create a nanopore detector chip for an instrument that generates base-dependent electronic signals from single strands of DNA as they pass through carbon nanotube probes. Inclusion of the resulting nanopores in the sequencing machine will resolve approximately 104 bases per second, allowing high quality sequencing of a mammalian genome within 20 hours for $1000.Tetrahymena Genome Sequencing Project, $1,373,429(7R01GM067012-04, National Institute of General Medical Sciences)Jonathan Eisen (University of California, Davis, Davis, CA)Goal: To complete whole-genome shotgun-sequencing, assembly, and annotation of the T. thermophila genome. Specifically, the project will result in sequence of high enough quality for post-genomic analysis, discovery of all genes, identification of putative genes, and prediction of gene function. The data will be immediately released on a TIGR website, a manually-curated Tetrahymena Genome Database, and a Tetrahymena-specific section on the NCBI website.Automated Alignment of MRI Rodent Brain to Sterotaxic Atlases, $175,750(1R03EB008134-01A1, National Institute of Biomedical Imaging and BioEngineering)Joseph Madeville (Massachusetts General Hospital, Boston, MA)Goal: To modify an optimized functional rodent MRI analysis scheme for open source distribution to the research community. The software resulting from this project will automate registration of MRI data to standardized stereotaxic coordinates and provide the means for transcribing anatomic regions based on histological atlases. This software will enable the use of extensive analysis packages developed for human MRI for rodent data.Application of Bioinformatics to Retinal Gene Regulation, $159,658(5R03EY015684-03, National Eye Institute)Jiang Qian (Johns Hopkins University, Baltimore, MD)Goal: To develop two algorithms for deciphering the transcriptional regulatory networks associated with retinal gene expression. Completion of the program will identify cis-regulatory motifs and predict transcription factor targets in retinal gene expression. Once established, these regulatory networks will be integrated with microarray data for prediction of transcription factor targets.FiguresReferencesRelatedDetails Vol. 46, No. 1 Follow us on social media for the latest updates Metrics History Published online 25 April 2018 Published in print January 2009 Information© 2009 Author(s)PDF download

Seed storage protein gene promoters contain conserved DNA motifs in Brassicaceae, Fabaceae and Poaceae

BackgroundAccurate computational identification of cis-regulatory motifs is difficult, particularly in eukaryotic promoters, which typically contain multiple short and degenerate DNA sequences bound by several interacting factors. Enrichment in combinations of rare motifs in the promoter sequence of functionally or evolutionarily related genes among several species is an indicator of conserved transcriptional regulatory mechanisms. This provides a basis for the computational identification of cis-regulatory motifs.ResultsWe have used a discriminative seeding DNA motif discovery algorithm for an in-depth analysis of 54 seed storage protein (SSP) gene promoters from three plant families, namely Brassicaceae (mustards), Fabaceae (legumes) and Poaceae (grasses) using backgrounds based on complete sets of promoters from a representative species in each family, namely Arabidopsis (Arabidopsis thaliana (L.) Heynh.), soybean (Glycine max (L.) Merr.) and rice (Oryza sativa L.) respectively. We have identified three conserved motifs (two RY-like and one ACGT-like) in Brassicaceae and Fabaceae SSP gene promoters that are similar to experimentally characterized seed-specific cis-regulatory elements. Fabaceae SSP gene promoter sequences are also enriched in a novel, seed-specific E2Fb-like motif. Conserved motifs identified in Poaceae SSP gene promoters include a GCN4-like motif, two prolamin-box-like motifs and an Skn-1-like motif. Evidence of the presence of a variant of the TATA-box is found in the SSP gene promoters from the three plant families. Motifs discovered in SSP gene promoters were used to score whole-genome sets of promoters from Arabidopsis, soybean and rice. The highest-scoring promoters are associated with genes coding for different subunits or precursors of seed storage proteins.ConclusionSeed storage protein gene promoter motifs are conserved in diverse species, and different plant families are characterized by a distinct combination of conserved motifs. The majority of discovered motifs match experimentally characterized cis-regulatory elements. These results provide a good starting point for further experimental analysis of plant seed-specific promoters and our methodology can be used to unravel more transcriptional regulatory mechanisms in plants and other eukaryotes.

Genetic and Computational Identification of a Conserved Bacterial Metabolic Module

We have experimentally and computationally defined a set of genes that form a conserved metabolic module in the α-proteobacterium Caulobacter crescentus and used this module to illustrate a schema for the propagation of pathway-level annotation across bacterial genera. Applying comprehensive forward and reverse genetic methods and genome-wide transcriptional analysis, we (1) confirmed the presence of genes involved in catabolism of the abundant environmental sugar myo-inositol, (2) defined an operon encoding an ABC-family myo-inositol transmembrane transporter, and (3) identified a novel myo-inositol regulator protein and cis-acting regulatory motif that control expression of genes in this metabolic module. Despite being encoded from non-contiguous loci on the C. crescentus chromosome, these myo-inositol catabolic enzymes and transporter proteins form a tightly linked functional group in a computationally inferred network of protein associations. Primary sequence comparison was not sufficient to confidently extend annotation of all components of this novel metabolic module to related bacterial genera. Consequently, we implemented the Graemlin multiple-network alignment algorithm to generate cross-species predictions of genes involved in myo-inositol transport and catabolism in other α-proteobacteria. Although the chromosomal organization of genes in this functional module varied between species, the upstream regions of genes in this aligned network were enriched for the same palindromic cis-regulatory motif identified experimentally in C. crescentus. Transposon disruption of the operon encoding the computationally predicted ABC myo-inositol transporter of Sinorhizobium meliloti abolished growth on myo-inositol as the sole carbon source, confirming our cross-genera functional prediction. Thus, we have defined regulatory, transport, and catabolic genes and a cis-acting regulatory sequence that form a conserved module required for myo-inositol metabolism in select α-proteobacteria. Moreover, this study describes a forward validation of gene-network alignment, and illustrates a strategy for reliably transferring pathway-level annotation across bacterial species.

The Promoter Signatures in Rice LEA Genes Can Be Used to Build a Co-expressing LEA Gene Network

Coordinated transcriptional modulation of large gene sets depends on the combinatorial use of cis-regulatory motifs in promoters. We postulate that promoter content similarities are diagnostic for co-expressing genes that function coherently during specific cellular responses. To find the co-expressing genes we propose an ab initio method that identifies motif families in promoters of target gene groups, map these families to the promoters of all genes in the genome, and determine the best matches of each of the target group gene promoters with all other promoters. When the method was tested in rice starting from a group of co-expressing Late Embryogenesis Abundant (LEA) genes, we obtained a promoter similarity-based network that contained candidate genes that could plausibly complement the function of LEA genes. Importantly, 73.36% of 244 genes predicted by our method were experimentally confirmed to co-express with the LEA genes in maturing rice embryos, making this methodology a promising tool for biological systems analyses.

Mitoferrin1 Transgenic Zebrafish Line Serves as a Model to Study Erythroid Cell Fate during Hematopoiesis

We previously identified the mitochondrial solute carrier, Mitoferrin1 (Slc25a37, Mfrn1) as the principal mitochondrial iron importer essential for heme and iron-sulfur (Fe-S) cluster synthesis in developing erythroblasts. Its closely related paralog, Mitoferrin2 (Slc25a28, Mfrn2) functions in an analogous role in non-erythroid cells. Zebrafish with mutations in Mfrn1 have defects in hemoglobinization and maturation of erythroid cells caused by defective acquisition of iron into the mitochondria (GC Shaw, et al., 2006 Nature 440:96–100). Mfrn1 is highly expressed in embryonic and definitive sites of hematopoiesis in zebrafish and mouse, such as the developing blood island (ICM), fetal liver and adult bone marrow. In contrast, Mfrn2 is ubiquitously expressed, including at very low levels in erythroid cells. To understand the transcriptional regulation of Mfrn1 and Mfrn2, we used bioinformatics tools to identify potential cis-regulatory motifs (CRM) within each gene from mouse. The Gateway-modified Tol-2 vector was used to rapidly clone these conserved, minimal CRM fragments from mouse upstream of the basal promoter and an EGFP-reporter. Each construct was then introduced into zebrafish embryos for transient and stable expression. Using this strategy, we identified CRM's that recapitulate the endogenous mRNA expression pattern of Mfrn genes during zebrafish development. Germ line stable transmission of the murine Tg(Mfrn1:EGFP) reporter in zebrafish showed robust EGFP expression in erythroid progenitors and mature erythrocytes and the remarkable conservation of function for CRM's across species. In contrast, the mouse Tg(Mfrn2:EGFP) was expressed in skeletal muscle, heart, liver, and pronephros. The Mfrn1 enhancer is located ~35 kb upstream of the transcription start site and contains two GATA consensus motifs, which bind GATA-1 by chromatin immunoprecipitation analysis. Moreover, the ~150 bp Mfrn1 enhancer fragment exhibits transcriptional activation when coupled to the minimal γ-globin promoter driving expression of a luciferase reporter in K562 cells. Site-directed mutagenesis revealed that both GATA motifs are required for robust erythroid expression. In a complementary approach, transient knockdown of GATA-1 in zebrafish embryos using anti-sense morpholinos selectively ablated Mfrn1 mRNA expression in the ICM, consistent with the epistatic relationship of GATA-1 and Mfrn1. The zebrafish transgenic lines harboring the two murine Mfrn enhancers have proven useful in studying the regulatory and developmental expression in the Mfrn genes in erythroid and non-hematopoietic organs, such as heart and liver. Our results show that the combined use of bioinformatics, Gateway-mediated cloning, and Tol-2 mediated transgenesis in zebrafish embryos is an effective approach to functionally interrogate the transcriptional activity of putative CRM's in vivo. The conservation and faithful expression of mouse CRM's in zebrafish demonstrate the utility of this functional approach for analyzing mammalian CRM's.