Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences.

Most homeodomains are unique within a genome, yet many are highly conserved across vast evolutionary distances, implying strong selection on their precise DNA-binding specificities. We determined the binding preferences of the majority (168) of mouse homeodomains to all possible 8-base sequences, revealing rich and complex patterns of sequence specificity and showing that there are at least 65 distinct homeodomain DNA-binding activities. We developed a computational system that successfully predicts binding sites for homeodomain proteins as distant from mouse as Drosophila and C. elegans, and we infer full 8-mer binding profiles for the majority of known animal homeodomains. Our results provide an unprecedented level of resolution in the analysis of this simple domain structure and suggest that variation in sequence recognition may be a factor in its functional diversity and evolutionary success.

sRNARFTarget: a fast machine-learning-based approach for transcriptome-wide sRNA target prediction.

Bacterial small regulatory RNAs (sRNAs) are key regulators of gene expression in many processes related to adaptive responses. A multitude of sRNAs have been identified in many bacterial species; however, their function has yet to be elucidated. A key step to understand sRNAs function is to identify the mRNAs these sRNAs bind to. There are several computational methods for sRNA target prediction, and the most accurate one is CopraRNA which is based on comparative-genomics. However, species-specific sRNAs are quite common and CopraRNA cannot be used for these sRNAs. The most commonly used transcriptome-wide sRNA target prediction method and second-most-accurate method is IntaRNA. However, IntaRNA can take hours to run on a bacterial transcriptome. Here we present sRNARFTarget, a machine-learning-based method for transcriptome-wide sRNA target prediction applicable to any sRNA. We comparatively assessed the performance of sRNARFTarget, CopraRNA and IntaRNA in three bacterial species. Our results show that sRNARFTarget outperforms IntaRNA in terms of accuracy, ranking of true interacting pairs, and running time. However, CopraRNA substantially outperforms the other two programsin terms of accuracy. Thus, we suggest using CopraRNA when homolog sequences of the sRNA are available, and sRNARFTarget for transcriptome-wide prediction or for species-specific sRNAs. sRNARFTarget is available at https://github.com/BioinformaticsLabAtMUN/sRNARFTarget.

A Study on the Geometric and Kinematic Descriptors of Trajectories in the Classification of Ship Types.

The classification of ships based on their trajectory descriptors is a common practice that is helpful in various contexts, such as maritime security and traffic management. For the most part, the descriptors are either geometric, which capture the shape of a ship's trajectory, or kinematic, which capture the motion properties of a ship's movement. Understanding the implications of the type of descriptor that is used in classification is important for feature engineering and model interpretation. However, this matter has not yet been deeply studied. This article contributes to feature engineering within this field by introducing proper similarity measures between the descriptors and defining sound benchmark classifiers, based on which we compared the predictive performance of geometric and kinematic descriptors. The performance profiles of geometric and kinematic descriptors, along with several standard tools in interpretable machine learning, helped us to provide an account of how different ships differ in movement. Our results indicated that the predictive performance of geometric and kinematic descriptors varied greatly, depending on the classification problem at hand. We also showed that the movement of certain ship classes solely differed geometrically while some other classes differed kinematically and that this difference could be formulated in simple terms. On the other hand, the movement characteristics of some other ship classes could not be delineated along these lines and were more complicated to express. Finally, this study verified the conjecture that the geometric-kinematic taxonomy could be further developed as a tool for more accessible feature selection.

Learning-induced mRNA alterations in olfactory bulb mitral cells in neonatal rats.

In the olfactory bulb, a cAMP/PKA/CREB-dependent form of learning occurs in the first week of life that provides a unique mammalian model for defining the epigenetic role of this evolutionarily ancient plasticity cascade. Odor preference learning in the week-old rat pup is rapidly induced by a 10-min pairing of odor and stroking. Memory is demonstrable at 24 h, but not 48 h, posttraining. Using this paradigm, pups that showed peppermint preference 30 min posttraining were sacrificed 20 min later for laser microdissection of odor-encoding mitral cells. Controls were given odor only. Microarray analysis revealed that 13 nonprotein-coding mRNAs linked to mRNA translation and splicing and 11 protein-coding mRNAs linked to transcription differed with odor preference training. MicroRNA23b, a translation inhibitor of multiple plasticity-related mRNAs, was down-regulated. Protein-coding transcription was up-regulated for Sec23b, Clic2, Rpp14, Dcbld1, Magee2, Mstn, Fam229b, RGD1566265, and Mgst2. Gng12 and Srcg1 mRNAs were down-regulated. Increases in Sec23b, Clic2, and Dcbld1 proteins were confirmed in mitral cells in situ at the same time point following training. The protein-coding changes are consistent with extracellular matrix remodeling and ryanodine receptor involvement in odor preference learning. A role for CREB and AP1 as triggers of memory-related mRNA regulation is supported. The small number of gene changes identified in the mitral cell input/output link for 24 h memory will facilitate investigation of the nature, and reversibility, of changes supporting temporally restricted long-term memory.

Cyclic di-GMP-Mediated Regulation of Gene Transfer and Motility in Rhodobacter capsulatus.

Gene transfer agents (GTAs) are bacteriophage-like particles produced by several bacterial and archaeal lineages that contain small pieces of the producing cells' genomes that can be transferred to other cells in a process similar to transduction. One well-studied GTA is RcGTA, produced by the alphaproteobacterium Rhodobacter capsulatus RcGTA gene expression is regulated by several cellular regulatory systems, including the CckA-ChpT-CtrA phosphorelay. The transcription of multiple other regulator-encoding genes is affected by the response regulator CtrA, including genes encoding putative enzymes involved in the synthesis and hydrolysis of the second messenger bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). To investigate whether c-di-GMP signaling plays a role in RcGTA production, we disrupted the CtrA-affected genes potentially involved in this process. We found that disruption of four of these genes affected RcGTA gene expression and production. We performed site-directed mutagenesis of key catalytic residues in the GGDEF and EAL domains responsible for diguanylate cyclase (DGC) and c-di-GMP phosphodiesterase (PDE) activities and analyzed the functions of the wild-type and mutant proteins. We also measured RcGTA production in R. capsulatus strains where intracellular levels of c-di-GMP were altered by the expression of either a heterologous DGC or a heterologous PDE. This adds c-di-GMP signaling to the collection of cellular regulatory systems controlling gene transfer in this bacterium. Furthermore, the heterologous gene expression and the four gene disruptions had similar effects on R. capsulatus flagellar motility as found for gene transfer, and we conclude that c-di-GMP inhibits both RcGTA production and flagellar motility in R. capsulatusIMPORTANCE Gene transfer agents (GTAs) are virus-like particles that move cellular DNA between cells. In the alphaproteobacterium Rhodobacter capsulatus, GTA production is affected by the activities of multiple cellular regulatory systems, to which we have now added signaling via the second messenger dinucleotide molecule bis-(3'-5')-cyclic dimeric GMP (c-di-GMP). Similar to the CtrA phosphorelay, c-di-GMP also affects R. capsulatus flagellar motility in addition to GTA production, with lower levels of intracellular c-di-GMP favoring increased flagellar motility and gene transfer. These findings further illustrate the interconnection of GTA production with global systems of regulation in R. capsulatus, providing additional support for the notion that the production of GTAs has been maintained in this and related bacteria because it provides a benefit to the producing organisms.

Genetic variation associated with healthy traits and environmental conditions in Vaccinium vitis-idaea.

BACKGROUND: Lingonberry (Vaccinium vitis-idaea L.), one of the least studied fruit crops in the Ericaceae family, has a dramatically increased worldwide demand due to its numerous health benefits. Genetic markers can facilitate the selection of berries with desirable climatic adaptations, agronomic and nutritious characteristics to improve cultivation programs. However, no genomic resources are available for this species. RESULTS: We used Genotyping-by-Sequencing (GBS) to analyze the genetic variation of 56 lingonberry samples from across Newfoundland and Labrador, Canada. To elucidate a potential adaptation to environmental conditions we searched for genotype-environment associations by applying three distinct approaches to screen the identified single nucleotide polymorphisms (SNPs) for correlation with six environmental variables. We also searched for an association between the identified SNPs and two phenotypic traits: the total phenolic content (TPC) and antioxidant capacity (AC) of fruit. We identified 1586 high-quality putative SNPs using the UNEAK pipeline available in TASSEL. We found 132 SNPs likely associated with at least one of the environmental or phenotypic variables. To obtain insights on the function of the genomic sequences containing the SNPs likely to be associated with the environmental or phenotypic variables, we performed a sequence-based functional annotation and identified homologous protein-coding sequences with functional roles related to abiotic stress response, pathogen defense, RNA metabolism, and, most interestingly, phenolic compound biosynthesis. CONCLUSIONS: The putative SNPs discovered are the first genomic resource for lingonberry. This resource might prove useful in high-density quantitative trait locus analysis, and association mapping. The identified candidate genes containing the SNPs need further studies on their potential role in local adaptation of lingonberry. Altogether, the present study provides new resources that can be used to breed for desirable traits in lingonberry.

Genome-wide identification and characterization of small RNAs in Rhodobacter capsulatus and identification of small RNAs affected by loss of the response regulator CtrA.

Small non-coding RNAs (sRNAs) are involved in the control of numerous cellular processes through various regulatory mechanisms, and in the past decade many studies have identified sRNAs in a multitude of bacterial species using RNA sequencing (RNA-seq). Here, we present the first genome-wide analysis of sRNA sequencing data in Rhodobacter capsulatus, a purple nonsulfur photosynthetic alphaproteobacterium. Using a recently developed bioinformatics approach, sRNA-Detect, we detected 422 putative sRNAs from R. capsulatus RNA-seq data. Based on their sequence similarity to sRNAs in a sRNA collection, consisting of published putative sRNAs from 23 additional bacterial species, and RNA databases, the sequences of 124 putative sRNAs were conserved in at least one other bacterial species; and, 19 putative sRNAs were assigned a predicted function. We bioinformatically characterized all putative sRNAs and applied machine learning approaches to calculate the probability of a nucleotide sequence to be a bona fide sRNA. The resulting quantitative model was able to correctly classify 95.2% of sequences in a validation set. We found that putative cis-targets for antisense and partially overlapping sRNAs were enriched with protein-coding genes involved in primary metabolic processes, photosynthesis, compound binding, and with genes forming part of macromolecular complexes. We performed differential expression analysis to compare the wild type strain to a mutant lacking the response regulator CtrA, an important regulator of gene expression in R. capsulatus, and identified 18 putative sRNAs with differing levels in the two strains. Finally, we validated the existence and expression patterns of four novel sRNAs by Northern blot analysis.

Two-color cell array screen reveals interdependent roles for histone chaperones and a chromatin boundary regulator in histone gene repression.

We describe a fluorescent reporter system that exploits the functional genomic tools available in budding yeast to systematically assess consequences of genetic perturbations on gene expression. We used our Reporter-Synthetic Genetic Array (R-SGA) method to screen for regulators of core histone gene expression. We discovered that the histone chaperone Rtt106 functions in a pathway with two other chaperones, Asf1 and the HIR complex, to create a repressive chromatin structure at core histone promoters. We found that activation of histone (HTA1) gene expression involves both relief of Rtt106-mediated repression by the activity of the histone acetyltransferase Rtt109 and restriction of Rtt106 to the promoter region by the bromodomain-containing protein Yta7. We propose that the maintenance of Asf1/HIR/Rtt106-mediated repressive chromatin domains is the primary mechanism of cell-cycle regulation of histone promoters. Our data suggest that this pathway may represent a chromatin regulatory mechanism that is broadly used across the genome.

Private rare deletions in SEC16A and MAMDC4 may represent novel pathogenic variants in familial axial spondyloarthritis.

OBJECTIVE: Axial spondyloarthritis (AxSpA) represents a group of inflammatory axial diseases that share common clinical and histopathological manifestations. Ankylosing spondylitis (AS) is the best characterised subset of AxSpA, and its genetic basis has been extensively investigated. Given that genome-wide association studies account for only 25% of AS heritability, the objective of this study was to discover rare, highly penetrant genetic variants in AxSpA pathogenesis using a well-characterised, multigenerational family. METHODS: HLA-B*27 genotyping and exome sequencing was performed on DNA collected from available family members. Variant frequency was assessed by mining publically available datasets and using fragment analysis of unrelated AxSpA cases and unaffected controls. Gene expression was performed by qPCR, and protein expression was assessed by western blot analysis and immunofluorescence microscopy using patient-derived B-cell lines. Circular dichroism spectroscopy was performed to assess the impact of discovered variants on secondary structure. RESULTS: This is the first report identifying two rare private familial variants in a multigenerational AxSpA family, an in-frame SEC16A deletion and an out-of-frame MAMDC4 deletion. Evidence suggests the causative mechanism for SEC16A appears to be a conformational change induced by deletion of three highly conserved amino acids from the intrinsically disordered Sec16A N-terminus and RNA-mediated decay for MAMDC4. CONCLUSIONS: The results suggest that it is the presence of rare syntenic SEC16A and MAMDC4 deletions that increases susceptibility to AxSpA in family members who carry the HLA-B*27 allele.

A library of yeast transcription factor motifs reveals a widespread function for Rsc3 in targeting nucleosome exclusion at promoters.

The sequence specificity of DNA-binding proteins is the primary mechanism by which the cell recognizes genomic features. Here, we describe systematic determination of yeast transcription factor DNA-binding specificities. We obtained binding specificities for 112 DNA-binding proteins representing 19 distinct structural classes. One-third of the binding specificities have not been previously reported. Several binding sequences have striking genomic distributions relative to transcription start sites, supporting their biological relevance and suggesting a role in promoter architecture. Among these are Rsc3 binding sequences, containing the core CGCG, which are found preferentially approximately 100 bp upstream of transcription start sites. Mutation of RSC3 results in a dramatic increase in nucleosome occupancy in hundreds of proximal promoters containing a Rsc3 binding element, but has little impact on promoters lacking Rsc3 binding sequences, indicating that Rsc3 plays a broad role in targeting nucleosome exclusion at yeast promoters.

Proteomics analysis of global regulatory cascades involved in clavulanic acid production and morphological development in Streptomyces clavuligerus.

The genus Streptomyces comprises bacteria that undergo a complex developmental life cycle and produce many metabolites of importance to industry and medicine. Streptomyces clavuligerus produces the ß-lactamase inhibitor clavulanic acid, which is used in combination with ß-lactam antibiotics to treat certain ß-lactam resistant bacterial infections. Many aspects of how clavulanic acid production is globally regulated in S. clavuligerus still remains unknown. We conducted comparative proteomics analysis using the wild type strain of S. clavuligerus and two mutants (ΔbldA and ΔbldG), which are defective in global regulators and vary in their ability to produce clavulanic acid. Approximately 33.5 % of the predicted S. clavuligerus proteome was detected and 192 known or putative regulatory proteins showed statistically differential expression levels in pairwise comparisons. Interestingly, the expression of many proteins whose corresponding genes contain TTA codons (predicted to require the bldA tRNA for translation) was unaffected in the bldA mutant.

Gene co-expression network analysis in Rhodobacter capsulatus and application to comparative expression analysis of Rhodobacter sphaeroides.

BACKGROUND: The genus Rhodobacter contains purple nonsulfur bacteria found mostly in freshwater environments. Representative strains of two Rhodobacter species, R. capsulatus and R. sphaeroides, have had their genomes fully sequenced and both have been the subject of transcriptional profiling studies. Gene co-expression networks can be used to identify modules of genes with similar expression profiles. Functional analysis of gene modules can then associate co-expressed genes with biological pathways, and network statistics can determine the degree of module preservation in related networks. In this paper, we constructed an R. capsulatus gene co-expression network, performed functional analysis of identified gene modules, and investigated preservation of these modules in R. capsulatus proteomics data and in R. sphaeroides transcriptomics data. RESULTS: The analysis identified 40 gene co-expression modules in R. capsulatus. Investigation of the module gene contents and expression profiles revealed patterns that were validated based on previous studies supporting the biological relevance of these modules. We identified two R. capsulatus gene modules preserved in the protein abundance data. We also identified several gene modules preserved between both Rhodobacter species, which indicate that these cellular processes are conserved between the species and are candidates for functional information transfer between species. Many gene modules were non-preserved, providing insight into processes that differentiate the two species. In addition, using Local Network Similarity (LNS), a recently proposed metric for expression divergence, we assessed the expression conservation of between-species pairs of orthologs, and within-species gene-protein expression profiles. CONCLUSIONS: Our analyses provide new sources of information for functional annotation in R. capsulatus because uncharacterized genes in modules are now connected with groups of genes that constitute a joint functional annotation. We identified R. capsulatus modules enriched with genes for ribosomal proteins, porphyrin and bacteriochlorophyll anabolism, and biosynthesis of secondary metabolites to be preserved in R. sphaeroides whereas modules related to RcGTA production and signalling showed lack of preservation in R. sphaeroides. In addition, we demonstrated that network statistics may also be applied within-species to identify congruence between mRNA expression and protein abundance data for which simple correlation measurements have previously had mixed results.

Assessing the reproducibility of machine-learning-based biomarker discovery in Parkinson's disease.

Feature selection and machine learning algorithms can be used to analyze Single Nucleotide Polymorphisms (SNPs) data and identify potential disease biomarkers. Reproducibility of identified biomarkers is critical for them to be useful for clinical research; however, genotyping platforms and selection criteria for individuals to be genotyped affect the reproducibility of identified biomarkers. To assess biomarkers reproducibility, we collected five SNPs datasets from the database of Genotypes and Phenotypes (dbGaP) and explored several data integration strategies. While combining datasets can lead to a reduction in classification accuracy, it has the potential to improve the reproducibility of potential biomarkers. We evaluated the agreement among different strategies in terms of the SNPs that were identified as potential Parkinson's disease (PD) biomarkers. Our findings indicate that, on average, 93% of the SNPs identified in a single dataset fail to be identified in other datasets. However, through dataset integration, this lack of replication is reduced to 62%. We discovered fifty SNPs that were identified at least twice, which could potentially serve as novel PD biomarkers. These SNPs are indirectly linked to PD in the literature but have not been directly associated with PD before. These findings open up new potential avenues of investigation.

Analysis of Escherichia coli RNase E and RNase III activity in vivo using tiling microarrays.

Tiling microarrays have proven to be a valuable tool for gaining insights into the transcriptomes of microbial organisms grown under various nutritional or stress conditions. Here, we describe the use of such an array, constructed at the level of 20 nt resolution for the Escherichia coli MG1655 genome, to observe genome-wide changes in the steady-state RNA levels in mutants defective in either RNase E or RNase III. The array data were validated by comparison to previously published results for a variety of specific transcripts as well as independent northern analysis of additional mRNAs and sRNAs. In the absence of RNase E, 60% of the annotated coding sequences showed either increases or decreases in their steady-state levels. In contrast, only 12% of the coding sequences were affected in the absence of RNase III. Unexpectedly, many coding sequences showed decreased abundance in the RNase E mutant, while more than half of the annotated sRNAs showed changes in abundance. Furthermore, the steady-state levels of many transcripts showed overlapping effects of both ribonucleases. Data are also presented demonstrating how the arrays were used to identify potential new genes, RNase III cleavage sites and the direct or indirect control of specific biological pathways.

Extracellular vesicle small RNA cargo discriminates non-cancer donors from pediatric B-lymphoblastic leukemia patients.

Pediatric B-acute lymphoblastic leukemia (B-ALL) is a disease of abnormally growing B lymphoblasts. Here we hypothesized that extracellular vesicles (EVs), which are nanosized particles released by all cells (including cancer cells), could be used to monitor B-ALL severity and progression by sampling plasma instead of bone marrow. EVs are especially attractive as they are present throughout the circulation regardless of the location of the originating cell. First, we used nanoparticle tracking analysis to compare EVs between non-cancer donor (NCD) and B-ALL blood plasma; we found that B-ALL plasma contains more EVs than NCD plasma. We then isolated EVs from NCD and pediatric B-ALL peripheral blood plasma using a synthetic peptide-based isolation technique (Vn96), which is clinically amenable and isolates a broad spectrum of EVs. RNA-seq analysis of small RNAs contained within the isolated EVs revealed a signature of differentially packaged and exclusively packaged RNAs that distinguish NCD from B-ALL. The plasma EVs contain a heterogenous mixture of miRNAs and fragments of long non-coding RNA (lncRNA) and messenger RNA (mRNA). Transcripts packaged in B-ALL EVs include those involved in negative cell cycle regulation, potentially suggesting that B-ALL cells may use EVs to discard gene sequences that control growth. In contrast, NCD EVs carry sequences representative of multiple organs, including brain, muscle, and epithelial cells. This signature could potentially be used to monitor B-ALL disease burden in pediatric B-ALL patients via blood draws instead of invasive bone marrow aspirates.

Promotech: a general tool for bacterial promoter recognition.

Promoters are genomic regions where the transcription machinery binds to initiate the transcription of specific genes. Computational tools for identifying bacterial promoters have been around for decades. However, most of these tools were designed to recognize promoters in one or few bacterial species. Here, we present Promotech, a machine-learning-based method for promoter recognition in a wide range of bacterial species. We compare Promotech's performance with the performance of five other promoter prediction methods. Promotech outperforms these other programs in terms of area under the precision-recall curve (AUPRC) or precision at the same level of recall. Promotech is available at https://github.com/BioinformaticsLabAtMUN/PromoTech .

Computational Methods for Elucidating Gene Expression Regulation in Bacteria.

Research over the past two decades has uncovered an unexpected complexity and intricacy of gene expression regulation in bacteria. Bacteria have (1) numerous small noncoding RNAs (sRNAs) which are ubiquitous regulators of gene expression, (2) a flexible and diverse promoter structure, and (3) transcription termination as another means of gene expression regulation.To understand bacteria gene expression regulation, one needs to identify promoters, terminators, and sRNAs together with their targets. Here we describe the state of the art in computational methods to perform promoter recognition, sRNA identification, and sRNA target prediction. Additionally, we provide step-by-step instructions to use current approaches to perform these tasks.

Predicting the binding preference of transcription factors to individual DNA k-mers.

MOTIVATION: Recognition of specific DNA sequences is a central mechanism by which transcription factors (TFs) control gene expression. Many TF-binding preferences, however, are unknown or poorly characterized, in part due to the difficulty associated with determining their specificity experimentally, and an incomplete understanding of the mechanisms governing sequence specificity. New techniques that estimate the affinity of TFs to all possible k-mers provide a new opportunity to study DNA-protein interaction mechanisms, and may facilitate inference of binding preferences for members of a given TF family when such information is available for other family members. RESULTS: We employed a new dataset consisting of the relative preferences of mouse homeodomains for all eight-base DNA sequences in order to ask how well we can predict the binding profiles of homeodomains when only their protein sequences are given. We evaluated a panel of standard statistical inference techniques, as well as variations of the protein features considered. Nearest neighbour among functionally important residues emerged among the most effective methods. Our results underscore the complexity of TF-DNA recognition, and suggest a rational approach for future analyses of TF families.

Parasite burden and CD36-mediated sequestration are determinants of acute lung injury in an experimental malaria model.

Although acute lung injury (ALI) is a common complication of severe malaria, little is known about the underlying molecular basis of lung dysfunction. Animal models have provided powerful insights into the pathogenesis of severe malaria syndromes such as cerebral malaria (CM); however, no model of malaria-induced lung injury has been definitively established. This study used bronchoalveolar lavage (BAL), histopathology and gene expression analysis to examine the development of ALI in mice infected with Plasmodium berghei ANKA (PbA). BAL fluid of PbA-infected C57BL/6 mice revealed a significant increase in IgM and total protein prior to the development of CM, indicating disruption of the alveolar-capillary membrane barrier-the physiological hallmark of ALI. In contrast to sepsis-induced ALI, BAL fluid cell counts remained constant with no infiltration of neutrophils. Histopathology showed septal inflammation without cellular transmigration into the alveolar spaces. Microarray analysis of lung tissue from PbA-infected mice identified a significant up-regulation of expressed genes associated with the gene ontology categories of defense and immune response. Severity of malaria-induced ALI varied in a panel of inbred mouse strains, and development of ALI correlated with peripheral parasite burden but not CM susceptibility. Cd36(-/-) mice, which have decreased parasite lung sequestration, were relatively protected from ALI. In summary, parasite burden and CD36-mediated sequestration in the lung are primary determinants of ALI in experimental murine malaria. Furthermore, differential susceptibility of mouse strains to malaria-induced ALI and CM suggests that distinct genetic determinants may regulate susceptibility to these two important causes of malaria-associated morbidity and mortality.

Comprehensive genetic analysis of transcription factor pathways using a dual reporter gene system in budding yeast.

The development and application of genomic reagents and techniques has fuelled progress in our understanding of regulatory networks that control gene expression in eukaryotic cells. However, a full description of the network of regulator-gene interactions that determine global gene expression programs remains elusive and will require systematic genetic as well as biochemical assays. Here, we describe a functional genomics approach that combines reporter technology, genome-wide array-based reagents and high-throughput imaging to discover new regulators controlling gene expression patterns in Saccharomyces cerevisiae. Our strategy utilizes the synthetic genetic array (SGA) method to systematically introduce promoter-GFP (green fluorescent protein) reporter constructs along with a control promoter-RFP (red fluorescent protein) gene into the array of approximately 4500 viable yeast deletion mutants. Fluorescence intensities from each reporter are assayed from individual colonies arrayed on solid agar plates using a scanning fluorimager and the ratio of GFP to RFP intensity reveals deletion mutants that cause differential GFP expression. We are exploiting this screening approach to construct a detailed map describing the interplay of regulators controlling the eukaryotic cell cycle. The method is extensible to any transcription factor or signalling pathway for which an appropriate reporter gene can be devised.