Robert H. Gross

Genome-wide analysis of light-inducible responses reveals hierarchical light signalling in Neurospora

White collar‐1 (WC‐1) and white collar‐2 (WC‐2) are essential for light‐mediated responses in Neurospora crassa, but the molecular mechanisms underlying gene induction and the roles of other real and putative photoreceptors remain poorly characterized. Unsupervised hierarchical clustering of genome‐wide microarrays reveals 5.6% of detectable transcripts, including several novel mediators, that are either early or late light responsive. Evidence is shown for photoreception in the absence of the dominant, and here confirmed, white collar complex (WCC) that regulates both types of light responses. VVD primarily modulates late responses, whereas light‐responsive submerged protoperithecia‐1 (SUB‐1), a GATA family transcription factor, is essential for most late light gene expression. After a 15‐min light stimulus, the WCC directly binds the sub‐1 promoter. Bioinformatics analysis detects many early light response elements (ELREs), as well as identifying a late light response element (LLRE) required for wild‐type activity of late light response promoters. The data provide a global picture of transcriptional response to light, as well as illuminating the cis‐ and trans‐acting elements comprising the regulatory signalling cascade that governs the photobiological response.

Phosphate‐dependent modulation of c‐di‐GMP levels regulates Pseudomonas fluorescens Pf0‐1 biofilm formation by controlling secretion of the adhesin LapA

Biofilm formation is commonly described as a developmental process regulated by environmental cues. In the current study we present a mechanistic model to explain regulation of Pseudomonas fluorescens biofilm formation by the environmentally relevant signal inorganic phosphate (Pi). We show that activation of the Pho regulon, the major pathway for adaptation to phosphate limitation, results in conditional expression of a c‐di‐GMP phosphodiesterase referred to as RapA. Genetic analysis indicated that RapA is an inhibitor of biofilm formation and required for loss of biofilm formation in response to limiting Pi. Our results suggest that RapA lowers the level of c‐di‐GMP, which in turn inhibits the secretion of LapA, a large adhesion required for biofilm formation by P. fluorescens. The ability of c‐di‐GMP to modulate protein secretion is a novel finding and further extends the biological influence of c‐di‐GMP beyond that of regulating exopolysaccharide synthesis and motility. Interestingly, Pho regulon expression does not impinge on the rate of attachment to a surface but rather inhibits the transition of cells to a more stable interaction with the surface. We hypothesize that Pho regulon expression confers a surface‐sensing mode on P. fluorescens and suggest this strategy may be broadly applicable to other bacteria.

SCOPE: a web server for practical de novo motif discovery

SCOPE is a novel parameter-free method for the de novo identification of potential regulatory motifs in sets of coordinately regulated genes. The SCOPE algorithm combines the output of three component algorithms, each designed to identify a particular class of motifs. Using an ensemble learning approach, SCOPE identifies the best candidate motifs from its component algorithms. In tests on experimentally determined datasets, SCOPE identified motifs with a significantly higher level of accuracy than a number of other web-based motif finders run with their default parameters. Because SCOPE has no adjustable parameters, the web server has an intuitive interface, requiring only a set of gene names or FASTA sequences and a choice of species. The most significant motifs found by SCOPE are displayed graphically on the main results page with a table containing summary statistics for each motif. Detailed motif information, including the sequence logo, PWM, consensus sequence and specific matching sites can be viewed through a single click on a motif. SCOPEs efficient, parameter-free search strategy has enabled the development of a web server that is readily accessible to the practising biologist while providing results that compare favorably with those of other motif finders. The SCOPE web server is at .

Developmentally-related candidate retinoic acid target genes regulated early during neuronal differentiation of human embryonal carcinoma

Embryonal carcinoma is a model of embryonic development as well as tumor cell differentiation. In response to all-trans retinoic acid (RA), the human embryonal carcinoma (EC) cell line, NT2/D1, differentiates toward a neuronal lineage with associated loss of cell growth and tumorigenicity. Through the use of cDNA-based microarrays we sought to identify the early downstream targets of RA during differentiation commitment of NT2/D1 cells. A total of 57 genes were induced and 37 genes repressed by RA. RA regulated genes were restricted at 8 h with 27 genes induced and five repressed. The total number of RA-responsive transcripts increased at 24 and 48 h and their pattern of expression was more symmetrical. For a given time point less than 1% of the 9128 cDNAs on the expression array were regulated by RA. Many of these gene products are associated with developmental pathways including those of TGF-β (Lefty A, NMA, follistatin), homeo domain (HoxD1, Meis2, Meis1, Gbx2), IGF (IGFBP3, IGFBP6, CTGF), Notch (manic fringe, ADAM11), Hedgehog (patched) and Wnt (Frat2, secreted frizzled-related protein 1) signaling. In addition a large cassette of genes induced by RA at 24–48 h are associated with cell adhesion, cytoskeletal and matrix remodeling, growth suppression and intracellular signaling cascades. The majority of repressed genes are associated with protein/RNA processing, turnover or metabolism. The early induced genes identified may play a regulatory role in RA-mediated growth suppression and terminal differentiation and may have physiologic or pharmacologic importance during normal human development and retinoid-based cancer therapy or prevention.

A novel ensemble learning method for de novo computational identification of DNA binding sites

BackgroundDespite the diversity of motif representations and search algorithms, the de novo computational identification of transcription factor binding sites remains constrained by the limited accuracy of existing algorithms and the need for user-specified input parameters that describe the motif being sought.ResultsWe present a novel ensemble learning method, SCOPE, that is based on the assumption that transcription factor binding sites belong to one of three broad classes of motifs: non-degenerate, degenerate and gapped motifs. SCOPE employs a unified scoring metric to combine the results from three motif finding algorithms each aimed at the discovery of one of these classes of motifs. We found that SCOPEs performance on 78 experimentally characterized regulons from four species was a substantial and statistically significant improvement over that of its component algorithms. SCOPE outperformed a broad range of existing motif discovery algorithms on the same dataset by a statistically significant margin.ConclusionSCOPE demonstrates that combining multiple, focused motif discovery algorithms can provide a significant gain in performance. By building on components that efficiently search for motifs without user-defined parameters, SCOPE requires as input only a set of upstream sequences and a species designation, making it a practical choice for non-expert users. A user-friendly web interface, Java source code and executables are available at http://genie.dartmouth.edu/scope.

Natural Selection Canalizes Expression Variation of Environmentally Induced Plasticity-Enabling Genes

Many organisms survive fluctuating and extreme environmental conditions by manifesting multiple distinct phenotypes during adulthood by means of developmental processes that enable phenotypic plasticity. We report on the discovery of putative plasticity-enabling genes that are involved in transforming the gill of the euryhaline teleost fish, Fundulus heteroclitus, from its freshwater to its seawater gill-type, a process that alters both morphology and function. Gene expression that normally enables osmotic plasticity is inhibited by arsenic. Gene sets defined by antagonistic interactions between arsenic and salinity show reduced transcriptional variation among individual fish, suggesting unusually accurate and precise regulatory control of these genes, consistent with the hypothesis that they participate in a canalized developmental response. We observe that natural selection acts to preserve canalized gene expression in populations of killifish that are most tolerant to abrupt salinity change and that these populations show the least variability in their transcription of genes enabling plasticity of the gill. We found that genes participating in this highly canalized and conserved plasticity-enabling response had significantly fewer and less complex associations with transcriptional regulators than genes that respond only to arsenic or salinity. Collectively these findings, which are drawn from the relationships between environmental challenge, plasticity, and canalization among populations, suggest that the selective processes that facilitate phenotypic plasticity do so by targeting the regulatory networks that gives rise to the response. These findings also provide a generalized, conceptual framework of how genes might interact with the environment and evolve toward the development of plastic traits.

Bounded search for de novo identification of degenerate cis-regulatory elements.

BackgroundThe identification of statistically overrepresented sequences in the upstream regions of coregulated genes should theoretically permit the identification of potential cis-regulatory elements. However, in practice many cis-regulatory elements are highly degenerate, precluding the use of an exhaustive word-counting strategy for their identification. While numerous methods exist for inferring base distributions using a position weight matrix, recent studies suggest that the independence assumptions inherent in the model, as well as the inability to reach a global optimum, limit this approach.ResultsIn this paper, we report PRISM, a degenerate motif finder that leverages the relationship between the statistical significance of a set of binding sites and that of the individual binding sites. PRISM first identifies overrepresented, non-degenerate consensus motifs, then iteratively relaxes each one into a high-scoring degenerate motif. This approach requires no tunable parameters, thereby lending itself to unbiased performance comparisons. We therefore compare PRISMs performance against nine popular motif finders on 28 well-characterized S. cerevisiae regulons. PRISM consistently outperforms all other programs. Finally, we use PRISM to predict the binding sites of uncharacterized regulons. Our results support a proposed mechanism of action for the yeast cell-cycle transcription factor Stb1, whose binding site has not been determined experimentally.ConclusionThe relationship between statistical measures of the binding sites and the set as a whole leads to a simple means of identifying the diverse range of cis-regulatory elements to which a protein binds. This approach leverages the advantages of word-counting, in that position dependencies are implicitly accounted for and local optima are more easily avoided. While we sacrifice guaranteed optimality to prevent the exponential blowup of exhaustive search, we prove that the error is bounded and experimentally show that the performance is superior to other methods. A Java implementation of this algorithm can be downloaded from our web server at http://genie.dartmouth.edu/prism.

BEAM: A Beam Search Algorithm for the Identification of Cis-Regulatory Elements in Groups of Genes

The identification of potential protein binding sites (cis-regulatory elements) in the upstream regions of genes is key to understanding the mechanisms that regulate gene expression. To this end, we present a simple, efficient algorithm, BEAM (beam-search enumerative algorithm for motif finding), aimed at the discovery of cis-regulatory elements in the DNA sequences upstream of a related group of genes. This algorithm dramatically limits the search space of expanded sequences, converting the problem from one that is exponential in the length of motifs sought to one that is linear. Unlike sampling algorithms, our algorithm converges and is capable of finding statistically overrepresented motifs with a low failure rate. Further, our algorithm is not dependent on the objective function or the organism used. Limiting the space of candidate motifs enables the algorithm to focus only on those motifs that are most likely to be biologically relevant and enables the algorithm to use direct evaluations of background frequencies instead of resorting to probabilistic estimates. In addition, limiting the space of candidate motifs makes it possible to use computationally expensive objective functions that are able to correctly identify biologically relevant motifs.

Splicing thermotolerance maintains pre-mRNA transcripts in the splicing pathway during severe heat shock

Thermotolerance, the ability of cells and organisms to withstand severe elevated temperatures after brief exposure to mild elevated temperatures, has been studied in numerous laboratories. Survival thermotolerance is defined as the increase in cell or organism survival at severe elevated temperatures after a pretreatment at mild elevated temperatures. This study examines splicing thermotolerance in Drosophila melanogaster, the ability to splice pre-mRNAs made at the severe temperature (38 degrees C) after a brief pretreatment at a milder temperature (35 degrees C). It is probably one of a number of mechanisms by which cells adapt to heat shock. These experiments demonstrate that pre-mRNAs synthesized at the severe temperatures in splicing thermotolerant cells, although protected in splicing-competent complexes, are not actually processed to mature mRNAs until the cells are returned to their normal temperature. We have also studied the kinetics of acquisition and loss of splicing thermotolerance. As little as 10 min of pretreatment at 35 degrees C was sufficient to provide full splicing thermotolerance to a 30-min severe heat shock of 38 degrees C. Pretreatments of less than 10 min provide partial splicing thermotolerance for a 30-min severe heat shock. Full splicing thermotolerance activity begins to decay about 4 h after the cessation of the 35 degrees C incubation and is completely lost by 8 h after the pretreatment. The kinetics experiments of pre-mRNAs synthesized during the 38 degrees C treatment in splicing thermotolerant cells indicate that one or more splicing thermotolerance factors are synthesized during the 35 degrees C pretreatment which interact with pre-mRNA-containing complexes to keep them in a splicing-competent state. These kinetic experiments also indicate that in cells which are partially splicing thermotolerant, the pre-mRNAs synthesized early during the 38 degrees C incubation are protected, whereas those synthesized late are not. In the absence of splicing thermotolerant factors, the pre-mRNA-containing complexes leave the normal splicing pathway and are allowed to exit to the cytoplasm.

Identifying functional relationships within sets of co-expressed genes by combining upstream regulatory motif analysis and gene expression information

BackgroundExisting clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.ResultsWe applied our method to several sets of co-expressed genes and were able to define subsets with enrichment in particular biological processes and specific upstream regulatory motifs.ConclusionsThese results show the potential of our technique for functional prediction and regulatory motif identification from microarray data.