Biomedical subjects

Eric D Siggia

Publications and source records attributed to Eric D Siggia.

10 recordsLinked to original sources

PubMed2003

Genome wide identification of regulatory motifs in Bacillus subtilis.

BACKGROUND: To explain the vastly different phenotypes exhibited by the same organism under different conditions, it is essential that we understand how the organism's genes are coordinately regulated. While there are many excellent tools for predicting sequences encoding proteins or RNA genes, few algorithms exist to predict regulatory sequences on a genome wide scale with no prior information. RESULTS: To identify motifs involved in the control of transcription, an algorithm was developed that searches upstream of operons for improbably frequent dimers. The algorithm was applied to the B. subtilis genome, which is predicted to encode for approximately 200 DNA binding proteins. The dimers found to be over-represented could be clustered into 317 distinct groups, each thought to represent a class of motifs uniquely recognized by some transcription factor. For each cluster of dimers, a representative weight matrix was derived and scored over the regions upstream of the operons to predict the sites recognized by the cluster's factor, and a putative regulon of the operons immediately downstream of the sites was inferred. The distribution in number of operons per predicted regulon is comparable to that for well characterized transcription factors. The most highly over-represented dimers matched sigmaA, the T-box, and sigmaW sites. We have evidence to suggest that at least 52 of our clusters of dimers represent actual regulatory motifs, based on the groups' weight matrix matches to experimentally characterized sites, the functional similarity of the component operons of the groups' regulons, and the positional biases of the weight matrix matches. All predictions are assigned a significance value, and thresholds are set to avoid false positives. Where possible, we examine our false negatives, drawing examples from known regulatory motifs and regulons inferred from RNA expression data. CONCLUSIONS: We have demonstrated that in the case of B. subtilis our algorithm allows for the genome wide identification of regulatory sites. As well as recovering known sites, we predict new sites of yet uncharacterized factors. Results can be viewed at http://www.physics.rockefeller.edu/~mwangi/.

Algorithms↗

PubMed2003

A probabilistic method to detect regulatory modules.

MOTIVATION: The discovery of cis-regulatory modules in metazoan genomes is crucial for understanding the connection between genes and organism diversity. RESULTS: We develop a computational method that uses Hidden Markov Models and an Expectation Maximization algorithm to detect such modules, given the weight matrices of a set of transcription factors known to work together. Two novel features of our probabilistic model are: (i) correlations between binding sites, known to be required for module activity, are exploited, and (ii) phylogenetic comparisons among sequences from multiple species are made to highlight a regulatory module. The novel features are shown to improve detection of modules, in experiments on synthetic as well as biological data.

Algorithms↗

PubMed2002

Computational detection of genomic cis-regulatory modules applied to body patterning in the early Drosophila embryo.

BACKGROUND: Regulation of gene transcription is crucial for the function and development of all organisms. While gene prediction programs that identify protein coding sequence are used with remarkable success in the annotation of genomes, the development of computational methods to analyze noncoding regions and to delineate transcriptional control elements is still in its infancy. RESULTS: Here we present novel algorithms to detect cis-regulatory modules through genome wide scans for clusters of transcription factor binding sites using three levels of prior information. When binding sites for the factors are known, our statistical segmentation algorithm, Ahab, yields about 150 putative gap gene regulated modules, with no adjustable parameters other than a window size. If one or more related modules are known, but no binding sites, repeated motifs can be found by a customized Gibbs sampler and input to Ahab, to predict genes with similar regulation. Finally using only the genome, we developed a third algorithm, Argos, that counts and scores clusters of overrepresented motifs in a window of sequence. Argos recovers many of the known modules, upstream of the segmentation genes, with no training data. CONCLUSIONS: We have demonstrated, in the case of body patterning in the Drosophila embryo, that our algorithms allow the genome-wide identification of regulatory modules. We believe that Ahab overcomes many problems of recent approaches and we estimated the false positive rate to be about 50%. Argos is the first successful attempt to predict regulatory modules using only the genome without training data. Complete results and module predictions across the Drosophila genome are available at http://uqbar.rockefeller.edu/~siggia/.

Algorithms↗

PubMed2002

Intrinsic and extrinsic contributions to stochasticity in gene expression.

Gene expression is a stochastic, or "noisy," process. This noise comes about in two ways. The inherent stochasticity of biochemical processes such as transcription and translation generates "intrinsic" noise. In addition, fluctuations in the amounts or states of other cellular components lead indirectly to variation in the expression of a particular gene and thus represent "extrinsic" noise. Here, we show how the total variation in the level of expression of a given gene can be decomposed into its intrinsic and extrinsic components. We demonstrate theoretically that simultaneous measurement of two identical genes per cell enables discrimination of these two types of noise. Analytic expressions for intrinsic noise are given for a model that involves all the major steps in transcription and translation. These expressions give the sensitivity to various parameters, quantify the deviation from Poisson statistics, and provide a way of fitting experiment. Transcription dominates the intrinsic noise when the average number of proteins made per mRNA transcript is greater than approximately 2. Below this number, translational effects also become important. Gene replication and cell division, included in the model, cause protein numbers to tend to a limit cycle. We calculate a general form for the extrinsic noise and illustrate it with the particular case of a single fluctuating extrinsic variable-a repressor protein, which acts on the gene of interest. All results are confirmed by stochastic simulation using plausible parameters for Escherichia coli.

Biophysical Phenomena↗

PubMed2002

Stochastic gene expression in a single cell.

Clonal populations of cells exhibit substantial phenotypic variation. Such heterogeneity can be essential for many biological processes and is conjectured to arise from stochasticity, or noise, in gene expression. We constructed strains of Escherichia coli that enable detection of noise and discrimination between the two mechanisms by which it is generated. Both stochasticity inherent in the biochemical process of gene expression (intrinsic noise) and fluctuations in other cellular components (extrinsic noise) contribute substantially to overall variation. Transcription rate, regulatory dynamics, and genetic factors control the amplitude of noise. These results establish a quantitative foundation for modeling noise in genetic networks and reveal how low intracellular copy numbers of molecules can fundamentally limit the precision of gene regulation.

Bacterial Proteins↗

PubMed2002

Identification of the binding sites of regulatory proteins in bacterial genomes.

We present an algorithm that extracts the binding sites (represented by position-specific weight matrices) for many different transcription factors from the regulatory regions of a genome, without the need for delineating groups of coregulated genes. The algorithm uses the fact that many DNA-binding proteins in bacteria bind to a bipartite motif with two short segments more conserved than the intervening region. It identifies all statistically significant patterns of the form W(1)N(x)W(2), where W(1) and W(2) are two short oligonucleotides separated by x arbitrary bases, and groups them into clusters of similar patterns. These clusters are then used to derive quantitative recognition profiles of putative regulatory proteins. For a given cluster, the algorithm finds the matching sequences plus the flanking regions in the genome and performs a multiple sequence alignment to derive position-specific weight matrices. We have analyzed the Escherichia coli genome with this algorithm and found approximately 1,500 significant patterns, which give rise to approximately 160 distinct position-specific weight matrices. A fraction of these matrices match the binding sites of one-third of the approximately 60 characterized transcription factors with high statistical significance. Many of the remaining matrices are likely to describe binding sites and regulons of uncharacterized transcription factors. The significance of these matrices was evaluated by their specificity, the location of the predicted sites, and the biological functions of the corresponding regulons, allowing us to suggest putative regulatory functions. The algorithm is efficient for analyzing newly sequenced bacterial genomes for which little is known about transcriptional regulation.

Algorithms↗

PubMed2002

Probabilistic clustering of sequences: inferring new bacterial regulons by comparative genomics.

Genome-wide comparisons between enteric bacteria yield large sets of conserved putative regulatory sites on a gene-by-gene basis that need to be clustered into regulons. Using the assumption that regulatory sites can be represented as samples from weight matrices (WMs), we derive a unique probability distribution for assignments of sites into clusters. Our algorithm, "PROCSE" (probabilistic clustering of sequences), uses Monte Carlo sampling of this distribution to partition and align thousands of short DNA sequences into clusters. The algorithm internally determines the number of clusters from the data and assigns significance to the resulting clusters. We place theoretical limits on the ability of any algorithm to correctly cluster sequences drawn from WMs when these WMs are unknown. Our analysis suggests that the set of all putative sites for a single genome (e.g., Escherichia coli) is largely inadequate for clustering. When sites from different genomes are combined and all the homologous sites from the various species are used as a block, clustering becomes feasible. We predict 50-100 new regulons as well as many new members of existing regulons, potentially doubling the number of known regulatory sites in E. coli.

Bacteria↗

PubMed2002

Dissection of COPI and Arf1 dynamics in vivo and role in Golgi membrane transport.

Cytosolic coat proteins that bind reversibly to membranes have a central function in membrane transport within the secretory pathway. One well-studied example is COPI or coatomer, a heptameric protein complex that is recruited to membranes by the GTP-binding protein Arf1. Assembly into an electron-dense coat then helps in budding off membrane to be transported between the endoplasmic reticulum (ER) and Golgi apparatus. Here we propose and corroborate a simple model for coatomer and Arf1 activity based on results analysing the distribution and lifetime of fluorescently labelled coatomer and Arf1 on Golgi membranes of living cells. We find that activated Arf1 brings coatomer to membranes. However, once associated with membranes, Arf1 and coatomer have different residence times: coatomer remains on membranes after Arf1-GTP has been hydrolysed and dissociated. Rapid membrane binding and dissociation of coatomer and Arf1 occur stochastically, even without vesicle budding. We propose that this continuous activity of coatomer and Arf1 generates kinetically stable membrane domains that are connected to the formation of COPI-containing transport intermediates. This role for Arf1/coatomer might provide a model for investigating the behaviour of other coat protein systems within cells.

ADP-Ribosylation Factor 1↗

PubMed2002

The role of proofreading in signal transduction specificity.

Many intracellular signaling proteins such as MAP kinases and transcription factors require multiple covalent modifications before activating downstream targets. This property suggests that signaling pathways are organized to facilitate proofreading, which expends energy to enhance the specificity of the pathway for the appropriate effector. Focusing on MAP kinases, we show that each phosphorylation of the kinase can act as an independent specificity test for that kinase. This is independent of whether MAP kinase activation is distributive, processive, or confined to a protein scaffold. We also highlight the importance of phosphatases in developing and maintaining specificity. Support for our proposals can be drawn from the existing literature.

Biophysical Phenomena↗

PubMed2002

The evolution of DNA regulatory regions for proteo-gamma bacteria by interspecies comparisons.

The comparison of homologous noncoding DNA for organisms a suitable evolutionary distance apart is a powerful tool for the identification of cis regulatory elements for transcription and translation and for the study of how they assemble into functional modules. We have fit the three parameters of an affine global probabilistic alignment algorithm to establish the background mutation rate of noncoding sequence between E. coli and a series of gamma proteobacteria ranging from Salmonella to Vibrio. The lower bound we find to the neutral mutation rate is sufficiently high, even for Salmonella, that most of the conservation of noncoding sequence is indicative of selective pressures rather than of insufficient time to evolve. We then use a local version of the alignment algorithm combined with our inferred background mutation rate to assign a significance to the degree of local sequence conservation between orthologous genes, and thereby deduce a probability profile for the upstream regulatory region of all E. coli protein-coding genes. We recover 75%-85% (depending on significance level) of all regulatory sites from a standard compilation for E. coli, and 66%-85% of sigma sites. We also trace the evolution of known regulatory sites and the groups associated with a given transcription factor. Furthermore, we find that approximately one-third of paralogous gene pairs in E. coli have a significant degree of correlation in their regulatory sequence. Finally, we demonstrate an inverse correlation between the rate of evolution of transcription factors and the number of genes they regulate. Our predictions are available at http://www.physics.rockefeller.edu/([tilde-see text])siggia.

Algorithms↗