Eric D. Siggia

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 ≃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.

Regulatory element detection using correlation with expression

We present here a new computational method for discovering cis-regulatory elements that circumvents the need to cluster genes based on their expression profiles. Based on a model in which upstream motifs contribute additively to the log-expression level of a gene, this method requires a single genome-wide set of expression ratios and the upstream sequence for each gene, and outputs statistically significant motifs. Analysis of publicly available expression data for Saccharomyces cerevisiae reveals several new putative regulatory elements, some of which plausibly control the early, transient induction of genes during sporulation. Known motifs generally have high statistical significance.

Tracking the in vivo evolution of multidrug resistance in Staphylococcus aureus by whole-genome sequencing

The spread of multidrug-resistant Staphylococcus aureus (MRSA) strains in the clinical environment has begun to pose serious limits to treatment options. Yet virtually nothing is known about how resistance traits are acquired in vivo. Here, we apply the power of whole-genome sequencing to identify steps in the evolution of multidrug resistance in isogenic S. aureus isolates recovered periodically from the bloodstream of a patient undergoing chemotherapy with vancomycin and other antibiotics. After extensive therapy, the bacterium developed resistance, and treatment failed. Sequencing the first vancomycin susceptible isolate and the last vancomycin nonsusceptible isolate identified genome wide only 35 point mutations in 31 loci. These mutations appeared in a sequential order in isolates that were recovered at intermittent times during chemotherapy in parallel with increasing levels of resistance. The vancomycin nonsusceptible isolates also showed a 100-fold decrease in susceptibility to daptomycin, although this antibiotic was not used in the therapy. One of the mutated loci associated with decreasing vancomycin susceptibility (the vraR operon) was found to also carry mutations in six additional vancomycin nonsusceptible S. aureus isolates belonging to different genetic backgrounds and recovered from different geographic sites. As costs drop, whole-genome sequencing will become a useful tool in elucidating complex pathways of in vivo evolution in bacterial pathogens.

Golgi Membranes Are Absorbed into and Reemerge from the ER during Mitosis

Quantitative imaging and photobleaching were used to measure ER/Golgi recycling of GFP-tagged Golgi proteins in interphase cells and to monitor the dissolution and reformation of the Golgi during mitosis. In interphase, recycling occurred every 1.5 hr, and blocking ER egress trapped cycling Golgi enzymes in the ER with loss of Golgi structure. In mitosis, when ER export stops, Golgi proteins redistributed into the ER as shown by quantitative imaging in vivo and immuno-EM. Comparison of the mobilities of Golgi proteins and lipids ruled out the persistence of a separate mitotic Golgi vesicle population and supported the idea that all Golgi components are absorbed into the ER. Moreover, reassembly of the Golgi complex after mitosis failed to occur when ER export was blocked. These results demonstrate that in mitosis the Golgi disperses and reforms through the intermediary of the ER, exploiting constitutive recycling pathways. They thus define a novel paradigm for Golgi genesis and inheritance.

The effects of molecular noise and size control on variability in the budding yeast cell cycle

Molecular noise in gene expression can generate substantial variability in protein concentration. However, its effect on the precision of a natural eukaryotic circuit such as the control of cell cycle remains unclear. We use single-cell imaging of fluorescently labelled budding yeast to measure times from division to budding (G1) and from budding to the next division. The variability in G1 decreases with the square root of the ploidy through a 1N/2N/4N ploidy series, consistent with simple stochastic models for molecular noise. Also, increasing the gene dosage of G1 cyclins decreases the variability in G1. A new single-cell reporter for cell protein content allows us to determine the contribution to temporal G1 variability of deterministic size control (that is, smaller cells extending G1). Cell size control contributes significantly to G1 variability in daughter cells but not in mother cells. However, even in daughters, size-independent noise is the largest quantitative contributor to G1 variability. Exit of the transcriptional repressor Whi5 from the nucleus partitions G1 into two temporally uncorrelated and functionally distinct steps. The first step, which depends on the G1 cyclin gene CLN3, corresponds to noisy size control that extends G1 in small daughters, but is of negligible duration in mothers. The second step, whose variability decreases with increasing CLN2 gene dosage, is similar in mothers and daughters. This analysis decomposes the regulatory dynamics of the Start transition into two independent modules, a size sensing module and a timing module, each of which is predominantly controlled by a different G1 cyclin.

PhyloGibbs: a Gibbs sampling motif finder that incorporates phylogeny.

A central problem in the bioinformatics of gene regulation is to find the binding sites for regulatory proteins. One of the most promising approaches toward identifying these short and fuzzy sequence patterns is the comparative analysis of orthologous intergenic regions of related species. This analysis is complicated by various factors. First, one needs to take the phylogenetic relationship between the species into account in order to distinguish conservation that is due to the occurrence of functional sites from spurious conservation that is due to evolutionary proximity. Second, one has to deal with the complexities of multiple alignments of orthologous intergenic regions, and one has to consider the possibility that functional sites may occur outside of conserved segments. Here we present a new motif sampling algorithm, PhyloGibbs, that runs on arbitrary collections of multiple local sequence alignments of orthologous sequences. The algorithm searches over all ways in which an arbitrary number of binding sites for an arbitrary number of transcription factors (TFs) can be assigned to the multiple sequence alignments. These binding site configurations are scored by a Bayesian probabilistic model that treats aligned sequences by a model for the evolution of binding sites and “background” intergenic DNA. This model takes the phylogenetic relationship between the species in the alignment explicitly into account. The algorithm uses simulated annealing and Monte Carlo Markov-chain sampling to rigorously assign posterior probabilities to all the binding sites that it reports. In tests on synthetic data and real data from five Saccharomyces species our algorithm performs significantly better than four other motif-finding algorithms, including algorithms that also take phylogeny into account. Our results also show that, in contrast to the other algorithms, PhyloGibbs can make realistic estimates of the reliability of its predictions. Our tests suggest that, running on the five-species multiple alignment of a single genes upstream region, PhyloGibbs on average recovers over 50% of all binding sites in S. cerevisiae at a specificity of about 50%, and 33% of all binding sites at a specificity of about 85%. We also tested PhyloGibbs on collections of multiple alignments of intergenic regions that were recently annotated, based on ChIP-on-chip data, to contain binding sites for the same TF. We compared PhyloGibbss results with the previous analysis of these data using six other motif-finding algorithms. For 16 of 21 TFs for which all other motif-finding methods failed to find a significant motif, PhyloGibbs did recover a motif that matches the literature consensus. In 11 cases where there was disagreement in the results we compiled lists of known target genes from the literature, and found that running PhyloGibbs on their regulatory regions yielded a binding motif matching the literature consensus in all but one of the cases. Interestingly, these literature gene lists had little overlap with the targets annotated based on the ChIP-on-chip data. The PhyloGibbs code can be downloaded from http://www.biozentrum.unibas.ch/~nimwegen/cgi-bin/phylogibbs.cgi or http://www.imsc.res.in/~rsidd/phylogibbs. The full set of predicted sites from our tests on yeast are available at http://www.swissregulon.unibas.ch.

Dynamics and retention of misfolded proteins in native ER membranes

When co-translationally inserted into endoplasmic reticulum (ER) membranes, newly synthesized proteins encounter the lumenal environment of the ER, which contains chaperone proteins that facilitate the folding reactions necessary for protein oligomerization, maturation and export from the ER. Here we show, using a temperature-sensitive variant of vesicular stomatitis virus G protein tagged with green fluorescent protein (VSVG–GFP), and fluorescence recovery after photobleaching (FRAP), the dynamics of association of folded and misfolded VSVG complexes with ER chaperones. We also investigate the potential mechanisms underlying protein retention in the ER. Misfolded VSVG–GFP complexes at 40 °C are highly mobile in ER membranes and do not reside in post-ER compartments, indicating that they are not retained in the ER by immobilization or retrieval mechanisms. These complexes are immobilized in ATP-depleted or tunicamycin-treated cells, in which VSVG–chaperone interactions are no longer dynamic. These results provide insight into the mechanisms of protein retention in the ER and the dynamics of protein-folding complexes in native ER membranes.

Analysis of combinatorial cis -regulation in synthetic and genomic promoters

Transcription factor binding sites are being discovered at a rapid pace. It is now necessary to turn attention towards understanding how these sites work in combination to influence gene expression. Quantitative models that accurately predict gene expression from promoter sequence will be a crucial part of solving this problem. Here we present such a model, based on the analysis of synthetic promoter libraries in yeast (Saccharomyces cerevisiae). Thermodynamic models based only on the equilibrium binding of transcription factors to DNA and to each other captured a large fraction of the variation in expression in every library. Thermodynamic analysis of these libraries uncovered several phenomena in our system, including cooperativity and the effects of weak binding sites. When applied to the S. cerevisiae genome, a model of repression by Mig1 (which was trained on synthetic promoters) predicts a number of Mig1-regulated genes that lack significant Mig1-binding sites in their promoters. The success of the thermodynamic approach suggests that the information encoded by combinations of cis-regulatory sites is interpreted primarily through simple protein–DNA and protein–protein interactions, with complicated biochemical reactions—such as nucleosome modifications—being downstream events. Quantitative analyses of synthetic promoter libraries will be an important tool in unravelling the rules underlying combinatorial cis-regulation.

Positive feedback of G1 cyclins ensures coherent cell cycle entry.

In budding yeast, Saccharomyces cerevisiae, the Start checkpoint integrates multiple internal and external signals into an all-or-none decision to enter the cell cycle. Here we show that Start behaves like a switch due to systems-level feedback in the regulatory network. In contrast to current models proposing a linear cascade of Start activation, transcriptional positive feedback of the G1 cyclins Cln1 and Cln2 induces the near-simultaneous expression of the ∼200-gene G1/S regulon. Nuclear Cln2 drives coherent regulon expression, whereas cytoplasmic Cln2 drives efficient budding. Cells with the CLN1 and CLN2 genes deleted frequently arrest as unbudded cells, incurring a large fluctuation-induced fitness penalty due to both the lack of cytoplasmic Cln2 and insufficient G1/S regulon expression. Thus, positive-feedback-amplified expression of Cln1 and Cln2 simultaneously drives robust budding and rapid, coherent regulon expression. A similar G1/S regulatory network in mammalian cells, comprised of non-orthologous genes, suggests either conservation of regulatory architecture or convergent evolution.

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.