Comparative analysis of global transcriptional profile of Escherichia coli and Schizosaccharomyces pombe in respiratory and fermentative growth: Inferring the evolution of analog circuits of post-transcriptional regulation
ABSTRACT: Comparative analysis of global transcriptional profile of Escherichia coli and Schizosaccharomyces pombe in respiratory and fermentative growth: Inferring the evolution of analog circuits of post-transcriptional regulation
Project description:Complex diseases involve dynamic perturbations of pathophysiological processes during disease progression. Transcriptional programs underlying such perturbations are unknown in many diseases. Here, we present core transcriptional regulatory circuits underlying early and late perturbations in prion disease. We first identified cellular processes perturbed early and late using time-course gene expression data from three prion-infected mouse strains. We then built a transcriptional regulatory network (TRN) describing regulation of early and late processes. We found over-represented feed-forward loops (FFLs) comprising transcription factor (TF) pairs and target genes in the TRN. Using gene expression data of brain cell types, we further selected active FFLs where TF pairs and target genes were expressed in the same cell type and showed correlated temporal expression changes in the brain. We finally determined core transcriptional regulatory circuits by combining these active FFLs. These circuits provide insights into transcriptional programs for early and late pathophysiological processes in prion disease.
Project description:The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed an algorithm, NetSurgeon, which uses genome-wide gene-regulatory networks to identify interventions that force a cell toward a desired expression state. We first validated NetSurgeon extensively on existing datasets. Next, we used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in Saccharomyces cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional state of cells using xylose toward that of cells producing large amounts of ethanol from glucose might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism.
Project description:An important goal of synthetic biology is the rational design and predictable implementation of synthetic gene circuits using standardized and interchangeable parts. However, engineering of complex circuits in mammalian cells is currently limited by the availability of well-characterized and orthogonal transcriptional repressors. Here, we introduce a library of 26 reversible transcription activator-like effector repressors (TALERs) that bind newly designed hybrid promoters and exert transcriptional repression through steric hindrance of key transcriptional initiation elements. We demonstrate that using the input-output transfer curves of our TALERs enables accurate prediction of the behavior of modularly assembled TALER cascade and switch circuits. We also show that TALER switches using feedback regulation exhibit improved accuracy for microRNA-based HeLa cancer cell classification versus HEK293 cells. Our TALER library is a valuable toolkit for modular engineering of synthetic circuits, enabling programmable manipulation of mammalian cells and helping elucidate design principles of coupled transcriptional and microRNA-mediated post-transcriptional regulation.
Project description:BACKGROUND: With the abundant information produced by microarray technology, various approaches have been proposed to infer transcriptional regulatory networks. However, few approaches have studied subtle and indirect interaction such as genetic compensation, the existence of which is widely recognized although its mechanism has yet to be clarified. Furthermore, when inferring gene networks most models include only observed variables whereas latent factors, such as proteins and mRNA degradation that are not measured by microarrays, do participate in networks in reality. RESULTS: Motivated by inferring transcriptional compensation (TC) interactions in yeast, a stepwise structural equation modeling algorithm (SSEM) is developed. In addition to observed variables, SSEM also incorporates hidden variables to capture interactions (or regulations) from latent factors. Simulated gene networks are used to determine with which of six possible model selection criteria (MSC) SSEM works best. SSEM with Bayesian information criterion (BIC) results in the highest true positive rates, the largest percentage of correctly predicted interactions from all existing interactions, and the highest true negative (non-existing interactions) rates. Next, we apply SSEM using real microarray data to infer TC interactions among (1) small groups of genes that are synthetic sick or lethal (SSL) to SGS1, and (2) a group of SSL pairs of 51 yeast genes involved in DNA synthesis and repair that are of interest. For (1), SSEM with BIC is shown to outperform three Bayesian network algorithms and a multivariate autoregressive model, checked against the results of qRT-PCR experiments. The predictions for (2) are shown to coincide with several known pathways of Sgs1 and its partners that are involved in DNA replication, recombination and repair. In addition, experimentally testable interactions of Rad27 are predicted. CONCLUSION: SSEM is a useful tool for inferring genetic networks, and the results reinforce the possibility of predicting pathways of protein complexes via genetic interactions.
Project description:The availability of more data of dynamic gene expression under multiple experimental conditions provides new information that makes the key goal of identifying not only the transcriptional regulators of a gene but also the underlying logical structure attainable.We propose a novel method for inferring transcriptional regulation using a simple, yet biologically interpretable, model to find the logic by which a set of candidate genes and their associated transcription factors (TFs) regulate the transcriptional process of a gene of interest. Our dynamic model links the mRNA transcription rate of the target gene to the activation states of the TFs assuming that these interactions are consistent across multiple experiments and over time. A trans-dimensional Markov Chain Monte Carlo (MCMC) algorithm is used to efficiently sample the regulatory logic under different combinations of parents and rank the estimated models by their posterior probabilities. We demonstrate and compare our methodology with other methods using simulation examples and apply it to a study of transcriptional regulation of selected target genes of Arabidopsis Thaliana from microarray time series data obtained under multiple biotic stresses. We show that our method is able to detect complex regulatory interactions that are consistent under multiple experimental conditions.Programs are written in MATLAB and Statistics Toolbox Release 2016b, The MathWorks, Inc., Natick, Massachusetts, United States and are available on GitHub https://github.com/giorgosminas/TRS and at http://firstname.lastname@example.org or email@example.com.Supplementary data are available at Bioinformatics online.
Project description:Cysteine is a commercially important amino acid; however, it lacks an efficient fermentative production method. Due to its cytotoxicity, intracellular cysteine levels are stringently controlled via several regulatory modes. Managing its toxic effects as well as understanding and deregulating the complexities of regulation are crucial for establishing the fermentative production of cysteine. The regulatory modes include feedback inhibition of key metabolic enzymes, degradation, efflux pumps, and the transcriptional regulation of biosynthetic genes by a master cysteine regulator, CysB. These processes have been extensively studied using <i>Escherichia coli</i> for overproducing cysteine by fermentation. In this study, we genetically engineered <i>Pantoea ananatis</i>, an emerging host for the fermentative production of bio-based materials, to identify key factors required for cysteine production. According to this and our previous studies, we identified a major cysteine desulfhydrase gene, <i>ccdA</i> (formerly PAJ_0331), involved in cysteine degradation, and the cysteine efflux pump genes <i>cefA</i> and <i>cefB</i> (formerly PAJ_3026 and PAJ_p0018, respectively), which may be responsible for downregulating the intracellular cysteine level. Our findings revealed that <i>ccdA</i> deletion and <i>cefA</i> and <i>cefB</i> overexpression are crucial factors for establishing fermentative cysteine production in <i>P. ananatis</i> and for obtaining a higher cysteine yield when combined with genes in the cysteine biosynthetic pathway. To our knowledge, this is the first demonstration of cysteine production in <i>P. ananatis</i>, which has fundamental implications for establishing overproduction in this microbe.<b>IMPORTANCE</b> The efficient production of cysteine is a major challenge in the amino acid fermentation industry. In this study, we identified cysteine efflux pumps and degradation pathways as essential elements and genetically engineered <i>Pantoea ananatis</i>, an emerging host for the fermentative production of bio-based materials, to establish the fermentative production of cysteine. This study provides crucial insights into the design and construction of cysteine-producing strains, which may play central roles in realizing commercial basis production.
Project description:A key obstacle to creating sophisticated genetic circuits has been the lack of scalable device libraries. Here we present a modular transcriptional repression architecture based on clustered regularly interspaced palindromic repeats (CRISPR) system and examine approaches for regulated expression of guide RNAs in human cells. Subsequently we demonstrate that CRISPR regulatory devices can be layered to create functional cascaded circuits, which provide a valuable toolbox for engineering purposes.
Project description:Purpose: The ability to rationally manipulate the transcriptional states of cells would be of great use in medicine and bioengineering. We have developed a novel algorithm, NetSurgeon, which utilizes genome-wide gene regulatory networks to identify interventions that force a cell toward a desired expression state. Results: We used NetSurgeon to select transcription factor deletions aimed at improving ethanol production in S. cerevisiae cultures that are catabolizing xylose. We reasoned that interventions that move the transcriptional states of cells utilizing xylose toward the fermentative state typical of cells that are producing ethanol rapidly (while utilizing glucose) might improve xylose fermentation. Some of the interventions selected by NetSurgeon successfully promoted a fermentative transcriptional state in the absence of glucose, resulting in strains with a 2.7-fold increase in xylose import rates, a 4-fold improvement in xylose integration into central carbon metabolism, or a 1.3-fold increase in ethanol production rate. Conclusions: We conclude by presenting an integrated model of transcriptional regulation and metabolic flux that will enable future metabolic engineering efforts aimed at improving xylose fermentation to prioritize functional regulators of central carbon metabolism. Overall design: Mutant and wildtype S. cerevisiae cells were put into 48 hour aerobic batch fermentations of synthetic complete medium supplmented with 2% glucose and 5% xylose and culture samples were taken at 4 hours and 24 hours for transcriptional profiling performed by RNA-Seq analysis. In addition, wildtype S. cerevisiae cells were grown in various single carbon sources for 12 hours and culture samples were taken for transcriptional profiling performed by RNA-Seq analysis.
Project description:Despite our increasing topological knowledge on regulation networks in model bacteria, it is largely unknown which of the many co-occurring regulatory events actually control metabolic function and the distribution of intracellular fluxes. Here, we unravel condition-dependent transcriptional control of Escherichia coli metabolism by large-scale (13)C-flux analysis in 91 transcriptional regulator mutants on glucose and galactose. In contrast to the canonical respiro-fermentative glucose metabolism, fully respiratory galactose metabolism depends exclusively on the phosphoenol-pyruvate (PEP)-glyoxylate cycle. While 2/3 of the regulators directly or indirectly affected absolute flux rates, the partitioning between different pathways remained largely stable with transcriptional control focusing primarily on the acetyl-CoA branch point. Flux distribution control was achieved by nine transcription factors on glucose, including ArcA, Fur, PdhR, IHF A and IHF B, but was exclusively mediated by the cAMP-dependent Crp regulation of the PEP-glyoxylate cycle flux on galactose. Five further transcription factors affected this flux only indirectly through cAMP and Crp by increasing the galactose uptake rate. Thus, E. coli actively limits its galactose catabolism at the expense of otherwise possible faster growth.
Project description:Individual genetic variation affects gene responsiveness to stimuli, often by influencing complex molecular circuits. Here we combine genomic and intermediate-scale transcriptional profiling with computational methods to identify variants that affect the responsiveness of genes to stimuli (responsiveness quantitative trait loci or reQTLs) and to position these variants in molecular circuit diagrams. We apply this approach to study variation in transcriptional responsiveness to pathogen components in dendritic cells from recombinant inbred mouse strains. We identify reQTLs that correlate with particular stimuli and position them in known pathways. For example, in response to a virus-like stimulus, a trans-acting variant responds as an activator of the antiviral response; using RNA interference, we identify Rgs16 as the likely causal gene. Our approach charts an experimental and analytic path to decipher the mechanisms underlying genetic variation in circuits that control responses to stimuli.