Jiri Vohradsky

Proteomic analysis of the bacterial cell cycle

A global approach was used to analyze protein synthesis and stability during the cell cycle of the bacterium Caulobacter crescentus. Approximately one-fourth (979) of the estimated C. crescentus gene products were detected by two-dimensional gel electrophoresis, 144 of which showed differential cell cycle expression patterns. Eighty-one of these proteins were identified by mass spectrometry and were assigned to a wide variety of functional groups. Pattern analysis revealed that coexpression groups were functionally clustered. A total of 48 proteins were rapidly degraded in the course of one cell cycle. More than half of these unstable proteins were also found to be synthesized in a cell cycle-dependent manner, establishing a strong correlation between rapid protein turnover and the periodicity of the bacterial cell cycle. This is, to our knowledge, the first evidence for a global role of proteolysis in bacterial cell cycle control.

Proteomic studies of diauxic lag in the differentiating prokaryote Streptomyces coelicolor reveal a regulatory network of stress‐induced proteins and central metabolic enzymes

Bacteria typically undergo intermittent periods of starvation and adaptation, emulated as diauxic growth in the laboratory. In association with growth arrest elicited by metabolic stress, the differentiating eubacterium Streptomyces coelicolor not only adapts its primary metabolism, but can also activate developmental programmes leading to morphogenesis and antibiotic biosynthesis. Here, we report combined proteomic and metabolomic data of S. coelicolor used to analyse global changes in gene expression during diauxic growth in a defined liquid medium. Cultures initially grew on glutamate, providing the nitrogen source and feeding carbon (as 2‐oxoglutarate) into the TCA cycle, followed by a diauxic delay allowing reorientation of metabolism and a second round of growth supported by NH4+, formed during prediauxic phase, and maltose, a glycolytic substrate. Cultures finally entered stationary phase as a result of nitrogen starvation. These four physiological states had previously been defined statistically by their distinct patterns of protein synthesis and heat shock responses. Together, these data demonstrated that the rates of synthesis of heat shock proteins are determined not only by temperature increase but also by the patterns and rates of metabolic flux in certain pathways. Synthesis profiles for metabolic‐ and stress‐induced proteins can now be interpreted by the identification of 204 spots (SWICZ database presented at http:proteom.biomed.cas.cz). Cluster analysis showed that the activity of central metabolic enzymes involved in glycolysis, the TCA cycle, starvation or proteolysis each displayed identifiable patterns of synthesis that logically underlie the metabolic state of the culture. Diauxic lag was accompanied by a structured regulatory programme involving the sequential activation of heat‐, salt‐, cold‐ and bacteriostatic antibiotic (pristinamycin I, PI)‐induced stimulons. Although stress stimulons presumably provide protection during environmental‐ or starvation‐induced stress, their identities did not reveal any coherent adaptive or developmental functions. These studies revealed interactive regulation of metabolic and stress response systems including some proteins known to support developmental programmes in S. coelicolor.

Numerical modelling of microRNA-mediated mRNA decay identifies novel mechanism of microRNA controlled mRNA downregulation

Post-transcriptional control of mRNA by micro-RNAs (miRNAs) represents an important mechanism of gene regulation. miRNAs act by binding to the 3′ untranslated region (3′UTR) of an mRNA, affecting the stability and translation of the target mRNA. Here, we present a numerical model of miRNA-mediated mRNA downregulation and its application to analysis of temporal microarray data of HepG2 cells transfected with miRNA-124a. Using the model our analysis revealed a novel mechanism of mRNA accumulation control by miRNA, predicting that specific mRNAs are controlled in a digital, switch-like manner. Specifically, the contribution of miRNAs to mRNA degradation is switched from maximum to zero in a very short period of time. Such behaviour suggests a model of control in which mRNA is at a certain moment protected from binding of miRNA and further accumulates with a basal rate. Genes associated with this process were identified and parameters of the model for all miRNA-124a affected mRNAs were computed.

Inference of active transcriptional networks by integration of gene expression kinetics modeling and multisource data

Inference of gene expression networks has become one of the primary challenges in computational biology. Analysis of microarray experiments using appropriate mathematical models can reveal interactions among protein regulators and target genes. This paper presents a combined approach to the inference of gene expression networks from time series measurements, ChIP-on-chip experiments, analyses of promoter sequences, and protein-protein interaction data. A recursive model of gene expression allowing for identification of active gene expression control networks with up to two regulators of one target gene is presented. The model was used to inspect all possible regulator-target gene combinations and predict those that are active during the underlying biological process. The procedure was applied to the inference of part of a regulatory network of the S. cerevisiae cell cycle, formed by 37 target genes and 128 transcription factors. A set of the most probable networks was suggested and analyzed.

A parallel genetic algorithm for single class pattern classification and its application for gene expression profiling in Streptomyces coelicolor

BackgroundIdentification of coordinately regulated genes according to the level of their expression during the time course of a process allows for discovering functional relationships among genes involved in the process.ResultsWe present a single class classification method for the identification of genes of similar function from a gene expression time series. It is based on a parallel genetic algorithm which is a supervised computer learning method exploiting prior knowledge of gene function to identify unknown genes of similar function from expression data. The algorithm was tested with a set of randomly generated patterns; the results were compared with seven other classification algorithms including support vector machines. The algorithm avoids several problems associated with unsupervised clustering methods, and it shows better performance then the other algorithms. The algorithm was applied to the identification of secondary metabolite gene clusters of the antibiotic-producing eubacterium Streptomyces coelicolor. The algorithm also identified pathways associated with transport of the secondary metabolites out of the cell. We used the method for the prediction of the functional role of particular ORFs based on the expression data.ConclusionThrough analysis of a time series of gene expression, the algorithm identifies pathways which are directly or indirectly associated with genes of interest, and which are active during the time course of the experiment.

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

Systems insight into the spore germination of Streptomyces coelicolor.

An example of bacterium, which undergoes a complex development, is the genus of Streptomyces whose importance lies in their wide capacity to produce secondary metabolites, including antibiotics. In this work, a proteomic approach was applied to the systems study of germination as a transition from dormancy to the metabolically active stage. The protein expression levels were examined throughout the germination time course, the kinetics of the accumulated and newly synthesized proteins were clustered, and proteins detected in each group were identified. Altogether, 104 2DE gel images at 13 time points, from dormant state until 5.5 h of growth, were analyzed. The mass spectrometry identified proteins were separated into functional groups and their potential roles during germination were further assessed. The results showed that the full competence of spores to effectively undergo active metabolism is derived from the sporulation step, which facilitates the rapid initiation of global protein expression during the first 10 min of cultivation. Within the first hour, the majority of proteins were synthesized. From this stage, the full capability of regulatory mechanisms to respond to environmental cues is presumed. The obtained results might also provide a data source for further investigations of the process of germination.

Stochastic simulation for the inference of transcriptional control network of yeast cyclins genes

Cell cycle is controlled by the activity of protein family of cyclins and cyclin-dependent kinases that are periodically expressed during cell cycle and that are conserved among different species. Genome-wide location analysis found that cyclins are controlled by a small number of transcription factors that form closed network of genes controlling each other. To investigate gene expression dynamics of this network, we developed a general procedure for stochastic simulation of gene expression process. Using the binding data, we simulated gene expression of all genes of the network for all possible combinations of regulatory interactions and by statistical comparison with experimentally measured time series excluded those interactions that formed gene expression temporal profiles significantly different from the measured ones. These experiments led to a new definition of the cyclins regulatory network coherent with the binding experiments which are kinetically plausible. Level of influence of individual regulators in control of the regulated genes is defined. Simulation results indicate particular mechanism of regulatory activity of protein complexes involved in the control of cyclins.

Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network

Inference of the topology of gene regulatory networks from experimental data is one of the primary challenges of systems biology. In an example of a genetic network of cyclins in the yeast cell cycle, we analyzed static genome‐wide location data together with microarray kinetic measurements using a recurrent neural network‐based model of gene expression and a newly developed, unbiased algorithm based on evolutionary programming principles. The modeling and simulation of gene expression dynamics identified cyclin genetic networks that were active during the cell cycle. We document that because there is inherent experimental variation, it is not possible to identify a single genetic network, only a set of equivalent networks with the same probability of occurrence. Analysis of these networks showed that each target gene was controlled by only a few regulators and that the control was robust. These results led to the reformulation of the cyclin genetic network in the yeast cell cycle as previously published. The analysis shows that with the methodologies that are currently available, it is not possible to predict only one genetic network; rather, we must work with the hypothesis of multiple, equivalent networks. Chromatin immunoprecipitation (ChIP)‐on‐chip experiments are not sufficient to predict the functional networks that are active during an investigated process. Such predictions must be considered as only potential, and their actual realization during particular cellular processes must be identified by incorporating both kinetic and other types of data.—To, C. C., Vohradsky, J. Measurement variation determines the gene network topology reconstructed from experimental data: a case study of the yeast cyclin network. FASEB J. 24, 3468–3478 (2010). www.fasebj.org

Effect of protein degradation on spot Mr distribution in 2-D gels – a case study of proteolysis during development of Streptomyces coelicolor cultures

Proteolysis, a regulated biological process, is reflected by protein spot molecular weight distribution in 2‐D gel electrophoretograms. Here we report studies of Streptomyces cultures as they undergo two different developmental processes involving proteolysis. Systematic changes in protein molecular weight distribution between the control samples and those with high activity of proteases were demonstrated. The observations were supported by a numerical model of degradation and its influence on the Mr distribution. Simple statistics could be used to distinguish between normal and degradative 2‐D gel electrophoretic patterns.