An integrated model of glucose and galactose metabolism regulated by the GAL genetic switch

Saccharomyces cerevisiae surface display of endolysin LysKB317 for control of bacterial contamination in corn ethanol fermentations

Control of bacterial contamination in bioethanol fermentation facilities has traditionally relied on chemical-based products such as hop acids and use of antibiotics. Recent emphasis on antibiotic stewardship has prompted new research into the development of alternative approaches to microbial remediation strategies. We recently described a recombinant peptidoglycan hydrolase, endolysin LysKB317, which inhibited Limosilactobacillus fermentum strains in corn mash fermentation. Here, Saccharomyces cerevisiae EBY100 was used to anchor recombinant LysKB317 using cell surface display with the a-agglutinin proteins Aga1p–Aga2p. Immunostaining and confocal fluorescence were used for localization of the extracellular interface of the cells. Yeast surface-expressed endolysin demonstrated an 83.8% decrease in bacterial cell counts compared to a 9.5% decrease in control yeast. Recombinant S. cerevisiae expressing LysKB317 used for small-scale corn mash fermentation, when infected with L. fermentum, could proactively control bacterial infection for 72 h with at least 1-log fold reduction. Analysis of fermentation products showed improved ethanol concentrations from 3.4% to at least 5.9% compared to the infection-only control and reduced levels of lactic and acetic acid from 34.7 mM to 13.8 mM and 25.5 mM to 18.1 mM, respectively. In an optimized yeast surface display system, proactive treatment of bacterial contaminants by endolysin LysKB317 can improve fermentation efficiency in the presence of L. fermentum contamination.

Enhancement of catabolite regulatory genes in Saccharomyces cerevisiae to increase ethanol production using hydrolysate from red seaweed Gloiopeltis furcata

Glucose and galactose are monosaccharides obtained from Gloiopeltis furcata (Red algae). A total monosaccharide yield of 62.3 g/L was obtained from G. furcata using thermal acid hydrolysis and enzymatic saccharification. Activated carbon was used to eliminate hydroxymethylfurfural (HMF) from the hydrolysate. Previously obtained monosaccharides are used for ethanol production by Saccharomyces cerevisiae. S. cerevisiae consumes glucose first, then galactose. The methods for reducing fermentation time and increasing the ethanol yield coefficient using the simultaneous consumption of glucose and galactose have been evaluated. Gal3p and Gal80p of S. cerevisiae act as signal transducers that govern the galactose inducer Gal4p mediated transcriptional activation of the Gal gene family. Gal80p binds to Gal4p for transcription deactivation. Therefore, Gal80p was deleted for Gal4p expression without interruption.

Synchronization of stochastic expressions drives the clustering of functionally related genes

Functionally related genes tend to be chromosomally clustered in eukaryotic genomes even after the exclusion of tandem duplicates, but the biological significance of this widespread phenomenon is unclear. We propose that stochastic expression fluctuations of neighboring genes resulting from chromatin dynamics are more or less synchronized such that their expression ratio is more stable than that for unlinked genes. Consequently, chromosomal clustering could be advantageous when the expression ratio of the clustered genes needs to stay constant, for example, because of the accumulation of toxic compounds when this ratio is altered. Evidence from manipulative experiments on the yeast GAL cluster, comprising three chromosomally adjacent genes encoding enzymes catalyzing consecutive reactions in galactose catabolism, unequivocally supports this hypothesis and elucidates how disorder in one biological phenomenon-gene expression noise-could prompt the emergence of order in another-genome organization.

Application of GC/MS-Based Metabonomic Profiling in Studying the Therapeutic Effects of Aconitum carmichaeli with Ampelopsis japonica Extract on Collagen-Induced Arthritis in Rats

Aconitum carmichaeli with Ampelopsis japonica (AA) is a classical traditional Chinese medicine (TCM) formula. There are a lot of examples showing that AA can be used to treat rheumatoid arthritis, but its mechanism of action is still not completely clear. In this research, collagen-induced arthritis (CIA) was chosen as a rheumatoid arthritis (RA) model. Rats of treated groups were continuously administered Aconitum carmichaeli (AC), Ampelopsis japonica (AJ) and Aconitum carmichaeli + Ampelopsis japonica (AA) orally once a day from the day after the onset of arthritis (day 7) until day 42. The results showed that AA not only significantly reduced paw swelling, but also improved the levels of TNF-α and IL-6 in serum. GC-MS-based urine metabonomics was established to analysis metabolic profiles and 21 biomarkers of RA rats were identified by the Partial Least Squares Discriminant Analysis (PLS-DA) and Support Vector Machine (SVM) methods. The prediction rate of the SVM method for the 21 biomarkers was 100%. Twenty of 21 biomarkers, including D-galactose, inositol and glycerol, gradually returned to normal levels after administration of AA. Metabolomic Pathway Analysis (MetPA) generated three related metabolic pathways-galactose metabolism, glycerolipid metabolism and inositol phosphate metabolism-which explain the mechanism of AA treatment of rheumatoid arthritis. This research provides a better understanding of the therapeutic effects and possible therapeutic mechanism of action of a complex TCM (AA) on rheumatoid arthritis.

LncRNAs: Bridging environmental sensing and gene expression

The survival of all organisms is dependent on complex, coordinated responses to environmental cues. Non-coding RNAs have been identified as major players in regulation of gene expression, with recent evidence supporting roles for long non-coding (lnc)RNAs in both transcriptional and post-transcriptional control. Evidence from our laboratory shows that lncRNAs have the ability to form hybridized structures called R-loops with specific DNA target sequences in S. cerevisiae, thereby modulating gene expression. In this Point of View, we provide an overview of the nature of lncRNA-mediated control of gene expression in the context of our studies using the GAL gene cluster as a model for controlling the timing of transcription.

Truncation of Gal4p explains the inactivation of the GAL/MEL regulon in both Saccharomyces bayanus and some Saccharomyces cerevisiae wine strains

In the past, the galactose-negative (Gal(-)) phenotype was a key physiological character used to distinguish Saccharomyces bayanus from S. cerevisiae In this work, we investigated the inactivation of GAL gene networks in S. bayanus, which is an S. uvarum/S. eubayanus hybrid, and in S. cerevisiae wine strains erroneously labelled 'S. bayanus'. We made an inventory of their GAL genes using genomes that were either available publicly, re-sequenced by us, or assembled from public data and completed with targeted sequencing. In the S. eubayanus/S. uvarum CBS 380(T) hybrid, the GAL/MEL network is composed of genes from both parents: from S. uvarum, an otherwise complete set that lacks GAL4, and from S. eubayanus, a truncated version of GAL4 and an additional copy of GAL3 and GAL80 Similarly, two different truncated GAL4 alleles were found in S. cerevisiae wine strains EC1118 and LalvinQA23. The lack of GAL4 activity in these strains was corrected by introducing a full-length copy of S. cerevisiae GAL4 on a CEN4/ARS plasmid. Transformation with this plasmid restored galactose utilisation in Gal(-) strains, and melibiose fermentation in strain CBS 380(T) The melibiose fermentation phenotype, formerly regarded as characteristic of S. uvarum, turned out to be widespread among Saccharomyces species.

Metabolomic profiling reveals distinct patterns of tricarboxylic acid disorders in blood stasis syndrome associated with coronary heart disease

OBJECTIVE

To investigate the underlying metabolomic profifiling of coronary heart disease (CHD) with blood stasis syndrome (BSS).

METHODS

CHD model was induced by a nameroid constrictor in Chinese miniature swine. Fifteen miniature swine were randomly divided into a model group (n=9) and a control group (n=6), respectively according to arandom number table. After 4 weeks, plasma hemorheology was detected by automatic hemorheological analyzer, indices including hematocrit, plasma viscosity, blood viscosity, rigidity index and erythrocyte sedimentation rate; cardiac function was assessed by echocardiograph to detect left ventricular end-systolic diameter (LVED), left ventricular end-diastolic diameter (LVEDd), ejection fraction (EF), fractional shortening (FS) and other indicators. Gas chromatography coupled with mass spectrometry (GC-MS) and bioinformatics were applied to analyze spectra of CHD plasma with BSS.

RESULTS

The results of hemorheology analysis showed signifificant changes in viscosity, with low shear whole blood viscosity being lower and plasma viscosity higher in the model group compared with the control group. Moreover, whole blood reduction viscosity at high shear rate and whole blood reduction viscosity at low shear rate increased signifificantly (P <0.05). The echocardiograph results demonstrated that cardiac EF and FS showed signifificant difference (P <0.05), with EF values being decreased to 50% or less. The GC-MS data showed that principal component analysis can clearly separate the animals with BSS from those in the control group. The enriched Kyoto Encyclopedia of Genes and Genomes biological pathways results suggested that the patterns involved were associated with dysfunction of energy metabolism including glucose and lipid disorders, especially in glycolysis/gluconeogenesis, galactose metabolism and adenosine-triphosphate-binding cassette transporters.

CONCLUSIONS

Glucose metabolism and lipid metabolism disorders were the major contributors to the syndrome classifification of CHD with BSS.

Development of an amperometric screen-printed galactose biosensor for serum analysis

The development of a disposable amperometric biosensor for the measurement of circulating galactose in serum is described. The biosensor comprises a screen-printed carbon electrode (SPCE), incorporating the electrocatalyst cobalt phthalocyanine (CoPC), which is covered by a permselective cellulose acetate (CA) membrane and a layer of immobilized galactose oxidase (GALOX). The optimal response of the biosensor, designated as GALOX-CA-CoPC-SPCE, was obtained by systematically examining the effects of enzyme loading, temperature, pH, and buffer strength. The optimal performance of the biosensor occurred with 2U of GALOX, at 35°C, using 50mM phosphate buffer solution (pH 7.0). The sensitivity was 7.00μAmM(-1)cm(-2) and the linear range from 0.1 to 25mM with a calculated limit of detection (LOD) of 0.02mM; this concentration range and LOD are appropriate to diagnose galactosemia, i.e., concentrations >1.1mM in infants. When the biosensor was used in conjunction with amperometry in stirred solution for the analysis of serum, the precision values obtained on unspiked (endogenous level of 0.153mM) and spiked serum (1mM added) (n=6) were 1.10% and 0.11%, respectively, with a calculated recovery of 99.9%.

Capsid protein expression and adeno-associated virus like particles assembly in Saccharomyces cerevisiae

Background

The budding yeast Saccharomyces cerevisiae supports replication of many different RNA or DNA viruses (e.g. Tombusviruses or Papillomaviruses) and has provided means for up-scalable, cost- and time-effective production of various virus-like particles (e.g. Human Parvovirus B19 or Rotavirus). We have recently demonstrated that S. cerevisiae can form single stranded DNA AAV2 genomes starting from a circular plasmid. In this work, we have investigated the possibility to assemble AAV capsids in yeast.

Results

To do this, at least two out of three AAV structural proteins, VP1 and VP3, have to be simultaneously expressed in yeast cells and their intracellular stoichiometry has to resemble the one found in the particles derived from mammalian or insect cells. This was achieved by stable co-transformation of yeast cells with two plasmids, one expressing VP3 from its natural p40 promoter and the other one primarily expressing VP1 from a modified AAV2 Cap gene under the control of the inducible yeast promoter Gal1. Among various induction strategies we tested, the best one to yield the appropriate VP1:VP3 ratio was 4.5 hour induction in the medium containing 0.5% glucose and 5% galactose. Following such induction, AAV virus like particles (VLPs) were isolated from yeast by two step ultracentrifugation procedure. The transmission electron microscopy analysis revealed that their morphology is similar to the empty capsids produced in human cells.

Conclusions

Taken together, the results show for the first time that yeast can be used to assemble AAV capsid and, therefore, as a genetic system to identify novel cellular factors involved in AAV biology.

Growth-related model of the GAL system in Saccharomyces cerevisiae predicts behaviour of several mutant strains

The genetic regulatory network responds dynamically to perturbations in the intracellular and extracellular environments of an organism. The GAL system in the yeast Saccharomyces cerevisiae has evolved to utilise galactose as an alternative carbon and energy source, in the absence of glucose in the environment. This work contains a modified dynamic model for GAL system in S. cerevisiae, which includes a novel mechanism for Gal3p activation upon induction with galactose. The modification enables the model to simulate the experimental observation that in absence of galactose, oversynthesis of Gal3p can also induce the GAL system. Subsequently, the model is related to growth on galactose and glucose in a structured manner. The growth-related models are validated with experimental data for growth on individual substrates as well as mixed substrates. Finally, the model is tested for its prediction of a variety of known mutant behaviours. The exercise shows that the authors' model with a single set of parameters is able to capture the rich behaviour of the GAL system in S. cerevisiae. [Includes supplementary material].

Identification of novel targets for breast cancer by exploring gene switches on a genome scale

BACKGROUND

An important feature that emerges from analyzing gene regulatory networks is the "switch-like behavior" or "bistability", a dynamic feature of a particular gene to preferentially toggle between two steady-states. The state of gene switches plays pivotal roles in cell fate decision, but identifying switches has been difficult. Therefore a challenge confronting the field is to be able to systematically identify gene switches.

RESULTS

We propose a top-down mining approach to exploring gene switches on a genome-scale level. Theoretical analysis, proof-of-concept examples, and experimental studies demonstrate the ability of our mining approach to identify bistable genes by sampling across a variety of different conditions. Applying the approach to human breast cancer data identified genes that show bimodality within the cancer samples, such as estrogen receptor (ER) and ERBB2, as well as genes that show bimodality between cancer and non-cancer samples, where tumor-associated calcium signal transducer 2 (TACSTD2) is uncovered. We further suggest a likely transcription factor that regulates TACSTD2.

CONCLUSIONS

Our mining approach demonstrates that one can capitalize on genome-wide expression profiling to capture dynamic properties of a complex network. To the best of our knowledge, this is the first attempt in applying mining approaches to explore gene switches on a genome-scale, and the identification of TACSTD2 demonstrates that single cell-level bistability can be predicted from microarray data. Experimental confirmation of the computational results suggest TACSTD2 could be a potential biomarker and attractive candidate for drug therapy against both ER+ and ER- subtypes of breast cancer, including the triple negative subtype.

A survey on methods for modeling and analyzing integrated biological networks

Understanding how cellular systems build up integrated responses to their dynamically changing environment is one of the open questions in Systems Biology. Despite their intertwinement, signaling networks, gene regulation and metabolism have been frequently modeled independently in the context of well-defined subsystems. For this purpose, several mathematical formalisms have been developed according to the features of each particular network under study. Nonetheless, a deeper understanding of cellular behavior requires the integration of these various systems into a model capable of capturing how they operate as an ensemble. With the recent advances in the "omics" technologies, more data is becoming available and, thus, recent efforts have been driven toward this integrated modeling approach. We herein review and discuss methodological frameworks currently available for modeling and analyzing integrated biological networks, in particular metabolic, gene regulatory and signaling networks. These include network-based methods and Chemical Organization Theory, Flux-Balance Analysis and its extensions, logical discrete modeling, Petri Nets, traditional kinetic modeling, Hybrid Systems and stochastic models. Comparisons are also established regarding data requirements, scalability with network size and computational burden. The methods are illustrated with successful case studies in large-scale genome models and in particular subsystems of various organisms.

Computational approaches to metabolomics

This chapter is intended to familiarize readers with the field of metabolomics and some of the algorithms, data analysis strategies, and computer programs used to analyze or interpret metabolomic data. Specifically, this chapter provides a brief overview of the experimental approaches and applications of metabolomics followed by a description of the spectral and statistical analysis tools for metabolomics. The chapter concludes with a discussion of the resources that can be used to interpret and analyze metabolomic data at a biological or clinical level. Emerging needs, challenges, and recent progress being made in these areas are also discussed.

Stochastic analysis of the GAL genetic switch in Saccharomyces cerevisiae: modeling and experiments reveal hierarchy in glucose repression

Background

Transcriptional regulation involves protein-DNA and protein-protein interactions. Protein-DNA interactions involve reactants that are present in low concentrations, leading to stochastic behavior. In addition, multiple regulatory mechanisms are typically involved in transcriptional regulation. In the GAL regulatory system of Saccharomyces cerevisiae, the inhibition of glucose is accomplished through two regulatory mechanisms: one through the transcriptional repressor Mig1p, and the other through regulating the amount of transcriptional activator Gal4p. However, the impact of stochasticity in gene expression and hierarchy in regulatory mechanisms on the phenotypic level is not clearly understood.

Results

We address the question of quantifying the effect of stochasticity inherent in these regulatory mechanisms on the performance of various genes under the regulation of Mig1p and Gal4p using a dynamic stochastic model. The stochastic analysis reveals the importance of both the mechanisms of regulation for tight expression of genes in the GAL network. The mechanism involving Gal4p is the dominant mechanism, yielding low variability in the expression of GAL genes. The mechanism involving Mig1p is necessary to maintain the switch-like response of certain GAL genes. The number of binding sites for Mig1p and Gal4p further influences the expression of the genes, with extra binding sites lowering the variability of expression. Our experiments involving growth on various substrates show that the trends predicted in mean expression and its variability are transmitted to the phenotypic level.

Conclusion

The mechanisms involved in the transcriptional regulation and their variability set up a hierarchy in the phenotypic response to growth on various substrates. Structural motifs, such as the number of binding sites and the mechanism of regulation, determine the level of stochasticity and eventually, the phenotypic response.

Metabolic gene regulation in a dynamically changing environment

Natural selection dictates that cells constantly adapt to dynamically changing environments in a context-dependent manner. Gene-regulatory networks often mediate the cellular response to perturbation, and an understanding of cellular adaptation will require experimental approaches aimed at subjecting cells to a dynamic environment that mimics their natural habitat. Here we monitor the response of Saccharomyces cerevisiae metabolic gene regulation to periodic changes in the external carbon source by using a microfluidic platform that allows precise, dynamic control over environmental conditions. We show that the metabolic system acts as a low-pass filter that reliably responds to a slowly changing environment, while effectively ignoring fast fluctuations. The sensitive low-frequency response was significantly faster than in predictions arising from our computational modelling, and this discrepancy was resolved by the discovery that two key galactose transcripts possess half-lives that depend on the carbon source. Finally, to explore how induction characteristics affect frequency response, we compare two S. cerevisiae strains and show that they have the same frequency response despite having markedly different induction properties. This suggests that although certain characteristics of the complex networks may differ when probed in a static environment, the system has been optimized for a robust response to a dynamically changing environment.