Transcriptional responses of Saccharomyces cerevisiae to shift from respiratory and respirofermentative to fully fermentative metabolism

Protein functional module identification method combining topological features and gene expression data

BACKGROUND

The study of protein complexes and protein functional modules has become an important method to further understand the mechanism and organization of life activities. The clustering algorithms used to analyze the information contained in protein-protein interaction network are effective ways to explore the characteristics of protein functional modules.

RESULTS

This paper conducts an intensive study on the problems of low recognition efficiency and noise in the overlapping structure of protein functional modules, based on topological characteristics of PPI network. Developing a protein function module recognition method ECTG based on Topological Features and Gene expression data for Protein Complex Identification.

CONCLUSIONS

The algorithm can effectively remove the noise data reflected by calculating the topological structure characteristic values in the PPI network through the similarity of gene expression patterns, and also properly use the information hidden in the gene expression data. The experimental results show that the ECTG algorithm can detect protein functional modules better.

Principal metabolic flux mode analysis

Motivation

In the analysis of metabolism, two distinct and complementary approaches are frequently used: Principal component analysis (PCA) and stoichiometric flux analysis. PCA is able to capture the main modes of variability in a set of experiments and does not make many prior assumptions about the data, but does not inherently take into account the flux mode structure of metabolism. Stoichiometric flux analysis methods, such as Flux Balance Analysis (FBA) and Elementary Mode Analysis, on the other hand, are able to capture the metabolic flux modes, however, they are primarily designed for the analysis of single samples at a time, and not best suited for exploratory analysis on a large sets of samples.

Results

We propose a new methodology for the analysis of metabolism, called Principal Metabolic Flux Mode Analysis (PMFA), which marries the PCA and stoichiometric flux analysis approaches in an elegant regularized optimization framework. In short, the method incorporates a variance maximization objective form PCA coupled with a stoichiometric regularizer, which penalizes projections that are far from any flux modes of the network. For interpretability, we also introduce a sparse variant of PMFA that favours flux modes that contain a small number of reactions. Our experiments demonstrate the versatility and capabilities of our methodology. The proposed method can be applied to genome-scale metabolic network in efficient way as PMFA does not enumerate elementary modes. In addition, the method is more robust on out-of-steady steady-state experimental data than competing flux mode analysis approaches.

Availability and implementation

Matlab software for PMFA and SPMFA and dataset used for experiments are available in https://github.com/aalto-ics-kepaco/PMFA.

Supplementary information

Supplementary data are available at Bioinformatics online.

Proteomic profiling and integrated analysis with transcriptomic data bring new insights in the stress responses of Kluyveromyces marxianus after an arrest during high-temperature ethanol fermentation

BACKGROUND

The thermotolerant yeast Kluyveromyces marxianus is a potential candidate for high-temperature fermentation. When K. marxianus was used for high-temperature ethanol fermentation, a fermentation arrest was observed during the late fermentation stage and the stress responses have been investigated based on the integration of RNA-Seq and metabolite data. In order to bring new insights into the cellular responses of K. marxianus after the fermentation arrest during high-temperature ethanol fermentation, quantitative proteomic profiling and integrated analysis with transcriptomic data were performed in this study.

RESULTS

Samples collected at 14, 16, 18, 20 and 22 h during high-temperature fermentation were subjected to isobaric tags for relative and absolute quantitation (iTRAQ)-based proteomic profiling and integrated analysis with transcriptomic data. The correlations between transcripts and proteins for the comparative group 16 h vs 14 h accounted for only 4.20% quantified proteins and 3.23% differentially expressed proteins (DEPs), respectively, much higher percentages of correlations (30.56%-59.11%) were found for other comparative groups (i.e., 18 h vs 14 h, 20 h vs 14 h, and 22 h vs 14 h). According to Spearman correlation tests between transcriptome and proteome (the absolute value of a correlation coefficient between 0.5 and 1 indicates a strong correlation), poor correlations were found for all quantified proteins (R = - 0.0355 to 0.0138), DEPs (R = - 0.0079 to 0.0233) and the DEPs with opposite expression trends to corresponding differentially expressed genes (DEGs) (R = - 0.0478 to 0.0636), whereas stronger correlations were observed in terms of the DEPs with the same expression trends as the correlated DEGs (R = 0.5593 to 0.7080). The results of multiple reaction monitoring (MRM) verification indicate that the iTRAQ results were reliable. After the fermentation arrest, a number of proteins involved in transcription, translation, oxidative phosphorylation and fatty acid metabolism were down-regulated, some molecular chaperones and proteasome proteins were up-regulated, the ATPase activity significantly decreased, and the total fatty acids gradually accumulated. In addition, the contents of palmitic acid, oleic acid, C16, C18, C22 and C24 fatty acids increased by 16.77%, 28.49%, 14.14%, 26.88%, 628.57% and 125.29%, respectively.

CONCLUSIONS

This study confirmed some biochemical and enzymatic alterations provoked by the stress conditions in the specific case of K. marxianus: such as decreases in transcription, translation and oxidative phosphorylation, alterations in cellular fatty acid composition, and increases in the abundance of molecular chaperones and proteasome proteins. These findings provide potential targets for further metabolic engineering towards improvement of the stress tolerance in K. marxianus.

Evaluating Transcription Factor Activity Changes by Scoring Unexplained Target Genes in Expression Data

Several methods predict activity changes of transcription factors (TFs) from a given regulatory network and measured expression data. But available gene regulatory networks are incomplete and contain many condition-dependent regulations that are not relevant for the specific expression measurement. It is not known which combination of active TFs is needed to cause a change in the expression of a target gene. A method to systematically evaluate the inferred activity changes is missing. We present such an evaluation strategy that indicates for how many target genes the observed expression changes can be explained by a given set of active TFs. To overcome the problem that the exact combination of active TFs needed to activate a gene is typically not known, we assume a gene to be explained if there exists any combination for which the predicted active TFs can possibly explain the observed change of the gene. We introduce the i-score (inconsistency score), which quantifies how many genes could not be explained by the set of activity changes of TFs. We observe that, even for these minimal requirements, published methods yield many unexplained target genes, i.e. large i-scores. This holds for all methods and all expression datasets we evaluated. We provide new optimization methods to calculate the best possible (minimal) i-score given the network and measured expression data. The evaluation of this optimized i-score on a large data compendium yields many unexplained target genes for almost every case. This indicates that currently available regulatory networks are still far from being complete. Both the presented Act-SAT and Act-A* methods produce optimal sets of TF activity changes, which can be used to investigate the difficult interplay of expression and network data. A web server and a command line tool to calculate our i-score and to find the active TFs associated with the minimal i-score is available from https://services.bio.ifi.lmu.de/i-score.

Can we predict the intracellular metabolic state of a cell based on extracellular metabolite data?

The analysis of extracellular metabolites presents many technical advantages over the analysis of intracellular compounds, which made this approach very popular in recent years as a high-throughput tool to assess the metabolic state of microbial cells. However, very little effort has been made to determine the actual relationship between intracellular and extracellular metabolite levels. The secretion of intracellular metabolites has been traditionally interpreted as a consequence of an intracellular metabolic overflow, which is based on the premise that for a metabolite to be secreted, it must be over-produced inside the cell. Therefore, we expect to find a secreted metabolite at increased levels inside the cells. Here we present a time-series metabolomics study of Saccharomyces cerevisiae growing on a glucose-limited chemostat with parallel measurements of intra- and extracellular metabolites. Although most of the extracellular metabolites were also detected in the intracellular samples and showed a typical metabolic overflow behaviour, we demonstrate that the secretion of many metabolites could not be explained by the metabolic overflow theory.

Reporter pathway analysis from transcriptome data: Metabolite-centric versus Reaction-centric approach

A systems-based investigation of the effect of perturbations on metabolic machinery is crucial to elucidate the mechanism behind perturbations. One way to investigate the perturbation-induced changes within the cell metabolism is to focus on pathway-level effects. In this study, three different perturbation types (genetic, environmental and disease-based) are analyzed to compute a list of reporter pathways, metabolic pathways which are significantly affected from a perturbation. The most common omics data type, transcriptome, is used as an input to the bioinformatic analysis. The pathways are scored by two alternative approaches: by averaging the changes in the expression levels of the genes controlling the associated reactions (reaction-centric), and by averaging the changes in the associated metabolites which were scored based on the associated genes (metabolite-centric). The analysis reveals the superiority of the novel metabolite-centric approach over the commonly used reaction-centric approach since it is based on metabolites which better represent the cross-talk among different pathways, enabling a more global and realistic cataloguing of network-wide perturbation effects.

Achieving Metabolic Flux Analysis for S. cerevisiae at a Genome-Scale: Challenges, Requirements, and Considerations

Recent advances in 13C-Metabolic flux analysis (13C-MFA) have increased its capability to accurately resolve fluxes using a genome-scale model with narrow confidence intervals without pre-judging the activity or inactivity of alternate metabolic pathways. However, the necessary precautions, computational challenges, and minimum data requirements for successful analysis remain poorly established. This review aims to establish the necessary guidelines for performing 13C-MFA at the genome-scale for a compartmentalized eukaryotic system such as yeast in terms of model and data requirements, while addressing key issues such as statistical analysis and network complexity. We describe the various approaches used to simplify the genome-scale model in the absence of sufficient experimental flux measurements, the availability and generation of reaction atom mapping information, and the experimental flux and metabolite labeling distribution measurements to ensure statistical validity of the obtained flux distribution. Organism-specific challenges such as the impact of compartmentalization of metabolism, variability of biomass composition, and the cell-cycle dependence of metabolism are discussed. Identification of errors arising from incorrect gene annotation and suggested alternate routes using MFA are also highlighted.

Transcriptional remodeling in response to transfer upon carbon-limited or metformin-supplemented media in S. cerevisiae and its effect on chronological life span

One of the factors affecting chronological life span (CLS) in budding yeast is nutrient, especially carbon limitation. Aside from metabolites in the growth medium such as glucose, amino acids, and acetic acid, many pharmaceuticals have also been proven to alter CLS. Besides their impact on life span, these drugs are also prospective chemicals to treat the age-associated diseases, so the identification of their action mechanism and their potential side effects is of crucial importance. In this study, the effects of caloric restriction and metformin, a dietary mimetic pharmaceutical, on yeast CLS are compared. Saccharomyces cerevisiae cells grown in synthetic dextrose complete (SDC) up to mid-exponential phase were either treated with metformin or were subjected to glucose limitation. The impacts of these perturbations were analyzed via transcriptomics, and the common (stimulation of glucose uptake, induction of mitochondrial maintenance, and reduction of protein translation) and divergent (stimulation of aerobic respiration and reprogramming of respiratory electron transport chain (ETC)) cellular responses specific to each treatment were determined. These results revealed that both glucose limitation and metformin treatment stimulate CLS extension and involve the mitochondrial function, probably by creating an efficient mitochondria-to-nucleus signaling of either aerobic respiration or ETC signaling stimulation, respectively.

The novel globin protein fungoglobin is involved in low oxygen adaptation of Aspergillus fumigatus

The human pathogenic fungus Aspergillus fumigatus normally lives as a soil saprophyte. Its environment includes poorly oxygenated substrates that also occur during tissue invasive growth of the fungus in the human host. Up to now, few cellular factors have been identified that allow the fungus to efficiently adapt its energy metabolism to hypoxia. Here, we cultivated A. fumigatus in an O2 -controlled fermenter and analysed its responses to O2 limitation on a minute timescale. Transcriptome sequencing revealed several genes displaying a rapid and highly dynamic regulation. One of these genes was analysed in detail and found to encode fungoglobin, a previously uncharacterized member of the sensor globin protein family widely conserved in filamentous fungi. Besides low O2 , iron limitation also induced transcription, but regulation was not entirely dependent on the two major transcription factors involved in adaptation to iron starvation and hypoxia, HapX and SrbA respectively. The protein was identified as a functional haemoglobin, as binding of this cofactor was detected for the recombinant protein. Gene deletion in A. fumigatus confirmed that haem-binding fungoglobins are important for growth in microaerobic environments with O2 levels far lower than in hypoxic human tissue.

Oxygen response of the wine yeast Saccharomyces cerevisiae EC1118 grown under carbon-sufficient, nitrogen-limited enological conditions

Discrete additions of oxygen play a critical role in alcoholic fermentation. However, few studies have quantitated the fate of dissolved oxygen and its impact on wine yeast cell physiology under enological conditions. We simulated the range of dissolved oxygen concentrations that occur after a pump-over during the winemaking process by sparging nitrogen-limited continuous cultures with oxygen-nitrogen gaseous mixtures. When the dissolved oxygen concentration increased from 1.2 to 2.7 μM, yeast cells changed from a fully fermentative to a mixed respirofermentative metabolism. This transition is characterized by a switch in the operation of the tricarboxylic acid cycle (TCA) and an activation of NADH shuttling from the cytosol to mitochondria. Nevertheless, fermentative ethanol production remained the major cytosolic NADH sink under all oxygen conditions, suggesting that the limitation of mitochondrial NADH reoxidation is the major cause of the Crabtree effect. This is reinforced by the induction of several key respiratory genes by oxygen, despite the high sugar concentration, indicating that oxygen overrides glucose repression. Genes associated with other processes, such as proline uptake, cell wall remodeling, and oxidative stress, were also significantly affected by oxygen. The results of this study indicate that respiration is responsible for a substantial part of the oxygen response in yeast cells during alcoholic fermentation. This information will facilitate the development of temporal oxygen addition strategies to optimize yeast performance in industrial fermentations.

Transcriptional regulation of fermentative and respiratory metabolism in Saccharomyces cerevisiae industrial bakers' strains

Bakers' yeast-producing companies grow cells under respiratory conditions, at a very high growth rate. Some desirable properties of bakers' yeast may be altered if fermentation rather than respiration occurs during biomass production. That is why differences in gene expression patterns that take place when industrial bakers' yeasts are grown under fermentative, rather than respiratory conditions, were examined. Macroarray analysis of V1 strain indicated changes in gene expression similar to those already described in laboratory Saccharomyces cerevisiae strains: repression of most genes related to respiration and oxidative metabolism and derepression of genes related to ribosome biogenesis and stress resistance in fermentation. Under respiratory conditions, genes related to the glyoxylate and Krebs cycles, respiration, gluconeogenesis, and energy production are activated. DOG21 strain, a partly catabolite-derepressed mutant derived from V1, displayed gene expression patterns quite similar to those of V1, although lower levels of gene expression and changes in fewer number of genes as compared to V1 were both detected in all cases. However, under fermentative conditions, DOG21 mutant significantly increased the expression of SNF1 -controlled genes and other genes involved in stress resistance, whereas the expression of the HXK2 gene, involved in catabolite repression, was considerably reduced, according to the pleiotropic stress-resistant phenotype of this mutant. These results also seemed to suggest that stress-resistant genes control desirable bakers' yeast qualities.