PheNetic: network-based interpretation of unstructured gene lists in E. coli

Role of spt23 in Saccharomyces cerevisiae thermal tolerance

spt23 plays multiple roles in the thermal tolerance of budding yeast. spt23 regulates unsaturated lipid acid (ULA) content in the cell, which can then significantly affect cellular thermal tolerance. Being a Ty suppressor, spt23 can also interact with transposons (Tys) that are contributors to yeast's adaptive evolution. Nevertheless, few studies have investigated whether and how much spt23 can exert its regulatory functions through transposons. In this study, expression quantitative trait loci (eQTL) analysis was conducted with thermal-tolerant Saccharomyces cerevisiae strains, and spt23 was identified as one of the most important genes in mutants. spt23-overexpression (OE), deletion (Del), and integrative-expressed (IE) strains were constructed. Their heat tolerance, ethanol production, the expression level of key genes, and lipid acid contents in the cell membranes were measured. Furthermore, LTR (long terminal repeat)-amplicon sequencing was used to profile yeast transposon activities in the treatments. The results showed the Del type had a higher survival rate, biomass, and ethanol production, revealing negative correlations between spt23 expression levels and thermal tolerance. Total unsaturated lipid acid (TULA) contents in cell membranes were lower in the Del type, indicating its negative association with spt23 expression levels. The Del type resulted in the lower richness and higher evenness in LTR distributions, as well as higher transposon activities. The intersection of 3 gene sets and regression analysis revealed the relative weight of spt23's direct and TY-induced influence is about 4:3. These results suggested a heat tolerance model in which spt23 increases cell thermal tolerance through transcriptional regulation in addition to spt23-transposon triggered unknown responses. KEY POINTS: • spt23 is a key gene for heat tolerance, important for LA contents but not vital. • Deletion of spt23 decreases in yeast's LTR richness but not in evenness. • The relative weight of spt23's direct and TY-induced influence is about 4:3.

Network-Based Analysis to Identify Drivers of Metastatic Prostate Cancer Using GoNetic

Most known driver genes of metastatic prostate cancer are frequently mutated. To dig into the long tail of rarely mutated drivers, we performed network-based driver identification on the Hartwig Medical Foundation metastatic prostate cancer data set (HMF cohort). Hereto, we developed GoNetic, a method based on probabilistic pathfinding, to identify recurrently mutated subnetworks. In contrast to most state-of-the-art network-based methods, GoNetic can leverage sample-specific mutational information and the weights of the underlying prior network. When applied to the HMF cohort, GoNetic successfully recovered known primary and metastatic drivers of prostate cancer that are frequently mutated in the HMF cohort (TP53, RB1, and CTNNB1). In addition, the identified subnetworks contain frequently mutated genes, reflect processes related to metastatic prostate cancer, and contain rarely mutated driver candidates. To further validate these rarely mutated genes, we assessed whether the identified genes were more mutated in metastatic than in primary samples using an independent cohort. Then we evaluated their association with tumor evolution and with the lymph node status of the patients. This resulted in forwarding several novel putative driver genes for metastatic prostate cancer, some of which might be prognostic for disease evolution.

Genome-wide expression and network analyses of mutants in key brassinosteroid signaling genes

BACKGROUND

Brassinosteroid (BR) signaling regulates plant growth and development in concert with other signaling pathways. Although many genes have been identified that play a role in BR signaling, the biological and functional consequences of disrupting those key BR genes still require detailed investigation.

RESULTS

Here we performed phenotypic and transcriptomic comparisons of A. thaliana lines carrying a loss-of-function mutation in BRI1 gene, bri1-5, that exhibits a dwarf phenotype and its three activation-tag suppressor lines that were able to partially revert the bri1-5 mutant phenotype to a WS2 phenotype, namely bri1-5/bri1-1D, bri1-5/brs1-1D, and bri1-5/bak1-1D. From the three investigated bri1-5 suppressors, bri1-5/bak1-1D was the most effective suppressor at the transcriptional level. All three bri1-5 suppressors showed altered expression of the genes in the abscisic acid (ABA signaling) pathway, indicating that ABA likely contributes to the partial recovery of the wild-type phenotype in these bri1-5 suppressors. Network analysis revealed crosstalk between BR and other phytohormone signaling pathways, suggesting that interference with one hormone signaling pathway affects other hormone signaling pathways. In addition, differential expression analysis suggested the existence of a strong negative feedback from BR signaling on BR biosynthesis and also predicted that BRS1, rather than being directly involved in signaling, might be responsible for providing an optimal environment for the interaction between BRI1 and its ligand.

CONCLUSIONS

Our study provides insights into the molecular mechanisms and functions of key brassinosteroid (BR) signaling genes, especially BRS1.

Comparison of Transcriptional Responses and Metabolic Alterations in Three Multidrug-Resistant Model Microorganisms, Staphylococcus aureus ATCC BAA-39, Escherichia coli ATCC BAA-196, and Acinetobacter baumannii ATCC BAA-1790, on Exposure to Iodine-Containing Nano-micelle Drug FS-1

Iodine is one of the oldest antimicrobial agents. Until now, there have been no reports on acquiring resistance to iodine. Recent studies showed promising results on application of iodine-containing nano-micelles, FS-1, against antibiotic-resistant pathogens as a supplement to antibiotic therapy. The mechanisms of the action, however, remain unclear. The aim of this study was to perform a holistic analysis and comparison of gene regulation in three phylogenetically distant multidrug-resistant reference strains representing pathogens associated with nosocomial infections from the ATCC culture collection: Escherichia coli BAA-196, Staphylococcus aureus BAA-39, and Acinetobacter baumannii BAA-1790. These cultures were treated by a 5-min exposure to sublethal concentrations of the iodine-containing drug FS-1 applied in the late lagging phase and the middle of the logarithmic growth phase. Complete genome sequences of these strains were obtained in the previous studies. Gene regulation was studied by total RNA extraction and Ion Torrent sequencing followed by mapping the RNA reads against the reference genome sequences and statistical processing of read counts using the DESeq2 algorithm. It was found that the treatment of bacteria with FS-1 profoundly affected the expression of many genes involved in the central metabolic pathways; however, alterations of the gene expression profiles were species specific and depended on the growth phase. Disruption of respiratory electron transfer membrane complexes, increased penetrability of bacterial cell walls, and osmotic and oxidative stresses leading to DNA damage were the major factors influencing the treated bacteria.IMPORTANCE Infections caused by antibiotic-resistant bacteria threaten public health worldwide. Combinatorial therapy in which antibiotics are administered together with supplementary drugs improving susceptibility of pathogens to the regular antibiotics is considered a promising way to overcome this problem. An induction of antibiotic resistance reversion by the iodine-containing nano-micelle drug FS-1 has been reported recently. This drug is currently under clinical trials in Kazakhstan against multidrug-resistant tuberculosis. The effects of released iodine on metabolic and regulatory processes in bacterial cells remain unexplored. The current work provides an insight into gene regulation in the antibiotic-resistant nosocomial reference strains treated with iodine-containing nanoparticles. This study sheds light on unexplored bioactivities of iodine and the mechanisms of its antibacterial effect when applied in sublethal concentrations. This knowledge will aid in the future design of new drugs against antibiotic-resistant infections.

The Effect of Iodine-Containing Nano-Micelles, FS-1, on Antibiotic Resistance, Gene Expression and Epigenetic Modifications in the Genome of Multidrug Resistant MRSA Strain Staphylococcus aureus ATCC BAA-39

Application of supplementary drugs which increase susceptibility of pathogenic bacteria to antibiotics is a promising yet unexplored approach to overcome the global problem of multidrug-resistant infections. The discovery of a new drug, an iodine-containing nano-molecular complex FS-1, which has proven to improve susceptibility to antibiotics in various pathogens, including MRSA strain Staphylococcus aureus ATCC BAA-39TM, allowed studying this phenomenon. Chromosomal DNA and total RNA samples extracted from the FS-1 treated strain (FS) and from the negative control (NC) cultures were sequenced by PacBio SMRT and Ion Torrent technologies, respectively. PacBio DNA reads were used to assemble chromosomal DNA of the NC and FS variants of S. aureus BAA-39 and to perform profiling of epigenetically modified nucleotides. Results of transcriptional profiling, variant calling and detection of epigenetic modifications in the FS variant were compared to the NC variant. Additionally, the genetic alterations caused by the treatment of S. aureus BAA-39 with FS-1 were compared to the results of a similar experiment conducted with another model organism, E. coli ATCC BAA-196. Several commonalities in responses of these phylogenetically distant microorganisms to the treatment with FS-1 were discovered, which included metabolic transition toward anaerobiosis and oxidative/osmotic stress response. S. aureus culture appeared to be more sensitive to FS-1 due to a higher penetrability of cells by iodine bound compounds, which caused carbonyl stress associated with nucleotide damaging by FS-1, abnormal epigenetic modifications and an increased rate of mutations. It was hypothesized that the disrupted pattern of adenine methylated loci within methicillin-resistance chromosome cassettes (SCCmec) may promote excision of this antibiotic resistance determinant from chromosomes while the altered pattern of cytosine methylation was behind the adaptive gene regulation in the culture FS. The selection against the antibiotic resistance in bacterial populations caused by abnormal epigenetic modifications exemplifies possible mechanisms of antibiotic resistance reversion induced by iodine-containing compounds. These finding will facilitate development of therapeutic agents against multidrug-resistant infections.

IAMBEE: a web-service for the identification of adaptive pathways from parallel evolved clonal populations

IAMBEE is a web server designed for the Identification of Adaptive Mutations in Bacterial Evolution Experiments (IAMBEE). Input data consist of genotype information obtained from independently evolved clonal populations or strains that show the same adapted behavior (phenotype). To distinguish adaptive from passenger mutations, IAMBEE searches for neighborhoods in an organism-specific interaction network that are recurrently mutated in the adapted populations. This search for recurrently mutated network neighborhoods, as proxies for pathways is driven by additional information on the functional impact of the observed genetic changes and their dynamics during adaptive evolution. In addition, the search explicitly accounts for the differences in mutation rate between the independently evolved populations. Using this approach, IAMBEE allows exploiting parallel evolution to identify adaptive pathways. The web-server is freely available at http://bioinformatics.intec.ugent.be/iambee/ with no login requirement.

Augmenting subnetwork inference with information extracted from the scientific literature

Many biological studies involve either (i) manipulating some aspect of a cell or its environment and then simultaneously measuring the effect on thousands of genes, or (ii) systematically manipulating each gene and then measuring the effect on some response of interest. A common challenge that arises in these studies is to explain how genes identified as relevant in the given experiment are organized into a subnetwork that accounts for the response of interest. The task of inferring a subnetwork is typically dependent on the information available in publicly available, structured databases, which suffer from incompleteness. However, a wealth of potentially relevant information resides in the scientific literature, such as information about genes associated with certain concepts of interest, as well as interactions that occur among various biological entities. We contend that by exploiting this information, we can improve the explanatory power and accuracy of subnetwork inference in multiple applications. Here we propose and investigate several ways in which information extracted from the scientific literature can be used to augment subnetwork inference. We show that we can use literature-extracted information to (i) augment the set of entities identified as being relevant in a subnetwork inference task, (ii) augment the set of interactions used in the process, and (iii) support targeted browsing of a large inferred subnetwork by identifying entities and interactions that are closely related to concepts of interest. We use this approach to uncover the pathways involved in interactions between a virus and a host cell, and the pathways that are regulated by a transcription factor associated with breast cancer. Our experimental results demonstrate that these approaches can provide more accurate and more interpretable subnetworks. Integer program code, background network data, and pathfinding code are available at https://github.com/Craven-Biostat-Lab/subnetwork_inference

There is a multitude of publicly available databases that contain information about biological entities (i.e., genes, proteins, and other small molecules) as well as information about how these entities interact together. However, these databases are often incomplete. There is a wealth of information present in the text of the scientific literature that is not yet available in these databases. Using tools that mine the scientific literature we are able to extract some of this potentially relevant information. In this work we show how we can use publicly available databases in conjunction with the information extracted from the scientific literature to infer the networks that are involved in specific biological processes, such as viral replication and cancer tumor growth.

Transcriptome analysis of hopanoid deficient mutant of Rhodopseuodomonas palustris TIE-1

All three domains of life have an ordered plasma membrane which is pivotal in the selective fitness of primitive life. Like cholesterol in eukaryotes, hopanoids are important in bacteria to modulate membrane order. Hopanoids are pentacyclic triterpenoid lipids biosynthesised in many eubacteria, few ferns and lichens. Hopanoid modulates outer membrane order and hopanoid deficiency results in the weakened structural integrity of the membrane which may in turn affect the other structures within or spanning the cell envelope and contributing to various membrane functions. Hence, to decipher the role of hopanoid, genome-wide transcriptome of wild-type and Δshc mutant of Rhodopseudomonas palustris TIE-1 was studied which indicated 299 genes were upregulated and 306 genes were downregulated in hopanoid deficient mutant, representing ∼11.5% of the genome. Thirty-eight genes involved in chemotaxis, response to stimuli and signal transduction were differentially regulated and impaired motility in hopanoid deficient mutant showed that hopanoid plays a crucial role in chemotaxis. The docking study demonstrated that diguanylate cyclase which catalyses the synthesis of secondary messenger exhibited the capability to interact with hopanoids and might be confederating in chemotaxis and signal transduction. Seventy-four genes involved in membrane transport were differentially expressed and cell assays also explicit that the multidrug transport is compromised in Δshc mutant. Membrane transport is reliant on hopanoids which may explain the basis for previous observations linking hopanoids to antibiotic resistance. Disturbing the membrane order by targeting lipid synthesis can be a possible novel approach in developing new antimicrobials and hopanoid biosynthesis could be a potential target.

The Crabtree Effect Shapes the Saccharomyces cerevisiae Lag Phase during the Switch between Different Carbon Sources

When faced with environmental changes, microbes often enter a temporary growth arrest during which they reprogram the expression of specific genes to adapt to the new conditions. A prime example of such a lag phase occurs when microbes need to switch from glucose to other, less-preferred carbon sources. Despite its industrial relevance, the genetic network that determines the duration of the lag phase has not been studied in much detail. Here, we performed a genome-wide Bar-Seq screen to identify genetic determinants of the Saccharomyces cerevisiae glucose-to-galactose lag phase. The results show that genes involved in respiration, and specifically those encoding complexes III and IV of the electron transport chain, are needed for efficient growth resumption after the lag phase. Anaerobic growth experiments confirmed the importance of respiratory energy conversion in determining the lag phase duration. Moreover, overexpression of the central regulator of respiration, HAP4, leads to significantly shorter lag phases. Together, these results suggest that the glucose-induced repression of respiration, known as the Crabtree effect, is a major determinant of microbial fitness in fluctuating carbon environments.IMPORTANCE The lag phase is arguably one of the prime characteristics of microbial growth. Longer lag phases result in lower competitive fitness in variable environments, and the duration of the lag phase is also important in many industrial processes where long lag phases lead to sluggish, less efficient fermentations. Despite the immense importance of the lag phase, surprisingly little is known about the exact molecular processes that determine its duration. Our study uses the molecular toolbox of S. cerevisiae combined with detailed growth experiments to reveal how the transition from fermentative to respirative metabolism is a key bottleneck for cells to overcome the lag phase. Together, our findings not only yield insight into the key molecular processes and genes that influence lag duration but also open routes to increase the efficiency of industrial fermentations and offer an experimental framework to study other types of lag behavior.

1-((2,4-Dichlorophenethyl)Amino)-3-Phenoxypropan-2-ol Kills Pseudomonas aeruginosa through Extensive Membrane Damage

The ever increasing multidrug-resistance of clinically important pathogens and the lack of novel antibiotics have resulted in a true antibiotic crisis where many antibiotics are no longer effective. Further complicating the treatment of bacterial infections are antibiotic-tolerant persister cells. Besides being responsible for the recalcitrant nature of chronic infections, persister cells greatly contribute to the observed antibiotic tolerance in biofilms and even facilitate the emergence of antibiotic resistance. Evidently, eradication of these persister cells could greatly improve patient outcomes and targeting persistence may provide an alternative approach in combatting chronic infections. We recently characterized 1-((2,4-dichlorophenethyl)amino)-3-phenoxypropan-2-ol (SPI009), a novel anti-persister molecule capable of directly killing persisters from both Gram-negative and Gram-positive pathogens. SPI009 potentiates antibiotic activity in several in vitro and in vivo infection models and possesses promising anti-biofilm activity. Strikingly, SPI009 restores antibiotic sensitivity even in resistant strains. In this study, we investigated the mode of action of this novel compound using several parallel approaches. Genetic analyses and a macromolecular synthesis assays suggest that SPI009 acts by causing extensive membrane damage. This hypothesis was confirmed by liposome leakage assay and membrane permeability studies, demonstrating that SPI009 rapidly impairs the bacterial outer and inner membranes. Evaluation of SPI009-resistant mutants, which only could be generated under severe selection pressure, suggested a possible role for the MexCD-OprJ efflux pump. Overall, our results demonstrate the extensive membrane-damaging activity of SPI009 and confirm its clinical potential in the development of novel anti-persister therapies.

Insufficient levels of the nrdAB-encoded ribonucleotide reductase underlie the severe growth defect of the Δhda E. coli strain

The ATP-bound form of the Escherichia coli DnaA replication initiator protein remodels the chromosomal origin of replication, oriC, to load the replicative helicase. The primary mechanism for regulating the activity of DnaA involves the Hda and β clamp proteins, which act together to dramatically stimulate the intrinsic DNA-dependent ATPase activity of DnaA via a process termed Regulatory Inactivation of DnaA. In addition to hyperinitiation, strains lacking hda function also exhibit cold sensitive growth at 30°C. Strains impaired for the other regulators of initiation (i.e., ΔseqA or ΔdatA) fail to exhibit cold sensitivity. The goal of this study was to gain insight into why loss of hda function impedes growth. We used a genetic approach to isolate 9 suppressors of Δhda cold sensitivity, and characterized the mechanistic basis by which these suppressors alleviated Δhda cold sensitivity. Taken together, our results provide strong support for the view that the fundamental defect associated with Δhda is diminished levels of DNA precursors, particularly dGTP and dATP. We discuss possible mechanisms by which the suppressors identified here may regulate dNTP pool size, as well as similarities in phenotypes between the Δhda strain and hda⁺ strains exposed to the ribonucleotide reductase inhibitor hydroxyurea.

Network-Based Analysis of eQTL Data to Prioritize Driver Mutations

In clonal systems, interpreting driver genes in terms of molecular networks helps understanding how these drivers elicit an adaptive phenotype. Obtaining such a network-based understanding depends on the correct identification of driver genes. In clonal systems, independent evolved lines can acquire a similar adaptive phenotype by affecting the same molecular pathways, a phenomenon referred to as parallelism at the molecular pathway level. This implies that successful driver identification depends on interpreting mutated genes in terms of molecular networks. Driver identification and obtaining a network-based understanding of the adaptive phenotype are thus confounded problems that ideally should be solved simultaneously. In this study, a network-based eQTL method is presented that solves both the driver identification and the network-based interpretation problem. As input the method uses coupled genotype-expression phenotype data (eQTL data) of independently evolved lines with similar adaptive phenotypes and an organism-specific genome-wide interaction network. The search for mutational consistency at pathway level is defined as a subnetwork inference problem, which consists of inferring a subnetwork from the genome-wide interaction network that best connects the genes containing mutations to differentially expressed genes. Based on their connectivity with the differentially expressed genes, mutated genes are prioritized as driver genes. Based on semisynthetic data and two publicly available data sets, we illustrate the potential of the network-based eQTL method to prioritize driver genes and to gain insights in the molecular mechanisms underlying an adaptive phenotype. The method is available at http://bioinformatics.intec.ugent.be/phenetic_eqtl/index.html

Adaptation to High Ethanol Reveals Complex Evolutionary Pathways

Tolerance to high levels of ethanol is an ecologically and industrially relevant phenotype of microbes, but the molecular mechanisms underlying this complex trait remain largely unknown. Here, we use long-term experimental evolution of isogenic yeast populations of different initial ploidy to study adaptation to increasing levels of ethanol. Whole-genome sequencing of more than 30 evolved populations and over 100 adapted clones isolated throughout this two-year evolution experiment revealed how a complex interplay of de novo single nucleotide mutations, copy number variation, ploidy changes, mutator phenotypes, and clonal interference led to a significant increase in ethanol tolerance. Although the specific mutations differ between different evolved lineages, application of a novel computational pipeline, PheNetic, revealed that many mutations target functional modules involved in stress response, cell cycle regulation, DNA repair and respiration. Measuring the fitness effects of selected mutations introduced in non-evolved ethanol-sensitive cells revealed several adaptive mutations that had previously not been implicated in ethanol tolerance, including mutations in PRT1, VPS70 and MEX67. Interestingly, variation in VPS70 was recently identified as a QTL for ethanol tolerance in an industrial bio-ethanol strain. Taken together, our results show how, in contrast to adaptation to some other stresses, adaptation to a continuous complex and severe stress involves interplay of different evolutionary mechanisms. In addition, our study reveals functional modules involved in ethanol resistance and identifies several mutations that could help to improve the ethanol tolerance of industrial yeasts.

Organisms can evolve resistance to specific stress factors, which allows them to thrive in environments where non-adapted organisms fail to grow. However, the molecular mechanisms that underlie adaptation to complex stress factors that interfere with basic cellular processes are poorly understood. In this study, we reveal how yeast populations adapt to high ethanol concentrations, an ecologically and industrially relevant stress that is still poorly understood. We exposed six independent populations of genetically identical yeast cells to gradually increasing ethanol levels, and we monitored the changes in their DNA sequence over a two-year period. Together with novel computational analyses, we could identify the mutational dynamics and molecular mechanisms underlying increased ethanol resistance. Our results show how adaptation to high ethanol is complex and can be reached through different mutational pathways. Together, our study offers a detailed picture of how populations adapt to a complex continuous stress and identifies several mutations that increase ethanol resistance, which opens new routes to obtain superior biofuel yeast strains.

PheNetic: network-based interpretation of molecular profiling data

Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.

Integrative study of Arabidopsis thaliana metabolomic and transcriptomic data with the interactive MarVis-Graph software

State of the art high-throughput technologies allow comprehensive experimental studies of organism metabolism and induce the need for a convenient presentation of large heterogeneous datasets. Especially, the combined analysis and visualization of data from different high-throughput technologies remains a key challenge in bioinformatics. We present here the MarVis-Graph software for integrative analysis of metabolic and transcriptomic data. All experimental data is investigated in terms of the full metabolic network obtained from a reference database. The reactions of the network are scored based on the associated data, and sub-networks, according to connected high-scoring reactions, are identified. Finally, MarVis-Graph scores the detected sub-networks, evaluates them by means of a random permutation test and presents them as a ranked list. Furthermore, MarVis-Graph features an interactive network visualization that provides researchers with a convenient view on the results. The key advantage of MarVis-Graph is the analysis of reactions detached from their pathways so that it is possible to identify new pathways or to connect known pathways by previously unrelated reactions. The MarVis-Graph software is freely available for academic use and can be downloaded at: http://marvis.gobics.de/marvis-graph.

COLOMBOS v2.0: an ever expanding collection of bacterial expression compendia

The COLOMBOS database (http://www.colombos.net) features comprehensive organism-specific cross-platform gene expression compendia of several bacterial model organisms and is supported by a fully interactive web portal and an extensive web API. COLOMBOS was originally published in PLoS One, and COLOMBOS v2.0 includes both an update of the expression data, by expanding the previously available compendia and by adding compendia for several new species, and an update of the surrounding functionality, with improved search and visualization options and novel tools for programmatic access to the database. The scope of the database has also been extended to incorporate RNA-seq data in our compendia by a dedicated analysis pipeline. We demonstrate the validity and robustness of this approach by comparing the same RNA samples measured in parallel using both microarrays and RNA-seq. As far as we know, COLOMBOS currently hosts the largest homogenized gene expression compendia available for seven bacterial model organisms.

Dynamics of the Saccharomyces cerevisiae transcriptome during bread dough fermentation

The behavior of yeast cells during industrial processes such as the production of beer, wine, and bioethanol has been extensively studied. In contrast, our knowledge about yeast physiology during solid-state processes, such as bread dough, cheese, or cocoa fermentation, remains limited. We investigated changes in the transcriptomes of three genetically distinct Saccharomyces cerevisiae strains during bread dough fermentation. Our results show that regardless of the genetic background, all three strains exhibit similar changes in expression patterns. At the onset of fermentation, expression of glucose-regulated genes changes dramatically, and the osmotic stress response is activated. The middle fermentation phase is characterized by the induction of genes involved in amino acid metabolism. Finally, at the latest time point, cells suffer from nutrient depletion and activate pathways associated with starvation and stress responses. Further analysis shows that genes regulated by the high-osmolarity glycerol (HOG) pathway, the major pathway involved in the response to osmotic stress and glycerol homeostasis, are among the most differentially expressed genes at the onset of fermentation. More importantly, deletion of HOG1 and other genes of this pathway significantly reduces the fermentation capacity. Together, our results demonstrate that cells embedded in a solid matrix such as bread dough suffer severe osmotic stress and that a proper induction of the HOG pathway is critical for optimal fermentation.