Systems biology from a yeast omics perspective

Metabolic engineering for compartmentalized biosynthesis of the valuable compounds in Saccharomyces cerevisiae

Saccharomyces cerevisiae is commonly used as a microbial cell factory to produce high-value compounds or bulk chemicals due to its genetic operability and suitable intracellular physiological environment. The current biosynthesis pathway for targeted products is primarily rewired in the cytosolic compartment. However, the related precursors, enzymes, and cofactors are frequently distributed in various subcellular compartments, which may limit targeted compounds biosynthesis. To overcome above mentioned limitations, the biosynthesis pathways are localized in different subcellular organelles for product biosynthesis. Subcellular compartmentalization in the production of targeted compounds offers several advantages, mainly relieving competition for precursors from side pathways, improving biosynthesis efficiency in confined spaces, and alleviating the cytotoxicity of certain hydrophobic products. In recent years, subcellular compartmentalization in targeted compound biosynthesis has received extensive attention and has met satisfactory expectations. In this review, we summarize the recent advances in the compartmentalized biosynthesis of the valuable compounds in S. cerevisiae, including terpenoids, sterols, alkaloids, organic acids, and fatty alcohols, etc. Additionally, we describe the characteristics and suitability of different organelles for specific compounds, based on the optimization of pathway reconstruction, cofactor supplementation, and the synthesis of key precursors (metabolites). Finally, we discuss the current challenges and strategies in the field of compartmentalized biosynthesis through subcellular engineering, which will facilitate the production of the complex valuable compounds and offer potential solutions to improve product specificity and productivity in industrial processes.

scGRN: a comprehensive single-cell gene regulatory network platform of human and mouse

Gene regulatory networks (GRNs) are interpretable graph models encompassing the regulatory interactions between transcription factors (TFs) and their downstream target genes. Making sense of the topology and dynamics of GRNs is fundamental to interpreting the mechanisms of disease etiology and translating corresponding findings into novel therapies. Recent advances in single-cell multi-omics techniques have prompted the computational inference of GRNs from single-cell transcriptomic and epigenomic data at an unprecedented resolution. Here, we present scGRN (https://bio.liclab.net/scGRN/), a comprehensive single-cell multi-omics gene regulatory network platform of human and mouse. The current version of scGRN catalogs 237 051 cell type-specific GRNs (62 999 692 TF-target gene pairs), covering 160 tissues/cell lines and 1324 single-cell samples. scGRN is the first resource documenting large-scale cell type-specific GRN information of diverse human and mouse conditions inferred from single-cell multi-omics data. We have implemented multiple online tools for effective GRN analysis, including differential TF-target network analysis, TF enrichment analysis, and pathway downstream analysis. We also provided details about TF binding to promoters, super-enhancers and typical enhancers of target genes in GRNs. Taken together, scGRN is an integrative and useful platform for searching, browsing, analyzing, visualizing and downloading GRNs of interest, enabling insight into the differences in regulatory mechanisms across diverse conditions.

Gene regulatory network inference in the era of single-cell multi-omics

The interplay between chromatin, transcription factors and genes generates complex regulatory circuits that can be represented as gene regulatory networks (GRNs). The study of GRNs is useful to understand how cellular identity is established, maintained and disrupted in disease. GRNs can be inferred from experimental data - historically, bulk omics data - and/or from the literature. The advent of single-cell multi-omics technologies has led to the development of novel computational methods that leverage genomic, transcriptomic and chromatin accessibility information to infer GRNs at an unprecedented resolution. Here, we review the key principles of inferring GRNs that encompass transcription factor-gene interactions from transcriptomics and chromatin accessibility data. We focus on the comparison and classification of methods that use single-cell multimodal data. We highlight challenges in GRN inference, in particular with respect to benchmarking, and potential further developments using additional data modalities.

Construction of versatile yeast plasmid vectors transferable by Agrobacterium-mediated transformation and their application to bread-making yeast strains

Agrobacterium-mediated transformation (AMT) potentially has great advantages over other DNA introduction methods: e.g., long DNA and numerous recipient strains can be dealt with at a time merely by co-cultivation with donor Agrobacterium cells. However, AMT was applied only to several laboratory yeast strains, and has never been considered as a standard gene-introduction method for yeast species. To disseminate the AMT method in yeast species, it is necessary to develop versatile AMT plasmid vectors including shuttle type ones, which have been unavailable yet for yeasts. In this study, we constructed a series of AMT plasmid vectors that consist of replicative (shuttle)- and integrative-types and harbor a gene conferring resistance to either G418 or aureobasidin A for application to prototrophic yeast strains. The vectors were successfully applied to five industrial yeast strains belonging to Saccharomyces cerevisiae after a modification of a previous AMT protocol, i.e., simply inputting a smaller number of yeast cells to the co-cultivation than that in the previous protocol. The revised protocol enabled all five yeast strains to generate recombinant colonies not only at high efficiency using replicative-type vectors, but also readily at an efficiency around 10-5 using integrative one. Further modification of the protocol demonstrated AMT for multiple yeast strains at a time with less labor. Therefore, AMT would facilitate molecular genetic approaches to many yeast strains in basic and applied sciences.

Active pharmaceutical ingredient (API) chemicals: a critical review of current biotechnological approaches

The aim of this article was to generate a framework of bio-based economy by an effective utilization of biomass from the perspectives of agriculture for developing potential end bio-based products (e.g. pharmaceuticals, active pharmaceutical ingredients). Our discussion is also extended to the conservatory ways of bioenergy along with development of bio-based products and biofuels. This review article further showcased the fundamental principles for producing these by-products. Thereby, the necessity of creating these products is to be efficaciously utilization by small-scale farmers that can aid the local needs for bio-based materials and energy. Concurrently, the building up of small markets will open up the avenues and linkages for bigger markets. In nutshell, the aim of the review is to explore the pathway of the biotechnological approaches so that less chosen producers and underdeveloped areas can be allied so that pressure on the systems of biomass production can be relaxed.

Yeast epigenetics: the inheritance of histone modification states

Saccharomyces cerevisiae (budding yeast) and Schizosaccharomyces pombe (fission yeast) are two of the most recognised and well-studied model systems for epigenetic regulation and the inheritance of chromatin states. Their silent loci serve as a proxy for heterochromatic chromatin in higher eukaryotes, and as such both species have provided a wealth of information on the mechanisms behind the establishment and maintenance of epigenetic states, not only in yeast, but in higher eukaryotes. This review focuses specifically on the role of histone modifications in governing telomeric silencing in S. cerevisiae and centromeric silencing in S. pombe as examples of genetic loci that exemplify epigenetic inheritance. We discuss the recent advancements that for the first time provide a mechanistic understanding of how heterochromatin, dictated by histone modifications specifically, is preserved during S-phase. We also discuss the current state of our understanding of yeast nucleosome dynamics during DNA replication, an essential component in delineating the contribution of histone modifications to epigenetic inheritance.

Yeast Systems Biology: The Continuing Challenge of Eukaryotic Complexity

Research on yeast has produced a plethora of tools and resources that have been central to the progress of systems biology. This chapter reviews these resources, explains the innovations that have been made since the first edition of this book, and introduces the constituent chapters of the current edition. The value of these resources not only in building and testing models of the functional networks of the yeast cell, but also in providing a foundation for network studies on the molecular basis of complex human diseases is considered. The gaps in this vast compendium of data, including enzyme kinetic characteristics, biomass composition, transport processes, and cell-cell interactions are discussed, as are the interactions between yeast cells and those of other species. The relevance of these studies to both traditional and advanced biotechnologies and to human medicine is considered, and the opportunities and challenges in using unicellular yeasts to model the systems of multicellular organisms are presented.

Protein Co-Aggregation Related to Amyloids: Methods of Investigation, Diversity, and Classification

Amyloids are unbranched protein fibrils with a characteristic spatial structure. Although the amyloids were first described as protein deposits that are associated with the diseases, today it is becoming clear that these protein fibrils play multiple biological roles that are essential for different organisms, from archaea and bacteria to humans. The appearance of amyloid, first of all, causes changes in the intracellular quantity of the corresponding soluble protein(s), and at the same time the aggregate can include other proteins due to different molecular mechanisms. The co-aggregation may have different consequences even though usually this process leads to the depletion of a functional protein that may be associated with different diseases. The protein co-aggregation that is related to functional amyloids may mediate important biological processes and change of protein functions. In this review, we survey the known examples of the amyloid-related co-aggregation of proteins, discuss their pathogenic and functional roles, and analyze methods of their studies from bacteria and yeast to mammals. Such analysis allow for us to propose the following co-aggregation classes: (i) titration: deposition of soluble proteins on the amyloids formed by their functional partners, with such interactions mediated by a specific binding site; (ii) sequestration: interaction of amyloids with certain proteins lacking a specific binding site; (iii) axial co-aggregation of different proteins within the same amyloid fibril; and, (iv) lateral co-aggregation of amyloid fibrils, each formed by different proteins.

Balanced trafficking between the ER and the Golgi apparatus increases protein secretion in yeast

The yeast Saccharomyces cerevisiae is widely used as a cell factory to produce recombinant proteins. However, S. cerevisiae naturally secretes only a few proteins, such as invertase and the mating alpha factor, and its secretory capacity is limited. It has been reported that engineering protein anterograde trafficking from the endoplasmic reticulum to the Golgi apparatus by the moderate overexpression of SEC16 could increase recombinant protein secretion in S. cerevisiae. In this study, the retrograde trafficking in a strain with moderate overexpression of SEC16 was engineered by overexpression of ADP-ribosylation factor GTP activating proteins, Gcs1p and Glo3p, which are involved in the process of COPI-coated vesicle formation. Engineering the retrograde trafficking increased the secretion of α-amylase but did not induce production of reactive oxygen species. An expanded ER membrane was detected in both the GCS1 and GLO3 overexpression strains. Physiological characterizations during batch fermentation showed that GLO3 overexpression had better effect on recombinant protein secretion than GCS1 overexpression. Additionally, the GLO3 overexpression strain had higher secretion of two other recombinant proteins, endoglucanase I from Trichoderma reesei and glucan-1,4-α-glucosidase from Rhizopus oryzae, indicating overexpression of GLO3 in a SEC16 moderate overexpression strain might be a general strategy for improving production of secreted proteins by yeast.

Enabling Science Support for Better Decision-Making when Responding to Chemical Spills

Chemical spills and accidents contaminate the environment and disrupt societies and economies around the globe. In the United States there were approximately 172,000 chemical spills that affected US waterbodies from 2004 to 2014. More than 8000 of these spills involved non-petroleum-related chemicals. Traditional emergency responses or incident command structures (ICSs) that respond to chemical spills require coordinated efforts by predominantly government personnel from multiple disciplines, including disaster management, public health, and environmental protection. However, the requirements of emergency response teams for science support might not be met within the traditional ICS. We describe the US ICS as an example of emergency-response approaches to chemical spills and provide examples in which external scientific support from research personnel benefitted the ICS emergency response, focusing primarily on nonpetroleum chemical spills. We then propose immediate, near-term, and long-term activities to support the response to chemical spills, focusing on nonpetroleum chemical spills. Further, we call for science support for spill prevention and near-term spill-incident response and identify longer-term research needs. The development of a formal mechanism for external science support of ICS from governmental and nongovernmental scientists would benefit rapid responders, advance incident- and crisis-response science, and aid society in coping with and recovering from chemical spills.

Mutant power: using mutant allele collections for yeast functional genomics

The budding yeast has long served as a model eukaryote for the functional genomic analysis of highly conserved signaling pathways, cellular processes and mechanisms underlying human disease. The collection of reagents available for genomics in yeast is extensive, encompassing a growing diversity of mutant collections beyond gene deletion sets in the standard wild-type S288C genetic background. We review here three main types of mutant allele collections: transposon mutagen collections, essential gene collections and overexpression libraries. Each collection provides unique and identifiable alleles that can be utilized in genome-wide, high-throughput studies. These genomic reagents are particularly informative in identifying synthetic phenotypes and functions associated with essential genes, including those modeled most effectively in complex genetic backgrounds. Several examples of genomic studies in filamentous/pseudohyphal backgrounds are provided here to illustrate this point. Additionally, the limitations of each approach are examined. Collectively, these mutant allele collections in Saccharomyces cerevisiae and the related pathogenic yeast Candida albicans promise insights toward an advanced understanding of eukaryotic molecular and cellular biology.

Recent advances in engineering yeast for pharmaceutical protein production

Currently available systems and synthetic biology tools can be applied to yeast engineering for improved biopharmaceutical protein production.

SLDR: a computational technique to identify novel genetic regulatory relationships

We developed a new computational technique called Step-Level Differential Response (SLDR) to identify genetic regulatory relationships. Our technique takes advantages of functional genomics data for the same species under different perturbation conditions, therefore complementary to current popular computational techniques. It can particularly identify "rare" activation/inhibition relationship events that can be difficult to find in experimental results. In SLDR, we model each candidate target gene as being controlled by N binary-state regulators that lead to ≤2^N observable states ("step-levels") for the target. We applied SLDR to the study of the GEO microarray data set GSE25644, which consists of 158 different mutant S. cerevisiae gene expressional profiles. For each target gene t, we first clustered ordered samples into various clusters, each approximating an observable step-level of t to screen out the "de-centric" target. Then, we ordered each gene x as a candidate regulator and aligned t to x for the purpose of examining the step-level correlations between low expression set of x (R_o) and high expression set of x (R_h) from the regulator x to t, by finding max f(t, x): |R_o-R_h| over all candidate × in the genome for each t. We therefore obtained activation and inhibitions events from different combinations of R_o and R_h. Furthermore, we developed criteria for filtering out less-confident regulators, estimated the number of regulators for each target t, and evaluated identified top-ranking regulator-target relationship. Our results can be cross-validated with the Yeast Fitness database. SLDR is also computationally efficient with o(N²) complexity. In summary, we believe SLDR can be applied to the mining of functional genomics big data for future network biology and network medicine applications.

Genomic insights into the evolution of industrial yeast species Brettanomyces bruxellensis

Brettanomyces bruxellensis, like its wine yeast counterpart Saccharomyces cerevisiae, is intrinsically linked with industrial fermentations. In wine, B. bruxellensis is generally considered to contribute negative influences on wine quality, whereas for some styles of beer, it is an essential contributor. More recently, it has shown some potential for bioethanol production. Our relatively poor understanding of B. bruxellensis biology, at least when compared with S. cerevisiae, is partly due to a lack of laboratory tools. As it is a nonmodel organism, efforts to develop methods for sporulation and transformation have been sporadic and largely unsuccessful. Recent genome sequencing efforts are now providing B. bruxellensis researchers unprecedented access to gene catalogues, the possibility of performing transcriptomic studies and new insights into evolutionary drivers. This review summarises these findings, emphasises the rich data sets already available yet largely unexplored and looks over the horizon at what might be learnt soon through comprehensive population genomics of B. bruxellensis and related species.

Multi-capillary column-ion mobility spectrometry of volatile metabolites emitted by Saccharomyces cerevisiae

Volatile organic compounds (VOCs) produced during microbial fermentations determine the flavor of fermented food and are of interest for the production of fragrances or food additives. However, the microbial synthesis of these compounds from simple carbon sources has not been well investigated so far. Here, we analyzed the headspace over glucose minimal salt medium cultures of Saccharomyces cerevisiae using multi-capillary column-ion mobility spectrometry (MCC-IMS). The high sensitivity and fast data acquisition of the MCC-IMS enabled online analysis of the fermentation off-gas and 19 specific signals were determined. To four of these volatile compounds, we could assign the metabolites ethanol, 2-pentanone, isobutyric acid, and 2,3-hexanedione by MCC-IMS measurements of pure standards and cross validation with thermal desorption-gas chromatography-mass spectrometry measurements. Despite the huge biochemical knowledge of the biochemistry of the model organism S. cerevisiae, only the biosynthetic pathways for ethanol and isobutyric acid are fully understood, demonstrating the considerable lack of research of volatile metabolites. As monitoring of VOCs produced during microbial fermentations can give valuable insight into the metabolic state of the organism, fast and non-invasive MCC-IMS analyses provide valuable data for process control.

An in vitro system for the rapid functional characterization of genes involved in carotenoid biosynthesis and accumulation

We have developed an assay based on rice embryogenic callus for rapid functional characterization of metabolic genes. We validated the assay using a selection of well-characterized genes with known functions in the carotenoid biosynthesis pathway, allowing rapid visual screening of callus phenotypes based on tissue color. We then used the system to identify the functions of two uncharacterized genes: a chemically synthesized β-carotene ketolase gene optimized for maize codon usage, and a wild-type Arabidopsis thaliana ortholog of the cauliflower Orange gene. In contrast to previous reports (Lopez, A.B., Van Eck, J., Conlin, B.J., Paolillo, D.J., O'Neill, J. and Li, L. () J. Exp. Bot. 59, 213-223; Lu, S., Van Eck, J., Zhou, X., Lopez, A.B., O'Halloran, D.M., Cosman, K.M., Conlin, B.J., Paolillo, D.J., Garvin, D.F., Vrebalov, J., Kochian, L.V., Küpper, H., Earle, E.D., Cao, J. and Li, L. () Plant Cell 18, 3594-3605), we found that the wild-type Orange allele was sufficient to induce chromoplast differentiation. We also found that chromoplast differentiation was induced by increasing the availability of precursors and thus driving flux through the pathway, even in the absence of Orange. Remarkably, we found that diverse endosperm-specific promoters were highly active in rice callus despite their restricted activity in mature plants. Our callus system provides a unique opportunity to predict the effect of metabolic engineering in complex pathways, and provides a starting point for quantitative modeling and the rational design of engineering strategies using synthetic biology. We discuss the impact of our data on analysis and engineering of the carotenoid biosynthesis pathway.

Physical and genetic-interaction density reveals functional organization and informs significance cutoffs in genome-wide screens

Genome-wide experiments often measure quantitative differences between treated and untreated cells to identify affected strains. For these studies, statistical models are typically used to determine significance cutoffs. We developed a method termed "CLIK" (Cutoff Linked to Interaction Knowledge) that overlays biological knowledge from the interactome on screen results to derive a cutoff. The method takes advantage of the fact that groups of functionally related interacting genes often respond similarly to experimental conditions and, thus, cluster in a ranked list of screen results. We applied CLIK analysis to five screens of the yeast gene disruption library and found that it defined a significance cutoff that differed from traditional statistics. Importantly, verification experiments revealed that the CLIK cutoff correlated with the position in the rank order where the rate of true positives drops off significantly. In addition, the gene sets defined by CLIK analysis often provide further biological perspectives. For example, applying CLIK analysis retrospectively to a screen for cisplatin sensitivity allowed us to identify the importance of the Hrq1 helicase in DNA crosslink repair. Furthermore, we demonstrate the utility of CLIK to determine optimal treatment conditions by analyzing genome-wide screens at multiple rapamycin concentrations. We show that CLIK is an extremely useful tool for evaluating screen quality, determining screen cutoffs, and comparing results between screens. Furthermore, because CLIK uses previously annotated interaction data to determine biologically informed cutoffs, it provides additional insights into screen results, which supplement traditional statistical approaches.

Integration of genomic medicine into pathology residency training: the stanford open curriculum

Next-generation sequencing methods provide an opportunity for molecular pathology laboratories to perform genomic testing that is far more comprehensive than single-gene analyses. Genome-based test results are expected to develop into an integral component of diagnostic clinical medicine and to provide the basis for individually tailored health care. To achieve these goals, rigorous interpretation of high-quality data must be informed by the medical history and the phenotype of the patient. The discipline of pathology is well positioned to implement genome-based testing and to interpret its results, but new knowledge and skills must be included in the training of pathologists to develop expertise in this area. Pathology residents should be trained in emerging technologies to integrate genomic test results appropriately with more traditional testing, to accelerate clinical studies using genomic data, and to help develop appropriate standards of data quality and evidence-based interpretation of these test results. We have created a genomic pathology curriculum as a first step in helping pathology residents build a foundation for the understanding of genomic medicine and its implications for clinical practice. This curriculum is freely accessible online.

Metabolic engineering of Saccharomyces cerevisiae: a key cell factory platform for future biorefineries

Metabolic engineering is the enabling science of development of efficient cell factories for the production of fuels, chemicals, pharmaceuticals, and food ingredients through microbial fermentations. The yeast Saccharomyces cerevisiae is a key cell factory already used for the production of a wide range of industrial products, and here we review ongoing work, particularly in industry, on using this organism for the production of butanol, which can be used as biofuel, and isoprenoids, which can find a wide range of applications including as pharmaceuticals and as biodiesel. We also look into how engineering of yeast can lead to improved uptake of sugars that are present in biomass hydrolyzates, and hereby allow for utilization of biomass as feedstock in the production of fuels and chemicals employing S. cerevisiae. Finally, we discuss the perspectives of how technologies from systems biology and synthetic biology can be used to advance metabolic engineering of yeast.

Systems biology of yeast cell death

Programmed cell death (PCD) (including apoptosis) is an essential process, and many human diseases of high prevalence such as neurodegenerative diseases and cancer are associated with deregulations in the cell death pathways. Yeast Saccharomyces cerevisiae, a unicellular eukaryotic organism, shares with multicellular organisms (including humans) key components and regulators of the PCD machinery. In this article, we review the current state of knowledge about cell death networks, including the modeling approaches and experimental strategies commonly used to study yeast cell death. We argue that the systems biology approach will bring valuable contributions to our understanding of regulations and mechanisms of the complex cell death pathways.

Systematic identification of gene annotation errors in the widely used yeast mutation collections

The baker's yeast mutation collections are extensively used genetic resources that are the basis for many genome-wide screens and new technologies. Anecdotal evidence has previously pointed to the putative existence of a neighboring gene effect (NGE) in these collections. NGE occurs when the phenotype of a strain carrying a particular perturbed gene is due to the lack of proper function of its adjacent gene. Here we performed a large-scale study of NGEs, presenting a network-based algorithm for detecting NGEs and validating software predictions using complementation experiments. We applied our approach to four datasets uncovering a similar magnitude of NGE in each (7-15%). These results have important consequences for systems biology, as the mutation collections are extensively used in almost every aspect of the field, from genetic network analysis to functional gene annotation.

ScreenTroll: a searchable database to compare genome-wide yeast screens

Systematic biological screens typically identify many genes or proteins that are implicated in a specific phenotype. However, deriving mechanistic insight from these screens typically involves focusing upon one or a few genes within the set in order to elucidate their precise role in producing the phenotype. To find these critical genes, researchers use a variety of tools to query the set of genes to uncover underlying common genetic or physical interactions or common functional annotations (e.g. gene ontology terms). Not only it is necessary to find previous screens containing genes in common with the new set, but also useful to easily access the individual manuscript or study that classified those genes. Unfortunately, no tool currently exists to facilitate this task. We have developed a web-based tool (ScreenTroll) that queries one or more genes against a database of systematic yeast screens. The software determines which genome-wide yeast screens also identified the queried gene(s) and the resulting screens are listed in an order based on the extent of the overlap between the queried gene(s) and the open reading frames (ORFs) characterized in each individual yeast screen. In a separate list, the corresponding ORFs that are found in both the queried set of genes and each individual genome-wide screen are displayed along with links to the relevant manuscript via NIH's PubMed database. ScreenTroll is useful for comparing a list of ORFs with genes identified in a wide array of published genome-wide screens. This comparison informs users whether any of their queried ORFs overlaps a previous study in the ScreenTroll database. By listing the manuscript of the published screen, users can read more about the phenotype associated with that study. Together, this information provides insight into the function of the queried genes and helps the user focus on a subset of them.

Genomic insights into the different layers of gene regulation in yeast

The model organism Saccharomyces cerevisiae has allowed the development of new functional genomics techniques devoted to the study of transcription in all its stages. With these techniques, it has been possible to find interesting new mechanisms to control gene expression that act at different levels and for different gene sets apart from the known cis-trans regulation in the transcription initiation step. Here we discuss a method developed in our laboratory, Genomic Run-On, and other new methods that have recently appeared with distinct technical features. A comparison between the datasets generated by them provides interesting genomic insights into the different layers of gene regulation in eukaryotes.

Cell growth and cell cycle in Saccharomyces cerevisiae: basic regulatory design and protein-protein interaction network

In this review we summarize the major connections between cell growth and cell cycle in the model eukaryote Saccharomyces cerevisiae. In S. cerevisiae regulation of cell cycle progression is achieved predominantly during a narrow interval in the late G1 phase known as START (Pringle and Hartwell, 1981). At START a yeast cell integrates environmental and internal signals (such as nutrient availability, presence of pheromone, attainment of a critical size, status of the metabolic machinery) and decides whether to enter a new cell cycle or to undertake an alternative developmental program. Several signaling pathways, that act to connect the nutritional status to cellular actions, are briefly outlined. A Growth & Cycle interaction network has been manually curated. More than one fifth of the edges within the Growth & Cycle network connect Growth and Cycle proteins, indicating a strong interconnection between the processes of cell growth and cell cycle. The backbone of the Growth & Cycle network is composed of middle-degree nodes suggesting that it shares some properties with HOT networks. The development of multi-scale modeling and simulation analysis will help to elucidate relevant central features of growth and cycle as well as to identify their system-level properties. Confident collaborative efforts involving different expertises will allow to construct consensus, integrated models effectively linking the processes of cell growth and cell cycle, ultimately contributing to shed more light also on diseases in which an altered proliferation ability is observed, such as cancer.

The future of model organisms in human disease research

Model organisms have played a huge part in the history of studies of human genetic disease, both in identifying disease genes and characterizing their normal and abnormal functions. But is the importance of model organisms diminishing? The direct discovery of disease genes and variants in humans has been revolutionized, first by genome-wide association studies and now by whole-genome sequencing. Not only is it now much easier to directly identify potential disease genes in humans, but the genetic architecture that is being revealed in many cases is hard to replicate in model organisms. Furthermore, disease modelling can be done with increasing effectiveness using human cells. Where does this leave non-human models of disease?

Tracking and synchronization of the yeast cell cycle using dielectrophoretic opacity

Cell cycle synchronization is an important tool for the study of the cell division stages and signalling. It provides homogeneous cell cultures that are of importance to develop and improve processes such as protein synthesis and drug screening. The main approach today is the use of metabolic agents that block the cell cycle at a particular phase and accumulate cells at this phase, disturbing the cell physiology. We provide here a non-invasive and label-free continuous cell sorting technique to analyze and synchronize yeast cell division. By balancing opposing dielectrophoretic forces at multiple frequencies, we maximize sensitivity to the characteristic shape and internal structure changes occurring during the yeast cell cycle, allowing us to synchronize the culture in late anaphase.

Unraveling condition-dependent networks of transcription factors that control metabolic pathway activity in yeast

While typically many expression levels change in transcription factor mutants, only few of these changes lead to functional changes. The predictive capability of expression and DNA binding data for such functional changes in metabolism is very limited.

Large-scale ¹³C-flux data reveal the condition specificity of transcriptional control of metabolic function.

Transcription control in yeast focuses on the switch between respiration and fermentation.

Follow-up modeling on the basis of transcriptomics and proteomics data suggest the newly discovered Gcn4 control of respiration to be mediated via PKA and Snf1.

Effective control and modulation of cellular behavior is of paramount importance in medicine (Kreeger and Lauffenburger, 2010) and biotechnology (Haynes and Silver, 2009), and requires profound understanding of control mechanisms. In this study, we aim to elucidate the extent to which transcription factors control the operation of yeast metabolism. As a quantitative readout of metabolic function, we monitored the traffic of small molecules through various pathways of central metabolism by ¹³C-flux analysis (Sauer, 2006). The choosen growth conditions represent two different regulatory states of reduced (galactose) and maximal carbon source repression (glucose), as well as a different nitrogen metabolism and two common, permanent stress conditions.

Depending on the growth condition, between 7 and 13% of the deleted transcription factors altered the determined flux ratios (Figure 3). Of the six quantified flux ratios, only the glycolysis/pentose phosphate pathway split, and the convergent ratio of anaplerosis and TCA cycle were controlled by the deleted transcription factors. Thus, we concluded that 23 transcription factors control flux distributions under at least one of the tested growth conditions, leading to 42 condition-dependent interactions of transcription factors with metabolic pathway activity (Figure 4). With two exceptions, all other identified transcription factors interactions controlled the TCA cycle flux. This condition-specific active control of metabolic function could not have been predicted from DNA binding and expression data; that is, 26.1% false negatives, 48.6% true positives.

Of the 23 transcription factors that controlled TCA cycle flux distributions under the tested conditions, only Bas1, Gcn4, Gcr2 and Pho2 exerted control under more than one condition. We identified Cit1, Mdh1 and Idh1/2 with a proteomics approach as the relevant target enzyme that increase the TCA cycle flux. Next, we asked whether Bas1, Gcr2, Gcn4 and Pho2 act directly on the TCA cycle or mediate their effect indirectly. Based on the transcriptomics data, the pattern of differentially activated transcription factors inferred by the differential expression of their target genes suggested reduced glucose repression in all four mutants as the common mechanism.

Starting from the currently largest set of ¹³C-based flux distributions, we identified networks of individual transcription factors that control metabolic pathway activity. These networks of active metabolic control have the following properties. First, they are highly condition dependent, as at most four transcription factors control the same metabolic flux distribution under more than one growth conditions. Second, they focus almost exclusively on the TCA cycle, thereby controlling the switch between respiratory and fermentative metabolism. Third, with four to 14 active transcription factors, they are small compared with gene regulation networks that were obtained from expression and DNA binding data. For the metabolic network studied here, robustness is also apparent from the fact that upregulated TCA cycle fluxes were not sufficient to achieve full respiratory metabolism; that is, absent or low ethanol formation. Several explanations could potentially explain the observed robustness. The most likely explanation is that environmental signals might be transmitted by different signaling pathways to several transcription factors, whose orchestrated action on multiple target genes is necessary to achieve a functional flux response. This hypothesis would explain why several transcription factors exert flux effects on the same pathway, but each flux effect is relatively small, as further, coordinated manipulations would be necessary to further improve the respiratory flux. Our findings demonstrate the importance of identifying and quantifying the extent to which regulatory effectors alter cellular function.

Which transcription factors control the distribution of metabolic fluxes under a given condition? We address this question by systematically quantifying metabolic fluxes in 119 transcription factor deletion mutants of Saccharomyces cerevisiae under five growth conditions. While most knockouts did not affect fluxes, we identified 42 condition-dependent interactions that were mediated by a total of 23 transcription factors that control almost exclusively the cellular decision between respiration and fermentation. This relatively sparse, condition-specific network of active metabolic control contrasts with the much larger gene regulation network inferred from expression and DNA binding data. Based on protein and transcript analyses in key mutants, we identified three enzymes in the tricarboxylic acid cycle as the key targets of this transcriptional control. For the transcription factor Gcn4, we demonstrate that this control is mediated through the PKA and Snf1 signaling cascade. The discrepancy between flux response predictions, based on the known regulatory network architecture and our functional ¹³C-data, demonstrates the importance of identifying and quantifying the extent to which regulatory effectors alter cellular functions.

Metabolic signatures in apoptotic human cancer cell lines

Cancer cells have several specific metabolic features, which have been explored for targeted therapies. Agents that promote apoptosis in tumors are currently considered as a powerful tool for cancer therapeutics. The present study aimed to design a fast, reliable and robust system for metabolite measurements in cells lines to observe impact of apoptosis on the metabolome. For that purpose the NBS (newborn screen) mass spectrometry-based metabolomics assay was adapted for cell culture approach. In HEK 293 and in cancer cell lines HepG2, PC3, and MCF7 we searched for metabolic biomarkers of apoptosis differing from that of necrosis. Already nontreated cell lines revealed distinct concentrations of metabolites. Several metabolites indicative for apoptotic processes in cell culture including aspartate, glutamate, methionine, alanine, glycine, propionyl carnitine (C3-carnitine), and malonyl carnitine (C3DC-carnitine) were observed. In some cell lines metabolite changes were visible as early as 4 h after apoptosis induction and preceeding the detection by caspase 3/7 assay. We demonstrated for the first time that the metabolomic signatures might be used in the tests of efficacy of agents causing apoptosis in cell culture. These signatures could be obtained in fast high-throughput screening.

A miniaturized technique for assessing protein thermodynamics and function using fast determination of quantitative cysteine reactivity

Protein thermodynamic stability is a fundamental physical characteristic that determines biological function. Furthermore, alteration of thermodynamic stability by macromolecular interactions or biochemical modifications is a powerful tool for assessing the relationship between protein structure, stability, and biological function. High-throughput approaches for quantifying protein stability are beginning to emerge that enable thermodynamic measurements on small amounts of material, in short periods of time, and using readily accessible instrumentation. Here we present such a method, fast quantitative cysteine reactivity, which exploits the linkage between protein stability, sidechain protection by protein structure, and structural dynamics to characterize the thermodynamic and kinetic properties of proteins. In this approach, the reaction of a protected cysteine and thiol-reactive fluorogenic indicator is monitored over a gradient of temperatures after a short incubation time. These labeling data can be used to determine the midpoint of thermal unfolding, measure the temperature dependence of protein stability, quantify ligand-binding affinity, and, under certain conditions, estimate folding rate constants. Here, we demonstrate the fQCR method by characterizing these thermodynamic and kinetic properties for variants of Staphylococcal nuclease and E. coli ribose-binding protein engineered to contain single, protected cysteines. These straightforward, information-rich experiments are likely to find applications in protein engineering and functional genomics.

Yeast systems biology: the challenge of eukaryotic complexity

In this chapter, we present an up-to-date view of the optimal characteristics of the yeast Saccharomyces cerevisiae as a model eukaryote for systems biology studies, with main molecular mechanisms, biological networks, and sub-cellular organization essentially conserved in all eukaryotes, derived from a complex common ancestor. The existence of advanced tools for molecular studies together with high-throughput experimental and computational methods, most of them being implemented and validated in yeast, with new ones being developed, is opening the way to the characterization of the core modular architecture and complex networks essential to all eukaryotes. Selected examples of the latest discoveries in eukaryote complexity and systems biology studies using yeast as a reference model and their applications in biotechnology and medicine are presented.

The developmental dynamics of the maize leaf transcriptome

We have analyzed the maize leaf transcriptome using Illumina sequencing. We mapped more than 120 million reads to define gene structure and alternative splicing events and to quantify transcript abundance along a leaf developmental gradient and in mature bundle sheath and mesophyll cells. We detected differential mRNA processing events for most maize genes. We found that 64% and 21% of genes were differentially expressed along the developmental gradient and between bundle sheath and mesophyll cells, respectively. We implemented Gbrowse, an electronic fluorescent pictograph browser, and created a two-cell biochemical pathway viewer to visualize datasets. Cluster analysis of the data revealed a dynamic transcriptome, with transcripts for primary cell wall and basic cellular metabolism at the leaf base transitioning to transcripts for secondary cell wall biosynthesis and C(4) photosynthetic development toward the tip. This dataset will serve as the foundation for a systems biology approach to the understanding of photosynthetic development.

Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses

Natural small compounds comprise most cellular molecules and bind proteins as substrates, products, cofactors, and ligands. However, a large-scale investigation of in vivo protein-small metabolite interactions has not been performed. We developed a mass spectrometry assay for the large-scale identification of in vivo protein-hydrophobic small metabolite interactions in yeast and analyzed compounds that bind ergosterol biosynthetic proteins and protein kinases. Many of these proteins bind small metabolites; a few interactions were previously known, but the vast majority are new. Importantly, many key regulatory proteins such as protein kinases bind metabolites. Ergosterol was found to bind many proteins and may function as a general regulator. It is required for the activity of Ypk1, a mammalian AKT/SGK kinase homolog. Our study defines potential key regulatory steps in lipid biosynthetic pathways and suggests that small metabolites may play a more general role as regulators of protein activity and function than previously appreciated.

Yeast proteomics and protein microarrays

Our understanding of biological processes as well as human diseases has improved greatly thanks to studies on model organisms such as yeast. The power of scientific approaches with yeast lies in its relatively simple genome, its facile classical and molecular genetics, as well as the evolutionary conservation of many basic biological mechanisms. However, even in this simple model organism, systems biology studies, especially proteomic studies had been an intimidating task. During the past decade, powerful high-throughput technologies in proteomic research have been developed for yeast including protein microarray technology. The protein microarray technology allows the interrogation of protein–protein, protein–DNA, protein–small molecule interaction networks as well as post-translational modification networks in a large-scale, high-throughput manner. With this technology, many groundbreaking findings have been established in studies with the budding yeast Saccharomyces cerevisiae, most of which could have been unachievable with traditional approaches. Discovery of these networks has profound impact on explicating biological processes with a proteomic point of view, which may lead to a better understanding of normal biological phenomena as well as various human diseases.

Deciphering the Arabidopsis floral transition process by integrating a protein-protein interaction network and gene expression data

In a plant, the progression from vegetative growth to reproductive growth is called the floral transition. Over the past several decades, the floral transition has been shown to be determined not by a single gene but by a complicated gene network. This important biological process, however, has not been investigated at a genome-wide network level. We collected Arabidopsis (Arabidopsis thaliana) protein-protein interaction data from several public databases and compiled them into a genome-wide Arabidopsis interactome. Then, we integrated gene expression profiles during the Arabidopsis floral transition process into the established protein-protein interaction network to identify two types of anticorrelated modules associated with vegetative and reproductive growth. Generally, the vegetative modules are conserved in plants, while the reproductive modules are more specific to advanced plants. The existence of floral transition switches demonstrates that vegetative and reproductive processes might be coordinated by the interacting interface of these modules. Our work also provides many candidates for mediating the interactions between these modules, which may play important roles during the Arabidopsis vegetative/reproductive switch.

2-DE based proteomic analysis of Saccharomyces cerevisiae wild and K+ transport-affected mutant (trk1,2) strains at the growth exponential and stationary phases

By using a 2-DE based workflow, the proteome of wild and potassium transport mutant trk1,2 under optimal growth potassium concentration (50mM) has been analyzed. At the exponential and stationary phases, both strains showed similar growth, morphology potassium content, and Vmax of rubidium transport, the only difference found being the Km values for this potassium analogue transport, higher for the mutant (20mM) than for the wild (3-6mM) cells. Proteins were buffer-extracted, precipitated, solubilized, quantified, and subjected to 2-DE analysis in the 5-8 pH range. More differences in protein content (37-64mgg(-1) cell dry weight) and number of resolved spots (178-307) were found between growth phases than between strains. In all, 164 spots showed no differences between samples and a total of 105 were considered to be differential after ANOVA test. 171 proteins, corresponding to 71 unique gene products have been identified, this set being dominated by cytosolic species and glycolitic enzymes. The ranking of the more abundant spots revealed no differences between samples and indicated fermentative metabolism, and active cell wall biosynthesis, redox homeostasis, biosynthesis of amino acids, coenzymes, nucleotides, and RNA, and protein turnover, apart from cell division and growth. PCA analysis allowed the separation of growth phases (PC1 and 2) and strains at the stationary phase (PC3 and 4), but not at the exponential one. These results are also supported by clustering analysis. As a general tendency, a number of spots newly appeared at the stationary phase in wild type, and to a lesser extent, in the mutant. These up-accumulated spots corresponded to glycolitic enzymes, indicating a more active glucose catabolism, accompanied by an accumulation of methylglyoxal detoxification, and redox-homeostasis enzymes. Also, more extensive proteolysis was observed at the stationary phase with this resulting in an accumulation of low Mr protein species.

A survey of yeast genomic assays for drug and target discovery

Over the past decade, the development and application of chemical genomic assays using the model organism Saccharomyces cerevisiae has provided powerful methods to identify the mechanism of action of known drugs and novel small molecules in vivo. These assays identify drug target candidates, genes involved in buffering drug target pathways and also help to define the general cellular response to small molecules. In this review, we examine current yeast chemical genomic assays and summarize the potential applications of each approach.

A unifying view of 21st century systems biology

The idea that multi-scale dynamic complex systems formed by interacting macromolecules and metabolites, cells, organs and organisms underlie some of the most fundamental aspects of life was proposed by a few visionaries half a century ago. We are witnessing a powerful resurgence of this idea made possible by the availability of nearly complete genome sequences, ever improving gene annotations and interactome network maps, the development of sophisticated informatic and imaging tools, and importantly, the use of engineering and physics concepts such as control and graph theory. Alongside four other fundamental "great ideas" as suggested by Sir Paul Nurse, namely, the gene, the cell, the role of chemistry in biological processes, and evolution by natural selection, systems-level understanding of "What is Life" may materialize as one of the major ideas of biology.