ChIP-exo analysis highlights Fkh1 and Fkh2 transcription factors as hubs that integrate multi-scale networks in budding yeast.

The understanding of the multi-scale nature of molecular networks represents a major challenge. For example, regulation of a timely cell cycle must be coordinated with growth, during which changes in metabolism occur, and integrate information from the extracellular environment, e.g. signal transduction. Forkhead transcription factors are evolutionarily conserved among eukaryotes, and coordinate a timely cell cycle progression in budding yeast. Specifically, Fkh1 and Fkh2 are expressed during a lengthy window of the cell cycle, thus are potentially able to function as hubs in the multi-scale cellular environment that interlocks various biochemical networks. Here we report on a novel ChIP-exo dataset for Fkh1 and Fkh2 in both logarithmic and stationary phases, which is analyzed by novel and existing software tools. Our analysis confirms known Forkhead targets from available ChIP-chip studies and highlights novel ones involved in the cell cycle, metabolism and signal transduction. Target genes are analyzed with respect to their function, temporal expression during the cell cycle, correlation with Fkh1 and Fkh2 as well as signaling and metabolic pathways they occur in. Furthermore, differences in targets between Fkh1 and Fkh2 are presented. Our work highlights Forkhead transcription factors as hubs that integrate multi-scale networks to achieve proper timing of cell division in budding yeast.

GEMMER: GEnome-wide tool for Multi-scale Modeling data Extraction and Representation for Saccharomyces cerevisiae.

Motivation: Multi-scale modeling of biological systems requires integration of various information about genes and proteins that are connected together in networks. Spatial, temporal and functional information is available; however, it is still a challenge to retrieve and explore this knowledge in an integrated, quick and user-friendly manner. Results: We present GEMMER (GEnome-wide tool for Multi-scale Modeling data Extraction and Representation), a web-based data-integration tool that facilitates high quality visualization of physical, regulatory and genetic interactions between proteins/genes in Saccharomyces cerevisiae. GEMMER creates network visualizations that integrate information on function, temporal expression, localization and abundance from various existing databases. GEMMER supports modeling efforts by effortlessly gathering this information and providing convenient export options for images and their underlying data. Availability and implementation: GEMMER is freely available at http://gemmer.barberislab.com. Source code, written in Python, JavaScript library D3js, PHP and JSON, is freely available at https://github.com/barberislab/GEMMER. Supplementary information: Supplementary data are available at Bioinformatics online.

Learning to read and write in evolution: from static pseudoenzymes and pseudosignalers to dynamic gear shifters.

We present a systems biology view on pseudoenzymes that acknowledges that genes are not selfish: the genome is. With network function as the selectable unit, there has been an evolutionary bonus for recombination of functions of and within proteins. Many proteins house a functionality by which they 'read' the cell's state, and one by which they 'write' and thereby change that state. Should the writer domain lose its cognate function, a 'pseudoenzyme' or 'pseudosignaler' arises. GlnK involved in Escherichia coli ammonia assimilation may well be a pseudosignaler, associating 'reading' the nitrogen state of the cell to 'writing' the ammonium uptake activity. We identify functional pseudosignalers in the cyclin-dependent kinase complexes regulating cell-cycle progression. For the mitogen-activated protein kinase pathway, we illustrate how a 'dead' pseudosignaler could produce potentially selectable functionalities. Four billion years ago, bioenergetics may have shuffled 'electron-writers', producing various networks that all served the same function of anaerobic ATP synthesis and carbon assimilation from hydrogen and carbon dioxide, but at different ATP/acetate ratios. This would have enabled organisms to deal with variable challenges of energy need and substrate supply. The same principle might enable 'gear-shifting' in real time, by dynamically generating different pseudo-redox enzymes, reshuffling their coenzymes, and rerouting network fluxes. Non-stationary pH gradients in thermal vents together with similar such shuffling mechanisms may have produced a first selectable proton-motivated pyrophosphate synthase and subsequent ATP synthase. A combination of functionalities into enzymes, signalers, and the pseudo-versions thereof may offer fitness in terms of plasticity, both in real time and in evolution.

Systems Pharmacology: An opinion on how to turn the impossible into grand challenges.

A pharmacology that hits single disease-causing molecules with a single drug passively distributing to the target tissue, was almost ready. Such a pharmacology is not (going to be) effective however: a great many diseases are systems biology diseases; complex networks of some hundred thousand types of molecule, determine the functions that constitute human health, through nonlinear interactions. Malfunctions are caused by a variety of molecular failures at the same time; rarely the same variety in different individuals; in complex constellations of OR and AND logics. Few molecules cause disease single-handedly and few drugs will cure the disease all by themselves when dosed for a limited amount of time. We here discuss the implications that this discovery of the network nature of disease should have for pharmacology. We suggest ways in which pharmacokinetics, pharmacodynamics, but also systems biology and genomics may have to change so as better to deal with systems-biology diseases.

Unveiling Forkhead-mediated regulation of yeast cell cycle and metabolic networks.

Transcription factors are regulators of the cell's genomic landscape. By switching single genes or entire molecular pathways on or off, transcription factors modulate the precise timing of their activation. The Forkhead (Fkh) transcription factors are evolutionarily conserved to regulate organismal physiology and cell division. In addition to molecular biology and biochemical efforts, genome-wide studies have been conducted to characterize the genomic landscape potentially regulated by Forkheads in eukaryotes. Here, we discuss and interpret findings reported in six genome-wide Chromatin ImmunoPrecipitation (ChIP) studies, with a particular focus on ChIP-chip and ChIP-exo. We highlight their power and challenges to address Forkhead-mediated regulation of the cellular landscape in budding yeast. Expression changes of the targets identified in the binding assays are investigated by taking expression data for Forkhead deletion and overexpression into account. Forkheads are revealed as regulators of the metabolic network through which cell cycle dynamics may be temporally coordinated further, in addition to their well-known role as regulators of the gene cluster responsible for cell division.

Simultaneous Integration of Gene Expression and Nutrient Availability for Studying the Metabolism of Hepatocellular Carcinoma Cell Lines.

How cancer cells utilize nutrients to support their growth and proliferation in complex nutritional systems is still an open question. However, it is certainly determined by both genetics and an environmental-specific context. The interactions between them lead to profound metabolic specialization, such as consuming glucose and glutamine and producing lactate at prodigious rates. To investigate whether and how glucose and glutamine availability impact metabolic specialization, we integrated computational modeling on the genome-scale metabolic reconstruction with an experimental study on cell lines. We used the most comprehensive human metabolic network model to date, Recon3D, to build cell line-specific models. RNA-Seq data was used to specify the activity of genes in each cell line and the uptake rates were quantitatively constrained according to nutrient availability. To integrated both constraints we applied a novel method, named Gene Expression and Nutrients Simultaneous Integration (GENSI), that translates the relative importance of gene expression and nutrient availability data into the metabolic fluxes based on an observed experimental feature(s). We applied GENSI to study hepatocellular carcinoma addiction to glucose/glutamine. We were able to identify that proliferation, and lactate production is associated with the presence of glucose but does not necessarily increase with its concentration when the latter exceeds the physiological concentration. There was no such association with glutamine. We show that the integration of gene expression and nutrient availability data into genome-wide models improves the prediction of metabolic phenotypes.

Clb3-centered regulations are recurrent across distinct parameter regions in minimal autonomous cell cycle oscillator designs.

Some biological networks exhibit oscillations in their components to convert stimuli to time-dependent responses. The eukaryotic cell cycle is such a case, being governed by waves of cyclin-dependent kinase (cyclin/Cdk) activities that rise and fall with specific timing and guarantee its timely occurrence. Disruption of cyclin/Cdk oscillations could result in dysfunction through reduced cell division. Therefore, it is of interest to capture properties of network designs that exhibit robust oscillations. Here we show that a minimal yeast cell cycle network is able to oscillate autonomously, and that cyclin/Cdk-mediated positive feedback loops (PFLs) and Clb3-centered regulations sustain cyclin/Cdk oscillations, in known and hypothetical network designs. We propose that Clb3-mediated coordination of cyclin/Cdk waves reconciles checkpoint and oscillatory cell cycle models. Considering the evolutionary conservation of the cyclin/Cdk network across eukaryotes, we hypothesize that functional ("healthy") phenotypes require the capacity to oscillate autonomously whereas dysfunctional (potentially "diseased") phenotypes may lack this capacity.

ROS networks: designs, aging, Parkinson's disease and precision therapies.

How the network around ROS protects against oxidative stress and Parkinson's disease (PD), and how processes at the minutes timescale cause disease and aging after decades, remains enigmatic. Challenging whether the ROS network is as complex as it seems, we built a fairly comprehensive version thereof which we disentangled into a hierarchy of only five simpler subnetworks each delivering one type of robustness. The comprehensive dynamic model described in vitro data sets from two independent laboratories. Notwithstanding its five-fold robustness, it exhibited a relatively sudden breakdown, after some 80 years of virtually steady performance: it predicted aging. PD-related conditions such as lack of DJ-1 protein or increased α-synuclein accelerated the collapse, while antioxidants or caffeine retarded it. Introducing a new concept (aging-time-control coefficient), we found that as many as 25 out of 57 molecular processes controlled aging. We identified new targets for "life-extending interventions": mitochondrial synthesis, KEAP1 degradation, and p62 metabolism.