A network biology approach to aging in yeast

Genetic and metabolomic architecture of variation in diet restriction-mediated lifespan extension in Drosophila

In most organisms, dietary restriction (DR) increases lifespan. However, several studies have found that genotypes within the same species vary widely in how they respond to DR. To explore the mechanisms underlying this variation, we exposed 178 inbred Drosophila melanogaster lines to a DR or ad libitum (AL) diet, and measured a panel of 105 metabolites under both diets. Twenty four out of 105 metabolites were associated with the magnitude of the lifespan response. These included proteinogenic amino acids and metabolites involved in α-ketoglutarate (α-KG)/glutamine metabolism. We confirm the role of α-KG/glutamine synthesis pathways in the DR response through genetic manipulations. We used covariance network analysis to investigate diet-dependent interactions between metabolites, identifying the essential amino acids threonine and arginine as “hub” metabolites in the DR response. Finally, we employ a novel metabolic and genetic bipartite network analysis to reveal multiple genes that influence DR lifespan response, some of which have not previously been implicated in DR regulation. One of these is CCHa2R, a gene that encodes a neuropeptide receptor that influences satiety response and insulin signaling. Across the lines, variation in an intronic single nucleotide variant of CCHa2R correlated with variation in levels of five metabolites, all of which in turn were correlated with DR lifespan response. Inhibition of adult CCHa2R expression extended DR lifespan of flies, confirming the role of CCHa2R in lifespan response. These results provide support for the power of combined genomic and metabolomic analysis to identify key pathways underlying variation in this complex quantitative trait.

Dietary restriction extends lifespan across most organisms in which it has been tested. However, several studies have now demonstrated that this effect can vary dramatically across different genotypes within a population. Within a population, dietary restriction might be beneficial for some, yet detrimental for others. Here, we measure the metabolome of 178 genetically characterized fly strains on fully fed and restricted diets. The fly strains vary widely in their lifespan response to dietary restriction. We then use information about each strain’s genome and metabolome (a measure of small molecules circulating in flies) to pinpoint cellular pathways that govern this variation in response. We identify a novel pathway involving the gene CCHa2R, which encodes a neuropeptide receptor that has not previously been implicated in dietary restriction or age-related signaling pathways. This study demonstrates the power of leveraging systems biology and network biology methods to understand how and why different individuals vary in their response to health and lifespan-extending interventions.

Identifiability and experimental design in perturbation studies

MOTIVATION

A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network's response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimizing the experimental design is essential to achieve a meaningful characterization of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters.

RESULTS

We show analytically that the identifiability of network parameters can be determined by an intuitive maximum-flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved, on average, with less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.

AVAILABILITY AND IMPLEMENTATION

IdentiFlow is available at github.com/GrossTor/IdentiFlow.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Divergent Aging of Isogenic Yeast Cells Revealed through Single-Cell Phenotypic Dynamics

Although genetic mutations that alter organisms' average lifespans have been identified in aging research, our understanding of the dynamic changes during aging remains limited. Here, we integrate single-cell imaging, microfluidics, and computational modeling to investigate phenotypic divergence and cellular heterogeneity during replicative aging of single S. cerevisiae cells. Specifically, we find that isogenic cells diverge early in life toward one of two aging paths, which are characterized by distinct age-associated phenotypes. We captured the dynamics of single cells along the paths with a stochastic discrete-state model, which accurately predicts both the measured heterogeneity and the lifespan of cells on each path within a cell population. Our analysis suggests that genetic and environmental factors influence both a cell's choice of paths and the kinetics of paths themselves. Given that these factors are highly conserved throughout eukaryotes, divergent aging might represent a general scheme in cellular aging of other organisms.

Conventional and emerging roles of the energy sensor Snf1/AMPK in Saccharomyces cerevisiae

All proliferating cells need to match metabolism, growth and cell cycle progression with nutrient availability to guarantee cell viability in spite of a changing environment. In yeast, a signaling pathway centered on the effector kinase Snf1 is required to adapt to nutrient limitation and to utilize alternative carbon sources, such as sucrose and ethanol. Snf1 shares evolutionary conserved functions with the AMP-activated Kinase (AMPK) in higher eukaryotes which, activated by energy depletion, stimulates catabolic processes and, at the same time, inhibits anabolism. Although the yeast Snf1 is best known for its role in responding to a number of stress factors, in addition to glucose limitation, new unconventional roles of Snf1 have recently emerged, even in glucose repressing and unstressed conditions. Here, we review and integrate available data on conventional and non-conventional functions of Snf1 to better understand the complexity of cellular physiology which controls energy homeostasis.

The role of autophagy in the regulation of yeast life span

The goal of the aging field is to develop novel therapeutic interventions that extend human health span and reduce the burden of age-related disease. While organismal aging is a complex, multifactorial process, a popular theory is that cellular aging is a significant contributor to the progressive decline inherent to all multicellular organisms. To explore the molecular determinants that drive cellular aging, as well as how to retard them, researchers have utilized the highly genetically tractable budding yeast Saccharomyces cerevisiae. Indeed, every intervention known to extend both cellular and organismal health span was identified in yeast, underlining the power of this approach. Importantly, a growing body of work has implicated the process of autophagy as playing a critical role in the delay of aging. This review summarizes recent reports that have identified a role for autophagy, or autophagy factors in the extension of yeast life span. These studies demonstrate (1) that yeast remains an invaluable tool for the identification and characterization of conserved mechanisms that promote cellular longevity and are likely to be relevant to humans, and (2) that the process of autophagy has been implicated in nearly all known longevity-promoting manipulations and thus represents an ideal target for interventions aimed at improving human health span.

AMPK orchestrates an elaborate cascade protecting tissue from fibrosis and aging

Fibrosis is a common process characterized by excessive extracellular matrix (ECM) accumulation after inflammatory injury, which is also a crucial cause of aging. The process of fibrosis is involved in the pathogenesis of most diseases of the heart, liver, kidney, lung, and other organs/tissues. However, there are no effective therapies for this pathological alteration. Annually, fibrosis represents a huge financial burden for the USA and the world. 5'-AMP-activated protein kinase (AMPK) is a pivotal energy sensor that alleviates or delays the process of fibrogenesis. In this review, we first present basic background information on AMPK and fibrogenesis and describe the protective roles of AMPK in three fibrogenic phases. Second, we analyze the protective action of AMPK during fibrosis in myocardial, hepatic, renal, pulmonary, and other organs/tissues. Third, we present a comprehensive discussion of AMPK during fibrosis and draw a conclusion. This review highlights recent advances, vital for basic research and clinical drug design, in the regulation of AMPK during fibrosis.

Similar environments but diverse fates: Responses of budding yeast to nutrient deprivation

Diploid budding yeast (Saccharomyces cerevisiae) can adopt one of several alternative differentiation fates in response to nutrient limitation, and each of these fates provides distinct biological functions. When different strain backgrounds are taken into account, these various fates occur in response to similar environmental cues, are regulated by the same signal transduction pathways, and share many of the same master regulators. I propose that the relationships between fate choice, environmental cues and signaling pathways are not Boolean, but involve graded levels of signals, pathway activation and master-regulator activity. In the absence of large differences between environmental cues, small differences in the concentration of cues may be reinforced by cell-to-cell signals. These signals are particularly essential for fate determination within communities, such as colonies and biofilms, where fate choice varies dramatically from one region of the community to another. The lack of Boolean relationships between cues, signaling pathways, master regulators and cell fates may allow yeast communities to respond appropriately to the wide range of environments they encounter in nature.

Deciphering hallmark processes of aging from interaction networks

BACKGROUND

Aging is broadly considered to be a dynamic process that accumulates unfavourable structural and functional changes in a time dependent fashion, leading to a progressive loss of physiological integrity of an organism, which eventually leads to age-related diseases and finally to death.

SCOPE OF REVIEW

The majority of aging-related studies are based on reductionist approaches, focusing on single genes/proteins or on individual pathways without considering possible interactions between them. Over the last few decades, several such genes/proteins were independently analysed and linked to a role that is affecting the longevity of an organism. However, an isolated analysis on genes and proteins largely fails to explain the mechanistic insight of a complex phenotype due to the involvement and integration of multiple factors.

MAJOR CONCLUSIONS

Technological advance makes it possible to generate high-throughput temporal and spatial data that provide an opportunity to use computer-based methods. These techniques allow us to go beyond reductionist approaches to analyse large-scale networks that provide deeper understanding of the processes that drive aging.

GENERAL SIGNIFICANCE

In this review, we focus on systems biology approaches, based on network inference methods to understand the dynamics of hallmark processes leading to aging phenotypes. We also describe computational methods for the interpretation and identification of important molecular hubs involved in the mechanistic linkage between aging related processes. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

Age-related changes in AMPK activation: Role for AMPK phosphatases and inhibitory phosphorylation by upstream signaling pathways

AMP-activated protein kinase (AMPK) is a fundamental regulator of energy metabolism, stress resistance, and cellular proteostasis. AMPK signaling controls an integrated signaling network which is involved in the regulation of healthspan and lifespan e.g. via FoxO, mTOR/ULK1, CRCT-1/CREB, and SIRT1 signaling pathways. Several studies have demonstrated that the activation capacity of AMPK signaling declines with aging, which impairs the maintenance of efficient cellular homeostasis and enhances the aging process. However, it seems that the aging process affects AMPK activation in a context-dependent manner since occasionally, it can also augment AMPK activation, possibly attributable to the type of insult and tissue homeostasis. Three protein phosphatases, PP1, PP2A, and PP2C, inhibit AMPK activation by dephosphorylating the Thr172 residue of AMPKα, required for AMPK activation. In addition, several upstream signaling pathways can phosphorylate Ser/Thr residues in the β/γ interaction domain of the AMPKα subunit that subsequently blocks the activation of AMPK. These inhibitory pathways include the insulin/AKT, cyclic AMP/PKA, and RAS/MEK/ERK pathways. We will examine the evidence whether the efficiency of AMPK responsiveness declines during the aging process. Next, we will review the mechanisms involved in curtailing the activation of AMPK. Finally, we will elucidate the potential age-related changes in the inhibitory regulation of AMPK signaling that might be a part of the aging process.

AMPK and HIF signaling pathways regulate both longevity and cancer growth: the good news and the bad news about survival mechanisms

The AMP-activated protein kinase (AMPK) and hypoxia-inducible factor (HIF) signaling pathways are evolutionarily-conserved survival mechanisms responding to two fundamental stresses, energy deficiency and/or oxygen deprivation. The AMPK and HIF pathways regulate the function of a survival network with several transcription factors, e.g. FOXO, NF-κB, NRF2, and p53, as well as with protein kinases and other factors, such as mTOR, ULK1, HDAC5, and SIRT1. Given that AMPK and HIF activation can enhance not only healthspan and lifespan but also cancer growth in a context-dependent manner; it seems that cancer cells can hijack certain survival factors to maintain their growth in harsh conditions. AMPK activation improves energy metabolism, stimulates autophagy, and inhibits inflammation, whereas HIF-1α increases angiogenesis and helps cells to adapt to severe conditions. First we will review how AMPK and HIF signaling mechanisms control the function of an integrated survival network which is able not only to improve the regulation of longevity but also support the progression of tumorigenesis. We will also describe distinct crossroads between the regulation of longevity and cancer, e.g. specific regulation through the AMPKα and HIF-α isoforms, the Warburg effect, mitochondrial dynamics, and cellular senescence.

The ceramide-activated protein phosphatase Sit4p controls lifespan, mitochondrial function and cell cycle progression by regulating hexokinase 2 phosphorylation

Sit4p is the catalytic subunit of a ceramide-activated PP2A-like phosphatase that regulates cell cycle, mitochondrial function, oxidative stress resistance and chronological lifespan in yeast. In this study, we show that hexokinase 2 (Hxk2p) is hyperphosphorylated in sit4Δ mutants grown in glucose medium by a Snf1p-independent mechanism and Hxk2p-S15A mutation suppresses phenotypes associated with SIT4 deletion, namely growth arrest at G1 phase, derepression of mitochondrial respiration, H2O2 resistance and lifespan extension. Consistently, the activation of Sit4p in isc1Δ mutants, which has been associated with premature aging, leads to Hxk2p hypophosphorylation, and the expression of Hxk2p-S15E increases the lifespan of isc1Δ cells. The overall results suggest that Hxk2p functions downstream of Sit4p in the control of cell cycle, mitochondrial function, oxidative stress resistance and chronological lifespan.

Integration of 'omics' data in aging research: from biomarkers to systems biology

Age is the strongest risk factor for many diseases including neurodegenerative disorders, coronary heart disease, type 2 diabetes and cancer. Due to increasing life expectancy and low birth rates, the incidence of age-related diseases is increasing in industrialized countries. Therefore, understanding the relationship between diseases and aging and facilitating healthy aging are major goals in medical research. In the last decades, the dimension of biological data has drastically increased with high-throughput technologies now measuring thousands of (epi) genetic, expression and metabolic variables. The most common and so far successful approach to the analysis of these data is the so-called reductionist approach. It consists of separately testing each variable for association with the phenotype of interest such as age or age-related disease. However, a large portion of the observed phenotypic variance remains unexplained and a comprehensive understanding of most complex phenotypes is lacking. Systems biology aims to integrate data from different experiments to gain an understanding of the system as a whole rather than focusing on individual factors. It thus allows deeper insights into the mechanisms of complex traits, which are caused by the joint influence of several, interacting changes in the biological system. In this review, we look at the current progress of applying omics technologies to identify biomarkers of aging. We then survey existing systems biology approaches that allow for an integration of different types of data and highlight the need for further developments in this area to improve epidemiologic investigations.

On predicting regulatory genes by analysis of functional networks in C. elegans

Background

Connectivity networks, which reflect multiple interactions between genes and proteins, possess not only a descriptive but also a predictive value, as new connections can be extrapolated and tested by means of computational analysis. Integration of different types of connectivity data (such as co-expression and genetic interactions) in one network has proven to benefit ‘guilt by association’ analysis. However predictive values of connectives of different types, that had their specific functional meaning and topological characteristics were not obvious, and have been addressed in this analysis.

Methods

eQTL data for 3 experimental C.elegans age groups were retrieved from WormQTL. WormNet has been used to obtain pair-wise gene interactions. The Shortest Path Function (SPF) has been adopted for statistical validation of the co-expressed gene clusters and for computational prediction of their potential gene expression regulators from a network context. A new SPF-based algorithm has been applied to genetic interactions sub-networks adjacent to the clusters of co-expressed genes for ranking the most likely gene expression regulators causal to eQTLs.

Results

We have demonstrated that known co-expression and genetic interactions between C. elegans genes can be complementary in predicting gene expression regulators. Several algorithms were compared in respect to their predictive potential in different network connectivity contexts. We found that genes associated with eQTLs are highly clustered in a C. elegans co-expression sub-network, and their adjacent genetic interactions provide the optimal functional connectivity environment for application of the new SPF-based algorithm. It was successfully tested in the reverse-prediction analysis on groups of genes with known regulators and applied to co-expressed genes and experimentally observed expression quantitative trait loci (eQTLs).

Conclusions

This analysis demonstrates differences in topology and connectivity of co-expression and genetic interactions sub-networks in WormNet. The modularity of less continuous genetic interaction network does not correspond to modularity of the dense network comprised by gene co-expression interactions. However the genetic interaction network can be used much more efficiently with the SPF method in prediction of potential regulators of gene expression. The developed method can be used for validation of functional significance of suggested eQTLs and a discovery of new regulatory modules.

Genome-wide expression analyses of the stationary phase model of ageing in yeast

Ageing processes involved in replicative lifespan (RLS) and chronological lifespan (CLS) have been found to be conserved among many organisms, including in unicellular Eukarya such as yeast Saccharomyces cerevisiae. Here we performed an integrated approach of genome wide expression profiles of yeast at different time points, during growth and starvation. The aim of the study was to identify transcriptional changes in those conditions by using several different computational analyses in order to propose transcription factors, biological networks and metabolic pathways that seem to be relevant during the process of chronological ageing in yeast. Specifically, we performed differential gene expression analysis, gene-set enrichment analysis and network-based analysis, and we identified pathways affected in the stationary phase and specific transcription factors driving transcriptional adaptations. The results indicate signal propagation from G protein-coupled receptors through signaling pathway components and other stress and nutrient-induced transcription factors resulting in adaptation of yeast cells to the lack of nutrients by activating metabolism associated with aerobic metabolism of carbon sources such as ethanol, glycerol and fatty acids. In addition, we found STE12, XBP1 and TOS8 as highly connected nodes in the subnetworks of ageing yeast.

Proteasomes, Sir2, and Hxk2 form an interconnected aging network that impinges on the AMPK/Snf1-regulated transcriptional repressor Mig1

Elevated proteasome activity extends lifespan in model organisms such as yeast, worms and flies. This pro-longevity effect might be mediated by improved protein homeostasis, as this protease is an integral module of the protein homeostasis network. Proteasomes also regulate cellular processes through temporal and spatial degradation of signaling pathway components. Here we demonstrate that the regulatory function of the proteasome plays an essential role in aging cells and that the beneficial impact of elevated proteasome capacity on lifespan partially originates from deregulation of the AMPK signaling pathway. Proteasome-mediated lifespan extension activity was carbon-source dependent and cells with enhancement proteasome function exhibited increased respiratory activity and oxidative stress response. These findings suggested that the pro-aging impact of proteasome upregulation might be related to changes in the metabolic state through a premature induction of respiration. Deletion of yeast AMPK, SNF1, or its activator SNF4 abrogated proteasome-mediated lifespan extension, supporting this hypothesis as the AMPK pathway regulates metabolism. We found that the premature induction of respiration in cells with increased proteasome activity originates from enhanced turnover of Mig1, an AMPK/Snf1 regulated transcriptional repressor that prevents the induction of genes required for respiration. Increasing proteasome activity also resulted in partial relocation of Mig1 from the nucleus to the mitochondria. Collectively, the results argue for a model in which elevated proteasome activity leads to the uncoupling of Snf1-mediated Mig1 regulation, resulting in a premature activation of respiration and thus the induction of a mitohormetic response, beneficial to lifespan. In addition, we observed incorrect Mig1 localization in two other long-lived yeast aging models: cells that overexpress SIR2 or deleted for the Mig1-regulator HXK2. Finally, compromised proteasome function blocks lifespan extension in both strains. Thus, our findings suggest that proteasomes, Sir2, Snf1 and Hxk2 form an interconnected aging network that controls metabolism through coordinated regulation of Mig1.

Advanced cellular age is associated with decreased efficiency of the proteostasis network. The proteasome, a protease in the cytoplasm and nuclei of eukaryotic cells, is an important component of this network. Recent studies demonstrate that increased proteasome capacity has a positive impact on longevity. The underlying mechanisms, however, have not been fully identified. Here we report that proteasomes are involved in regulating the AMP-activated kinase (AMPK) pathway and thus participate in correct metabolic adaptation. We find that Mig1, a transcriptional repressor downstream of yeast AMPK, Snf1, is a proteasome target and a negative regulator of lifespan. Increased proteasome activity results in enhanced turnover and incorrect localization of Mig1. The reduced Mig1 levels result in the induction of respiration and upregulation of the oxidative stress response. Premature Mig1 inactivation is also observed in two additional long-lived strains that overexpress SIR2 or are deleted for HXK2 and lifespan extension in both strains requires correct proteasome function. Our results uncover an interconnected network comprised of the proteasome, Sir2 and AMPK/Hxk2 signaling that impacts longevity through regulation of Mig1 and modulates respiratory metabolism. Mechanistic information on the cross-communication between these pathways is expected to facilitate the identification of novel pro-aging interventions.

Avoiding pitfalls in L1-regularised inference of gene networks

L₁ regularisation methods fail to infer the correct network even when the data are so informative that all existing links can be proven to exist.

Gene differential coexpression analysis based on biweight correlation and maximum clique

Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.

Deteriorated stress response in stationary-phase yeast: Sir2 and Yap1 are essential for Hsf1 activation by heat shock and oxidative stress, respectively

Stationary-phase cultures have been used as an important model of aging, a complex process involving multiple pathways and signaling networks. However, the molecular processes underlying stress response of non-dividing cells are poorly understood, although deteriorated stress response is one of the hallmarks of aging. The budding yeast Saccharomyces cerevisiae is a valuable model organism to study the genetics of aging, because yeast ages within days and are amenable to genetic manipulations. As a unicellular organism, yeast has evolved robust systems to respond to environmental challenges. This response is orchestrated largely by the conserved transcription factor Hsf1, which in S. cerevisiae regulates expression of multiple genes in response to diverse stresses. Here we demonstrate that Hsf1 response to heat shock and oxidative stress deteriorates during yeast transition from exponential growth to stationary-phase, whereas Hsf1 activation by glucose starvation is maintained. Overexpressing Hsf1 does not significantly improve heat shock response, indicating that Hsf1 dwindling is not the major cause for Hsf1 attenuated response in stationary-phase yeast. Rather, factors that participate in Hsf1 activation appear to be compromised. We uncover two factors, Yap1 and Sir2, which discretely function in Hsf1 activation by oxidative stress and heat shock. In Δyap1 mutant, Hsf1 does not respond to oxidative stress, while in Δsir2 mutant, Hsf1 does not respond to heat shock. Moreover, excess Sir2 mimics the heat shock response. This role of the NAD+-dependent Sir2 is supported by our finding that supplementing NAD+ precursors improves Hsf1 heat shock response in stationary-phase yeast, especially when combined with expression of excess Sir2. Finally, the combination of excess Hsf1, excess Sir2 and NAD+ precursors rejuvenates the heat shock response.

Cell-autonomous mechanisms of chronological aging in the yeast Saccharomyces cerevisiae

A body of evidence supports the view that the signaling pathways governing cellular aging - as well as mechanisms of their modulation by longevity-extending genetic, dietary and pharmacological interventions - are conserved across species. The scope of this review is to critically analyze recent advances in our understanding of cell-autonomous mechanisms of chronological aging in the budding yeast Saccharomyces cerevisiae. Based on our analysis, we propose a concept of a biomolecular network underlying the chronology of cellular aging in yeast. The concept posits that such network progresses through a series of lifespan checkpoints. At each of these checkpoints, the intracellular concentrations of some key intermediates and products of certain metabolic pathways - as well as the rates of coordinated flow of such metabolites within an intricate network of intercompartmental communications - are monitored by some checkpoint-specific ʺmaster regulatorʺ proteins. The concept envisions that a synergistic action of these master regulator proteins at certain early-life and late-life checkpoints modulates the rates and efficiencies of progression of such processes as cell metabolism, growth, proliferation, stress resistance, macromolecular homeostasis, survival and death. The concept predicts that, by modulating these vital cellular processes throughout lifespan (i.e., prior to an arrest of cell growth and division, and following such arrest), the checkpoint-specific master regulator proteins orchestrate the development and maintenance of a pro- or anti-aging cellular pattern and, thus, define longevity of chronologically aging yeast.

Longevity pathways and maintenance of the proteome: the role of autophagy and mitophagy during yeast ageing

Ageing is a complex and multi-factorial process that results in the progressive accumulation of molecular alterations that disrupt different cellular functions. The budding yeast Saccharomyces cerevisiae is an important model organism that has significantly contributed to the identification of conserved molecular and cellular determinants of ageing. The nutrient-sensing pathways are well-recognized modulators of longevity from yeast to mammals, but their downstream effectors and outcomes on different features of ageing process are still poorly understood. A hypothesis that is attracting increased interest is that one of the major functions of these “longevity pathways” is to contribute to the maintenance of the proteome during ageing. In support of this hypothesis, evidence shows that TOR/Sch9 and Ras/PKA pathways are important regulators of autophagy that in turn are essential for healthy cellular ageing. It is also well known that mitochondria homeostasis and function regulate lifespan, but how mitochondrial dynamics, mitophagy and biogenesis are regulated during ageing remains to be elucidated. This review describes recent findings that shed light on how longevity pathways and metabolic status impact maintenance of the proteome in both yeast ageing paradigms. These findings demonstrate that yeast remain a powerful model system for elucidating these relationships and their influence on ageing regulation.

Identification of the dichotomous role of age-related LCK in calorie restriction revealed by integrative analysis of cDNA microarray and interactome

Among the many experimental paradigms used for the investigation of aging, the calorie restriction (CR) model has been proven to be the most useful in gerontological research. Exploration of the mechanisms underlying CR has produced a wealth of data. To identify key molecules controlled by aging and CR, we integrated data from 84 mouse and rat cDNA microarrays with a protein-protein interaction network. On the basis of this integrative analysis, we selected three genes that are upregulated in aging but downregulated by CR and two genes that are downregulated in aging but upregulated by CR. One of these key molecules is lymphocyte-specific protein tyrosine kinase (LCK). To further confirm this result on LCK, we performed a series of experiments in vitro and in vivo using kidneys obtained from aged ad libitum-fed and CR rats. Our major significant findings are as follows: (1) identification of LCK as a key molecule using integrative analysis; (2) confirmation that the age-related increase in LCK was modulated by CR and that protein tyrosine kinase activity was decreased using a LCK-specific inhibitor; and (3) upregulation of LCK leads to NF-κB activation in a ONOO(-) generation-dependent manner, which is modulated by CR. These results indicate that LCK could be considered a target attenuated by the anti-aging effects of CR. Integrative analysis of cDNA microarray and interactome data are powerful tools for identifying target molecules that are involved in the aging process and modulated by CR.

Structure and dynamics of molecular networks: a novel paradigm of drug discovery: a comprehensive review

Despite considerable progress in genome- and proteome-based high-throughput screening methods and in rational drug design, the increase in approved drugs in the past decade did not match the increase of drug development costs. Network description and analysis not only give a systems-level understanding of drug action and disease complexity, but can also help to improve the efficiency of drug design. We give a comprehensive assessment of the analytical tools of network topology and dynamics. The state-of-the-art use of chemical similarity, protein structure, protein-protein interaction, signaling, genetic interaction and metabolic networks in the discovery of drug targets is summarized. We propose that network targeting follows two basic strategies. The "central hit strategy" selectively targets central nodes/edges of the flexible networks of infectious agents or cancer cells to kill them. The "network influence strategy" works against other diseases, where an efficient reconfiguration of rigid networks needs to be achieved by targeting the neighbors of central nodes/edges. It is shown how network techniques can help in the identification of single-target, edgetic, multi-target and allo-network drug target candidates. We review the recent boom in network methods helping hit identification, lead selection optimizing drug efficacy, as well as minimizing side-effects and drug toxicity. Successful network-based drug development strategies are shown through the examples of infections, cancer, metabolic diseases, neurodegenerative diseases and aging. Summarizing >1200 references we suggest an optimized protocol of network-aided drug development, and provide a list of systems-level hallmarks of drug quality. Finally, we highlight network-related drug development trends helping to achieve these hallmarks by a cohesive, global approach.

New approaches to an age-old problem

Progress in the last decades indicated that ageing might be a universal fact of life. However, the molecular mechanisms underlying this process remain a major challenge in biology. Our relatively long life span and huge variations in lifestyle make detailed studies of ageing in humans difficult to interpret. In contrast, the relatively simple yeast Saccharomyces cerevisiae (budding yeast) has been a critical model in the field of ageing research for decades. Systems biology has contributed to the ageing field by mapping complex regulatory networks and resolving the dynamics of signal transduction pathways. In this review we first review the current understanding of ageing in yeast, then highlight the recent high-throughput systems and system biology approaches that could be used to further our understanding of ageing in yeast.

Genome-scale studies of aging: challenges and opportunities

Whole-genome studies involving a phenotype of interest are increasingly prevalent, in part due to a dramatic increase in speed at which many high throughput technologies can be performed coupled to simultaneous decreases in cost. This type of genome-scale methodology has been applied to the phenotype of lifespan, as well as to whole-transcriptome changes during the aging process or in mutants affecting aging. The value of high throughput discovery-based science in this field is clearly evident, but will it yield a true systems-level understanding of the aging process? Here we review some of this work to date, focusing on recent findings and the unanswered puzzles to which they point. In this context, we also discuss recent technological advances and some of the likely future directions that they portend.

The emerging paradigm of network medicine in the study of human disease

The molecular pathways that govern human disease consist of molecular circuits that coalesce into complex, overlapping networks. These network pathways are presumably regulated in a coordinated fashion, but such regulation has been difficult to decipher using only reductionistic principles. The emerging paradigm of "network medicine" proposes to utilize insights garnered from network topology (eg, the static position of molecules in relation to their neighbors) as well as network dynamics (eg, the unique flux of information through the network) to understand better the pathogenic behavior of complex molecular interconnections that traditional methods fail to recognize. As methodologies evolve, network medicine has the potential to capture the molecular complexity of human disease while offering computational methods to discern how such complexity controls disease manifestations, prognosis, and therapy. This review introduces the fundamental concepts of network medicine and explores the feasibility and potential impact of network-based methods for predicting individual manifestations of human disease and designing rational therapies. Wherever possible, we emphasize the application of these principles to cardiovascular disease.

Lithocholic acid extends longevity of chronologically aging yeast only if added at certain critical periods of their lifespan

Our studies revealed that LCA (lithocholic bile acid) extends yeast chronological lifespan if added to growth medium at the time of cell inoculation. We also demonstrated that longevity in chronologically aging yeast is programmed by the level of metabolic capacity and organelle organization that they developed before entering a quiescent state and, thus, that chronological aging in yeast is likely to be the final step of a developmental program progressing through at least one checkpoint prior to entry into quiescence. Here, we investigate how LCA influences longevity and several longevity-defining cellular processes in chronologically aging yeast if added to growth medium at different periods of the lifespan. We found that LCA can extend longevity of yeast under CR (caloric restriction) conditions only if added at either of two lifespan periods. One of them includes logarithmic and diauxic growth phases, whereas the other period exists in early stationary phase. Our findings suggest a mechanism linking the ability of LCA to increase the lifespan of CR yeast only if added at either of the two periods to its differential effects on various longevity-defining processes. In this mechanism, LCA controls these processes at three checkpoints that exist in logarithmic/diauxic, post-diauxic and early stationary phases. We therefore hypothesize that a biomolecular longevity network progresses through a series of checkpoints, at each of which (1) genetic, dietary and pharmacological anti-aging interventions modulate a distinct set of longevity-defining processes comprising the network; and (2) checkpoint-specific master regulators monitor and govern the functional states of these processes.

Catching transcriptional regulation by thermostatistical modeling

Gene expression is frequently regulated by multiple transcription factors (TFs). Thermostatistical methods allow for a quantitative description of interactions between TFs, RNA polymerase and DNA, and their impact on the transcription rates. We illustrate three different scales of the thermostatistical approach: the microscale of TF molecules, the mesoscale of promoter energy levels and the macroscale of transcriptionally active and inactive cells in a cell population. We demonstrate versatility of combinatorial transcriptional activation by exemplifying logic functions, such as AND and OR gates. We discuss a metric for cell-to-cell transcriptional activation variability known as Fermi entropy. Suitability of thermostatistical modeling is illustrated by describing the experimental data on transcriptional induction of NFκB and the c-Fos protein.

Nutritional stress in eukaryotic cells: oxidative species and regulation of survival in time of scarceness

The survival response to glucose limitation in eukaryotic cells involves different signaling pathways highly conserved from yeasts to mammals. Upon nutritional restriction, a network driven by kinases such as the AMP dependent protein kinase (AMPK/Snf1), the Target of Rapamycin kinase (TOR), the Protein kinases A (PKA) or B (PKB/Akt) control stress defenses, cell cycle regulators, pro and anti apoptotic proteins, respiratory complexes, etc. In this work we review the state of the art in this scenario of kinase pathways, i.e. their principal effectors and links, both in yeasts and mammals. We also focus in downstream actors such as sirtuins and the Forkhead box class O transcription factors. Besides, we particularly analyze the participation of these kinases on the balance of Reactive Oxygen Species and their role in the regulation of survival during glucose deprivation. Key results on yeast stationary phase survival and the contribution of such genetics studies are discussed.

Reverse-engineering human regulatory networks

The explosion of genomic, transcriptomic, proteomic, metabolomic, and other omics data is challenging the research community to develop rational models for their organization and interpretation to generate novel biological knowledge. The development and use of gene regulatory networks to mechanistically interpret this data is an important development in molecular biology, usually captured under the banner of systems biology. As a result, the repertoire of methods for the reconstruction of comprehensive and cell-context-specific maps of regulatory interactions, or interactomes, has also exploded in the past few years. In this review, we focus on Network Biology and more specifically on methods for reverse engineering transcriptional, post-transcriptional, and post-translational human interaction networks and show how their interrogation is starting to impact our understanding of cellular pathophysiology and one's ability to predict cellular phenotypes from genome-wide molecular observations.

Calorie restriction: is AMPK a key sensor and effector?

Dietary restriction can extend life span in most organisms tested to date, suggesting that mechanisms sensing nutrient and energy availability might regulate longevity. The AMP-activated protein kinase (AMPK) has emerged as a key energy sensor with the ability to transcriptionally reprogram the cell and metabolically adapt to external cues. In this review, we will discuss the possible role of AMPK in the beneficial effects of calorie restriction on health and life span.

A network-based approach on elucidating the multi-faceted nature of chronological aging in S. cerevisiae

BACKGROUND

Cellular mechanisms leading to aging and therefore increasing susceptibility to age-related diseases are a central topic of research since aging is the ultimate, yet not understood mechanism of the fate of a cell. Studies with model organisms have been conducted to ellucidate these mechanisms, and chronological aging of yeast has been extensively used as a model for oxidative stress and aging of postmitotic tissues in higher eukaryotes.

METHODOLOGY/PRINCIPAL FINDINGS

The chronological aging network of yeast was reconstructed by integrating protein-protein interaction data with gene ontology terms. The reconstructed network was then statistically "tuned" based on the betweenness centrality values of the nodes to compensate for the computer automated method. Both the originally reconstructed and tuned networks were subjected to topological and modular analyses. Finally, an ultimate "heart" network was obtained via pooling the step specific key proteins, which resulted from the decomposition of the linear paths depicting several signaling routes in the tuned network.

CONCLUSIONS/SIGNIFICANCE

The reconstructed networks are of scale-free and hierarchical nature, following a power law model with γ  =  1.49. The results of modular and topological analyses verified that the tuning method was successful. The significantly enriched gene ontology terms of the modular analysis confirmed also that the multifactorial nature of chronological aging was captured by the tuned network. The interplay between various signaling pathways such as TOR, Akt/PKB and cAMP/Protein kinase A was summarized in the "heart" network originated from linear path analysis. The deletion of four genes, TCB3, SNA3, PST2 and YGR130C, was found to increase the chronological life span of yeast. The reconstructed networks can also give insight about the effect of other cellular machineries on chronological aging by targeting different signaling pathways in the linear path analysis, along with unraveling of novel proteins playing part in these pathways.

Identification of the CRE-1 cellulolytic regulon in Neurospora crassa

BACKGROUND

In filamentous ascomycete fungi, the utilization of alternate carbon sources is influenced by the zinc finger transcription factor CreA/CRE-1, which encodes a carbon catabolite repressor protein homologous to Mig1 from Saccharomyces cerevisiae. In Neurospora crassa, deletion of cre-1 results in increased secretion of amylase and β-galactosidase.

METHODOLOGY/PRINCIPAL FINDINGS

Here we show that a strain carrying a deletion of cre-1 has increased cellulolytic activity and increased expression of cellulolytic genes during growth on crystalline cellulose (Avicel). Constitutive expression of cre-1 complements the phenotype of a N. crassa Δcre-1 strain grown on Avicel, and also results in stronger repression of cellulolytic protein secretion and enzyme activity. We determined the CRE-1 regulon by investigating the secretome and transcriptome of a Δcre-1 strain as compared to wild type when grown on Avicel versus minimal medium. Chromatin immunoprecipitation-PCR of putative target genes showed that CRE-1 binds to only some adjacent 5'-SYGGRG-3' motifs, consistent with previous findings in other fungi, and suggests that unidentified additional regulatory factors affect CRE-1 binding to promoter regions. Characterization of 30 mutants containing deletions in genes whose expression level increased in a Δcre-1 strain under cellulolytic conditions identified novel genes that affect cellulase activity and protein secretion.

CONCLUSIONS/SIGNIFICANCE

Our data provide comprehensive information on the CRE-1 regulon in N. crassa and contribute to deciphering the global role of carbon catabolite repression in filamentous ascomycete fungi during plant cell wall deconstruction.

The AMPK/SNF1/SnRK1 fuel gauge and energy regulator: structure, function and regulation

All life forms on earth require a continuous input and monitoring of carbon and energy supplies. The AMP-activated kinase (AMPK)/sucrose non-fermenting1 (SNF1)/Snf1-related kinase1 (SnRK1) protein kinases are evolutionarily conserved metabolic sensors found in all eukaryotic organisms from simple unicellular fungi (yeast SNF1) to animals (AMPK) and plants (SnRK1). Activated by starvation and energy-depleting stress conditions, they enable energy homeostasis and survival by up-regulating energy-conserving and energy-producing catabolic processes, and by limiting energy-consuming anabolic metabolism. In addition, they control normal growth and development as well as metabolic homeostasis at the organismal level. As such, the AMPK/SNF1/SnRK1 kinases act in concert with other central signaling components to control carbohydrate uptake and metabolism, fatty acid and lipid biosynthesis and the storage of carbon energy reserves. Moreover, they have a tremendous impact on developmental processes that are triggered by environmental changes such as nutrient depletion or stress. Although intensive research by many groups has partly unveiled the factors that regulate AMPK/SNF1/SnRK1 kinase activity as well as the pathways and substrates they control, several fundamental issues still await to be clarified. In this review, we will highlight these issues and focus on the structure, function and regulation of the AMPK/SNF1/SnRK1 kinases.

Identification of potential calorie restriction-mimicking yeast mutants with increased mitochondrial respiratory chain and nitric oxide levels

Calorie restriction (CR) induces a metabolic shift towards mitochondrial respiration; however, molecular mechanisms underlying CR remain unclear. Recent studies suggest that CR-induced mitochondrial activity is associated with nitric oxide (NO) production. To understand the role of mitochondria in CR, we identify and study Saccharomyces cerevisiae mutants with increased NO levels as potential CR mimics. Analysis of the top 17 mutants demonstrates a correlation between increased NO, mitochondrial respiration, and longevity. Interestingly, treating yeast with NO donors such as GSNO (S-nitrosoglutathione) is sufficient to partially mimic CR to extend lifespan. CR-increased NO is largely dependent on mitochondrial electron transport and cytochrome c oxidase (COX). Although COX normally produces NO under hypoxic conditions, CR-treated yeast cells are able to produce NO under normoxic conditions. Our results suggest that CR may derepress some hypoxic genes for mitochondrial proteins that function to promote the production of NO and the extension of lifespan.

Computational biology for ageing

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions.

Tunable signal processing in synthetic MAP kinase cascades

The flexibility of MAPK cascade responses enables regulation of a vast array of cell fate decisions, but elucidating the mechanisms underlying this plasticity is difficult in endogenous signaling networks. We constructed insulated mammalian MAPK cascades in yeast to explore how intrinsic and extrinsic perturbations affect the flexibility of these synthetic signaling modules. Contrary to biphasic dependence on scaffold concentration, we observe monotonic decreases in signal strength as scaffold concentration increases. We find that augmenting the concentration of sequential kinases can enhance ultrasensitivity and lower the activation threshold. Further, integrating negative regulation and concentration variation can decouple ultrasensitivity and threshold from the strength of the response. Computational analyses show that cascading can generate ultrasensitivity and that natural cascades with different kinase concentrations are innately biased toward their distinct activation profiles. This work demonstrates that tunable signal processing is inherent to minimal MAPK modules and elucidates principles for rational design of synthetic signaling systems.

A systems biology model of the regulatory network in Populus leaves reveals interacting regulators and conserved regulation

BACKGROUND

Green plant leaves have always fascinated biologists as hosts for photosynthesis and providers of basic energy to many food webs. Today, comprehensive databases of gene expression data enable us to apply increasingly more advanced computational methods for reverse-engineering the regulatory network of leaves, and to begin to understand the gene interactions underlying complex emergent properties related to stress-response and development. These new systems biology methods are now also being applied to organisms such as Populus, a woody perennial tree, in order to understand the specific characteristics of these species.

RESULTS

We present a systems biology model of the regulatory network of Populus leaves. The network is reverse-engineered from promoter information and expression profiles of leaf-specific genes measured over a large set of conditions related to stress and developmental. The network model incorporates interactions between regulators, such as synergistic and competitive relationships, by evaluating increasingly more complex regulatory mechanisms, and is therefore able to identify new regulators of leaf development not found by traditional genomics methods based on pair-wise expression similarity. The approach is shown to explain available gene function information and to provide robust prediction of expression levels in new data. We also use the predictive capability of the model to identify condition-specific regulation as well as conserved regulation between Populus and Arabidopsis.

CONCLUSIONS

We outline a computationally inferred model of the regulatory network of Populus leaves, and show how treating genes as interacting, rather than individual, entities identifies new regulators compared to traditional genomics analysis. Although systems biology models should be used with care considering the complexity of regulatory programs and the limitations of current genomics data, methods describing interactions can provide hypotheses about the underlying cause of emergent properties and are needed if we are to identify target genes other than those constituting the "low hanging fruit" of genomic analysis.

Towards a unifying, systems biology understanding of large-scale cellular death and destruction caused by poorly liganded iron: Parkinson's, Huntington's, Alzheimer's, prions, bactericides, chemical toxicology and others as examples

Exposure to a variety of toxins and/or infectious agents leads to disease, degeneration and death, often characterised by circumstances in which cells or tissues do not merely die and cease to function but may be more or less entirely obliterated. It is then legitimate to ask the question as to whether, despite the many kinds of agent involved, there may be at least some unifying mechanisms of such cell death and destruction. I summarise the evidence that in a great many cases, one underlying mechanism, providing major stresses of this type, entails continuing and autocatalytic production (based on positive feedback mechanisms) of hydroxyl radicals via Fenton chemistry involving poorly liganded iron, leading to cell death via apoptosis (probably including via pathways induced by changes in the NF-κB system). While every pathway is in some sense connected to every other one, I highlight the literature evidence suggesting that the degenerative effects of many diseases and toxicological insults converge on iron dysregulation. This highlights specifically the role of iron metabolism, and the detailed speciation of iron, in chemical and other toxicology, and has significant implications for the use of iron chelating substances (probably in partnership with appropriate anti-oxidants) as nutritional or therapeutic agents in inhibiting both the progression of these mainly degenerative diseases and the sequelae of both chronic and acute toxin exposure. The complexity of biochemical networks, especially those involving autocatalytic behaviour and positive feedbacks, means that multiple interventions (e.g. of iron chelators plus antioxidants) are likely to prove most effective. A variety of systems biology approaches, that I summarise, can predict both the mechanisms involved in these cell death pathways and the optimal sites of action for nutritional or pharmacological interventions.

Fourier analysis and systems identification of the p53 feedback loop

A key circuit in the response of cells to damage is the p53-mdm2 feedback loop. This circuit shows sustained, noisy oscillations in individual human cells following DNA breaks. Here, we apply an engineering approach known as systems identification to quantify the in vivo interactions in the circuit on the basis of accurate measurements of its power spectrum. We obtained oscillation time courses of p53 and Mdm2 protein levels from several hundred cells and analyzed their Fourier spectra. We find characteristic spectra with distinct low-frequency components that are well-described by a third-order linear model with white noise. The model identifies the sign and strength of the known interactions, including a negative feedback loop between p53 and its upstream regulator. It also implies that noise can trigger and maintain the oscillations. The model also captures the power spectra of p53 dynamics without DNA damage. Parameters such as noise amplitudes and protein lifetimes are estimated. This approach employs natural biological noise as a diagnostic that stimulates the system at many frequencies at once. It seems to be a useful way to find the in vivo design of circuits and may be applied to other systems by monitoring their power spectrum in individual cells.

Lessons on longevity from budding yeast

The past decade has seen fundamental advances in our understanding of the ageing process and raised optimism that interventions to slow ageing may be on the horizon. Studies of budding yeast have made immense contributions to this progress. Yeast longevity factors have now been shown to modulate ageing in invertebrate and mammalian models, and studies of yeast have resulted in some of the best candidates for anti-ageing drugs currently in development. The first interventions to slow human ageing may spring from the humble yeast.

Network reconstruction reveals new links between aging and calorie restriction in yeast

Aging affects all known organisms and has been studied extensively. Yet, the underlying mechanisms are insufficiently understood, possibly due to the multiscale complexity involved in this process: the aging of multicellular organisms depends on the aging of their cells, which depends on molecular events occurring in each cell. However, the aging of unicellular populations seeded in new niches and the aging of metazoans are surprisingly similar, indicating that the multiscale aspects of aging may have been conserved since the beginnings of cellular life on Earth. This underlines the importance of aging research in unicellular organisms such as a recent study by Lorenz et al., [(2009) Proc. Natl. Acad. Sci. U.S.A. 106, 1145-1150]. In their paper, the authors combine computational network identification with extensive experimentation and literature mining to discover and validate numerous regulatory interactions among ten genes involved in the cellular response to glucose starvation. Since low levels of glucose (calorie restriction) have been known to extend the longevity of various eukaryotes, the authors test the effect of Snf1 kinase overexpression on chronological aging and discover that this key regulator of glucose repression and two of its newly discovered synergistic repressors significantly affect the chronological lifespan of baker's yeast.

Linking toxicant physiological mode of action with induced gene expression changes in Caenorhabditis elegans

Background

Physiologically based modelling using DEBtox (dynamic energy budget in toxicology) and transcriptional profiling were used in Caenorhabditis elegans to identify how physiological modes of action, as indicated by effects on system level resource allocation were associated with changes in gene expression following exposure to three toxic chemicals: cadmium, fluoranthene (FA) and atrazine (AZ).

Results

For Cd, the physiological mode of action as indicated by DEBtox model fitting was an effect on energy assimilation from food, suggesting that the transcriptional response to exposure should be dominated by changes in the expression of transcripts associated with energy metabolism and the mitochondria. While evidence for effect on genes associated with energy production were seen, an ontological analysis also indicated an effect of Cd exposure on DNA integrity and transcriptional activity. DEBtox modelling showed an effect of FA on costs for growth and reproduction (i.e. for production of new and differentiated biomass). The microarray analysis supported this effect, showing an effect of FA on protein integrity and turnover that would be expected to have consequences for rates of somatic growth. For AZ, the physiological mode of action predicted by DEBtox was increased cost for maintenance. The transcriptional analysis demonstrated that this increase resulted from effects on DNA integrity as indicated by changes in the expression of genes chromosomal repair.

Conclusions

Our results have established that outputs from process based models and transcriptomics analyses can help to link mechanisms of action of toxic chemicals with resulting demographic effects. Such complimentary analyses can assist in the categorisation of chemicals for risk assessment purposes.

Data integration and analysis of biological networks

During the past decade, bottom-up and top-down approaches of network reconstruction have greatly facilitated integration and analysis of biological networks, including transcriptional, protein interaction, and metabolic networks. As increasing amounts of multidimensional high-throughput data become available, biological networks have also been upgraded, allowing more accurate understanding of whole cellular characteristics. The network size is constantly expanding as larger volume of information and omics data are further integrated into the biological networks previously built upon a single type of data. Such effort more recently led to the modeling of human metabolic network and prediction of its tissue-specific metabolism, reconstruction of consensus yeast metabolic network, and simulation of mutual interactions among multiple microorganisms. It is expected that this trend will continue, the outcomes of which will allow development of more sophisticated networks integrating diverse omics data, and enhance our understanding of biological systems.

Growth landscape formed by perception and import of glucose in yeast

An important challenge in systems biology is to quantitatively describe microbial growth using a few measurable parameters that capture the essence of this complex phenomenon. Two key events at the cell membrane – extracellular glucose sensing and uptake – initiate the budding yeast’s growth on glucose. However, conventional growth models focus almost exclusively on glucose uptake. Here we present results from growth-rate experiments that cannot be explained by focusing on glucose uptake alone. By imposing a glucose uptake rate independent of the sensed extracellular glucose level, we show that despite increasing both the sensed glucose concentration and uptake rate, the cell’s growth rate can decrease or even approach zero. We resolve this puzzle by showing that the interaction between glucose perception and import, not their individual actions, determines the central features of growth and characterize this interaction using a quantitative model. Disrupting this interaction by knocking out two key glucose sensors significantly changes the cell’s growth rate, yet uptake rates are unchanged. This is due to a decrease in burden that glucose perception places on the cells. Our work shows that glucose perception and import are separate and pivotal modules of yeast growth whose interplay can be precisely tuned and measured.