Tradeoff between enzyme and metabolite efficiency maintains metabolic homeostasis upon perturbations in enzyme capacity

Substrate metabolite concentrations are inversely related to the in vivo capacity of their converting enzymes.Local metabolite responses represent a passive mechanism to achieve metabolic homeostasis upon perturbations in enzyme capacity.Enzyme capacity and metabolite concentration control the metabolic reaction rate

Transcriptional regulation and steady-state modeling of metabolic networks

Biologiske systemer er karakteriseret ved en høj grad af kompleksitet, hvori de individuelle komponenter (f.eks. proteiner) er indbyrdes forbundet på en måde, der fører til en opførsel, der er vanskelig at forstå i detaljer. Udredning af systemets kompleksitet kræver i det mindste svar på følgende tre spørgsmål: hvad er komponenterne af systemerne, hvordan er de forskellige komponenter sammenkoblet, og hvordan udfører disse netværk de funktioner, der resulterer i systemernes adfærd? Moderne analytiske teknologier giver os mulighed for at optrævle de bestanddele og interaktioner der findes i et givet system, men det tredje spørgsmål er den ultimative udfordring for systembiologi. Nærværende afhandling behandler dette spørgsmål systematisk i forbindelse med metaboliske netværk, som velsagtens er de mest velbeskrevne biologiske netværk hvad angår komponenter og samspillet mellem dem. Desuden er der stor interesse for at forstå og manipulere cellestofskiftet ud fra såvel sundhedsmæssige som bioteknologiske perspektiver. Fundamentalt forskellige biologiske spørgsmål undersøges i forskellige centrale kapitler i afhandlingen, selv om de alle er forbundet af det fælles tema omkring, hvordan det cellulære stofskifte fungerer. De tre vigtigste emner, der behandles, er: i) Transkriptionel regulering af metabolit-koncentrationer, ii) transkriptionel dys-regulering af skeletmuskulaturens stofskifte i type-2 diabetes, og iii) metaboliske interaktioner i mikrobielle økosystemer. Det overordnede mål er at opnå ny forståelse bag de operationelle principper for metaboliske netværk.Cellers reaktioner på forstyrrelser i vækstvilkår og genetiske/epigenetiske ændringer styres i høj grad gennem transkription, som er en af de grundlæggende mekanismer for cellulær regulering. Et vigtigt spørgsmål er, i hvilket omfang genekspression kan forklare metaboliske fænotyper; med andre ord, hvor godt kan ændringer i metabolitkoncentrationer forklares med ændringer i mængderne af mRNA kodende for de ansvarlige enzymer? Forsøg på at forudsige ændringer i metabolomet ud fra genekspressionsdata har hidtil ikke ladet sig gøre. Her udfordrer jeg dette spørgsmål ved at foreslå en mekanistisk forklaring af samspillet mellem metabolitkoncentrationer, transkripter og flux baseret på Michaelis-Menten kinetik på netværks-skala. Dette arbejde viser, at i steady-state systemer er ændringer i intracellulære metabolit-koncentrationer forbundet med ændringer i genekspression af både reaktioner, der producerer, og reaktioner, der forbruger en bestemt metabolit. I modsætning til tidligere tænkning tyder analyse af en stor samling af genekspressionsdata endvidere på, at transkriptionel regulering ved metaboliske forgreningspunkter er meget plastisk, og i flere tilfælde synes den selektive fordel ved reguleringen at være metabolit-orienteret snarere end pathway-orienteret. Undersøgelsen giver således et fundamentalt og nyt syn på metabolisk netværksregulering i Saccharomyces cerevisiae.Metabolisme er et i høj grad bevaret system på tværs af hele biologien. I dag er stofskifte blevet et centralt punkt i diagnosticering og behandling af sygdomme såsom diabetes og kræft. Type 2-diabetes mellitus er en kompleks metabolisk sygdom, der er anerkendt som en af de største trusler mod menneskers sundhed i det 21. århundrede. Nylige undersøgelser af genekspressionsniveauer i humane vævsprøver har vist, at flere metaboliske veje er dysreguleret i diabetes og hos personer med risiko for diabetes; hvilke af disse veje der er primære og/eller centrale for patogenesen, er fortsat et centralt spørgsmål. Cellulære metaboliske netværk er meget tæt forbundne og ofte stramt regulerede; eventuelle forstyrrelser ved et enkelt forbindelsespunkt kan således hurtigt udbrede sig til resten af netværket. En sådan kompleksitet udgør en betydelig udfordring i at indkredse de vigtigste molekylære mekanismer og kendetegn, der er forbundet med insulinresistens og type 2 diabetes. Det foreliggende arbejde løser dette problem ved at bruge en metode, der integrerer genekspressionsdata med det humane cellulære metaboliske netværk. Denne fremgangsmåde demonstreres ved analyse af to datasæt fra skeletmusklers genekspression. Den foreslåede metode identificerede transkriptionsfaktorer og metabolitter, der udgør potentielle mål for farmaka og fremtidig klinisk diagnose for type 2-diabetes og forringet glukosemetabolisme. I en bredere sammenhæng frembyder undersøgelsen en ramme for analyse af genekspression-data indsamlet ved komplekse heterogene sygdomme, genetiske og miljømæssige perturbationer, der afspejles i og/eller er medieret via ændringer i stofskiftet.I naturen eksisterer mikroorganismer normalt ikke som rene kulturer, men udvikler sig og sameksisterer med andre arter. Mikrobielle samfund har en bred vifte af mulige anvendelser, herunder behandling af metaboliske sygdomme og bioteknologi. Eksempelvis kan mikrobielle konsortier bestående af forskellige bakterier og svampe udføre biologisk nedbrydning bedre end rene kulturer, hvilket gør dem attraktive at udforske. Det er almindeligt antaget, at ernæring spiller en afgørende rolle i udformningen af mikrobielle samfund, og indbyrdes udveksling og udnyttelse af metabolitter kan give flere fordele for samfundet som helhed. For eksempel kan en mere effektiv og fuldstændig anvendelse af de tilgængelige næringsstoffer, eller en forbedret evne til at tilpasse sig skiftende ernæringsforhold, føre til forbedret overlevelse af individerne. Det tredje emne i denneafhandling undersøger de metaboliske interaktioners rolle i blandede mikrobielle samfund. Formålet med undersøgelsen er at identificere de egenskaber ved metabolismen, der er bestemmende for strukturerne af de blandede samfund. Analysen er baseret på et globalt metagenomisk datasæt, og metaboliske modeller i genom-skala pegede på, at arter inden for sameksisterende samfund har et større potentiale for metabolisk samarbejde i forhold til tilfældigt sammensatte samfund. Dette arbejde førte til en ny metode (kaldet species metabolic coupling analysis) for at studere metaboliskinteraktion og indbyrdes afhængighed inden for mikrobielle samfund. Metoden har en vifte af konkrete anvendelser, herunder undersøgelse af metaboliske interaktioner i menneskets mikrobiom, værtspatogene interaktioner og udvikling af stabile mikrobielle samfund.Samlet set bidrager dette arbejde med nye indsigter, værktøjer og metoder til at studere hvordan cellulært stofskifte fungerer.Biological systems are characterized by a high degree of complexity wherein the individual components (e.g. proteins) are inter-linked in a way that leads to emergent behaviors that are difficult to decipher. Uncovering system complexity requires, at least, answers to the following three questions: what are the components of the systems, how are the different components interconnected and how do these networks perform the functions that make the resulting system behavior? Modern analytical technologies allow us to unravel the constituents and interactions happening in a given system; however, the third question is the ultimate challenge for systems biology. The work of this thesis systematically addresses this question in the context of metabolic networks, which are arguably the most well characterized cellular networks in terms of their constituting components and interactions among them. Furthermore, there is large interest in understanding and manipulating cellular metabolism from health as well as biotechnological perspectives. Fundamentally different biological questions are investigated in different core chapters of the thesis, though all are linked by the common thread of the functioning of cellular metabolism. The three main topics addressed are: i) transcriptional regulation of metabolite concentration, ii) transcriptional dys-regulation of skeletal muscle metabolism in type 2 diabetes, and iii) metabolic interactions in microbial ecosystems. The overall objective is to obtain novel understanding underlying the operating principles of metabolic networks. Cellular responses to environmental perturbations and genetic/epigenetic modifications are to a large extent controlled through transcription, which is one of the fundamental mechanism/means of cellular regulation. An important question is to what extent gene expression can explain metabolic phenotype, in other words, how well changes in metabolite concentrations can be explained by the changes in related enzyme-coding transcripts? Attempts to predict changes in the metabolome from gene expression data have so far remained unsolved. Here, I challenge this question by proposing a mechanistic explanation of the interplay between metabolite concentrations, transcripts and fluxes based on Michaelis-Menten kinetics at the network-scale. The work demonstrates that in steadystate systems, changes of intracellular metabolites concentrations are linked with the changes in gene expression of both reactions that produce and reactions that consume a given metabolite. Analysis of a large compendium of gene expression data further suggested that, contrary to previous thinking, transcriptional regulation at metabolic branch points is highly plastic and, in several cases, the objective of the regulation appears to be metabolite-oriented as opposed to pathway-oriented. The study thus provides a fundamental and novel view of metabolic network regulation in Saccharomyces cerevisiae. Metabolism is a conserved system across all domains of life. Nowadays, metabolism has become a focal point in diagnosing and treating diseases such as diabetes and cancer. Type 2 diabetes mellitus is a complex metabolic disease which is recognized as one of the largest threats to human health in the 21st century. Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dys-regulated in diabetes and in individuals at risk for diabetes; which of these are primary, or central to disease pathogenesis, remains a key question. Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network. Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and signatures associated with insulin resistance and type 2 diabetes. The present work addresses this problem by using a methodology that integrates gene expression data with the human cellular metabolic network. The approach is demonstrated by analysis of two skeletal muscle gene expression datasets. The proposed methodology identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader context, the study provides a framework for analysis of gene expression datasets from complex heterogeneous diseases, genetic, and environmental perturbations that are reflected in and/or mediated through changes in metabolism.In nature, microorganisms do not exist as pure cultures, but evolve and co-exist with other species. Microbial communities have a variety of potential applications, including metabolic disease therapies and biotechnology. For example, microbial consortia consisting of various bacteria and fungi are known to exhibit a biodegradation performance superior to pure cultures, making them attractive research targets. It is believed that nutrition plays a crucial role in shaping microbial communities. Interspecies metabolite cross-feeding can confer several advantages to the community as a whole. For example, more efficient and complete use of available nutrients, or increased ability to survive under diverse/changing nutrition availability potentially induces fitness of individuals. The third topic of this thesis investigates the role of metabolic interaction in co-occurring microbial communities. The study aims to identify metabolic properties that shape the community structures. The analysis based on a global metagenomic dataset and genome-scale metabolic models suggested that species within coexisting communities have higher potential of metabolic cooperation compared to random controls. This work yielded a novel methodology (termed species metabolic coupling analysis) for studying metabolic interaction and interdependencies within microbial communities. Species metabolic coupling analysis has a spectrum of applications to real-world problems, including investigation of metabolic interactions within the human microbiome, host -pathogen interactions and development of stable microbial communities. Overall, this work contributes with novel insights, tools and methodologies to study the operation of cellular metabolism

When transcriptome meets metabolome: fast cellular responses of yeast to sudden relief of glucose limitation

Within the first 5 min after a sudden relief from glucose limitation, Saccharomyces cerevisiae exhibited fast changes of intracellular metabolite levels and a major transcriptional reprogramming. Integration of transcriptome and metabolome data revealed tight relationships between the changes at these two levels. Transcriptome as well as metabolite changes reflected a major investment in two processes: adaptation from fully respiratory to respiro-fermentative metabolism and preparation for growth acceleration. At the metabolite level, a severe drop of the AXP pools directly after glucose addition was not accompanied by any of the other three NXP. To counterbalance this loss, purine biosynthesis and salvage pathways were transcriptionally upregulated in a concerted manner, reflecting a sudden increase of the purine demand. The short-term dynamics of the transcriptome revealed a remarkably fast decrease in the average half-life of downregulated genes. This acceleration of mRNA decay can be interpreted both as an additional nucleotide salvage pathway and an additional level of glucose-induced regulation of gene expression

Contribution of Network Connectivity in Determining the Relationship between Gene Expression and Metabolite Concentration Changes

One of the primary mechanisms through which a cell exerts control over its metabolic state is by modulating expression levels of its enzyme-coding genes. However, the changes at the level of enzyme expression allow only indirect control over metabolite levels, for two main reasons. First, at the level of individual reactions, metabolite levels are non-linearly dependent on enzyme abundances as per the reaction kinetics mechanisms. Secondly, specific metabolite pools are tightly interlinked with the rest of the metabolic network through their production and consumption reactions. While the role of reaction kinetics in metabolite concentration control is well studied at the level of individual reactions, the contribution of network connectivity has remained relatively unclear. Here we report a modeling framework that integrates both reaction kinetics and network connectivity constraints for describing the interplay between metabolite concentrations and mRNA levels. We used this framework to investigate correlations between the gene expression and the metabolite concentration changes in Saccharomyces cerevisiae during its metabolic cycle, as well as in response to three fundamentally different biological perturbations, namely gene knockout, nutrient shock and nutrient change. While the kinetic constraints applied at the level of individual reactions were found to be poor descriptors of the mRNA-metabolite relationship, their use in the context of the network enabled us to correlate changes in the expression of enzyme-coding genes to the alterations in metabolite levels. Our results highlight the key contribution of metabolic network connectivity in mediating cellular control over metabolite levels, and have implications towards bridging the gap between genotype and metabolic phenotype

Quantifying absolute gene expression profiles reveals distinct regulation of central carbon metabolism genes in yeast

In addition to controlled expression of genes by specific regulatory circuits, the abundance of proteins and transcripts can also be influenced by physiological states of the cell such as growth rate and metabolism. Here we examine the control of gene expression by growth rate and metabolism, by analyzing a multi-omics dataset consisting of absolute-quantitative abundances of the transcriptome, proteome, and amino acids in 22 steady-state yeast cultures. We find that transcription and translation are coordinately controlled by the cell growth rate via RNA polymerase II and ribosome abundance, but they are independently controlled by nitrogen metabolism via amino acid and nucleotide availabilities. Genes in central carbon metabolism, however, are distinctly regulated and do not respond to the cell growth rate or nitrogen metabolism as all other genes. Understanding these effects allows the confounding factors of growth rate and metabolism to be accounted for in gene expression profiling studies

Systems-biology dissection of eukaryotic cell growth

A recent article in BMC Biology illustrates the use of a systems-biology approach to integrate data across the transcriptome, proteome and metabolome of budding yeast in order to dissect the relationship between nutrient conditions and cell growth

mRNA stability and the unfolding of gene expression in the long-period yeast metabolic cycle

<p>Abstract</p> <p>Background</p> <p>In yeast, genome-wide periodic patterns associated with energy-metabolic oscillations have been shown recently for both short (approx. 40 min) and long (approx. 300 min) periods.</p> <p>Results</p> <p>The dynamical regulation due to mRNA stability is found to be an important aspect of the genome-wide coordination of the long-period yeast metabolic cycle. It is shown that for periodic genes, arranged in classes according either to expression profile or to function, the pulses of mRNA abundance have phase and width which are directly proportional to the corresponding turnover rates.</p> <p>Conclusion</p> <p>The cascade of events occurring during the yeast metabolic cycle (and their correlation with mRNA turnover) reflects to a large extent the gene expression program observable in other dynamical contexts such as the response to stresses/stimuli.</p

Reconstruction of the yeast Snf1 kinase regulatory network reveals its role as a global energy regulator

Highly conserved among eukaryotic cells, the AMP-activated kinase (AMPK) is a central regulator of carbon metabolism. To map the complete network of interactions around AMPK in yeast (Snf1) and to evaluate the role of its regulatory subunit Snf4, we measured global mRNA, protein and metabolite levels in wild type, Δsnf1, Δsnf4, and Δsnf1Δsnf4 knockout strains. Using four newly developed computational tools, including novel DOGMA sub-network analysis, we showed the benefits of three-level ome-data integration to uncover the global Snf1 kinase role in yeast. We for the first time identified Snf1's global regulation on gene and protein expression levels, and showed that yeast Snf1 has a far more extensive function in controlling energy metabolism than reported earlier. Additionally, we identified complementary roles of Snf1 and Snf4. Similar to the function of AMPK in humans, our findings showed that Snf1 is a low-energy checkpoint and that yeast can be used more extensively as a model system for studying the molecular mechanisms underlying the global regulation of AMPK in mammals, failure of which leads to metabolic diseases

Integration of metabolome data with metabolic networks reveals reporter reactions

Interpreting quantitative metabolome data is a difficult task owing to the high connectivity in metabolic networks and inherent interdependency between enzymatic regulation, metabolite levels and fluxes. Here we present a hypothesis-driven algorithm for the integration of such data with metabolic network topology. The algorithm thus enables identification of reporter reactions, which are reactions where there are significant coordinated changes in the level of surrounding metabolites following environmental/genetic perturbations. Applicability of the algorithm is demonstrated by using data from Saccharomyces cerevisiae. The algorithm includes preprocessing of a genome-scale yeast model such that the fraction of measured metabolites within the model is enhanced, and thus it is possible to map significant alterations associated with a perturbation even though a small fraction of the complete metabolome is measured. By combining the results with transcriptome data, we further show that it is possible to infer whether the reactions are hierarchically or metabolically regulated. Hereby, the reported approach represents an attempt to map different layers of regulation within metabolic networks through combination of metabolome and transcriptome data