Co-Regulation of Metabolic Genes Is Better Explained by Flux Coupling Than by Network Distance

To what extent can modes of gene regulation be explained by systems-level properties of metabolic networks? Prior studies on co-regulation of metabolic genes have mainly focused on graph-theoretical features of metabolic networks and demonstrated a decreasing level of co-expression with increasing network distance, a naïve, but widely used, topological index. Others have suggested that static graph representations can poorly capture dynamic functional associations, e.g., in the form of dependence of metabolic fluxes across genes in the network. Here, we systematically tested the relative importance of metabolic flux coupling and network position on gene co-regulation, using a genome-scale metabolic model of Escherichia coli. After validating the computational method with empirical data on flux correlations, we confirm that genes coupled by their enzymatic fluxes not only show similar expression patterns, but also share transcriptional regulators and frequently reside in the same operon. In contrast, we demonstrate that network distance per se has relatively minor influence on gene co-regulation. Moreover, the type of flux coupling can explain refined properties of the regulatory network that are ignored by simple graph-theoretical indices. Our results underline the importance of studying functional states of cellular networks to define physiologically relevant associations between genes and should stimulate future developments of novel functional genomic tools

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

Asymmetric relationships between proteins shape genome evolution

An investigation of metabolic networks in E. coli and S. cerevisiae reveals that asymmetric protein interactions affect gene expression, the relative effect of gene-knockouts and genome evolution

The landscape of tiered regulation of breast cancer cell metabolism

Altered metabolism is a hallmark of cancer, but little is still known about its regulation. In this study, we measure transcriptomic, proteomic, phospho-proteomic and fluxomics data in a breast cancer cell-line (MCF7) across three different growth conditions. Integrating these multiomics data within a genome scale human metabolic model in combination with machine learning, we systematically chart the different layers of metabolic regulation in breast cancer cells, predicting which enzymes and pathways are regulated at which level. We distinguish between two types of reactions, directly and indirectly regulated. Directly-regulated reactions include those whose flux is regulated by transcriptomic alterations (~890) or via proteomic or phospho-proteomics alterations (~140) in the enzymes catalyzing them. We term the reactions that currently lack evidence for direct regulation as (putative) indirectly regulated (~930). Many metabolic pathways are predicted to be regulated at different levels, and those may change at different media conditions. Remarkably, we find that the flux of predicted indirectly regulated reactions is strongly coupled to the flux of the predicted directly regulated ones, uncovering a tiered hierarchical organization of breast cancer cell metabolism. Furthermore, the predicted indirectly regulated reactions are predominantly reversible. Taken together, this architecture may facilitate rapid and efficient metabolic reprogramming in response to the varying environmental conditions incurred by the tumor cells. The approach presented lays a conceptual and computational basis for mapping metabolic regulation in additional cancers

Predicting functional associations from metabolism using bi-partite network algorithms

<p>Abstract</p> <p>Background</p> <p>Metabolic reconstructions contain detailed information about metabolic enzymes and their reactants and products. These networks can be used to infer functional associations between metabolic enzymes. Many methods are based on the number of metabolites shared by two enzymes, or the shortest path between two enzymes. Metabolite sharing can miss associations between non-consecutive enzymes in a serial pathway, and shortest-path algorithms are sensitive to high-degree metabolites such as water and ATP that create connections between enzymes with little functional similarity.</p> <p>Results</p> <p>We present new, fast methods to infer functional associations in metabolic networks. A local method, the degree-corrected Poisson score, is based only on the metabolites shared by two enzymes, but uses the known metabolite degree distribution. A global method, based on graph diffusion kernels, predicts associations between enzymes that do not share metabolites. Both methods are robust to high-degree metabolites. They out-perform previous methods in predicting shared Gene Ontology (GO) annotations and in predicting experimentally observed synthetic lethal genetic interactions. Including cellular compartment information improves GO annotation predictions but degrades synthetic lethal interaction prediction. These new methods perform nearly as well as computationally demanding methods based on flux balance analysis.</p> <p>Conclusions</p> <p>We present fast, accurate methods to predict functional associations from metabolic networks. Biological significance is demonstrated by identifying enzymes whose strong metabolic correlations are missed by conventional annotations in GO, most often enzymes involved in transport vs. synthesis of the same metabolite or other enzyme pairs that share a metabolite but are separated by conventional pathway boundaries. More generally, the methods described here may be valuable for analyzing other types of networks with long-tailed degree distributions and high-degree hubs.</p