Modern Views of Ancient Metabolic Networks 1 1,2,3,4 61 Q8 Joshua E
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COISB145_proof ■ 5 February 2018 ■ 1/8 Available online at www.sciencedirect.com Current Opinion in ScienceDirect Systems Biology 59 60 Q9 Modern views of ancient metabolic networks 1 1,2,3,4 61 Q8 Joshua E. Goldford and Daniel Segrè 62 63 1 64 2 Abstract problem at the whole-cell level [3,4]. A common 65 3 perspective in the analysis of cellular self-reproduction is 66 4 Metabolism is a molecular, cellular, ecological and planetary phenomenon, whose fundamental principles are likely at the the notion that the genome, with its crucial information- 67 5 68 heart of what makes living matter different from inanimate one. storage role, is the central molecule of the cell, and that 6 69 Systems biology approaches developed for the quantitative everything else can be collectively regarded as the ma- 7 70 analysis of metabolism at multiple scales can help understand chinery whose role is to produce a copy of the DNA. It is 8 71 9 metabolism’s ancient history. In this review, we highlight work therefore not surprising that, as we struggle with the fascinating question of how life started on a lifeless 72 10 that uses network-level approaches to shed light on key in- 73 planet, it is tempting to look for how a single information- 11 novations in ancient life, including the emergence of proto- 74 containing molecule could arise spontaneously from 12 metabolic networks, collective autocatalysis and bioenergetics 75 13 coupling. Recent experiments and computational analyses prebiotic compounds. However, in spite of the appeal of 76 14 have revealed new aspects of this ancient history, paving the thinking of DNA (or its historically older predecessor, 77 15 way for the use of large datasets to further improve our un- RNA) as the central molecule who is being replicated in 78 16 derstanding of life’s principles and abiogenesis. the cell, no molecule in the cell really self-replicates: the 79 17 cell is a network of chemical transformations capable of 80 18 Addresses collective autocatalytic self-reproduction. Collective 81 1 Bioinformatics Program and Biological Design Center, Boston 19 autocatalysis is the capacity for a collection of chemicals 82 20 University, Boston, MA 02215, United States 2 Department of Biology, Boston University, Boston MA 02215, United to enhance or catalyze the synthesis or import of its own 83 21 Q1 States components, enabling a positive feedback mechanism 84 22 3 Department of Biomedical Engineering, Boston University, Boston that can lead to their sustained amplification. Combining 85 23 MA 02215, United States this systems-level view of a cell with the argument of 86 4 24 Department of Physics, Boston University, Boston MA 02215, United what is usually called the “metabolism first” view of the 87 States 25 origin of life, one could propose that the ability of a 88 26 Corresponding author: Segrè, Daniel, Bioinformatics Program and chemical network to produce more of itself (or to grow 89 27 Biological Design Center, Boston University, Boston, MA 02215, autocatalytically) is and has always been a key hallmark of 90 28 Q2 United States. ([email protected]) life [5e9]. An interesting modern version of this very 91 29 same principle is embedded in one of the most popular 92 30 93 31 Current Opinion in Systems Biology 2018, -:1–8 systems biology approaches for the study of whole cell metabolism: this approach, based on reaction network 94 32 This review comes from a themed issue on Regulatory and metabolic stoichiometry and efficient constraint-based optimiza- 95 33 networks 96 tion algorithms, is commonly known as flux balance 34 Edited by Bas Teusink and Uwe Sauer 97 35 analysis (FBA) [3]. FBA solves mathematically the For a complete overview see the Issue and the Editorial 98 36 resource allocation problem that every living cell needs to 99 Available online xxx 37 solve in real life in order to transform available nutrients 100 38 https://doi.org/10.1016/j.coisb.2018.01.004 into the macromolecular building blocks that are neces- 101 39 2452-3100/© 2018 Published by Elsevier Ltd. sary for maintenance and reproduction. When an FBA 102 40 calculation estimates the maximal growth capacity of a 103 41 cell, it essentially computes the set of reaction network 104 42 Keywords Metabolism, Evolution, Network expansion, Origin of life. fluxes that enable optimally efficient autocatalytic self- 105 43 reproduction. While in cellular life this process is finely 106 44 regulated and controlled, ancient life must have gone 107 45 Introduction through many different stages of similar, but much less 108 46 The metabolic network of a cell transforms free energy organized collectively autocatalytic processes. Thus, one 109 47 of the key problems of the origin of life is the question of 110 48 and environmentally available molecules into more cells, moving electrons step by step along gradients in a com- how an initially random path in the space of possible 111 49 112 plex energetic landscape [1,2]. The ability of a cell to chemical transformations driven far from thermodynamic 50 equilibrium could have ended up being dynamically 113 51 efficiently and simultaneously manage hundreds of “trapped” in a collectively autocatalytic state. 114 52 metabolic processes so as to accurately balance the pro- 115 53 duction of its internal components constitutes a very The focus on cellular self-reproduction as the funda- 116 54 complex resource allocation problem. In fact, it is only 117 55 through recent systems biology research that we have mental level at which life and its origin should be un- derstood is however too narrow. An exciting recent 118 56 begun to quantitatively assess this resource allocation 119 57 120 58 www.sciencedirect.com Current Opinion in Systems Biology 2018, -:1–8 121 COISB145_proof ■ 5 February 2018 ■ 2/8 2 Regulatory and metabolic networks 1 64 2 development in systems biology of metabolism is the properties of life at the cellular and subcellular 65 3 rise of methods to extend FBA models from the genome level [16]. Bridging this gap could greatly benefit from 66 4 scale to the ecosystem level [10e12]. In addition to the use of integrative models similar to the ones used in 67 5 solving the resource allocation problem of metabolism systems biology research and data science. In this 68 6 for individual organisms in a given environment, these perspective, we will discuss some recent system-level 69 7 approaches take into account the fact that metabolites approaches that have provided new important insight 70 8 can be exchanged across species, giving rise to into life’s ancient history at multiple scales, highlighting 71 9 metabolically-driven ecological networks [11]. These the fact the metabolism and its multiscale nature e 72 10 advances suggest that metabolism may be best under- from the single reaction to the biosphere e are taking a 73 11 stood as an ecosystem-level phenomenon (Figure 1b), center stage role in this endeavor. 74 12 where the collective biochemical capabilities of multi- 75 13 e 76 ple co-existing organisms may reflect better than any Protometabolism before enzymes 14 e 77 individual metabolic network an optimal capacity of A top-down reconstruction of ancient metabolic net- 15 life to utilize resources present in a given environment 78 16 works can be achieved based on the inferred history of 79 [9,13]. The ecosystem-level nature of metabolism is 17 gene families, using traditional phylogenomic tech- 80 another feature of present-day life whose roots likely 18 niques [17e20]. Leveraging information on the newly 81 19 date back to the early stages of life on our planet. For mapped genomic diversity of modern life [21], Martin 82 20 example, the chemical networks that gradually gave rise and colleagues recently proposed a comprehensive 83 21 to reproducing protocells may have wandered for quite phylogenetic reconstruction of the metabolic capabil- 84 22 some time in a broader chemical space, effectively ities of the last universal common ancestor (LUCA), 85 23 generating molecular ecosystems before the rise of suggesting that LUCA was an autotrophic, thermophilic, 86 spatially and chemically well-defined cellular structures 24 N2-fixing anaerobic prokaryote, living in hydrothermal 87 25 Q3 (see Figure 2). vents and equipped with life’s most complex molecular 88 26 machines (e.g. ATP synthase) [22]. Although the details 89 27 At an even larger scale, metabolism could be viewed as of LUCA’s specific repertoire of metabolic enzymes are 90 28 operating not just at the level of individual cells or still subject of debate [23,24], these results corroborate 91 29 ecosystems, but even as a planetary phenomenon, in the notion that LUCA was very complex, highlighting a 92 30 which cellular processes collectively affect (and are massive gap in knowledge with regard to the transition 93 31 94 affected by) the flow of molecules at geological scales from prebiotic geochemical processes to the biochem- 32 95 (Figure 1c). The strong coupling between the metabolic ical complexity of LUCA and its progeny. 33 processes of ecosystems and planetary-scale geochem- 96 34 istry [14,15] suggest that biosphere-level metabolism 97 35 A major challenge in the study of the origin of metabolic 98 should be viewed as one of the natural scales for the networks is to gain insight on the structure of metabolic 36 study of life’s history. A paramount challenge in the 99 37 networks before LUCA and before the rise of genetic 100 study of life’s history is thus bridging the gap between coding. At the core of this challenge is the question of 38 material and energy fluxes at the biosphere scale, and 101 39 whether and how metabolic reactions which depend on 102 detailed molecular mechanisms responsible for the 40 genome-encoded enzymes in modern cells could have 103 41 104 42 Figure 1 105 43 106 44 107 45 108 46 109 47 110 48 111 49 112 50 113 51 114 52 115 53 116 54 117 55 118 56 119 57 120 58 Metabolism at different scales.