Enzyme promiscuity shapes adaptation to novel growth substrates
2019; Springer Nature; Volume: 15; Issue: 4 Linguagem: Inglês
10.15252/msb.20188462
ISSN1744-4292
AutoresGabriela I. Guzmán, Troy E. Sandberg, Ryan A. LaCroix, Ákos Nyerges, Henrietta Papp, Markus de Raad, Zachary A. King, Ying Hefner, Trent R. Northen, Richard A. Notebaart, Csaba Pál, Bernhard Ø. Palsson, Balázs Papp, Adam M. Feist,
Tópico(s)Fungal and yeast genetics research
ResumoReport8 April 2019Open Access Transparent process Enzyme promiscuity shapes adaptation to novel growth substrates Gabriela I Guzmán Gabriela I Guzmán Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Troy E Sandberg Troy E Sandberg orcid.org/0000-0003-3240-3659 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ryan A LaCroix Ryan A LaCroix Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ákos Nyerges Ákos Nyerges orcid.org/0000-0002-1581-490X Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Henrietta Papp Henrietta Papp Virological Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary Search for more papers by this author Markus de Raad Markus de Raad Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, Berkeley, CA, USA Search for more papers by this author Zachary A King Zachary A King Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ying Hefner Ying Hefner Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Trent R Northen Trent R Northen Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, Berkeley, CA, USA Search for more papers by this author Richard A Notebaart Richard A Notebaart Laboratory of Food Microbiology, Wageningen University and Research, Wageningen, The Netherlands Search for more papers by this author Csaba Pál Csaba Pál Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Bernhard O Palsson Bernhard O Palsson orcid.org/0000-0003-2357-6785 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Balázs Papp Balázs Papp Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Adam M Feist Corresponding Author Adam M Feist [email protected] orcid.org/0000-0002-8630-4800 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Gabriela I Guzmán Gabriela I Guzmán Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Troy E Sandberg Troy E Sandberg orcid.org/0000-0003-3240-3659 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ryan A LaCroix Ryan A LaCroix Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ákos Nyerges Ákos Nyerges orcid.org/0000-0002-1581-490X Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Henrietta Papp Henrietta Papp Virological Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary Search for more papers by this author Markus de Raad Markus de Raad Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, Berkeley, CA, USA Search for more papers by this author Zachary A King Zachary A King Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Ying Hefner Ying Hefner Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Trent R Northen Trent R Northen Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, Berkeley, CA, USA Search for more papers by this author Richard A Notebaart Richard A Notebaart Laboratory of Food Microbiology, Wageningen University and Research, Wageningen, The Netherlands Search for more papers by this author Csaba Pál Csaba Pál Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Bernhard O Palsson Bernhard O Palsson orcid.org/0000-0003-2357-6785 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA Search for more papers by this author Balázs Papp Balázs Papp Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary Search for more papers by this author Adam M Feist Corresponding Author Adam M Feist [email protected] orcid.org/0000-0002-8630-4800 Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark Search for more papers by this author Author Information Gabriela I Guzmán1, Troy E Sandberg1, Ryan A LaCroix1, Ákos Nyerges2, Henrietta Papp3, Markus de Raad4, Zachary A King1, Ying Hefner1, Trent R Northen4, Richard A Notebaart5, Csaba Pál2, Bernhard O Palsson1,6,7, Balázs Papp2 and Adam M Feist *,1,6 1Department of Bioengineering, University of California, San Diego, La Jolla, CA, USA 2Synthetic and Systems Biology Unit, Institute of Biochemistry, Biological Research Centre of the Hungarian Academy of Sciences, Szeged, Hungary 3Virological Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary 4Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory Berkeley, Berkeley, CA, USA 5Laboratory of Food Microbiology, Wageningen University and Research, Wageningen, The Netherlands 6Novo Nordisk Foundation Center for Biosustainability, Technical University of Denmark, Lyngby, Denmark 7Department of Pediatrics, University of California, San Diego, La Jolla, CA, USA *Corresponding author. Tel: +1 858 534 9592; E-mail: [email protected] Molecular Systems Biology (2019)15:e8462https://doi.org/10.15252/msb.20188462 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are not well characterized. Furthermore, it remains untested whether knowledge of an organism's promiscuous reaction set, or underground metabolism, can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in Escherichia coli K-12 MG1655. After as few as approximately 20 generations, evolved populations repeatedly acquired the capacity to grow on five predicted non-native substrates—D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Altered promiscuous activities were shown to be directly involved in establishing high-efficiency pathways. Structural mutations shifted enzyme substrate turnover rates toward the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Synopsis Computational modeling of underground metabolism, laboratory evolution and omics analyses reveal that enzyme promiscuity can play a major role during adaptation to new growth environments and indicate that the genes underlying the phenotypic innovations can be predicted. Enzyme promiscuity can confer a fitness benefit in novel growth environments and open routes for achieving innovative growth states. Mutation events which enable growth on non-native carbon sources can be structural or regulatory in nature and single mutation events related to a promiscuous activity can be sufficient to support growth while some cases require multiple mutations. Metabolic network analysis and constraint-based modeling can predict adaptation to non-native carbon sources through promiscuous enzyme activities. Laboratory evolution can be used to select for enzymes with structural mutations enabling an improved substrate affinity for a non-native carbon source. Introduction Understanding how novel metabolic pathways arise during adaptation to environmental changes remains a central issue in evolutionary biology. The prevailing view is that enzymes often display promiscuous (i.e., side or secondary) activities and evolution takes advantage of such pre-existing weak activities to generate metabolic novelties (Jensen, 1976; Copley, 2000; Schmidt et al, 2003; Khersonsky & Tawfik, 2010; Huang et al, 2012; Nam et al, 2012; Näsvall et al, 2012; Voordeckers et al, 2012; Notebaart et al, 2014). However, it remains to be fully explored how these metabolic novelties are achieved via mutation events during periods of adaptation in short-term evolution experiments. Do genetic elements associated with promiscuous activities mutate mostly early on in adaptation when the initial innovative phenotype of growth on a new nutrient source is observed (Copley, 2000; Barrick & Lenski, 2013; Mortlock, 2013) or do promiscuous activities continue to play a role throughout the optimization process of continued fitness improvement on a non-native nutrient source (Barrick & Lenski, 2013)? In this work, mutational events that resulted in the ability of an organism to grow on a new, non-native carbon source were examined. These types of innovations have previously been linked to beneficial mutations that endow an organism with novel capabilities and expand into a new ecological niche (Wagner, 2011; Barrick & Lenski, 2013). Further, mutational events that were associated with more gradual enhancements of growth fitness (Barrick & Lenski, 2013) on the non-native carbon source were also examined. Such gradual improvements may stem from mutational events leading to regulatory improvements that fine-tune expression of desirable or undesirable pathways or possibly the fine-tuning of enzyme kinetics or substrate specificity of enzymes involved in key metabolic pathways (Copley, 2000; Barrick & Lenski, 2013). Enzyme promiscuity has been prominently linked to early mutation events, where mutations enhancing secondary activities may result in dramatic phenotypic improvements or new capabilities (Khersonsky & Tawfik, 2010; Barrick & Lenski, 2013). Therefore, in this work, we explored a diverse range of evolutionary routes taken during adaptation to new carbon sources. Specifically, we examined the role of enzyme promiscuity in both early mutations linked to innovative phenotypes and growth-optimizing mutations throughout various short-term laboratory evolution experiments. A second open question in understanding the role of enzyme promiscuity in adaptation concerns our ability to predict the future evolution of broad genetic and phenotypic changes (Papp et al, 2011; Lässig et al, 2017). While there has been an increasing interest in studying empirical fitness landscapes to assess the predictability of evolutionary routes (de Visser & Krug, 2014; Notebaart et al, 2018), these approaches assess predictability only in retrospect. There is a need for computational frameworks that forecast the specific genes that accumulate mutations based on mechanistic knowledge of the evolving trait. A recent study suggested that a detailed knowledge of an organism's promiscuous reaction set (the so-called "underground metabolism"; D'Ari & Casadesús, 1998) enables the computational prediction of genes that confer new metabolic capabilities when artificially overexpressed (Notebaart et al, 2014). However, it remains unclear whether this approach could predict evolution in a population of cells adapting to a new nutrient environment through spontaneous mutations. First, phenotypes conferred by artificial overexpression might not be accessible through single mutations arising spontaneously. Second, and more fundamentally, mutations in distinct genes may lead to the same phenotype. Such alternative mutational trajectories may render genetic evolution largely unpredictable. Furthermore, computational approaches can aid in predicting and discovering overlapping physiological functions of enzymes (Guzmán et al, 2015; Notebaart et al, 2018), but these have also yet to be explored in the context of adaptation. In this study, we address these issues by performing controlled laboratory evolution experiments to adapt Escherichia coli to predicted novel carbon sources and by monitoring the temporal dynamics of adaptive mutations. Results Computational prediction and experimental evolution of non-native carbon source utilizations Based on our knowledge of underground metabolism, we utilized a genome-scale model of E. coli metabolism that includes a comprehensive network reconstruction of underground metabolism (Notebaart et al, 2014) to test our ability to predict evolutionary adaptation to novel (non-native) carbon sources. This model was previously shown to correctly predict growth on non-native carbon sources if a given enabling gene was artificially overexpressed in a growth screen (Notebaart et al, 2014). This previous work identified a list of ten carbon sources that the native E. coli metabolic network is not able to utilize for growth in simulations but that can be utilized for growth in silico with the addition of a single underground reaction (Appendix Table S1). Based on this list—as well as substrate cost, availability, and solubility properties to maximize compatibility with our laboratory evolution procedures—we selected seven carbon sources (D-lyxose, D-tartrate, D-2-deoxyribose, D-arabinose, ethylene glycol, m-tartrate, monomethyl succinate) that cannot be utilized by wild-type E. coli MG1655 but are predicted to be growth-sustaining carbon sources after adaptive laboratory evolution. Next, we initiated laboratory evolution experiments to adapt E. coli to these non-native carbon sources. Adaptive laboratory evolution experiments were conducted in two distinct phases: first, a "weaning/dynamic environment" (Copley, 2000; Mortlock, 2013) stage during which cells acquired the ability to grow solely on the non-native carbon sources and, second, a "static environment" (Barrick & Lenski, 2013) stage during which a strong selection pressure was placed to select for the fastest growing cells on the novel carbon sources (Fig 1A). Figure 1. Laboratory evolution method schematic and the growth trajectory of D-lyxose experiments A schematic of the two-part adaptive laboratory evolution (ALE) experiments. The "weaning/dynamic environment" stage involved growing cells in supplemented flasks containing the non-native substrate (blue) and growth-promoting supplement (red). As cultures were serially passed, they were split into another supplemented flask as well as an "non-native substrate test flask" containing only the non-native nutrient (no supplement) to test for the desired evolved growth phenotype. The "static environment" stage consisted of selecting for the fastest growing cells and passing in mid log phase. Growth rate trajectories for duplicate experiments (n = 2 evolution experiments per substrate condition) (green and purple) for the example case of D-lyxose. Population growth rates are plotted against cumulative cell divisions. Clones were isolated for whole-genome sequencing at notable growth rate plateaus as indicated by the arrows. Mutations gained at each plateau are highlighted beside the arrows (mutations arising earlier along the trajectory persisted in later sequenced clones). Download figure Download PowerPoint During the "weaning/dynamic environment" stage of laboratory evolution experiments (Fig 1A, see Materials and Methods), E. coli was successfully adapted to grow on five non-native substrates individually in separate experiments. Duplicate laboratory evolution experiments were conducted in batch growth conditions for each individual substrate and in parallel on an automated adaptive laboratory evolution (ALE) platform using a protocol that uniquely selected for adaptation to conditions where the ancestor (i.e., wild type) was unable to grow (Fig 1A; LaCroix et al, 2015). In the weaning phase, E. coli was dynamically weaned off of a growth-supporting nutrient (glycerol) onto the novel substrates individually (Fig 1A, Appendix Table S2). A description of the complex passage protocol is given in the Fig 1 legend and expanded in the methods for both phases of the evolution. This procedure successfully adapted E. coli to grow on five out of seven non-native substrates, specifically, D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate. Unsuccessful cases could be attributed to various experimental and biological factors such as experimental duration limitations, the requirement of multiple mutation events, or stepwise adaptation events, as observed in an experiment evolving E. coli to utilize ethylene glycol (Szappanos et al, 2016). The "static environment" stage of the evolution experiments consisted of serially passing cultures in the early exponential phase of growth in order to select for cells with the highest growth rates (Fig 1A). Cultures were grown in a static media composition environment containing a single non-native carbon source. Marked and repeatable increases in growth rates on the non-native carbon sources were observed in as few as 180–420 generations (Appendix Table S1). Whole-genome sequencing of clones was performed at each distinct growth rate "jump" or plateau during the static environment phase (see arrows in Fig 1B, Appendix Fig S1). Such plateaus represent regions where a causal mutation has fixed in a population and it was assumed that the mutation(s) enabling the jump in growth rate were stable and maintained throughout the plateau region (LaCroix et al, 2015). Thus, clones were isolated at any point within this plateau region where frozen stock samples were available (LaCroix et al, 2015). Modeling with underground metabolism accurately predicted key genes mutated during laboratory evolution experiments To analyze genotypic changes underlying the nutrient utilizations, clones were isolated and sequenced shortly after an innovative growth phenotype was achieved; mutations were identified (see Materials and Methods) and analyzed for their associated causality (Fig 1B, Appendix Fig S1, Dataset EV1). Strong signs of parallel evolution were observed at the level of mutated genes in the replicate evolution experiments (Fig 1B, Appendix Fig S1, Table 1, Dataset EV1). Such parallelism provided evidence of the beneficial nature of the observed mutations and is a prerequisite for predicting the genetic basis of adaptation (Bailey et al, 2015). Mutations detected in the evolved isolated clones for each experiment demonstrated a striking agreement with such predicted "underground" utilization pathways (Notebaart et al, 2014). Specifically, for four out of the five different substrate conditions, key mutations were linked to the predicted enzyme with promiscuous activity, which would be highly unlikely by chance (P < 10−8, Fisher's exact test; Table 1, Appendix Fig S2). Not only were the specific genes (or their direct regulatory elements) mutated in four out of five cases, but few additional mutations (0–2 per strain, Dataset EV1) were observed directly following the weaning phase, indicating that the innovative phenotypes observed required a small number of mutational steps and the method utilized was highly selective. For the one case where the prediction and observed mutations did not align—D-arabinose—a detailed inspection of the literature revealed existing evidence that three fuc operon-associated enzymes can metabolize D-arabinose—FucI, FucK, and FucA (LeBlanc & Mortlock, 1971). The mutations observed in the D-arabinose evolution experiments after the weaning stage were in the fucR gene (Table 1), a DNA-transcriptional activator associated with regulating the expression of the transcription units fucAO and fucPIK (Podolny et al, 1999). Thus, it was inferred that the strains evolved to grow on D-arabinose in our experiments were utilizing the fuc operon-associated enzymes to metabolize D-arabinose in agreement with prior work (LeBlanc & Mortlock, 1971). In this case, the genome-scale model did not identify the promiscuous reactions responsible for growth on D-arabinose because the promiscuous (underground) reaction database was incomplete (see section "Mutations in regulatory elements linked to increased expression of underground activities: D-arabinose evolution" for more details on D-arabinose metabolism). Table 1. Key mutations associated with growth phenotypes after weaning phase Gene mutated Substrate Gene prediction Protein change(s) (Experiment #) Perceived impact (Structural (S) or Regulatory (R)) yihS D-Lyxose yihS R315S (1) Substrate bindinga (S) R315C (2) Substrate bindinga (S) yihW D-Lyxose yihS Frameshift (1) Loss of function, large truncation (R) I156S (2) - (R) rbsK D-2-Deox. rbsK N20Y (1) - (S) rbsR D-2-Deox. rbsK Insertion Sequence (1) Loss of function; increased rbsK expression (R) 181 kbp and 281 kbp Regions D-2-Deox. rbsK - (1) Increased gene expression (R) fucR D-Arabinose rbsK D82Y (1) Pfam: DeoRC C terminal substrate sensor domainb (R) S75R (1 and 2) Pfam: DeoRC C terminal substrate sensor domainb (R) *244C (2) - (R) dmlA m-Tartrate dmlA A242T (1) - (S) dmlR/dmlA m-Tartrate dmlA Intergenic −50/−53 (2) Sigma 70 binding: close proximity to −10 of dmlRp3 promoterc (R) Intergenic −35/−68 (2) dmlRp3 promoter regionc (R) ybfF/seqA Mon. Succ. ybfF Intergenic −73/−112 (1) Sigma 24 binding: −35 of ybfFp1 promoterc (R) Intergenic −51/−123 (2) Sigma 24 binding: −10 of ybfFp1 promoterc (R) Substrates D-2-deoxyribose and monomethyl succinate are abbreviated D-2-Deox. and Mon. Succ., respectively. The detailed locations of the mutations listed in this table are available in Dataset EV1 and Appendix Fig S3. a Substrate binding information about YihS previously published (Itoh et al, 2008). b Protein family information listed in the Pfam database (Finn et al, 2016). c Promoter/sigma factor binding regions found on EcoCyc (Keseler et al, 2013) based on computational predictions (Huerta & Collado-Vides, 2003). In general, key mutations observed shortly after strains achieved reproducible growth on the non-native substrate could be categorized as regulatory (R) or structural (S) (Table 1). Of the fifteen mutation events outlined in Table 1, eleven were categorized as regulatory (observed in all five successful substrate conditions) and four were categorized as structural (three of five successful substrate conditions). For D-lyxose, D-2-deoxyribose, and m-tartrate evolution experiments, mutations were observed within the coding regions of the predicted genes, namely yihS, rbsK, and dmlA (Table 1, Appendix Fig S1). Regulatory mutations occurring in transcriptional regulators or within intergenic regions—likely affecting sigma factor binding and transcription of the predicted gene target—were observed for D-lyxose, D-2-deoxyribose, m-tartrate, and monomethyl succinate (Table 1). Observing more regulatory mutations is broadly consistent with previous reports (Mortlock, 2013; Toll-Riera et al, 2016). The regulatory mutations were believed to increase the expression of the target enzyme, thereby increasing the dose of the typically low-level side activity (Guzmán et al, 2015). This observation is consistent with "gene sharing" models of promiscuity and adaptation where diverging mutations that alter enzyme specificity are not necessary to acquire the growth innovation (Piatigorsky et al, 1988; Guzmán et al, 2015). Furthermore, although enzyme dosage could also be increased through duplication of genomic segments, this scenario was not commonly observed shortly after the weaning phase of our experiments. The one exception was observed in the D-2-deoxyribose evolution experiment where two large duplication events (containing 165 genes (yqiG-yhcE) and 262 genes (yhiS-rbsK), respectively) were observed (Appendix Fig S3). Notably, one of these regions did include the rbsK gene with the underground activity predicted to support growth on D-2-deoxyribose (Table 1). To identify the causal mutation events relevant to the observed innovative nutrient utilization phenotypes, each key mutation (Table 1) was introduced into the ancestral wild-type strain using the genome engineering method pORTMAGE (Nyerges et al, 2016). This genome editing approach was performed to screen for mutation causality (Herring et al, 2006) on all novel substrate conditions, except for monomethyl succinate, which only contained a single mutation (Table 1). Individual mutants were isolated after pORTMAGE reconstruction, and their growth was monitored in a binary fashion on the growth medium containing the non-native substrate over the course of 1 week. These growth tests revealed that single mutations were sufficient for growth on D-lyxose, D-arabinose, and m-tartrate (Appendix Table S3). Interestingly, in the case of D-2-deoxyribose, an individual mutation (either the RbsK N20Y or the rbsR insertion mutation) was not sufficient for growth, thereby suggesting that the mechanism of adaptation to this substrate was more complex. To address this, a pORTMAGE library containing the RbsK N20Y and rbsR insertion mutations individually and in combination was grown on three M9 minimal medium + 2 g l−1 D-2-deoxyribose agar plates alongside a wild-type MG1655 ancestral strain control. The large duplications in the D-2-deoxyribose strain (Table 1) could not be reconstructed due to the limitations of the pORTMAGE method. After 10 days of incubation, visible colonies could be seen resulting from the reverse engineered library, but not from the wild-type strain (Appendix Fig S4A). Subsequently, 16 colonies were chosen and colony PCR was performed to sequence the regions of rbsK and rbsR where the mutations were introduced (Appendix Fig S4B). All 16 colonies sequenced contained both the RbsK N20Y and rbsR insertion mutations. Fifteen of the 16 colonies showed an additional mutation at RbsK residue Asn14—7 colonies showed a AAT to GAT codon change resulting in an RbsK N14D mutation and 8 colonies showed a AAT to AGT codon change resulting in an RbsK N14S mutation. The Asn14 residue has been previously associated with ribose substrate binding of the ribokinase RbsK enzyme (Sigrell et al, 1999). Only one of the 16 colonies sequenced did not acquire the residue 14 mutation, but instead acquired a GCA to ACA codon change at residue Ala4 resulting in an RbsK A4T mutation. It is unclear if the additional mutations occurred spontaneously during growth prior to plating, but it is possible that these Asn14 and Ala4 residue mutations were introduced at a low frequency during MAGE-oligonucleotide DNA synthesis (< 0.1% error rate at each nucleotide position) (Isaacs et al, 2011; Nyerges et al, 2018). In either case, these results suggested that the observed mutations in rbsK and rbsR enabled growth on the non-native D-2-deoxyribose substrate and that there was a strong selection pressure on the ribokinase underground activity. Further, there were multiple ways to impact rbsK, as both duplication events and structural mutations (Table 1) or multiple structural mutations were separately observed in strains which grew solely on D-2-deoxyribose. Overall, these causality assessments support the notion that underground activities can open short adaptive paths toward novel phenotypes and may play prominent roles in innovation events. Examination of growth-optimizing evolutionary routes Once the causality of the observed mutations was established, adaptive mechanisms required for further optimizing or fine-tuning growth on the novel carbon sources were explored. Discovery of these growth-optimizing activities was driven by a systems-level analysis consisting of mutation, enzyme activity, and transcriptome analyses coupled with computational modeling of optimized growth states on the novel carbon sources. Out of the total set
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