Biology subjects

Keasling, J. D.

Publications and source records attributed to Keasling, J. D..

At least 37 records · Page 2Linked to original sources

bioRxiv2023

BayFlux: A Bayesian method to quantify metabolic Fluxes and their uncertainty at the genome scale

Metabolic fluxes, the number of metabolites traversing each biochemical reaction in a cell per unit time, are crucial for assessing and understanding cell function. 13C Metabolic Flux Analysis (13C MFA) is considered to be the gold standard for measuring metabolic fluxes. 13C MFA typically works by leveraging extracellular exchange fluxes as well as data from 13C labeling experiments to calculate the flux profile which best fit the data for a small, central carbon, metabolic model. However, the nonlinear nature of the 13C MFA fitting procedure means that several flux profiles fit the experimental data within the experimental error, and traditional optimization methods offer only a partial or skewed picture, especially in "non-gaussian" situations where multiple very distinct flux regions fit the data equally well. Here, we present a method for flux space sampling through Bayesian inference (BayFlux), that identifies the full distribution of fluxes compatible with experimental data for a comprehensive genome-scale model. This Bayesian approach allows us to accurately quantify uncertainty in calculated fluxes. We also find that, surprisingly, the genome-scale model of metabolism produces narrower flux distributions (reduced uncertainty) than the small core metabolic models traditionally used in 13C MFA. The different results for some reactions when using genome-scale models vs core metabolic models advise caution in assuming strong inferences from 13C MFA since the results may depend significantly on the completeness of the model used. Based on BayFlux, we developed and evaluated novel methods (P-13C MOMA and ROOM) to predict the biological results of a gene knockout, that improve on the traditional MOMA and ROOM methods. We provide an open source Python implementation of BayFlux at https://github.com/JBEI/bayflux. Author summary13C MFA practitioners know that modeling results can be sensitive to minor modifications of the metabolic model. Certain parts of the metabolic model that are not well mapped to a molecular mechanism (e.g. drains to biomass or ATP maintenance) can have an inordinate impact on the final fluxes. The only way to ascertain the validity of the model is by checking that the result does not significantly differ from previously observed flux profiles. However, that approach diminishes the possibility of discovering truly novel flux profiles. Because of this strong dependence on metabolic model details, it would be very useful to have a systematic and repeatable way to produce these metabolic models. And indeed there is one: genome-scale metabolic models can be systematically obtained from genomic sequences, and represent all the known genomically encoded metabolic information. However, these models are much larger than the traditionally used central carbon metabolism models. Hence, the number of degrees of freedom of the model (fluxes) significantly exceeds the number of measurements (metabolite labeling profiles and exchange fluxes). As a result, one expects many flux profiles compatible with the experimental data. The best way to represent these is by identifying all fluxes compatible with the experimental data. Our novel method BayFlux, based on Bayesian inference and Markov Chain Monte Carlo sampling, provides this capability. Interestingly, this approach leads to the observation that traditional optimization approaches can significantly overestimate flux uncertainty, and that genome-scale models of metabolism produce narrower flux distributions than the small core metabolic models that are traditionally used in 13C MFA. Furthermore, we show that the extra information provided by this approach allows us to improve knockout predictions, compared to traditional methods. Although the method scales well with more reactions, improvements will be needed to tackle the large metabolic models found in microbiomes and human metabolism.

systems biology↗

bioRxiv2023

Ensemble and Iterative Engineering for Maximized Bioconversion to the Blue Pigment, Indigoidine from Non-Canonical Sustainable Carbon Sources

While many heterologous molecules can be produced via microbial bioconversion processes, maximizing their titers, rates, and yields from lignin-derived carbon streams remains challenging. Growth coupling can not only increase titers and yields but also shift the production period from stationary phase to growth phase. These methods for designing growth-coupling strains however require multi-gene edits for implementation which may be perceived as impractical. Here, we computationally evaluated 4,114 potential solutions for growth coupling para-coumarate to indigoidine production and prototype two cut sets in Pseudomonas putida KT2440. We used adaptive laboratory evolution (ALE) on the initial triple deletion strain to restore growth on p-CA. Using X-ray tomography on this post-ALE strain we revealed increased cell density and decreased cell volume. Proteomics identified upregulated peroxidases that mitigate reactive oxygen species formation. Nine iterative stepwise modifications further informed by model-guided and rational approaches realized a growth coupled strain that produced 7.3 g/L indigoidine at 77% MTY in para-coumarate minimal medium. These ensemble strategies provide a blueprint for producing target molecules at high product titers, rates, and yields. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=80 SRC="FIGDIR/small/532821v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@f0754aorg.highwire.dtl.DTLVardef@a45d24org.highwire.dtl.DTLVardef@df0596org.highwire.dtl.DTLVardef@1e7e140_HPS_FORMAT_FIGEXP M_FIG C_FIG

systems biology↗

bioRxiv2023

The pGinger family of expression plasmids

The pGinger suite of expression plasmids comprises 43 plasmids that will enable precise constitutive and inducible gene expression in a wide range of gram-negative bacterial species. Constitutive vectors are composed of 16 synthetic constitutive promoters upstream of RFP, with a broad host range BBR1 origin and a kanamycin resistance marker. The family also has seven inducible systems (Jungle Express, Psal/NahR, Pm/XylS, Prha/RhaS, LacO1/LacI, LacUV5/LacI, and Ptet/TetR) controlling RFP expression on BBR1/kanamycin plasmid backbones. For four of these inducible systems (Jungle Express, Psal/NahR, LacO1/LacI, and Ptet/TetR), we created variants that utilize the RK2 origin and spectinomycin or gentamicin selection. Relevant RFP expression and growth data have been collected in the model bacterium Escherichia coli as well as Pseudomonas putida. All pGinger vectors are available via the Joint BioEnergy Institute (JBEI) Public Registry.

synthetic biology↗

bioRxiv2022

A highly active Burkholderia polyketoacyl-CoA thiolase for production of triacetic acid lactone

Triacetic acid lactone (TAL) is a platform chemical biosynthesized primarily through decarboxylative Claisen condensation by type III polyketide synthase 2-pyrone synthase (2-PS). However, this reaction suffers from intrinsic energy inefficiency and feedback inhibition by and competition for malonyl-CoA. TAL production through non-decarboxylative Claisen condensation by polyketoacyl-CoA thiolase alleviates many of these disadvantages. We discovered five more thiolases with TAL production activity by exploring homologs of a previously reported polyketoacyl-CoA thiolase, BktB, from Cupriavidus necator. Among them, the BktB homolog from Burkholderia sp. RF2-non_BP3 has [~] 30 times higher in vitro and in vivo TAL production activity and led to [~]10 times higher TAL titer than 2-PS when expressed in Escherichia coli, achieving a titer of 2.8 g/L in fed-batch fermentations. This discovery of a novel polyketoacyl-CoA thiolase with superior TAL production activity paves the way for realization of total biomanufacturing of TAL.

bioengineering↗

bioRxiv2022

Engineering Pseudomonas putida KT2440 for chain length tailored free fatty acid and oleochemical production

Despite advances in understanding the metabolism of Pseudomonas putida KT2440, a promising bacterial host for producing valuable chemicals from plant-derived feedstocks, a strain capable of producing free fatty acid-derived chemicals has not been developed. Guided by functional genomics, we engineered P. putida to produce medium- and long-chain free fatty acids (FFAs) to titers of up to 670 mg/L, paving the road for the production of high-value oleochemicals and biofuels from cheap feedstocks, such as plant biomass, using this host. Additionally, by taking advantage of the varying substrate preferences of paralogous native fatty acyl-CoA ligases, we employed a strategy to control FFA chain length that resulted in a P. putida strain specialized in producing medium-chain FFAs. Finally, we demonstrate the production of oleochemicals in these strains by synthesizing medium-chain fatty acid methyl esters, compounds useful as biodiesel blending agents, in various media including sorghum hydrolysate at titers greater than 300 mg/L.

synthetic biology↗

bioRxiv2021

Nitrogen metabolism in Pseudomonas putida: functional analysis using random barcode transposon sequencing

Pseudomonas putida KT2440 has long been studied for its diverse and robust metabolisms, yet many genes and proteins imparting these growth capacities remain uncharacterized. Using pooled mutant fitness assays, we identified genes and proteins involved in the assimilation of 52 different nitrogen containing compounds. To assay amino acid biosynthesis, 19 amino acid drop- out conditions were also tested. From these 71 conditions, significant fitness phenotypes were elicited in 672 different genes including 100 transcriptional regulators and 112 transport-related proteins. We divide these conditions into 6 classes, and propose assimilatory pathways for the compounds based on this wealth of genetic data. To complement these data, we characterize the substrate range of three promiscuous aminotransferases relevant to metabolic engineering efforts in vitro. Furthermore, we examine the specificity of five transcriptional regulators, explaining some fitness data results and exploring their potential to be developed into useful synthetic biology tools. In addition, we use manifold learning to create an interactive visualization tool for interpreting our BarSeq data, which will improve the accessibility and utility of this work to other researchers. IMPORTANCEUnderstanding the genetic basis of P. putidas diverse metabolism is imperative for us to reach its full potential as a host for metabolic engineering. Many target molecules of the bioeconomy and their precursors contain nitrogen. This study provides functional evidence linking hundreds of genes to their roles in the metabolism of nitrogenous compounds, and provides an interactive tool for visualizing these data. We further characterize several aminotransferases, lactamases, and regulators--which are of particular interest for metabolic engineering.

microbiology↗

bioRxiv2021

A synthetic RNA-mediated evolution system in yeast

Laboratory evolution is a powerful approach to search for genetic adaptations to new or improved phenotypes, yet either relies on labour-intensive human-guided iterative rounds of mutagenesis and selection, or prolonged adaptation regimes based on naturally evolving cell populations. Here we present CRISPR- and RNA-assisted in vivo directed evolution (CRAIDE) of genomic loci using evolving chimeric donor gRNAs continuously delivered from an error-prone T7 RNA polymerase, and directly introduced as RNA repair donors into genomic targets under either Cas9 or dCas9 guidance. We validate CRAIDE by evolving novel functional variants of an auxotrophic marker gene, and by conferring resistance to a toxic amino acid analogue in bakers yeast Saccharomyces cerevisiae with a mutation rate >3,000-fold higher compared to spontaneous native rate, thus enabling the first demonstrations of in vivo delivery and information transfer from long evolving RNA donor templates into genomic context without the use of in vitro supplied and pre-programmed repair donors.

bioengineering↗

bioRxiv2020

Integrating continuous hypermutation with high-throughput screening for optimization of cis,cis-muconic acid production in yeast

Directed evolution is a powerful method to optimize proteins and metabolic reactions towards user-defined goals. It usually involves subjecting genes or pathways to iterative rounds of mutagenesis, selection, and amplification. While powerful, systematic searches through large sequence-spaces is a labor-intensive task, and can be further limited by a priori knowledge about the optimal initial search space, and/or limits in terms of screening throughput. Here we demonstrate an integrated directed evolution workflow for metabolic pathway enzymes that continuously generates enzyme variants using the recently developed orthogonal replication system, OrthoRep, and screens for optimal performance in high-throughput using a transcription factor-based biosensor. We demonstrate the strengths of this workflow by evolving a ratelimiting enzymatic reaction of the biosynthetic pathway for cis,cis-muconic acid (CCM), a precursor used for bioplastic and coatings, in Saccharomyces cerevisiae. After two weeks of simply iterating between passaging of cells to generate variant enzymes via OrthoRep and high-throughput sorting of best-performing variants using a transcription factor-based biosensor for CCM, we ultimately identified variant enzymes improving CCM titers >13-fold compared to reference enzymes. Taken together, the combination of synthetic biology tools as adopted in this study, is an efficient approach to debottleneck repetitive workflows associated with directed evolution of metabolic enzymes.

synthetic biology↗

bioRxiv2020

Functional analysis of the fatty acid and alcohol metabolism of Pseudomonas putida using RB-TnSeq

ABSTRACTWith its ability to catabolize a wide variety of carbon sources and a growing engineering toolkit, Pseudomonas putida KT2440 is emerging as an important chassis organism for metabolic engineering. Despite advances in our understanding of this organism, many gaps remain in our knowledge of the genetic basis of its metabolic capabilities. These gaps are particularly noticeable in our understanding of both fatty acid and alcohol catabolism, where many paralogs putatively coding for similar enzymes co-exist making biochemical assignment via sequence homology difficult. To rapidly assign function to the enzymes responsible for these metabolisms, we leveraged Random Barcode Transposon Sequencing (RB-TnSeq). Global fitness analyses of transposon libraries grown on 13 fatty acids and 10 alcohols produced strong phenotypes for hundreds of genes. Fitness data from mutant pools grown on varying chain length fatty acids indicated specific enzyme substrate preferences, and enabled us to hypothesize that DUF1302/DUF1329 family proteins potentially function as esterases. From the data we also postulate catabolic routes for the two biogasoline molecules isoprenol and isopentanol, which are catabolized via leucine metabolism after initial oxidation and activation with CoA. Because fatty acids and alcohols may serve as both feedstocks or final products of metabolic engineering efforts, the fitness data presented here will help guide future genomic modifications towards higher titers, rates, and yields.IMPORTANCE To engineer novel metabolic pathways into P. putida, a comprehensive understanding of the genetic basis of its versatile metabolism is essential. Here we provide functional evidence for the putative roles of hundreds of genes involved in the fatty acid and alcohol metabolism of this bacterium. These data provide a framework facilitating precise genetic changes to prevent product degradation and channel the flux of specific pathway intermediates as desired.Competing Interest StatementJ.D.K. has financial interests in Amyris, Lygos, Demetrix, Napigen, Maple Bio, and Apertor Labs. C.B.E has a financial interest in Perlumi Chemicals.View Full Text

microbiology↗

bioRxiv2020

Enhanced production of taxadiene in Saccharomyces cerevisiae

Cost-effective production of the highly effective anti-cancer drug, paclitaxel (Taxol®), remains limited despite growing global demands. Low yields of the critical taxadiene precursor remains a key bottleneck in microbial production. In this study, the key challenge of poor taxadiene synthase (TASY) solubility in S. cerevisiae was revealed, and the strains were strategically engineered to relieve this bottleneck. Multi-copy chromosomal integration of TASY harbouring a selection of fusion solubility tags improved taxadiene titres 22-fold, up to 57 ± 3 mg/L at 30 °C at shake flask scale. The scalability of the process was highlighted through achieving similar titres during scale up to 25 mL and 250 mL in shake flask and bioreactor cultivations, respectively. Maximum taxadiene titres of 129 ± 15 mg/L and 119 mg/L were achieved through shake flask and bioreactor cultivation, respectively, of the optimal strain at a reduced temperature of 20 °C. The results highlight the positive effect of coupling molecular biology tools with bioprocess variable optimisation on synthetic pathway development.HighlightsMaximum taxadiene titre of 129 ± 15 mg/L in Saccharomyces cerevisiae at 20 °CIntegrating fusion protein tagged-taxadiene synthase improved taxadiene titre.Consistent taxadiene titres were achieved at the micro-and mini-bioreactor scales.Competing Interest StatementJ.D.K. has financial interests in Amyris, Lygos, Demetrix, Napigen, Maple Bio, Apertor Labs, Ansa Biotechnologies, and Berkeley Brewing Sciences.AbbreviationsBTS1Geranylgeranyl diphosphate synthasecrtEGeranylgeranyl diphosphate synthaseDMAPPDimethylallyl pyrophosphateERG8Phosphomevalonate kinaseERG9Farnesyl-diphosphate farnesyl transferase (squalene synthase)ERG103-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthaseERG12Mevalonate kinaseERG133-hydroxy-3-methylglutaryl-CoA (HMG-CoA) synthaseERG19Mevalonate pyrophosphate decarboxylaseERG20Farnesyl pyrophosphate synthetaseFPPFarnesyl diphosphateGGOH(E,E,E)-geranylgeraniolGGPP(E,E,E)-Geranylgeranyl diphosphateGPPGeranyl diphosphateHMG13-hydroxy-3-methylglutaryl-coenzyme A reductase 1HMG23-hydroxy-3-methylglutaryl-coenzyme A reductase 2IDIIsopentenyl-diphosphate delta-isomeraseIPPIsopentenyl pyrophosphateMBPMaltose binding proteinmvaEAcetyl-CoA acetyltransferasemvaSHydroxymethylglutaryl-CoA synthaseMVA pathwayMevalonate pathwayTASYTaxadiene synthase.View Full Text

synthetic biology↗

bioRxiv2020

High titer methyl ketone production with tailored Pseudomonas taiwanensis VLB120

Methyl ketones present a group of highly reduced platform chemicals industrially produced from petroleum-derived hydrocarbons. They find applications in the fragrance, flavor, pharmacological, and agrochemical industries, and are further discussed as biodiesel blends. In recent years, intense research has been carried out to achieve sustainable production of these molecules by re-arranging the fatty acid metabolism of various microbes. One challenge in the development of a highly productive microbe is the high demand for reducing power. Here, we engineered Pseudomonas taiwanensis VLB120 for methyl ketone production as this microbe has been shown to sustain exceptionally high NAD(P)H regeneration rates. The implementation of published strategies resulted in 2.1 g Laq-1 methyl ketones in fed-batch fermentation. We further increased the production by eliminating competing reactions suggested by metabolic analyses. These efforts resulted in the production of 9.8 g Laq-1 methyl ketones (corresponding to 69.3 g Lorg-1 in the in situ extraction phase) at 53 % of the maximum theoretical yield. This represents a 4-fold improvement in product titer compared to the initial production strain and the highest titer of recombinantly produced methyl ketones reported to date. Accordingly, this study underlines the high potential of P. taiwanensis VLB120 to produce methyl ketones and emphasizes model-driven metabolic engineering to rationalize and accelerate strain optimization efforts.Competing Interest StatementJay D. Keasling has a financial interest in Amyris, Lygos, Demetrix, Napigen, Maple Bio, Apertor Labs, Ansa Biotechnologies, and Berkeley Brewing Sciences.Abbreviationsaqaqueous phaseCDWcell dry weightDOdissolved oxygenGCgas chromatographyGLCglucoseHPLChigh-performance liquid chromatographyMiMBlMinimization of Metabolites BalancesMKmethyl ketoneMSmass spectrometryOD600nmoptical density determined at 600 nmorgorganic phase(p)FBA(parsimonious) flux balance analysisView Full Text

synthetic biology↗

bioRxiv2020

A bimodular PKS platform that expands the biological design space

Traditionally engineered to produce novel bioactive molecules, Type I modular polyketide synthases (PKSs) could be engineered as a new biosynthetic platform for the production of de novo fuels, commodity chemicals, and specialty chemicals. Previously, our investigations manipulated the first module of the lipomycin PKS to produce short chain ketones, 3-hydroxy acids, and saturated, branched carboxylic acids. Building upon this work, we have expanded to multi-modular systems by engineering the first two modules of lipomycin to generate unnatural polyketides as potential biofuels and specialty chemicals in Streptomyces albus. First, we produce 20.6 mg/L of the ethyl ketone, 4,6 dimethylheptanone through a reductive loop exchange in LipPKS1 and a ketoreductase knockouts in LipPKS2. We then show that an AT swap in LipPKS1 and a reductive loop exchange in LipPKS2 can produce the potential fragrance 3-isopropyl-6-methyltetrahydropyranone. Highlighting the challenge of maintaining product fidelity, in both bimodular systems we observed side products from premature hydrolysis in the engineered first module and stalled dehydration in reductive loop exchanges. Collectively, our work expands the biological design space and moves the field closer to the production of “designer” biomolecules.HighlightsEngineered lipomycin module 1 and module 2 to produce unnatural polyketides as valuable bio-based chemicalsA reductive loop swap and ketoreductase knockout used to produce 20 mg/mL of a novel ethyl ketone, a gasoline replacementAn acyltransferase swap and reductive loop swap successfully produced δ-lactone, a potential fragrant compoundIncomplete reduction and premature hydrolysis observed in engineered modulesCompeting Interest StatementJ.D.K. has a financial interest in Amyris, Lygos, Demetrix, Napigen, Maple Bio, Berkeley Brewing Sciences, Ansa Biotech and Apertor Labs.View Full Text

synthetic biology↗

bioRxiv2020

Population dynamics analysis of Saccharomyces cerevisiae deletion library during fed-batch cultivation using Bar-seq

To understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance, we employed a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime. Our results demonstrate the utility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions. We find that mutant population diversity is maintained through multiple seed trains, enabling for large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant external stresses, such as the accumulation of the fermentative byproduct ethanol. The mutants that were lost during the time of most extreme population selection suggest that specific biological processes may be required to cope with these specific stresses. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.

microbiology↗

bioRxiv2020

Regulatory control circuits for stabilizing long-term anabolic product formation in yeast

Engineering living cells for production of chemicals, enzymes and therapeutics can burden cells due to use of limited native co-factor availability and/or expression burdens, totalling a fitness deficit compared to parental cells encoded through long evolutionary trajectories to maximise fitness. Ultimately, this discrepancy puts a selective pressure against fitness-burdened engineered cells under prolonged bioprocesses, and potentially leads to complete eradication of high-performing engineered cells at the population level. Here we present the mutation landscapes of fitness-burdened yeast cells engineered for vanillin-{beta}-glucoside production. Next, we design synthetic control circuits based on transcriptome analysis and biosensors responsive to vanillin-{beta}-glucoside pathway intermediates in order to stabilize vanillin-{beta}-glucoside production over [~]55 generations in sequential passage experiments. Furthermore, using biosensors with two different modes of action we identify control circuits linking vanillin-{beta}-glucoside pathway flux to various essential cellular functions, and demonstrate control circuits robustness and 92% higher vanillin-{beta}-glucoside production, including 5-fold increase in total vanillin-{beta}-glucoside pathway metabolite accumulation, in a fed-batch fermentation compared to vanillin-{beta}-glucoside producing cells without control circuits.

synthetic biology↗

bioRxiv2020

Genome-scale metabolic rewiring to achieve predictable titers rates and yield of a non-native product at scale

Achieving high titer rates and yields (TRY) remains a bottleneck in the production of heterologous products through microbial systems, requiring elaborate engineering and many iterations. Reliable scaling of engineered strains is also rarely addressed in the first designs of the engineered strains. Both high TRY and scale are challenging metrics to achieve due to the inherent trade-off between cellular use of carbon towards growth vs. target metabolite production. We hypothesized that being able to strongly couple product formation with growth may lead to improvements across both metrics. In this study, we use elementary mode analysis to predict metabolic reactions that could be targeted to couple the production of indigoidine, a sustainable pigment, with the growth of the chosen host, Pseudomonas putida KT2440. We then filtered the set of 16 predicted reactions using -omics data. We implemented a total of 14 gene knockdowns using a CRISPRi method optimized for P. putida and show that the resulting engineered P. putida strain could achieve high TRY. The engineered pairing of product formation with carbon use also shifted production from stationary to exponential phase and the high TRY phenotype was maintained across scale. In one design cycle, we constructed an engineered P. putida strain that demonstrates close to 50% maximum theoretical yield (0.33 g indigoidine/g glucose consumed), reaching 25.6 g/L indigoidine and a rate of 0.22g/l/h in exponential phase. These desirable phenotypes were maintained from batch to fed-batch cultivation mode, and from 100ml shake flasks to 250 mL ambr(R) and 2 L bioreactors.

bioengineering↗

bioRxiv2020

Characterization of an ECF56-family sigma factor from Streptomyces venezuelae reveals a highly conserved regulome

Bacteria often possess alternative sigma factors that initiate the transcription of specific genes under environmental stresses, the largest and most diverse group being the extracytoplasmic function (ECF) sigma factors. The regulation of ECF activity is crucial for ensuring the distinct transcription of stress responsive genes only occurs under the appropriate conditions. While most ECFs are comprised of only the core {sigma}2 and {sigma}4 regions, a unique form of ECF sigma factor regulation also contains a C-terminal extension bearing homology to the NTF2 superfamily of protein domains. While previous work has shown that this NTF2 domain can affect transcriptional activity in vivo in ECF41 and ECF42, its role in the newly classified ECF56 subgroup is unknown. In this work, we show that truncation of the C-terminus of the ECF56 sigma factor SVEN_4562 of Streptomyces venezuelae upregulates its activity in a hybrid assay. Through transcriptomics in S. venezuelae, we found that this truncated ECF56 sigma factor has a highly conserved promoter sequence in vivo. Bioinformatic assays illustrated that deep branches of the Actinobacteria phylum contained putative ECF56 promoter motifs identical to those found in the S. venezuelae ECF56 regulon. We validated these findings through ex situ hybrid assays illustrating that truncated ECF56 sigma factors from phylogenetically diverse Actinobacteria activate transcription from these promoters. Importantly, our work shows that the genetic infrastructure of the ECF56 family of sigma factors is highly conserved and performs important functions yet to be understood in Actinobacteria. ImportanceMost ECF sigma-factors rely on anti-sigma factor regulation; in contrast, the unique classes of ECF sigma-factors that contain a C-terminal extension are thought to respond directly to an environmental signal. Here we show that the cis-acting regulatory element of the ECF56 regulon is likely highly conserved in many Actinobacteria, with exact nucleotide level conservation over ~2 billion years of evolution. The high conservation of this genetic architecture, as well as a conserved gene content within the regulon, strongly point to a specialized and important role in Actinobacteria biology.

microbiology↗

bioRxiv2020

An iron (II) dependent oxygenase performs the last missing step of plant lysine catabolism

Due to low abundance in many staple food crops, the essential amino acid lysine must be produced industrially to meet global food supply needs. Despite intensive study, manipulation, and agricultural importance, the steps of plant lysine catabolism beyond the 2-oxoadipate (2OA) intermediate remain undescribed. Recently we described a missing step in the D-lysine catabolic pathway of the bacterium Pseudomonas putida in which 2OA is converted to D-2-hydroxyglutarate (D2HG) via hydroxyglutarate synthase (HglS), an enzyme belonging to the previously uncharacterized DUF1338 protein family. Here we solve the structure of HglS to 1.1[A] resolution in the substrate-free form and in complex with 2OA. Structural similarity to hydroxymandelate synthase suggested a successive decarboxylation and intramolecular hydroxylation mechanism forming 2HG in a Fe(II)- and O2-dependent manner, which is validated experimentally. 2OA specificity was mediated by a single arginine (R74), highly conserved across nearly all DUF1338 family proteins, including in 76% of plant enzymes. In Arabidopsis thaliana, a DUF1338 homolog is coexpressed with known lysine catabolism enzymes, and mutants show significant germination rate defects consistent with disrupted lysine catabolism. Structural and biochemical analysis of the Oryza sativa homolog FLO7 revealed identical activity to HglS despite low sequence identity. Our results suggest that nearly all DUF1338 containing enzymes likely catalyze the same biochemical reaction, exerting the same physiological function across bacteria and eukaryotes. SignificanceTo meet human demands, millions of tons of lysine are produced by bacterial fermentation annually due to its low abundance in staple crops. Here, we show the last missing step of lysine catabolism in nearly all plant endosperms is likely catalyzed by an iron-dependant DUF1338-containing enzyme homologous to the bacterial hydroxyglutarate synthase. Structural and bioninformatic analyses of DUF1338-containing enzymes showed high conservation of critical catalytic and specificity-conferring residues across multiple domains of life despite low sequence identity. These results suggest that the DUF1338 family evolved a specific physiological function within lysine catabolism across multiple domains of life.

molecular biology↗

bioRxiv2019

Predictive engineering and optimization of tryptophan metabolism in yeast through a combination of mechanistic and machine learning models

In combination with advanced mechanistic modeling and the generation of high-quality multi-dimensional data sets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can complement each other and be used in a combined approach to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets and produce a large combinatorial library of metabolic pathway designs with different promoters which, once phenotyped, provide the basis for machine learning algorithms to be trained and used for new design recommendations. The approach enables successful forward engineering of aromatic amino acid metabolism in yeast, with the new recommended designs improving tryptophan production by up to 17% compared to the best designs used for algorithm training, and ultimately producing a total increase of 106% in tryptophan accumulation compared to optimized reference designs. Based on a single high-throughput data-generation iteration, this study highlights the power of combining mechanistic and machine learning models to enhance their predictive power and effectively direct metabolic engineering efforts.

bioengineering↗