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Sunscreen has been used for thousands of years to protect skin from ultraviolet radiation. However, the use of modern commercial sunscreen containing oxybenzone, ZnO, and TiO 2 has raised concerns due to their negative effects on human health and the environment. In this study, we aim to establish an efficient microbial platform for production of shinorine, a UV light absorbing compound with anti-aging properties. First, we methodically selected an appropriate host for shinorine production by analyzing central carbon flux distribution data from prior studies alongside predictions from genome-scale metabolic models (GEMs). We enhanced shinorine productivity through CRISPRi-mediated downregulation and utilized shotgun proteomics to pinpoint potential competing pathways. Simultaneously, we improved the shinorine biosynthetic pathway by refining its design, optimizing promoter usage, and altering the strength of ribosome binding sites. Finally, we conducted amino acid feeding experiments under various conditions to identify the key limiting factors in shinorine production. The study combines meta-analysis of 13 C-metabolic flux analysis, GEMs, synthetic biology, CRISPRi-mediated gene downregulation, and omics analysis to improve shinorine production, demonstrating the potential of Pseudomonas putida KT2440 as platform for shinorine production., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

The Lipomyces clade contains oleaginous yeast species with advantageous metabolic features for biochemical and biofuel production. Limited knowledge about the metabolic networks of the species and limited tools for genetic engineering have led to a relatively small amount of research on the microbes. Here, a genome-scale metabolic model (GSM) of Lipomyces starkeyi NRRL Y-11557 was built using orthologous protein mappings to model yeast species. Phenotypic growth assays were used to validate the GSM (66% accuracy) and indicated that NRRL Y-11557 utilized diverse carbohydrates but had more limited catabolism of organic acids. The final GSM contained 2,193 reactions, 1,909 metabolites, and 996 genes and was thus named iLst996. The model contained 96 of the annotated carbohydrate-active enzymes. iLst996 predicted a flux distribution in line with oleaginous yeast measurements and was utilized to predict theoretical lipid yields. Twenty-five other yeasts in the Lipomyces clade were then genome sequenced and annotated. Sixteen of the Lipomyces species had orthologs for more than 97% of the iLst996 genes, demonstrating the usefulness of iLst996 as a broad GSM for Lipomyces metabolism. Pathways that diverged from iLst996 mainly revolved around alternate carbon metabolism, with ortholog groups excluding NRRL Y-11557 annotated to be involved in transport, glycerolipid, and starch metabolism, among others. Overall, this study provides a useful modeling tool and data for analyzing and understanding Lipomyces species metabolism and will assist further engineering efforts in Lipomyces ., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision., (Copyright © 2024 Czajka, Han, Kim, Mondo, Hofstad, Robles, Haridas, Riley, LaButti, Pangilinan, Andreopoulos, Lipzen, Yan, Wang, Ng, Grigoriev, Spatafora, Magnuson, Baker and Pomraning.)

Sustainable aviation fuel (SAF) will significantly impact global warming in the aviation sector, and important SAF targets are emerging. Isoprenol is a precursor for a promising SAF compound DMCO (1,4-dimethylcyclooctane) and has been produced in several engineered microorganisms. Recently, Pseudomonas putida has gained interest as a future host for isoprenol bioproduction as it can utilize carbon sources from inexpensive plant biomass. Here, we engineer metabolically versatile host P. putida for isoprenol production. We employ two computational modeling approaches (Bilevel optimization and Constrained Minimal Cut Sets) to predict gene knockout targets and optimize the "IPP-bypass" pathway in P. putida to maximize isoprenol production. Altogether, the highest isoprenol production titer from P. putida was achieved at 3.5 g/L under fed-batch conditions. This combination of computational modeling and strain engineering on P. putida for an advanced biofuels production has vital significance in enabling a bioproduction process that can use renewable carbon streams., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

Many fungal species have been used industrially for production of biofuels and bioproducts. Developing strains with better performance in biomanufacturing contexts requires a systematic understanding of cellular metabolism. Genome-scale metabolic models (GEMs) offer a comprehensive view of interconnected pathways and a mathematical framework for downstream analysis. Recently, GEMs have been developed or updated for several industrially important fungi. Some of them incorporate enzyme constraints, enabling improved predictions of cell states and proteome allocation. Here, we provide an overview of these newly developed GEMs and computational methods that facilitate construction of enzyme-constrained GEMs and utilize flux predictions from GEMs. Furthermore, we highlight the pivotal roles of these GEMs in iterative design-build-test-learn cycles, ultimately advancing the field of fungal biomanufacturing., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s), Battelle Memorial Institute. Published by Elsevier Ltd.. All rights reserved.)

Aconitic acid is an unsaturated tricarboxylic acid that is attractive for its potential use in manufacturing biodegradable and biocompatible polymers, plasticizers, and surfactants. Previously Aspergillus pseudoterreus was engineered as a platform to produce aconitic acid by deleting the cadA (cis-aconitic acid decarboxylase) gene in the itaconic acid biosynthetic pathway. In this study, the aconitic acid transporter gene (aexA) was identified using comparative global discovery proteomics analysis between the wild-type and cadA deletion strains. The protein AexA belongs to the Major Facilitator Superfamily (MFS). Deletion of aexA almost abolished aconitic acid secretion, while its overexpression led to a significant increase in aconitic acid production. Transportation of aconitic acid across the plasma membrane is a key limiting step in its production. In vitro, proteoliposome transport assay further validated AexA's function and substrate specificity. This research provides new approaches to efficiently pinpoint and characterize exporters of fungal organic acids and accelerate metabolic engineering to improve secretion capability and lower the cost of bioproduction., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Fungi have been utilized for centuries in medical, agricultural, and industrial applications. Development of systems biology techniques has enabled the design and metabolic engineering of these fungi to produce novel fuels, chemicals, and enzymes from renewable feedstocks. Many genetic tools have been developed for manipulating the genome and creating mutants rapidly. However, screening and confirmation of transformants remain an inefficient step within the design, build, test, and learn cycle in many industrial fungi because extracting fungal genomic DNA is laborious, time-consuming, and involves toxic chemicals., Results: In this study we developed a rapid and robust technique called "Squash-PCR" to break open the spores and release fungal genomic DNA as a template for PCR. The efficacy of Squash-PCR was investigated in eleven different filamentous fungal strains. Clean PCR products with high yields were achieved in all tested fungi. Spore age and type of DNA polymerase did not affect the efficiency of Squash-PCR. However, spore concentration was found to be the crucial factor for Squash-PCR in Aspergillus niger, with the dilution of starting material often resulting in higher PCR product yield. We then further evaluated the applicability of the squashing procedure for nine different yeast strains. We found that Squash-PCR can be used to improve the quality and yield of colony PCR in comparison to direct colony PCR in the tested yeast strains., Conclusion: The developed technique will enhance the efficiency of screening transformants and accelerate genetic engineering in filamentous fungi and yeast., (© 2023. The Author(s).)

Microbial production of valuable bioproducts is a promising route towards green and sustainable manufacturing. The oleaginous yeast, Rhodosporidium toruloides, has emerged as an attractive host for the production of biofuels and bioproducts from lignocellulosic hydrolysates. 3-hydroxypropionic acid (3HP) is an attractive platform molecule that can be used to produce a wide range of commodity chemicals. This study focuses on establishing and optimizing the production of 3HP in R. toruloides. As R. toruloides naturally has a high metabolic flux towards malonyl-CoA, we exploited this pathway to produce 3HP. Upon finding the yeast capable of catabolizing 3HP, we then implemented functional genomics and metabolomic analysis to identify the catabolic pathways. Deletion of a putative malonate semialdehyde dehydrogenase gene encoding an oxidative 3HP pathway was found to significantly reduce 3HP degradation. We further explored monocarboxylate transporters to promote 3HP transport and identified a novel 3HP transporter in Aspergillus pseudoterreus by RNA-seq and proteomics. Combining these engineering efforts with media optimization in a fed-batch fermentation resulted in 45.4 g/L 3HP production. This represents one of the highest 3HP titers reported in yeast from lignocellulosic feedstocks. This work establishes R. toruloides as a host for 3HP production from lignocellulosic hydrolysate at high titers, and paves the way for further strain and process optimization towards enabling industrial production of 3HP in the future., Competing Interests: Declaration of competing interest The authors claim no competing financial interests., (Copyright © 2023 International Metabolic Engineering Society. All rights reserved.)

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13 C-Metabolic Flux Analysis ( 13 C-MFA). Under glucose-fed conditions, E-Flux2 presents an R 2 value of 0.54, while predictions based on pFBA had an inferior R 2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2's flux predictions display a high R 2 of 0.96 while pFBA showed an R 2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design.

Multidimensional measurements using state-of-the-art separations and mass spectrometry provide advantages in untargeted metabolomics analyses for studying biological and environmental bio-chemical processes. However, the lack of rapid analytical methods and robust algorithms for these heterogeneous data has limited its application. Here, we develop and evaluate a sensitive and high-throughput analytical and computational workflow to enable accurate metabolite profiling. Our workflow combines liquid chromatography, ion mobility spectrometry and data-independent acquisition mass spectrometry with PeakDecoder, a machine learning-based algorithm that learns to distinguish true co-elution and co-mobility from raw data and calculates metabolite identification error rates. We apply PeakDecoder for metabolite profiling of various engineered strains of Aspergillus pseudoterreus, Aspergillus niger, Pseudomonas putida and Rhodosporidium toruloides. Results, validated manually and against selected reaction monitoring and gas-chromatography platforms, show that 2683 features could be confidently annotated and quantified across 116 microbial sample runs using a library built from 64 standards., (© 2023. Battelle Memorial Institute and the Author(s).)

Background: Fuels and chemicals derived from non-fossil sources are needed to lessen human impacts on the environment while providing a healthy and growing economy. 3-hydroxypropionic acid (3-HP) is an important chemical building block that can be used for many products. Biosynthesis of 3-HP is possible; however, low production is typically observed in those natural systems. Biosynthetic pathways have been designed to produce 3-HP from a variety of feedstocks in different microorganisms., Results: In this study, the 3-HP β-alanine pathway consisting of aspartate decarboxylase, β-alanine-pyruvate aminotransferase, and 3-hydroxypropionate dehydrogenase from selected microorganisms were codon optimized for Aspergillus species and placed under the control of constitutive promoters. The pathway was introduced into Aspergillus pseudoterreus and subsequently into Aspergillus niger, and 3-HP production was assessed in both hosts. A. niger produced higher initial 3-HP yields and fewer co-product contaminants and was selected as a suitable host for further engineering. Proteomic and metabolomic analysis of both Aspergillus species during 3-HP production identified genetic targets for improvement of flux toward 3-HP including pyruvate carboxylase, aspartate aminotransferase, malonate semialdehyde dehydrogenase, succinate semialdehyde dehydrogenase, oxaloacetate hydrolase, and a 3-HP transporter. Overexpression of pyruvate carboxylase improved yield in shake-flasks from 0.09 to 0.12 C-mol 3-HP C-mol -1 glucose in the base strain expressing 12 copies of the β-alanine pathway. Deletion or overexpression of individual target genes in the pyruvate carboxylase overexpression strain improved yield to 0.22 C-mol 3-HP C-mol -1 glucose after deletion of the major malonate semialdehyde dehydrogenase. Further incorporation of additional β-alanine pathway genes and optimization of culture conditions (sugars, temperature, nitrogen, phosphate, trace elements) for 3-HP production from deacetylated and mechanically refined corn stover hydrolysate improved yield to 0.48 C-mol 3-HP C-mol -1 sugars and resulted in a final titer of 36.0 g/L 3-HP., Conclusions: The results of this study establish A. niger as a host for 3-HP production from a lignocellulosic feedstock in acidic conditions and demonstrates that 3-HP titer and yield can be improved by a broad metabolic engineering strategy involving identification and modification of genes participated in the synthesis of 3-HP and its precursors, degradation of intermediates, and transport of 3-HP across the plasma membrane., (© 2023. Battelle Memorial Institute.)

The global regulator LaeA controls secondary metabolism in diverse Aspergillus species. Here we explored its role in regulation of itaconic acid production in Aspergillus pseudoterreus . To understand its role in regulating metabolism, we deleted and overexpressed laeA, and assessed the transcriptome, proteome, and secreted metabolome prior to and during initiation of phosphate limitation induced itaconic acid production. We found that secondary metabolite clusters, including the itaconic acid biosynthetic gene cluster, are regulated by laeA and that laeA is required for high yield production of itaconic acid. Overexpression of LaeA improves itaconic acid yield at the expense of biomass by increasing the expression of key biosynthetic pathway enzymes and attenuating the expression of genes involved in phosphate acquisition and scavenging. Increased yield was observed in optimized conditions as well as conditions containing excess nutrients that may be present in inexpensive sugar containing feedstocks such as excess phosphate or complex nutrient sources. This suggests that global regulators of metabolism may be useful targets for engineering metabolic flux that is robust to environmental heterogeneity., Competing Interests: The authors declare no competing interests., (© 2022 The Authors.)

Oleaginous yeast, such as Lipomyces starkeyi , are logical organisms for production of higher energy density molecules like lipids and terpenes. We demonstrate that transgenic L. starkeyi strains expressing an α-zingiberene synthase gene from lemon basil or Hall's panicgrass can produce up to 17 mg/L α-zingiberene in yeast extract peptone dextrose (YPD) medium containing 4% glucose. The transgenic strain was further examined in 8% glucose media with C/N ratios of 20 or 100, and YPD. YPD medium resulted in 59 mg/L α-zingiberene accumulation. Overexpression of selected genes from the mevalonate pathway achieved 145% improvement in α-zingiberene synthesis. Optimization of the growth medium for α-zingiberene production led to 15% higher titer than YPD medium. The final transgenic strain produced 700 mg/L α-zingiberene in fed-batch bioreactor culture. This study opens a new synthetic route to produce α-zingiberene or other terpenoids in L. starkeyi and establishes this yeast as a platform for jet fuel biosynthesis.

Background: Mitigation of climate change requires that new routes for the production of fuels and chemicals be as oil-independent as possible. The microbial conversion of lignocellulosic feedstocks into terpene-based biofuels and bioproducts represents one such route. This work builds upon previous demonstrations that the single-celled carotenogenic basidiomycete, Rhodosporidium toruloides, is a promising host for the production of terpenes from lignocellulosic hydrolysates., Results: This study focuses on the optimization of production of the monoterpene 1,8-cineole and the sesquiterpene α-bisabolene in R. toruloides. The α-bisabolene titer attained in R. toruloides was found to be proportional to the copy number of the bisabolene synthase (BIS) expression cassette, which in turn influenced the expression level of several native mevalonate pathway genes. The addition of more copies of BIS under a stronger promoter resulted in production of α-bisabolene at 2.2 g/L from lignocellulosic hydrolysate in a 2-L fermenter. Production of 1,8-cineole was found to be limited by availability of the precursor geranylgeranyl pyrophosphate (GPP) and expression of an appropriate GPP synthase increased the monoterpene titer fourfold to 143 mg/L at bench scale. Targeted mevalonate pathway metabolite analysis suggested that 3-hydroxy-3-methyl-glutaryl-coenzyme A reductase (HMGR), mevalonate kinase (MK) and phosphomevalonate kinase (PMK) may be pathway bottlenecks are were therefore selected as targets for overexpression. Expression of HMGR, MK, and PMK orthologs and growth in an optimized lignocellulosic hydrolysate medium increased the 1,8-cineole titer an additional tenfold to 1.4 g/L. Expression of the same mevalonate pathway genes did not have as large an impact on α-bisabolene production, although the final titer was higher at 2.6 g/L. Furthermore, mevalonate pathway intermediates accumulated in the mevalonate-engineered strains, suggesting room for further improvement., Conclusions: This work brings R. toruloides closer to being able to make industrially relevant quantities of terpene from lignocellulosic biomass.

Biological engineering of microorganisms to produce value-added chemicals is a promising route to sustainable manufacturing. However, overproduction of metabolic intermediates at high titer, rate, and yield from inexpensive substrates is challenging in non-model systems where limited information is available regarding metabolic flux and its control in production conditions. Integrated multi-omic analyses of engineered strains offers an in-depth look at metabolites and proteins directly involved in growth and production of target and non-target bioproducts. Here we applied multi-omic analyses to overproduction of the polymer precursor 3-hydroxypropionic acid (3HP) in the filamentous fungus Aspergillus pseudoterreus . A synthetic pathway consisting of aspartate decarboxylase, beta-alanine pyruvate transaminase, and 3HP dehydrogenase was designed and built for A. pseudoterreus . Strains with single- and multi-copy integration events were isolated and multi-omics analysis consisting of intracellular and extracellular metabolomics and targeted and global proteomics was used to interrogate the strains in shake-flask and bioreactor conditions. Production of a variety of co-products (organic acids and glycerol) and oxidative degradation of 3HP were identified as metabolic pathways competing with 3HP production. Intracellular accumulation of nitrogen as 2,4-diaminobutanoate was identified as an off-target nitrogen sink that may also limit flux through the engineered 3HP pathway. Elimination of the high-expression oxidative 3HP degradation pathway by deletion of a putative malonate semialdehyde dehydrogenase improved the yield of 3HP by 3.4 × after 10 days in shake-flask culture. This is the first report of 3HP production in a filamentous fungus amenable to industrial scale biomanufacturing of organic acids at high titer and low pH., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Pomraning, Dai, Munoz, Kim, Gao, Deng, Kim, Hofstad, Swita, Lemmon, Collett, Panisko, Webb-Robertson, Zucker, Nicora, De Paoli, Baker, Burnum-Johnson, Hillson and Magnuson.)

Biology has changed radically in the past two decades, growing from a purely descriptive science into also a design science. The availability of tools that enable the precise modification of cells, as well as the ability to collect large amounts of multimodal data, open the possibility of sophisticated bioengineering to produce fuels, specialty and commodity chemicals, materials, and other renewable bioproducts. However, despite new tools and exponentially increasing data volumes, synthetic biology cannot yet fulfill its true potential due to our inability to predict the behavior of biological systems. Here, we showcase a set of computational tools that, combined, provide the ability to store, visualize, and leverage multiomics data to predict the outcome of bioengineering efforts. We show how to upload, visualize, and output multiomics data, as well as strain information, into online repositories for several isoprenol-producing strain designs. We then use these data to train machine learning algorithms that recommend new strain designs that are correctly predicted to improve isoprenol production by 23%. This demonstration is done by using synthetic data, as provided by a novel library, that can produce credible multiomics data for testing algorithms and computational tools. In short, this paper provides a step-by-step tutorial to leverage these computational tools to improve production in bioengineered strains., Competing Interests: NH declares financial interests in TeselaGen Biotechnologies, and Ansa Biotechnologies. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Roy, Radivojevic, Forrer, Marti, Jonnalagadda, Backman, Morrell, Plahar, Kim, Hillson and Garcia Martin.)

An oleaginous yeast Rhodosporidium toruloides is a promising host for converting lignocellulosic biomass to bioproducts and biofuels. In this work, we performed multi-omics analysis of lignocellulosic carbon utilization in R. toruloides and reconstructed the genome-scale metabolic network of R. toruloides . High-quality metabolic network models for model organisms and orthologous protein mapping were used to build a draft metabolic network reconstruction. The reconstruction was manually curated to build a metabolic model using functional annotation and multi-omics data including transcriptomics, proteomics, metabolomics, and RB-TDNA sequencing. The multi-omics data and metabolic model were used to investigate R. toruloides metabolism including lipid accumulation and lignocellulosic carbon utilization. The developed metabolic model was validated against high-throughput growth phenotyping and gene fitness data, and further refined to resolve the inconsistencies between prediction and data. We believe that this is the most complete and accurate metabolic network model available for R. toruloides to date., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Kim, Coradetti, Kim, Gao, Yaegashi, Zucker, Munoz, Zink, Burnum-Johnson, Baker, Simmons, Skerker, Gladden and Magnuson.)

Machine learning provides researchers a unique opportunity to make metabolic engineering more predictable. In this review, we offer an introduction to this discipline in terms that are relatable to metabolic engineers, as well as providing in-depth illustrative examples leveraging omics data and improving production. We also include practical advice for the practitioner in terms of data management, algorithm libraries, computational resources, and important non-technical issues. A variety of applications ranging from pathway construction and optimization, to genetic editing optimization, cell factory testing, and production scale-up are discussed. Moreover, the promising relationship between machine learning and mechanistic models is thoroughly reviewed. Finally, the future perspectives and most promising directions for this combination of disciplines are examined., (Copyright © 2020. Published by Elsevier Inc.)

Targeted proteomics is a mass spectrometry-based protein quantification technique with high sensitivity, accuracy, and reproducibility. As a key component in the multi-omics toolbox of systems biology, targeted liquid chromatography-selected reaction monitoring (LC-SRM) measurements are critical for enzyme and pathway identification and design in metabolic engineering. To fulfill the increasing need for analyzing large sample sets with faster turnaround time in systems biology, high-throughput LC-SRM is greatly needed. Even though nanoflow LC-SRM has better sensitivity, it lacks the speed offered by microflow LC-SRM. Recent advancements in mass spectrometry instrumentation significantly enhance the scan speed and sensitivity of LC-SRM, thereby creating opportunities for applying the high speed of microflow LC-SRM without losing peptide multiplexing power or sacrificing sensitivity. Here, we studied the performance of microflow LC-SRM relative to nanoflow LC-SRM by monitoring 339 peptides representing 132 enzymes in Pseudomonas putida KT2440 grown on various carbon sources. The results from the two LC-SRM platforms are highly correlated. In addition, the response curve study of 248 peptides demonstrates that microflow LC-SRM has comparable sensitivity for the majority of detected peptides and better mass spectrometry signal and chromatography stability than nanoflow LC-SRM., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer LB declared a past co-authorship with one of the authors GTB to the handling editor., (Copyright © 2020 Gao, Fillmore, Munoz, Bentley, Johnson, Kim, Meadows, Zucker, Burnet, Lipton, Bilbao, Orton, Kim, Moore, Robinson, Baker, Webb-Robertson, Guss, Gladden, Beckham, Magnuson and Burnum-Johnson.)

Currently, microbial conversion of lignocellulose-derived glucose and xylose to biofuels is hindered by the fact that most microbes (including Escherichia coli [E. coli], Saccharomyces cerevisiae, and Zymomonas mobilis) preferentially consume glucose first and consume xylose slowly after glucose is depleted in lignocellulosic hydrolysates. In this study, E. coli strains are developed that simultaneously utilize glucose and xylose in lignocellulosic biomass hydrolysate using genome-scale models and adaptive laboratory evolution. E. coli strains are designed and constructed that coutilize glucose and xylose and adaptively evolve them to improve glucose and xylose utilization. Whole-genome resequencing of the evolved strains find relevant mutations in metabolic and regulatory genes and the mutations' involvement in sugar coutilization is investigated. The developed strains show significantly improved coconversion of sugars in lignocellulosic biomass hydrolysates and provide a promising platform for producing next-generation biofuels., (© 2019 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

Background: Rhodosporidium toruloides is a promising host for the production of bioproducts from lignocellulosic biomass. A key prerequisite for efficient pathway engineering is the availability of robust genetic tools and resources. However, there is a lack of characterized promoters to drive expression of heterologous genes for strain engineering in R. toruloides., Results: This data describes a set of native R. toruloides promoters, characterized over time in four different media commonly used for cultivation of this yeast. The promoter sequences were selected using transcriptional analysis and several of them were found to drive expression bidirectionally. Promoter expression strength was determined by measurement of EGFP and mRuby2 reporters by flow cytometry. A total of 20 constitutive promoters (12 monodirectional and 8 bidirectional) were found, and are expected to be of potential value for genetic engineering of R. toruloides., Conclusions: A set of robust and constitutive promoters to facilitate genetic engineering of R. toruloides is presented here, ranging from a promoter previously used for this purpose (P7, glyceraldehyde 3-phosphate dehydrogenase, GAPDH) to stronger monodirectional (e.g., P15, mitochondrial adenine nucleotide translocator, ANT) and bidirectional (e.g., P9 and P9R, histones H3 and H4, respectively) promoters. We also identified promoters that may be useful for specific applications such as late-stage expression (e.g., P3, voltage-dependent anion channel protein 2, VDAC2). This set of characterized promoters significantly expands the range of engineering tools available for this yeast and can be applied in future metabolic engineering studies.

Background: Efficient and economically viable production of biofuels from lignocellulosic biomass is dependent on mechanical and chemical pretreatment and enzymatic hydrolysis of plant material. These processing steps yield simple sugars as well as plant-derived and process-added organic acids, sugar-derived dehydration products, aldehydes, phenolics and other compounds that inhibit the growth of many microorganisms. Lipomyces starkeyi is an oleaginous yeast capable of robust growth on a variety of sugars and lipid accumulation on pretreated lignocellulosic substrates making it attractive as an industrial producer of biofuels. Here, we examined gene expression during batch growth and lipid accumulation in a 20-L bioreactor with either a blend of pure glucose and xylose or pretreated corn stover (PCS) that had been enzymatically hydrolyzed as the carbon sources., Results: We monitored sugar and ammonium utilization as well as biomass accumulation and found that growth of L. starkeyi is inhibited with PCS hydrolysate as the carbon source. Both acetic acid and furfural are present at concentrations toxic to L. starkeyi in PCS hydrolysate. We quantified gene expression at seven time-points for each carbon source during batch growth and found that gene expression is similar at physiologically equivalent points. Analysis of promoter regions revealed that gene expression during the transition to lipid accumulation is regulated by carbon and nitrogen catabolite repression, regardless of carbon source and is associated with decreased expression of the translation machinery and suppression of the cell cycle. We identified 73 differentially expressed genes during growth phase in the bioreactor that may be involved in detoxification of corn stover hydrolysate., Conclusions: Growth of L. starkeyi is inhibited by compounds present in PCS hydrolysate. Here, we monitored key metabolites to establish physiologically equivalent comparisons during a batch bioreactor run comparing PCS hydrolysate and purified sugars. L. starkeyi 's response to PCS hydrolysate is primarily at the beginning of the run during growth phase when inhibitory compounds are presumably at their highest concentration and inducing the general detoxification response by L. starkeyi . Differentially expressed genes identified herein during growth phase will aid in the improvement of industrial strains capable of robust growth on substrates containing various growth inhibitory compounds., Competing Interests: Competing interestsThe authors declare that they have no competing interests.

Isopentenyl pyrophosphate (IPP) toxicity presents a challenge in engineered microbial systems since its formation is unavoidable in terpene biosynthesis. In this work, we develop an experimental platform to study IPP toxicity in isoprenol-producing Escherichia coli. We first characterize the physiological response to IPP accumulation, demonstrating that elevated IPP levels are linked to growth inhibition, reduced cell viability, and plasmid instability. We show that IPP toxicity selects for pathway "breakage", using proteomics to identify a reduction in phosphomevalonate kinase (PMK) as a probable recovery mechanism. Next, using multi-omics data, we demonstrate that endogenous E. coli metabolism is globally impacted by IPP accumulation, which slows nutrient uptake, decreases ATP levels, and perturbs nucleotide metabolism. We also observe the extracellular accumulation of IPP and present preliminary evidence that IPP can be transported by E. coli, findings that might be broadly relevant for the study of isoprenoid biosynthesis. Finally, we discover that IPP accumulation leads to the formation of ApppI, a nucleotide analog of IPP that may contribute to observed toxicity phenotypes. This comprehensive assessment of IPP stress suggests potential strategies for the alleviation of prenyl diphosphate toxicity and highlights possible engineering targets for improved IPP flux and high titer isoprenoid production., (Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.)

Plant biomass, once reduced to its composite sugars, can be converted to fuel substitutes. One means of overcoming the recalcitrance of lignocellulose is pretreatment followed by enzymatic hydrolysis. However, currently available commercial enzyme cocktails are inhibited in the presence of residual pretreatment chemicals. Recent studies have identified a number of cellulolytic enzymes from bacteria that are tolerant to pretreatment chemicals such as ionic liquids. The challenge now is generation of these enzymes in copious amounts, an arena where fungal organisms such as Aspergillus niger have proven efficient. Fungal host strains still need to be engineered to increase production titers of heterologous protein over native enzymes, which has been a difficult task. Here, we developed a forward genetics screen coupled with whole-genome resequencing to identify specific lesions responsible for a protein hyper-production phenotype in A. niger. This strategy successfully identified novel targets, including a low-affinity glucose transporter, MstC, whose deletion significantly improved secretion of recombinant proteins driven by a glucoamylase promoter.

Advances in genome-scale metabolic modeling allow us to investigate and engineer metabolism at a systems level. Metabolic network reconstructions have been made for many organisms and computational approaches have been developed to convert these reconstructions into predictive models. However, due to incomplete knowledge these reconstructions often have missing or extraneous components and interactions, which can be identified by reconciling model predictions with experimental data. Recent studies have provided methods to further improve metabolic model predictions by incorporating transcriptional regulatory interactions and high-throughput omics data to yield context-specific metabolic models. Here we discuss recent approaches for resolving model-data discrepancies and building context-specific metabolic models. Once developed highly accurate metabolic models can be used in a variety of biotechnology applications., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

Efficient microbial conversion of lignocellulosic hydrolysates to biofuels is a key barrier to the economically viable deployment of lignocellulosic biofuels. A chief contributor to this barrier is the impact on microbial processes and energy metabolism of lignocellulose-derived inhibitors, including phenolic carboxylates, phenolic amides (for ammonia-pretreated biomass), phenolic aldehydes, and furfurals. To understand the bacterial pathways induced by inhibitors present in ammonia-pretreated biomass hydrolysates, which are less well studied than acid-pretreated biomass hydrolysates, we developed and exploited synthetic mimics of ammonia-pretreated corn stover hydrolysate (ACSH). To determine regulatory responses to the inhibitors normally present in ACSH, we measured transcript and protein levels in an Escherichia coli ethanologen using RNA-seq and quantitative proteomics during fermentation to ethanol of synthetic hydrolysates containing or lacking the inhibitors. Our study identified four major regulators mediating these responses, the MarA/SoxS/Rob network, AaeR, FrmR, and YqhC. Induction of these regulons was correlated with a reduced rate of ethanol production, buildup of pyruvate, depletion of ATP and NAD(P)H, and an inhibition of xylose conversion. The aromatic aldehyde inhibitor 5-hydroxymethylfurfural appeared to be reduced to its alcohol form by the ethanologen during fermentation, whereas phenolic acid and amide inhibitors were not metabolized. Together, our findings establish that the major regulatory responses to lignocellulose-derived inhibitors are mediated by transcriptional rather than translational regulators, suggest that energy consumed for inhibitor efflux and detoxification may limit biofuel production, and identify a network of regulators for future synthetic biology efforts.

Predicting cellular responses to perturbations is an important task in systems biology. We report a new approach, RELATCH, which uses flux and gene expression data from a reference state to predict metabolic responses in a genetically or environmentally perturbed state. Using the concept of relative optimality, which considers relative flux changes from a reference state, we hypothesize a relative metabolic flux pattern is maintained from one state to another, and that cells adapt to perturbations using metabolic and regulatory reprogramming to preserve this relative flux pattern. This constraint-based approach will have broad utility where predictions of metabolic responses are needed.

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ∼10 days to ∼5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.

Integrated constraint-based metabolic and regulatory models can accurately predict cellular growth phenotypes arising from genetic and environmental perturbations. Challenges in constructing such models involve the limited availability of information about transcription factor--gene target interactions and computational methods to quickly refine models based on additional datasets. In this study, we developed an algorithm, GeneForce, to identify incorrect regulatory rules and gene-protein-reaction associations in integrated metabolic and regulatory models. We applied the algorithm to refine integrated models of Escherichia coli and Salmonella typhimurium, and experimentally validated some of the algorithm's suggested refinements. The adjusted E. coli model showed improved accuracy (∼80.0%) for predicting growth phenotypes for 50,557 cases (knockout mutants tested for growth in different environmental conditions). In addition to identifying needed model corrections, the algorithm was used to identify native E. coli genes that, if over-expressed, would allow E. coli to grow in new environments. We envision that this approach will enable the rapid development and assessment of genome-scale metabolic and regulatory network models for less characterized organisms, as such models can be constructed from genome annotations and cis-regulatory network predictions.

Background: Computational modeling and analysis of metabolic networks has been successful in metabolic engineering of microbial strains for valuable biochemical production. Limitations of currently available computational methods for metabolic engineering are that they are often based on reaction deletions rather than gene deletions and do not consider the regulatory networks that control metabolism. Due to the presence of multi-functional enzymes and isozymes, computational designs based on reaction deletions can sometimes result in strategies that are genetically complicated or infeasible. Additionally, strains might not be able to grow initially due to regulatory restrictions. To overcome these limitations, we have developed a new approach (OptORF) for identifying metabolic engineering strategies based on gene deletion and overexpression., Results: Here we propose an effective method to systematically integrate transcriptional regulatory networks and metabolic networks. This allows for the formulation of linear optimization problems that search for metabolic and/or regulatory perturbations that couple biomass and biochemical production, thus proposing adaptive evolutionary strain designs. Using genome-scale models of Escherichia coli, we have implemented the OptORF algorithm (which considers gene deletions and transcriptional regulation) and compared its metabolic engineering strategies for ethanol production to those found using OptKnock (which considers reaction deletions). Our results found that the reaction-based strategies often require more gene deletions to remove the identified reactions (2 more genes than reactions), and result in lethal growth phenotypes when transcriptional regulation is considered (162 out of 200 cases). Finally, we present metabolic engineering strategies for producing ethanol and higher alcohols (e.g. isobutanol) in E. coli using our OptORF approach. We have found common genetic modifications such as deletion of pgi and overexpression of edd, as well as chemical specific strategies for producing different alcohols., Conclusions: By taking regulatory effects into account, OptORF can propose changes such as the overexpression of metabolic genes or deletion of transcriptional factors, in addition to the deletion of metabolic genes, that may lead to faster evolutionary trajectories. While biofuel production in E. coli is evaluated here, the developed OptORF approach is general and can be applied to optimize the production of different compounds in other biological systems.