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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. • Comparative global discovery proteomics analysis identified the aconitic acid transporter. • AexA identified as specific aconitic acid transporter in Aspergillus pseudoterreus. • AexA belongs to the major facilitator superfamily subclass Drug:H+ Antiporter-1 (DHA1, TC# 2.A.1.2). • AexA was validated by deletion and overexpression in vivo. • AexA was validated by proteoliposome transport assay in vitro. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 13C-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. • Meta-Analysis of 13C-MFA and GEMs show P. putida to be an ideal host for shinorine production. • CRISPRi-mediated gene downregulation of PP_1444 improves shinorine production. • Shinorine was produced at high titers and amino acid supplementation improved the titer. • Shinorine accumulates exclusively in the supernatant in P. putida cultures. [ABSTRACT FROM AUTHOR]

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. • Biosynthetic isoprenol is an emerging precursor for sustainable aviation fuel DMCO. • Pseudomonas putida was engineered for isoprenol production via the IPP-bypass pathway. • Genome scale metabolic modeling-aided gene knockouts improves isoprenol production. • Frequency scoring of two computational methods enables to prioritize knockout targets. • Combination of genome and pathway engineering results in isoprenol titer improvement. [ABSTRACT FROM AUTHOR]

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. • R. toruloides was engineered to produce 3HP at high titers. • 3HP catabolic routes in R. toruloides were studied by RB-TnSeq • A novel 3HP transporter was identified from A. pseudoterreus. • With process optimization 3HP titer reached 45.4 g/L from biomass hydrolysate. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. • In this review, we offer an introduction to Machine learning in terms that are relatable to metabolic engineers. • We include practical advice for the practitioner in terms of data management, algorithm libraries, and computational resourcessoci. • A variety of applications ranging from pathway construction and optimization, to scale-up are discussed. • Finally, the future perspectives and most promising directions for this combination of disciplines are examined. [ABSTRACT FROM AUTHOR]

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. [Display omitted] • Genome-scale models are available for many industrially important fungi. • Enzyme-constrained genome-scale models improve simulation performance. • Machine learning methods facilitate the reconstruction of enzyme-constrained models. • Fluxes predicted by genome-scale models can be features in machine learning models. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]