Massively Parallel Fitness Profiling Reveals Multiple Novel Enzymes in Pseudomonas putida Lysine Metabolism

Modulating bacterial function utilizing A knowledge base of transcriptional regulatory modules

Synthetic biology enables the reprogramming of cellular functions for various applications. However, challenges in scalability and predictability persist due to context-dependent performance and complex circuit-host interactions. This study introduces an iModulon-based engineering approach, utilizing machine learning-defined co-regulated gene groups (iModulons) as design parts containing essential genes for specific functions. This approach identifies the necessary components for genetic circuits across different contexts, enhancing genome engineering by improving target selection and predicting module behavior. We demonstrate several distinct uses of iModulons: (i) discovery of unknown iModulons to increase protein productivity, heat tolerance and fructose utilization; (ii) an iModulon boosting approach, which amplifies the activity of specific iModulons, improved cell growth under osmotic stress with minimal host regulation disruption; (iii) an iModulon rebalancing strategy, which adjusts the activity levels of iModulons to balance cellular functions, significantly increased oxidative stress tolerance while minimizing trade-offs and (iv) iModulon-based gene annotation enabled natural competence activation by predictably rewiring iModulons. Comparative experiments with traditional methods showed our approach offers advantages in efficiency and predictability of strain engineering. This study demonstrates the potential of iModulon-based strategies to systematically and predictably reprogram cellular functions, offering refined and adaptable control over complex regulatory networks.

Machine learning analysis of RB-TnSeq fitness data predicts functional gene modules in Pseudomonas putida KT2440

There is growing interest in engineering Pseudomonas putida KT2440 as a microbial chassis for the conversion of renewable and waste-based feedstocks, and metabolic engineering of P. putida relies on the understanding of the functional relationships between genes. In this work, independent component analysis (ICA) was applied to a compendium of existing fitness data from randomly barcoded transposon insertion sequencing (RB-TnSeq) of P. putida KT2440 grown in 179 unique experimental conditions. ICA identified 84 independent groups of genes, which we call fModules ("functional modules"), where gene members displayed shared functional influence in a specific cellular process. This machine learning-based approach both successfully recapitulated previously characterized functional relationships and established hitherto unknown associations between genes. Selected gene members from fModules for hydroxycinnamate metabolism and stress resistance, acetyl coenzyme A assimilation, and nitrogen metabolism were validated with engineered mutants of P. putida. Additionally, functional gene clusters from ICA of RB-TnSeq data sets were compared with regulatory gene clusters from prior ICA of RNAseq data sets to draw connections between gene regulation and function. Because ICA profiles the functional role of several distinct gene networks simultaneously, it can reduce the time required to annotate gene function relative to manual curation of RB-TnSeq data sets.

IMPORTANCE

This study demonstrates a rapid, automated approach for elucidating functional modules within complex genetic networks. While Pseudomonas putida randomly barcoded transposon insertion sequencing data were used as a proof of concept, this approach is applicable to any organism with existing functional genomics data sets and may serve as a useful tool for many valuable applications, such as guiding metabolic engineering efforts in other microbes or understanding functional relationships between virulence-associated genes in pathogenic microbes. Furthermore, this work demonstrates that comparison of data obtained from independent component analysis of transcriptomics and gene fitness datasets can elucidate regulatory-functional relationships between genes, which may have utility in a variety of applications, such as metabolic modeling, strain engineering, or identification of antimicrobial drug targets.

Genome-scale and pathway engineering for the sustainable aviation fuel precursor isoprenol production in Pseudomonas putida

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.

Characterization and Diversification of AraC/XylS Family Regulators Guided by Transposon Sequencing

In this study, we explored the development of engineered inducible systems. Publicly available data from previous transposon sequencing assays were used to identify regulators of metabolism in Pseudomonas putida KT2440. For AraC family regulators (AFRs) represented in these data, we posited AFR/promoter/inducer groupings. Twelve promoters were characterized for a response to their proposed inducers in P. putida, and the resultant data were used to create and test nine two-plasmid sensor systems in Escherichia coli. Several of these were further developed into a palette of single-plasmid inducible systems. From these experiments, we observed an unreported inducer response from a previously characterized AFR, demonstrated that the addition of a P. putida transporter improved the sensor dynamics of an AFR in E. coli, and identified an uncharacterized AFR with a novel potential inducer specificity. Finally, targeted mutations in an AFR, informed by structural predictions, enabled the further diversification of these inducible plasmids.

An enzymic l-2-hydroxyglutarate biosensor based on l-2-hydroxyglutarate dehydrogenase from Azoarcus olearius

l-2-Hydroxyglutarate (l-2-HG) is a critical signaling and immune metabolite but its excessive accumulation can lead to l-2-hydroxyglutaric aciduria, renal cancer, and other diseases. Development of efficient and high-throughput methods for selective l-2-HG detection is urgently required. In this study, l-2-HG dehydrogenase in Azoarcus olearius BH72 (AoL2HGDH) was screened from ten homologs and identified as an enzyme with high specificity and activity toward l-2-HG dehydrogenation. Then, an enzymatic assay-based l-2-HG-sensing fluorescent reporter, EaLHGFR which consists of AoL2HGDH and resazurin, was developed for the detection of l-2-HG. The response magnitude and limit of detection of EaLHGFR were systematically optimized using a single-factor screening strategy. The optimal biosensor EaLHGFR-2 exhibited a response magnitude of 2189.25 ± 26.89% and a limit of detection of 0.042 μM. It can accurately detect the concentration of l-2-HG in bacterial and cellular samples as well as human body fluids. Considering its desirable properties, EaLHGFR-2 may be a promising alternative for quantitation of l-2-HG in biological samples.

REC protein family expansion by the emergence of a new signaling pathway

IMPORTANCE

We explore when and why large classes of proteins expand into new sequence space. We used an unsupervised machine learning approach to observe the sequence landscape of REC domains of bacterial response regulator proteins. We find that within-gene recombination can switch effector domains and, consequently, change the regulatory context of the duplicated protein.

Development of a 2-hydroxyglutarate production system by Corynebacterium glutamicum

2-Oxoglutarate (2-OG) is a tricarboxylate cycle intermediate that can be biologically converted into several industrially important compounds. However, studies on the fermentative production of compounds synthesized from 2-OG, but not via glutamate (defined as 2-OG derivatives), have been limited. Herein, a system that can efficiently produce 2-hydroxyglutarate (2-HG), a 2-OG derivative biosynthesized by the hgdH-encoded NADH-dependent 2-HG dehydrogenase of Acidaminococcus fermentans, was developed as a model using Corynebacterium glutamicum. First, the D3 strain, which lacked the two NADH-consuming enzymes, lactate dehydrogenase and malate dehydrogenase, as well as isocitrate lyase, was constructed as a starting strain. Next, the growth conditions that induced the accumulation of 2-OG were investigated, and it was found that the biotin- and nitrogen-limited (B/N-limited) aerobic growth conditions were suitable for this purpose. Finally, the hgdH gene of A. fermentans became overexpressed in the D3 strain by inserting it into the intergenic regions with the strong constitutive promoter of the tuf gene of C. glutamicum; the engineered strain was cultured under the B/N-limited aerobic growth conditions. The engineered strain produced 80.1 mM 2-HG with a yield of 0.390 mol/mol glucose, which are the highest titer and yield reported thus far, to the best of our knowledge. Furthermore, reverse genetics showed that the produced 2-HG was partially exported via the YggB protein (NCgl1221 protein, a mechanosensitive channel) known as an exporter for glutamate under the conditions used herein. KEY POINTS: • An efficient 2-HG production system was developed with Corynebacterium glutamicum. • Biotin- and nitrogen-limited aerobic growth conditions induced 2-OG production. • Produced 2-HG was partially excreted via the glutamate exporter, YggB.

RB-TnSeq identifies genetic targets for improved tolerance of Pseudomonas putida towards compounds relevant to lignin conversion

Lignin-derived mixtures intended for bioconversion commonly contain high concentrations of aromatic acids, aliphatic acids, and salts. The inherent toxicity of these chemicals places a significant bottleneck upon the effective use of microbial systems for the valorization of these mixtures. Pseudomonas putida KT2440 can tolerate stressful quantities of several lignin-related compounds, making this bacterium a promising host for converting these chemicals to valuable bioproducts. Nonetheless, further increasing P. putida tolerance to chemicals in lignin-rich substrates has the potential to improve bioprocess performance. Accordingly, we employed random barcoded transposon insertion sequencing (RB-TnSeq) to reveal genetic determinants in P. putida KT2440 that influence stress outcomes during exposure to representative constituents found in lignin-rich process streams. The fitness information obtained from the RB-TnSeq experiments informed engineering of strains via deletion or constitutive expression of several genes. Namely, ΔgacAS, ΔfleQ, ΔlapAB, ΔttgR::P_tac:ttgABC, P_tac:PP_1150:PP_1152, ΔrelA, and ΔPP_1430 mutants showed growth improvement in the presence of single compounds, and some also exhibited greater tolerance when grown using a complex chemical mixture representative of a lignin-rich chemical stream. Overall, this work demonstrates the successful implementation of a genome-scale screening tool for the identification of genes influencing stress tolerance against notable compounds within lignin-enriched chemical streams, and the genetic targets identified herein offer promising engineering targets for improving feedstock tolerance in lignin valorization strains of P. putida KT2440.

Tuning a high performing multiplexed-CRISPRi Pseudomonas putida strain to further enhance indigoidine production

In this study, a 14-gene edited Pseudomonas putida KT2440 strain for heterologous indigoidine production was examined using three distinct omic datasets. Transcriptomic data indicated that CRISPR/dCpf1-interference (CRISPRi) mediated multiplex repression caused global gene expression changes, implying potential undesirable changes in metabolic flux. ¹³C-metabolic flux analysis (¹³C-MFA) revealed that the core P. putida flux network after CRISPRi repression was conserved, with moderate reduction of TCA cycle and pyruvate shunt activity along with glyoxylate shunt activation during glucose catabolism. Metabolomic results identified a change in intracellular TCA metabolites and extracellular metabolite secretion profiles (sugars and succinate overflow) in the engineered strains. These omic analyses guided further strain engineering, with a random mutagenesis screen first identifying an optimal ribosome binding site (RBS) for Cpf1 that enabled stronger product-substrate pairing (1.6-fold increase). Then, deletion strains were constructed with excision of the PHA operon (ΔphaAZC-IID) resulting in a 2.2-fold increase in indigoidine titer over the optimized Cpf1-RBS construct at the end of the growth phase (∼6 h). The maximum indigoidine titer (at 72 h) in the ΔphaAZC-IID strain had a 1.5-fold and 1.8-fold increase compared to the optimized Cpf1-RBS construct and the original strain, respectively. Overall, this study demonstrated that integration of omic data types is essential for understanding responses to complex metabolic engineering designs and directly quantified the effect of such modifications on central metabolism.

Experimental and Analytical Approaches for Improving the Resolution of Randomly Barcoded Transposon Insertion Sequencing (RB-TnSeq) Studies

Randomly barcoded transposon insertion sequencing (RB-TnSeq) is an efficient, multiplexed method to determine microbial gene function during growth under a selection condition of interest. This technique applies to growth, tolerance, and persistence studies in a variety of hosts, but the wealth of data generated can complicate the identification of the most critical gene targets. Experimental and analytical methods for improving the resolution of RB-TnSeq are proposed, using Pseudomonas putida KT2440 as an example organism. Several key parameters, such as baseline media selection, substantially influence the determination of gene fitness. We also present options to increase statistical confidence in gene fitness, including increasing the number of biological replicates and passaging the baseline culture in parallel with selection conditions. These considerations provide practitioners with several options to identify genes of importance in TnSeq data sets, thereby streamlining metabolic characterization.

Identification of Pseudomonas asiatica subsp. bavariensis str. JM1 as the first Nε -carboxy(m)ethyllysine degrading soil bacterium

Thermal food processing leads to the formation of advanced glycation end products (AGE) such as N_ε -carboxymethyllysine (CML). Accordingly, these non-canonical amino acids are an important part of the human diet. However, CML is only partially decomposed by our gut microbiota and up to 30% are excreted via feces and, hence, enter the environment. In frame of this study, we isolated a soil bacterium that can grow on CML as well as its higher homologue N_ε -carboxyethyllysine (CEL) as sole source of carbon. Bioinformatic analyses upon whole genome sequencing revealed a subspecies of Pseudomonas asiatica, which we named 'bavariensis'. We performed a metabolite screening of P. asiatica subsp. bavariensis str. JM1 grown either on CML or CEL and identified N-carboxymethylaminopentanoic acid (CM-APA), and N-carboxyethylaminopentanoic acid (CE-APA), respectively. We further detected α-aminoadipate as intermediate in the metabolism of CML. These reaction products suggest two routes of degradation: While CEL seems to be predominantly processed from the α-C-atom, decomposition of CML can also be initiated with cleavage of the carboxymethyl group and under the release of acetate. Thus, our study provides novel insights into the metabolism of two important AGEs and how these are processed by environmental bacteria. This article is protected by copyright. All rights reserved.

Filling gaps in bacterial catabolic pathways with computation and high-throughput genetics

To discover novel catabolic enzymes and transporters, we combined high-throughput genetic data from 29 bacteria with an automated tool to find gaps in their catabolic pathways. GapMind for carbon sources automatically annotates the uptake and catabolism of 62 compounds in bacterial and archaeal genomes. For the compounds that are utilized by the 29 bacteria, we systematically examined the gaps in GapMind's predicted pathways, and we used the mutant fitness data to find additional genes that were involved in their utilization. We identified novel pathways or enzymes for the utilization of glucosamine, citrulline, myo-inositol, lactose, and phenylacetate, and we annotated 299 diverged enzymes and transporters. We also curated 125 proteins from published reports. For the 29 bacteria with genetic data, GapMind finds high-confidence paths for 85% of utilized carbon sources. In diverse bacteria and archaea, 38% of utilized carbon sources have high-confidence paths, which was improved from 27% by incorporating the fitness-based annotations and our curation. GapMind for carbon sources is available as a web server (http://papers.genomics.lbl.gov/carbon) and takes just 30 seconds for the typical genome.

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. IMPORTANCE Understanding the genetic basis of P. putida's 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.

Exceptionally versatile take II: post-translational modifications of lysine and their impact on bacterial physiology

Among the 22 proteinogenic amino acids, lysine sticks out due to its unparalleled chemical diversity of post-translational modifications. This results in a wide range of possibilities to influence protein function and hence modulate cellular physiology. Concomitantly, lysine derivatives form a metabolic reservoir that can confer selective advantages to those organisms that can utilize it. In this review, we provide examples of selected lysine modifications and describe their role in bacterial physiology.

Biosensor-based isolation of amino acid-producing Vibrio natriegens strains

The marine bacterium Vibrio natriegens has recently been demonstrated to be a promising new host for molecular biology and next generation bioprocesses. V. natriegens is a Gram-negative, non-pathogenic slight-halophilic bacterium, with a high nutrient versatility and a reported doubling time of under 10 min. However, V. natriegens is not an established model organism yet, and further research is required to promote its transformation into a microbial workhorse. In this work, the potential of V. natriegens as an amino acid producer was investigated. First, the transcription factor-based biosensor LysG, from Corynebacterium glutamicum, was adapted for expression in V. natriegens to facilitate the detection of positively charged amino acids. A set of different biosensor variants were constructed and characterized, using the expression of a fluorescent protein as sensor output. After random mutagenesis, one of the LysG-based sensors was used to screen for amino acid producer strains. Here, fluorescence-activated cell sorting enabled the selective sorting of highly fluorescent cells, i.e. potential producer cells. Using this approach, individual L-lysine, L-arginine and L-histidine producers could be obtained producing up to 1 mM of the effector amino acid, extracellularly. Genome sequencing of the producer strains provided insight into the amino acid production metabolism of V. natriegens. This work demonstrates the successful expression and application of transcription factor-based biosensors in V. natriegens and provides insight into the underlying physiology, forming a solid basis for further development of this promising microbe.

Deciphering Microbial Metal Toxicity Responses via Random Bar Code Transposon Site Sequencing and Activity-Based Metabolomics

To uncover metal toxicity targets and defense mechanisms of the facultative anaerobe Pantoea sp. strain MT58 (MT58), we used a multiomic strategy combining two global techniques, random bar code transposon site sequencing (RB-TnSeq) and activity-based metabolomics. MT58 is a metal-tolerant Oak Ridge Reservation (ORR) environmental isolate that was enriched in the presence of metals at concentrations measured in contaminated groundwater at an ORR nuclear waste site. The effects of three chemically different metals found at elevated concentrations in the ORR contaminated environment were investigated: the cation Al³⁺, the oxyanion CrO₄^2-, and the oxycation UO₂²⁺. Both global techniques were applied using all three metals under both aerobic and anaerobic conditions to elucidate metal interactions mediated through the activity of metabolites and key genes/proteins. These revealed that Al³⁺ binds intracellular arginine, CrO₄^2- enters the cell through sulfate transporters and oxidizes intracellular reduced thiols, and membrane-bound lipopolysaccharides protect the cell from UO₂²⁺ toxicity. In addition, the Tol outer membrane system contributed to the protection of cellular integrity from the toxic effects of all three metals. Likewise, we found evidence of regulation of lipid content in membranes under metal stress. Individually, RB-TnSeq and metabolomics are powerful tools to explore the impact various stresses have on biological systems. Here, we show that together they can be used synergistically to identify the molecular actors and mechanisms of these pertubations to an organism, furthering our understanding of how living systems interact with their environment. IMPORTANCE Studying microbial interactions with their environment can lead to a deeper understanding of biological molecular mechanisms. In this study, two global techniques, RB-TnSeq and activity metabolomics, were successfully used to probe the interactions between a metal-resistant microorganism, Pantoea sp. strain MT58, and metals contaminating a site where the organism can be located. A number of novel metal-microbe interactions were uncovered, including Al³⁺ toxicity targeting arginine synthesis, which could lead to a deeper understanding of the impact Al³⁺ contamination has on microbial communities as well as its impact on higher-level organisms, including plants for whom Al³⁺ contamination is an issue. Using multiomic approaches like the one described here is a way to further our understanding of microbial interactions and their impacts on the environment overall.

An L-2-hydroxyglutarate biosensor based on specific transcriptional regulator LhgR

l-2-Hydroxyglutarate (l-2-HG) plays important roles in diverse physiological processes, such as carbon starvation response, tumorigenesis, and hypoxic adaptation. Despite its importance and intensively studied metabolism, regulation of l-2-HG metabolism remains poorly understood and none of regulator specifically responded to l-2-HG has been identified. Based on bacterial genomic neighborhood analysis of the gene encoding l-2-HG oxidase (LhgO), LhgR, which represses the transcription of lhgO in Pseudomonas putida W619, is identified in this study. LhgR is demonstrated to recognize l-2-HG as its specific effector molecule, and this allosteric transcription factor is then used as a biorecognition element to construct an l-2-HG-sensing FRET sensor. The l-2-HG sensor is able to conveniently monitor the concentrations of l-2-HG in various biological samples. In addition to bacterial l-2-HG generation during carbon starvation, biological function of the l-2-HG dehydrogenase and hypoxia induced l-2-HG accumulation are also revealed by using the l-2-HG sensor in human cells.

L-2-hydroxyglutarate (L-2-HG) is an important metabolite but its regulation is poorly understood. Here the authors report an L-2-HG FRET biosensor based on the allosteric transcription factor, LhgR, to monitor L-2-HG in cells and biological samples.

Computationally Prospecting Potential Pathways from Lignin Monomers and Dimers toward Aromatic Compounds

The heterogeneity of the aromatic products originating from lignin catalytic depolymerization remains one of the major challenges associated with lignin valorization. Microbes have evolved catabolic pathways that can funnel heterogeneous intermediates to a few central aromatic products. These aromatic compounds can subsequently undergo intra- or extradiol ring opening to produce value-added chemicals. However, such funneling pathways are only partially characterized for a few organisms such as Sphingobium sp. SYK-6 and Pseudomonas putida KT2440. Herein, we apply the de novo pathway design tool (novoStoic) to computationally prospect possible ways of funneling lignin-derived mono- and biaryls. novoStoic employs reaction rules between molecular moieties to hypothesize de novo conversions by flagging known enzymes that carry out the same biotransformation on the most similar substrate. Both reaction rules and known reactions are then deployed by novoStoic to identify a mass-balanced biochemical network that converts a source to a target metabolite while minimizing the number of de novo steps. We demonstrate the application of novoStoic for (i) designing alternative pathways of funneling S, G, and H lignin monomers, and (ii) exploring cleavage pathways of β-1 and β-β dimers. By exploring the uncharted chemical space afforded by enzyme promiscuity, novoStoic can help predict previously unknown native pathways leveraging enzyme promiscuity and propose new carbon/energy efficient lignin funneling pathways with few heterologous enzymes.

Engineering Pseudomonas putida for efficient aromatic conversion to bioproduct using high throughput screening in a bioreactor

Pseudomonas putida KT2440 is an emerging biomanufacturing host amenable for use with renewable carbon streams including aromatics such as para-coumarate. We used a pooled transposon library disrupting nearly all (4,778) non-essential genes to characterize this microbe under common stirred-tank bioreactor parameters with quantitative fitness assays. Assessing differential fitness values by monitoring changes in mutant strain abundance identified 33 gene mutants with improved fitness across multiple stirred-tank bioreactor formats. Twenty-one deletion strains from this subset were reconstructed, including GacA, a regulator, TtgB, an ABC transporter, and PP_0063, a lipid A acyltransferase. Thirteen deletion strains with roles in varying cellular functions were evaluated for conversion of para-coumarate, to a heterologous bioproduct, indigoidine. Several mutants, such as the ΔgacA strain improved fitness in a bioreactor by 35 fold and showed an 8-fold improvement in indigoidine production (4.5 g/L, 0.29 g/g, 23% of maximum theoretical yield) from para-coumarate as the carbon source.

Fermentative Production of l-2-Hydroxyglutarate by Engineered Corynebacterium glutamicum via Pathway Extension of l-Lysine Biosynthesis

l-2-hydroxyglutarate (l-2HG) is a trifunctional building block and highly attractive for the chemical and pharmaceutical industries. The natural l-lysine biosynthesis pathway of the amino acid producer Corynebacterium glutamicum was extended for the fermentative production of l-2HG. Since l-2HG is not native to the metabolism of C. glutamicum metabolic engineering of a genome-streamlined l-lysine overproducing strain was required to enable the conversion of l-lysine to l-2HG in a six-step synthetic pathway. To this end, l-lysine decarboxylase was cascaded with two transamination reactions, two NAD(P)-dependent oxidation reactions and the terminal 2-oxoglutarate-dependent glutarate hydroxylase. Of three sources for glutarate hydroxylase the metalloenzyme CsiD from Pseudomonas putida supported l-2HG production to the highest titers. Genetic experiments suggested a role of succinate exporter SucE for export of l-2HG and improving expression of its gene by chromosomal exchange of its native promoter improved l-2HG production. The availability of Fe²⁺ as cofactor of CsiD was identified as a major bottleneck in the conversion of glutarate to l-2HG. As consequence of strain engineering and media adaptation product titers of 34 ± 0 mM were obtained in a microcultivation system. The glucose-based process was stable in 2 L bioreactor cultivations and a l-2HG titer of 3.5 g L⁻¹ was obtained at the higher of two tested aeration levels. Production of l-2HG from a sidestream of the starch industry as renewable substrate was demonstrated. To the best of our knowledge, this study is the first description of fermentative production of l-2HG, a monomeric precursor used in electrochromic polyamides, to cross-link polyamides or to increase their biodegradability.

Biotin, a universal and essential cofactor: Synthesis, ligation and regulation

Biotin is a covalently attached enzyme cofactor required for intermediary metabolism in all three domains of life. Several important human pathogens (e.g. Mycobacterium tuberculosis) require biotin synthesis for pathogenesis. Humans lack a biotin synthetic pathway hence bacterial biotin synthesis is a prime target for new therapeutic agents. The biotin synthetic pathway is readily divided into early and late segments. Although pimelate, a seven carbon α,ω-dicarboxylic acid that contributes seven of the ten biotin carbons atoms, was long known to be a biotin precursor, its biosynthetic pathway was a mystery until the E. coli pathway was discovered in 2010. Since then, diverse bacteria encode evolutionarily distinct enzymes that replace enzymes in the E. coli pathway. Two new bacterial pimelate synthesis pathways have been elucidated. In contrast to the early pathway the late pathway, assembly of the fused rings of the cofactor, was long thought settled. However, a new enzyme that bypasses a canonical enzyme was recently discovered as well as homologs of another canonical enzyme that functions in synthesis of another protein-bound coenzyme, lipoic acid. Most bacteria tightly regulate transcription of the biotin synthetic genes in a biotin-responsive manner. The bifunctional biotin ligases which catalyze attachment of biotin to its cognate enzymes and repress biotin gene transcription are best understood regulatory system.

α-proteobacteria synthesize biotin precursor pimeloyl-ACP using BioZ 3-ketoacyl-ACP synthase and lysine catabolism

Pimelic acid, a seven carbon α,ω-dicarboxylic acid (heptanedioic acid), is known to provide seven of the ten biotin carbon atoms including all those of the valeryl side chain. Distinct pimelate synthesis pathways were recently elucidated in Escherichia coli and Bacillus subtilis where fatty acid synthesis plus dedicated biotin enzymes produce the pimelate moiety. In contrast, the α-proteobacteria which include important plant and mammalian pathogens plus plant symbionts, lack all of the known pimelate synthesis genes and instead encode bioZ genes. Here we report a pathway in which BioZ proteins catalyze a 3-ketoacyl-acyl carrier protein (ACP) synthase III-like reaction to produce pimeloyl-ACP with five of the seven pimelate carbon atoms being derived from glutaryl-CoA, an intermediate in lysine degradation. Agrobacterium tumefaciens strains either deleted for bioZ or which encode a BioZ active site mutant are biotin auxotrophs, as are strains defective in CaiB which catalyzes glutaryl-CoA synthesis from glutarate and succinyl-CoA.

Genome-scale metabolic rewiring improves titers rates and yields of the non-native product indigoidine at scale

High titer, rate, yield (TRY), and scalability are challenging metrics to achieve due to trade-offs between carbon use for growth and production. To achieve these metrics, we take the minimal cut set (MCS) approach that predicts metabolic reactions for elimination to couple metabolite production strongly with growth. We compute MCS solution-sets for a non-native product indigoidine, a sustainable pigment, in Pseudomonas putida KT2440, an emerging industrial microbe. From the 63 solution-sets, our omics guided process identifies one experimentally feasible solution requiring 14 simultaneous reaction interventions. We implement a total of 14 genes knockdowns using multiplex-CRISPRi. MCS-based solution shifts production from stationary to exponential phase. We achieve 25.6 g/L, 0.22 g/l/h, and ~50% maximum theoretical yield (0.33 g indigoidine/g glucose). These phenotypes are maintained from batch to fed-batch mode, and across scales (100-ml shake flasks, 250-ml ambr®, and 2-L bioreactors).

Fatty Acid and Alcohol Metabolism in Pseudomonas putida: Functional Analysis Using Random Barcode Transposon Sequencing

With 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 the organism, many gaps remain in our knowledge of the genetic basis of its metabolic capabilities. The gaps are particularly noticeable in our understanding of both fatty acid and alcohol catabolism, where many paralogs putatively coding for similar enzymes coexist, 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-Tn-Seq). 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 fatty acids of varying chain lengths 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 coenzyme A (CoA). Because fatty acids and alcohols may serve as both feedstocks and final products of metabolic-engineering efforts, the fitness data presented here will help guide future genomic modifications toward 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 the bacterium. These data provide a framework facilitating precise genetic changes to prevent product degradation and to channel the flux of specific pathway intermediates as desired.

Investigation of Bar-seq as a method to study population dynamics of Saccharomyces cerevisiae deletion library during bioreactor cultivation

Background

Despite the latest advancements in metabolic engineering for genome editing and characterization of host performance, the successful development of robust cell factories used for industrial bioprocesses and accurate prediction of the behavior of microbial systems, especially when shifting from laboratory-scale to industrial conditions, remains challenging. To increase the probability of success of a scale-up process, data obtained from thoroughly performed studies mirroring cellular responses to typical large-scale stimuli may be used to derive crucial information to better understand potential implications of large-scale cultivation on strain performance. This study assesses the feasibility to employ a barcoded yeast deletion library to assess genome-wide strain fitness across a simulated industrial fermentation regime and aims to understand the genetic basis of changes in strain physiology during industrial fermentation, and the corresponding roles these genes play in strain performance.

Results

We find that mutant population diversity is maintained through multiple seed trains, enabling large scale fermentation selective pressures to act upon the community. We identify specific deletion mutants that were enriched in all processes tested in this study, independent of the cultivation conditions, which include MCK1, RIM11, MRK1, and YGK3 that all encode homologues of mammalian glycogen synthase kinase 3 (GSK-3). Ecological analysis of beta diversity between all samples revealed significant population divergence over time and showed feed specific consequences of population structure. Further, we show that significant changes in the population diversity during fed-batch cultivations reflect the presence of significant stresses. Our observations indicate that, for this yeast deletion collection, the selection of the feeding scheme which affects the accumulation of the fermentative by-product ethanol impacts the diversity of the mutant pool to a higher degree as compared to the pH of the culture broth. 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.

Conclusions

Our results demonstrate the feasibility of Bar-seq to assess fermentation associated stresses in yeast populations under industrial conditions and to understand critical stages of a scale-up process where variability emerges, and selection pressure gets imposed. Overall our work highlights a promising avenue to identify genetic loci and biological stress responses required for fitness under industrial conditions.

Isocitrate dehydrogenase variants in cancer - Cellular consequences and therapeutic opportunities

Abnormal metabolism is common in cancer cells and often correlates with mutations in genes encoding for enzymes involved in small-molecule metabolism. Isocitrate dehydrogenase 1 (IDH1) is the most frequently mutated metabolic gene in cancer. Cancer-associated substitutions in IDH1 and IDH2 impair wild-type production of 2-oxoglutarate and reduced nicotinamide adenine dinucleotide phosphate (NADPH) from isocitrate and oxidised nicotinamide adenine dinucleotide phosphate (NADP⁺ ), and substantially promote the IDH variant catalysed conversion of 2-oxoglutarate to d-2-hydroxyglutarate (d-2HG). Elevated d-2HG is a biomarker for some cancers, and inhibition of IDH1 and IDH2 variants is being pursued as a medicinal chemistry target. We provide an overview of the types of cancer-associated IDH variants, discuss some of the proposed consequences of altered metabolism as a result of elevated d-2HG, summarise therapeutic efforts targeting IDH variants and identify areas for future research.

You get what you screen for: on the value of fermentation characterization in high-throughput strain improvements in industrial settings

While design and high-throughput build approaches in biotechnology have increasingly gained attention over the past decade, approaches to test strain performance in high-throughput have received less discussion in the literature. Here, we describe how fermentation characterization can be used to improve the overall efficiency of high-throughput DBTAL (design-build-test-analyze-learn) cycles in an industrial context. Fermentation characterization comprises an in-depth study of strain performance in a bioreactor setting and involves semi-frequent sampling and analytical measurement of substrates, cell densities and viabilities, and (by)products. We describe how fermentation characterization can be used to (1) improve (high-throughput) strain design approaches; (2) enable the development of bench-scale fermentation processes compatible with a wide diversity of strains; and (3) inform the development of high-throughput plate-based strain testing procedures for improved performance at larger scales.

Iron Supplementation Eliminates Antagonistic Interactions Between Root-Associated Bacteria

The rhizosphere microbiome (rhizobiome) plays a critical role in plant health and development. However, the processes by which the constituent microbes interact to form and maintain a community are not well understood. To investigate these molecular processes, we examined pairwise interactions between 11 different microbial isolates under select nutrient-rich and nutrient-limited conditions. We observed that when grown with media supplemented with 56 mM glucose, two microbial isolates were able to inhibit the growth of six other microbes. The interaction between microbes persisted even after the antagonistic microbe was removed, upon exposure to spent media. To probe the genetic basis for these antagonistic interactions, we used a barcoded transposon library in a proxy bacterium, Pseudomonas putida, to identify genes which showed enhanced sensitivity to the antagonistic factor(s) secreted by Acinetobacter sp. 02. Iron metabolism-related gene clusters in P. putida were implicated by this systems-level analysis. The supplementation of iron prevented the antagonistic interaction in the original microbial pair, supporting the hypothesis that iron limitation drives antagonistic microbial interactions between rhizobionts. We conclude that rhizobiome community composition is influenced by competition for limiting nutrients, with implications for growth and development of the plant.

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

Despite intensive study, plant lysine catabolism beyond the 2-oxoadipate (2OA) intermediate remains unvalidated. Recently we described a missing step in the D-lysine catabolism of Pseudomonas putida in which 2OA is converted to D-2-hydroxyglutarate (2HG) via hydroxyglutarate synthase (HglS), a DUF1338 family protein. Here we solve the structure of HglS to 1.1 Å resolution in substrate-free form and in complex with 2OA. We propose a successive decarboxylation and intramolecular hydroxylation mechanism forming 2HG in a Fe(II)- and O₂-dependent manner. Specificity is mediated by a single arginine, highly conserved across most DUF1338 proteins. An Arabidopsis thaliana HglS homolog coexpresses with known lysine catabolism enzymes, and mutants show phenotypes consistent with disrupted lysine catabolism. Structural and biochemical analysis of Oryza sativa homolog FLO7 reveals identical activity to HglS despite low sequence identity. Our results suggest DUF1338-containing enzymes catalyze the same biochemical reaction, exerting the same physiological function across bacteria and eukaryotes.

Hydroxyglutarate synthase (HglS) converts 2-oxoadipate to D-2- hydroxyglutarate during lysine catabolism in bacteria. Here the authors use structural and biochemical approaches to show that HglS acts via successive decarboxylation and intramolecular hydroxylation and that homologous enzymes catalyze the final step of lysine catabolism in plants.

Leveraging host metabolism for bisdemethoxycurcumin production in Pseudomonas putida

Pseudomonas putida is a saprophytic bacterium with robust metabolisms and strong solvent tolerance making it an attractive host for metabolic engineering and bioremediation. Due to its diverse carbon metabolisms, its genome encodes an array of proteins and enzymes that can be readily applied to produce valuable products. In this work we sought to identify design principles and bottlenecks in the production of type III polyketide synthase (T3PKS)-derived compounds in P. putida. T3PKS products are widely used as nutraceuticals and medicines and often require aromatic starter units, such as coumaroyl-CoA, which is also an intermediate in the native coumarate catabolic pathway of P. putida. Using a randomly barcoded transposon mutant (RB-TnSeq) library, we assayed gene functions for a large portion of aromatic catabolism, confirmed known pathways, and proposed new annotations for two aromatic transporters. The 1,3,6,8-tetrahydroxynapthalene synthase of Streptomyces coelicolor (RppA), a microbial T3PKS, was then used to rapidly assay growth conditions for increased T3PKS product accumulation. The feruloyl/coumaroyl CoA synthetase (Fcs) of P. putida was used to supply coumaroyl-CoA for the curcuminoid synthase (CUS) of Oryza sativa, a plant T3PKS. We identified that accumulation of coumaroyl-CoA in this pathway results in extended growth lag times in P. putida. Deletion of the second step in coumarate catabolism, the enoyl-CoA hydratase-lyase (Ech), resulted in increased production of the type III polyketide bisdemethoxycurcumin.

Identification, Characterization, and Application of a Highly Sensitive Lactam Biosensor from Pseudomonas putida

Caprolactam is an important polymer precursor to nylon traditionally derived from petroleum and produced on a scale of 5 million tons per year. Current biological pathways for the production of caprolactam are inefficient with titers not exceeding 2 mg/L, necessitating novel pathways for its production. As development of novel metabolic routes often require thousands of designs and result in low product titers, a highly sensitive biosensor for the final product has the potential to rapidly speed up development times. Here we report a highly sensitive biosensor for valerolactam and caprolactam from Pseudomonas putida KT2440 which is >1000× more sensitive to an exogenous ligand than previously reported sensors. Manipulating the expression of the sensor oplR (PP_3516) substantially altered the sensing parameters, with various vectors showing K_d values ranging from 700 nM (79.1 μg/L) to 1.2 mM (135.6 mg/L). Our most sensitive construct was able to detect in vivo production of caprolactam above background at ∼6 μg/L. The high sensitivity and range of OplR is a powerful tool toward the development of novel routes to the biological synthesis of caprolactam.

Catabolism of biogenic amines in Pseudomonas species

Biogenic amines (BAs; 2-phenylethylamine, tyramine, dopamine, epinephrine, norepinephrine, octopamine, histamine, tryptamine, serotonin, agmatine, cadaverine, putrescine, spermidine, spermine and certain aliphatic amines) are widely distributed organic molecules that play basic physiological functions in animals, plants and microorganisms. Pseudomonas species can grow in media containing different BAs as carbon and energy sources, a reason why these bacteria are excellent models for studying such catabolic pathways. In this review, we analyse most of the routes used by different species of Pseudomonas (P. putida, P. aeruginosa, P. entomophila and P. fluorescens) to degrade BAs. Analysis of these pathways has led to the identification of a huge number of genes, catabolic enzymes, transport systems and regulators, as well as to understanding of their hierarchy and functional evolution. Knowledge of these pathways has allowed the design and collection of genetically manipulated microbes useful for eliminating BAs from different sources, highlighting the biotechnological applications of these studies.

Omics-driven identification and elimination of valerolactam catabolism in Pseudomonas putida KT2440 for increased product titer

Pseudomonas putida is a promising bacterial chassis for metabolic engineering given its ability to metabolize a wide array of carbon sources, especially aromatic compounds derived from lignin. However, this omnivorous metabolism can also be a hindrance when it can naturally metabolize products produced from engineered pathways. Herein we show that P. putida is able to use valerolactam as a sole carbon source, as well as degrade caprolactam. Lactams represent important nylon precursors, and are produced in quantities exceeding one million tons per year (Zhang et al., 2017). To better understand this metabolism we use a combination of Random Barcode Transposon Sequencing (RB-TnSeq) and shotgun proteomics to identify the oplBA locus as the likely responsible amide hydrolase that initiates valerolactam catabolism. Deletion of the oplBA genes prevented P. putida from growing on valerolactam, prevented the degradation of valerolactam in rich media, and dramatically reduced caprolactam degradation under the same conditions. Deletion of oplBA, as well as pathways that compete for precursors L-lysine or 5-aminovalerate, increased the titer of valerolactam from undetectable after 48 h of production to ~90 mg/L. This work may serve as a template to rapidly eliminate undesirable metabolism in non-model hosts in future metabolic engineering efforts.

High-quality genome-scale metabolic modelling of Pseudomonas putida highlights its broad metabolic capabilities

Genome-scale reconstructions of metabolism are computational species-specific knowledge bases able to compute systemic metabolic properties. We present a comprehensive and validated reconstruction of the biotechnologically relevant bacterium Pseudomonas putida KT2440 that greatly expands computable predictions of its metabolic states. The reconstruction represents a significant reactome expansion over available reconstructed bacterial metabolic networks. Specifically, iJN1462 (i) incorporates several hundred additional genes and associated reactions resulting in new predictive capabilities, including new nutrients supporting growth; (ii) was validated by in vivo growth screens that included previously untested carbon (48) and nitrogen (41) sources; (iii) yielded gene essentiality predictions showing large accuracy when compared with a knock-out library and Bar-seq data; and (iv) allowed mapping of its network to 82 P. putida sequenced strains revealing functional core that reflect the large metabolic versatility of this species, including aromatic compounds derived from lignin. Thus, this study provides a thoroughly updated metabolic reconstruction and new computable phenotypes for P. putida, which can be leveraged as a first step toward understanding the pan metabolic capabilities of Pseudomonas.

Robust Characterization of Two Distinct Glutarate Sensing Transcription Factors of Pseudomonas putida l-Lysine Metabolism

A significant bottleneck in synthetic biology involves screening large genetically encoded libraries for desirable phenotypes such as chemical production. However, transcription factor-based biosensors can be leveraged to screen thousands of genetic designs for optimal chemical production in engineered microbes. In this study we characterize two glutarate sensing transcription factors (CsiR and GcdR) from Pseudomonas putida. The genomic contexts of csiR homologues were analyzed, and their DNA binding sites were bioinformatically predicted. Both CsiR and GcdR were purified and shown to bind upstream of their coding sequencing in vitro. CsiR was shown to dissociate from DNA in vitro when exogenous glutarate was added, confirming that it acts as a genetic repressor. Both transcription factors and cognate promoters were then cloned into broad host range vectors to create two glutarate biosensors. Their respective sensing performance features were characterized, and more sensitive derivatives of the GcdR biosensor were created by manipulating the expression of the transcription factor. Sensor vectors were then reintroduced into P. putida and evaluated for their ability to respond to glutarate and various lysine metabolites. Additionally, we developed a novel mathematical approach to describe the usable range of detection for genetically encoded biosensors, which may be broadly useful in future efforts to better characterize biosensor performance.

Glutarate L-2-hydroxylase (CsiD/GlaH) is an archetype Fe(II)/2-oxoglutarate-dependent dioxygenase

The Escherichia coli gene initially named ygaT is located adjacent to lhgO, encoding L-2-hydroxyglutarate oxidase/dehydrogenase, and the gabDTP gene cluster, utilized for γ-aminobutyric acid (GABA) metabolism. Because this gene is transcribed specifically during periods of carbon starvation, it was renamed csiD for carbon starvation induced. The CsiD protein was structurally characterized and shown to possess a double-stranded ß-helix fold, characteristic of a large family of non-heme Fe(II)- and 2-oxoglutarate (2OG)-dependent oxygenases. Consistent with a role in producing the substrate for LhgO, CsiD was shown to be a glutarate L-2-hydroxylase. We review the kinetic and structural properties of glutarate L-2-hydroxylase from E. coli and other species, and we propose a catalytic mechanism for this archetype 2OG-dependent hydroxylase. Glutarate can be derived from l-lysine within the cell, with the gabDT genes exhibiting expanded reactivities beyond those known for GABA metabolism. The complete CsiD-containing pathway provides a means for the cell to obtain energy from the metabolism of l-lysine during periods of carbon starvation. To reflect the role of this protein in the cell, a renaming of csiD to glaH has been proposed.