Fungal communities (mycobiome) have an important role in sustaining the resilience of complex microbial communities and maintenance of homeostasis. The mycobiome remains relatively unexplored compared to the bacteriome despite increasing evidence highlighting their contribution to host-microbiome interactions in health and disease. Despite being a small proportion of the total species, fungi constitute a large proportion of the biomass within the human microbiome and thus serve as a potential target for metabolic reprogramming in pathogenesis and disease mechanism. Metabolites produced by fungi shape host niches, induce immune tolerance and changes in their levels prelude changes associated with metabolic diseases and cancer. Given the complexity of microbial interactions, studying the metabolic interplay of the mycobiome with both host and microbiome is a demanding but crucial task. However, genome-scale modelling and synthetic biology can provide an integrative platform that allows elucidation of the multifaceted interactions between mycobiome, microbiome and host. The inferences gained from understanding mycobiome interplay with other organisms can delineate the key role of the mycobiome in pathophysiology and reveal its role in human disease. In contrast, the mycobiome  study remains in its formative years.4–6 To date, there are substantially fewer studies focused on fungi within the microbiome due to difficulties in nucleic acid extraction, rudimentary fungal genome annotations and limited bioinformatic tools to analyse these data or established standards. Early studies using culture-dependent studies suggested that only 70% of the population harboured a fungal population.7 A total of 267 fungal taxa have been identified in the human gut mycobiome by several studies. However, the validity of some of these fungal species (e.g. Penicillium) is unclear as they cannot be grown under human gut conditions in the laboratory.8 The long-term residence of fungi in the gut microbiome is thus still the subject of debate as the longevity of fungal species within a mycobiome is undetermined.8 Further, fungal genes within the microbial metagenome have been estimated to only make up 0.1% of the total microbial genes.9 However, the importance of fungi to the host cannot be determined by measures of the overall relative abundance of their genetic material within the microbiome. Production of metabolites and small molecules by fungi potentially has a more significant impact in the microbiota and host compared to bacterial production, owing to the greater fungal biomass and associated higher occupation of surface area, however, this remains largely unproven.9–11 A summary table of mycobiome studies associated with dysbiosis highlights the increasing relevance of fungi  tract is a contributing factor to IBD onset.31–36 Further, a shift in Ascomycota and Basidiomycota proportions potentially drives inflammatory processes in IBD.37 The distal effect of mycobiome dysbiosis is chronic immune-mediated inflammation and appears to be related to central nervous system (CNS) infection associated with neurological diseases.38–40 Fungal products have been found in Alzheimer patients,41 whilst another study confirmed the presence of 14 fungal species, including Malassezia, Candida, Alternaria and Botrytis,42 in different brain regions of Alzheimer’s patients.43 Similarly, fungi have been associated as triggers of autoimmunity and multiple sclerosis,44 whilst the presence of C. albicans and S. cerevisiae antibodies in schizophrenia patients has been linked to the gastrointestinal disorder, supporting the hypothesis of a gut-brain axis.45Fungal dysbiosis potentially affects a significant portion of the population, being implicated in irritable bowel syndrome (IBS) and Crohn’s disease (CD) and a direct contributing factor in IBD30 see . For exa40 For example, the mycobiome is associated with cancer development and progression,46 where Malassezia spp. are increased in pancreatic ductal adenocarcinoma (PDA) in an in vivo (murine) study.46 In contrast, Candida tropicalis, another commensal fungus, is associated with slower or lack of tumour development.46 This study, among others, indicates that mycobiome composition is paramount to host health and disease status. However, specific fungal species do not exclusively drive these interactions. In order to survive, fungi adapt to their local environment; this includes adjusting to acidic environments through morphology switching and metabolic plasticity. Biofilms are a classic example of fungi changing their growth pattern in response to host responses and environmental cues. Fungi-microbe interactions play a key role in adaptation. Fungi cooperate with bacteria to increase fungal biofilm potential, resulting in significant increases in the severity of inflammatory diseases – most notably in IBD.37 Alternatively, Pichia spp. interaction with Candida results in the reduction of biofilm due to competition in the environment.47 Thus, mycobiome composition plays a significant role in the maintenance of the homeostatic relationship within the microbiota. Determining whether fungal-bacterial-host relationships drive compositional changes and whether controlling each element in these networks could potentially prevent disease is a fundamental goal for future microbiome research.Mycobiome dysbiosis is a predisposing factor in other diseases as well.48 or modulate immunological responses.49 These host immune responses to fungal infection have been reviewed extensively elsewhere.50–54 To creates symbiotic relationship with microbiota for host immune-tolerance, there is a positive exchange of compounds and nutrients. With evidence of fungi producing vitamins and fatty acids,55–57 there is an opportunity and potential for these exchanges with the mycobiome that remains to be investigated.The interplay between the mycobiome and host immune responses involves physical interactions at the cell surface, metabolic reprogramming, regulatory pathways, and secretion of small molecules. Exposure to host responses and environmental stressors provides a platform for metabolic plasticity to manipulate host interactions and promote invasion58–60 For instance, short-chain fatty acids (SCFA), tryptophan and branched-chain amino acids have all been identified as playing a role in immune and homeostatic responses.61 Short-chain fatty acids (SCFAs) available within the host are necessary for regulating some fungal growth as well as modulating immune responses. Fungi are capable of producing vitamins, notably including vitamin D and B6, that can impact the immune system.62–66 The different fungal moieties, such as cell wall polysaccharides and melanin,67 drive metabolic reprogramming of innate immune cells to confer protective inflammation and activate antimicrobial defences.68–70 Equally, contact with host cells drives shifts in fungal metabolism.52 For instance, host recognition of ß-glucan from fungal cell walls reprogrammes macrophage glucose metabolism towards aerobic glycolysis, promoting more rapid ATP creation.67 Importantly, however, C. albicans undergoes a similar metabolic reprogramming, meaning that local glucose is rapidly depleted, leading to the premature death of macrophages.52 As C. albicans has a significant degree of metabolic plasticity, it can then revert to using alternative carbon sources to survive. Melanin produced by Aspergillus fumigatus is recognised by macrophages via MelLec (a C-type melanin-sensing receptor) via calcium sequestering in phagosomes, leading to activation of antifungal defences within the host.71A. fumigatus responds further by reducing melanin for fungal persistence, leading to conidia germination and increased glycolysis allows metabolic repurposing of macrophage response for effective fungal defence.70 Hosts defensive metabolic responses and the parallel evasive mechanistic pathways in fungi demonstrate a need to understand metabolic networks in host mycobiome in health and disease.52Microbial metabolism, including fungal metabolism, has been associated with driving inflammation, various diseases and even cancer progression.in vivo data set sources is listed in 72–74 The interaction of fungi and bacteria occurs on several different levels. Physical contact can occur between members of microbial communities leading to the exchange of secreted products and communication between the two cell surfaces. Cell signalling between microbes also provides regulation and trade of metabolites and genetic materials through different mechanisms, such as horizontal gene transfer, signal transduction and interaction with the host.76 This exchange allows for an interplay between microbes that can maintain the ecosystem’s resilience or drive a dysbiosis state. This interplay within the host means there is a knock-on effect for the host, potentially resulting in disease pathology. For example, C. tropicalis is significantly more abundant in Crohn’s disease (CD) patients in comparison to non-CD relatives.37 This increased abundance correlates with increased abundance of some bacterial species and related improved biofilm formation and disease severity. These interactions can potentially be bi-directional. For example, Escherichia coli, Serratia marcescens, and Candida tropicalis together exhibit enhanced biofilm formation, giving a thicker, more robust film.37 Furthermore, the production of bile acids (e.g. cholic acid) from the microbiota and environment have been shown to promote the growth and morphogenetic plasticity of C. albicans,77 whilst hexadecanedioic and caproic acids have demonstrated antifungal activity.29In a series of animal model studies, the interaction between host-mycobiome and fungus-bacterium has been linked to the health status of the host  affect both confirmation and community structure of the mycobiome, potentially leading to dysbiosis.Alteration of the bacteriome composition drives diverse changes in metabolite levels in the gut and effects on the mycobiome. For example, antibiotic-treated mice have increased levels of 29 The natural ability of fungi to produce this cornucopia leads to ripple effects in their environment.97 The metabolic activity of fungal communities has a pivotal role in microbiome modulation, contributing to disease pathogenesis and pathology. Fungi can catabolise a panoply of different substrates, including carbohydrates, amino acids, lipids, proteins, and vitamins utilising both primary and secondary metabolism. Primary metabolism is involved in the growth and development of the cell, whilst secondary metabolism increases fungal viability in the environment. Fungi produce a variety of secondary metabolites, including acids, toxins, polyols and sugars that mediate cross-talk and are considered a by-product of other reactions.98Fungi produce an expansive variety of metabolites with varied impacts on their survival, as well as host and microbiota interactions. For example, the fungal metabolite N-acetyl-L-glutamic acid has hypotensive effects.99–102 Polyketide (PKS) class consists of derivatives of fatty acids and the output of acetyl CoA pathways that allow for various structures and functions.103 Non-ribosomal peptides (NRPS) are derived from amino acids and cannot perform RNA transcription.104 Terpenes class are generated from terpene synthases, a product of the mevalonic acid pathway and produced as steroid compounds.105 Terpenoids are a modified class of terpenes. Dimethyallyl tryptophan (DMATs) class is comparable to the ergot alkaloid class and are usually pharmacologically active compounds responsible for producing indoles.106–108 As the detection of growing numbers of metabolites progresses, methods in finding genes responsible for secondary metabolites are also being developed. These genes are called biosynthetic gene clusters (BGCs) and are based on the chromosomal arrangement of the synthesis of secondary metabolites. These BGCs ranges from smaller clusters relating to synthases to a larger cluster of genes that include transcription factors, as detailed in Keller et al. (2019). These genes are globally regulated by transcriptional activity, not all necessarily from within the cluster, thus indicating that transcription and epigenetic activation of BGCs is dependent on external factors. These factors include environmental stimuli, metal presence, nutritional availability, temperature, pH and light, triggered by regulatory pathways.76Secondary metabolites are key by-products of primary reactions and play a crucial role in shaping the interaction within a niche. In the elucidation of enzymatic processes, fungal secondary metabolites have been classed into several groups depending on the biosynthesis of these products. The most notable involves polyketide synthases (PKS), non-ribosomal peptides synthases (NRPS), fatty acid derivatives, terpenoids, and steroids.Candida, Penicillium and Aspergillus species and their possible metabolic pathways have been illustrated in The mycobiome includes metabolic processes with an extensive pool of secondary metabolite production that can be key in driving or preventing a pathogenic switch. For instance, metabolites and small molecules can activate the host immune system via recognition of foreign material or the release of toxins, driving inflammatory responses. Potential effects of known metabolites produced by Aspergillus species is naptho-γ-pyrones. This natural metabolite has various biological effects, including anti-bacterial and anti-oxidant activity.120–124 The wide variety of fungal metabolite functions makes them ideal targets for therapeutic use. This can be done by targeting metabolic pathways to derive benefits. For example, by driving immunometabolic shifts that affect glucose competition and redirecting pathways for cancer treatment.125 As well as direct impacts on the host whether as colonisers or during infection, these metabolites are ideal potential drugs for treating the microbiome directly. For example Penicillium produces a mycotoxin called citrinin that has antibiotic properties against bacteria and can have a toxic effect on kidney and liver.126–128 Secondary metabolites can thus conceivably both provide diagnostic markers for the detection of disease and manipulate metabolic pathways, altering host health status. System biology contributes useful tools in investigating metabolic pathways in fungi in the context of the human mycobiome.Secondary metabolites can have a knock-on effect on host metabolism, and this has been used extensively for therapeutic effects . One met4 The development of molecular techniques such as 18S ribosomal RNA and inter transcribed spacer (ITS) sequencing has profoundly impacted the detection of fungi and the characterisation of the mycobiome. However, these techniques are limited in understanding the mycobiome community’s overall structure, function, and dynamic existence on a large-scale platform.129 Many mycologists face the obstacle of limited bioinformatic tools and databases available compared to bacteriologists. With improving next generation sequencing (NGS) technologies and lower costs, multi-omics data generation is increasing rapidly. Multi-omics data includes metagenomics, transcriptomics, proteomics, metabolomics and fluxomics that impart in-depth information that has enabled us to learn about microbiota interactions  has proven to enable the genotype-phenotype relationships through a series of connected gene-protein-reaction links, providing a powerful predictive toolkit.135 GSMM have been applied in different microbial and human studies as a discovery platform to propose novel markers, treatments and bioproduction enhancement.138–141 GSMM predictions and discovery can be further evaluated and validated using synthetic biology, such as CRISPR-cas systems  for genome editing to test and manipulate molecular and metabolic pathways.142To date, mycobiome studies have primarily been grounded in culture-based techniques that are hostage to the diverse requirements of each community member, meaning many fungi remain undetected and undiscovered.ractions .130–132 S. cerevisiae model is the benchmarking fungal GSMM with a comprehensive network structure including the 3D structure and protein kinetics.143 This GSMM is a reference model used to generate the scaffold of other fungal species models.30 Other well-constructed fungal reference models that have been generated include Scheffersomyces stipitis,144C. glabrata146 and Pichia pastoris.147 Fungal GSMM are mainly used to inform the metabolic engineering experiments, increase the efficiency of the process and markers, such as modelling yeast suggested methods for increasing vanillin production by 5-fold.148 Similarly, the model’s biological interpretation of Yarrowia lipolytica metabolic pathways identified that this fungal species is an ideal producer of di-carboxylic acid using a lipid pathway.149The 139 This has led to a recent increase in studies predicting biological behaviour, the use of biotics for metabolic diseases, personalised medicine, and drug targets for pathogens.149–154 Modelling has implicated the production of SCFAs from microbial fermentation in the host in CD aetiology; thus, adjusting diet can control the presence of SCFAs in patients and modulate disease.155 Exploring IBD compared to healthy cohorts showed an increase in vitamin metabolism in IBD patients, whilst a closer inspection of species in the microbiome using GSMMs unveiled vitamin and biotin producing gut organisms in those suffering from IBD,156 whilst using modelling of the microbiome in neurological diseases including Parkinson’s Disease157–160 and Alzheimer’s disease162 has identified microbiome perturbations, causing flux changes in pathways and production of SCFAs that ultimately potentially cause disease. Studies on community modelling of the microbiome predict metabolic interactions between species with the overall metabolic contribution of each species determined.163–165 Research in this area remains in its infancy as the development of algorithms and tools used to explore community modelling continues, but the potential is beyond doubt.The application of GSMM technologies to the human microbiome has identified cross-feeding of metabolites between host and microbiota in health and disease.C. albicans have been used to investigate its regulatory system in the clinical context.166As there are several available fungal GSMM, they could be implemented in studying the mycobiome to elucidate the complex interactions between mycobiome, bacteriome and host, thereby explaining the metabolic role of fungi in disease pathophysiology. Exploring the mycobiome with GSMM will significantly improve our understanding of fungal interactions with the host in health and disease. Synthetic biology can then be used to validate and assess genetic circuits and metabolic frameworks for complex interactions. Recently, CRISPR-based systems for repression and activation in 167The difficulties lie in exploiting fungal models and synthetic biology due to annotation accuracy with high plasticity of metabolic pathways, diverse morphology, and evolutionary adaptive traits. There is a lagging development of fungal GSMM and applying CRISPR system in clinical and healthcare settings due to a lack of quantitative tools to study fungal genomes a lack of annotation and validation in the model. The global overview provided by this approach offers the potential to drive strategies to manipulate the mycobiome to make predictions, test hypotheses, propose clinical areas for manipulation, identify new diagnostics target areas with better understanding, and discover novel therapeutic targets.in silico models as a predictive tool to validate current datasets and knowledge. In doing so, we will generate a platform to answer how mycobiome metabolism affects the host in health and disease.Understanding the behaviour of fungi, both individually and as a community, is essential in elucidating the role of the mycobiome in health and disease. Untethering the distinct metabolic interactions and effects of secondary metabolism provide an opportunity for disease intervention, determining the point at which dysbiosis arises, identifying biomarkers for better diagnostics and novel therapeutic targets. Metabolism is a holistic and integrative subject that combines genetics, molecular pathways, signalling and environmental factors. Multi-disciplinary tools and methods such as system biology are needed to develop in silico while sustaining full metabolic flux functionality of the system. The functionality of a model refers to the natural ability of the model to undertake reactions, a realistic rate of energy consumption, rate of energy production and apply physio-chemical laws and environmental input to create a system that is true-to-life.168 The conversion of the reactions into a stoichiometric matrix allows mathematical inferences to integrate data into a predictive biological framework called constraint-based modelling.169 This developmental process of integration requires automated and manual curation for efficient quality standards. The GSMM community has developed a MEMOTE suite package to ensure the models’ standardisation and functional feasibility.170 With synthetic biology approach of genome engineering techniques pave the way for validating fungal species’ biological mechanisms underlying these systematic alterations in response to different environmental changes such as response to anti-fungal drug and the host organism’s reaction during fungal infection .142Genome-scale metabolic models (GSMM) are a systematic and curated method to establish genotype-phenotype relationships. GSMM aim to bridge together the complex network of genes, reactions and thousands of metabolites 171 The current approach to reconstructing GSMM combines top-down and bottom-up approaches using automated tools such as the RAVEN toolbox .172 GSMM can be used in constraint-based modelling to predict the flux distribution and secretion of metabolites within the organism under certain constraints such as the intake of substrates and governing specific objective functions such as growth, thereby providing information that would be difficult to generate in vitro experiments .169 As of 2019, there are reconstructed GSMM for 434 bacteria, 40 archaea and 117 eukaryotes .30 The majority of these GSMM are used in the simulation of a single organism. However, their use can go further to study the metabolic cross-talk of microbial communities.The creation of a GSMM includes two methods: firstly, a top-down approach, which integrates experimental data into a mathematical model. This method makes it possible to create a network of reactions and metabolic outputs through pathways. The study of metabolism can provide more accuracy and specificity with further predictive analysis and assessment. Secondly, the bottom-up approach includes defined knowledge and information sourced from readily available studies and the input of current literature .173 What makes Type-II CRISPR-Cas9 a well-established CRISPR approach in the genome-editing field is the ability of manipulation at genome level using just chimeric single guide RNA, making double-strand breaks to insert-deletion gene (indel) using cas9 nuclease, followed by homologous/non-homologues recombination and multiple genes indel .175CRISPR  is a robust and customised method of gene editing which is adapted from the immune mechanism in bacteria and archaea against viral (bacteriophage) invasion .Click here for additional data file.


The successful realization of a sustainable manufacturing bioprocess and the maximization of its production potential and capacity are the main concerns of a bioprocess engineer. A main step towards this endeavor is the development of an efficient biocatalyst. Isolated enzyme(s), microbial cells, or (immobilized) formulations thereof can serve as biocatalysts. Living cells feature, beside active enzymes, metabolic modules that can be exploited to support energy-dependent and multi-step enzyme-catalyzed reactions. Metabolism can sustainably supply necessary cofactors or cosubstrates at the expense of readily available and cheap resources, rendering external addition of costly cosubstrates unnecessary. However, for the development of an efficient whole-cell biocatalyst, in depth comprehension of metabolic modules and their interconnection with cell growth, maintenance, and product formation is indispensable. In order to maximize the flux through biosynthetic reactions and pathways to an industrially relevant product and respective key performance indices , existing metabolic modules can be redesigned and/or novel artificial ones established. This review focuses on whole-cell bioconversions that are coupled to heterotrophic or phototrophic metabolism and discusses metabolic engineering efforts aiming at 1) increasing regeneration and supply of redox equivalents, such as NAD(P/H), 2) blocking competing fluxes, and 3) increasing the availability of metabolites serving as (co)substrates of desired biosynthetic routes. They can theoretically produce all the metabolites present in their metabolic network from simple carbon sources such as sugars, organic acids, or even CO2. If naturally occurring microorganisms are employed, the production efficiencies typically are rather low , generate a proton gradient via the photosynthetic electron transport chain (PETC), and thereby regenerate NADPH and ATP to fuel CO2 fixation via the Calvin-Benson-Bassham cycle , the Entner-Doudoroff pathway (EDP), the pentose phosphate pathway (PPP), as well as the tricarboxylic acid (TCA) cycle , with fed to CO2 . Their tam cycle . The latam cycle  and is ham cycle .2, and water for growth and product formation. This allows biocatalyst production, regeneration, and operation at minimal economical and ecological costs in overall carbon neutral and land-saving processes. Recent results show the high potential of cyanobacteria, green algae, and other phototrophs to produce biomass  and NADP(H) play in heterotrophic and phototrophic cells. In the latter, NADPH constitutes a main product of the photosynthetic light reaction, the electron donor for COmination . This chmination . The dif+ ↔ NAD+ + NADPH) is catalyzed by the membrane-bound, proton-translocating PntAB (EC 1.6.1.2) or the soluble, energy-independent UdhA (EC 1.6.1.1) , Sauer et al. . pgi mutant with the PPP as primary route of glucose catabolism, UdhA activity could efficiently restore the redox balance and improve the growth rate by 25%. They concluded that, in prokaryotes, PntAB catalyzes energy- and NADH-dependent NADP+ reduction at low intracellular NADPH levels, whereas UdhA oxidizes NADPH at high intracellular NADPH levels (The function of NAD(P/H) transhydrogenases differs among organisms, especially between phototrophic and heterotrophic bacteria, and is not completely understood, yet. The transhydrogenase reaction (NADH + NADP1.6.1.1) . Up to n1.6.1.1) . Using mr et al.  identifiH levels .Synechocystis sp. PCC 6803, however, pntA transcription was shown to be high under nitrogen and phosphate limitation -2-chloropropionate production from 2-chloroacrylate catalyzed by the NADPH-dependent 2-haloacrylate reductase, E. coli  or udhA. In chemostat cultures, the NADPH/NADP+ ratios at steady state correlated positively with thymidine yields H-dependent biotransformations both in phototrophs and heterotrophs. Because membrane proteins are usually considered more tedious to be heterologously expressed, soluble UdhA is more commonly used in metabolic engineering. Such engineering was successfully employed for the syntheses of several industrially useful compounds. In the study of  E. coli . CoexpreDPH sink . Using ae yields . Fatty aand nadK .udhA  together with lactate dehydrogenase  improves the production due to enhanced NADH supply -precursor synthesizing reductase, accumulated poly with a 13-fold higher 3-hydroxyvalerate content compared to the wild type strain  catalyze the phosphorylation of NAD+ to NADP+ using ATP as the phosphoryl donor /(NADPH + NAD+) ratio remained constant probably by regulating pntAB and udhA expression levels to compensate for the effects of nadK overexpression (nadK overexpression in a recombinant E. coli harboring a polyhydroxybutyrate (PHB) synthesis pathway doubled the overall PHB accumulation in a bioreactor experiment and came along with a 3- to 6-fold increase in NADP+ concentration  and Sll1415 type (group 2) . It is hnditions . A slr04 enzymes . Further+ to NAD(P)H, the latter the NAD+-dependent oxidation of formate to CO2.Apart from the metabolic engineering of intrinsic NAD(P)H generating pathways, studies have focused on exploiting heterologous systems to increase intracellular NAD(P)H availability and enhance the efficiency and productivity of NAD(P)H-dependent whole-cell biotransformations . Of thosE. coli by regenerating NADH through the heterologous expression of Candida boidinii FDH regenerating one mole of NADH per mole of formate converted to CO2. Natively, this FDH converts formate to CO2 and H2 without cofactor involvement. Under anaerobic conditions, the NADH availability increased from 2 to 4 mol NADH per mol glucose consumed and promoted ethanol formation. Under aerobic conditions, when formate was not formed by metabolism but added to the medium, the increased NADH availability stimulated usually inactive fermentation pathways, i.e., ethanol, lactate, and succinate formation  dehydrogenase bearing E. coli for the production of -2,3-BD from diacetyl, i.e., a liquid fuel and a building block in asymmetric synthesis. The engineering not only improved the efficiency of -2,3-BD production, but also simplified product isolation by driving the reaction to completion and preventing the accumulation of gluconic, acetic, and lactic acid that are by-produced when only glucose is used for internal cofactor regeneration. Introduction of FDH resulted in higher -2,3-BD concentration, productivity, and yield, coming along with a large increase in the intracellular NADH concentration. However, GDH introduction poorly improved whole-cell biocatalysis efficiency, probably due to poor glucose permeation over cellular membranes and intracellular gluconic acid accumulation. E. coli essentially takes up glucose via active transport, i.e., the phosphotransferase system (PTS). Thereby, glucose is directly phosphorylated and does not become available for GDH. In crude extracts, GDH showed high activity, whereas the slow glucose permeation into cells limited GDH-mediated NADH regeneration H regeneration, mainly because of its dual cofactor specificity and ease of expression in E. coli, interesting biochemical properties  and cofactor preference have been reported for GDHs from different organisms (E. coli and B. subtilis a GDH gene from B. amyloliquefaciens (GDH-BA) and evaluated the kinetics and biochemical properties of the purified enzyme. They suggested that the high specific activity as well as the low KM-value towards glucose and NADP+ make this GDH a promising candidate for in vivo applications. Recombinant B. subtilis harboring GDH-BA was successfully used to regenerate NADPH for n-hexane hydroxylation catalyzed by B. subtilis containing the CYP450 BM3 variant F87V -3-hydroxy butanoate from methyl acetoacetate with 100% yield. Also gdh coexpression together with genes encoding carbonyl reductases in E. coli has been shown to be valuable for the production of chiral alcohols  from NADH to NADPH. The engineered LdhD in Synechococcus elongatus PCC 7942 then enabled efficient light-driven production of optically pure D-lactate from CO2. Mutation of LdhD increased D-lactate productivity by over 3.6-fold. The additional introduction of a lactic acid transporter and bubbling CO2-enriched air further enhanced D-lactate concentration and productivity H regeneration systems have been widely studied, NAD(P)+ regeneration has been less explored and can be considered less demanding. Using the H2O producing NADH oxidase from L. brevis as heterologous NAD+ regenerating enzyme together with a NAD+-dependent -2,3-BD dehydrogenase from B. subtilis, E. coli biocatalyst for the production of (3S)-acetoin from meso-2,3-BD, (3R)-acetoin from -2,3-BD, and -2,3-BD from a substrate mixture. Their work demonstrated that coupling NADH oxidase from L. brevis with NAD+-dependent dehydrogenases provides an efficient platform for NAD+-dependent whole-cell biotransformations.For the production of chemicals from COuctivity . For pho2 fixation and thus constitutes the main carrier of reduction equivalents under light conditions. Heterotrophs rely on catabolism for NAD(P)H regeneration with a bias towards NADH. Catabolic modules have been widely exploited to fuel redox-intensive production processes. Key enzymes involved in catabolic NAD(P)H regeneration are summarized in Central carbon metabolism generally refers to the main NAD(P)H-generating catabolic pathways. Whereas NADH especially fuels aerobic energy metabolism, NADPH is consumed especially in anabolic pathways. In photoautotrophs, NADPH additionally functions as provider of reduction equivalents for COzwf  and gnd  both encoding NADPH forming enzymes  and pfkA/B     . Fuhrer  glucose . The ∆pg the PPP . While d the PPP .zwf and gnd also was found to enhance NADPH-dependent PHB synthesis by recombinant E. coli, with zwf overexpression being three times more effective than gnd overexpression for increasing the NADPH -3-hydroxybutyrate (3HB) production, Halomonas boliviensis into E. coli, involving acetoacetyl-CoA reductase, which accepts both NADH and NADPH. The Vmax for the latter is however eight times higher so that the less abundant NADPH limits production. Overexpression of zwf together with nitrogen depletion gave the highest yield and a 50% increase in 3HB concentration. Overexpression of PPP genes also has successfully been applied in organisms other than E. coli. For instance, the production of the poly-γ-glutamic acid (γ-PGA), a multifunctional non-toxic, biodegradable biopolymer, in Bacillus licheniformis was improved by 35% when zwf was overexpressed . In the case of NADPH-dependent GDP-L-fucose biosynthesis from glucose, for instance, zwf overexpression decreased the production under glucose-limiting condition even though the intracellular NADPH level was increased compared to the control strain. Fine-tuning of the glucose feeding strategy improved the GDP-L-fucose production by 21% with zwf overexpression, highlighting that sufficient supplementation of the carbon and energy source is critical when the PPP is engineered for NADPH-dependent biotransformations  via the PPP. Using a theoretical approach, the NADPH production rate was proposed to increase by 300% upon pgi deletion  and the TCA cycle. Under resting cell conditions, this flux through EMPP and TCA cycle is necessary and beneficial for energy generation and PTS-driven glucose uptake  from L. brevis. The MHB yield on glucose was almost doubled via pfkA deletion, which was attributed to partial PPP cyclization, which was confirmed by 13C-MFA revealing a negative net flux via PGI , and, for the Δpgi and ΔpfkA strains, the specific MHB production rate increased by 15 and 20%, respectively. pfkA or gapA (encoding G3P dehydrogenase) deletion in ADH carrying C. glutamicum. The WT showed a specific MHB production rate of 3.1 mmol gCDW−1 h−1 and a MHB yield of 2.7 mol per mol glucose, whereas the ∆pfkA mutant showed a similar rate, but a yield of 4.8 mol mol−1. The specific activity of the ΔgapA mutant decreased by 62% compared to the other strains, but the MHB yield increased to 7.9 mol per mol glucose.Deletions in hosphate . This pae uptake . For thee uptake , the pro via PGI . For furStreptomyces, a genus widely used for the synthesis of various antibiotics, was successfully modulated. pfkA2 gene, one of the three annotated pfkA homologues in S. coelicolor A3, for the overproduction of the pigmented antibiotics actinorhodin and undecylprodigiosin. The pfkA2 deletion increased the carbon flux through the PPP, as measured by 13C-MFA, involving increased intracellular glucose 6-phosphate and fructose 6-phosphate levels. Similarly, PPP flux was enhanced by deleting gap1 and gap2 in S. clavuligerus, thereby increasing the supply of the precursor G3P for the production of clavulanic acid, a β-lactamase inhibitor , and gluconate doping of Y. lipolytica accelerated acetate-driven lipogenesis by NADPH synthesis through cyclic PPP. Together, synergistic cofeeding was calculated to produce CO2-derived lipids with up to 38% energy yield (referred to H2 energy). Their stoichiometric analysis suggested that glucose and gluconate complemented ATP and NADPH generation, alleviating limitations seen in acetogenesis and lipogenesis, respectively. Tracing 13C-labelled glucose and gluconate revealed that cells used these substrates almost exclusively for ATP and NADPH production locally within glycolysis and the PPP, respectively. In particular, PPP activation by gluconate cofeeding solved the challenge of NADPH limitation in acetate-fed cells.It is well reported that the NADPH supply, e.g., by yeast . Apart fby yeast , medium C. glutamicum strain for high-titer hyaluronic acid biosynthesis  attenuation, lactate/acetate pathway knockout, and pyruvate dehydrogenase complex (PDH) attenuation to drive carbon flux from the precursor metabolites glucose-6-phosphate (G6P), fructose-6-phosphate (F6P), and byproducts  to hyaluronic acid biosynthesis. The engineered C. glutamicum strain produced 28.7 g L−1 HA in a fed-batch culture, while the byproduct concentration was reduced by half.It should be noted that strategies involving a PPP knockout in combination with attenuated glycolysis can also be beneficial for biosynthetic processes, e.g., hyaluronic acid production. Based on a genome-scale metabolic model and respective simulation, Cheng et al., constructed a superior ynthesis . GeneticSaccharomyces cerevisiae is one of the most studied examples of how switching the cofactor dependency of the involved metabolic pathways can enhance product yield and decrease unwanted side products such as xylitol and CO2  production by increasing biosynthetic gene copy numbers and improving flux towards precursors and redox cofactors. 3HP was produced via a malonyl-CoA reductase (MCR) that reduces malonyl-CoA to 3HP at the expense of two NADPH. Genomic integration of multiple copies of Chloroflexus aurantiacus MCR and phosphorylation-deficient acetyl-CoA carboxylase genes increased the 3HP titer 5-fold in comparison with single integration. Furthermore, the native NAD+-dependent glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was exchanged with a heterologous NADP+-dependent version from Chlostridium acetobutylicum to increase NADPH formation  (E. coli was shown to enhance glucose consumption as well as G3P and pyruvate formation serving as precursors of isoprenoid synthesis via the methylerythritol 4-phosphate (MEP) pathway  . E. coliynthesis . In thesynthesis . Physiolectively . With angh rates . EDP-derrmation) . Recentl pathway . The lattabolism .E. coli, In a similar attempt to improve isoprenoid biosynthesis from glucose and xylose in E. coli strain expressing the Enterococcus faecalis genes mvaE and mvaS, one mevalonate molecule is synthesized from three acetyl-CoA molecules using two molecules of NADPH in a 3-step reaction. Nagai et al. studied thermodynamic states of the central metabolism to identify metabolic reactions with regulatory function. Based on metabolite concentration data, they calculated ΔG and substrate saturation for each metabolic reaction, comparing control and mevalonate producing strains. Their analyses showed that further activation of thermodynamically feasible reactions in the upper part of glycolysis and the PPP appeared difficult and that metabolic bypassing via the EDP was a promising strategy to increase acetyl-CoA and NADPH supply. Deletion of gnd and gntR (encoding a negative regulator of the expression of two EDP genes) increased mevalonate yield and specific production rate by 113 and 158%, respectively  dependent dioxygenases . Here, i example . This an+, NADP+, or both cofactors. In E. coli, there are 2 genes, i.e., maeA (encoding a NAD+-ME) and maeB (encoding a NADP+-ME), which are part of the anaplerotic node that links glycolysis and gluconeogenesis to the TCA cycle (icd) can also be an important NAD(P)H supplier , more malate is formed, which in turn can be converted to pyruvate by the ME involving NADPH formation  is also considered a major source of NAD(P)H and a promising target for redox metabolism engineering . ME cataCA cycle . Isocitrsupplier . WhereasCA cycle . Finallyormation .zwf, icd, and maeB genes in order to improve NADPH-dependent GDP-l-fucose productivity in recombinant E. coli bearing GDP-l-fucose biosynthetic enzymes. Overexpression of icd resulted in 30% higher GDP-L-fucose production compared with the control, similarly as zwf overexpression. Overexpression of maeB however resulted in a 24% reduction of GDP-L-fucose production. B. licheniformis DW2 to allow a high TCA cycle flux and study whether the improved ATP and NADPH levels could concomitantly enhance bacitracin biosynthesis. What they found was that the increased TCA cycle flux resulted in more energy generation and improved amino acid uptake and intracellular biosynthesis leading to larger pools of precursor amino acids for bacitracin biosynthesis. The contribution of the TCA cycle in NADPH regeneration was also confirmed by E. coli bearing NADPH-dependent xylose reductase from C. boidinii. They reported that succinyl-CoA synthetase (sucC) deletion significantly reduced the xylitol yield on glucose provided as cosubstrate, indicating that a functional TCA cycle contributes to NADPH allocation for xylitol production.maeB overexpression in Mucor cicinelloides, a commercial oil-producing fungus, resulted in a 2.5-fold increase in lipid accumulation  without affecting fatty acid profiles  from A. calcoaceticus to improve malonyl-CoA formation and the maeB gene from E. coli to improve NADPH availability. The overexpression of the acc genes alone improved malonyl-CoA formation resulting in a 3-fold increase in intracellular lipids, whereas maeB overexpression together with the addition of malate resulted in a 4-fold increase in intracellular lipids (197.74 mg g−1). Combining the approaches, a 5.6-fold increase of intracellular lipids was achieved (284.56 mg g−1). Similarly, the overexpression of ME from the oleaginous fungus Mortierell alpine in fatty alcohol producing S. cerevisiae strains lead to increased final fatty alcohol titers, with the reduction of fatty acyl-CoA to fatty alcohol requiring stoichiometric amounts of NADPH  high-level expression of the NADP+-dependent glyceraldhyde-3-phosphate dehydrogenase (GAPDH) from C. acetobutylicum, 2) overexpression of NAD+ kinase, and 3) introduction of cytosolic NADP+-dependent malic enzyme. Interestingly, the conversion of NADH to NADPH reduced the O2 demand of the culture enabling higher cell densities with the same aeration capacity.These studies implicate ME to be a key supplier of lipogenic NADPH. However,  strains . Moreoveetyl-CoA . For thiE. coli containing recombinant NADH-dependent styrene monooxygenase (Pseudomonas taiwanensis VLB120ΔCΔttgV (MFA has shown that the competition for NAD(P)H between redox biocatalysis and the energy metabolism becomes critical during asymmetric styrene epoxidation catalyzed by growing xygenase . Engineexygenase . This, h0ΔCΔttgV  as host 0ΔCΔttgV .E. coli cells bearing P4H from Dactylosporangium sp. strain RH1 requires stoichiometric amounts of the TCA cycle intermediate α-KG. In this hydroxylation reaction, one atom of molecular oxygen is introduced into L-proline to yield hyp, while the other oxygen atom is used for oxidative decarboxylation of α-KG giving rise to succinate and CO2. This decarboxylation added a shortcut of the TCA. For the synthesis of hyp from proline, a commercially used whole-cell process has been reported (sucA (α-KG dehydrogenase E1 subunit) or sucC (succinyl-CoA synthetase β subunit) together with aceA (isocitrate lyase), disrupting both glyoxylate shunt and TCA cycle (E. coli strain (ΔputA) expressing the p4h gene. Whereas the ΔsucCΔaceAΔputA strain grew in minimal medium in the absence of P4H, relying on the activity of fumarate reductase, growth of the ΔsucAΔaceAΔputA strictly depended on P4H activity, thus coupling growth to proline hydroxylation. Employing a Na+/L-proline transporter (putP gene) in the ΔsucAΔaceAΔputA strain, the specific proline hydroxylation rate doubled (E. coli carrying an α-ketoglutarate-dependent L-isoleucine dioxygenase (IDO)  from penicillin G  was used to reconstitute the TCA cycle. Additional engineering (poxB deletion and acs overexpression) reduced acetate accumulation in order to prevent medium acidification and carbon loss. Together with the deletion of the host β-lactamase involved in penicillin G and G-7-ADCA degradation, the G-7-ADCA titer could be increased 11-fold to 29.01 mM. Interestingly, the constructed vector systems did not require antibiotic selection, as survival depended on the expression of the dioxygenase encoded on the vector.As mentioned above, synthetically interesting biocatalytic reactions can directly depend on central carbon metabolites that serve as cosubstrates. The hydroxylation of L-proline to trans-4-hydroxy-L-proline (hyp) using an α-KG dependent L-proline-4-hydroxylase (P4H) is such an example . The reareported  and the reported . In an aCA cycle , in a pr doubled . In a sise (IDO) . Also hecillin G . In that2 offers a promising sustainable alternative , the primary electron acceptor of photosystem I. CDW−1 were achieved. Both enzyme specific and host intrinsic Fds have been found to shuttle electrons, with differing efficiency. Phototrophs contain multiple Fds with, e.g., at least nine Fd in the model cyanobacterium Synechocystis sp. PCC 6803  affecting both the biocatalytic reaction and the host metabolism. The introduction of heterologous pathways relying on C-fixation and reducing power showed a way to avoid such imbalance , cinnamaldehyde (cinnamon), octanal (citrus), 2-heptanone (banana), ionones (rose), and vanillin (vanilla)  . Laccasevanilla) , are prevanilla) .1) How efficiently does the biocatalyst use the incident light? What are the resulting electron transfer rates (ETRs)?2) At which point can photosynthesis be tapped with what efficiency?3) Which strains or strain character traits are desirable to improve photosynthesis-driven biotechnology?For the envisioned photosynthesis-driven redox biocatalysis, the light-to-product efficiency is of special interest. Major questions to be answered, are:k, where the incident light is just sufficient to supply the metabolism with the reductive power needed, e.g., for biomass production. In natural environments, phototrophic organisms face a “light usage dilemma”  reach the surface with light intensities above Ek  At photosystem II (PSII), a part of the energy is dissipated via heat or fluorescence emission and non-photochemical quenching (NPQ), which decreases the operative quantum yield at PSII (ФPSII). ФPSII can be assessed via pulse-amplitude-modulation (PAM) fluorometry to calculate the ETR in PSII. 3) Alternative electron flux (AEF) via terminal oxidases and Mehler- or Mehler-type reactions further compromises the light-to-biomass efficiency. As a result, only a fraction of the incident light energy can be used for biomass formation or productive  reactions. To illustrate these theoretical considerations, 2 evolution, electron supply, and growth for typical phototrophic microorganisms.Within the photosynthetic apparatus, (excess) energy is lost at different levels, often by mechanisms important to protect the apparatus from over-excitation: 1) Depending on their pigmentation, photosynthetic organisms absorb only a share of PAR, expressed as the share of photosynthetically absorbed quanta Qvia Fd or NADPH as described above) involves electron losses to respiratory elements, like oxidases, or Mehler- and Mehler-type reactions. Taken together, these losses account for 20–30%, reducing the light use efficiency to 15–20%. Beside these theoretical efficiencies, the redox potentials of tapped PETC cofactors or electron carriers are of practical importance as reviewed by Synechocystis sp. PCC 6803  energy on account of a higher light harvesting capacity  in combination with systems biology approaches, were successfully applied for hococcus . Synechocapacity , augurincapacity . Here, tTEX 2973  and the progress . For thehococcus .As mentioned above, phototrophs experience light excess under normal/natural conditions and have evolved multiple mechanisms for excessive energy dissipation . Approacin situ product removal (ISPR) concepts. As an example, a two-liquid-phase system has been demonstrated to stabilize a cyanobacterial CYP450-based process by reducing substrate toxicity and volatility  mass transfer, strain-specific nutrient supply, water evaporation, harvesting costs, and sterility. Interested readers are referred to relevant reviews H-dependent productive reactions. The introduction of heterologous NAD(P)H generating enzyme systems into whole-cell biocatalysts on the other hand relieves the dependency of target reactions on the cellular redox metabolism and can be suitable for specific biotransformations. Targeted modifications in central carbon metabolism, such as overexpression and deletion of genes encoding critical enzymes or regulators, can effectively enhance redox-dependent production processes. Respective investigations also provide insight into the intricate regulatory network guiding future pathway and genome-scale engineering efforts to further boost productivities. Whereas the PPP has high potential for the optimization of NADPH availability, EDP, TCA cycle, and respective engineering have been shown to be valuable targets to achieve high NAD(P)H regeneration rates. The TCA-cycle also has high value to drive redox reactions depending on metabolites other than NAD(P)H, e.g., α-KG for oxyfunctionalizations catalyzed by α-KG-dependent dioxygenases.Transhydrogenases and NAD6f, attachment to PSI, and targeted manipulation of protection mechanisms. Further, strategies to avoid intracellular competition are needed, such as attenuating FNR affinity for ferredoxin and thereby making more ferredoxin available for target reactions. Finally, future approaches have to tackle imbalances among redox and energy status of the cells.Cyanobacteria and green algae have great potential as whole-cell redox biocatalysts, making use of water and sunlight to provide reduction equivalents. Recent studies showed that introduction of an artificial electron sink could not only benefit from, but also enhance photosynthetic performance. For high light-to-product efficiencies, efficient electron transport in the photosynthetic apparatus is required. Promising strategies include overexpression of limiting elements, e.g., cytochrome b


Saccharomyces cerevisiae, Yarrowia lipolytica and Kluyveromyces marxianus are generated to study their long-term adaptation to several stress factors by incorporation of proteomics data. Predictions reveal that upregulation and high saturation of enzymes in amino acid metabolism are common across organisms and conditions, suggesting the relevance of metabolic robustness in contrast to optimal protein utilization as a cellular objective for microbial growth under stress and nutrient-limited conditions. The functionality of GECKO is expanded with an automated framework for continuous and version-controlled update of enzyme-constrained GEMs, also producing such models for Escherichia coli and Homo sapiens. In this work, we facilitate the utilization of enzyme-constrained GEMs in basic science, metabolic engineering and synthetic biology purposes.Genome-scale metabolic models (GEMs) have been widely used for quantitative exploration of the relation between genotype and phenotype. Streamlined integration of enzyme constraints and proteomics data into such models was first enabled by the GECKO toolbox, allowing the study of phenotypes constrained by protein limitations. Here, we upgrade the toolbox in order to enhance models with enzyme and proteomics constraints for any organism with a compatible GEM reconstruction. With this, enzyme-constrained models for the budding yeasts  Genome-scale metabolic models have been widely used for quantitative exploration of the relation between genotype and phenotype. Here the authors present GECKO 2, an automated framework for continuous and version controlled update of enzyme-constrained models of metabolism, producing an interesting catalogue of high-quality models for diverse yeasts, bacteria and human metabolism, aiming to facilitate their use in basic science, metabolic engineering and synthetic biology purposes. Their myriads of applications span from model-driven development of efficient cell factories9, to their utilization for understanding mechanisms underlying complex human diseases12. One of the most common simulation techniques for enabling phenotype predictions with these models is flux balance analysis (FBA), which assumes that there is balancing of fluxes around each metabolite in the metabolic network. This means that fluxes are constrained by stoichiometries of the biochemical reactions in the network, and that cells have evolved in order to operate their metabolism according to optimality principles14. Quantitative determination of biologically meaningful flux distribution profiles is a major challenge for constraint-based methods, as optimal phenotypes can be attained by alternate flux distribution profiles15, caused by the presence of network redundancies that provide organisms with robustness to environmental and genetic perturbations. This limitation is often addressed by incorporation of experimental measurements of exchange fluxes (secretion of byproducts and uptake of substrates) as numerical flux constraints for the FBA problem. However, such measurements are not readily available for a wide variety of conditions and organisms.Genome-scale metabolic models (GEMs) have become an established tool for systematic analyses of metabolism for a wide variety of organisms18, a finite membrane surface area for expression of transporter proteins19 and a bounded total protein mass available for metabolic enzymes25. All of these modeling frameworks have been successful at expanding the range of predictions of classical FBA, providing explanations for overflow metabolism and cellular growth on diverse environments for Escherichia coli25, Saccharomyces cerevisiae26, Lactococus lactis27, and even human cells24. However, these modeling approaches were applied to metabolic networks of extensively studied model organisms, which are usually well represented in specialized resources for kinetic parameters such as the BRENDA28 and SABIO RK29 databases. Furthermore, collecting the necessary parameters for the aforementioned models was mostly done manually; therefore, no generalized model parameterization procedure was provided as an integral part of these methods.In order to overcome these limitations, the concept of enzymatic limitations on metabolic reactions has been explored and incorporated by several constraint-based methods. Some of these have modeled enzyme demands of metabolic reactions by constraining metabolic networks with kinetic parameters and physiological limitations of cells, such as a crowded intracellular volumeE. coli33, Thermotoga maritima34 and Lactococus lactis35; and resource balance analysis models (RBA), on reconstructions for E. coli36 and Bacillus subtilis37. These formalisms succeeded at merging genome-scale metabolic networks together with comprehensive representations of macromolecular expression processes, enabling detailed exploration of the constraints that govern cellular growth on diverse environments. Despite the great advances for understanding cell physiology provided by these modeling formalisms, accuracy on phenotype predictions is compromised by the large number of parameters that are required , with most of these not being readily available in the literature. Moreover, these models encompass processes that differ radically in their temporal scales  and their mathematical representation (presence of non-linear expressions in ME-models), requiring the implementation of more elaborate techniques for numerical simulation.Enzyme limitations have also been introduced into models of metabolism by other formalisms, for instance, Metabolic and gene Expression models (ME-models), implemented on reconstructions for S. cerevisiae, Yeast738. This method extends the classical FBA approach by incorporating a detailed description of the enzyme demands for the metabolic reactions in a network, accounting for all types of enzyme-reaction relations, including isoenzymes, promiscuous enzymes and enzymatic complexes. Moreover, GECKO enables direct integration of proteomics abundance data, if available, as constraints for individual protein demands, represented as enzyme usage pseudo-reactions, whilst all the unmeasured enzymes in the network are constrained by a pool of remaining protein mass. Additionally, this method incorporates a hierarchical and automated procedure for retrieval of kinetic parameters from the BRENDA database, which yielded a high coverage of kinetic constraints for the S. cerevisiae network. The resulting enzyme-constrained model, ecYeast7, was used for successful prediction of the Crabtree effect in wild-type and mutant strains of S. cerevisiae and cellular growth on diverse environments and genetic backgrounds, but also provided a simple framework for prediction of protein allocation profiles and study of proteomics data in a metabolic context. Furthermore, the model formed the basis for modeling yeast growth at different temperatures39.GECKO, a method for enhancement of GEMs with Enzymatic Constraints using Kinetic and Omics data, was developed in 2017 and applied to the consensus GEM for 38, its principles of enzyme constraints have been incorporated into GEMs for B. subtilis40, E. coli41, B. coagulans42, Streptomyces coelicolor43 and even for diverse human cancer cell-lines2, showing the applicability of the method even for non-model organisms. Despite the rapid adoption of the method by the constraint-based modeling community, there is still a need for automating the model generation and enabling identification of kinetic parameters for less studied organisms.Since the first implementation of the GECKO methodIn this work, we updated the GECKO toolbox to its 2.0 version, expanding its use it for building enzyme-constrained models (ecModels) for more organisms. Among other improvements, we generalized its structure to facilitate its applicability to a wide variety of GEMs, and we improved its parameterization procedure to ensure high coverage of kinetic constraints, even for poorly studied organisms. Additionally, we incorporated simulation utility functions, and developed an automated pipeline for updating ecModels, named ecModels container. This container is directly connected to the original sources of version-controlled GEMs and the GECKO toolbox, offering a continuously updated catalog of diverse ecModels.44 and the COBRApy package45. The development of GECKO has been continuously tracked in a public repository  since 2017, providing a platform for open and collaborative development. The generation of output model files in.txt and SBML L3V1 FBC246 formats enabled the use of the ecYeastGEM1 structure as a standard test to track the effects of any modifications in the toolbox algorithm through the use of the Git version control system, contributing to reproducibility of results and backwards compatibility of code.To ensure wide application and enable future development by the research community, we established the GECKO toolbox as open-source software, mostly encoded in MATLAB. It integrates modules for enhancement of GEMs with kinetic and proteomics constraints, automated retrieval of kinetic parameters from the BRENDA database (python module), as well as simulation utilities and export of ecModel files compatible with both the COBRA toolboxhttps://gitter.im/SysBioChalmers/GECKO), reinforcing transparent and continuous communication between users and developers.Interaction with users of the GECKO toolbox and the ecYeastGEM model has also been facilitated through the use of the GECKO repository, allowing users to raise issues related with the programming of the toolbox or even about conceptual assumptions of the method, which has guided cumulative enhancements. Additionally, technical support for installation and utilization of the toolbox and ecYeastGEM is now provided through an open community chat room , or even non-specific to their reaction mechanism  were needed to be incorporated, in order to fill the gaps in the available data for the reconstruction of the first S. cerevisiae ecModel, ecYeast7. Moreover, detailed manual curation of kcat numbers was needed for several key enzymes in order to achieve biologically meaningful predictions.The previous implementation of the GECKO method in GECKO 1.0 significantly improved phenotype predictions for 47 is the main source of kinetic parameters for GECKO, all of the available kcat and specific activity entries for non-mutant enzymes were retrieved. In total, 38,280 entries for 4130 unique E.C. numbers were obtained and classified according to biochemical mechanisms, phylogeny of host organisms and metabolic context , even without filtering the dataset for entries reported exclusively for natural substrates, as previously done by other studies49 , adding 8,118 new entries to the catalog of kinetic parameters in the toolbox. A phylogenetic distance-based criterion, based on the phylogenetic tree available in the KEGG database48, was introduced for cases in which no organism-specific entries are available for a given query in the kinetic parameters dataset. Specifically, where GECKO 1.0 chooses kcat available in BRENDA regardless of organism, GECKO 2.0 chooses the values available in BRENDA for the phylogenetically closest organism by iteratively introducing a wildcard into the E.C. number, as exemplified in the Brenda kinetic data analysis section in the Supplementary Information File kcat matching algorithm, including the estimation of the phylogenetic distance, and its comparison with the predecessor are shown in the supplementary methods section in Supplementary File In the new version of the GECKO toolbox (GECKO 2.0), a modified set of hierarchical kcat assignment algorithm on an ecModel, ecYeast7 was reconstructed using both the first and GECKO 2.0. A classification of the matched kcat values according to the new matching algorithm is provided in Fig. P-values 2.1 × 10−11 and 3.9 × 10−8, when compared to the BRENDA fungi and S. cerevisiae distributions, respectively, under a two-tailed Kolmogorov–Smirnov test)  Fig. . The quast) Fig. ; in contkcat numbers in a metabolic network has been proven to increase the quality of phenotype predictions for S. cerevisiae38; nevertheless, this is an intensive and time-consuming procedure that is hard to ensure for a large number of models subject to continuous modifications. In order to ensure applicability of the GECKO method to any standard GEM, a unified procedure for curation of kinetic parameters was developed based on parameter sensitivity analysis. For automatically generated ecModels that are not able to reach the provided experimental value for maximum batch growth rate, an automatic module performs a series of steps in which the top enzymatic limitation on growth rate is identified through the quantification of enzyme control coefficients. For such enzymes, the E.C. number is obtained and then its correspondent kcat value is substituted by the highest one available in BRENDA for the given enzyme class. This procedure iterates until the specific growth rate predicted by the model reaches the provided experimental value.The introduction of manually curated 44 or RAVEN50 formats, all of the organism-specific parameters required by the method  can be provided in a single MATLAB initialization script, minimizing the modifications needed for the generation of a new ecModel.Finally, as the first version of the toolbox relied on the structure and nomenclature of the model Yeast7, its applicability to other reconstructions was not possible in a straightforward way. In order to provide compatibility with any other GEM, based on COBRA6, this renders GEMs to be dynamic structures that can change rapidly. In order to integrate such continuous updates into the enzyme-constrained version of a model in an organized way, an automated pipeline named ecModels container was developed.Several GEMs that have been published are still subject to continuous development and maintenancehttps://github.com/SysBioChalmers/ecModels) in a version-controlled way, requiring minimal human interaction and maintenance. The GECKO toolbox ensures the creation of functional and calibrated ecModels that are compatible with the provided experimental data . This whole computational pipeline is illustrated in Fig. The ecModels container is a continuous integration implementation whose main functionality is to provide a catalog of ecModels for several relevant organisms that are automatically updated every time a modification is detected either in the original GEM source repository or in the GECKO toolbox, i.e., new releases in their respective repositories. The pipeline generates ecModels in different formats, including the standard SBML and MATLAB files, and stores them in a container repository  basic simulation and analysis purposes, (2) accessible retrieval of kinetic parameters, (3) automated generation of condition-dependent ecModels with proteomic abundance constraints, (4) comparative flux variability analysis between a GEM and its ecModel counterpart, and (5) prediction of metabolic engineering targets for enhanced production with an implementation of the FSEOF method56 for ecModels. Detailed information about the inputs and outputs for each utility can be found on their respective documentation, available at: https://github.com/SysBioChalmers/GECKO/tree/master/geckomat/utilities. All of these utilities were developed in MATLAB due to their dependency on some RAVEN toolbox functions50.As ecModels are defined in an irreversible format and incorporate additional elements such as enzymes (as new pseudo-metabolites) and their usages (represented as pseudo-reactions), they might sometimes not be directly compatible with all of the functionalities offered by currently available constraint-based simulation softwareecModels container pipeline, batch growth under 11 different carbon sources was simulated with eciML1515 for E. coli. Figure iML1515 and the iJL1678 ME-model32, the latter accounting for both metabolism and macromolecular expression processes. The sum squared error (SSE) for batch growth rate predictions across the 11 carbon sources using eciML1515 was 0.27, a drastic improvement when compared to the 1.21 SSE of iJL1678 ME-model predictions32. Figure iML1515 predicts values that lie within the range of predictions of the iJL1678 ME-model  for 10 out of the 11 carbon sources (see “Methods” for simulation details). This shows that the new version of the GECKO toolbox ensures the generation of functional ecModels that can be readily used for simulation of metabolism, due to its systematic parameter flexibilization step, which reduces the need of extensive manual curation for new ecModels. Furthermore, iML1515 is a model available as a static file at the BiGG models repository57; therefore, its integration to the ecModels container for continuous update demonstrates the flexibility of our pipeline, regarding compatibility with original GEM sources, which can be provided as a link to their git-based repositories or even as static URLs.In order to test the quality of the phenotype predictions of an ecModel automatically generated by the 62, a major caveat of using proteomics data as constraints for quantitative models is their intrinsic high biological and technical variability63, therefore some of the incorporated data constraints need to be loosened in order to obtain functional ecModels. When needed, additional condition-dependent exchange fluxes of byproducts can also be used as constraints in order to limit the feasible solution space. A detailed description of the proteomics integration algorithm implemented in GECKO is given in the supplementary methods section in the Supplementary Information File The previously mentioned module for integration of proteomics data generates a condition-dependent ecModel with proteomics constraints for each condition/replicate in a provided dataset of absolute protein abundances [mmol/gDw]. Even though absolute quantification of proteins is becoming more accessible and integrated into systems biology studiesiYali, eciSM996). We measured absolute protein abundances for S. cerevisiae, Y. lipolytica and K. marxianus, grown in chemostats at 0.1 h−1 dilution rate and subject to several experimental conditions 64, and incorporated these data into the ecModels as upper bounds for individual enzyme usage pseudo-reactions. Then, exchange fluxes for CO2 and oxygen corresponding to the same chemostat experiments were used as a comparison basis to evaluate quality of phenotype predictions. For each organism- condition pair, 3 models were generated and compared in terms of predictions: a pure stoichiometric metabolic model, an enzyme-constrained model with a limited shared protein pool, and an enzyme-constrained model with proteomics constraints. It was found that the addition of the enzyme pool constraint enables major reduction of the relative error in prediction of gaseous exchange fluxes in some of the studied conditions. Additionally, the incorporation of individual protein abundance constraints improves even further the predictive accuracy of gaseous exchanges, for 5 out of the 11 evaluated cases , showed that central carbon and energy metabolism is the most affected sector in the ecYeastGEM network by integration of proteomics constraints, as protein burden predictions were higher, at least by 20%, for 3 out of the 4 simulated conditions when compared with predictions of the ecYeastGEM without proteomics data  hinders their compatibility with the flux variability analysis (FVA) functions already available in COBRA44 and RAVEN50 toolboxes. As a solution to this, an FVA module was integrated to the utilities repertoire in GECKO, whose applicability has been previously tested on studies with ecModels for S. cerevisiae1 and human cell lines2. This module contains the necessary functions to perform FVA on any set of reactions of an ecModel, enabling also a direct comparison of flux variability ranges between an ecModel and its GEM counterpart in a consistent way . In all cases the FVA comparisons were carried out for both chemostat and batch growth conditions in order to span different degrees of constraining of the metabolic networks , which restrains these models to an energy efficient respiratory mode  are integrated to it, providing a version-controlled and continuously updated repository for high-quality ecModels. Moreover, the implemented automation facilitates the application of the GECKO method to other organisms for which sufficient data is available.In order to further showcase the functionality of the GECKO toolbox 2.0, a family of new high-quality ecModels were generated for S. cerevisiae, K. marxianus and Y. lipolytica grown under multiple environmental conditions, were incorporated as constraints into their ecModels by using the proteomics integration module added to the GECKO toolbox. Analysis of metabolic flux distributions revealed that net reaction fluxes predicted by GEMs are not significantly affected by the incorporation of kinetic and proteomics constraints, however, the explicit integration of enzymes into ecModels extends the range of predictions of classical FBA and enables computation of enzyme demands at the reaction and pathway levels. It was found that incorporation of proteomics constraints does not affect enzyme demand predictions significantly for most of the active enzymes at low dilution rates across the simulated conditions. However, we observed that a diversified utilization of isoenzymes, enforced by proteomics constraints, increases the predicted total protein mass allocated to central carbon and energy metabolism, in comparison to optimal enzyme allocation profiles. This result suggests the relevance of metabolic robustness in contrast to optimal protein utilization for microbial growth under environmental stress and nutrient-limited conditions.Absolute proteomics measurements for the budding yeasts 69 and protein levels64. For all studied stress conditions in this study, we identified upregulated proteins (increased abundance) that are needed to operate at high saturation levels in stress conditions, while showing low usage at reference conditions, creating lists of potential gene amplification targets for enhancing stress tolerance in three industrially relevant yeast species .Our results on drastic reduction of median flux variability ranges and the number of totally unbounded fluxes for eciML1515 and iJL1678 ME-model (both for E. coli) for cellular growth and global protein demands on diverse environments were compared. The major improvement in predicted maximum growth rates, together with a comparable performance on quantification of protein demands, shown by eciML1515 suggest that, despite its mathematical and conceptual simplicity, the GECKO formalism is a suitable framework for quantitative probing of metabolic capabilities, compatible with the widely used FBA method and without the need of excessive complexity or computational power. Nevertheless, ME-models provide a much wider range of predictions that explore additional processes in cell physiology with great detail. Direct comparison between the predictions of these modeling formalisms, suggest that ME-models performance can be improved by incorporation of either curated or systematically retrieved kinetic parameters that are suitable for the modeled organisms.Here, the predictive capabilities of ec17, metabolic modeling with enzyme kinetics (MOMENT)23, and constrained allocation flux balance analysis (CAFBA)21, have also been developed and used to explore microbial cellular growth21 and overflow metabolism23. These methods have overcome the lack of reported parameters for some specific reactions either by incorporation of proteomics measurements and prior flux distributions23, manual curation and sampling procedures17 or even by lumping protein demands by functionally related proteome groups. In contrast, the new version of the GECKO toolbox provides a systematic and robust parameterization procedure, leveraging the vastly accumulated knowledge of biochemistry research stored in public databases, ensuring the incorporation of biologically meaningful kinetic parameters even for poorly studied reactions and organisms.Simpler modeling frameworks that account for protein or enzyme constraints in metabolism, such as flux balance analysis with molecular crowding (FBAwMC)70. The pipeline was tested on the generation of an enzyme-constrained version of the iJO1366 metabolic reconstruction for E. coli, which also showed consistency with experimental data. This work represented a step forward in the field of constrain-based metabolic modeling, as it contributed to standardization of model generation and facilitating their utilization and applicability to other cases. However, due to the intrinsic trade-off between model simplicity and descriptive representation, a limitation of the sMOMENT method is its simplification of redundancies in metabolism, which just accounts for the optimal way of catalyzing a given biochemical reaction, discarding the representation of alternative isoforms that might be relevant under certain conditions. In GECKO ecModels, all enzymes for which a gene-E.C. number relationship exists are included in the model structure. As traditional FBA simulations rely on optimality principles one could, in principle, expect the same predicted flux distributions by sMOMENT and GECKO ecModels. Nonetheless, the explicit incorporation of all enzymes in a metabolic network enables explanation of protein expression profiles that deviate from optimality in order to gain robustness to changes in the environment, as it has been recently shown by the integration of a regulatory nutrient-signaling Boolean network together with an ecModel for S. cerevisiae’s central carbon metabolism71.The applicability of these other simple modeling formalisms to models for diverse species is limited as none of these methods has been provided as part of a generalized model-agnostic software implementation. Recently, a simplified variant of the MOMENT method (sMOMENT) was developed and embedded into an automated pipeline for generation and calibration of enzyme-constrained models of metabolism (AutoPACMEN)ecModels container facilitates the generation, standardization, utilization, exchange and community development of ecModels through a transparent version-controlled environment. This tool provides a dynamic, and potentially increasing, catalog of updated ecModels trying to close the gap between model developers and final users and reduce the time-consuming tasks of model maintenance. We are confident that this will enable wide use of ecModels in basic science for obtaining novel insight into the function of metabolism, as well as in synthetic biology and metabolic engineering for design of strains with improved functionalities, e.g., for high-level production of valuable chemicals.In conclusion, GECKO 2.0 together with the development of the automated pipeline The ecModels repository is used to version-control the pipeline code and the resulting models. The pipeline is restricted to 2 short Python files, whose role is to decide when models need to be updated based on a configuration file config.ini, and to consequently invoke the use of GECKO for each model. Updates are deemed necessary when either the underlying dependencies  or the source GEMs are independently updated to a new version (release) in their respective repositories.Actions tab of the GitHub repository. The computation is performed through a self-hosted GitHub runner, further leveraging the transparent nature of the GitHub platform and the git version- control system. The resulting ecModel and updated configuration are committed to the repository, with the changes being made available for review through a pull request. Additionally, the GECKO output is also replicated in the pull request body. The ecModels container thus continues the transparency and reproducibility of the source models.The pipeline is designed be automatic and to not require supervision. It was developed to work with both version-controlled GEMs and GEMs downloadable from a URL, updating the version in the configuration after a new ecModel is obtained. For easy review, the pipeline log is publicly available under the Saccharomyces cerevisiae CEN.PK113-7D , Kluyveromyces marxianus CBS6556  and Yarrowia lipolytica W29  was conducted as described in the supplementary methods section in the Supplementary Information File Total protein extraction for the strains 72. Data filtering was set to peptide E-value < 0.01 and protein log < –3. Relative quantification of protein abundances was carried out using the Normalized Spectral Abundance Factor (NSAF)73 and the NSAF values obtained from UPS2 proteins in bulk samples were used to determine the suitable regression curves that allowed the conversion from relative protein abundance into absolute terms. The regression curves parameters for protein abundance quantification are shown in the supplementary methods section in the Supplementary Information File Protein identification was performed using the open-source search engine X!Tandem pipeline 3.4.464. Then the biomass production rate was constrained (both upper and lower bounds) with the experimental dilution rate (0.1 h−1). Maximization of the non-growth associated maintenance pseudo-reaction was set as an objective function for the parsimonious FBA problem as a representation of the additional energy demands for regulation of cellular growth at non-optimal conditions. The same procedure was followed for simulations with ecModels constrained by a total protein pool. For the case of ecModels with proteomics constraints, the same set of constraints was used but the objective function was set as minimization of the total usage of unmeasured proteins, assuming that the regulatory machinery for stress tolerance is represented by the condition-specific protein expression profile.Simulation of cellular phenotypes for conditions of environmental stress at low dilution rates with GEMs were performed by first setting bounds on measured glucose uptake and byproduct secretion rates according to experimental data from previous studies on chemostatsBatch cellular growth was simulated by allowing unconstrained uptake of all nutrients present in minimal mineral media, enabling a specific carbon source uptake reaction for each case while blocking the rest of the uptake reactions and allowing unconstrained secretion rates for all exchangeable metabolites. Maximization of the biomass production rate was used as an objective function for the resulting FBA problem. For prediction of total protein demands on unlimited nutrient conditions, media constraints were set as expressed above and experimental batch growth rate values were fixed as both lower and upper bounds for the biomass production pseudo-reaction. The total protein pool exchange pseudo-reaction was then unconstrained and set as an objective function to minimize, assuming that when exposed to unlimited availability of nutrients the total mass of protein available for catalyzing metabolic reactions becomes the limiting resource for cells. The solveLP function, available in the RAVEN toolbox (v2.4.3), was used for solving all FBA problems in this study.Further information on research design is available in the Supplementary information Filereporting summaryPeer Review File


UGT2B17 and UGT2B28 genes are linked to prostate cancer (PCa) risk and progression, leukaemia, autoimmune and other diseases.Naturally occurring germline gene deletions (KO) represent a unique setting to interrogate gene functions. Complete deletions and differential expression of the human glycosyltransferase The systemic metabolic consequences of UGT deficiencies were examined using untargeted and targeted mass spectrometry-based metabolomics profiling of carefully matched, treatment-naive PCa cases.UGT2B17 KO, reduced levels of inactive steroid-glucuronides were compensated by sulfated derivatives that constitute circulating steroid reservoirs. UGT2B28 KO presented remarkably lower levels of oxylipins paralleled by reduced inflammatory mediators, but higher ceramides unveiled as substrates of the enzyme in PCa cells.Each UGT KO differentially affected over 5% of the 1545 measured metabolites, with divergent metabolic perturbations influencing the same pathways. Several of the perturbed metabolites are known to promote PCa growth, invasion and metastasis, including steroids, ceramides and kynurenine. In The distinctive and broad metabolic rewiring caused by UGT KO reinforces the need to examine their unique and divergent functions in PCa biology.  APOC3 and PCSK9 genes reduce levels of triglycerides and low-density lipoprotein, respectively, and afford protection against coronary heart disease . In. In24]. UGT2B17 or UGT2B28 genes and gene-proficient individuals. By untargeted and targeted mass spectrometry (MS) approaches, we identify an unexpected global rewiring of metabolism in both classes of KO individuals well beyond steroid hormones. Still, we reveal divergent metabolic perturbations in each KO group and different classes of metabolites affected by the absence of functional UGT2B17 and UGT2B28 pathways. We thus expose several important metabolic perturbations in KO PCa cases that help to understand how UGT2B17 and UGT2B28 influence PCa progression.Building on these observations and given their wide tissue distribution  or both copies of UGT2B28  genes, or were UGT2B17/UGT2B28-gene-proficient controls . These patients were matched for age and adverse pathological and clinical features (prostate serum antigen (PSA), Gleason score and tumour stage). Characteristics of matched groups are provided in Table Patients from the PROCURE Prostate Cancer cohort were recruited between 2007 and 2012 at four university hospital centres in the Province of Québec, Canada  . The cohc status . Deletioc status . PatientPlasma sample aliquots were analysed for profiling of global metabolites by the metabolomics platform at Metabolon Inc. . Samples were prepared using the automated MicroLab STAR system . A recovery standard was added prior to the first step in the extraction process for quality control purposes. Metabolites were extracted by vigorous agitation after the precipitation of proteins with methanol. The resulting extract was divided in four fractions analysed as follows: two for reverse phase (RP)/ultra-performance liquid chromatography (UPLC)-MS/MS methods with positive ion mode electrospray ionisation (ESI), one for analysis by RP/UPLC-MS/MS with negative ion mode ESI, one for analysis by HILIC/UPLC-MS/MS with negative ion mode ESI utilising a Waters ACQUITY UPLC system coupled to a Thermo Scientific Q-Exactive high-resolution MS equipped with a heated ESI source and an Orbitrap mass analyser. Raw data extraction, peak identification, and quality control processing were carried out using the Metabolon proprietary hardware and software. Compound identification was done through comparison with a library of chromatographic and MS data from authenticated standards. Peaks were quantified using the area under the curve (AUC) method, and data were normalised for inter-day signal differences. The analytical variability was ≤10%.Complex lipid profiling was conducted according to a modified version of a previously described protocol by the metabolomics platform at Metabolon Inc . Lipids Plasma steroid levels were measured by LC/MS-MS and GC-MS using previously published methods , 40. In Oxylipins were quantified in plasma samples at the West Coast Metabolomics Center  as previously described . Interna2, 5 μg/mL pepstatin, 0.5 μg/mL leupeptin, 0.5 mM UDP-glucuronic acid, 20 μg/mL alamethicin and 200 μM of substrates in a final volume of 100 μL. Reaction assays were incubated at 37 °C for 4 hours, stopped with 100 μL methanol. Chemicals were from Sigma . Glucuronide formation was assessed by LC/MS-MS as previously described [Glycosyltransferase activity was assessed with protein preparations from human livers as a positive control  and LNCaP and LAPC4 PCa cell models expressing UGT2B17 or UGT2B28, as indicated in the legend of figures. Reaction assays contained 50 µg homogenates or 20 µg microsomal proteins, 50 mM Tris-HCl (pH 7.5), 10 mM MgClescribed , 43.UGT2B28 KO, UGT2B17 KO and gene-proficient controls and six additional pairs of UGT2B17 KO and gene-proficient cases. Metabolomics data were log-transformed prior to statistical comparisons using paired sample two-sided t test, and fold changes (FCs) were calculated based on the mean. The 30 UGT2B17 KO were compared with the 30 matched gene-proficient controls. The 24 UGT2B28 KO samples were compared with the 24 matched gene-proficient controls and with all 30 controls, with similar conclusions. Compiled metabolomics data and statistical analysis are provided in Supplementary Tables Samples included 24 matched triplets of Further information on research design is available in the UGT2B17 KO, 24 UGT2B28 KO and 30 gene-proficient treatment-naive PCa cases undergoing prostatectomy . By contrast, a large majority of changed metabolites in each class were lower in UGT2B28 KO individuals relative to gene-proficient cases (85% of changed metabolites were decreased)  Fig. .Fig. 2SuUGT2B17 KO, five of the ten most upregulated metabolites were steroid sulfate conjugates (fold change (FC) of 1.4–2.1X; P < 0.05), whereas two of the most downregulated metabolites were glucuronide conjugates, with deoxycholate-glucuronide  being the most discriminating metabolite in UGT2B17 KO individuals  whereas two markers of inflammation, leukotriene B4 and the peptide HWESASLLR, reduced by 12% and 33%, respectively, were among the most discriminating metabolites of UGT2B28 KO  but unchanged in UGT2B28 KO relative to gene-proficient individuals , lower levels of three glucuronide conjugates , FC −1.7X; P < 0.05), higher levels of cortolone-G , and higher levels of 11-oxy-androsterone  , and higher 11-oxy-androsterone  were observed in UGT2B28 KO . Functional glucuronidation assays support that the pregnanediol derivatives 5α-pregnan-3α,20α-diol and 5α-pregnan-3β,20α-diol are conjugated by UGT2B28 in LNCaP prostate cancer cells  (Supplementary Table The steroidome of 05) Fig.  and S4A. KO Fig. . UGT2B28lls Fig. . For C18P < 0.01) whereas metabolites of the neutral pathway were unchanged  in UGT2B17 KO). UGT2B17 KO individuals also had remarkably lower levels of deoxycholate-glucuronide by −8.3X (P < 10–6), which constituted a circulating metabolite discriminating UGT2B17 KO from proficient individuals , short and medium chain fatty acid acylcarnitines  and fatty acid dicarboxylates   Fig. . None of05) Fig. .UGT2B17 KO. This included levels of dihydroceramides, ceramides, lactosylceramides and sphingomyelins, increased by 10–38% in UGT2B17 KO  and sphingosine   Figs. . This ob2) Figs. .Fig. 4UGUGT2B28 KO individuals, first hinted by the untargeted metabolomics assay  were unaffected. These observations were paralleled by lower systemic levels of other inflammatory mediators in UGT2B28 KO, namely of the pro-inflammatory peptides bradykinin and HWESASLLR, associated with the kallikrein–kinin system [UGT2B28 KO cases  of several gamma-glutamyl amino acids whereas UGT2B28 KO displayed a reduced branched-chain amino acid metabolism , whereas UGT2B28 KO had unchanged kynurenine levels, but reduced levels of anthranilate, by 39%  as a sugar donor in the conjugation reaction. We thus examined metabolites linked to the uronic and hexosamine pathways, which are synthesised from glycolytic intermediates. In UGT2B17 and UGT2B28 complete gene deletions differentially influence the systemic metabolome, affecting levels of important classes of metabolites. Some of these metabolites were reported to be altered in previous PCa metabolomic studies and to promote PCa cancer growth and invasion/metastasis, such as steroids and ceramides [UGT2B17 and UGT2B28 expression levels have been associated with the progression of PCa [UGT2B17 and UGT2B28 KO PCa cases, despite a shared 84.3% nucleotide sequence identity between the two genes, a similar tissue distribution (present in hepatic and prostate tissues) and overlapping steroidogenic substrates  to biochemical recurrence of PCa after prostatectomy and lower plasma levels of LPC (18:0) to an increased risk of PCa [UGT2B28 KO individuals. Because the inflammatory shift affected many oxylipins and other inflammatory metabolites, a feedforward loop or mechanism independent of the known enzymatic activity of UGT2B28, such as protein interactions between UGT2B28 and biosynthetic enzymes such as LOX or CYPs, may be envisioned. Consistent with this hypothesis, functional protein interactions between other UGTs and CYP enzymes have been reported, with an influence on enzymatic functions and cancer cell phenotypes [UGT2B28 KO, which appears related to levels of bradykinin [UGT2B28 KO. Both peptides are produced by kallikrein peptidases that include the PSA encoded by KLK3, used in the screening and monitoring of PCa [Circulating levels of bile acids were reduced in both cs study . Furthercs study . The lowk of PCa , 57. Amok of PCa . The oveenotypes , 60. Besadykinin , also reg of PCa .UGT2B17 KO, the higher kynurenine and downstream metabolites suggested activation of this pathway. Higher kynurenine concentrations have been reported in prostatic tumoral tissues and in serum samples of PCa cases compared to controls and are associated with a more aggressive disease [UGT2B17 KO. However, the mechanism by which UGT2B17 affects the kynurenine pathway remains to be elucidated and does not seem to be caused by direct glucuronidation of tryptophan or kynurenine on the basis of our functional in vitro assays (not shown).In  disease –64 in keUGT KO remain to be fully examined. As blood samples were collected at surgery prior to removal of primary tumours, metabolic profiles may reflect in part tumour activity in addition to the systemic disease state in the context of UGT KO. Nonetheless, control PCa cases were carefully matched for the aggressiveness of the disease as well as patient age, limiting the impact of disease-specific metabolic changes. UDP-activated sugars, including the co-substrate UDP-GlcA preferentially used in the glucuronidation reaction by UGT2B enzymes, were not measured by the MS approaches used. The study confirms our hypothesis of a broad metabolic rewiring caused by germline UGT KOs. The circulating metabolome reflects the overall impact of the complete absence of UGT2B17 and UGT2B28 in each tissue where they are normally expressed. The metabolic activity of UGTs in each tissue, and also their interplay with other metabolic pathways, are likely to contribute to the systemic metabolic alterations associated with UGT KO. Some of the observed changes may represent the consequences of direct conjugation of metabolites but also adaptive metabolism in the absence of UGT enzyme, indirectly resulting from the perturbed homoeostasis of endogenous metabolites created by the UGT gene losses and feedforward regulatory loops. Other mechanisms may be involved, such as protein–protein interactions with an impact on metabolite levels possibly unrelated to their transferase activity. Our studies have recently hinted that the UGT enzymes may participate in other cellular and metabolic functions by this process that is also supported by their subcellular localisation not limited to the endoplasmic reticulum and depending on their tissue-specific expression [Limitations of this study focused on PCa include that only men were analysed and the consequences of pression .UGT KO genotypes, especially for UGT2B28, this study is the first to include sufficient cases to inform on global metabolic perturbations of these complete human gene KO. The important classes of metabolic pathways modified in UGT2B17 KO are elevated ceramides, kynurenine and triacylglycerol, all associated with adverse PCa outcomes [UGT2B28 KO, including oxylipins and other inflammatory mediators but with an increase in ceramides/sphingolipids precursors, also linked to PCa cancer growth and invasion/metastasis [UGT gene loss at the systemic level, well beyond steroidogenesis, providing potential novel insights on how UGT2B17 and UGT2B28 may differentially influence the course of PCa. Findings may also be relevant to several other clinical conditions also associated with UGT KO, including solid and haematological cancers, bone mineral density and osteoporosis, and autoimmune diseases [UGT KO pinpoint possible vulnerabilities that deserve further characterisation, including those of the steroidome and inflammation.Given the relatively low frequency of the outcomes , 65, 66,tastasis , 49. Ourdiseases , 67, 68.Supplemental MaterialSupplementary Table S3ASupplementary Table S3BSupplementary Table S4ASupplementary Table S4BSupplementary Table S5Reporting Summary form