Expanding flavone and flavonol production capabilities in Escherichia coli.

Flavones and flavonols are important classes of flavonoids with nutraceutical and pharmacological value, and their production by fermentation with recombinant microorganisms promises to be a scalable and economically favorable alternative to extraction from plant sources. Flavones and flavonols have been produced recombinantly in a number of microorganisms, with Saccharomyces cerevisiae typically being a preferred production host for these compounds due to higher yields and titers of precursor compounds, as well as generally improved ability to functionally express cytochrome P450 enzymes without requiring modification to improve their solubility. Recently, a rapid prototyping platform has been developed for high-value compounds in E. coli, and a number of gatekeeper (2S)-flavanones, from which flavones and flavonols can be derived, have been produced to high titers in E. coli using this platform. In this study, we extended these metabolic pathways using the previously reported platform to produce apigenin, chrysin, luteolin and kaempferol from the gatekeeper flavonoids naringenin, pinocembrin and eriodictyol by the expression of either type-I flavone synthases (FNS-I) or type-II flavone synthases (FNS-II) for flavone biosynthesis, and by the expression of flavanone 3-dioxygenases (F3H) and flavonol synthases (FLS) for the production of the flavonol kaempferol. In our best-performing strains, titers of apigenin and kaempferol reached 128 mg L-1 and 151 mg L-1 in 96-DeepWell plates in cultures supplemented with an additional 3 mM tyrosine, though titers for chrysin (6.8 mg L-1) from phenylalanine, and luteolin (5.0 mg L-1) from caffeic acid were considerably lower. In strains with upregulated tyrosine production, apigenin and kaempferol titers reached 80.2 mg L-1 and 42.4 mg L-1 respectively, without the further supplementation of tyrosine beyond the amount present in the rich medium. Notably, the highest apigenin, chrysin and luteolin titers were achieved with FNS-II enzymes, suggesting that cytochrome P450s can show competitive performance compared with non-cytochrome P450 enzymes in prokaryotes for the production of flavones.

Mapping the Initial Stages of a Protective Pathway that Enhances Catalytic Turnover by a Lytic Polysaccharide Monooxygenase.

Oxygenase and peroxygenase enzymes generate intermediates at their active sites which bring about the controlled functionalization of inert C-H bonds in substrates, such as in the enzymatic conversion of methane to methanol. To be viable catalysts, however, these enzymes must also prevent oxidative damage to essential active site residues, which can occur during both coupled and uncoupled turnover. Herein, we use a combination of stopped-flow spectroscopy, targeted mutagenesis, TD-DFT calculations, high-energy resolution fluorescence detection X-ray absorption spectroscopy, and electron paramagnetic resonance spectroscopy to study two transient intermediates that together form a protective pathway built into the active sites of copper-dependent lytic polysaccharide monooxygenases (LPMOs). First, a transient high-valent species is generated at the copper histidine brace active site following treatment of the LPMO with either hydrogen peroxide or peroxyacids in the absence of substrate. This intermediate, which we propose to be a CuII-(histidyl radical), then reacts with a nearby tyrosine residue in an intersystem-crossing reaction to give a ferromagnetically coupled (S = 1) CuII-tyrosyl radical pair, thereby restoring the histidine brace active site to its resting state and allowing it to re-enter the catalytic cycle through reduction. This process gives the enzyme the capacity to minimize damage to the active site histidine residues "on the fly" to increase the total turnover number prior to enzyme deactivation, highlighting how oxidative enzymes are evolved to protect themselves from deleterious side reactions during uncoupled turnover.

Metabolomics of Escherichia coli for Disclosing Novel Metabolic Engineering Strategies for Enhancing Hydrogen and Ethanol Production.

The biological production of hydrogen is an appealing approach to mitigating the environmental problems caused by the diminishing supply of fossil fuels and the need for greener energy. Escherichia coli is one of the best-characterized microorganisms capable of consuming glycerol-a waste product of the biodiesel industry-and producing H2 and ethanol. However, the natural capacity of E. coli to generate these compounds is insufficient for commercial or industrial purposes. Metabolic engineering allows for the rewiring of the carbon source towards H2 production, although the strategies for achieving this aim are difficult to foresee. In this work, we use metabolomics platforms through GC-MS and FT-IR techniques to detect metabolic bottlenecks in the engineered ΔldhΔgndΔfrdBC::kan (M4) and ΔldhΔgndΔfrdBCΔtdcE::kan (M5) E. coli strains, previously reported as improved H2 and ethanol producers. In the M5 strain, increased intracellular citrate and malate were detected by GC-MS. These metabolites can be redirected towards acetyl-CoA and formate by the overexpression of the citrate lyase (CIT) enzyme and by co-overexpressing the anaplerotic human phosphoenol pyruvate carboxykinase (hPEPCK) or malic (MaeA) enzymes using inducible promoter vectors. These strategies enhanced specific H2 production by up to 1.25- and 1.49-fold, respectively, compared to the reference strains. Other parameters, such as ethanol and H2 yields, were also enhanced. However, these vectors may provoke metabolic burden in anaerobic conditions. Therefore, alternative strategies for a tighter control of protein expression should be addressed in order to avoid undesirable effects in the metabolic network.

Volatile Aroma Compound Production Is Affected by Growth Rate in S. cerevisiae.

The initial growth rate of a yeast strain is a key parameter in the production of fermented beverages. Fast growth is linked with higher fermentative capacity and results in less slow and stuck fermentations unable to reach the expected final gravity. As concentrations of metabolites are in a constant state of flux, quantitative data on how growth rate affects the production of aromatic compounds becomes an important factor for brewers. Chemostats allow to set and keep a specific dilution rate throughout the fermentation and are ideal system to study the effect of growth on aroma production. In this study, we ran chemostats alongside batch and fed-batch cultures, compared volatile profiles detected at different growth rates, and identified those affected by the different feeding profiles. Specifically, we quantified six abundant aroma compounds produced in anaerobic glucose-limited continuous cultivations of S. cerevisiae at different dilution rates. We found that volatile production was affected by the growth rate in four out of six compounds assayed, with higher alcohols and esters following opposite trends. Batch and fed-batch fermentations were devised to study the extent by which the final concentration of volatile compounds is influenced by glucose availability. Compared with the batch system, fed-batch fermentations, where the yeast growth was artificially limited by a slow constant release of nutrients in the media, resulted in a significant increase in concentration of higher alcohols, mirroring the results obtained in continuous fermentations. This study paves the way to further process development optimization for the production of fermented beverages. IMPORTANCE The production of fermentation beverages will need to quickly adapt to changes in both the climate and customer demands, requiring the development of new strains and processes. Breakthroughs in the field are hindered by the limited knowledge on the interplay between physiology and aroma compound production in yeast. No quantitative data on how growth rate affects aroma profile is available in the literature to guide optimization of the complex flavors in fermented beverages. In this study, we exploited the chemostat system, alongside with batch and fed-batch cultures, to compare volatile profiles at different growth rates. We identified the aromatic compounds affected by the different feeding profiles and nutrient limitations. Moreover, we uncovered the correlation between yeast growth, esters, and higher alcohols production. This study showcases the potential of the application of feeding profiles for the manipulation of aroma in the craft beverage industry.

Untargeted saliva metabolomics by liquid chromatography-Mass spectrometry reveals markers of COVID-19 severity.

BACKGROUND: The COVID-19 pandemic is likely to represent an ongoing global health issue given the potential for new variants, vaccine escape and the low likelihood of eliminating all reservoirs of the disease. Whilst diagnostic testing has progressed at a fast pace, the metabolic drivers of outcomes-and whether markers can be found in different biofluids-are not well understood. Recent research has shown that serum metabolomics has potential for prognosis of disease progression. In a hospital setting, collection of saliva samples is more convenient for both staff and patients, and therefore offers an alternative sampling matrix to serum. METHODS: Saliva samples were collected from hospitalised patients with clinical suspicion of COVID-19, alongside clinical metadata. COVID-19 diagnosis was confirmed using RT-PCR testing, and COVID-19 severity was classified using clinical descriptors (respiratory rate, peripheral oxygen saturation score and C-reactive protein levels). Metabolites were extracted and analysed using high resolution liquid chromatography-mass spectrometry, and the resulting peak area matrix was analysed using multivariate techniques. RESULTS: Positive percent agreement of 1.00 between a partial least squares-discriminant analysis metabolomics model employing a panel of 6 features (5 of which were amino acids, one that could be identified by formula only) and the clinical diagnosis of COVID-19 severity was achieved. The negative percent agreement with the clinical severity diagnosis was also 1.00, leading to an area under receiver operating characteristics curve of 1.00 for the panel of features identified. CONCLUSIONS: In this exploratory work, we found that saliva metabolomics and in particular amino acids can be capable of separating high severity COVID-19 patients from low severity COVID-19 patients. This expands the atlas of COVID-19 metabolic dysregulation and could in future offer the basis of a quick and non-invasive means of sampling patients, intended to supplement existing clinical tests, with the goal of offering timely treatment to patients with potentially poor outcomes.

Metabolomics for the design of new metabolic engineering strategies for improving aerobic succinic acid production in Escherichia coli.

INTRODUCTION: Glycerol is a byproduct from the biodiesel industry that can be biotransformed by Escherichia coli to high added-value products such as succinate under aerobic conditions. The main genetic engineering strategies to achieve this aim involve the mutation of succinate dehydrogenase (sdhA) gene and also those responsible for acetate synthesis including acetate kinase, phosphate acetyl transferase and pyruvate oxidase encoded by ackA, pta and pox genes respectively in the ΔsdhAΔack-ptaΔpox (M4) mutant. Other genetic manipulations to rewire the metabolism toward succinate consist on the activation of the glyoxylate shunt or blockage the pentose phosphate pathway (PPP) by deletion of isocitrate lyase repressor (iclR) or gluconate dehydrogenase (gnd) genes on M4-ΔiclR and M4-Δgnd mutants respectively. OBJECTIVE: To deeply understand the effect of the blocking of the pentose phosphate pathway (PPP) or the activation of the glyoxylate shunt, metabolite profiles were analyzed on M4-Δgnd, M4-ΔiclR and M4 mutants. METHODS: Metabolomics was performed by FT-IR and GC-MS for metabolite fingerprinting and HPLC for quantification of succinate and glycerol. RESULTS: Most of the 65 identified metabolites showed lower relative levels in the M4-ΔiclR and M4-Δgnd mutants than those of the M4. However, fructose 1,6-biphosphate, trehalose, isovaleric acid and mannitol relative concentrations were increased in M4-ΔiclR and M4-Δgnd mutants. To further improve succinate production, the synthesis of mannitol was suppressed by deletion of mannitol dehydrogenase (mtlD) on M4-ΔgndΔmtlD mutant that increase ~ 20% respect to M4-Δgnd. CONCLUSION: Metabolomics can serve as a holistic tool to identify bottlenecks in metabolic pathways by a non-rational design. Genetic manipulation to release these restrictions could increase the production of succinate.

Comparing root exudate collection techniques: An improved hybrid method.

1. Plant-microbe interactions are critical for ecosystem functioning and drive rhizosphere processes. Root exudates are an important soil carbon (C) input, as well as a mechanism for communication between plants and rhizosphere microbes, but are notoriously difficult to extract and characterise. Common methods produce either substantial noise from the soil or do not mimic natural systems. Optimising methods for root exudate collection in soil is crucial for advancing our understanding of root-microbe interactions under changing environmental conditions. 2. Hybrid root exudate collection methods, where plants are grown in soil and transferred to hydroponics for exudate collection after root washing, might offer an ecologically relevant alternative to existing approaches. However, this method causes potential root damage as well as osmosis and subsequent leaking of cell contents. Here, we assessed different 'root recovery' periods after root washing and before hybrid root exudate collection, by comparing root exudate quantity and quality with both damaged root extracts and with leachates collected from the intact root-soil system. This was done across three common grassland species representing three functional groups. 3. We found that root exudate profiles of the shortest recovery period (0 days) were similar to damaged root extracts and were very high in C. With an increasing period of root recovery, profiles were more similar to leachates collected from the intact root-soil system, and C concentrations decreased. While both hybrid and leachate collection methods separated species by their root exudate profiles, the hybrid method was less variable in terms of the amount of C measured and provided a more diverse and abundant metabolome with better identification of metabolites. 4. Our results show that a recovery period after root washing of at least 3 days is critical to prevent root damage bias in hybrid collection methods, and that our hybrid method yields exudates that discriminate between species. Our data also suggest that exudates collected with this hybrid method are ecologically valid, which is vital for gaining a mechanistic understanding of their role in ecosystem functioning.

Prototyping of microbial chassis for the biomanufacturing of high-value chemical targets.

Metabolic engineering technologies have been employed with increasing success over the last three decades for the engineering and optimization of industrial host strains to competitively produce high-value chemical targets. To this end, continued reductions in the time taken from concept, to development, to scale-up are essential. Design-Build-Test-Learn pipelines that are able to rapidly deliver diverse chemical targets through iterative optimization of microbial production strains have been established. Biofoundries are employing in silico tools for the design of genetic parts, alongside combinatorial design of experiments approaches to optimize selection from within the potential design space of biological circuits based on multi-criteria objectives. These genetic constructs can then be built and tested through automated laboratory workflows, with performance data analysed in the learn phase to inform further design. Successful examples of rapid prototyping processes for microbially produced compounds reveal the potential role of biofoundries in leading the sustainable production of next-generation bio-based chemicals.

Validating Differential Volatilome Profiles in Parkinson's Disease.

Parkinson's disease (PD) is a progressive neurodegenerative disorder that does not currently have a robust clinical diagnostic test. Nonmotor symptoms such as skin disorders have long since been associated with the disease, and more recently a characteristic odor emanating from the skin of people with Parkinson's has been identified. Here, dynamic head space (DHS) thermal desorption (TD) gas chromatography-mass spectrometry (GC-MS) is implemented to directly measure the volatile components of sebum on swabs sampled from people with Parkinson's-both drug naïve and those on PD medications (n = 100) and control subjects (n = 29). Supervised multivariate analyses of data showed 84.4% correct classification of PD cases using all detected volatile compounds. Variable importance in projection (VIP) scores were generated from these data, which revealed eight features with VIP > 1 and p < 0.05 which all presented a downregulation within the control cohorts. Purified standards based on previously annotated analytes of interest eicosane and octadecanal did not match to patient sample data, although multiple metabolite features are annotated with these compounds all with high spectral matches indicating the presence of a series of similar structured species. DHS-TD-GC-MS analysis of a range of lipid standards has revealed the presence of common hydrocarbon species rather than differentiated intact compounds which are hypothesized to be breakdown products of lipids. This replication study validates that a differential volatile profile between control and PD cohorts can be measured using an analytical method that measures volatile compounds directly from skin swabs.

Assessment of Transdermal Delivery of Topical Compounds in Skin Scarring Using a Novel Combined Approach of Raman Spectroscopy and High-Performance Liquid Chromatography.

Objective: The goal of any topical formulation is efficient transdermal delivery of its active components. However, delivery of compounds can be problematic with penetration through tough layers of fibrotic dermal scar tissue. Approach: We propose a new approach combining high-performance liquid chromatography (HPLC) and Raman spectroscopy (RS) using a topical of unknown composition against a well-known antiscar topical (as control). Results: Positive detection of compounds within the treatment topical using both techniques was validated with mass spectrometry. RS detected conformational structural changes; the 1,655/1,446 cm-1 ratio estimating collagen content significantly decreased (p < 0.05) over weeks 4, 12, and 16 compared with day 0. The amide I band, known to represent collagen and protein in skin, shifted from 1,667 to 1,656 cm-1, which may represent a change from ß-sheets in elastin to α-helices in collagen. Confirmatory elastin immunohistochemistry decreased compared with day 0, conversely the collagen I/III ratio increased in the same samples by week 12 (p < 0.05, and p < 0.0001, respectively), in keeping with normal scar formation. Optical coherence tomography attenuation coefficient representing collagen deposition was significantly decreased at week 4 compared with day 0 and increased at week 16 (p < 0.05). Innovation: This study provides a platform for further research on the simultaneous evaluation of the effects of compounds in cutaneous scarring by RS and HPLC, and identifies a role for RS in the therapeutic evaluation and theranostic management of skin scarring. Conclusions: RS can provide noninvasive information on the effects of topicals on scar pathogenesis and structural composition, validated by other analytical techniques.

Engineering Escherichia coli towards de novo production of gatekeeper (2S)-flavanones: naringenin, pinocembrin, eriodictyol and homoeriodictyol.

Natural plant-based flavonoids have drawn significant attention as dietary supplements due to their potential health benefits, including anti-cancer, anti-oxidant and anti-asthmatic activities. Naringenin, pinocembrin, eriodictyol and homoeriodictyol are classified as (2S)-flavanones, an important sub-group of naturally occurring flavonoids, with wide-reaching applications in human health and nutrition. These four compounds occupy a central position as branch point intermediates towards a broad spectrum of naturally occurring flavonoids. Here, we report the development of Escherichia coli production chassis for each of these key gatekeeper flavonoids. Selection of key enzymes, genetic construct design and the optimization of process conditions resulted in the highest reported titers for naringenin (484 mg/l), improved production of pinocembrin (198 mg/l) and eriodictyol (55 mg/l from caffeic acid), and provided the first example of in vivo production of homoeriodictyol directly from glycerol (17 mg/l). This work provides a springboard for future production of diverse downstream natural and non-natural flavonoid targets.

Targeting Methionine Synthase in a Fungal Pathogen Causes a Metabolic Imbalance That Impacts Cell Energetics, Growth, and Virulence.

There is an urgent need to develop novel antifungals to tackle the threat fungal pathogens pose to human health. Here, we have performed a comprehensive characterization and validation of the promising target methionine synthase (MetH). We show that in Aspergillus fumigatus the absence of this enzymatic activity triggers a metabolic imbalance that causes a reduction in intracellular ATP, which prevents fungal growth even in the presence of methionine. Interestingly, growth can be recovered in the presence of certain metabolites, which shows that metH is a conditionally essential gene and consequently should be targeted in established infections for a more comprehensive validation. Accordingly, we have validated the use of the tetOFF genetic model in fungal research and improved its performance in vivo to achieve initial validation of targets in models of established infection. We show that repression of metH in growing hyphae halts growth in vitro, which translates into a beneficial effect when targeting established infections using this model in vivo Finally, a structure-based virtual screening of methionine synthases reveals key differences between the human and fungal structures and unravels features in the fungal enzyme that can guide the design of novel specific inhibitors. Therefore, methionine synthase is a valuable target for the development of new antifungals.IMPORTANCE Fungal pathogens are responsible for millions of life-threatening infections on an annual basis worldwide. The current repertoire of antifungal drugs is very limited and, worryingly, resistance has emerged and already become a serious threat to our capacity to treat fungal diseases. The first step to develop new drugs is often to identify molecular targets in the pathogen whose inhibition during infection can prevent its growth. However, the current models are not suitable to validate targets in established infections. Here, we have characterized the promising antifungal target methionine synthase in great detail, using the prominent fungal pathogen Aspergillus fumigatus as a model. We have uncovered the underlying reason for its essentiality and confirmed its druggability. Furthermore, we have optimized the use of a genetic system to show a beneficial effect of targeting methionine synthase in established infections. Therefore, we believe that antifungal drugs to target methionine synthase should be pursued and additionally, we provide a model that permits gaining information about the validity of antifungal targets in established infections.

Rapid prototyping of microbial production strains for the biomanufacture of potential materials monomers.

Bio-based production of industrial chemicals using synthetic biology can provide alternative green routes from renewable resources, allowing for cleaner production processes. To efficiently produce chemicals on-demand through microbial strain engineering, biomanufacturing foundries have developed automated pipelines that are largely compound agnostic in their time to delivery. Here we benchmark the capabilities of a biomanufacturing pipeline to enable rapid prototyping of microbial cell factories for the production of chemically diverse industrially relevant material building blocks. Over 85 days the pipeline was able to produce 17 potential material monomers and key intermediates by combining 160 genetic parts into 115 unique biosynthetic pathways. To explore the scale-up potential of our prototype production strains, we optimized the enantioselective production of mandelic acid and hydroxymandelic acid, achieving gram-scale production in fed-batch fermenters. The high success rate in the rapid design and prototyping of microbially-produced material building blocks reveals the potential role of biofoundries in leading the transition to sustainable materials production.

A microbiome and metabolomic signature of phases of cutaneous healing identified by profiling sequential acute wounds of human skin: An exploratory study.

Profiling skin microbiome and metabolome has been utilised to gain further insight into wound healing processes. The aims of this multi-part temporal study in 11 volunteers were to analytically profile the dynamic wound tissue and headspace metabolome and sequence microbial communities in acute wound healing at days 0, 7, 14, 21 and 28, and to investigate their relationship to wound healing, using non-invasive quantitative devices. Metabolites were obtained using tissue extraction, sorbent and polydimethylsiloxane patches and analysed using GCMS. PCA of wound tissue metabolome clearly separated time points with 10 metabolites of 346 being involved in separation. Analysis of variance-simultaneous component analysis identified a statistical difference between the wound headspace metabolome, sites (P = 0.0024) and time points (P<0.0001), with 10 out of the 129 metabolites measured involved with this separation between sites and time points. A reciprocal relationship between Staphylococcus spp. and Propionibacterium spp. was observed at day 21 (P<0.05) with a statistical correlation between collagen and Propionibacterium (r = 0.417; P = 0.038) and Staphylococcus (r = -0.434; P = 0.03). Procrustes analysis showed a statistically significant similarity between wound headspace and tissue metabolome with non-invasive wound devices. This exploratory study demonstrates the temporal and dynamic nature of acute wound metabolome and microbiome presenting a novel class of biomarkers that correspond to wound healing, with further confirmatory studies now necessary.

Integrated Probabilistic Annotation: A Bayesian-Based Annotation Method for Metabolomic Profiles Integrating Biochemical Connections, Isotope Patterns, and Adduct Relationships.

In a typical untargeted metabolomics experiment, the huge amount of complex data generated by mass spectrometry necessitates automated tools for the extraction of useful biological information. Each metabolite generates numerous mass spectrometry features. The association of these experimental features to the underlying metabolites still represents one of the major bottlenecks in metabolomics data processing. While certain identification (e.g., by comparison to authentic standards) is always desirable, it is usually achievable only for a limited number of compounds, and scientists often deal with a significant amount of putatively annotated metabolites. The confidence in a specific annotation is usually assessed by considering different sources of information (e.g., isotope patterns, adduct formation, chromatographic retention times, and fragmentation patterns). IPA (integrated probabilistic annotation) offers a rigorous and reproducible method to automatically annotate metabolite profiles and evaluate the resulting confidence of the putative annotations. It is able to provide a rigorous measure of our confidence in any putative annotation and is also able to update and refine our beliefs (i.e., background prior knowledge) by incorporating different sources of information in the annotation process, such as isotope patterns, adduct formation and biochemical relations. The IPA package is freely available on GitHub ( https://github.com/francescodc87/IPA ), together with the related extensive documentation.

An automated pipeline for the screening of diverse monoterpene synthase libraries.

Monoterpenoids are a structurally diverse group of natural products with applications as pharmaceuticals, flavourings, fragrances, pesticides, and biofuels. Recent advances in synthetic biology offer new routes to this chemical diversity through the introduction of heterologous isoprenoid production pathways into engineered microorganisms. Due to the nature of the branched reaction mechanism, monoterpene synthases often produce multiple products when expressed in monoterpenoid production platforms. Rational engineering of terpene synthases is challenging due to a lack of correlation between protein sequence and cyclisation reaction catalysed. Directed evolution offers an attractive alternative protein engineering strategy as limited prior sequence-function knowledge is required. However, directed evolution of terpene synthases is hampered by the lack of a convenient high-throughput screening assay for the detection of multiple volatile terpene products. Here we applied an automated pipeline for the screening of diverse monoterpene synthase libraries, employing robotic liquid handling platforms coupled to GC-MS, and automated data extraction. We used the pipeline to screen pinene synthase variant libraries, with mutations in three areas of plasticity, capable of producing multiple monoterpene products. We successfully identified variants with altered product profiles and demonstrated good agreement between the results of the automated screen and traditional shake-flask cultures. In addition, useful insights into the cyclisation reaction catalysed by pinene synthase were obtained, including the identification of positions with the highest level of plasticity, and the significance of region 2 in carbocation cyclisation. The results obtained will aid the prediction and design of novel terpene synthase activities towards clean monoterpenoid products.

Enhanced Fatty Acid Scavenging and Glycerophospholipid Metabolism Accompany Melanocyte Neoplasia Progression in Zebrafish.

Alterations in lipid metabolism in cancer cells impact cell structure, signaling, and energy metabolism, making lipid metabolism a potential diagnostic marker and therapeutic target. In this study, we combined PET, desorption electrospray ionization-mass spectrometry (DESI-MS), nonimaging MS, and transcriptomic analyses to interrogate changes in lipid metabolism in a transgenic zebrafish model of oncogenic RAS-driven melanocyte neoplasia progression. Exogenous fatty acid uptake was detected in melanoma tumor nodules by PET using the palmitic acid surrogate tracer 14(R,S)-18F-fluoro-6-thia-heptadecanoic acid ([18F]-FTHA), consistent with upregulation of genes associated with fatty acid uptake found through microarray analysis. DESI-MS imaging revealed that FTHA uptake in tumors was heterogeneous. Transcriptome and lipidome analyses further highlighted dysregulation of glycerophospholipid pathways in melanoma tumor nodules, including increased abundance of phosphatidyl ethanolamine and phosphatidyl choline species, corroborated by DESI-MS, which again revealed heterogeneous phospholipid composition in tumors. Overexpression of the gene encoding lipoprotein lipase (LPL), which was upregulated in zebrafish melanocyte tumor nodules and expressed in the majority of human melanomas, accelerated progression of oncogenic RAS-driven melanocyte neoplasia in zebrafish. Depletion or antagonism of LPL suppressed human melanoma cell growth; this required simultaneous fatty acid synthase (FASN) inhibition when FASN expression was also elevated. Collectively, our findings implicate fatty acid acquisition as a possible therapeutic target in melanoma, and the methods we developed for monitoring fatty acid uptake have potential for diagnosis, patient stratification, and monitoring pharmacologic response. SIGNIFICANCE: These findings demonstrate the translational potential of monitoring fatty acid uptake and identify lipoprotein lipase as a potential therapeutic target in melanoma.

High throughput screening of complex biological samples with mass spectrometry - from bulk measurements to single cell analysis.

High throughput screening (HTS) of molecular analytes is in high demand from and implemented in many areas of chemistry, medicine and industrial biotechnology including the discovery of biomarkers and the development of new chemical entities. Despite its prevalence, technical challenges remain in many of the new application areas of HTS which require rapid results from complex mixtures, for example in: screening biotransformations; targeted metabolomics; and in locating drugs and/or metabolites in biological matrices. Common to all of these are lengthy and costly sample preparation stages, involving recovery from cell cultures, extractions followed by low throughput LC-MS/MS methods or specific fluorescence measurements. In the latter the target molecules need to be inherently fluorescent or to include a fluorescent label or tag which can adversely influence a cellular system. Direct infusion mass spectrometry coupled with robotic sample infusion is a viable contender for information rich HTS with sub-second analysis times, and recent developments in ambient ionisation have heralded a new era where screening can be performed on crude cell lysates or even from live cells. Besides commercially available technologies such as RapidFire, Acoustic Mist Ionisation, and the TriVersa ChipMate there are promising new developments from academic groups. Novel applications using desorption electrospray ionisation, microfluidics, rapid LC-separation and 'one cell' direct infusion methods offer much potential for increasing throughput from 'messy' complex samples and for significantly reducing the amount of material that needs to be analysed. Here we review recent advances in HTS coupled with MS with an emphasis on methods that reduce or remove all sample preparation and will facilitate single cell screening approaches.

Highly multiplexed, fast and accurate nanopore sequencing for verification of synthetic DNA constructs and sequence libraries.

Synthetic biology utilizes the Design-Build-Test-Learn pipeline for the engineering of biological systems. Typically, this requires the construction of specifically designed, large and complex DNA assemblies. The availability of cheap DNA synthesis and automation enables high-throughput assembly approaches, which generates a heavy demand for DNA sequencing to verify correctly assembled constructs. Next-generation sequencing is ideally positioned to perform this task, however with expensive hardware costs and bespoke data analysis requirements few laboratories utilize this technology in-house. Here a workflow for highly multiplexed sequencing is presented, capable of fast and accurate sequence verification of DNA assemblies using nanopore technology. A novel sample barcoding system using polymerase chain reaction is introduced, and sequencing data are analyzed through a bespoke analysis algorithm. Crucially, this algorithm overcomes the problem of high-error rate nanopore data (which typically prevents identification of single nucleotide variants) through statistical analysis of strand bias, permitting accurate sequence analysis with single-base resolution. As an example, 576 constructs (6 × 96 well plates) were processed in a single workflow in 72 h (from Escherichia coli colonies to analyzed data). Given our procedure's low hardware costs and highly multiplexed capability, this provides cost-effective access to powerful DNA sequencing for any laboratory, with applications beyond synthetic biology including directed evolution, single nucleotide polymorphism analysis and gene synthesis.

Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli.

The field of synthetic biology aims to make the design of biological systems predictable, shrinking the huge design space to practical numbers for testing. When designing microbial cell factories, most optimization efforts have focused on enzyme and strain selection/engineering, pathway regulation, and process development. In silico tools for the predictive design of bacterial ribosome binding sites (RBSs) and RBS libraries now allow translational tuning of biochemical pathways; however, methods for predicting optimal RBS combinations in multigene pathways are desirable. Here we present the implementation of machine learning algorithms to model the RBS sequence-phenotype relationship from representative subsets of large combinatorial RBS libraries allowing the accurate prediction of optimal high-producers. Applied to a recombinant monoterpenoid production pathway in Escherichia coli, our approach was able to boost production titers by over 60% when screening under 3% of a library. To facilitate library screening, a multiwell plate fermentation procedure was developed, allowing increased screening throughput with sufficient resolution to discriminate between high and low producers. High producers from one library did not translate during scale-up, but the reduced screening requirements allowed rapid rescreening at the larger scale. This methodology is potentially compatible with any biochemical pathway and provides a powerful tool toward predictive design of bacterial production chassis.