Ethanol-lactate transition of Lachancea thermotolerans is linked to nitrogen metabolism.

Climate change increases sugar content in grapes, resulting in unwanted increase in ethanol content of wine. Lachancea thermotolerans ferments glucose and fructose into both ethanol and lactate, decreasing final ethanol content and positively affecting wine acidity. Reported Lachancea thermotolerans strains show big variation in lactate production during fermentation. However, a mechanistic understanding of this lactate producing phenotype is currently lacking. Through a combination of metabolomics, transcriptomics, genomics and computational methods we show that the lactate production is induced by amino acid limitation in a high lactate producing strain. We found in fermentations in synthetic grape juice media that lactate production starts in the last stages of growth, marked by decreased growth rate and increased expression levels of stress related genes. This onset of lactate production is specific for the high lactate producing strain and independent of oxygen availability. The onset of lactate production was changed by increased amino acid content of the media, and it is shown by both computational methods and amino acid measurements that at the onset of lactate production amino acids become limiting for growth. This study shows that lactate production of Lachancea thermotolerans is directly linked to nitrogen availability in the media, an insight that can further aid in the improvement of wine quality.

A microbial supply chain for production of the anti-cancer drug vinblastine.

Monoterpene indole alkaloids (MIAs) are a diverse family of complex plant secondary metabolites with many medicinal properties, including the essential anti-cancer therapeutics vinblastine and vincristine1. As MIAs are difficult to chemically synthesize, the world's supply chain for vinblastine relies on low-yielding extraction and purification of the precursors vindoline and catharanthine from the plant Catharanthus roseus, which is then followed by simple in vitro chemical coupling and reduction to form vinblastine at an industrial scale2,3. Here, we demonstrate the de novo microbial biosynthesis of vindoline and catharanthine using a highly engineered yeast, and in vitro chemical coupling to vinblastine. The study showcases a very long biosynthetic pathway refactored into a microbial cell factory, including 30 enzymatic steps beyond the yeast native metabolites geranyl pyrophosphate and tryptophan to catharanthine and vindoline. In total, 56 genetic edits were performed, including expression of 34 heterologous genes from plants, as well as deletions, knock-downs and overexpression of ten yeast genes to improve precursor supplies towards de novo production of catharanthine and vindoline, from which semisynthesis to vinblastine occurs. As the vinblastine pathway is one of the longest MIA biosynthetic pathways, this study positions yeast as a scalable platform to produce more than 3,000 natural MIAs and a virtually infinite number of new-to-nature analogues.

Analysing intracellular isoprenoid metabolites in diverse prokaryotic and eukaryotic microbes.

Isoprenoids, also known as terpenes or terpenoids, are a very large and diverse group of natural compounds. These compounds fulfil a myriad of critical roles in biology as well as having a wide range of industrial uses. Isoprenoids are produced via two chemically distinct metabolic pathways, the mevalonate (MVA) pathway and the methylerythritol phosphate (MEP) pathway. Downstream of these two pathways is the shared prenyl phosphate pathway. Because of their importance in both basic physiology and industrial biotechnology, extraction, identification, and quantification of isoprenoid pathway intermediates is an important protocol. Here we describe methods for extraction and analysis of intracellular metabolites from the MVA, MEP, and prenyl phosphate pathways for five key model microbes: the yeast Saccharomyces cerevisiae, the bacterium Escherichia coli, the diatom Phaeodactylum tricornutum, the green algae Chlamydomonas reinhardtii, and the cyanobacterium Synechocystis sp. PCC 6803. These methods also detect several central carbon intermediates. These protocols will likely work effectively, or be readily adaptable, to a variety of related microorganisms and metabolic pathways.

SmartPeak Automates Targeted and Quantitative Metabolomics Data Processing.

Technological advances in high-resolution mass spectrometry (MS) vastly increased the number of samples that can be processed in a life science experiment, as well as volume and complexity of the generated data. To address the bottleneck of high-throughput data processing, we present SmartPeak (https://github.com/AutoFlowResearch/SmartPeak), an application that encapsulates advanced algorithms to enable fast, accurate, and automated processing of capillary electrophoresis-, gas chromatography-, and liquid chromatography (LC)-MS(/MS) data and high-pressure LC data for targeted and semitargeted metabolomics, lipidomics, and fluxomics experiments. The application allows for an approximate 100-fold reduction in the data processing time compared to manual processing while enhancing quality and reproducibility of the results.

Adaptation of hydroxymethylbutenyl diphosphate reductase enables volatile isoprenoid production.

Volatile isoprenoids produced by plants are emitted in vast quantities into the atmosphere, with substantial effects on global carbon cycling. Yet, the molecular mechanisms regulating the balance between volatile and non-volatile isoprenoid production remain unknown. Isoprenoids are synthesised via sequential condensation of isopentenyl pyrophosphate (IPP) to dimethylallyl pyrophosphate (DMAPP), with volatile isoprenoids containing fewer isopentenyl subunits. The DMAPP:IPP ratio could affect the balance between volatile and non-volatile isoprenoids, but the plastidic DMAPP:IPP ratio is generally believed to be similar across different species. Here we demonstrate that the ratio of DMAPP:IPP produced by hydroxymethylbutenyl diphosphate reductase (HDR/IspH), the final step of the plastidic isoprenoid production pathway, is not fixed. Instead, this ratio varies greatly across HDRs from phylogenetically distinct plants, correlating with isoprenoid production patterns. Our findings suggest that adaptation of HDR plays a previously unrecognised role in determining in vivo carbon availability for isoprenoid emissions, directly shaping global biosphere-atmosphere interactions.

A White-Box Machine Learning Approach for Revealing Antibiotic Mechanisms of Action.

Current machine learning techniques enable robust association of biological signals with measured phenotypes, but these approaches are incapable of identifying causal relationships. Here, we develop an integrated "white-box" biochemical screening, network modeling, and machine learning approach for revealing causal mechanisms and apply this approach to understanding antibiotic efficacy. We counter-screen diverse metabolites against bactericidal antibiotics in Escherichia coli and simulate their corresponding metabolic states using a genome-scale metabolic network model. Regression of the measured screening data on model simulations reveals that purine biosynthesis participates in antibiotic lethality, which we validate experimentally. We show that antibiotic-induced adenine limitation increases ATP demand, which elevates central carbon metabolism activity and oxygen consumption, enhancing the killing effects of antibiotics. This work demonstrates how prospective network modeling can couple with machine learning to identify complex causal mechanisms underlying drug efficacy.

RapidRIP quantifies the intracellular metabolome of 7 industrial strains of E. coli.

Fast metabolite quantification methods are required for high throughput screening of microbial strains obtained by combinatorial or evolutionary engineering approaches. In this study, a rapid RIP-LC-MS/MS (RapidRIP) method for high-throughput quantitative metabolomics was developed and validated that was capable of quantifying 102 metabolites from central, amino acid, energy, nucleotide, and cofactor metabolism in less than 5 minutes. The method was shown to have comparable sensitivity and resolving capability as compared to a full length RIP-LC-MS/MS method (FullRIP). The RapidRIP method was used to quantify the metabolome of seven industrial strains of E. coli revealing significant differences in glycolytic, pentose phosphate, TCA cycle, amino acid, and energy and cofactor metabolites were found. These differences translated to statistically and biologically significant differences in thermodynamics of biochemical reactions between strains that could have implications when choosing a host for bioprocessing.

Quantifying the Metabolome of Pseudomonas taiwanensis VLB120: Evaluation of Hot and Cold Combined Quenching/Extraction Approaches.

Absolute quantification of free intracellular metabolites is a valuable tool in both pathway discovery and metabolic engineering. In this study, we conducted a comprehensive examination of different hot and cold combined quenching/extraction approaches to extract and quantify intracellular metabolites of Pseudomonas taiwanensis (P. taiwanensis) VLB120 to provide a useful reference data set of absolute intracellular metabolite concentrations. The suitability of commonly used metabolomics tools including a pressure driven fast filtration system followed by combined quenching/extraction techniques (such as cold methanol/acetonitrile/water, hot water, and boiling ethanol/water, as well as cold ethanol/water) were tested and evaluated for P. taiwanensis VLB120 metabolome analysis. In total 94 out of 107 detected intracellular metabolites were quantified using an isotope-ratio-based approach. The quantified metabolites include amino acids, nucleotides, central carbon metabolism intermediates, redox cofactors, and others. The acquired data demonstrate that the pressure driven fast filtration approach followed by boiling ethanol quenching/extraction is the most adequate technique for P. taiwanensis VLB120 metabolome analysis based on quenching efficiency, extraction yields of metabolites, and experimental reproducibility.

Analysis of glyphosate and aminomethylphosphonic acid in leaves from Coffea arabica using high performance liquid chromatography with quadrupole mass spectrometry detection.

Glyphosate is a commonly applied herbicide in coffee plantations. Because of its non-selective mode of action it can damage the crop exposed through spray drift. Therefore, it is of interest to study glyphosate fate in coffee plants. The aim of this study was to develop an analytical method for accurate and precise quantification of glyphosate and its main metabolite aminomethylphosphonic acid (AMPA) at trace levels in coffee leaves using liquid chromatography with single-quadrupole mass spectrometry detection. The method is based on a two-step solid phase extraction (SPE) with an intermediate derivatization reaction using 9-fluorenylmethylchloroformate (FMOC). An isotope dilution method was used to account for matrix effects and to enhance the confidence in analyte identification. The limit of quantification (LOQ) for glyphosate and AMPA in coffee leaves was 41 and 111 µg kg(-1) dry weight, respectively. For the method optimization a design of experiments (DOE) approach was used. The sample clean-up procedure can be simplified for the analysis of less challenging matrices, for laboratories having a tandem mass spectrometry detector and for cases in which quantification limits above 0.1 mg kg(-1) are acceptable, which is often the case for glyphosate. The method is robust, possesses high identification confidence, while being suitable for most commercial and academic laboratories. All leaf samples from five coffee fields analyzed (n=21) contained glyphosate, while AMPA was absent. The simplified clean-up procedure was successfully validated for coffee leaves, rice, black beans and river water.

Glyphosate spray drift in Coffea arabica - sensitivity of coffee plants and possible use of shikimic acid as a biomarker for glyphosate exposure.

Glyphosate is widely used in coffee plantations to control weeds. Lacking selectivity, glyphosate spray drift is suspected to cause adverse effects in coffee plants. Symptoms caused by glyphosate can be similar to those produced by other stress factors. However, shikimic acid accumulation should be a useful biomarker for glyphosate exposure as shown for other crops. The aim of this study was to assess the sensitivity of coffee plants towards glyphosate on different biological response variables and to evaluate the use of shikimic acid as biomarker. Dose-response experiments yielded ED50 values (50% effect dose) in the range of 38-550 ga.e.ha(-1) depending on the quantitative or qualitative variable monitored. The frequency of plants showing symptoms was the most sensitive variable. The best sampling time for shikimic acid accumulation was 1-2 weeks after glyphosate application, depending on experimental conditions. The highest shikimic acid accumulation was observed in young leaves. Shikimic acid is a suitable biomarker for a glyphosate exposure in coffee, using only young leaves for the analysis. Young coffee plants are susceptible to glyphosate damage. If symptoms are absent the risk of severe crop damage or yield loss is low.