SCALiR: A Web Application for Automating Absolute Quantification of Mass Spectrometry-Based Metabolomics Data.

Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming an important approach for studying complex biological systems but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process that is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (standard curve application for determining linear ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signals into absolute quantitative data (https://www.lewisresearchgroup.org/software). SCALiR uses an algorithm that automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from the LC-MS signal. Using a standard mix containing 77 metabolites, we show a close correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99 for a y = x curve fitting). Moreover, we demonstrate that SCALiR reproducibly calculates concentrations of midrange standards across ten analytical batches (average coefficient of variation 0.091). SCALiR can be used to calculate metabolite concentrations either using external calibration curves or by using internal standards to correct for matrix effects. This open-source and vendor agnostic software offers users several advantages in that (1) it requires only 10 s of analysis time to compute concentrations of >75 compounds, (2) it facilitates automation of quantitative workflows, and (3) it performs deterministic evaluations of compound quantification limits. SCALiR therefore provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

SCALiR: a web application for automating absolute quantification of mass spectrometry-based metabolomics data.

Metabolomics is an important approach for studying complex biological systems. Quantitative liquid chromatography-mass spectrometry (LC-MS)-based metabolomics is becoming a mainstream strategy but presents several technical challenges that limit its widespread use. Computing metabolite concentrations using standard curves generated from standard mixtures of known concentrations is a labor-intensive process which is often performed manually. Currently, there are few options for open-source software tools that can automatically calculate metabolite concentrations. Herein, we introduce SCALiR (Standard Curve Application for determining Linear Ranges), a new web-based software tool specifically built for this task, which allows users to automatically transform LC-MS signal data into absolute quantitative data (https://www.lewisresearchgroup.org/software). The algorithm used in SCALiR automatically finds the equation of the line of best fit for each standard curve and uses this equation to calculate compound concentrations from their LC-MS signal. Using a standard mix containing 77 metabolites, we found excellent correlation between the concentrations calculated by SCALiR and the expected concentrations of each compound (R2 = 0.99) and that SCALiR reproducibly calculated concentrations of mid-range standards across ten analytical batches (average coefficient of variation 0.091). SCALiR offers users several advantages, including that it (1) is open-source and vendor agnostic; (2) requires only 10 seconds of analysis time to compute concentrations of >75 compounds; (3) facilitates automation of quantitative workflows; and (4) performs deterministic evaluation of compound quantification limits. SCALiR provides the metabolomics community with a simple and rapid tool that enables rigorous and reproducible quantitative metabolomics studies.

Rapid LC-MS assay for targeted metabolite quantification by serial injection into isocratic gradients.

Liquid chromatography mass spectrometry (LC-MS) has emerged as a mainstream strategy for metabolomics analyses. One advantage of LC-MS is that it can serve both as a biomarker discovery tool and as a platform for clinical diagnostics. Consequently, it offers an exciting opportunity to potentially transition research studies into real-world clinical tools. One important distinction between research versus diagnostics-based applications of LC-MS is throughput. Clinical LC-MS must enable quantitative analyses of target molecules in hundreds or thousands of samples each day. Currently, the throughput of these clinical applications is limited by the chromatographic gradient lengths, which-when analyzing complex metabolomics samples-are difficult to conduct in under ~ 3 min per sample without introducing serious quantitative analysis problems. To address this shortcoming, we developed sequential quantification using isotope dilution (SQUID), an analytical strategy that combines serial sample injections into a continuous isocratic mobile phase to maximize throughput. SQUID uses internal isotope-labelled standards to correct for changes in LC-MS response factors over time. We show that SQUID can detect microbial polyamines in human urine specimens (lower limit of quantification; LLOQ = 106 nM) with less than 0.019 normalized root mean square error. Moreover, we show that samples can be analyzed in as little as 57 s. We propose SQUID as a new, high-throughput LC-MS tool for quantifying small sets of target biomarkers across large cohorts.

Microbial containment device: A platform for comprehensive analysis of microbial metabolism without sample preparation.

Metabolomics is a mainstream strategy for investigating microbial metabolism. One emerging application of metabolomics is the systematic quantification of metabolic boundary fluxes - the rates at which metabolites flow into and out of cultured cells. Metabolic boundary fluxes can capture complex metabolic phenotypes in a rapid assay, allow computational models to be built that predict the behavior of cultured organisms, and are an emerging strategy for clinical diagnostics. One advantage of quantifying metabolic boundary fluxes rather than intracellular metabolite levels is that it requires minimal sample processing. Whereas traditional intracellular analyses require a multi-step process involving extraction, centrifugation, and solvent exchange, boundary fluxes can be measured by simply analyzing the soluble components of the culture medium. To further simplify boundary flux analyses, we developed a custom 96-well sampling system-the Microbial Containment Device (MCD)-that allows water-soluble metabolites to diffuse from a microbial culture well into a bacteria-free analytical well via a semi-permeable membrane. The MCD was designed to be compatible with the autosamplers present in commercial liquid chromatography-mass spectrometry systems, allowing metabolic fluxes to be analyzed with minimal sample handling. Herein, we describe the design, evaluation, and performance testing of the MCD relative to traditional culture methods. We illustrate the utility of this platform, by quantifying the unique boundary fluxes of four bacterial species and demonstrate antibiotic-induced perturbations in their metabolic activity. We propose the use of the MCD for enabling single-step metabolomics sample preparation for microbial identification, antimicrobial susceptibility testing, and other metabolic boundary flux applications where traditional sample preparation methods are impractical.

Biomarker enrichment medium: A defined medium for metabolomic analysis of microbial pathogens.

Microbes have diverse metabolic capabilities and differences in these phenotypes are critical for differentiating strains, species, and broader taxa of microorganisms. Recent advances in liquid chromatography-mass spectrometry (LC-MS) allow researchers to track the complex combinations of molecules that are taken up by each cell type and to quantify the rates that individual metabolites enter or exit the cells. This metabolomics-based approach allows complex metabolic phenotypes to be captured in a single assay, enables computational models of microbial metabolism to be constructed, and can serve as a diagnostic approach for clinical microbiology. Unfortunately, metabolic phenotypes are directly affected by the molecular composition of the culture medium and many traditional media are subject to molecular-level heterogeneity. Herein, we show that commercially sourced Mueller Hinton (MH) medium, a Clinical and Laboratory Standards Institute (CLSI) approved medium for clinical microbiology, has significant lot-to-lot and supplier-to-supplier variability in the concentrations of individual nutrients. We show that this variability does not affect microbial growth rates but does affect the metabolic phenotypes observed in vitro-including metabolic phenotypes that distinguish six common pathogens. To address this, we used a combination of isotope-labeling, substrate exclusion, and nutritional supplementation experiments using Roswell Park Memorial Institute (RPMI) medium to identify the specific nutrients used by the microbes to produce diagnostic biomarkers, and to formulate a Biomarker Enrichment Medium (BEM) as an alternative to complex undefined media for metabolomics research, clinical diagnostics, antibiotic susceptibility testing, and other applications where the analysis of stable microbial metabolic phenotypes is important.

Methods for Quantifying the Metabolic Boundary Fluxes of Cell Cultures in Large Cohorts by High-Resolution Hydrophilic Liquid Chromatography Mass Spectrometry.

Metabolomics is a mainstream approach for investigating the metabolic underpinnings of complex biological phenomena and is increasingly being applied to large-scale studies involving hundreds or thousands of samples. Although metabolomics methods are robust in smaller-scale studies, they can be challenging to apply to larger cohorts due to the inherent variability of liquid chromatography mass spectrometry (LC-MS). Much of this difficulty results from the time-dependent changes in the LC-MS system, which affects both the qualitative and quantitative performances of the instrument. Herein, we introduce an analytical strategy for addressing this problem in large-scale microbial studies. Our approach quantifies microbial boundary fluxes using two zwitterionic hydrophilic interaction liquid chromatography (ZIC-HILIC) columns that are plumbed to enable offline column equilibration. Using this strategy, we show that over 397 common metabolites can be resolved in 4.5 min per sample and that metabolites can be quantified with a median coefficient of variation of 0.127 across 1100 technical replicates. We illustrate the utility of this strategy via an analysis of 960 strains of Staphylococcus aureus isolated from bloodstream infections. These data capture the diversity of metabolic phenotypes observed in clinical isolates and provide an example of how large-scale investigations can leverage our novel analytical strategy.

Metabolic preference assay for rapid diagnosis of bloodstream infections.

Bloodstream infections (BSIs) cause >500,000 infections and >80,000 deaths per year in North America. The length of time between the onset of symptoms and administration of appropriate antimicrobials is directly linked to mortality rates. It currently takes 2-5 days to identify BSI pathogens and measure their susceptibility to antimicrobials - a timeline that directly contributes to preventable deaths. To address this, we demonstrate a rapid metabolic preference assay (MPA) that uses the pattern of metabolic fluxes observed in ex-vivo microbial cultures to identify common pathogens and determine their antimicrobial susceptibility profiles. In a head-to-head race with a leading platform (VITEK 2, BioMérieux) used in diagnostic laboratories, MPA decreases testing timelines from 40 hours to under 20. If put into practice, this assay could reduce septic shock mortality and reduce the use of broad spectrum antibiotics.

Untargeted Metabolomics Investigation on Selenite Reduction to Elemental Selenium by Bacillus mycoides SeITE01.

Bacillus mycoides SeITE01 is an environmental isolate that transforms the oxyanion selenite ( SeO 3 2 - ) into the less bioavailable elemental selenium (Se0) forming biogenic selenium nanoparticles (Bio-SeNPs). In the present study, the reduction of sodium selenite (Na2SeO3) by SeITE01 strain and the effect of SeO 3 2 - exposure on the bacterial cells was examined through untargeted metabolomics. A time-course approach was used to monitor both cell pellet and cell free spent medium (referred as intracellular and extracellular, respectively) metabolites in SeITE01 cells treated or not with SeO 3 2 - . The results show substantial biochemical changes in SeITE01 cells when exposed to SeO 3 2 - . The initial uptake of SeO 3 2 - by SeITE01 cells (3h after inoculation) shows both an increase in intracellular levels of 4-hydroxybenzoate and indole-3-acetic acid, and an extracellular accumulation of guanosine, which are metabolites involved in general stress response adapting strategies. Proactive and defensive mechanisms against SeO 3 2 - are observed between the end of lag (12h) and beginning of exponential (18h) phases. Glutathione and N-acetyl-L-cysteine are thiol compounds that would be mainly involved in Painter-type reaction for the reduction and detoxification of SeO 3 2 - to Se0. In these growth stages, thiol metabolites perform a dual role, both acting against the toxic and harmful presence of the oxyanion and as substrate or reducing sources to scavenge ROS production. Moreover, detection of the amino acids L-threonine and ornithine suggests changes in membrane lipids. Starting from stationary phase (24 and 48h), metabolites related to the formation and release of SeNPs in the extracellular environment begin to be observed. 5-hydroxyindole acetate, D-[+]-glucosamine, 4-methyl-2-oxo pentanoic acid, and ethanolamine phosphate may represent signaling strategies following SeNPs release from the cytoplasmic compartment, with consequent damage to SeITE01 cell membranes. This is also accompanied by intracellular accumulation of trans-4-hydroxyproline and L-proline, which likely represent osmoprotectant activity. The identification of these metabolites suggests the activation of signaling strategies that would protect the bacterial cells from SeO 3 2 - toxicity while it is converting into SeNPs.

Bacterial cyclic diguanylate signaling networks sense temperature.

Many bacteria use the second messenger cyclic diguanylate (c-di-GMP) to control motility, biofilm production and virulence. Here, we identify a thermosensory diguanylate cyclase (TdcA) that modulates temperature-dependent motility, biofilm development and virulence in the opportunistic pathogen Pseudomonas aeruginosa. TdcA synthesizes c-di-GMP with catalytic rates that increase more than a hundred-fold over a ten-degree Celsius change. Analyses using protein chimeras indicate that heat-sensing is mediated by a thermosensitive Per-Arnt-SIM (PAS) domain. TdcA homologs are widespread in sequence databases, and a distantly related, heterologously expressed homolog from the Betaproteobacteria order Gallionellales also displayed thermosensitive diguanylate cyclase activity. We propose, therefore, that thermotransduction is a conserved function of c-di-GMP signaling networks, and that thermosensitive catalysis of a second messenger constitutes a mechanism for thermal sensing in bacteria.

Staphylococcus aureus induces an itaconate-dominated immunometabolic response that drives biofilm formation.

Staphylococcus aureus is a prominent human pathogen that readily adapts to host immune defenses. Here, we show that, in contrast to Gram-negative pathogens, S. aureus induces a distinct airway immunometabolic response dominated by the release of the electrophilic metabolite, itaconate. The itaconate synthetic enzyme, IRG1, is activated by host mitochondrial stress, which is induced by staphylococcal glycolysis. Itaconate inhibits S. aureus glycolysis and selects for strains that re-direct carbon flux to fuel extracellular polysaccharide (EPS) synthesis and biofilm formation. Itaconate-adapted strains, as illustrated by S. aureus isolates from chronic airway infection, exhibit decreased glycolytic activity, high EPS production, and proficient biofilm formation even before itaconate stimulation. S. aureus thus adapts to the itaconate-dominated immunometabolic response by producing biofilms, which are associated with chronic infection of the human airway.

Thermogenic hydrocarbon biodegradation by diverse depth-stratified microbial populations at a Scotian Basin cold seep.

At marine cold seeps, gaseous and liquid hydrocarbons migrate from deep subsurface origins to the sediment-water interface. Cold seep sediments are known to host taxonomically diverse microorganisms, but little is known about their metabolic potential and depth distribution in relation to hydrocarbon and electron acceptor availability. Here we combined geophysical, geochemical, metagenomic and metabolomic measurements to profile microbial activities at a newly discovered cold seep in the deep sea. Metagenomic profiling revealed compositional and functional differentiation between near-surface sediments and deeper subsurface layers. In both sulfate-rich and sulfate-depleted depths, various archaeal and bacterial community members are actively oxidizing thermogenic hydrocarbons anaerobically. Depth distributions of hydrocarbon-oxidizing archaea revealed that they are not necessarily associated with sulfate reduction, which is especially surprising for anaerobic ethane and butane oxidizers. Overall, these findings link subseafloor microbiomes to various biochemical mechanisms for the anaerobic degradation of deeply-sourced thermogenic hydrocarbons.

Stirred suspension bioreactors maintain naïve pluripotency of human pluripotent stem cells.

Due to their ability to standardize key physiological parameters, stirred suspension bioreactors can potentially scale the production of quality-controlled pluripotent stem cells (PSCs) for cell therapy application. Because of differences in bioreactor expansion efficiency between mouse (m) and human (h) PSCs, we investigated if conversion of hPSCs, from the conventional "primed" pluripotent state towards the "naïve" state prevalent in mPSCs, could be used to enhance hPSC production. Through transcriptomic enrichment of mechano-sensing signaling, the expression of epigenetic regulators, metabolomics, and cell-surface protein marker analyses, we show that the stirred suspension bioreactor environment helps maintain a naïve-like pluripotent state. Our research corroborates that converting hPSCs towards a naïve state enhances hPSC manufacturing and indicates a potentially important role for the stirred suspension bioreactor's mechanical environment in maintaining naïve-like pluripotency.

Microbiome-derived inosine modulates response to checkpoint inhibitor immunotherapy.

Several species of intestinal bacteria have been associated with enhanced efficacy of checkpoint blockade immunotherapy, but the underlying mechanisms by which the microbiome enhances antitumor immunity are unclear. In this study, we isolated three bacterial species-Bifidobacterium pseudolongum, Lactobacillus johnsonii, and Olsenella species-that significantly enhanced efficacy of immune checkpoint inhibitors in four mouse models of cancer. We found that intestinal B. pseudolongum modulated enhanced immunotherapy response through production of the metabolite inosine. Decreased gut barrier function induced by immunotherapy increased systemic translocation of inosine and activated antitumor T cells. The effect of inosine was dependent on T cell expression of the adenosine A2A receptor and required costimulation. Collectively, our study identifies a previously unknown microbial metabolite immune pathway activated by immunotherapy that may be exploited to develop microbial-based adjuvant therapies.

PelX is a UDP-N-acetylglucosamine C4-epimerase involved in Pel polysaccharide-dependent biofilm formation.

Pel is a GalNAc-rich bacterial polysaccharide that contributes to the structure and function of Pseudomonas aeruginosa biofilms. The pelABCDEFG operon is highly conserved among diverse bacterial species, and Pel may therefore be a widespread biofilm determinant. Previous annotation of pel gene clusters has helped us identify an additional gene, pelX, that is present adjacent to pelABCDEFG in >100 different bacterial species. The pelX gene is predicted to encode a member of the short-chain dehydrogenase/reductase (SDR) superfamily, but its potential role in Pel-dependent biofilm formation is unknown. Herein, we have used Pseudomonas protegens Pf-5 as a model to elucidate PelX function as Pseudomonas aeruginosa lacks a pelX homologue in its pel gene cluster. We found that P. protegens forms Pel-dependent biofilms; however, despite expression of pelX under these conditions, biofilm formation was unaffected in a ΔpelX strain. This observation led us to identify a pelX paralogue, PFL_5533, which we designate here PgnE, that appears to be functionally redundant to pelX In line with this, a ΔpelX ΔpgnE double mutant was substantially impaired in its ability to form Pel-dependent biofilms. To understand the molecular basis for this observation, we determined the structure of PelX to 2.1 Å resolution. The structure revealed that PelX resembles UDP-GlcNAc C4-epimerases. Using 1H NMR analysis, we show that PelX catalyzes the epimerization between UDP-GlcNAc and UDP-GalNAc. Our results indicate that Pel-dependent biofilm formation requires a UDP-GlcNAc C4-epimerase that generates the UDP-GalNAc precursors required by the Pel synthase machinery for polymer production.

Metabolic potential of uncultured bacteria and archaea associated with petroleum seepage in deep-sea sediments.

The lack of microbial genomes and isolates from the deep seabed means that very little is known about the ecology of this vast habitat. Here, we investigate energy and carbon acquisition strategies of microbial communities from three deep seabed petroleum seeps (3 km water depth) in the Eastern Gulf of Mexico. Shotgun metagenomic analysis reveals that each sediment harbors diverse communities of chemoheterotrophs and chemolithotrophs. We recovered 82 metagenome-assembled genomes affiliated with 21 different archaeal and bacterial phyla. Multiple genomes encode enzymes for anaerobic oxidation of aliphatic and aromatic compounds, including those of candidate phyla Aerophobetes, Aminicenantes, TA06 and Bathyarchaeota. Microbial interactions are predicted to be driven by acetate and molecular hydrogen. These findings are supported by sediment geochemistry, metabolomics, and thermodynamic modelling. Overall, we infer that deep-sea sediments experiencing thermogenic hydrocarbon inputs harbor phylogenetically and functionally diverse communities potentially sustained through anaerobic hydrocarbon, acetate and hydrogen metabolism.

An N-methyltransferase from Ephedra sinica catalyzing the formation of ephedrine and pseudoephedrine enables microbial phenylalkylamine production.

Phenylalkylamines, such as the plant compounds ephedrine and pseudoephedrine and the animal neurotransmitters dopamine and adrenaline, compose a large class of natural and synthetic molecules with important physiological functions and pharmaceutically valuable bioactivities. The final steps of ephedrine and pseudoephedrine biosynthesis in members of the plant genus Ephedra involve N-methylation of norephedrine and norpseudoephedrine, respectively. Here, using a plant transcriptome screen, we report the isolation and characterization of an N-methyltransferase (NMT) from Ephedra sinica able to catalyze the formation of (pseudo)ephedrine and other naturally occurring phenylalkylamines, including N-methylcathinone and N-methyl(pseudo)ephedrine. Phenylalkylamine N-methyltransferase (PaNMT) shares substantial amino acid sequence identity with enzymes of the NMT family involved in benzylisoquinoline alkaloid (BIA) metabolism in members of the higher plant order Ranunculales, which includes opium poppy (Papaver somniferum). PaNMT accepted a broad range of substrates with phenylalkylamine, tryptamine, ß-carboline, tetrahydroisoquinoline, and BIA structural scaffolds, which is in contrast to the specificity for BIA substrates of NMT enzymes within the Ranunculales. PaNMT transcript levels were highest in young shoots of E. sinica, which corresponded to the location of NMT activity yielding (pseudo)ephedrine, N-methylcathinone, and N-methyl(pseudo)ephedrine, and with in planta accumulation of phenylalkylamines. Co-expression of recombinant genes encoding PaNMT and an ω-transaminase (PP2799) from Pseudomonas putida in Escherichia coli enabled the conversion of exogenous (R)-phenylacetylcarbinol (PAC) and (S)-PAC to ephedrine and pseudoephedrine, respectively. Our work further demonstrates the utility of plant biochemical genomics for the isolation of key enzymes that facilitate microbial engineering for the production of medicinally important metabolites.

Iron Sequestration in Microbiota Biofilms As A Novel Strategy for Treating Inflammatory Bowel Disease.

Significant alterations of intestinal microbiota and anemia are hallmarks of inflammatory bowel disease (IBD). It is widely accepted that iron is a key nutrient for pathogenic bacteria, but little is known about its impact on microbiota associated with IBD. We used a model device to grow human mucosa-associated microbiota in its physiological anaerobic biofilm phenotype. Compared to microbiota from healthy donors, microbiota from IBD patients generate biofilms ex vivo that were larger in size and cell numbers, contained higher intracellular iron concentrations, and exhibited heightened virulence in a model of human intestinal epithelia in vitro and in the nematode Caenorhabditis elegans. We also describe an unexpected iron-scavenging property for an experimental hydrogen sulfide-releasing derivative of mesalamine. The findings demonstrate that this new drug reduces the virulence of IBD microbiota biofilms through a direct reduction of microbial iron intake and without affecting bacteria survival or species composition within the microbiota. Metabolomic analyses indicate that this drug reduces the intake of purine nucleosides (guanosine), increases the secretion of metabolite markers of purine catabolism (urate and hypoxanthine), and reduces the secretion of uracil (a pyrimidine nucleobase) in complex multispecies human biofilms. These findings demonstrate a new pathogenic mechanism for dysbiotic microbiota in IBD and characterize a novel mode of action for a class of mesalamine derivatives. Together, these observations pave the way towards a new therapeutic strategy for treatment of patients with IBD.

Transcriptome profiling of khat (Catha edulis) and Ephedra sinica reveals gene candidates potentially involved in amphetamine-type alkaloid biosynthesis.

Amphetamine analogues are produced by plants in the genus Ephedra and by khat (Catha edulis), and include the widely used decongestants and appetite suppressants (1S,2S)-pseudoephedrine and (1R,2S)-ephedrine. The production of these metabolites, which derive from L-phenylalanine, involves a multi-step pathway partially mapped out at the biochemical level using knowledge of benzoic acid metabolism established in other plants, and direct evidence using khat and Ephedra species as model systems. Despite the commercial importance of amphetamine-type alkaloids, only a single step in their biosynthesis has been elucidated at the molecular level. We have employed Illumina next-generation sequencing technology, paired with Trinity and Velvet-Oases assembly platforms, to establish data-mining frameworks for Ephedra sinica and khat plants. Sequence libraries representing a combined 200,000 unigenes were subjected to an annotation pipeline involving direct searches against public databases. Annotations included the assignment of Gene Ontology (GO) terms used to allocate unigenes to functional categories. As part of our functional genomics program aimed at novel gene discovery, the databases were mined for enzyme candidates putatively involved in alkaloid biosynthesis. Queries used for mining included enzymes with established roles in benzoic acid metabolism, as well as enzymes catalyzing reactions similar to those predicted for amphetamine alkaloid metabolism. Gene candidates were evaluated based on phylogenetic relationships, FPKM-based expression data, and mechanistic considerations. Establishment of expansive sequence resources is a critical step toward pathway characterization, a goal with both academic and industrial implications.