Metabolite Measurement: Pitfalls to Avoid and Practices to Follow.

Metabolites are the small biological molecules involved in energy conversion and biosynthesis. Studying metabolism is inherently challenging due to metabolites' reactivity, structural diversity, and broad concentration range. Herein, we review the common pitfalls encountered in metabolomics and provide concrete guidelines for obtaining accurate metabolite measurements, focusing on water-soluble primary metabolites. We show how seemingly straightforward sample preparation methods can introduce systematic errors (e.g., owing to interconversion among metabolites) and how proper selection of quenching solvent (e.g., acidic acetonitrile:methanol:water) can mitigate such problems. We discuss the specific strengths, pitfalls, and best practices for each common analytical platform: liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS), nuclear magnetic resonance (NMR), and enzyme assays. Together this information provides a pragmatic knowledge base for carrying out biologically informative metabolite measurements.

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.

MINNO: An Open Source Software for Refining Metabolic Networks and Investigating Complex Network Activity Using Empirical Metabolomics Data.

Metabolomics is a powerful tool for uncovering biochemical diversity in a wide range of organisms. Metabolic network modeling is commonly used to frame metabolomics data in the context of a broader biological system. However, network modeling of poorly characterized nonmodel organisms remains challenging due to gene homology mismatches which lead to network architecture errors. To address this, we developed the Metabolic Interactive Nodular Network for Omics (MINNO), a web-based mapping tool that uses empirical metabolomics data to refine metabolic networks. MINNO allows users to create, modify, and interact with metabolic pathway visualizations for thousands of organisms, in both individual and multispecies contexts. Herein, we illustrate the use of MINNO in elucidating the metabolic networks of understudied species, such as those of the Borrelia genus, which cause Lyme and relapsing fever diseases. Using a hybrid genomics-metabolomics modeling approach, we constructed species-specific metabolic networks for threeBorrelia species. Using these empirically refined networks, we were able to metabolically differentiate these species via their nucleotide metabolism, which cannot be predicted from genomic networks. Additionally, using MINNO, we identified 18 missing reactions from the KEGG database, of which nine were supported by the primary literature. These examples illustrate the use of metabolomics for the empirical refining of genetically constructed networks and show how MINNO can be used to study nonmodel organisms.

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.

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.

Unique metabolic phenotype and its transition during maturation of juvenile male germ cells.

Human male reproductive development has a prolonged prepubertal period characterized by juvenile quiescence of germ cells with immature spermatogonial stem cell (SSC) precursors (gonocytes) present in the testis for an extended period of time. The metabolism of gonocytes is not defined. We demonstrate with mitochondrial ultrastructure studies via TEM and IHC and metabolic flux studies with UHPLC-MS that a distinct metabolic transition occurs during the maturation to SSCs. The mitochondrial ultrastructure of prepubertal human spermatogonia is shared with prepubertal pig spermatogonia. The metabolism of early prepubertal porcine spermatogonia (gonocytes) is characterized by the reliance on OXPHOS fuelled by oxidative decarboxylation of pyruvate. Interestingly, at the same time, a high amount of the consumed pyruvate is also reduced and excreted as lactate. With maturation, prepubertal spermatogonia show a metabolic shift with decreased OXHPOS and upregulation of the anaerobic metabolism-associated uncoupling protein 2 (UCP2). This shift is accompanied with stem cell specific promyelocytic leukemia zinc finger protein (PLZF) protein expression and glial cell-derived neurotropic factor (GDNF) pathway activation. Our results demonstrate that gonocytes differently from mature spermatogonia exhibit unique metabolic demands that must be attained to enable their maintenance and growth in vitro.

Colitis-associated microbiota drives changes in behaviour in male mice in the absence of inflammation.

Inflammatory bowel diseases (IBD) are chronic inflammatory conditions of the gastrointestinal tract. IBD are associated with a high prevalence of cognitive, behavioural and emotional comorbidities, including anxiety and depression. The link between IBD and the development of behavioural comorbidities is poorly understood. As the intestinal microbiota profoundly influences host behaviour, we sought to determine whether the altered gut microbiota associated with intestinal inflammation contributes to the development of behavioural abnormalities. Using the dextran sulphate sodium (DSS) model of colitis, we characterized intestinal inflammation, behaviour (elevated plus maze and tail suspension test) and the composition of the microbiota in male mice. Cecal contents from colitic mice were transferred into germ-free (GF) or antibiotic (Abx)-treated mice, and behaviour was characterized in recipient mice. Gene expression was measured using qPCR. DSS colitis was characterized by a significant reduction in body weight and an increase in colonic inflammatory markers. These changes were accompanied by increased anxiety-like behaviour, an altered gut microbiota composition, and increased central Tnf expression. Transfer of the cecal matter from colitic mice induced similar behavioural changes in both GF and Abx-treated recipient mice, with no signs of colonic or neuroinflammation. Upon characterization of the microbiota in donor and recipient mice, specific taxa were found to be associated with behavioural changes, notably members of the Lachnospiraceae family. Behavioural abnormalities associated with intestinal inflammation are transmissible via transfer of cecal matter, suggesting that alterations in the composition of the gut microbiota play a key role in driving behavioural changes in colitis.

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.

nmrML: A Community Supported Open Data Standard for the Description, Storage, and Exchange of NMR Data.

NMR is a widely used analytical technique with a growing number of repositories available. As a result, demands for a vendor-agnostic, open data format for long-term archiving of NMR data have emerged with the aim to ease and encourage sharing, comparison, and reuse of NMR data. Here we present nmrML, an open XML-based exchange and storage format for NMR spectral data. The nmrML format is intended to be fully compatible with existing NMR data for chemical, biochemical, and metabolomics experiments. nmrML can capture raw NMR data, spectral data acquisition parameters, and where available spectral metadata, such as chemical structures associated with spectral assignments. The nmrML format is compatible with pure-compound NMR data for reference spectral libraries as well as NMR data from complex biomixtures, i.e., metabolomics experiments. To facilitate format conversions, we provide nmrML converters for Bruker, JEOL and Agilent/Varian vendor formats. In addition, easy-to-use Web-based spectral viewing, processing, and spectral assignment tools that read and write nmrML have been developed. Software libraries and Web services for data validation are available for tool developers and end-users. The nmrML format has already been adopted for capturing and disseminating NMR data for small molecules by several open source data processing tools and metabolomics reference spectral libraries, e.g., serving as storage format for the MetaboLights data repository. The nmrML open access data standard has been endorsed by the Metabolomics Standards Initiative (MSI), and we here encourage user participation and feedback to increase usability and make it a successful standard.

Evolution of Fitness Cost-Neutral Mutant PfCRT Conferring P. falciparum 4-Aminoquinoline Drug Resistance Is Accompanied by Altered Parasite Metabolism and Digestive Vacuole Physiology.

Southeast Asia is an epicenter of multidrug-resistant Plasmodium falciparum strains. Selective pressures on the subcontinent have recurrently produced several allelic variants of parasite drug resistance genes, including the P. falciparum chloroquine resistance transporter (pfcrt). Despite significant reductions in the deployment of the 4-aminoquinoline drug chloroquine (CQ), which selected for the mutant pfcrt alleles that halted CQ efficacy decades ago, the parasite pfcrt locus is continuously evolving. This is highlighted by the presence of a highly mutated allele, Cam734 pfcrt, which has acquired the singular ability to confer parasite CQ resistance without an associated fitness cost. Here, we used pfcrt-specific zinc-finger nucleases to genetically dissect this allele in the pathogenic setting of asexual blood-stage infection. Comparative analysis of drug resistance and growth profiles of recombinant parasites that express Cam734 or variants thereof, Dd2 (the most common Southeast Asian variant), or wild-type pfcrt, revealed previously unknown roles for PfCRT mutations in modulating parasite susceptibility to multiple antimalarial agents. These results were generated in the GC03 strain, used in multiple earlier pfcrt studies, and might differ in natural isolates harboring this allele. Results presented herein show that Cam734-mediated CQ resistance is dependent on the rare A144F mutation that has not been observed beyond Southeast Asia, and reveal distinct impacts of this and other Cam734-specific mutations on CQ resistance and parasite growth rates. Biochemical assays revealed a broad impact of mutant PfCRT isoforms on parasite metabolism, including nucleoside triphosphate levels, hemoglobin catabolism and disposition of heme, as well as digestive vacuole volume and pH. Results from our study provide new insights into the complex molecular basis and physiological impact of PfCRT-mediated antimalarial drug resistance, and inform ongoing efforts to characterize novel pfcrt alleles that can undermine the efficacy of first-line antimalarial drug regimens.

Metabolic QTL analysis links chloroquine resistance in Plasmodium falciparum to impaired hemoglobin catabolism.

Drug resistant strains of the malaria parasite, Plasmodium falciparum, have rendered chloroquine ineffective throughout much of the world. In parts of Africa and Asia, the coordinated shift from chloroquine to other drugs has resulted in the near disappearance of chloroquine-resistant (CQR) parasites from the population. Currently, there is no molecular explanation for this phenomenon. Herein, we employ metabolic quantitative trait locus mapping (mQTL) to analyze progeny from a genetic cross between chloroquine-susceptible (CQS) and CQR parasites. We identify a family of hemoglobin-derived peptides that are elevated in CQR parasites and show that peptide accumulation, drug resistance, and reduced parasite fitness are all linked in vitro to CQR alleles of the P. falciparum chloroquine resistance transporter (pfcrt). These findings suggest that CQR parasites are less fit because mutations in pfcrt interfere with hemoglobin digestion by the parasite. Moreover, our findings may provide a molecular explanation for the reemergence of CQS parasites in wild populations.

Hierarchy in pentose sugar metabolism in Clostridium acetobutylicum.

Bacterial metabolism of polysaccharides from plant detritus into acids and solvents is an essential component of the terrestrial carbon cycle. Understanding the underlying metabolic pathways can also contribute to improved production of biofuels. Using a metabolomics approach involving liquid chromatography-mass spectrometry, we investigated the metabolism of mixtures of the cellulosic hexose sugar (glucose) and hemicellulosic pentose sugars (xylose and arabinose) in the anaerobic soil bacterium Clostridium acetobutylicum. Simultaneous feeding of stable isotope-labeled glucose and unlabeled xylose or arabinose revealed that,as expected, glucose was preferentially used as the carbon source. Assimilated pentose sugars accumulated in pentose phosphate pathway (PPP) intermediates with minimal flux into glycolysis. Simultaneous feeding of xylose and arabinose revealed an unexpected hierarchy among the pentose sugars, with arabinose utilized preferentially over xylose. The phosphoketolase pathway (PKP) provides an alternative route of pentose catabolism in C. acetobutylicum that directly converts xylulose-5-phosphate into acetyl-phosphate and glyceraldehyde-3-phosphate, bypassing most of the PPP. When feeding the mixture of pentose sugars, the labeling patterns of lower glycolytic intermediates indicated more flux through the PKP than through the PPP and upper glycolysis, and this was confirmed by quantitative flux modeling. Consistent with direct acetyl-phosphate production from the PKP, growth on the pentose mixture resulted in enhanced acetate excretion. Taken collectively, these findings reveal two hierarchies in clostridial pentose metabolism: xylose is subordinate to arabinose, and the PPP is used less than the PKP.

Kinetic flux profiling elucidates two independent acetyl-CoA biosynthetic pathways in Plasmodium falciparum.

The malaria parasite Plasmodium falciparum depends on glucose to meet its energy requirements during blood-stage development. Although glycolysis is one of the best understood pathways in the parasite, it is unclear if glucose metabolism appreciably contributes to the acetyl-CoA pools required for tricarboxylic acid metabolism (TCA) cycle and fatty acid biosynthesis. P. falciparum possesses a pyruvate dehydrogenase (PDH) complex that is localized to the apicoplast, a specialized quadruple membrane organelle, suggesting that separate acetyl-CoA pools are likely. Herein, we analyze PDH-deficient parasites using rapid stable-isotope labeling and show that PDH does not appreciably contribute to acetyl-CoA synthesis, tricarboxylic acid metabolism, or fatty acid synthesis in blood stage parasites. Rather, we find that acetyl-CoA demands are supplied through a "PDH-like" enzyme and provide evidence that the branched-chain keto acid dehydrogenase (BCKDH) complex is performing this function. We also show that acetyl-CoA synthetase can be a significant contributor to acetyl-CoA biosynthesis. Interestingly, the PDH-like pathway contributes glucose-derived acetyl-CoA to the TCA cycle in a stage-independent process, whereas anapleurotic carbon enters the TCA cycle via a stage-dependent phosphoenolpyruvate carboxylase/phosphoenolpyruvate carboxykinase process that decreases as the parasite matures. Although PDH-deficient parasites have no blood-stage growth defect, they are unable to progress beyond the oocyst phase of the parasite mosquito stage.

Boundary flux analysis: an emerging strategy for investigating metabolic pathway activity in large cohorts.

Many biological phenotypes are rooted in metabolic pathway activity rather than the concentrations of individual metabolites. Despite this, most metabolomics studies only capture steady-state metabolism - not metabolic flux. Although sophisticated metabolic flux analysis strategies have been developed, these methods are technically challenging and difficult to implement in large-cohort studies. Recently, a new boundary flux analysis (BFA) approach has emerged that captures large-scale metabolic flux phenotypes by quantifying changes in metabolite levels in the media of cultured cells. This approach is advantageous because it is relatively easy to implement yet captures complex metabolic flux phenotypes. We describe the opportunities and challenges of BFA and illustrate how it can be harnessed to investigate a wide transect of biological phenomena.

Staphylococcus aureus adapts to exploit collagen-derived proline during chronic infection.

Staphylococcus aureus is a pulmonary pathogen associated with substantial human morbidity and mortality. As vaccines targeting virulence determinants have failed to be protective in humans, other factors are likely involved in pathogenesis. Here we analysed transcriptomic responses of human clinical isolates of S. aureus from initial and chronic infections. We observed upregulated collagenase and proline transporter gene expression in chronic infection isolates. Metabolomics of bronchiolar lavage fluid and fibroblast infection, growth assays and analysis of bacterial mutant strains showed that airway fibroblasts produce collagen during S. aureus infection. Host-adapted bacteria upregulate collagenase, which degrades collagen and releases proline. S. aureus then imports proline, which fuels oxidative metabolism via the tricarboxylic acid cycle. Proline metabolism provides host-adapted S. aureus with a metabolic benefit enabling out-competition of non-adapted strains. These data suggest that clinical settings characterized by airway repair processes and fibrosis provide a milieu that promotes S. aureus adaptation and supports infection.

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.

Staphylococcus aureus stimulates neutrophil itaconate production that suppresses the oxidative burst.

Neutrophils are critical in the host defense against Staphylococcus aureus, a major human pathogen. However, even in the setting of a robust neutrophil response, S. aureus can evade immune clearance. Here, we demonstrate that S. aureus impairs neutrophil function by triggering the production of the anti-inflammatory metabolite itaconate. The enzyme that synthesizes itaconate, Irg1, is selectively expressed in neutrophils during S. aureus pneumonia. Itaconate inhibits neutrophil glycolysis and oxidative burst, which impairs survival and bacterial killing. In a murine pneumonia model, neutrophil Irg1 expression protects the lung from excessive inflammation but compromises bacterial clearance. S. aureus is thus able to evade the innate immune response by targeting neutrophil metabolism and inducing the production of the anti-inflammatory metabolite itaconate.

Microbiota alters the metabolome in an age- and sex- dependent manner in mice.

Commensal bacteria are major contributors to mammalian metabolism. We used liquid chromatography mass spectrometry to study the metabolomes of germ-free, gnotobiotic, and specific-pathogen-free mice, while also evaluating the influence of age and sex on metabolite profiles. Microbiota modified the metabolome of all body sites and accounted for the highest proportion of variation within the gastrointestinal tract. Microbiota and age explained similar amounts of variation the metabolome of urine, serum, and peritoneal fluid, while age was the primary driver of variation in the liver and spleen. Although sex explained the least amount of variation at all sites, it had a significant impact on all sites except the ileum. Collectively, these data illustrate the interplay between microbiota, age, and sex in the metabolic phenotypes of diverse body sites. This provides a framework for interpreting complex metabolic phenotypes and will help guide future studies into the role that the microbiome plays in disease.

Butyrate reduces adherent-invasive E. coli-evoked disruption of epithelial mitochondrial morphology and barrier function: involvement of free fatty acid receptor 3.

Gut bacteria provide benefits to the host and have been implicated in inflammatory bowel disease (IBD), where adherent-invasive E. coli (AIEC) pathobionts (e.g., strain LF82) are associated with Crohn's disease. E. coli-LF82 causes fragmentation of the epithelial mitochondrial network, leading to increased epithelial permeability. We hypothesized that butyrate would limit the epithelial mitochondrial disruption caused by E. coli-LF82. Human colonic organoids and the T84 epithelial cell line infected with E. coli-LF82 (MOI = 100, 4 h) showed a significant increase in mitochondrial network fission that was reduced by butyrate (10 mM) co-treatment. Butyrate reduced the loss of mitochondrial membrane potential caused by E. coli-LF82 and increased expression of PGC-1α mRNA, the master regulator of mitochondrial biogenesis. Metabolomics revealed that butyrate significantly altered E. coli-LF82 central carbon metabolism leading to diminished glucose uptake and increased succinate secretion. Correlating with preservation of mitochondrial network form/function, butyrate reduced E. coli-LF82 transcytosis across T84-cell monolayers. The use of the G-protein inhibitor, pertussis toxin, implicated GPCR signaling as critical to the effect of butyrate, and the free fatty acid receptor three (FFAR3, GPR41) agonist, AR420626, reproduced butyrate's effect in terms of ameliorating the loss of barrier function and reducing the mitochondrial fragmentation observed in E. coli-LF82 infected T84-cells and organoids. These data indicate that butyrate helps maintain epithelial mitochondrial form/function when challenged by E. coli-LF82 and that this occurs, at least in part, via FFAR3. Thus, loss of butyrate-producing bacteria in IBD in the context of pathobionts would contribute to loss of epithelial mitochondrial and barrier functions that could evoke disease and/or exaggerate a low-grade inflammation.

Semiautomated device for batch extraction of metabolites from tissue samples.

Metabolomics has become a mainstream analytical strategy for investigating metabolism. The quality of data derived from these studies is proportional to the consistency of the sample preparation. Although considerable research has been devoted to finding optimal extraction protocols, most of the established methods require extensive sample handling. Manual sample preparation can be highly effective in the hands of skilled technicians, but an automated tool for purifying metabolites from complex biological tissues would be of obvious utility to the field. Here, we introduce the semiautomated metabolite batch extraction device (SAMBED), a new tool designed to simplify metabolomics sample preparation. We discuss SAMBED's design and show that SAMBED-based extractions are of comparable quality to extracts produced through traditional methods (13% mean coefficient of variation from SAMBED versus 16% from manual extractions). Moreover, we show that aqueous SAMBED-based methods can be completed in less than a quarter of the time required for manual extractions.