Search

The human microbiota impacts a variety of diseases and responses to therapeutics. Due to a lack of robust in vitro models, detailed mechanistic explanations of host-microbiota interactions cannot often be recapitulated. We describe the design and development of a novel, versatile and modular in vitro system that enables indirect coculture of human epithelial cells with anaerobic bacteria for the characterization of host-microbe secreted metabolite interactions. This system was designed to compartmentalize anaerobes and human cells in separate chambers conducive to each organism's requisite cell growth conditions. Using perfusion, fluidic mixing, and automated sample collection, the cells continuously received fresh media, while in contact with their corresponding compartments conditioned supernatant. Supernatants from each chamber were collected in a cell-free time-resolved fashion. The system sustained low oxygen conditions in the anaerobic chamber, while also supporting the growth of a representative anaerobe (Bacteroides thetaiotaomicron) and a human colonic epithelial cell line (Caco-2) in the aerobic chamber. Caco-2 global gene expression changes in response to coculture with B. thetaiotaomicron was characterized using RNA sequencing. Extensive, targeted metabolomics analysis of over 150 central carbon metabolites was performed on the serially collected supernatants. We observed broad metabolite changes in host-microbe coculture, compared to respective mono-culture controls. These effects were dependent both on sampling time and the compartment probed (apical vs. basolateral). Coculturing resulted in the depletion of several important metabolites, including guanine, uridine 5'-monophosphate, asparagine, and thiamine. Additionally, while Caco-2 cells cultured alone predominantly affected the basolateral metabolite milieu, increased abundance of 2,3-dihydroxyisovalerate and thymine on the basolateral side, occurred when the cells were cocultured with B. thetaiotaomicron. Thus, our system can capture the dynamic, competitive and cooperative processes between host cells and gut microbes., (© 2024 Wiley Periodicals LLC.)

Type 2 diabetes (T2D) is a global public health issue characterized by excess weight, abdominal obesity, dyslipidemia, hyperglycemia, and a progressive increase in insulin resistance. Human population studies of T2D development and its effects on systemic metabolism are confounded by many factors that cannot be controlled, complicating the interpretation of results and the identification of early biomarkers. Aged, sedentary, and overweight/obese non-human primates (NHPs) are one of the best animal models to mimic spontaneous T2D development in humans. We sought to identify and distinguish a set of plasma and/or fecal metabolite biomarkers, that have earlier disease onset predictability, and that could be evaluated for their predictability in subsequent T2D studies in human cohorts. In this study, a single plasma and fecal sample was collected from each animal in a colony of 57 healthy and dysmetabolic NHPs and analyzed for metabolomics and lipidomics. The samples were comprehensively analyzed using untargeted and targeted LC/MS/MS. The changes in each animal's disease phenotype were monitored using IVGTT, HbA1c, and other clinical metrics, and correlated with their metabolic profile. The plasma and fecal lipids, as well as bile acid profiles, from Healthy, Dysmetabolic (Dys), and Diabetic (Dia) animals were compared. Following univariate and multivariate analyses, including adjustments for weight, age, and sex, several plasma lipid species were identified to be significantly different between these animal groups. Medium and long-chain plasma phosphatidylcholines (PCs) ranked highest at distinguishing Healthy from Dys animals, whereas plasma triglycerides (TG) primarily distinguished Dia from Dys animals. Random Forest (RF) analysis of fecal bile acids showed a reduction in the secondary bile acid glycoconjugate, GCDCA, in diseased animals (AUC 0.76[0.64, 0.89]). Moreover, metagenomics results revealed several bacterial species, belonging to the genera Roseburia , Ruminococcus , Clostridium , and Streptococcus , to be both significantly enriched in non-healthy animals and associated with secondary bile acid levels. In summary, our results highlight the detection of several elevated circulating plasma PCs and microbial species associated with fecal secondary bile acids in NHP dysmetabolic states. The lipids and metabolites we have identified may help researchers to differentiate individual NHPs more precisely between dysmetabolic and overtly diabetic states. This could help assign animals to study groups that are more likely to respond to potential therapies where a difference in efficacy might be anticipated between early vs. advanced disease.

Importance: We show that simultaneous study of stool and nasopharyngeal microbiome reveals divergent timing and patterns of maturation, suggesting that local mucosal factors may influence microbiome composition in the gut and respiratory system. Antibiotic exposure in early life as occurs commonly, may have an adverse effect on vaccine responsiveness. Abundance of gut and/or nasopharyngeal bacteria with the machinery to produce lipopolysaccharide-a toll-like receptor 4 agonist-may positively affect future vaccine protection, potentially by acting as a natural adjuvant. The increased levels of serum phenylpyruvic acid in infants with lower vaccine-induced antibody levels suggest an increased abundance of hydrogen peroxide, leading to more oxidative stress in low vaccine-responding infants., Competing Interests: All authors who are/were employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA, may hold stocks and/or stock options in Merck & Co., Inc., Rahway, NJ, USA. Eduardo Gonzalez, Steven Schulz, and Michael Pichichero are employees of Rochester General Hospital, and that institution received a collaborative research grant from Merck & Co., Inc. for conducting the prospective trial, aspects of the laboratory work, data management, and analysis.

Sepsis is a life-threatening condition caused by a dysregulated host response to infection. Despite continued efforts to understand the pathophysiology of sepsis, no effective therapies are currently available. While singular components of the aberrant immune response have been investigated, comprehensive studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and concomitant changes in cytokines and metabolites in patients with sepsis. Our findings identify differentially expressed mature and immature neutrophil subsets in patients with sepsis. These subsets correlate with various proteins, metabolites, and lipids, including pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in patients with sepsis. These results enabled the construction of a statistical model based on weighted multi-omics linear regression analysis for sepsis biomarker identification. These findings could help inform early patient stratification and treatment options, and facilitate further mechanistic studies targeting the trifecta of surface marker expression, cytokines, and metabolites., Competing Interests: The authors declare no competing interests., (© 2023 Merck Sharp & Dohme LLC., a subsidiary Merck & Co, The Author(s).)

Donor-to-donor variability in primary human organoid cultures has not been well characterized. As these cultures contain multiple cell types, there is greater concern that variability could lead to increased noise. In this work we investigated donor-to-donor variability in human gut adult stem cell (ASC) organoids. We examined intestinal developmental pathways during culture differentiation in ileum- and colon-derived cultures established from multiple donors, showing that differentiation patterns were consistent among cultures. This finding indicates that donor-to-donor variability in this system remains at a manageable level. Intestinal metabolic activity was evaluated by targeted analysis of central carbon metabolites and by analyzing hormone production patterns. Both experiments demonstrated similar metabolic functions among donors. Importantly, this activity reflected intestinal biology, indicating that these ASC organoid cultures are appropriate for studying metabolic processes. This work establishes a framework for generating high-confidence data using human primary cultures through thorough characterization of variability., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

Recent reports show that colorectal tumors contain microbiota that are distinct from those that reside in a 'normal' colon environment, and that these microbiota can contribute to cancer progression. Fusobacterium nucleatum is the most commonly observed species in the colorectal tumor microenvironment and reportedly influences disease progression through numerous mechanisms. However, a detailed understanding of the role of this organism in cancer progression is limited, in part due to challenges in maintaining F. nucleatum viability under standard aerobic cell culture conditions. Herein we describe the development of a 3-dimensional (3D) tumor spheroid model that can harbor and promote the growth of anaerobic bacteria. Bacteria-tumor cell interactions and metabolic crosstalk were extensively studied by measuring the kinetics of bacterial growth, cell morphology and lysis, cancer-related gene expression, and metabolomics. We observed that viable F. nucleatum assembles biofilm-like structures in the tumor spheroid microenvironment, whereas heat-killed F. nucleatum is internalized and sequestered in the cancer cells. Lastly, we use the model to co-culture 28 Fusobacterium clinical isolates and demonstrate that the model successfully supports co-culture with diverse fusobacterial species. This bacteria-spheroid co-culture model enables mechanistic investigation of the role of anaerobic bacteria in the tumor microenvironment.

Previous studies have shown that calcium stressed Saccharomyces cerevisiae, challenged with immunosuppressant drugs FK506 and Cyclosporin A, responds with comprehensive gene expression changes and attenuation of the generalized calcium stress response. Here, we describe a global metabolomics workflow for investigating the utility of tracking corresponding phenotypic changes. This was achieved by efficiently analyzing relative abundance differences between intracellular metabolite pools from wild-type and calcium stressed cultures, with and without prior immunosuppressant drugs exposure. We used pathway database content from WikiPathways and YeastCyc to facilitate the projection of our metabolomics profiling results onto biological pathways. A key challenge was to increase the coverage of the detected metabolites. This was achieved by applying both reverse phase (RP) and aqueous normal phase (ANP) chromatographic separations, as well as electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) sources for detection in both ion polarities. Unsupervised principle component analysis (PCA) and ANOVA results revealed differentiation between wild-type controls, calcium stressed and immunosuppressant/calcium challenged cells. Untargeted data mining resulted in 247 differentially expressed, annotated metabolites, across at least one pair of conditions. A separate, targeted data mining strategy identified 187 differential, annotated metabolites. All annotated metabolites were subsequently mapped onto curated pathways from YeastCyc and WikiPathways for interactive pathway analysis and visualization. Dozens of pathways showed differential responses to stress conditions based on one or more matches to the list of annotated metabolites or to metabolites that had been identified further by MS/MS. The purine salvage, pantothenate and sulfur amino acid pathways were flagged as being enriched, which is consistent with previously published literature for transcriptomics analysis. Thus, broad discovery-based data mining combined with targeted pathway projections can be an important asset for rapidly distilling, testing and evaluating a large amount of information for further investigation.

Malaria is a global infectious disease that threatens the lives of millions of people. Transcriptomics, proteomics and functional genomics studies, as well as sequencing of the Plasmodium falciparum and Homo sapiens genomes, have shed new light on this host-parasite relationship. Recent advances in accurate mass measurement mass spectrometry, sophisticated data analysis software, and availability of biological pathway databases, have converged to facilitate our global, untargeted biochemical profiling study of in vitro P. falciparum-infected (IRBC) and uninfected (NRBC) erythrocytes. In order to expand the number of detectable metabolites, several key analytical steps in our workflows were optimized. Untargeted and targeted data mining resulted in detection of over one thousand features or chemical entities. Untargeted features were annotated via matching to the METLIN metabolite database. For targeted data mining, we queried the data using a compound database derived from a metabolic reconstruction of the P. falciparum genome. In total, over one hundred and fifty differential annotated metabolites were observed. To corroborate the representation of known biochemical pathways from our data, an inferential pathway analysis strategy was used to map annotated metabolites onto the BioCyc pathway collection. This hypothesis-generating approach resulted in over-representation of many metabolites onto several IRBC pathways, most prominently glycolysis. In addition, components of the "branched" TCA cycle, partial urea cycle, and nucleotide, amino acid, chorismate, sphingolipid and fatty acid metabolism were found to be altered in IRBCs. Interestingly, we detected and confirmed elevated levels for cyclic ADP ribose and phosphoribosyl AMP in IRBCs, a novel observation. These metabolites may play a role in regulating the release of intracellular Ca(2+) during P. falciparum infection. Our results support a strategy of global metabolite profiling by untargeted data acquisition. Untargeted and targeted data mining workflows, when used together to perform pathway-inferred metabolomics, have the benefit of obviating MS/MS confirmation for every detected compound.

Although alterations in xenobiotic metabolism are considered causal in the development of bladder cancer, the precise mechanisms involved are poorly understood. In this study, we used high-throughput mass spectrometry to measure over 2,000 compounds in 58 clinical specimens, identifying 35 metabolites which exhibited significant changes in bladder cancer. This metabolic signature distinguished both normal and benign bladder from bladder cancer. Exploratory analyses of this metabolomic signature in urine showed promise in distinguishing bladder cancer from controls and also nonmuscle from muscle-invasive bladder cancer. Subsequent enrichment-based bioprocess mapping revealed alterations in phase I/II metabolism and suggested a possible role for DNA methylation in perturbing xenobiotic metabolism in bladder cancer. In particular, we validated tumor-associated hypermethylation in the cytochrome P450 1A1 (CYP1A1) and cytochrome P450 1B1 (CYP1B1) promoters of bladder cancer tissues by bisulfite sequence analysis and methylation-specific PCR and also by in vitro treatment of T-24 bladder cancer cell line with the DNA demethylating agent 5-aza-2'-deoxycytidine. Furthermore, we showed that expression of CYP1A1 and CYP1B1 was reduced significantly in an independent cohort of bladder cancer specimens compared with matched benign adjacent tissues. In summary, our findings identified candidate diagnostic and prognostic markers and highlighted mechanisms associated with the silencing of xenobiotic metabolism. The metabolomic signature we describe offers potential as a urinary biomarker for early detection and staging of bladder cancer, highlighting the utility of evaluating metabolomic profiles of cancer to gain insights into bioprocesses perturbed during tumor development and progression.

Prostate cancer is the second leading cause of cancer related death in American men. Development and progression of clinically localized prostate cancer is highly dependent on androgen signaling. Metastatic tumors are initially responsive to anti-androgen therapy, however become resistant to this regimen upon progression. Genomic and proteomic studies have implicated a role for androgen in regulating metabolic processes in prostate cancer. However, there have been no metabolomic profiling studies conducted thus far that have examined androgen-regulated biochemical processes in prostate cancer. Here, we have used unbiased metabolomic profiling coupled with enrichment-based bioprocess mapping to obtain insights into the biochemical alterations mediated by androgen in prostate cancer cell lines. Our findings indicate that androgen exposure results in elevation of amino acid metabolism and alteration of methylation potential in prostate cancer cells. Further, metabolic phenotyping studies confirm higher flux through pathways associated with amino acid metabolism in prostate cancer cells treated with androgen. These findings provide insight into the potential biochemical processes regulated by androgen signaling in prostate cancer. Clinically, if validated, these pathways could be exploited to develop therapeutic strategies that supplement current androgen ablative treatments while the observed androgen-regulated metabolic signatures could be employed as biomarkers that presage the development of castrate-resistant prostate cancer.

Bacterial leaf blight (BLB), caused by Xanthomonas oryzae pv. oryzae (Xoo), gives rise to devastating crop losses in rice. Disease resistant rice cultivars are the most economical way to combat the disease. The TP309 cultivar is susceptible to infection by Xoo strain PXO99. A transgenic variety, TP309_Xa21, expresses the pattern recognition receptor Xa21, and is resistant. PXO99 big up tri, openraxST, a strain lacking the raxST gene, is able to overcome Xa21-mediated immunity. We used a single extraction solvent to demonstrate comprehensive metabolomics and transcriptomics profiling under sample limited conditions, and analyze the molecular responses of two rice lines challenged with either PXO99 or PXO99 big up tri, openraxST. LC-TOF raw data file filtering resulted in better within group reproducibility of replicate samples for statistical analyses. Accurate mass match compound identification with molecular formula generation (MFG) ranking of 355 masses was achieved with the METLIN database. GC-TOF analysis yielded an additional 441 compounds after BinBase database processing, of which 154 were structurally identified by retention index/MS library matching. Multivariate statistics revealed that the susceptible and resistant genotypes possess distinct profiles. Although few mRNA and metabolite differences were detected in PXO99 challenged TP309 compared to mock, many differential changes occurred in the Xa21-mediated response to PXO99 and PXO99 big up tri, openraxST. Acetophenone, xanthophylls, fatty acids, alkaloids, glutathione, carbohydrate and lipid biosynthetic pathways were affected. Significant transcriptional induction of several pathogenesis related genes in Xa21 challenged strains, as well as differential changes to GAD, PAL, ICL1 and Glutathione-S-transferase transcripts indicated limited correlation with metabolite changes under single time point global profiling conditions. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11306-010-0218-7) contains supplementary material, which is available to authorized users.

Aqueous normal-phase chromatography was used for the analysis of metabolites in human saliva and urine samples. The column was packed with a silica hydride type separation material. Several gradients were tested with different mobile phase additives in order to produce retention for amino acids, small organic acids, and carbohydrates. Detection was done by TOF MS. In some cases the relative concentration levels of various metabolites in human saliva were compared for normal patients and patients with pancreatic cancer or pancreatitis. The reproducibility of retention of individual metabolites in these complex matrices was tested for several compounds.

A novel silica hydride-based stationary phase was used to evaluate the retention behavior in the aqueous normal-phase (ANP) mode of standards representing three classes of metabolites. The effects on retention behavior of amino acids, carbohydrates and small organic acids were examined by altering the column temperature, and by adding different additives to both the mobile phase and sample solvent. Gradient mode results revealed the repeatability of retention times to be very stable for these compound classes. At both 15 and 30 degrees C, excellent RSD values were obtained with less than 1% variation for over 50 injections of an amino acid mixture. The ability to separate the 19 nonderivatized amino acid standards, organic acids and carbohydrates was demonstrated as well as the potential for this material to separate polar metabolites in complex fluids such as urine.

In an effort to simplify and streamline compound identification from metabolomics data generated by liquid chromatography time-of-flight mass spectrometry, we have created software for constructing Personalized Metabolite Databases with content from over 15,000 compounds pulled from the public METLIN database (http://metlin.scripps.edu/). Moreover, we have added extra functionalities to the database that (a) permit the addition of user-defined retention times as an orthogonal searchable parameter to complement accurate mass data; and (b) allow interfacing to separate software, a Molecular Formula Generator (MFG), that facilitates reliable interpretation of any database matches from the accurate mass spectral data. To test the utility of this identification strategy, we added retention times to a subset of masses in this database, representing a mixture of 78 synthetic urine standards. The synthetic mixture was analyzed and screened against this METLIN urine database, resulting in 46 accurate mass and retention time matches. Human urine samples were subsequently analyzed under the same analytical conditions and screened against this database. A total of 1387 ions were detected in human urine; 16 of these ions matched both accurate mass and retention time parameters for the 78 urine standards in the database. Another 374 had only an accurate mass match to the database, with 163 of those masses also having the highest MFG score. Furthermore, MFG calculated a formula for a further 849 ions that had no match to the database. Taken together, these results suggest that the METLIN Personal Metabolite database and MFG software offer a robust strategy for confirming the formula of database matches. In the event of no database match, it also suggests possible formulas that may be helpful in interpreting the experimental results.

Reproducible and comprehensive sample extraction and detection of metabolites with a broad range of physico-chemical properties from biological matrices can be a highly challenging process. A single LC/MS separation method was developed for a 2.1 mm x 100 mm, 1.8 microm ZORBAX SB-Aq column that was used to separate human erythrocyte metabolites extracted under sample extraction solvent conditions where the pH was neutral or had been adjusted to either, pH 2, 6 or 9. Internal standards were included and evaluated for tracking sample extraction efficiency. Through the combination of electrospray ionization (ESI) and atmospheric pressure chemical ionization (APCI) techniques in both positive (+) and negative (-) ion modes, a total of 2370 features (compounds and associated compound related components: isotopes, adducts and dimers) were detected across all pHs. Broader coverage of the detected metabolome was achieved by observing that (1) performing extractions at pH 2 and 9, leads to a combined 92% increase in detected features over pH 7 alone; and (2) including APCI in the analysis results in a 34% increase in detected features, across all pHs, than the total number detected by ESI. A significant dependency of extraction solvent pH on the recovery of heme and other compounds was observed in erythrocytes and underscores the need for a comprehensive sample extraction strategy and LC/MS analysis in metabolomics profiling experiments.

Diverse and complex microbial ecosystems are found in virtually every environment on earth, yet we know very little about their composition and ecology. Comprehensive identification and quantification of the constituents of these microbial communities--a 'census'--is an essential foundation for understanding their biology. To address this problem, we developed, tested and optimized a DNA oligonucleotide microarray composed of 10,462 small subunit (SSU) ribosomal DNA (rDNA) probes (7167 unique sequences) selected to provide quantitative information on the taxonomic composition of diverse microbial populations. Using our optimized experimental approach, this microarray enabled detection and quantification of individual bacterial species present at fractional abundances of <0.1% in complex synthetic mixtures. The estimates of bacterial species abundance obtained using this microarray are similar to those obtained by phylogenetic analysis of SSU rDNA sequences from the same samples--the current 'gold standard' method for profiling microbial communities. Furthermore, probes designed to represent higher order taxonomic groups of bacterial species reliably detected microbes for which there were no species-specific probes. This simple, rapid microarray procedure can be used to explore and systematically characterize complex microbial communities, such as those found within the human body.

To investigate the potential molecular mediators of tissue-specific recruitment, we explored the influence of different cytokine challenges on gene expression regulation in five primary endothelial cells (ECs), representing two different phenotypes: iliac artery and aortic (macrovascular); lung, colon and dermal (microvascular). We challenged ECs with cytokines that elicit different patterns of inflammatory and immune responses in immune cells: tumor necrosis factor (TNF-alpha), interferon-gamma (IFN-gamma) or interleukin-4 (IL-4), and used microarrays containing approximately 40,000 unique cDNAs, to assess changes in differential gene expression relative to untreated cells. Five hundred and sixty three sequences changed by at least 2.5 fold in one or more of the 15 possible EC /cytokine combinations. The list included highly regulated adhesion molecules, chemokines, cytokines, metalloproteases, and IFN-gamma-induced genes. Overall, IFN-gamma caused the largest number of gene expression changes and its profile was least correlated with IL-4. In addition to clusters that were predominantly EC/cytokine specific, we also observed several clusters that were regulated by more than one cytokine across several ECs. Furthermore, we identified genes that were reciprocally expressed in response to different cytokines that could serve as markers of inflammatory and immune expression. These results confirm the importance of microenvironment in primary ECs that could have important applications in developing targeted therapies for vascular diseases.