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.

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.

Sepsis is a life-threatening condition caused by an aberrant immune response to infection and subsequent organ dysfunction. Further, due to shared mechanisms such as severe inflammation and endothelial dysfunction, sepsis remains the most common etiology in acute respiratory distress syndrome (ARDS) patients. Despite multiple efforts to identify a distinct signature to support the development of new therapeutics, sepsis remains a significant unmet clinical need. While singular components of the dysregulated immune response have been explored, studies linking different data layers are lacking. Using an integrated systems immunology approach, we evaluated neutrophil phenotypes and related changes in soluble plasma factors in sepsis versus healthy donors. Results from our studies identify differentially expressed mature and immature neutrophil subsets in sepsis patients. Further, we show that expression of neutrophil CD10 is associated with factors such as pentraxin-3, angiopoietin-2, and lysophosphatidylcholines, in the sepsis cohort. These findings detail a novel correlation between increased presence of immature neutrophils and endothelial dysfunction/metabolic alterations in sepsis patients. Additionally, we profiled the neutrophil landscape and evaluated levels of soluble plasma factors, in a subset of ARDS patients and healthy donors. Here, we present distinct findings that reveal a novel crosstalk between neutrophil subsets and parameters indicative of endothelial dysfunction, in sepsis and ARDS patients. Overall, our study reinforces the importance of data integration and provides novel insights that could help inform future mechanistic studies, early patient stratification and treatment options.

ABSTRACT Despite multiple vaccine doses early in life, a substantial proportion of infants do not mount protective responses. In this study, we followed a cohort of children over the first 2 years of life, collecting microbiome and metabolome data longitudinally to investigate correlates of lower and higher responses to primary vaccinations. We found that the stool and nasopharyngeal microbiome developed with age, though demonstrated divergent timing and patterns in maturation. When measured at child age 2 months, evenness of genera in the stool microbiome correlated with lower vaccine responses, upregulated metabolome genes that encode for lipid A biosynthesis and oxidative phosphorylation correlated with higher vaccine responses, and abundance of phenylpyruvic acid in serum correlated with lower vaccine responses, measured 10 months later. Antibiotic exposure was associated with low vaccine response, and microbiome/metabolome features at child age 2 months, before childhood vaccinations commenced, correlated with variations in vaccine responses measured at child age 1 year. These results indicate that there may be potential to intervene before first childhood vaccinations to improve later protection. 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.

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.

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.

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.

γδ T cells are a unique subset of T cells found in both blood (Vδ2) and tissues (Vδ1). Unlike canonical αβ T cells that recognize peptide antigens through MHC class I/II molecules, γδ T cells recognize lipids and metabolites presented by non-MHC molecules. γδ T cells are highly cytotoxic and can rapidly kill both infected and cancer cells. Their cytotoxicity and lack of MHC restriction makes γδ T cells a promising target for allogeneic cell transfer therapy. In collaboration with MRL Oncology and ID/Vax we are investigating γδ T cells as a potential “off-the-shelf” cell transfer therapy. The ESC is currently optimizing ex vivo expansion protocols for both Vδ1 and Vδ2 T cells. Vδ2 cells rapidly expand from PBMCs after exposure to Zometa/IL-2, and are efficient cancer killers in vitro. To assess activation state, targeted metabolomics analysis was performed during Vδ2 expansion, and displayed significant differences in glutamate utilization, lactic acid, myo-inositol, and several essential and non-essential amino acids. We have also isolated and expanded Vδ1 T cells from PBMCs and human tissues (colon). These cells express high levels of NKG2D, which is suggestive of cytotoxic potential and we are currently assessing their cancer cell killing capacity in vitro. In collaboration with GpGx, we are identifying the dominant T-cell receptor (TCR) repertoires present in these populations by performing sc-TCRseq, sc-REAPseq, and comprehensive immunophenotyping. The results of these assays will be used to identify the optimum expansion protocol as well as to inform on the mechanisms underlying γδ T cell cancer targeting.

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.

This article describes a nontargeted, discovery-based approach for analyzing the yeast metabolome in response to calcium and immunosuppressant drug treatment, using multiple liquid chromatography (LC) conditions and quadrupole time-of-flight mass spectrometry (QTOF-MS) analysis. Yeast cultures were exposed to calcium only or calcium and one of two immunosuppressant drugs with the goal of perturbing the calcineurin- and any other calcium- or immunosuppressant-responsive pathways. Compounds were extracted from the raw data using an unbiased extraction algorithm. These results were subsequently analyzed using multidimensional statistical analysis and data visualization software and reduced to a list of compounds that were differentially expressed according to culture condition. Accurate-mass based identifications were done using an endogenous metabolite database. MS-MS spectra were acquired for a subset of compounds, allowing for matching at multiple fragmentation energies against an endogenous metabolite library to provide higher confidence identifications.

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 Ca2+ 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.

AbstractAlthough 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. Cancer Res; 71(24); 7376–86. ©2011 AACR.

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.

A successful metabolonnics study requires a workflow strategy that can unravel the complexity of the data and reveal the real changes that occur in the system. A workflow that combines untargeted (discovery) quadrupole time-of-flight (Q-TOF) liquid chromatography mass spectrometry (LC-MS), targeted (confirmation) triple-quadrupole LC-MS-MS, and sophisticated data mining is an effective means to elucidate metabolite changes.

Introduction: Bladder cancer (BCa) is the second most prevalent urological malignancy and the fourth highest cause of cancer-related death in the United States. Earlier studies have linked BCa development to alterations in metabolic pathways. Significant among these are decreased activity of N-acetyl transferases causing a slow-acetylator phenotype leading to inefficient detoxification of aromatic hydrocarbons causal to onset of BCa. Interestingly, Afro-American patients inherently exhibit such slow acetylator phenotype and are known to have a more aggressive form of the tumor compared to Caucasians. This indicates existence of a metabolic niche that governs the racial disparity in BCa, which is not well understood. Also, there is an imminent need to develop non-invasive markers for early detection and prognosis of BCa, since urine cytology which is the current clinical standard is not specific to the tumor. Using mass spectrometry we report metabolic alterations in BCa and delineate bioprocesses that are altered during its progression. Our data for the first-time demonstrates role of methylation in attenuation of xenotbiotic metabolism in BCa. Furthermore, the metabolic profiles seed further analysis to examine the racial disparity in these tumors. Methods: Total metabolome from flash frozen clinically annotated bladder-derived tissues (n=58,31 benign adjacent and 27 BCa, 26 matched pairs) were examined using a combination of Q-TOF (unbiased) and triple-quadrupole (targeted) mass spectrometry. Panel of well-defined standards were used to ensure reproducibility of the profiling process. The metabolites were pre-fractionated using liquid chromatography prior to mass spectrometry in both the positive and negative ionization mode. The unbiased mass spectral data was searched using Metlin library to identify the compounds. The metabolomic profiles thus generated were analyzed to delineate class-specific signatures which were interrogated for altered bioprocesses using Molecular Concept Map (OCM, www.oncomine.org). The altered bioprocesses were validated in cell line models using a combination of Q-PCR, immunoblot analysis and functional assays. Results and discussion: A total of 2019 compounds were detected across the 58 bladder-derived specimens of which, 423 compounds were significantly altered in BCa compared to adjacent benign. 50 of the differential compounds were named and used for developing a classificatory signature and bioprocess mapping. Included among these were polycylic compounds like aniline, catechols, aromatic amino acids, polyamines and S-adenosyl methionine (SAM). Interestingly this BCa-specific metabolic signature in tissues was able to delineate tumor from benign with an accuracy of 75 %. Importantly the functional mapping of the metabolic data revealed enhanced methylation potential in tumors as being one of the factors de-regulating the xenobiotic metabolism. In vitro experiments using bisulfite sequencing and methyltransferase inhibitor 5-Aza-cytidine confirmed this methylation-induced attenuation of phase I/II metabolic genes namely CYP1A1, CYP1B1, EPHX1 and GSTT1 in BCa. In summary, using unbiased metabolomic profiling report metabolic fingerprint for bladder cancer. Importantly our data for the first time reveals methylation-induced silencing of xenobiotic metabolism in bladder tumors. Citation Information: Cancer Epidemiol Biomarkers Prev 2010;19(10 Suppl):A52.

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△raxST, 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△raxST. 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△raxST. 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.

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.