Faculty Bibliography

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Pancreatic ductal adenocarcinoma (PDAC) evolves a complex microenvironment comprised of multiple cell types, including pancreatic stellate cells (PSCs). Previous studies have demonstrated that stromal supply of alanine, lipids, and nucleotides supports the metabolism, growth, and therapeutic resistance of PDAC. Here we demonstrate that alanine crosstalk between PSCs and PDAC is orchestrated by the utilization of specific transporters. PSCs utilize SLC1A4 and other transporter(s) to rapidly exchange and maintain environmental alanine concentrations. Moreover, PDAC cells upregulate SLC38A2 to supply their increased alanine demand. Cells lacking SLC38A2 fail to concentrate intracellular alanine and undergo a profound metabolic crisis resulting in markedly impaired tumor growth. Our results demonstrate that stromal-cancer metabolic niches can form through differential transporter expression, creating unique therapeutic opportunities to target metabolic demands of cancer.

Metabolomics is increasingly important for biomedical research, but large-scale metabolite identification in untargeted metabolomics is still challenging. Here, we present Jumbo Mass spectrometry-based Program of Metabolomics (JUMPm) software, a streamlined software tool for identifying potential metabolite formulas and structures in mass spectrometry. During database search, the false discovery rate is evaluated by a target-decoy strategy, where the decoys are produced by breaking the octet rule of chemistry. We illustrated the utility of JUMPm by detecting metabolite formulas and structures from liquid chromatography coupled tandem mass spectrometry (LC-MS/MS) analyses of unlabeled and stable-isotope labeled yeast samples. We also benchmarked the performance of JUMPm by analyzing a mixed sample from a commercially available metabolite library in both hydrophilic and hydrophobic LC-MS/MS. These analyses confirm that metabolite identification can be significantly improved by estimating the element composition in formulas using stable isotope labeling, or by introducing LC retention time during a spectral library search, which are incorporated into JUMPm functions. Finally, we compared the performance of JUMPm and two commonly used programs, Compound Discoverer 3.1 and MZmine 2, with respect to putative metabolite identifications. Our results indicate that JUMPm is an effective tool for metabolite identification of both unlabeled and labeled data in untargeted metabolomics.

OBJECTIVE:To characterize the ecological effects of biologic therapies on the gut bacterial and fungal microbiome of psoriatic arthritis (PsA)/spondyloarthritis (SpA) patients. METHODS:Fecal samples from PsA/SpA patients pre- and post-treatment with tumor necrosis factor inhibitors (TNFi; n=15) or an anti-interleukin (IL)-17A monoclonal antibody inhibitor (IL-17i; n=14) underwent sequencing (16S, ITS and shotgun metagenomics) and computational microbiome analysis. Fecal levels of fatty acid metabolites and cytokines/proteins implicated in PsA/SpA pathogenesis or intestinal inflammation were correlated with sequence data. Additionally, ileal biopsies obtained from SpA patients who developed clinically overt Crohn's disease (CD) after treatment with IL-17i (n=5) were analyzed for expression of IL-23/Th-17 related cytokines, IL-25/IL-17E-producing cells and type-2 innate lymphoid cells (ILC2s). RESULTS:There were significant shifts in abundance of specific taxa after treatment with IL-17i compared to TNFi, particularly Clostridiales (p=0.016) and Candida albicans (p=0.041). These subclinical alterations correlated with changes in bacterial community co-occurrence, metabolic pathways, IL-23/Th17-related cytokines and various fatty acids. Ileal biopsies showed that clinically overt CD was associated with expansion of IL-25/IL-17E-producing tuft cells and ILC2s (p<0.05) compared to pre-IL-17i treatment levels. CONCLUSION/CONCLUSIONS:In a subgroup of SpA patients, the initiation of IL-17A blockade correlated with features of subclinical gut inflammation and intestinal dysbiosis of certain bacterial and fungal taxa, most notably C. albicans. Further, IL-17i-related CD was associated with overexpression of IL-25/IL-17E-producing tuft cells and ILC2s. These results may help to explain the potential link between inhibition of a specific IL-17 pathway and the (sub)clinical gut inflammation observed in SpA.

Background and aims: Chronic granulomatous disease (CGD) is characterized by recurrent infections and inflammatory dysregulation, especially in the gut. Almost 50% of patients with CGD have CGD-associated inflammatory bowel disease (CGD-IBD), yet its pathophysiology remains poorly understood. We characterized the intestinal microbiome and metabolome in patients with CGD to determine if intestinal microbiome and metabolomic signatures could distinguish subpopulations of patients with CGD while using the metabolome to add a functional dimension to observed microbiome signatures.
Method(s): Clinical metadata and fecal samples were collected crosssectionally from healthy volunteers (HV; n=16) and patients with CGD (n=77). Metabolomic profiling and 16S rRNA (V4) sequencing was performed on fecal samples (total samples: 108; reads/sample: 15,254 to 191,415; median: 60,816).
Result(s): Samples from patients with CGD had distinct intestinal microbiome signatures and metabolomic profiles depending on genotype, presence of CGD-IBD and specific interventions (e.g. treatment with an elemental diet). Notably, samples from patients with active CGD-IBD (compared to samples from patients without a history of CGD-IBD) had significantly different alpha- and betadiversities, and were enriched for Enterococcus spp. (8.5 vs. 1.5%), Serratia spp. (8.6 vs. 3.9%) and Raoultella spp. (6.1 vs. 0.6%), while being depleted of Bacteroides spp. (9.3 vs. 23.6%). Metabolomic profiles from CGD patient samples pointed toward an aberrant metabolism of toxic ammonia waste by the intestinal microbiota compared to HV. Interestingly, use of an elemental diet to treat a patient with CGD-IBD induced long-term changes in the alpha- and beta-diversities of the patients intestinal microbiota, stabilized the intestinal metabolome, and allowed his microbial and metabolic profiles to resemble those of patients without CGD-IBD.
Conclusion(s): Intestinal microbiome and metabolomic signatures can distinguish subpopulations of patients CGD based on genotype, presence of intestinal inflammation and certain treatment interventions

The use of fixed fibroblasts from familial and sporadic Alzheimer's disease patients has previously indicated an upregulation of mitochondria-ER contacts (MERCs) as a hallmark of Alzheimer's disease. Despite its potential significance, the relevance of these results is limited because they were not extended to live neurons. Here we performed a dynamic in vivo analysis of MERCs in hippocampal neurons from McGill-R-Thy1-APP transgenic rats, a model of Alzheimer's disease-like amyloid pathology. Live FRET imaging of neurons from transgenic rats revealed perturbed 'lipid-MERCs' (gap width <10 nm), while 'Ca²⁺-MERCs' (10-20 nm gap width) were unchanged. In situ TEM showed no significant differences in the lipid-MERCs:total MERCs or lipid-MERCs:mitochondria ratios; however, the average length of lipid-MERCs was significantly decreased in neurons from transgenic rats as compared to controls. In accordance with FRET results, untargeted lipidomics showed significant decreases in levels of 12 lipids and bioenergetic analysis revealed respiratory dysfunction of mitochondria from transgenic rats. Thus, our results reveal changes in MERC structures coupled with impaired mitochondrial functions in Alzheimer's disease-related neurons.This article has an associated First Person interview with the first author of the paper.

Advanced, metastatic melanomas frequently grow in subcutaneous tissues and portend a poor prognosis. Though subcutaneous tissues are largely composed of adipocytes, the mechanisms by which adipocytes influence melanoma are poorly understood. Using in vitro and in vivo models, we find that adipocytes increase proliferation and invasion of adjacent melanoma cells. Additionally, adipocytes directly transfer lipids to melanoma cells, which alters tumor cell metabolism. Adipocyte-derived lipids are transferred to melanoma cells through the FATP/SLC27A family of lipid transporters expressed on the tumor cell surface. Among the six FATP/SLC27A family members, melanomas significantly overexpress FATP1/SLC27A1. Melanocyte-specific FATP1 expression cooperates with BRAFV600E in transgenic zebrafish to accelerate melanoma development, an effect that is similarly seen in mouse xenograft studies. Pharmacologic blockade of FATPs with the small molecule Lipofermata abrogates lipid transport into melanoma cells and reduces melanoma growth and invasion. These data demonstrate that stromal adipocytes can drive melanoma progression through FATP lipid transporters, and represents a new target aimed at interrupting adipocyte-melanoma cross-talk.

High throughput untargeted metabolomics usually relies on complementary liquid chromatography-mass spectrometry (LC-MS) methods to expand the coverage of diverse metabolites, but the integration of those methods is not fully characterized. We systematically investigated the performance of hydrophilic interaction liquid chromatography (HILIC)-MS and nanoflow reverse-phase liquid chromatography (nRPLC)-MS under 8 LC-MS settings, varying stationary phases (HILIC and C18), mobile phases (acidic and basic pH), and MS ionization modes (positive and negative). Whereas nRPLC-MS optimization was previously reported, we found in HILIC-MS (2.1 mm × 150 mm) that the optimal performance was achieved in a 90 min gradient with 100 μL/min flow rate by loading metabolite extracts from 2 mg of cell/tissue samples. Since peak features were highly compromised by contaminants, we used stable isotope labeled yeast to enhance formula identification for comparing different LC-MS conditions. The 8 LC-MS settings enabled the detection of a total of 1050 formulas, among which 78%, 73%, and 62% formulas were recovered by the best combination of 4, 3, and 2 LC-MS settings, respectively. Moreover, these yeast samples were harvested in the presence or absence of nitrogen starvation, enabling quantitative comparisons of altered formulas and metabolite structures, followed by validation with selected synthetic metabolites. The results revealed that nitrogen starvation downregulated amino acid components but upregulated uridine-related metabolism. In summary, this study introduces a thorough evaluation of hydrophilicity and hydrophobicity based LC-MS and provides information for selecting complementary settings to balance throughput and efficiency during metabolomics experiments.

Metabolite identification is a crucial step in mass spectrometry (MS)-based metabolomics. However, it is still challenging to assess the confidence of assigned metabolites. We report a novel method for estimating the false discovery rate (FDR) of metabolite assignment with a target-decoy strategy, in which the decoys are generated through violating the octet rule of chemistry by adding small odd numbers of hydrogen atoms. The target-decoy strategy was integrated into JUMPm, an automated metabolite identification pipeline for large-scale MS analysis and was also evaluated with two other metabolomics tools, mzMatch and MZmine 2. The reliability of FDR calculation was examined by false data sets, which were simulated by altering MS1 or MS2 spectra. Finally, we used the JUMPm pipeline coupled to the target-decoy strategy to process unlabeled and stable-isotope-labeled metabolomic data sets. The results demonstrate that the target-decoy strategy is a simple and effective method for evaluating the confidence of high-throughput metabolite identification.