Biology subjects

Metz, T. O.

Publications and source records attributed to Metz, T. O..

14 recordsLinked to original sources

bioRxiv2026

Assessing extracellular vesicle proteins as predictive biomarkers for developing type 1 diabetes

Plasma extracellular vesicles (EVs) are considered excellent sources for biomarker discovery since they carry signatures of their cellular origin and disease processes. In this paper, we evaluate the potential of plasma EV proteomics analysis for identifying predictive biomarkers of developing type 1 diabetes (T1D), which results from autoimmune destruction of insulin-producing {beta} cells in the islet. We used strong anion exchange beads (Mag-Net) to capture plasma EVs from 19 donors with islet autoimmunity (diagnosed by circulating autoantibodies against islet proteins - AAB+) vs. 17 control individuals and analyzed their protein cargo by mass spectrometry. The analysis identified and quantified 5,480 proteins, a 3.2-fold increase in proteome coverage compared to our previous T1D biomarker proteomics study that used whole plasma depleted of the 14 most abundant proteins. The Mag-Net approach also detected 1,306 out of the 1,717 proteins (76%) that we previously verified as EV proteins. Statistical tests revealed 448 proteins to be differentially abundant in AAB+ vs control volunteers, including 69 previously verified EV proteins. A functional-enrichment analysis resulted in overrepresentation of 25 pathways among the differentially abundant proteins, including pathways related to autoimmune response and lipid metabolism. The capacity of this data to predict AAB+ was tested with a machine learning analysis using a random forest model, resulting in a receiver operating characteristic-area under the curve of 0.81. Overall, our study indicates that plasma EV proteomics analysis can be an exciting approach for studying biomarkers for developing T1D. Significance of the studyType 1 diabetes (T1D) is a disease characterized by the bodys inability to produce insulin and consequently, to control blood glucose levels. Despite the initial trigger being unclear, the disease development process involves an autoimmune response to the islets of Langerhans, resulting in the death of insulin-producing {beta} cells. There is no cure for the disease, and treatment relies on exogenous administration of insulin. Therefore, preventive therapies that block the autoimmune process are attractive for treating T1D. In fact, anti-CD3 antibody (Teplizumab) delays the onset of T1D by 2 years by targeting T cells. Predictive biomarkers for developing T1D are needed to aid the development and implementation of new therapies and to identify the initial trigger and mechanisms of the islet autoimmune process. In this paper, we assess the potential of plasma extracellular vesicle (EV) proteomics analysis for identifying predictive biomarkers of T1D. Our results show excellent potential of the approach, opening opportunities to perform broader studies to identify biomarkers for developing T1D.

systems biology↗

bioRxiv2024

Metabolomic and transcriptomic remodeling of bone marrow myeloid cells in response to maternal obesity

Maternal obesity puts the offspring at high risk of developing obesity and cardio-metabolic diseases in adulthood. Here, using a mouse model of maternal high-fat diet (HFD)-induced obesity, we show that whole body fat content of the offspring of HFD-fed mothers (Off-HFD) increases significantly from very early age when compared to the offspring regular diet-fed mothers (Off-RD). We have previously shown significant metabolic and immune perturbations in the bone marrow of newly-weaned offspring of obese mothers. Therefore, we hypothesized that lipid metabolism is altered in the bone marrow Off-HFD in newly-weaned offspring of obese mothers when compared to the Off-RD. To test this hypothesis, we investigated the lipidomic profile of bone marrow cells collected from three-week-old offspring of regular and high fat diet-fed mothers. Diacylgycerols (DAGs), triacylglycerols (TAGs), sphingolipids and phospholipids, including plasmalogen, and lysophospholipids were remarkably different between the groups, independent of fetal sex. Levels of cholesteryl esters were significantly decreased in offspring of obese mothers, suggesting reduced delivery of cholesterol to bone marrow cells. This was accompanied by age-dependent progression of mitochondrial dysfunction in bone marrow cells. We subsequently isolated CD11b+ myeloid cells from three-week-old mice and conducted metabolomics, lipidomics, and transcriptomics analyses. The lipidomic profiles of these bone marrow myeloid cells were largely similar to that seen in bone marrow cells and included increases in DAGs and phospholipids alongside decreased TAGs, except for long-chain TAGs, which were significantly increased. Our data also revealed significant sex-dependent changes in amino acids and metabolites related to energy metabolism. Transcriptomic analysis revealed altered expression of genes related to major immune pathways including macrophage alternative activation, B-cell receptor signaling, TGF{beta} signaling, and communication between the innate and adaptive immune systems. All told, this study revealed lipidomic, metabolomic, and gene expression abnormalities in bone marrow cells broadly, and in bone marrow myeloid cells particularly, in the newly-weaned offspring of obese mothers, which might at least partially explain the progression of metabolic and cardiovascular diseases in their adulthood.

molecular biology↗

bioRxiv2024

Introducing identification probability for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies

Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.

biochemistry↗

bioRxiv2023

The unknown lipids project: harmonized methods improve compound identification and data reproducibility in an inter-laboratory untargeted lipidomics study

Untargeted lipidomics allows analysis of a broader range of lipids than targeted methods and permits discovery of unknown compounds. Previous ring trials have evaluated the reproducibility of targeted lipidomics methods, but inter-laboratory comparison of compound identification and unknown feature detection in untargeted lipidomics has not been attempted. To address this gap, five laboratories analyzed a set of mammalian tissue and biofluid reference samples using both their own untargeted lipidomics procedures and a common chromatographic and data analysis method. While both methods yielded informative data, the common method improved chromatographic reproducibility and resulted in detection of more shared features between labs. Spectral search against the LipidBlast in silico library enabled identification of over 2,000 unique lipids. Further examination of LC-MS/MS and ion mobility data, aided by hybrid search and spectral networking analysis, revealed spectral and chromatographic patterns useful for classification of unknown features, a subset of which were highly reproducible between labs. Overall, our method offers enhanced compound identification performance compared to targeted lipidomics, demonstrates the potential of harmonized methods to improve inter-site reproducibility for quantitation and feature alignment, and can serve as a reference to aid future annotation of untargeted lipidomics data.

systems biology↗

bioRxiv2022

Meta-analysis refinement of plasma extracellular vesicle composition identifies proplatelet basic protein as a signaling messenger in type 1 diabetes

Extracellular vesicles (EVs) play important roles in cell-to-cell communication and are potential biomarkers as they carry markers of their derived tissues and disease signatures. However, obtaining pure EV preparations from biofluids is challenging due to contaminants with similar physicochemical properties. Here, we performed a meta-analysis of plasma EV proteomics data deposited in public repositories to refine the protein composition of EVs and investigate potential roles in type 1 diabetes development. With the concept that each purification method yields different proportions of distinct contaminants, we grouped proteins into clusters based on their abundance profiles. This allowed us to separate clusters with classical EV markers, such as CD9, CD40, C63 and CD81, from clusters of well-known contaminants, such as serum albumin, apolipoproteins and components of the complement and coagulation pathways. Two clusters containing a total of 1720 proteins combined were enriched with EV markers and depleted in common contaminants; therefore, they were considered to contain bona fide EV components. As possible origins of plasma EVs, these clusters had markers of tissues such as spleen, liver, brain, lungs, pancreas, and blood/immune cells. These clusters were also enriched in cell surface markers CD antigens, and proteins from cell-to-cell communication and signaling pathways, such as chemokine signaling and antigen presentation. We also show that the EV component and type 1 diabetes biomarker, platelet basic protein (PPBP/CXCL7) regulates apoptosis in both beta and macrophage cell lines. Overall, our meta-analysis refined the composition of plasma EVs, reinforcing a primary function as messengers for cell-to-cell communication and signaling. Furthermore, this analysis identifies optimal avenues to target EVs for development of disease biomarkers.

cell biology↗

bioRxiv2022

A global lipid map reveals host dependency factors conserved across SARS-CoV-2 variants

A comprehensive understanding of host dependency factors for SARS-CoV-2 remains elusive. We mapped alterations in host lipids following SARS-CoV-2 infection using nontargeted lipidomics. We found that SARS-CoV-2 rewires host lipid metabolism, altering 409 lipid species up to 64-fold relative to controls. We correlated these changes with viral protein activity by transfecting human cells with each viral protein and performing lipidomics. We found that lipid droplet plasticity is a key feature of infection and that viral propagation can be blocked by small-molecule glycerolipid biosynthesis inhibitors. We found that this inhibition was effective against the main variants of concern (alpha, beta, gamma, and delta), indicating that glycerolipid biosynthesis is a conserved host dependency factor that supports this evolving virus.

microbiology↗

bioRxiv2020

The role of islet lipid composition remodeling in regulation of beta-cell death via ADP-ribosyl-acceptor glycohydrolase ARH3 signaling in insulitis

Lipids have been implicated as mediators of insulitis and {beta}-cell death in type 1 diabetes development, but the mechanisms underlying this association are poorly understood. Here, we investigated the changes in islet/{beta}-cell lipid composition using three models of insulitis: human islets and EndoC-{beta}H1 {beta}-cells treated with the cytokines IL-1{beta} and IFN-{gamma}, and islets from non-obese diabetic mice. Across all three models, lipidomic analyses showed a consistent change in abundance of the lysophosphatidylcholine, phosphatidylcholine and triacylglycerol species. We also showed that lysophosphatidylcholine and its biosynthetic enzyme PLA2G6 are enriched in murine islets. We determined that the ADP-ribosyl-acceptor glycohydrolase ARH3 is regulated by cytokines downstream of PLA2G6, which in turn regulates proteins involved in apoptosis, lipid metabolism, antigen processing and presentation and chemokines. ARH3 reduced cytokine-induced apoptosis, which may represent a negative feedback mechanism. Overall, these data show the importance of lipid metabolism in regulating {beta}-cell death in type 1 diabetes. HighlightsO_LILipidomics of 3 insulitis models revealed commonly regulated lipid classes. C_LIO_LIIdentification of 35 proteins regulated by cytokines via PLA2G6 signaling. C_LIO_LIARH3 reduces cytokine-induced apoptosis via PLA2G6 regulation. C_LIO_LIARH3 regulates the levels of proteins related to insulitis and type 1 diabetes. C_LI

systems biology↗

bioRxiv2020

A generalizable method for false-discovery rate estimation in mass spectrometry-based lipidomics

Mass spectrometry (MS)-based lipidomics is revolutionizing lipid research with high throughput identification and quantification of hundreds to thousands of lipids with the goal of elucidating lipid metabolism and function. Estimates of statistical confidence in lipid identification are essential for downstream data interpretation in a biological context. In the related field of proteomics, a variety of methods for estimating false-discovery are available, and understanding the statistical confidence of identifications is typically required for data analysis and hypothesis testing. However, there is no current method for estimating the false discovery rate (FDR) or statistical confidence for MS-based lipid identifications. This has slowed the adoption of MS-based lipidomics research, as all identifications require manual inspection and validation to ensure their accuracy. We present here the first generalizable method for FDR estimation, a target/decoy approach, that allows those conducting MS-based lipidomics research to confidently adjust spectral score thresholds to minimize false discovery and to enable full automation of data analysis.

bioinformatics↗

bioRxiv2020

A global lipid map defines a network essential for Zika virus replication

Zika virus (ZIKV), an arbovirus of global concern, remodels intracellular membranes to form replication sites. How ZIKV dysregulates lipid networks to allow this, and consequences for disease, is poorly understood. Here, we performed comprehensive lipidomics to create a lipid network map during ZIKV infection. We found that ZIKV significantly alters host lipid composition, with the most striking changes seen within subclasses of sphingolipids. Ectopic expression of ZIKV NS4B protein resulted in similar changes, demonstrating a role for NS4B in modulating sphingolipid pathways. Disruption of sphingolipid biosynthesis in various cell types, including human neural progenitor cells, blocked ZIKV infection. Additionally, the sphingolipid ceramide redistributes to ZIKV replication sites and increasing ceramide levels by multiple pathways sensitizes cells to ZIKV infection. Thus, we identify a sphingolipid metabolic network with a critical role in ZIKV replication and show that ceramide flux is a key mediator of ZIKV infection.

microbiology↗

bioRxiv2020

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

Gas chromatography-mass spectrometry (GC-MS) represents an analytical technique with significant practical societal impact. Spectral deconvolution is an essential step for interpreting GC-MS data. No public GC-MS repositories that also enable repository-scale analysis exist, in part because deconvolution requires significant user input. We therefore engineered a scalable machine learning workflow for the Global Natural Product Social Molecular Networking (GNPS) analysis platform to enable the mass spectrometry community to store, process, share, annotate, compare, and perform molecular networking of GC-MS data. The workflow performs auto-deconvolution of compound fragmentation patterns via unsupervised non-negative matrix factorization, using a Fast Fourier Transform-based strategy to overcome scalability limitations. We introduce a "balance score" that quantifies the reproducibility of fragmentation patterns across all samples. We demonstrate the utility of the platform with breathomics analysis applied to the early detection of oesophago-gastric cancer, and by creating the first molecular spatial map of the human volatilome.

bioinformatics↗

bioRxiv2019

The impact of pro-inflammatory cytokines on the β-cell regulatory landscape provides new insights into the genetics of type 1 diabetes

Early stages of type 1 diabetes (T1D) are characterized by local autoimmune inflammation and progressive loss of insulin-producing pancreatic {beta} cells. We show here that exposure to pro-inflammatory cytokines unmasks a marked plasticity of the {beta}-cell regulatory landscape. We expand the repertoire of human islet regulatory elements by mapping stimulus-responsive enhancers linked to changes in the {beta}-cell transcriptome, proteome and 3D chromatin structure. Our data indicates that the {beta} cell response to cytokines is mediated by the induction of novel regulatory regions as well as the activation of primed regulatory elements pre-bound by islet-specific transcription factors. We found that T1D-associated loci are enriched of the newly mapped cis-regulatory regions and identify T1D-associated variants disrupting cytokine-responsive enhancer activity in human {beta} cells. Our study illustrates how {beta} cells respond to a pro-inflammatory environment and implicate a role for stimulus-response islet enhancers in T1D.

genomics↗

bioRxiv2019

MetFish: A Metabolomics Platform for Studying Microbial Communities in Chemically Extreme Environments

Metabolites have essential roles in microbial communities, including as mediators of nutrient and energy exchange, cell-to-cell communication, and antibiosis. However, detecting and quantifying metabolites and other chemicals in samples having extremes in salt or mineral content using liquid chromatography-mass spectrometry (LC-MS)-based methods remains a significant challenge. Here we report a facile method based on in situ chemical derivatization followed by extraction for analysis of metabolites and other chemicals in hypersaline samples, enabling for the first time direct LC-MS-based exo-metabolomics analysis in sample matrices containing up to 2 molar total dissolved salts. The method, MetFish, is applicable to molecules containing amine, carboxylic acid, carbonyl, or hydroxyl functional groups, and can be integrated into either targeted or untargeted analysis pipelines. In targeted analyses, MetFish provided limits of quantification as low as 1 nM, broad linear dynamic ranges (up to 5-6 orders of magnitude) with excellent linearity, and low median inter-day reproducibility (e.g. 2.6%). MetFish was successfully applied in targeted and untargeted exo-metabolomics analyses of microbial consortia, quantifying amino acid dynamics in the exo-metabolome during community succession; in situ in a native prairie soil, whose exo-metabolome was isolated using a hypersaline extraction; and in input and produced fluids from a hydraulically fractured well, identifying dramatic changes in the exo-metabolome over time in the well.

microbiology↗

bioRxiv2018

Rapidly Assessing the Quality of Targeted Proteomics Experiments Through Monitoring Stable-isotope Labeled Standards

Targeted proteomics experiments based on selected reaction monitoring (SRM) have gained wide adoption in clinical biomarker, cellular modeling and numerous other biological experiments due to their highly accurate and reproducible quantification. The quantitative accuracy in targeted proteomics experiments is reliant on the stable-isotope, heavy-labeled peptide standards which are spiked into a sample and used as a reference when calculating the abundance of endogenous peptides. Therefore, the quality of measurement for these standards is a critical factor in determining whether data acquisition was successful. With improved MS instrumentation that enables the monitoring of hundreds of peptides in hundreds to thousands of samples, quality assessment is increasingly important and cannot be performed manually. We present Q4SRM, a software tool that rapidly checks the signal from all heavy labeled peptides and flags those that fail quality control metrics. Using four metrics, the tool detects problems both with individual SRM transitions and the collective group of transitions that monitor a single peptide. The programs speed enables its use at the point of data acquisition and can be ideally run immediately upon the completion of an LC-SRM-MS analysis.

bioinformatics↗

bioRxiv2018

Lactobacillus acidophilus disrupts collaborative multispecies bile acid metabolism

Bile acids are metabolic links between hosts and their gut microbiomes, yet little is known about the roles they play in microbe-to-microbe interactions. Here we present a study designed to investigate the effect that a common probiotic, Lactobacillus acidophilus, has on microbial interactions that lead to formation of secondary bile acids. A model microbial consortium was built from three human gut isolates, Clostridium scindens, Collinsella aerofaciens, and Blautia obeum, and cultured under different bile acid and probiotic treatments. A multi-omics platform that included mass spectrometry-based metabolomics and activity-based proteomic probes was used to produce two major results. The first, was that an uncommon secondary bile acid - ursocholate - was produced by a multi-species chemical synthesis pathway. This result highlights a new microbe-to-microbe interaction mediated by bile acids. The second finding was that the probiotic strain, L. acidophilus, quenched the observed interactions and effectively halted consortial synthesis of ursocholate. Little is known about the role that ursocholate plays in human health and development. However, we did discover that a decrease in ursocholate abundance corresponded with successful weight loss in patients after gastric bypass surgery versus those who did not lose weight after surgery. Hence, this study uncovered basic knowledge that may aid future designs of custom probiotic therapies to combat obesity.

microbiology↗