Role of UDP-Glycosyltransferase (ugt) Genes in Detoxification and Glycosylation of 1-Hydroxyphenazine (1-HP) in Caenorhabditis elegans.

Caenorhabditis elegans is a useful model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins, but the mechanisms of phase II detoxification are understudied. 1-Hydroxyphenazine (1-HP), a toxin produced by the bacterium Pseudomonas aeruginosa, kills C. elegans. We previously showed that C. elegans detoxifies 1-HP by adding one, two, or three glucose molecules in N2 worms. Our current study evaluates the roles that some UDP-glycosyltransferase (ugt) genes play in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP than reference strains N2 or PD1074. Our data also show that ugt-23 knockout mutants produce reduced amounts of the trisaccharide sugars, while the ugt-49 knockout mutants produce reduced amounts of all 1-HP derivatives except for the glucopyranosyl product compared to the reference strains. We characterized the structure of the trisaccharide sugar phenazines made by C. elegans and showed that one of the sugar modifications contains an N-acetylglucosamine (GlcNAc) in place of glucose. This implies broad specificity regarding UGT function and the role of genes other than ogt-1 in adding GlcNAc, at least in small-molecule detoxification.

Perspective: use and reuse of NMR-based metabolomics data: what works and what remains challenging.

BACKGROUND: The National Cancer Institute issued a Request for Information (RFI; NOT-CA-23-007) in October 2022, soliciting input on using and reusing metabolomics data. This RFI aimed to gather input on best practices for metabolomics data storage, management, and use/reuse. AIM OF REVIEW: The nuclear magnetic resonance (NMR) Interest Group within the Metabolomics Association of North America (MANA) prepared a set of recommendations regarding the deposition, archiving, use, and reuse of NMR-based and, to a lesser extent, mass spectrometry (MS)-based metabolomics datasets. These recommendations were built on the collective experiences of metabolomics researchers within MANA who are generating, handling, and analyzing diverse metabolomics datasets spanning experimental (sample handling and preparation, NMR/MS metabolomics data acquisition, processing, and spectral analyses) to computational (automation of spectral processing, univariate and multivariate statistical analysis, metabolite prediction and identification, multi-omics data integration, etc.) studies. KEY SCIENTIFIC CONCEPTS OF REVIEW: We provide a synopsis of our collective view regarding the use and reuse of metabolomics data and articulate several recommendations regarding best practices, which are aimed at encouraging researchers to strengthen efforts toward maximizing the utility of metabolomics data, multi-omics data integration, and enhancing the overall scientific impact of metabolomics studies.

Changes in primary metabolite content may affect thrips feeding preference in soybean crops.

Past research has characterized the induction of plant defenses in response to chewing insect damage. However, little is known about plant responses to piercing-sucking insects that feed on plant cell-contents like thrips (Caliothrips phaseoli). In this study, we used NMR spectroscopy to measure metabolite changes in response to six days of thrips damage from two field-grown soybean cultivars (cv.), known for their different susceptibility to Caliothrips phaseoli. We observed that thrips damage reduces sucrose concentration in both cultivars, while pinitol, the most abundant leaf soluble carbohydrate, is induced in cv. Charata but not in cv. Williams. Thrips did not show preference for leaves where sucrose or pinitol were externally added, at tested concentration. In addition, we also noted that cv. Charata was less naturally colonized and contained higher levels of trigonelline, tyrosine as well as several compounds that we have not yet identified. We have established that preference-feeding clues are not dependent on the plants major soluble carbohydrates but may depend on other types of compounds or leaf physical characteristics.

SAND: Automated Time-Domain Modeling of NMR Spectra Applied to Metabolite Quantification.

Developments in untargeted nuclear magnetic resonance (NMR) metabolomics enable the profiling of thousands of biological samples. The exploitation of this rich source of information requires a detailed quantification of spectral features. However, the development of a consistent and automatic workflow has been challenging because of extensive signal overlap. To address this challenge, we introduce the software Spectral Automated NMR Decomposition (SAND). SAND follows on from the previous success of time-domain modeling and automatically quantifies entire spectra without manual interaction. The SAND approach uses hybrid optimization with Markov chain Monte Carlo methods, employing subsampling in both time and frequency domains. In particular, SAND randomly divides the time-domain data into training and validation sets to help avoid overfitting. We demonstrate the accuracy of SAND, which provides a correlation of ∼0.9 with ground truth on cases including highly overlapped simulated data sets, a two-compound mixture, and a urine sample spiked with different amounts of a four-compound mixture. We further demonstrate an automated annotation using correlation networks derived from SAND decomposed peaks, and on average, 74% of peaks for each compound can be recovered in single clusters. SAND is available in NMRbox, the cloud computing environment for NMR software hosted by the Network for Advanced NMR (NAN). Since the SAND method uses time-domain subsampling (i.e., random subset of time-domain points), it has the potential to be extended to a higher dimensionality and nonuniformly sampled data.

Computational analysis of variation in C. elegans ugts.

Caenorhabditis elegans are free-living nematodes with a relatively short life cycle and a wealth of genomic information across multiple databases. Uridine diphosphate-glycosyltransferases (UGTs) are a family of enzymes involved in Phase II modification of xenobiotics in C. elegans , which is the addition of a sizeable water-soluble molecule to a xenobiotic to allow for its excretion out of a cell. Little is known about the variation in UGTs across wild isolates and how that might affect their innate immune response. We analyzed the diversity in ugt genes across C. elegans isolates from different geographical locations from the Caenorhabditis elegans Natural Diversity Resource (CaeNDR) database. This was accomplished using whole genome data and data identifying genome regions as hyper-divergent for each isotype. We implemented three steps to identify ugt genes and make inferences based on their variation. First, we created a catalog of UGTs in the N2 reference strain and used them to create a phylogenetic tree that depicts the relationships between the UGT protein sequences. We then quantified ugt variation using the strains from the CaeNDR database and used their data to remove hyper-divergent ugt genes. The third step was to catalog the occurrence of minor allele frequency (MAF) > 0.05 for all the ugts to compare how that aligned with genes classified as hyper-divergent by CaeNDR. Of the 67 ugt genes analyzed, 18 were hyper-divergent. This research will help improve our understanding of ugt variation in C. elegans .

Development of a Robust Consensus Modeling Approach for Identifying Cellular and Media Metabolites Predictive of Mesenchymal Stromal Cell Potency.

Mesenchymal stromal cells (MSCs) have shown promise in regenerative medicine applications due in part to their ability to modulate immune cells. However, MSCs demonstrate significant functional heterogeneity in terms of their immunomodulatory function because of differences in MSC donor/tissue source, as well as non-standardized manufacturing approaches. As MSC metabolism plays a critical role in their ability to expand to therapeutic numbers ex vivo, we comprehensively profiled intracellular and extracellular metabolites throughout the expansion process to identify predictors of immunomodulatory function (T-cell modulation and indoleamine-2,3-dehydrogenase (IDO) activity). Here, we profiled media metabolites in a non-destructive manner through daily sampling and nuclear magnetic resonance (NMR), as well as MSC intracellular metabolites at the end of expansion using mass spectrometry (MS). Using a robust consensus machine learning approach, we were able to identify panels of metabolites predictive of MSC immunomodulatory function for 10 independent MSC lines. This approach consisted of identifying metabolites in 2 or more machine learning models and then building consensus models based on these consensus metabolite panels. Consensus intracellular metabolites with high predictive value included multiple lipid classes (such as phosphatidylcholines, phosphatidylethanolamines, and sphingomyelins) while consensus media metabolites included proline, phenylalanine, and pyruvate. Pathway enrichment identified metabolic pathways significantly associated with MSC function such as sphingolipid signaling and metabolism, arginine and proline metabolism, and autophagy. Overall, this work establishes a generalizable framework for identifying consensus predictive metabolites that predict MSC function, as well as guiding future MSC manufacturing efforts through identification of high-potency MSC lines and metabolic engineering.

Label-free in vitro assays predict the potency of anti-disialoganglioside chimeric antigen receptor T-cell products.

BACKGROUND AIMS: Chimeric antigen receptor (CAR) T cells have demonstrated remarkable efficacy against hematological malignancies; however, they have not experienced the same success against solid tumors such as glioblastoma (GBM). There is a growing need for high-throughput functional screening platforms to measure CAR T-cell potency against solid tumor cells. METHODS: We used real-time, label-free cellular impedance sensing to evaluate the potency of anti-disialoganglioside (GD2) targeting CAR T-cell products against GD2+ patient-derived GBM stem cells over a period of 2 days and 7 days in vitro. We compared CAR T products using two different modes of gene transfer: retroviral transduction and virus-free CRISPR-editing. Endpoint flow cytometry, cytokine analysis and metabolomics data were acquired and integrated to create a predictive model of CAR T-cell potency. RESULTS: Results indicated faster cytolysis by virus-free CRISPR-edited CAR T cells compared with retrovirally transduced CAR T cells, accompanied by increased inflammatory cytokine release, CD8+ CAR T-cell presence in co-culture conditions and CAR T-cell infiltration into three-dimensional GBM spheroids. Computational modeling identified increased tumor necrosis factor α concentrations with decreased glutamine, lactate and formate as being most predictive of short-term (2 days) and long-term (7 days) CAR T cell potency against GBM stem cells. CONCLUSIONS: These studies establish impedance sensing as a high-throughput, label-free assay for preclinical potency testing of CAR T cells against solid tumors.

13C NMR metabolomics: J-resolved STOCSY meets INADEQUATE.

Robust annotation of metabolites is a challenging task in metabolomics. Among available applications, 13C NMR experiment INADEQUATE determines direct 13C-13C connectivity unambiguously, offering indispensable information on molecular structure. Despite its great utility, it is not always practical to collect INADEQUATE data on every sample in a large metabolomics study because of its relatively long experiment time. Here, we propose an alternative approach that maintains the quality of information but saves experiment time. In this approach, individual samples in a study are first screened by 13C homonuclear J-resolved experiment (JRES). Next, JRES data are processed by statistical total correlation spectroscopy (STOCSY) to extract peaks that behave similarly among samples. Finally, INADEQUATE is collected on one internal pooled sample to select STOCSY peaks that originate from the same compound. We tested this concept using the 13C-labeled endometabolome of a model marine diatom strain incubated under various settings, intending to cover a range of metabolites produced under different external conditions. This scheme was able to extract known diatom metabolites proline, 2,3-dihydroxypropane-1-sulfonate (DHPS), ß-1,3-glucan, choline, and glutamate. This pipeline also detected unknown compounds with structural information, which is valuable in metabolomics where a priori knowledge of metabolites is not always available. The ability of this scheme was seen even in sugar regions, which are usually challenging in 1H NMR due to severe peak overlap. JRES and INADEQUATE were highly complementary; INADEQUATE provided directly-bonded 13C networks, whereas JRES linked INADEQUATE networks within the same compound but broken by nitrogen or sulfur atoms, highlighting the advantage of this integrated approach.

Unknown Metabolite Identification Using Machine Learning Collision Cross-Section Prediction and Tandem Mass Spectrometry.

Ion mobility (IM) spectrometry provides semiorthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics data sets. While current literature has showcased IM-MS for identifying unknowns under near ideal circumstances, less work has been conducted to evaluate the performance of this approach in metabolomics studies involving highly complex samples with difficult matrices. Here, we present a workflow incorporating de novo molecular formula annotation and MS/MS structure elucidation using SIRIUS 4 with experimental IM collision cross-section (CCS) measurements and machine learning CCS predictions to identify differential unknown metabolites in mutant strains of Caenorhabditis elegans. For many of those ion features, this workflow enabled the successful filtering of candidate structures generated by in silico MS/MS predictions, though in some cases, annotations were challenged by significant hurdles in instrumentation performance and data analysis. While for 37% of differential features we were able to successfully collect both MS/MS and CCS data, fewer than half of these features benefited from a reduction in the number of possible candidate structures using CCS filtering due to poor matching of the machine learning training sets, limited accuracy of experimental and predicted CCS values, and lack of candidate structures resulting from the MS/MS data. When using a CCS error cutoff of ±3%, on average, 28% of candidate structures could be successfully filtered. Herein, we identify and describe the bottlenecks and limitations associated with the identification of unknowns in non-targeted metabolomics using IM-MS to focus and provide insights into areas requiring further improvement.

Postgraduate perspectives on mentoring undergraduate researchers for talent development.

Undergraduate research experiences are critical for the talent development of the STEM research workforce, and research mentors play an influential role in this process. Given the many life science majors seeking research experiences at universities, graduate and postdoctoral researchers (i.e., postgraduates) provide much of the daily mentoring of undergraduate researchers. Yet, there remains little research on how postgraduates contribute to talent development among undergraduate researchers. To begin to address this knowledge gap, we conducted an exploratory study of the experiences of 32 postgraduates who mentored life science undergraduate researchers. We identified four factors that they perceived as enabling undergraduate researcher talent development: undergraduate researcher characteristics, research project characteristics, and mentoring implementation as well as outcomes for both the postgraduate and undergraduate. We then describe a team-based approach to postgraduate mentoring of undergraduate researchers that attends to these factors to provide an example that practitioners can adapt or adopt for their own research groups.

Dissolved organic metabolite extraction from high-salt media.

We describe considerations and strategies for developing a nuclear magnetic resonance (NMR) sample preparation method to extract low molecular weight metabolites from high-salt spent media in a model coculture system of phytoplankton and marine bacteria. Phytoplankton perform half the carbon fixation and oxygen generation on Earth. A substantial fraction of fixed carbon becomes part of a metabolite pool of small molecules known as dissolved organic matter (DOM), which are taken up by marine bacteria proximate to phytoplankton. There is an urgent need to elucidate these metabolic exchanges due to widespread anthropogenic transformations on the chemical, phenotypic, and species composition of seawater. These changes are increasing water temperature and the amount of CO2 absorbed by the ocean at energetic costs to marine microorganisms. Little is known about the metabolite-mediated, structured interactions occurring between phytoplankton and associated marine bacteria, in part because of challenges in studying high-salt solutions on various analytical platforms. NMR analysis is problematic due to the high-salt content of both natural seawater and culture media for marine microbes. High-salt concentration degrades the performance of the radio frequency coil, reduces the efficiency of some pulse sequences, limits signal-to-noise, and prolongs experimental time. The method described herein can reproducibly extract low molecular weight DOM from small-volume, high-salt cultures. It is a promising tool for elucidating metabolic flux between marine microorganisms and facilitates genetic screens of mutant microorganisms.

Functional assessment of homozygous ALDH18A1 variants reveals alterations in amino acid and antioxidant metabolism.

Mono- and bi-allelic variants in ALDH18A1 cause a spectrum of human disorders associated with cutaneous and neurological findings that overlap with both cutis laxa and spastic paraplegia. ALDH18A1 encodes the bifunctional enzyme pyrroline-5-carboxylate synthetase (P5CS) that plays a role in the de novo biosynthesis of proline and ornithine. Here we characterize a previously unreported homozygous ALDH18A1 variant (p.Thr331Pro) in four affected probands from two unrelated families, and demonstrate broad-based alterations in amino acid and antioxidant metabolism. These four patients exhibit variable developmental delay, neurological deficits and loose skin. Functional characterization of the p.Thr331Pro variant demonstrated a lack of any impact on the steady-state level of the P5CS monomer or mitochondrial localization of the enzyme, but reduced incorporation of the monomer into P5CS oligomers. Using an unlabeled NMR-based metabolomics approach in patient fibroblasts and ALDH18A1-null human embryonic kidney cells expressing the variant P5CS, we identified reduced abundance of glutamate and several metabolites derived from glutamate, including proline and glutathione. Biosynthesis of the polyamine putrescine, derived from ornithine, was also decreased in patient fibroblasts, highlighting the functional consequence on another metabolic pathway involved in antioxidant responses in the cell. RNA sequencing of patient fibroblasts revealed transcript abundance changes in several metabolic and extracellular matrix-related genes, adding further insight into pathogenic processes associated with impaired P5CS function. Together these findings shed new light on amino acid and antioxidant pathways associated with ALDH18A1-related disorders, and underscore the value of metabolomic and transcriptomic profiling to discover new pathways that impact disease pathogenesis.

An anchored experimental design and meta-analysis approach to address batch effects in large-scale metabolomics.

Untargeted metabolomics studies are unbiased but identifying the same feature across studies is complicated by environmental variation, batch effects, and instrument variability. Ideally, several studies that assay the same set of metabolic features would be used to select recurring features to pursue for identification. Here, we developed an anchored experimental design. This generalizable approach enabled us to integrate three genetic studies consisting of 14 test strains of Caenorhabditis elegans prior to the compound identification process. An anchor strain, PD1074, was included in every sample collection, resulting in a large set of biological replicates of a genetically identical strain that anchored each study. This enables us to estimate treatment effects within each batch and apply straightforward meta-analytic approaches to combine treatment effects across batches without the need for estimation of batch effects and complex normalization strategies. We collected 104 test samples for three genetic studies across six batches to produce five analytical datasets from two complementary technologies commonly used in untargeted metabolomics. Here, we use the model system C. elegans to demonstrate that an augmented design combined with experimental blocks and other metabolomic QC approaches can be used to anchor studies and enable comparisons of stable spectral features across time without the need for compound identification. This approach is generalizable to systems where the same genotype can be assayed in multiple environments and provides biologically relevant features for downstream compound identification efforts. All methods are included in the newest release of the publicly available SECIMTools based on the open-source Galaxy platform.

NMR and Metabolomics-A Roadmap for the Future.

Metabolomics investigates global metabolic alterations associated with chemical, biological, physiological, or pathological processes. These metabolic changes are measured with various analytical platforms including liquid chromatography-mass spectrometry (LC-MS), gas chromatography-mass spectrometry (GC-MS) and nuclear magnetic resonance spectroscopy (NMR). While LC-MS methods are becoming increasingly popular in the field of metabolomics (accounting for more than 70% of published metabolomics studies to date), there are considerable benefits and advantages to NMR-based methods for metabolomic studies. In fact, according to PubMed, more than 926 papers on NMR-based metabolomics were published in 2021-the most ever published in a given year. This suggests that NMR-based metabolomics continues to grow and has plenty to offer to the scientific community. This perspective outlines the growing applications of NMR in metabolomics, highlights several recent advances in NMR technologies for metabolomics, and provides a roadmap for future advancements.

Predicting T-cell quality during manufacturing through an artificial intelligence-based integrative multiomics analytical platform.

Large-scale, reproducible manufacturing of therapeutic cells with consistently high quality is vital for translation to clinically effective and widely accessible cell therapies. However, the biological and logistical complexity of manufacturing a living product, including challenges associated with their inherent variability and uncertainties of process parameters, currently make it difficult to achieve predictable cell-product quality. Using a degradable microscaffold-based T-cell process, we developed an artificial intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters and critical quality attributes from heterogeneous, high-dimensional, time-dependent multiomics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, design-of-experiment-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from early in-culture media assessment that were highly predictive of the end-product CD4/CD8 ratio and total live CD4+ and CD8+ naïve and central memory T cells (CD63L+CCR7+). Our results demonstrate a broadly applicable platform tool to predict end-product quality and composition from early time point in-process measurements during therapeutic cell manufacturing.

Uncovering in vivo biochemical patterns from time-series metabolic dynamics.

System biology relies on holistic biomolecule measurements, and untangling biochemical networks requires time-series metabolomics profiling. With current metabolomic approaches, time-series measurements can be taken for hundreds of metabolic features, which decode underlying metabolic regulation. Such a metabolomic dataset is untargeted with most features unannotated and inaccessible to statistical analysis and computational modeling. The high dimensionality of the metabolic space also causes mechanistic modeling to be rather cumbersome computationally. We implemented a faster exploratory workflow to visualize and extract chemical and biochemical dependencies. Time-series metabolic features (about 300 for each dataset) were extracted by Ridge Tracking-based Extract (RTExtract) on measurements from continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) in Neurospora crassa under different conditions. The metabolic profiles were then smoothed and projected into lower dimensions, enabling a comparison of metabolic trends in the cultures. Next, we expanded incomplete metabolite annotation using a correlation network. Lastly, we uncovered meaningful metabolic clusters by estimating dependencies between smoothed metabolic profiles. We thus sidestepped the processes of time-consuming mechanistic modeling, difficult global optimization, and labor-intensive annotation. Multiple clusters guided insights into central energy metabolism and membrane synthesis. Dense connections with glucose 1-phosphate indicated its central position in metabolism in N. crassa. Our approach was benchmarked on simulated random network dynamics and provides a novel exploratory approach to analyzing high-dimensional metabolic dynamics.

Microbial metabolites in the marine carbon cycle.

One-quarter of photosynthesis-derived carbon on Earth rapidly cycles through a set of short-lived seawater metabolites that are generated from the activities of marine phytoplankton, bacteria, grazers and viruses. Here we discuss the sources of microbial metabolites in the surface ocean, their roles in ecology and biogeochemistry, and approaches that can be used to analyse them from chemistry, biology, modelling and data science. Although microbial-derived metabolites account for only a minor fraction of the total reservoir of marine dissolved organic carbon, their flux and fate underpins the central role of the ocean in sustaining life on Earth.

In Silico Collision Cross Section Calculations to Aid Metabolite Annotation.

The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.

Application of Counter-wound Multi-arm Spirals in HTS Resonator Design.

Significant sensitivity improvements have been achieved by utilizing high temperature superconducting (HTS) resonators in nuclear magnetic resonance (NMR) probes. Many nuclei such as 13C benefit from strong excitation fields which cannot be produced by traditional HTS resonator designs. We investigate the use of double-sided, counter-wound multi-arm spiral HTS resonators with the aim of increasing the excitation field at the required nuclear Larmor frequency for 13C. When compared to double-sided, counter-wound spiral resonators with similar geometry, simulations indicate that the multi-arm spiral version develops a more uniform current distribution. Preliminary tests of a two-arm resonator indicate that it may produce a stronger excitation field.

Growth-stage-related shifts in diatom endometabolome composition set the stage for bacterial heterotrophy.

Phytoplankton-derived metabolites fuel a large fraction of heterotrophic bacterial production in the global ocean, yet methodological challenges have limited our understanding of the organic molecules transferred between these microbial groups. In an experimental bloom study consisting of three heterotrophic marine bacteria growing together with the diatom Thalassiosira pseudonana, we concurrently measured diatom endometabolites (i.e., potential exometabolite supply) by nuclear magnetic resonance (NMR) spectroscopy and bacterial gene expression (i.e., potential exometabolite uptake) by metatranscriptomic sequencing. Twenty-two diatom endometabolites were annotated, with nine increasing in internal concentration in the late stage of the bloom, eight decreasing, and five showing no variation through the bloom progression. Some metabolite changes could be linked to shifts in diatom gene expression, as well as to shifts in bacterial community composition and their expression of substrate uptake and catabolism genes. Yet an overall low match indicated that endometabolome concentration was not a good predictor of exometabolite availability, and that complex physiological and ecological interactions underlie metabolite exchange. Six diatom endometabolites accumulated to higher concentrations in the bacterial co-cultures compared to axenic cultures, suggesting a bacterial influence on rates of synthesis or release of glutamate, arginine, leucine, 2,3-dihydroxypropane-1-sulfonate, glucose, and glycerol-3-phosphate. Better understanding of phytoplankton metabolite production, release, and transfer to assembled bacterial communities is key to untangling this nearly invisible yet pivotal step in ocean carbon cycling.