Biology subjects

Edison, A. S.

Publications and source records attributed to Edison, A. S..

17 recordsLinked to original sources

bioRxiv2026

Singular Value Decomposition for the Automated Analysis of NMR Fraction Libraries

Identifying and quantifying molecular components of biological fluids offers great potential for the diagnosis of disease, detecting environmental contaminants, and process monitoring. The use of simple 1D 1H NMR spectra to accomplish this task offers advantages in terms of broad applicability and quantitative output. However, it also encounters obstacles in extracting individual components from complex superimposed spectra and in compound identification through comparison with limited database entries. Here, we present a new protocol that includes automatically separating component spectra using singular value decomposition (SVD) and matching them to database entries that carry both chemical shift and scalar coupling information. The procedure is illustrated by application to a fungal extract from Neurospora crassa that is chromatographically fractionated to form a two-dimensional spectral fraction library. Results are compared to a previous analysis that used a semi-manual curation of separated spectra followed by chemical shift matching.

bioinformatics↗

bioRxiv2026

Higher Lipid Saturation in Well-Irrigated Georgia Cotton Plants: A Field-Based NMR Metabolomics Study

Cotton (Gossypium hirsutum) is an economically important crop, but it faces increasing pest outbreaks, especially in non-irrigated areas. In this study, 20 cotton farms using center-pivot irrigation were sampled in southern Georgia to assess chemical differences between non-irrigated and irrigated areas. Proton (1H) nuclear magnetic resonance (NMR) data were obtained from cotton leaves, and Principal Component Analysis (PCA) was performed to assess differences in chemical composition. Across all samples, farm site accounted for most of the variability, but within each farm site, the PCA scores plots showed clear separation between non-irrigated and irrigated conditions in 10 sites. Inspecting the PCA loadings revealed significant resonances resembling a lipid-like signal. After reverse-phase fractionation, we observed that many of these resonances appeared together in later fractions, suggesting a lipid, specifically a fatty acid such as linoleic acid. We hypothesized that differences in net lipid saturation level may drive separation between non-irrigated and irrigated samples. Six farm sites had a significantly or marginally significantly higher degree of unsaturation in irrigated samples, while one farm site had significantly higher unsaturation in non-irrigated samples. Our results indicate that drought stress likely affects lipid profile composition, which could be driving higher herbivorous pest densities in drought-stressed crops.

plant biology↗

bioRxiv2026

PFOA induced metabolic and immune perturbations in a SARS-2 infection model

This study evaluates the impact of PFOA exposure on the metabolome and immune response to SARS-2 using a ferret model. Ferrets were separated into control or PFOA-exposed groups (10/mg/kg/day) and challenged with SARS-2. Longitudinal analyses encompassing clinical assessments, serological profiling, histopathology, and untargeted nuclear magnetic resonance (NMR) metabolomics revealed significant metabolic and immunological perturbations. We found prominent effects of PFOA exposure on metabolism, which resulted in altered metabolic responses to SARS-2 infection. PFOA exposure was also associated with impaired immune function, as evidenced by decreased serum IgG levels, increased viral loads, and prolonged peak infectivity. These findings underscore the consequences of PFOA exposure on host metabolism and immunity during infectious diseases.

pharmacology and toxicology↗

bioRxiv2025

Metabolite Fraction Libraries for Quantitative NMR Metabolomics

Nuclear Magnetic Resonance (NMR) has unique strengths in metabolomics studies, particularly in quantifying mixtures and elucidating the structures of unknown molecules. One-dimensional (1D) proton (1H) NMR is the most common method; however, spectral overlap is significant, making analysis challenging. We present a new approach that utilizes chromatographically separated fractions from a pooled sample, henceforth called a metabolite fraction library (mFL). We developed an algorithm to extract highly correlated peaks from the mFL, collectively forming a metabolite basis set (mBS). The mBS can be fit to NMR profiling data, enabling comprehensive quantification. Applied to 10 mixtures of 53 metabolites, our approach accurately quantified 50, quantified an impurity and an oxidation product, and described between 91-96% of total spectral intensity. The method is demonstrated using the fungus Neurospora crassa, resulting in the identification of 45 metabolites with high confidence, 45 with medium confidence, and accounting for 94% of total spectral intensity.

bioinformatics↗

bioRxiv2025

Dynamic Reworking of Marine Diatom Endometabolomes

A large annual carbon flux occurs through the oceans labile dissolved organic carbon (DOC) pool, with carbon influx dominated by phytoplankton-derived metabolites and outflux by heterotrophic bacterioplankton uptake. We addressed the dynamics of this flux between marine primary and secondary producers through analysis of the Thalassiosira pseudonana CCMP1335 endometabolome, a proxy for labile DOC release during phytoplankton excretion and mortality. Diatom strains acclimated at one of three different temperatures (14{degrees}C, 20{degrees}C, or 28{degrees}C) were then cultured either axenically or with the bacterium Ruegeria pomeroyi DSS-3, and their endometabolites analyzed by NMR. Osmolytes were by far the most dynamic, exhibiting concentration differences up to 150-fold between conditions; median concentration variation across identified endometabolites was [~]1.5-fold. Differential expression of diatom metabolic pathways suggested changes in synthesis rates as a mechanism for endometabolome remodeling. Consistent with expectations of high turnover, endometabolite mean lifetimes prior to bacterial uptake were <2 h to 12 h. ImportanceThe role of labile DOC in the transfer of marine carbon between phytoplankton and heterotrophic bacteria was first recognized 40 years ago, yet the identity and dynamics of phytoplankton metabolites entering the labile DOC pool are still poorly known. Using metabolome and transcriptome profiling, we found dynamic composition and concentration of diatom endometabolites, depending on growth conditions and arising over time frames as short as a single growth cycle. This strong response to external conditions, both biotic and abiotic, has implications for downstream processing and fate of ocean carbon by heterotrophic bacteria.

ecology↗

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↗

bioRxiv2024

PyINETA: Open-source platform for INADEQUATE-JRES integration in NMR metabolomics

Annotating compounds with high confidence is a critical element in metabolomics. 13C-detection NMR experiment INADEQUATE (incredible natural abundance double-quantum transfer experiment) stands out as a powerful tool for structural elucidation, whereas this valuable experiment is not often included in metabolomics studies. This is partly due to the lack of community platform that provides structural information based INADEQUATE. Also, it is often the case that a single study uses various NMR experiments synergistically to improve the quality of information or balance total NMR experiment time, but there is no public platform that can integrate the outputs of INADEQUATE and other NMR experiments either. Here, we introduce PyINETA, Python-based INADEQUATE network analysis. PyINETA is an open-source platform that provides structural information of molecules using INADEQUATE, conducts database search, and integrates information of INADEQUATE and a complementary NMR experiment 13C J-resolved experiment (13C-JRES). Those steps are carried out automatically, and PyINETA keeps track of all the pipeline parameters and outputs, ensuring the transparency of annotation in metabolomics. Our evaluation of PyINETA using a model mouse study showed that our pipeline successfully integrated INADEQUATE and 13C-JRES. The results showed that 13C-labeled amino acids that were fed to mice were transferred to different tissues, and, also, they were transformed to other metabolites. The distribution of those compounds was tissue-specific, showing enrichment of particular metabolites in liver, spleen, pancreas, muscle, or lung. The value of PyINETA was not limited to those known compounds; PyINETA also provided fragment information for unknown compounds. PyINETA is available on NMRbox.

systems biology↗

bioRxiv2023

Role of ugt genes in detoxification and glycosylation of 1-hydroxy phenazine (1-HP) in Caenorhabditis elegans

Caenorhabditis elegans is an ideal model organism to study the xenobiotic detoxification pathways of various natural and synthetic toxins. One toxin shown to cause death in C. elegans is 1-hydroxyphenazine (1-HP), a molecule produced by the bacterium Pseudomonas aeruginosa. We previously showed that the median lethal dose (LD50) for 1-HP in C elegans is 179 M in PD1074 and between 150-200 M in N2 (C. elegans lab strain). We also showed that C. elegans detoxifies 1-HP by glycosylation by adding one, two, or three glucose molecules in N2 worms. This study tested whether UDP-glycosyltransferase (ugt) genes play a role in 1-HP detoxification. We show that ugt-23 and ugt-49 knockout mutants are more sensitive to 1-HP. 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. We have also characterized the structure of the trisaccharide sugar phenazine structures made by C. elegans and show 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.

biochemistry↗

bioRxiv2022

Ion Mobility for Unknown Metabolite Identification: Hope or Hype?

Ion mobility (IM) spectrometry provides semi-orthogonal data to mass spectrometry (MS), showing promise for identifying unknown metabolites in complex non-targeted metabolomics datasets. 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%, an 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 insight on areas requiring further improvement.

biochemistry↗

bioRxiv2022

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

Large-scale untargeted metabolomics studies suffer from individual variation, batch effects and instrument variability, making comparisons of common spectral features across studies difficult. One solution is to compare studies after compound identification. However, compound identification is expensive and time consuming. We successfully identify common spectral features across multiple studies, with a generalizable experimental design approach. First, we included an anchor strain, PD1074, during sample and data collection. Second, we collected data in blocks with multiple controls. These anchors enabled us to successfully integrate three studies of Caenorhabditis elegans for nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS) data from five different assays. We found 34% and 14% of features to be significant in LC-MS and NMR, respectively. Between 20-50% of spectral features differ in a mutant and among a set of genetically diverse natural strains, suggesting this reduced set of spectral features are excellent targets for compound identification. GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=108 SRC="FIGDIR/small/485859v1_ufig1.gif" ALT="Figure 1"> View larger version (47K): org.highwire.dtl.DTLVardef@84abe1org.highwire.dtl.DTLVardef@3066fforg.highwire.dtl.DTLVardef@1ba166corg.highwire.dtl.DTLVardef@164cf0_HPS_FORMAT_FIGEXP M_FIG C_FIG Fourteen C. elegans strains are used in three individual studies. PD1074, the anchor control strain (orange), is grown alongside test strains (green, yellow, purple). Multiple biological replicates of PD1074 captures environmental variation in growth conditions. Non-polar and polar metabolic data across the three studies (i.e., natural strains, central metabolism mutants, and UGT mutants) were collected by nuclear magnetic resonance (NMR) spectroscopy and liquid chromatography-mass spectrometry (LC-MS). Data acquisition controls in each block included biological reference material and pooled PD1074 samples. Biological replicates of PD1074 (n = 42 for LC-MS, n = 52 for NMR) were included in all batches. Meta-analysis provided comparable inferences to mixed effects models, and the estimated relative effects of each test strain to PD1074 and straightforward comparisons of test strains across experiments.

systems biology↗

bioRxiv2021

Multi-omics characterization of mesenchymal stem/stromal cells for the identification of putative critical quality attributes

BackgroundMesenchymal stromal cells (MSCs) have shown great promise in the field of regenerative medicine as many studies have shown that MSCs possess immunomodulatory function. Despite this promise, no MSC therapies have been granted licensure from the FDA. This lack of successful clinical translation is due in part to MSC heterogeneity and a lack of critical quality attributes (CQAs). While MSC Indoleamine 2,3-dioxygnease (IDO) activity has been shown to correlate with MSC function, multiple CQAs may be needed to better predict MSC function. MethodsThree MSC lines (two bone marrow, one iPSC) were expanded to three passages. At the time of harvest for each passage, cell pellets were collected for nuclear magnetic resonance (NMR) and ultra-performance liquid chromatography mass spectrometry (UPLC-MS), and media was collected for cytokine profiling. Harvested cells were also cryopreserved for assessing function using T cell proliferation and IDO activity assays. Linear regression was performed on functional and multiomics data to reduce the number of important features, and partial least squares regression (PLSR) was used to obtain putative CQAs based on variable importance in projection (VIP) scores. ResultsSignificant functional heterogeneity (in terms of T cell suppression and IDO activity) was observed between the three MSC lines, as well as donor-dependent differences based on passage. Omics characterization revealed distinct differences between cell lines using principal component analysis (PCA). Cell lines separated along principal component 1 based on tissue source (bone marrow vs. iPSC-derived) for NMR, MS, and cytokine profiles. PLSR modeling of important features predicts MSC functional capacity with NMR (R2=0.86), MS (R2=0.83), cytokines (R2=0.70), and a combination of all features (R2=0.88). DiscussionThe work described here provides a platform for identifying putative CQAs for predicting MSC functional capacity using PLSR modeling that could be used as release criteria and guide future manufacturing strategies for MSCs and other cell therapies.

bioengineering↗

bioRxiv2021

Predicting T Cell Quality During Manufacturing Through an Artificial Intelligence-based Integrative Multi-Omics 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 as an example, we developed an Artificial Intelligence (AI)-driven experimental-computational platform to identify a set of critical process parameters (CPP) and critical quality attributes (CQA) from heterogeneous, high dimensional, time-dependent multi-omics data, measurable during early stages of manufacturing and predictive of end-of-manufacturing product quality. Sequential, Design-of-Experiment (DOE)-based studies, coupled with an agnostic machine-learning framework, were used to extract feature combinations from media assessment that were highly predictive of total live CD4+ and CD8+ naive and central memory (CD63L+CCR7+) T cells and their ratio in the end-product. This computational workflow could be broadly applied to any cell therapy and provide a roadmap for discovering CQAs and CPPs in cell manufacturing.

bioengineering↗

bioRxiv2021

Long-Term Metabolomics Reference Material

The use of quality control samples in metabolomics ensures data quality, reproducibility and comparability between studies, analytical platforms and laboratories. Long-term, stable and sustainable reference materials (RMs) are a critical component of the QA/QC system, however, the limited selection of currently available matrix matched RMs reduce their applicability for widespread use. To produce a RM in any context, for any matrix that is robust to changes over the course of time we developed IBAT (Iterative Batch Averaging meThod). To illustrate this method, we generated 11 independently grown E. coli batches and made a RM over the course of 10 IBAT iterations. We measured the variance of these materials by NMR and showed that IBAT produces a stable and sustainable RM over time. This E. coli RM was then used as food source to produce a C. elegans RM for a metabolomics experiment. The metabolite extraction of this material alongside 41 independently grown individual C. elegans samples of the same genotype, allowed to estimate the proportion of sample variation in pre-analytical steps. From the NMR data, we found that 40% of the metabolite variance is due to the metabolite extraction process and analysis and 60% is due to sample-to-sample variance. The availability of RMs in untargeted metabolomics is one of the predominant needs of the metabolomics community that reach beyond quality control practices. IBAT addresses this need by facilitating the production of biologically relevant RMs and increasing their widespread use.

biochemistry↗

bioRxiv2020

Diel Investments in Phytoplankton Metabolite Production Influenced by Associated Heterotrophic Bacteria

Organic carbon transfer between photoautotrophic and heterotrophic microbes in the surface ocean mediated through metabolites dissolved in seawater is a central but poorly understood process in the global carbon cycle. In a synthetic microbial community in which diatom extracellular release of organic molecules sustained growth of a co-cultured bacterium, metabolite transfer was assessed over two diel cycles based on per cell quantification of phytoplankton endometabolites and bacterial transcripts. Of 31 phytoplankton endometabolites identified and classified into temporal abundance patterns, eight could be matched to patterns of bacterial transcripts mediating their uptake and catabolism. A model simulating the coupled endometabolite-transcription relationships hypothesized that one category of outcomes required an increase in phytoplankton metabolite synthesis in response to the presence of the bacterium. An experimental test of this hypothesis confirmed higher endometabolome accumulation in the presence of bacteria for all five compounds assigned to this category - leucine, glycerol-3-phosphate, glucose, and the organic sulfur compounds dihydroxypropanesulfonate and dimethylsulfoniopropionate. Partitioning of photosynthate into rapidly-cycling dissolved organic molecules at the expense of phytoplankton biomass production has implications for carbon sequestration in the deep ocean. That heterotrophic bacteria can impact this partitioning suggests a previously unrecognized influence on the oceans carbon reservoirs. Significance StatementMicrobes living in the surface ocean are critical players in the global carbon cycle, carrying out a particularly key role in the flux of carbon between the ocean and atmosphere. The release of metabolites by marine phytoplankton and their uptake by heterotrophic bacteria is one of the major routes of microbial carbon turnover. Yet the identity of these metabolites, their concentration in seawater, and the factors that affect their synthesis and release are poorly known. Here we provide experimental evidence that marine heterotrophic bacteria can affect phytoplankton production and extracellular release of metabolites. This microbial interaction has relevance for the partitioning of photosynthate between dissolved and particulate carbon reservoirs in the ocean, an important factor in oceanic carbon sequestration.

ecology↗

bioRxiv2020

A novel redox-active switch in Fructosamine-3-kinases expands the regulatory repertoire of the protein kinase superfamily

Aberrant regulation of metabolic kinases by altered redox homeostasis is a major contributing factor in aging and disease such as diabetes. However, the biochemical mechanisms by which metabolic kinases are regulated under oxidative stress is poorly understood. In this study, we demonstrate that the catalytic activity of a conserved family of Fructosamine-3-kinases (FN3Ks), which are evolutionarily related to eukaryotic protein kinases (ePKs), are regulated by redox-active cysteines in the kinase domain. By solving the crystal structure of FN3K homolog from Arabidopsis thaliana (AtFN3K), we demonstrate that it forms an unexpected strand-exchange dimer in which the ATP binding P-loop and adjoining beta strands are swapped between two chains in the dimer. This dimeric configuration is characterized by strained inter-chain disulfide bonds that stabilize the P-loop in an extended conformation. Mutational analysis and solution studies confirm that the strained disulfides function as redox "switches" to reversibly regulate FN3K activity and dimerization. Consistently, we find that human FN3K (HsFN3K), which contains an equivalent P-loop Cys, is also redox-sensitive, whereas ancestral bacterial FN3K homologs, which lack a P-loop Cys, are not. Furthermore, CRISPR knockout of FN3K in human HepG2 cells results in significant upregulation of redox metabolites including glutathione. We propose that redox regulation evolved progressively in FN3Ks in response to changing cellular redox conditions. Our studies provide important new insights into the origin and evolution of redox regulation in the protein kinase superfamily and open new avenues for targeting HsFN3K in diabetic complications.

biochemistry↗

bioRxiv2019

Deep evolutionary analysis reveals the design principles of fold A glycosyltransferases

Glycosyltransferases (GTs) are prevalent across the tree of life and regulate nearly all aspects of cellular functions by catalyzing synthesis of glycosidic linkages between diverse donor and acceptor substrates. Despite the availability of GT sequences from diverse organisms, the evolutionary basis for their complex and diverse modes of catalytic and regulatory functions remain enigmatic. Here, based on deep mining of over half a million GT-A fold sequences from diverse organisms, we define a minimal core component shared among functionally diverse enzymes. We find that variations in the common core and the emergence of hypervariable loops extending from the core contributed to the evolution of catalytic and functional diversity. We provide a phylogenetic framework relating diverse GT-A fold families for the first time and show that inverting and retaining mechanisms emerged multiple times independently during the course of evolution. We identify conserved modes of donor and acceptor recognition in evolutionarily divergent families and pinpoint the sequence and structural features for functional specialization. Using the evolutionary information encoded in primary sequences, we trained a machine learning classifier to predict donor specificity with nearly 88% accuracy and deployed it for the annotation of understudied GTs in five model organisms. Our studies provide an evolutionary framework for investigating the complex relationships connecting GT-A fold sequence, structure, function and regulation.

bioinformatics↗

bioRxiv2018

Continuous in vivo metabolism by NMR

Dense time-series metabolomics data are essential for unraveling the underlying dynamic properties of metabolism. Here we extend high-resolution-magic angle spinning (HR-MAS) to enable continuous in vivo monitoring of metabolism by NMR (CIVM-NMR) and provide analysis tools for these data. First, we reproduced a result in human chronic lymphoid leukemia cells by using isotope-edited CIVM-NMR to rapidly and unambiguously demonstrate unidirectional flux in branched-chain amino acid metabolism. We then collected untargeted CIVM-NMR datasets for Neurospora crassa, a classic multicellular model organism, and uncovered dynamics between central carbon metabolism, amino acid metabolism, energy storage molecules, and lipid and cell wall precursors. Virtually no sample preparation was required to yield a dynamic metabolic fingerprint over hours to days at ~4-min temporal resolution with little noise. CIVM-NMR is simple and readily adapted to different types of cells and microorganisms, offering an experimental complement to kinetic models of metabolism for diverse biological systems.

systems biology↗