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Variation in insect herbivory across an urbanization gradient: The role of abiotic factors and leaf secondary metabolites

Urbanization impacts plant-herbivore interactions, which are crucial for ecosystem functions such as carbon sequestration and nutrient cycling. While some studies have reported reductions in insect herbivory in urban areas (relative to rural or natural forests), this trend is not consistent and the underlying causes for such variation remain unclear. We conducted a continental-scale study on insect herbivory along urbanization gradients for three European tree species: Quercus robur, Tilia cordata, and Fraxinus excelsior, and further investigated their biotic and abiotic correlates to get at mechanisms. To this end, we quantified insect leaf herbivory and foliar secondary metabolites (phenolics, terpenoids, alkaloids) for 176 trees across eight European cities. Additionally, we collected data on microclimate (air temperature) and soil characteristics (pH, carbon, nutrients) to test for abiotic correlates of urbanization effects directly or indirectly (through changes in plant secondary chemistry) linked to herbivory. Our results showed that urbanization was negatively associated with herbivory for Q. robur and F. excelsior, but not for T. cordata. In addition, urbanization was positively associated with secondary metabolite concentrations, but only for Q. robur. Urbanization was positively associated with air temperature for Q. robur and F. excelsior, and negatively with soil nutrients (magnesium) in the case of F. excelsior, but these abiotic variables were not associated with herbivory. Contrary to expectations, we found no evidence for indirect effects of abiotic factors via plant defences on herbivory for either Q. robur or F. excelsior. Additional biotic or abiotic drivers must therefore be accounted for to explain observed urbanization gradients in herbivory and their interspecific variation.

Characterizing and Tailoring the Substrate Profile of a γ-Glutamyltransferase Variant

Xenobiology is an emerging field that focuses on the extension and redesign of biological systems through the use of laboratory-derived xenomolecules, which are molecules that are new to the metabolism of the cell. Despite the enormous potential of using xenomolecules in living organisms, most noncanonical building blocks still need to be supplied externally, and often poor uptake into cells limits wider applicability. To improve the cytosolic availability of noncanonical molecules, a synthetic transport system based on portage transport was developed, in which molecules of interest "cargo" are linked to a synthetic transport vector that enables piggyback transport through the alkylsulfonate transporter (SsuABC) of Escherichia coli. Upon cytosolic delivery, the vector-cargo conjugate is enzymatically cleaved by GGTxe, leading to the release of the cargo molecule. To deepen our understanding of the synthetic transport system, we focused on the characterization and further development of the enzymatic cargo release step. Hence, the substrate scope of GGTxe was characterized using a library of structurally diverse vector-cargo conjugates and MS/MS-based quantification of hydrolysis products in a kinetic manner. The resulting substrate tolerance characterization revealed that vector-amino acid conjugates were significantly unfavored. To overcome this shortcoming, a selection system based on metabolic auxotrophy complementation and directed evolution of GGTxe was established. In a directed evolution campaign, we improved the enzymatic activity of GGTxe for vector-amino acid conjugates and revealed the importance of residue D386 in the cargo unloading step.

Cardiac NAD+ depletion in mice promotes hypertrophic cardiomyopathy and arrhythmias prior to impaired bioenergetics

Nicotinamide adenine dinucleotide (NAD+) is an essential co-factor in metabolic reactions and co-substrate for signaling enzymes. Failing human hearts display decreased expression of the major NAD+ biosynthetic enzyme nicotinamide phosphoribosyltransferase (Nampt) and lower NAD+ levels, and supplementation with NAD+ precursors is protective in preclinical models. Here we show that Nampt loss in adult cardiomyocytes caused depletion of NAD+ along with marked metabolic derangements, hypertrophic remodeling and sudden cardiac deaths, despite unchanged ejection fraction, endurance and mitochondrial respiratory capacity. These effects were directly attributable to NAD+ loss as all were ameliorated by restoring cardiac NAD+ levels with the NAD+ precursor nicotinamide riboside (NR). Electrocardiograms revealed that loss of myocardial Nampt caused a shortening of QT intervals with spontaneous lethal arrhythmias causing sudden cardiac death. Thus, changes in NAD+ concentration can have a profound influence on cardiac physiology even at levels sufficient to maintain energetics.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

MSIGen: An Open-Source Python Package for Processing and Visualizing Mass Spectrometry Imaging Data

Mass spectrometry imaging (MSI) provides information about the spatial localization of molecules in complex samples with high sensitivity and molecular selectivity. Although point-wise data acquisition, in which mass spectra are acquired at predefined points in a grid pattern, is common in MSI, several MSI techniques use line-wise data acquisition. In line-wise mode, the imaged surface is continuously sampled along consecutive parallel lines and MSI data are acquired as a collection of line scans across the sample. Furthermore, aside from the standard imaging mode in which full mass spectra are acquired, other acquisition modes have been developed to enhance molecular specificity, enable separation of isobaric and isomeric species, and improve sensitivity to facilitate the imaging of low abundance species. These methods, including MS/MS-MSI in both MS2 and MS3 modes, multiple-reaction monitoring (MRM)-MSI, and ion mobility spectrometry (IMS)-MSI have all demonstrated their capabilities, but their broader implementation is limited by the existing MSI analysis software. Here, we present MSIGen, an open-source Python package for the visualization of MSI experiments performed in line-wise acquisition mode containing MS1, MS2, MRM, and IMS data, which is available at https://github.com/LabLaskin/MSIGen. The package supports multiple vendor-specific and open-source data formats and contains tools for targeted extraction of ion images, normalization, and exportation as images, arrays, or publication-style images. MSIGen offers multiple interfaces, allowing for accessibility and easy integration with other workflows. Considering its support for a wide variety of MSI imaging modes and vendor formats, MSIGen is a valuable tool for the visualization and analysis of MSI data.

Reproducible mass spectrometry data processing and compound annotation in MZmine 3

Untargeted mass spectrometry (MS) experiments produce complex, multidimensional data that are practically impossible to investigate manually. For this reason, computational pipelines are needed to extract relevant information from raw spectral data and convert it into a more comprehensible format. Depending on the sample type and/or goal of the study, a variety of MS platforms can be used for such analysis. MZmine is an open-source software for the processing of raw spectral data generated by different MS platforms. Examples include liquid chromatography-MS, gas chromatography-MS and MS-imaging. These data might typically be associated with various applications including metabolomics and lipidomics. Moreover, the third version of the software, described herein, supports the processing of ion mobility spectrometry (IMS) data. The present protocol provides three distinct procedures to perform feature detection and annotation of untargeted MS data produced by different instrumental setups: liquid chromatography-(IMS-)MS, gas chromatography-MS and (IMS-)MS imaging. For training purposes, example datasets are provided together with configuration batch files (i.e., list of processing steps and parameters) to allow new users to easily replicate the described workflows. Depending on the number of data files and available computing resources, we anticipate this to take between 2 and 24 h for new MZmine users and nonexperts. Within each procedure, we provide a detailed description for all processing parameters together with instructions/recommendations for their optimization. The main generated outputs are represented by aligned feature tables and fragmentation spectra lists that can be used by other third-party tools for further downstream analysis.

Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model

Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric "mirror" branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z = 674.25 and m/z = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra.

Regulation of bacterial stringent response by an evolutionarily conserved ribosomal protein L11 methylation

Lysine and arginine methylation is an important regulator of enzyme activity and transcription in eukaryotes. However, little is known about this covalent modification in bacteria. In this work, we investigated the role of methylation in bacteria. By reanalyzing a large phyloproteomics data set from 48 bacterial strains representing six phyla, we found that almost a quarter of the bacterial proteome is methylated. Many of these methylated proteins are conserved across diverse bacterial lineages, including those involved in central carbon metabolism and translation. Among the proteins with the most conserved methylation sites is ribosomal protein L11 (bL11). bL11 methylation has been a mystery for five decades, as the deletion of its methyltransferase PrmA causes no cell growth defects. Comparative proteomics analysis combined with inorganic polyphosphate and guanosine tetra/pentaphosphate assays of the ΔprmA mutant in Escherichia coli revealed that bL11 methylation is important for stringent response signaling. In the stationary phase, we found that the ΔprmA mutant has impaired guanosine tetra/pentaphosphate production. This leads to a reduction in inorganic polyphosphate levels, accumulation of RNA and ribosomal proteins, and an abnormal polysome profile. Overall, our investigation demonstrates that the evolutionarily conserved bL11 methylation is important for stringent response signaling and ribosomal activity regulation and turnover.

IMPORTANCE

Protein methylation in bacteria was first identified over 60 years ago. Since then, its functional role has been identified for only a few proteins. To better understand the functional role of methylation in bacteria, we analyzed a large phyloproteomics data set encompassing 48 diverse bacteria. Our analysis revealed that ribosomal proteins are often methylated at conserved residues, suggesting that methylation of these sites may have a functional role in translation. Further analysis revealed that methylation of ribosomal protein L11 is important for stringent response signaling and ribosomal homeostasis.

Restoring hippocampal glucose metabolism rescues cognition across Alzheimer's disease pathologies

Impaired cerebral glucose metabolism is a pathologic feature of Alzheimer's disease (AD), with recent proteomic studies highlighting disrupted glial metabolism in AD. We report that inhibition of indoleamine-2,3-dioxygenase 1 (IDO1), which metabolizes tryptophan to kynurenine (KYN), rescues hippocampal memory function in mouse preclinical models of AD by restoring astrocyte metabolism. Activation of astrocytic IDO1 by amyloid β and tau oligomers increases KYN and suppresses glycolysis in an aryl hydrocarbon receptor-dependent manner. In amyloid and tau models, IDO1 inhibition improves hippocampal glucose metabolism and rescues hippocampal long-term potentiation in a monocarboxylate transporter-dependent manner. In astrocytic and neuronal cocultures from AD subjects, IDO1 inhibition improved astrocytic production of lactate and uptake by neurons. Thus, IDO1 inhibitors presently developed for cancer might be repurposed for treatment of AD.

Co-exposure to polyethylene fiber and Salmonella enterica serovar Typhimurium alters microbiome and metabolome of in vitro chicken cecal mesocosms

Humans and animals encounter a summation of exposures during their lifetime (the exposome). In recent years, the scope of the exposome has begun to include microplastics. Microplastics (MPs) have increasingly been found in locations, including in animal gastrointestinal tracts, where there could be an interaction with Salmonella enterica serovar Typhimurium, one of the commonly isolated serovars from processed chicken. However, there is limited knowledge on how gut microbiomes are affected by microplastics and if an effect would be exacerbated by the presence of a pathogen. In this study, we aimed to determine if acute exposure to microplastics in vitro altered the gut microbiome membership and activity. The microbiota response to a 24 h co-exposure to Salmonella enterica serovar Typhimurium and/or low-density polyethylene (PE) microplastics in an in vitro broiler cecal model was determined using 16S rRNA amplicon sequencing (Illumina) and untargeted metabolomics. Community sequencing results indicated that PE fiber with and without S. Typhimurium yielded a lower Firmicutes/Bacteroides ratio compared with other treatment groups, which is associated with poor gut health, and overall had greater changes to the cecal microbial community composition. However, changes in the total metabolome were primarily driven by the presence of S. Typhimurium. Additionally, the co-exposure to PE fiber and S. Typhimurium caused greater cecal microbial community and metabolome changes than either exposure alone. Our results indicate that polymer shape is an important factor in effects resulting from exposure. It also demonstrates that microplastic-pathogen interactions cause metabolic alterations to the chicken cecal microbiome in an in vitro chicken cecal mesocosm.

IMPORTANCE

Researching the exposome, a summation of exposure to one's lifespan, will aid in determining the environmental factors that contribute to disease states. There is an emerging concern that microplastic-pathogen interactions in the gastrointestinal tract of broiler chickens may lead to an increase in Salmonella infection across flocks and eventually increased incidence of human salmonellosis cases. In this research article, we elucidated the effects of acute co-exposure to polyethylene microplastics and Salmonella enterica serovar Typhimurium on the ceca microbial community in vitro. Salmonella presence caused strong shifts in the cecal metabolome but not the microbiome. The inverse was true for polyethylene fiber. Polyethylene powder had almost no effect. The co-exposure had worse effects than either alone. This demonstrates that exposure effects to the gut microbial community are contaminant-specific. When combined, the interactions between exposures exacerbate changes to the gut environment, necessitating future experiments studying low-dose chronic exposure effects with in vivo model systems.

A metabolic constraint in the kynurenine pathway drives mucosal inflammation in IBD

Inflammatory bowel disease (IBD) is associated with perturbed metabolism of the essential amino acid tryptophan (Trp). Whether increased degradation of Trp directly fuels mucosal inflammation or acts as a compensatory attempt to restore cellular energy levels via de-novo nicotinamide adenine dinucleotide (NAD + ) synthesis is not understood. Employing a systems medicine approach on longitudinal IBD therapy intervention cohorts and targeted screening in preclinical IBD models, we discover that steady increases in Trp levels upon therapy success coincide with a rewiring of metabolic processes within the kynurenine pathway (KP). In detail, we identify that Trp catabolism in IBD is metabolically constrained at the level of quinolinate phosphorybosyltransferase (QPRT), leading to accumulation of quinolinic acid (Quin) and a decrease of NAD + . We further demonstrate that Trp degradation along the KP occurs locally in the inflamed intestinal mucosa and critically depends on janus kinase / signal transducers and activators of transcription (JAK/STAT) signalling. Subsequently, knockdown of QPRT in-vitro induces NAD + depletion and a pro-inflammatory state, which can largely be rescued by bypassing QPRT via other NAD + precursors. We hence propose a model of impaired de-novo NAD + synthesis from Trp in IBD. These findings point towards the replenishment of NAD + precursors as a novel therapeutic pathway in IBD.

MOSCA 2.0: A bioinformatics framework for metagenomics, metatranscriptomics and metaproteomics data analysis and visualization

The analysis of meta-omics data requires the utilization of several bioinformatics tools and proficiency in informatics. The integration of multiple meta-omics data is even more challenging, and the outputs of existing bioinformatics solutions are not always easy to interpret. Here, we present a meta-omics bioinformatics pipeline, Meta-Omics Software for Community Analysis (MOSCA), which aims to overcome these limitations. MOSCA was initially developed for analysing metagenomics (MG) and metatranscriptomics (MT) data. Now, it also performs MG and metaproteomics (MP) integrated analysis, and MG/MT analysis was upgraded with an additional iterative binning step, metabolic pathways mapping, and several improvements regarding functional annotation and data visualization. MOSCA handles raw sequencing data and mass spectra and performs pre-processing, assembly, annotation, binning and differential gene/protein expression analysis. MOSCA shows taxonomic and functional analysis in large tables, performs metabolic pathways mapping, generates Krona plots and shows gene/protein expression results in heatmaps, improving omics data visualization. MOSCA is easily run from a single command while also providing a web interface (MOSGUITO). Relevant features include an extensive set of customization options, allowing tailored analyses to suit specific research objectives, and the ability to restart the pipeline from intermediary checkpoints using alternative configurations. Two case studies showcased MOSCA results, giving a complete view of the anaerobic microbial communities from anaerobic digesters and insights on the role of specific microorganisms. MOSCA represents a pivotal advancement in meta-omics research, offering an intuitive, comprehensive, and versatile solution for researchers seeking to unravel the intricate tapestry of microbial communities.

Comprehensive Glycomic and Proteomic Analysis of Mouse Striatum and Lateral Hypothalamus Following Repeated Exposures to Cocaine or Methamphetamine

Substance use disorder is a major concern, with few therapeutic options. Heparan sulfate (HS) and chondroitin sulfate (CS) interact with a plethora of growth factors and their receptors and have profound effects on cellular signaling. Thus, targeting these dynamic interactions might represent a potential novel therapeutic modality. In the present study, we performed mass spectrometry-based glycomic and proteomic analysis to understand the effects of cocaine and methamphetamine (METH) on HS, CS, and the proteome of two brain regions critically involved in drug addiction: the lateral hypothalamus and the striatum. We observed that cocaine and METH significantly alter HS and CS abundances as well as sulfate contents and composition. In particular, repeated METH or cocaine treatments reduced CS 4-O-sulfation and increased CS 6-O-sulfation. Since C4S and C6S exercise differential effects on axon growth, regeneration, and plasticity, these changes likely contribute to drug-induced neural plasticity in these brain regions. Notably, we observed that restoring these alterations by increasing CS 4-0 levels in the lateral hypothalamus by adeno-associated virus delivery of an shRNA to arylsulfatase B (N-acetylgalactosamine-4-sulfatase) ameliorated anxiety and prevented the expression of preference for cocaine in a novelty induced conditioned place preference test during cocaine withdrawal. Finally, proteomics analyses revealed a number of aberrant proteins in METH- and cocaine-treated versus saline-treated mice, including myelin proteolipid protein, calcium/calmodulin-dependent protein kinase type II subunit alpha, synapsin-2, tenascin-R, calnexin, annexin A7, hepatoma-derived growth factor, neurocan, and CSPG5, and oxidative phosphorylation among the top perturbed pathway. Taken together, these data support the role of HS, CS, and associated proteins in stimulants abuse and suggest that manipulation of HSPGs can represent a novel therapeutic strategy.

Specificity profiling of deubiquitylases against endogenously generated ubiquitin-protein conjugates

Deubiquitylating enzymes (DUBs) remove ubiquitin from proteins thereby regulating their stability or activity. Our understanding of DUB-substrate specificity is limited because DUBs are typically not compared to each other against many physiological substrates. By broadly inhibiting DUBs in Xenopus egg extract, we generated hundreds of ubiquitylated proteins and compared the ability of 30 DUBs to deubiquitylate them using quantitative proteomics. We identified five high-impact DUBs (USP7, USP9X, USP36, USP15, and USP24) that each reduced ubiquitylation of over 10% of the isolated proteins. Candidate substrates of high-impact DUBs showed substantial overlap and were enriched for disordered regions, suggesting this feature may promote substrate recognition. Other DUBs showed lower impact and non-overlapping specificity, targeting distinct non-disordered proteins including complexes such as the ribosome or the proteasome. Altogether our study identifies candidate DUB substrates and defines patterns of functional redundancy and specificity, revealing substrate characteristics that may influence DUB-substrate recognition.

Chinese traditional medicine DZGP beneficially affects gut microbiome, serum metabolites and recovery from rheumatoid arthritis through mediating NF-κB signaling pathway

Rheumatoid arthritis (RA) is globally treated with several commercially available anti-inflammatory and analgesic drugs, which pose adverse side effects in many cases. Due to increasing population affected by autoimmune disorder of joints inflammation, it is crucial to use natural therapies, which are less toxic at metabolic level and promote gut health. In this study, we investigated the potential role of a locally developed traditional Chinese medicine (TCM), namely Duzheng tablet (DZGP) in controlling the RA. For this purpose, we introduced RA in male mice and divided them into 5 different groups. High throughput transcriptome analysis of synovial cells after DZGP treatment in arthritic mice revealed a significant alteration of gene expression. The correlation analysis of transcriptome with metabolites revealed that DZGP specifically targeted the B cells mediated immunity pathways. Treatment with DZGP inhibited the cytokines production, while reducing the production of inflammatory TNF-α, which led to the alleviation of inflammatory response in arthritic mice. Additionally, we applied integrated approach using 16S rDNA sequencing to understand the microbial population in relation to metabolites accumulation. The results showed that DZGP promoted the healthy gut microbiota by maintaining the ratio of Firmicutes and Bacteroidota and introduction of two additional phyla namely, Verrucomicrobiota and Cyanobacteria. Therefore, it is concluded that DZGP offers an advantage over commercial drug by changing the metabolic profile, gut microbiota while exhibiting lower cellular toxicity.

PANAMA-enabled high-sensitivity dual nanoflow LC-MS metabolomics and proteomics analysis

High-sensitivity nanoflow liquid chromatography (nLC) is seldom employed in untargeted metabolomics because current sample preparation techniques are inefficient at preventing nanocapillary column performance degradation. Here, we describe an nLC-based tandem mass spectrometry workflow that enables seamless joint analysis and integration of metabolomics (including lipidomics) and proteomics from the same samples without instrument duplication. This workflow is based on a robust solid-phase micro-extraction step for routine sample cleanup and bioactive molecule enrichment. Our method, termed proteomic and nanoflow metabolomic analysis (PANAMA), improves compound resolution and detection sensitivity without compromising the depth of coverage as compared with existing widely used analytical procedures. Notably, PANAMA can be applied to a broad array of specimens, including biofluids, cell lines, and tissue samples. It generates high-quality, information-rich metabolite-protein datasets while bypassing the need for specialized instrumentation.

MetaboAnalyst 6.0: towards a unified platform for metabolomics data processing, analysis and interpretation

We introduce MetaboAnalyst version 6.0 as a unified platform for processing, analyzing, and interpreting data from targeted as well as untargeted metabolomics studies using liquid chromatography - mass spectrometry (LC-MS). The two main objectives in developing version 6.0 are to support tandem MS (MS2) data processing and annotation, as well as to support the analysis of data from exposomics studies and related experiments. Key features of MetaboAnalyst 6.0 include: (i) a significantly enhanced Spectra Processing module with support for MS2 data and the asari algorithm; (ii) a MS2 Peak Annotation module based on comprehensive MS2 reference databases with fragment-level annotation; (iii) a new Statistical Analysis module dedicated for handling complex study design with multiple factors or phenotypic descriptors; (iv) a Causal Analysis module for estimating metabolite - phenotype causal relations based on two-sample Mendelian randomization, and (v) a Dose-Response Analysis module for benchmark dose calculations. In addition, we have also improved MetaboAnalyst's visualization functions, updated its compound database and metabolite sets, and significantly expanded its pathway analysis support to around 130 species. MetaboAnalyst 6.0 is freely available at https://www.metaboanalyst.ca.

Metabolic Profiles of Encapsulated Chondrocytes Exposed to Short-Term Simulated Microgravity

The mechanism by which chondrocytes respond to reduced mechanical loading environments and the subsequent risk of developing osteoarthritis remains unclear. This is of particular concern for astronauts. In space the reduced joint loading forces during prolonged microgravity (10-6 g) exposure could lead to osteoarthritis (OA), compromising quality of life post-spaceflight. In this study, we encapsulated human chondrocytes in an agarose gel of similar stiffness to the pericellular matrix to mimic the cartilage microenvironment. We then exposed agarose-chondrocyte constructs to simulated microgravity (SM) using a rotating wall vessel (RWV) bioreactor to better assess the cartilage health risks associated with spaceflight. Global metabolomic profiling detected a total of 1205 metabolite features across all samples, with 497 significant metabolite features identified by ANOVA (FDR-corrected p-value < 0.05). Specific metabolic shifts detected in response to SM exposure resulted in clusters of co-regulated metabolites, as well as key metabolites identified by variable importance in projection scores. Microgravity-induced metabolic shifts in gel constructs and media were indicative of protein synthesis, energy metabolism, nucleotide metabolism, and oxidative catabolism. The microgravity associated-metabolic shifts were consistent with early osteoarthritic metabolomic profiles in human synovial fluid, which suggests that even short-term exposure to microgravity (or other reduced mechanical loading environments) may lead to the development of OA.

MICOS Complex Loss Governs Age-Associated Murine Mitochondrial Architecture and Metabolism in the Liver, While Sam50 Dictates Diet Changes

The liver, the largest internal organ and a metabolic hub, undergoes significant declines due to aging, affecting mitochondrial function and increasing the risk of systemic liver diseases. How the mitochondrial three-dimensional (3D) structure changes in the liver across aging, and the biological mechanisms regulating such changes confers remain unclear. In this study, we employed Serial Block Face-Scanning Electron Microscopy (SBF-SEM) to achieve high-resolution 3D reconstructions of murine liver mitochondria to observe diverse phenotypes and structural alterations that occur with age, marked by a reduction in size and complexity. We also show concomitant metabolomic and lipidomic changes in aged samples. Aged human samples reflected altered disease risk. To find potential regulators of this change, we examined the Mitochondrial Contact Site and Cristae Organizing System (MICOS) complex, which plays a crucial role in maintaining mitochondrial architecture. We observe that the MICOS complex is lost during aging, but not Sam50. Sam50 is a component of the sorting and assembly machinery (SAM) complex that acts in tandem with the MICOS complex to modulate cristae morphology. In murine models subjected to a high-fat diet, there is a marked depletion of the mitochondrial protein SAM50. This reduction in Sam50 expression may heighten the susceptibility to liver disease, as our human biobank studies corroborate that Sam50 plays a genetically regulated role in the predisposition to multiple liver diseases. We further show that changes in mitochondrial calcium dysregulation and oxidative stress accompany the disruption of the MICOS complex. Together, we establish that a decrease in mitochondrial complexity and dysregulated metabolism occur with murine liver aging. While these changes are partially be regulated by age-related loss of the MICOS complex, the confluence of a murine high-fat diet can also cause loss of Sam50, which contributes to liver diseases. In summary, our study reveals potential regulators that affect age-related changes in mitochondrial structure and metabolism, which can be targeted in future therapeutic techniques.

Predicting glycan structure from tandem mass spectrometry via deep learning

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Restoring hippocampal glucose metabolism rescues cognition across Alzheimer's disease pathologies

Impaired cerebral glucose metabolism is a pathologic feature of Alzheimer Disease (AD), and recent proteomic studies highlight a disruption of glial carbohydrate metabolism with disease progression. Here, we report that inhibition of indoleamine-2,3-dioxygenase 1 (IDO1), which metabolizes tryptophan to kynurenine (KYN) in the first step of the kynurenine pathway, rescues hippocampal memory function and plasticity in preclinical models of amyloid and tau pathology by restoring astrocytic metabolic support of neurons. Activation of IDO1 in astrocytes by amyloid-beta 42 and tau oligomers, two major pathological effectors in AD, increases KYN and suppresses glycolysis in an AhR-dependent manner. Conversely, pharmacological IDO1 inhibition restores glycolysis and lactate production. In amyloid-producing APP Swe -PS1 ΔE9 and 5XFAD mice and in tau-producing P301S mice, IDO1 inhibition restores spatial memory and improves hippocampal glucose metabolism by metabolomic and MALDI-MS analyses. IDO1 blockade also rescues hippocampal long-term potentiation (LTP) in a monocarboxylate transporter (MCT)-dependent manner, suggesting that IDO1 activity disrupts astrocytic metabolic support of neurons. Indeed, in vitro mass-labeling of human astrocytes demonstrates that IDO1 regulates astrocyte generation of lactate that is then taken up by human neurons. In co-cultures of astrocytes and neurons derived from AD subjects, deficient astrocyte lactate transfer to neurons was corrected by IDO1 inhibition, resulting in improved neuronal glucose metabolism. Thus, IDO1 activity disrupts astrocytic metabolic support of neurons across both amyloid and tau pathologies and in a model of AD iPSC-derived neurons. These findings also suggest that IDO1 inhibitors developed for adjunctive therapy in cancer could be repurposed for treatment of amyloid- and tau-mediated neurodegenerative diseases.

MS-PyCloud: A Cloud Computing-Based Pipeline for Proteomic and Glycoproteomic Data Analyses

Rapid development and wide adoption of mass spectrometry-based glycoproteomic technologies have empowered scientists to study proteins and protein glycosylation in complex samples on a large scale. This progress has also created unprecedented challenges for individual laboratories to store, manage, and analyze proteomic and glycoproteomic data, both in the cost for proprietary software and high-performance computing and in the long processing time that discourages on-the-fly changes of data processing settings required in explorative and discovery analysis. We developed an open-source, cloud computing-based pipeline, MS-PyCloud, with graphical user interface (GUI), for proteomic and glycoproteomic data analysis. The major components of this pipeline include data file integrity validation, MS/MS database search for spectral assignments to peptide sequences, false discovery rate estimation, protein inference, quantitation of global protein levels, and specific glycan-modified glycopeptides as well as other modification-specific peptides such as phosphorylation, acetylation, and ubiquitination. To ensure the transparency and reproducibility of data analysis, MS-PyCloud includes open-source software tools with comprehensive testing and versioning for spectrum assignments. Leveraging public cloud computing infrastructure via Amazon Web Services (AWS), MS-PyCloud scales seamlessly based on analysis demand to achieve fast and efficient performance. Application of the pipeline to the analysis of large-scale LC-MS/MS data sets demonstrated the effectiveness and high performance of MS-PyCloud. The software can be downloaded at https://github.com/huizhanglab-jhu/ms-pycloud.

Impact of four different extraction methods and three different reconstitution solvents on the untargeted metabolomics analysis of human and rat urine samples

Unsuitable sample preparation may result in loss of important analytes and consequently affect the outcome of untargeted metabolomics. Due to species differences, different sample preparations may be required within the same biological matrix. The study aimed to compare the in-house sample preparation method for urine with methods from literature and to investigate the transferability of sample preparation from human urine to rat urine. A total of 12 different conditions for protein precipitation were tested, combining four different extraction solvents and three different reconstitution solvents using an untargeted liquid-chromatography high resolution mass spectrometry (LC-HRMS) metabolomics analysis. Evaluation was done based on the impact on feature count, their detectability, as well as the reproducibility of selected compounds. Results showed that a combination of methanol as extraction and acetonitrile/water (75/25) as reconstitution solvent provided improved results at least regarding the total feature count. Additionally, it was found that a higher amount of methanol was most suitable for extraction of rat urine among the tested conditions. In comparison, human urine requires significantly less volume of extraction solvent. Overall, it is recommended to systematically optimize both, the extraction method, and the reconstitution solvent for the used biofluid and the individual analytical settings.

The MICOS Complex Regulates Mitochondrial Structure and Oxidative Stress During Age-Dependent Structural Deficits in the Kidney

The kidney filters nutrient waste and bodily fluids from the bloodstream, in addition to secondary functions of metabolism and hormone secretion, requiring an astonishing amount of energy to maintain its functions. In kidney cells, mitochondria produce adenosine triphosphate (ATP) and help maintain kidney function. Due to aging, the efficiency of kidney functions begins to decrease. Dysfunction in mitochondria and cristae, the inner folds of mitochondria, is a hallmark of aging. Therefore, age-related kidney function decline could be due to changes in mitochondrial ultrastructure, increased reactive oxygen species (ROS), and subsequent alterations in metabolism and lipid composition. We sought to understand if there is altered mitochondrial ultrastructure, as marked by 3D morphological changes, across time in tubular kidney cells. Serial block facing-scanning electron microscope (SBF-SEM) and manual segmentation using the Amira software were used to visualize murine kidney samples during the aging process at 3 months (young) and 2 years (old). We found that 2-year mitochondria are more fragmented, compared to the 3-month, with many uniquely shaped mitochondria observed across aging, concomitant with shifts in ROS, metabolomics, and lipid homeostasis. Furthermore, we show that the mitochondrial contact site and cristae organizing system (MICOS) complex is impaired in the kidney due to aging. Disruption of the MICOS complex shows altered mitochondrial calcium uptake and calcium retention capacity, as well as generation of oxidative stress. We found significant, detrimental structural changes to aged kidney tubule mitochondria suggesting a potential mechanism underlying why kidney diseases occur more readily with age. We hypothesize that disruption in the MICOS complex further exacerbates mitochondrial dysfunction, creating a vicious cycle of mitochondrial degradation and oxidative stress, thus impacting kidney health.

Novel FFPE proteomics method suggests prolactin induced protein as hormone induced cytoskeleton remodeling spatial biomarker

Robotically assisted proteomics provides insights into the regulation of multiple proteins achieving excellent spatial resolution. However, developing an effective method for spatially resolved quantitative proteomics of formalin fixed paraffin embedded tissue (FFPE) in an accessible and economical manner remains challenging. We introduce non-robotic In-insert FFPE proteomics approach, combining glass insert FFPE tissue processing with spatial quantitative data-independent mass spectrometry (DIA). In-insert approach identifies 450 proteins from a 5 µm thick breast FFPE tissue voxel with 50 µm lateral dimensions covering several tens of cells. Furthermore, In-insert approach associated a keratin series and moesin (MOES) with prolactin-induced protein (PIP) indicating their prolactin and/or estrogen regulation. Our data suggest that PIP is a spatial biomarker for hormonally triggered cytoskeletal remodeling, potentially useful for screening hormonally affected hotspots in breast tissue. In-insert proteomics represents an alternative FFPE processing method, requiring minimal laboratory equipment and skills to generate spatial proteotype repositories from FFPE tissue.

A Portable and Reusable Database Infrastructure for Mass Spectrometry, and Its Associated Toolkit (The DIMSpec Project)

Nontargeted analysis (NTA) is a rapidly growing field of techniques that includes the identification of unknown chemical analytes in complex mixtures such as environmental, biological, and food matrices. The use of reference mass spectral databases is a key component of most NTA workflows, providing a high level of confidence for chemical identification when analytical standards are not available, yet effective interlaboratory sharing of research grade spectra remains challenging. The Database Infrastructure for Mass Spectrometry (DIMSpec) project focused on the creation of an open-source toolkit supporting storage and sharing of high-resolution mass spectra with attached sample and methodological metadata. As a demonstration of its utility, the DIMSpec toolkit was used to create a database of curated mass spectra for per- and polyfluoroalkyl substances (PFAS) generated from various sources. While the underlying toolkit is agnostic to analytical targets, this initial release (along with the database schema, mass spectral data, and database tools) should enable PFAS researchers to use these data for their own studies, including the identification of novel PFAS in the environment.

Top-Down Proteomics Analysis of Picogram-Level Complex Samples Using Spray-Capillary-Based Capillary Electrophoresis-Mass Spectrometry

Proteomics analysis of mass-limited samples has become increasingly important for understanding biological systems in physiologically relevant contexts such as patient samples, multicellular organoids, spheroids, and single cells. However, relatively low sensitivity in top-down proteomics methods makes their application to mass-limited samples challenging. Capillary electrophoresis (CE) has emerged as an ideal separation method for mass-limited samples due to its high separation resolution, ultralow detection limit, and minimal sample volume requirements. Recently, we developed "spray-capillary", an electrospray ionization (ESI)-assisted device, that is capable of quantitative ultralow-volume sampling (e.g., pL-nL level). Here, we developed a spray-capillary-CE-MS platform for ultrasensitive top-down proteomics analysis of intact proteins in mass-limited complex biological samples. Specifically, to improve the sensitivity of the spray-capillary platform, we incorporated a polyethylenimine (PEI)-coated capillary and optimized the spray-capillary inner diameter. Under optimized conditions, we successfully detected over 200 proteoforms from 50 pg of E. coli lysate. To our knowledge, the spray-capillary CE-MS platform developed here represents one of the most sensitive detection methods for top-down proteomics. Furthermore, in a proof-of-principle experiment, we detected 261 ± 65 and 174 ± 45 intact proteoforms from fewer than 50 HeLa and OVCAR-8 cells, respectively, by coupling nanodroplet-based sample preparation with our optimized CE-MS platform. Overall, our results demonstrate the capability of the modified spray-capillary CE-MS platform to perform top-down proteomics analysis on picogram amounts of samples. This advancement presents the possibility of meaningful top-down proteomics analysis of mass-limited samples down to the level of single mammalian cells.

Campylobacter jejuni and casein hydrolysate addition: Impact on poultry in vitro cecal microbiota and metabolome

This study investigates the impact of casein hydrolysates on the poultry ceca inoculated with Campylobacter focusing on microbial molecular preferences for different protein sources in the presence of Campylobacter jejuni. Three casein sources (intact casein (IN), casein enzyme hydrolysate (EH), and casein acid hydrolysate (AH)) were introduced to cecal contents in combination with inoculated C. jejuni in an in vitro model system incubated for 48 h at 42°C under microaerophilic conditions. Samples were collected at 0, 24, and 48 h. Genomic DNA was extracted and amplified using custom dual-indexed primers, followed by sequencing on an Illumina MiSeq platform. The obtained sequencing data were then analyzed via QIIME2-2021.11. Metabolite extracts were analyzed with ultra-high-performance liquid orbitrap chromatography-mass spectrometry (UHPLC-MS). Statistical analysis of metabolites was conducted using MetaboAnalyst 5.0, while functional analysis was performed using Mummichog 2.0 with a significance threshold set at P < 0.00001. DNA sequencing and metabolomic analyses revealed that C. jejuni was most abundant in the EH group. Microbial diversity and richness improved in casein supplemented groups, with core microbial differences observed, compared to non-supplemented groups. Vitamin B-associated metabolites significantly increased in the supplemented groups, displaying distinct patterns in vitamin B6 and B9 metabolism between EH and AH groups (P < 0.05). Faecalibacterium and Phascolarctobacterium were associated with AH and EH groups, respectively. These findings suggest microbial interactions in the presence of C. jejuni and casein supplementation are influenced by microbial community preferences for casein hydrolysates impacting B vitamin production and shaping competitive dynamics within the cecal microbial community. These findings underscore the potential of nutritional interventions to modulate the poultry GIT microbiota for improved health outcomes.

Environment modulates protein heterogeneity through transcriptional and translational stop codon readthrough

Stop codon readthrough events give rise to longer proteins, which may alter the protein's function, thereby generating short-lasting phenotypic variability from a single gene. In order to systematically assess the frequency and origin of stop codon readthrough events, we designed a library of reporters. We introduced premature stop codons into mScarlet, which enabled high-throughput quantification of protein synthesis termination errors in E. coli using fluorescent microscopy. We found that under stress conditions, stop codon readthrough may occur at rates as high as 80%, depending on the nucleotide context, suggesting that evolution frequently samples stop codon readthrough events. The analysis of selected reporters by mass spectrometry and RNA-seq showed that not only translation but also transcription errors contribute to stop codon readthrough. The RNA polymerase was more likely to misincorporate a nucleotide at premature stop codons. Proteome-wide detection of stop codon readthrough by mass spectrometry revealed that temperature regulated the expression of cryptic sequences generated by stop codon readthrough in E. coli. Overall, our findings suggest that the environment affects the accuracy of protein production, which increases protein heterogeneity when the organisms need to adapt to new conditions.

Tracing the geographical origin of endangered fungus Ophiocordyceps sinensis, especially from Nagqu, using UPLC-Q-TOF-MS

Ophiocordyceps sinensis (OS), known as "soft gold", played an important role in local economic development. OS from different producing areas was difficult to be discriminated by the appearance. Nagqu OS, a distinguished and safeguarded geographical indication product, commands a premium price in market. The real claim of OS geographical origins is urgently required. Here, 81 OS samples were collected from Tibetan Plateau in China to explore markers for tracing origins. OS from Xigazê can be distinguished by dark color of head of caterpillar. Then 57 samples, a fully representative training-sample set, were used to set up OPLS-DA models by nontargeted metabolomics from UPLC-QTOF-MS. Certain markers were successfully identified and validation using 21 blind test samples confirmed that the markers can trace the geographical origin of OS, especially Nagqu samples. It was affirmed that UPLC-QTOF-MS-based untargeted metabolomics coupled with OPLS-DA was a reliable strategy to trace the geographical origins of OS.

BERNN: Enhancing classification of Liquid Chromatography Mass Spectrometry data with batch effect removal neural networks

Liquid Chromatography Mass Spectrometry (LC-MS) is a powerful method for profiling complex biological samples. However, batch effects typically arise from differences in sample processing protocols, experimental conditions, and data acquisition techniques, significantly impacting the interpretability of results. Correcting batch effects is crucial for the reproducibility of omics research, but current methods are not optimal for the removal of batch effects without compressing the genuine biological variation under study. We propose a suite of Batch Effect Removal Neural Networks (BERNN) to remove batch effects in large LC-MS experiments, with the goal of maximizing sample classification performance between conditions. More importantly, these models must efficiently generalize in batches not seen during training. A comparison of batch effect correction methods across five diverse datasets demonstrated that BERNN models consistently showed the strongest sample classification performance. However, the model producing the greatest classification improvements did not always perform best in terms of batch effect removal. Finally, we show that the overcorrection of batch effects resulted in the loss of some essential biological variability. These findings highlight the importance of balancing batch effect removal while preserving valuable biological diversity in large-scale LC-MS experiments.

Time-resolved profiling of RNA binding proteins throughout the mRNA life cycle

mRNAs continually change their protein partners throughout their lifetimes, yet our understanding of mRNA-protein complex (mRNP) remodeling is limited by a lack of temporal data. Here, we present time-resolved mRNA interactome data by performing pulse metabolic labeling with photoactivatable ribonucleoside in human cells, UVA crosslinking, poly(A)+ RNA isolation, and mass spectrometry. This longitudinal approach allowed the quantification of over 700 RNA binding proteins (RBPs) across ten time points. Overall, the sequential order of mRNA binding aligns well with known functions, subcellular locations, and molecular interactions. However, we also observed RBPs with unexpected dynamics: the transcription-export (TREX) complex recruited posttranscriptionally after nuclear export factor 1 (NXF1) binding, challenging the current view of transcription-coupled mRNA export, and stress granule proteins prevalent in aged mRNPs, indicating roles in late stages of the mRNA life cycle. To systematically identify mRBPs with unknown functions, we employed machine learning to compare mRNA binding dynamics with Gene Ontology (GO) annotations. Our data can be explored at chronology.rna.snu.ac.kr.

TREM1 disrupts myeloid bioenergetics and cognitive function in aging and Alzheimer disease mouse models

Human genetics implicate defective myeloid responses in the development of late-onset Alzheimer disease. A decline in peripheral and brain myeloid metabolism, triggering maladaptive immune responses, is a feature of aging. The role of TREM1, a pro-inflammatory factor, in neurodegenerative diseases is unclear. Here we show that Trem1 deficiency prevents age-dependent changes in myeloid metabolism, inflammation and hippocampal memory function in mice. Trem1 deficiency rescues age-associated declines in ribose 5-phosphate. In vitro, Trem1-deficient microglia are resistant to amyloid-β42 oligomer-induced bioenergetic changes, suggesting that amyloid-β42 oligomer stimulation disrupts homeostatic microglial metabolism and immune function via TREM1. In the 5XFAD mouse model, Trem1 haploinsufficiency prevents spatial memory loss, preserves homeostatic microglial morphology, and reduces neuritic dystrophy and changes in the disease-associated microglial transcriptomic signature. In aging APPSwe mice, Trem1 deficiency prevents hippocampal memory decline while restoring synaptic mitochondrial function and cerebral glucose uptake. In postmortem Alzheimer disease brain, TREM1 colocalizes with Iba1+ cells around amyloid plaques and its expression is associated with Alzheimer disease clinical and neuropathological severity. Our results suggest that TREM1 promotes cognitive decline in aging and in the context of amyloid pathology.

Comparison of reversed-phase, hydrophilic interaction, and porous graphitic carbon chromatography columns for an untargeted toxicometabolomics study in pooled human liver microsomes, rat urine, and rat plasma

INTRODUCTION

Untargeted metabolomics studies are expected to cover a wide range of compound classes with high chemical diversity and complexity. Thus, optimizing (pre-)analytical parameters such as the analytical liquid chromatography (LC) column is crucial and the selection of the column depends primarily on the study purpose.

OBJECTIVES

The current investigation aimed to compare six different analytical columns. First, by comparing the chromatographic resolution of selected compounds. Second, on the outcome of an untargeted toxicometabolomics study using pooled human liver microsomes (pHLM), rat plasma, and rat urine as matrices.

METHODS

Separation and analysis were performed using three different reversed-phase (Phenyl-Hexyl, BEH C18, and Gold C18), two hydrophilic interaction chromatography (HILIC) (ammonium-sulfonic acid and sulfobetaine), and one porous graphitic carbon (PGC) columns coupled to high-resolution mass spectrometry (HRMS). Their impact was evaluated based on the column performance and the size of feature count, amongst others.

RESULTS

All three reversed-phase columns showed a similar performance, whereas the PGC column was superior to both HILIC columns at least for polar compounds. Comparing the size of feature count across all datasets, most features were detected using the Phenyl-Hexyl or sulfobetaine column. Considering the matrices, most significant features were detected in urine and pHLM after using the sulfobetaine and in plasma after using the ammonium-sulfonic acid column.

CONCLUSION

The results underline that the outcome of this untargeted toxicometabolomic study LC-HRMS metabolomic study was highly influenced by the analytical column, with the Phenyl-Hexyl or sulfobetaine column being the most suitable. However, column selection may also depend on the investigated compounds as well as on the investigated matrix.

Genomic and Metabolomic Analyses of Nocardiopsis maritima YSL2 as the Mycorrhizosphere Bacterium of Suaeda maritima (L.) Dumort

Nine bacteria were isolated from the episphere of Suaeda maritima (L.) Dumort. Among them, the bacterial strain YSL2 displayed the highest antimicrobial activity on agar plates and exhibited significant novelty compared with other bacteria based on 16S rRNA analysis. Consequently, Nocardiopsis maritima YSL2T was subjected to phenotypic characterization and whole-genome sequencing. Phylogenetic analysis revealed its close association with Nocardiopsis aegyptia SNG49T. Furthermore, genomic analysis of strain YSL2T revealed the presence of various gene clusters, indicating its potential for producing antimicrobial secondary metabolites. Upon cultivation on a large scale, maritiamides A and B (1 and 2) were isolated and characterized as cyclic hexapeptides based on nuclear magnetic resonance, ultraviolet, infrared, and mass spectrometric data. The absolute configurations of the amino acid residues in the maritiamides were determined through chiral derivatization, utilizing FDAA and GITC. Maritiamides 1 and 2 exhibited promising antibacterial activities against Staphylococcus epidermidis and weakly inhibited the growth of Escherichia coli and Pseudomonas fluorescens.

Tepuazines A-E: Phenazine Glycosides from a Venezuelan Quartz-Rich (Tepui) Cave Soil-Derived Streptomyces virginiae CMB-CA091

Investigation of the secondary metabolites of Streptomyces virginiae CMB-CA091 isolated from the quartz-rich (tepui) soil of a cave in Venezuela yielded two new dimeric phenazine glycosides, tepuazines A and B (1 and 2); three new monomeric phenazine glycosides, tepuazines C-E (3-5); and a series of known analogues, baraphenazine G (6), phenazinolin D (7), izumiphenazine C (8), 4-methylaminobenzoyl-l-rhamnopyranoside (9), and 2-acetamidophenol (10). Structures were assigned to 1-10 on the basis of detailed spectroscopic analysis and biosynthetic considerations, with 1 and 2 featuring a rare 2-oxabicyclo[3.3.1]nonane-like ring C/D bridge shared with only a handful of known Streptomyces natural products. We propose a plausible convergent biosynthetic relationship linking all known members of this structure class that provides a rationale for the observed ring C/D configuration.

Pullenvalenes A-D: Nitric Oxide-Mediated Transcriptional Activation (NOMETA) Enables Discovery of Triterpene Aminoglycosides from Australian Termite Nest-Derived Fungi

We report on the use of nitric oxide-mediated transcriptional activation (NOMETA) as an innovative means to detect and access new classes of microbial natural products encoded within silent biosynthetic gene clusters. A small library of termite nest- and mangrove-derived fungi and actinomyces was subjected to cultivation profiling using a miniaturized 24-well format approach (MATRIX) in the presence and absence of nitric oxide, with the resulting metabolomes subjected to comparative chemical analysis using UPLC-DAD and GNPS molecular networking. This strategy prompted study of Talaromyces sp. CMB-TN6F and Coccidiodes sp. CMB-TN39F, leading to discovery of the triterpene glycoside pullenvalenes A-D (1-4), featuring an unprecedented triterpene carbon skeleton and rare 6-O-methyl-N-acetyl-d-glucosaminyl glycoside residues. Structure elucidation of 1-4 was achieved by a combination of detailed spectroscopic analysis, chemical degradation, derivatization and synthesis, and biosynthetic considerations.

Time series of chicken stool metagenomics and egg metabolomics in changing production systems: preliminary insights from a proof-of-concept

BACKGROUND

Different production systems of livestock animals influence various factors, including the gut microbiota.

METHODS

We investigated whether changing the conditions from barns to free-range chicken farming impacts the microbiome over the course of three weeks. We compared the stool microbiota of chicken from industrial barns after introducing them either in community or separately to a free-range environment.

RESULTS

Over the six time points, 12 taxa-mostly lactobacilli-changed significantly. As expected, the former barn chicken cohort carries more resistances to common antibiotics. These, however, remained positive over the observed period. At the end of the study, we collected eggs and compared metabolomic profiles of the egg white and yolk to profiles of eggs from commercial suppliers. Here, we observed significant differences between commercial and fresh collected eggs as well as differences between the former barn chicken and free-range chicken.

CONCLUSION

Our data indicate that the gut microbiota can undergo alterations over time in response to changes in production systems. These changes subsequently exert an influence on the metabolites found in the eggs. The preliminary results of our proof-of-concept study motivate larger scale observations with more individual chicken and longer observation periods.

Metabolite Profiling of Colvillea racemosa via UPLC-ESI-QTOF-MS Analysis in Correlation to the In Vitro Antioxidant and Cytotoxic Potential against A549 Non-Small Cell Lung Cancer Cell Line

In this study, flower and leaf extracts of Colvillea racemosa were considered a source of bioactive compounds. In this context, the objective of the study focused on investigating the anticancer potential as well as the phytochemical composition of both extracts. The extracts were analyzed by UPLC-ESI-QTOF-MS, and the bioactivity was tested using in vitro antioxidant assays (FRAP, DPPH, and ABTS) in addition to cytotoxic assays on non-small cell lung cancer cell line (A549). Our results clearly indicated the potent radical scavenging capacity of both extracts. Importantly, the flower extract exhibited a greater antioxidant capacity than the leaf extract. In terms of cytotoxic activity, leaf and flower extracts significantly inhibited cell viability with IC50 values of 17.0 and 17.2 µg/mL, respectively. The phytochemical characterization enabled the putative annotation of 42 metabolites, such as saccharides, phenolic acids, flavonoids, amino acids, and fatty acids. Among them, the flavonoid C-glycosides stand out due to their high relative abundance and previous reports on their anticancer bioactivity. For a better understanding of the bioactive mechanisms, four flavonoids (vitexin, kaempferol-3-O-rutinoside, luteolin, and isoorientin) were selected for molecular docking on hallmark protein targets in lung cancer as represented by γ-PI3K, EGFR, and CDK2 through in-silico studies. In these models, kaempferol-3-O-rutinoside and vitexin had the highest binding scores on γ-PI3K and CDK2, followed by isoorientin, so they could be highly responsible for the bioactive properties of C. racemosa extracts.

Screening Metabolic Biomarkers in KRAS Mutated Mouse Acinar and Human Pancreatic Cancer Cells via Single-Cell Mass Spectrometry

Pancreatic cancer is a highly aggressive and rapidly progressing disease, often diagnosed in advanced stages due to the absence of early noticeable symptoms. The KRAS mutation is a hallmark of pancreatic cancer, yet the underlying mechanisms driving pancreatic carcinogenesis remain elusive. Cancer cells display significant metabolic heterogeneity, which is relevant to the pathogenesis of cancer. Population measurements may obscure information about the metabolic heterogeneity among cancer cells. Therefore, it is crucial to analyze metabolites at the single-cell level to gain a more comprehensive understanding of metabolic heterogeneity. In this study, we employed a 3D-printed ionization source for metabolite analysis in both mice and human pancreatic cancer cells at the single-cell level. Using advanced machine learning algorithms and mass spectral feature selection, we successfully identified 23 distinct metabolites that are statistically significantly different in KRAS mutant human pancreatic cancer cells and mouse acinar cells bearing the oncogenic KRAS mutation. These metabolites encompass a variety of chemical classes, including organic nitrogen compounds, organic acids and derivatives, organoheterocyclic compounds, benzenoids, and lipids. These findings shed light on the metabolic remodeling associated with KRAS-driven pancreatic cancer initiation and indicate that the identified metabolites hold promise as potential diagnostic markers for early detection in pancreatic cancer patients.

Metabolism of the 4-Hydroxyphenylpyruvate Dioxygenase Inhibitor, Mesotrione, in Multiple-Herbicide-Resistant Palmer amaranth (Amaranthus palmeri)

Metabolic resistance to the maize-selective, HPPD-inhibiting herbicide, mesotrione, occurs via Phase I ring hydroxylation in resistant waterhemp and Palmer amaranth; however, mesotrione detoxification pathways post-Phase I are unknown. This research aims to (1) evaluate Palmer amaranth populations for mesotrione resistance via survivorship, foliar injury, and aboveground biomass, (2) determine mesotrione metabolism rates in Palmer amaranth populations during a time course, and (3) identify mesotrione metabolites including and beyond Phase I oxidation. The Palmer amaranth populations, SYNR1 and SYNR2, exhibited higher survival rates (100%), aboveground biomass (c.a. 50%), and lower injury (25-30%) following mesotrione treatment than other populations studied. These two populations also metabolized mesotrione 2-fold faster than sensitive populations, PPI1 and PPI2, and rapidly formed 4-OH-mesotrione. Additionally, SYNR1 and SYNR2 formed 5-OH-mesotrione, which is not produced in high abundance in waterhemp or naturally tolerant maize. Metabolite features derived from 4/5-OH-mesotrione and potential Phase II mesotrione-conjugates were detected and characterized by liquid chromatography-mass spectrometry (LCMS).

Genomic insights and anti-phytopathogenic potential of siderophore metabolome of endolithic Nocardia mangyaensis NH1

Actinobacteria are one of the predominant groups that successfully colonize and survive in various aquatic, terrestrial and rhizhospheric ecosystems. Among actinobacteria, Nocardia is one of the most important agricultural and industrial bacteria. Screening and isolation of Nocardia related bacteria from extreme habitats such as endolithic environments are beneficial for practical applications in agricultural and environmental biotechnology. In this work, bioinformatics analysis revealed that a novel strain Nocardia mangyaensis NH1 has the capacity to produce structurally varied bioactive compounds, which encoded by non-ribosomal peptide synthases (NRPS), polyketide synthase (PKS), and post-translationally modified peptides (RiPPs). Among NRPS, five gene clusters have a sequence homology with clusters encoding for siderophore synthesis. We also show that N. mangyaensis NH1 accumulates both catechol- and hydroxamate-type siderophores simultaneously under iron-deficient conditions. Untargeted LC-MS/MS analysis revealed a variety of metabolites, including siderophores, lipopeptides, cyclic peptides, and indole-3-acetic acid (IAA) in the culture medium of N. mangyaensis NH1 grown under iron deficiency. We demonstrate that four CAS (chrome azurol S)-positive fractions display variable affinity to metals, with a high Fe3+ chelating capability. Additionally, three of these fractions exhibit antioxidant activity. A combination of iron scavenging metabolites produced by N. mangyaensis NH1 showed antifungal activity against several plant pathogenic fungi. We have shown that the pure culture of N. mangyaensis NH1 and its metabolites have no adverse impact on Arabidopsis seedlings. The ability of N. mangyaensis NH1 to produce siderophores with antifungal, metal-chelating, and antioxidant properties, when supplemented with phytohormones, has the potential to improve the release of macro- and micronutrients, increase soil fertility, promote plant growth and development, and enable the production of biofertilizers across diverse soil systems.

Bioassay-Driven, Fractionation-Empowered, Focused Metabolomics for Discovering Bacterial Activators of Aryl Hydrocarbon Receptor

Aryl hydrocarbon receptor (AhR) is a transcription factor that regulates gene expression upon ligand activation, enabling microbiota-dependent induction, training, and function of the host immune system. A spectrum of metabolites, encompassing indole and tryptophan derivatives, have been recognized as activators. This work introduces an integrated, mass spectrometry-centric workflow that employs a bioassay-guided, fractionation-based methodology for the identification of AhR activators derived from human bacterial isolates. By leveraging the workflow efficiency, the complexities inherent in metabolomics profiling are significantly reduced, paving the way for an in-depth and focused mass spectrometry analysis of bioactive fractions isolated from bacterial culture supernatants. Validation of AhR activator candidates used multiple criteria─MS/MS of the synthetic reference compound, bioassay of AhR activity, and elution time confirmation using a C-13 isotopic reference─and was demonstrated for N-formylkynurenine (NFK). The workflow reported provides a roadmap update for improved efficiency of identifying bioactive metabolites using mass spectrometry-based metabolomics. Mass spectrometry datasets are accessible at the National Metabolomics Data Repository (PR001479, Project DOI: 10.21228/M8JM7Q).

First comprehensive untargeted metabolomics study of suramin-treated Trypanosoma brucei: an integrated data analysis workflow from multifactor data modelling to functional analysis

INTRODUCTION

Human African trypanosomiasis, commonly known as sleeping sickness, is a vector-borne parasitic disease prevalent in sub-Saharan Africa and transmitted by the tsetse fly. Suramin, a medication with a long history of clinical use, has demonstrated varied modes of action against Trypanosoma brucei. This study employs a comprehensive workflow to investigate the metabolic effects of suramin on T. brucei, utilizing a multimodal metabolomics approach.

OBJECTIVES

The primary aim of this study is to comprehensively analyze the metabolic impact of suramin on T. brucei using a combined liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance spectroscopy (NMR) approach. Statistical analyses, encompassing multivariate analysis and pathway enrichment analysis, are applied to elucidate significant variations and metabolic changes resulting from suramin treatment.

METHODS

A detailed methodology involving the integration of high-resolution data from LC-MS and NMR techniques is presented. The study conducts a thorough analysis of metabolite profiles in both suramin-treated and control T. brucei brucei samples. Statistical techniques, including ANOVA-simultaneous component analysis (ASCA), principal component analysis (PCA), ANOVA 2 analysis, and bootstrap tests, are employed to discern the effects of suramin treatment on the metabolomics outcomes.

RESULTS

Our investigation reveals substantial differences in metabolic profiles between the control and suramin-treated groups. ASCA and PCA analysis confirm distinct separation between these groups in both MS-negative and NMR analyses. Furthermore, ANOVA 2 analysis and bootstrap tests confirmed the significance of treatment, time, and interaction effects on the metabolomics outcomes. Functional analysis of the data from LC-MS highlighted the impact of treatment on amino-acid, and amino-sugar and nucleotide-sugar metabolism, while time effects were observed on carbon intermediary metabolism (notably glycolysis and di- and tricarboxylic acids of the succinate production pathway and tricarboxylic acid (TCA) cycle).

CONCLUSION

Through the integration of LC-MS and NMR techniques coupled with advanced statistical analyses, this study identifies distinctive metabolic signatures and pathways associated with suramin treatment in T. brucei. These findings contribute to a deeper understanding of the pharmacological impact of suramin and have the potential to inform the development of more efficacious therapeutic strategies against African trypanosomiasis.

Host-gut microbiota interactions shape parasite infections in farmed Atlantic salmon

Animals and their associated microbiota share long evolutionary histories. However, it is not always clear how host genotype and microbiota interact to affect phenotype. We applied a hologenomic approach to explore how host-microbiota interactions shape lifetime growth and parasite infection in farmed Atlantic salmon (Salmo salar). Multi-omics data sets were generated from the guts of 460 salmon, 82% of which were naturally infected with an intestinal cestode. A single Mycoplasma bacterial strain, MAG01, dominated the gut metagenome of large, non-parasitized fish, consistent with previous studies showing high levels of Mycoplasma in the gut microbiota of healthy salmon. While small and/or parasitized salmon also had high abundance of MAG01, we observed increased alpha diversity in these individuals, driven by increased frequency of low-abundance Vibrionaceae and other Mycoplasma species that carried known virulence genes. Colonization by one of these cestode-associated Mycoplasma strains was associated with host individual genomic variation in long non-coding RNAs. Integrating the multi-omic data sets revealed coordinated changes in the salmon gut mRNA transcriptome and metabolome that correlated with shifts in the microbiota of smaller, parasitized fish. Our results suggest that the gut microbiota of small and/or parasitized fish is in a state of dysbiosis that partly depends on the host genotype, highlighting the value of using a hologenomic approach to incorporate the microbiota into the study of host-parasite dynamics.IMPORTANCEStudying host-microbiota interactions through the perspective of the hologenome is gaining interest across all life sciences. Intestinal parasite infections are a huge burden on human and animal health; however, there are few studies investigating the role of the hologenome during parasite infections. We address this gap in the largest multi-omics fish microbiota study to date using natural cestode infection of farmed Atlantic salmon. We find a clear association between cestode infection, salmon lifetime growth, and perturbation of the salmon gut microbiota. Furthermore, we provide the first evidence that the genetic background of the host may partly determine how the gut microbiota changes during parasite-associated dysbiosis. Our study therefore highlights the value of a hologenomic approach for gaining a more in-depth understanding of parasitism.

Multispecies Benchmark Analysis for LC-MS/MS Validation and Performance Evaluation in Bottom-Up Proteomics

We present an instrument-independent benchmark procedure and software (LFQ_bout) for the validation and comparative evaluation of the performance of LC-MS/MS and data processing workflows in bottom-up proteomics. The procedure enables a back-to-back comparison of common and emerging workflows, e.g., diaPASEF or ScanningSWATH, and evaluates the impact of arbitrary and inadequately documented settings or black-box data processing algorithms. It enhances the overall performance and quantification accuracy by recognizing and reporting common quantification errors.

Application of SWATH Mass Spectrometry and Machine Learning in the Diagnosis of Inflammatory Bowel Disease Based on the Stool Proteome

Inflammatory bowel disease (IBD) flare-ups exhibit symptoms that are similar to other diseases and conditions, making diagnosis and treatment complicated. Currently, the gold standard for diagnosing and monitoring IBD is colonoscopy and biopsy, which are invasive and uncomfortable procedures, and the fecal calprotectin test, which is not sufficiently accurate. Therefore, it is necessary to develop an alternative method. In this study, our aim was to provide proof of concept for the application of Sequential Window Acquisition of All Theoretical Mass Spectra-Mass spectrometry (SWATH-MS) and machine learning to develop a non-invasive and accurate predictive model using the stool proteome to distinguish between active IBD patients and symptomatic non-IBD patients. Proteome profiles of 123 samples were obtained and data processing procedures were optimized to select an appropriate pipeline. The differentially abundant analysis identified 48 proteins. Utilizing correlation-based feature selection (Cfs), 7 proteins were selected for proceeding steps. To identify the most appropriate predictive machine learning model, five of the most popular methods, including support vector machines (SVMs), random forests, logistic regression, naive Bayes, and k-nearest neighbors (KNN), were assessed. The generated model was validated by implementing the algorithm on 45 prospective unseen datasets; the results showed a sensitivity of 96% and a specificity of 76%, indicating its performance. In conclusion, this study illustrates the effectiveness of utilizing the stool proteome obtained through SWATH-MS in accurately diagnosing active IBD via a machine learning model.

Enhanced efficiency of MS/MS all-ion fragmentation for non-targeted analysis of trace contaminants in surface water using multivariate curve resolution and data fusion

Data-independent acquisition-all-ion fragmentation (DIA-AIF) mode of mass spectrometry can facilitate wide-scope non-target analysis of contaminants in surface water due to comprehensive spectral identification. However, because of the complexity of the resulting MS2 AIF spectra, identifying unknown pollutants remains a significant challenge, with a significant bottleneck in translating non-targeted chemical signatures into environmental impacts. The present study proposes to process fused MS1 and MS2 data sets obtained from LC-HRMS/MS measurements in non-targeted AIF workflows on surface water samples using multivariate curve resolution-alternating least squares (MCR-ALS). This enables straightforward assignment between precursor ions obtained from resolved MS1 spectra and their corresponding MS2 spectra. The method was evaluated for two sets of tap water and surface water contaminated with 14 target chemicals as a proof of concept. The data set of surface water samples consisting of 3506 MS1 and 2170 MS2 AIF mass spectral features was reduced to 81 components via a fused MS1-MS2 MCR model that describes at least 98.8% of the data. Each component summarizes the distinct chromatographic elution of components together with their corresponding MS1 and MS2 spectra. MS2 spectral similarity of more than 82% was obtained for most target chemicals. This highlights the potential of this method for unraveling the composition of MS/MS complex data in a water environment. Ultimately, the developed approach was applied to the retrospective non-target analysis of an independent set of surface water samples.

Characterizing the D-Amino Acid Position in Peptide Epimers by Using Higher-Energy Collisional Dissociation Tandem Mass Spectrometry: A Case Study of Liraglutide

D-amino acid-containing peptides (DAACPs) occur in biological and artificial environments. Since the importance of DAACPs has been recognized, various mass spectrometry-based analytical approaches have been developed. However, the capability of higher-energy collisional dissociation (HCD) fragmentation to characterize DAACP sites has not been evaluated. In this study, we compared the normalized spectra intensity under different conditions of HCD and used liraglutide along with its DAACPs as examples. Our results indicated that the difference in the intensity of y ions between DAACPs and all-L liraglutide could not only distinguish them but also localize the sites of D-amino acids in the DAACPs. Our data demonstrate the potential of using HCD for the site characterization of DAACPs, which may have great impact in biological studies and peptide drug development.