Metabolomic Features Research Articles

Abstract 5815: Metabolomics in an Apc-mutant genetic background: Mechanistic insights from acute vs. chronic spinach intake

Abstract There is growing interest in the crosstalk between the gut microbiome, metabolomic features, and disease pathogenesis. Colorectal cancer is a major health burden worldwide, linked in part to modifiable risk factors associated with diet and lifestyle (1). The current investigation compared long-term (26 week) and acute (3 day) dietary spinach intake in a genetic model of colorectal cancer. Metabolomic analyses in the polyposis in rat colon (Pirc) model and in wildtype animals corroborated key contributions to anticancer outcomes by spinach-derived linoleate bioactives and a butanoate metabolite linked to increased α-diversity of the gut microbiome (2). Combining linoleate and butanoate metabolites in human colon cancer cells revealed enhanced apoptosis and reduced cell viability, paralleling the apoptosis induction observed in colon tumors from rats given long-term spinach treatment. Mechanistic studies indicated reactivation of immune-associated major histocompatibility complex genes in cell-based assays and in vivo, plus differential roles of the metabolites in targeting Wnt/β-catenin signaling (3). Clinical translation of the findings from this investigation to at-risk patients might provide valuable quality-of-life benefits by delaying surgical interventions and drug therapies with adverse side effects (4,5).

Abstract 3487: Molecular clusters and functional drivers of IgM monoclonal gammopathies

Abstract IgM MGUS and Waldenstrom Macroglobulinemia (WM) represent a disease spectrum with highly dissimilar therapeutic management ranging from observation to chemoimmunotherapy. The current classification relies solely on clinical features and does not explain the heterogeneity that exists within each of these conditions. To shed light on the biology that may account for the clinical differences, we used bone marrow (BM) CD19+/CD138+ sorted cells and matched BM plasma from 32 patients (pts) (7 IgM MGUS, 25 WM) and 5 healthy controls to perform the first comprehensive multiomics approach including whole exome sequencing, RNA seq, proteomics and metabolomics. Applying principal component analysis to gene expression profiling, most of WM pts clustered together, while a small subset of them grouped separately with MGUS pts, suggesting a biologic dichotomy within WM. The healthy controls formed a distinct group from most WM and MGUS. We then applied a non-negative matrix factorization consensus clustering to the gene expression data and identified three robust clusters. Cluster 1 (C1) included only WM pts, cluster 2 (C2) included both WM and MGUS pts, and cluster 3 (C3) included all controls as well as a small number of WM and MGUS pts. When mutations commonly identified in WM were analyzed, there was no difference among the three groups (excluding controls) in mutation burden of MYD88 L265P and CXCR4. Interestingly, aberrant expression of TNFAIP3 seemed a distinct feature of C1 as deletion of 6q (which encodes for TNFAIP3) and TNFAIP3 mutations were each significantly enriched in C1 (47%) compared to C2 (0%) and C3 (20%; p=0.04). Individual clusters associated with specific transcriptional signature and clinical features. While C1 displayed enrichment of cell growth, downregulation of inflammatory pathways (eg IL6 and IL8 signaling) and aggressive behavior, C2 showed increased inflammatory signaling and cell survival with indolent behavior. C3 had an intermediate feature with combined proliferative and inflammatory signatures. In accordance with the transcriptomic data, the hallmark of C1 was upregulation of proliferation-associated proteins (eg AKT, MAPK) and downregulation of inflammatory proteins (eg IL4, IL10) while the opposite was observed in C2. Once more, C3 confirmed intermediate features with combined upregulation of proliferation and inflammatory proteins. The metabolism was rewired towards fatty acid catabolism in C1, glycolysis in C2 and anabolism in C3. Accordingly, C1 showed undetectable concentration of 3-hydroxybutyric acid as opposed to C2 which had increased levels of malic and lactic acids, as end products of fatty acid oxidation and glycolysis respectively. Those metabolites had intermediate levels in C3. In conclusion, we identified three molecular clusters with distinct clinical, proteomic and metabolomic features, suggesting a potential biologic classification that may have therapeutic implications. Citation Format: Patrizia Mondello, Jonas Paludo, Joseph Novak, Kerstin Wenzl, Shahrzad Jalali, Jordan Krull, Esteban Braggio, Surendra Dasari, Michelle Manske, Jithma Abeykoon, Asher Chanan-Khan, Robert Kyle, Morie Gertz, Zhi Zhang Yang, Anne Novak, Stephen Ansell. Molecular clusters and functional drivers of IgM monoclonal gammopathies [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3487.

Elution with 1,2-Hexanediol Enables Coupling of ICPMS with Reversed-Pase Liquid Chromatography under Standard Conditions.

The inductively coupled plasma mass spectrometry (ICPMS) has been attracting increasing attention for many applications as an element-selective chromatographic detector. A major and fundamental limitation in coupling ICPMS with liquid chromatography is the limited compatibility with organic solvents, which has so far been addressed via a tedious approach, collectively referred to as the “organic ICPMS mode”, that can decrease detection sensitivity by up to 100-fold. Herein, we report 1,2-hexanediol as a new eluent in high-performance liquid chromatography–ICPMS which enables avoiding the current limitations. Unlike commonly used eluents, 1,2-hexanediol was remarkably compatible with ICPMS detection at high flow rates of 1.5 mL min–1 and concentrations of at least 30% v/v, respectively, under the standard conditions and instrumental setup normally used with 100% aqueous media. Sensitivity for all tested elements (P, S, Cl, Br, Se, and As) was enhanced with 10% v/v 1,2-hexanediol relative to that of 100% aqueous media by 1.5–7-fold depending on the element. Concentrations of 1,2-hexanediol at ≤30% v/v were superior in elution strength to concentrations at >90% v/v of the common organic phases, which greatly decreases the amount of carbon required to elute highly hydrophobic compounds such as lipids and steroids, enabling detection at ultra-trace levels. The proposed approach was applied to detect arsenic-containing fatty acids in spiked human urine, and detection limits of <0.01 μg As L–1 were achieved, which is >100-fold lower than those previously reported using the organic ICPMS mode. Nontargeted speciation analysis in Allium sativum revealed the presence of a large number of hydrophobic sulfur-containing metabolomic features at trace levels.

159-LB: The Lipidomic Signature of Prediabetes among the Oldest Old—The Long Life Family Study

The epidemiology of prediabetes and its metabolomic features remain poorly characterized in the oldest old. Therefore, we characterized the serum lipidome in 635 nonagenarians and centenarians without type 2 diabetes (T2D) from the Long Life Family Study, a multicenter study of 583 families with a clustering of exceptional longevity. Despite a low T2D prevalence and incidence in the LLFS cohort, 361 (56.9%) of participants aged 90+ years had prediabetes at study entry based on either glycated hemoglobin or fasting serum glucose levels. We used LC/MS to initially assess 193 lipid metabolites in fasting serum in those with and without prediabetes. We tested the association of log transformed lipid metabolites with prediabetes using logistic regression with the Benjamini-Hochberg false-discovery rate method (FDR&amp;lt;0.05) , while adjusting for age, sex, fasting time, field center, and familial relatedness. 17 out of 193 annotated lipid metabolites were associated with prevalent prediabetes, with the strongest association found for phosphatidylethanolamine (PE 40:7) , which remained significant after additional adjustment for BMI (odds ratio (OR) =2.83; 95% confidence interval (CI) : 1.7-4.7) . Phosphatidylethanolamine might have proinflammatory properties and act as a positive modulator of membrane disruption induced by islet amyloid polypeptide, an amyloidogenic protein involved in T2D. Our data suggest that among the oldest old with exceptional longevity prediabetes burden is high, and that the phosphatidylethanolamine pathway might have an important role in the development of prediabetes. Future untargeted metabolomic analyses will investigate potential biomarkers that predict the transition from prediabetes to diabetes among the oldest old in this unique family study of healthy aging and exceptional longevity. Disclosure I. Miljkovic: None. R. Cvejkus: None. P. An: None. B. Thyagarajan: n/a. J. Mengel-from: None. M. Feitosa: None. A. N. Benbourenane: None. J. M. Zmuda: None. Funding National Institute of Health (U01AG023746)

NMR-based metabolomic profiling can differentiate follicular lymphoma from benign lymph node tissues and may be predictive of outcome

Follicular lymphoma (FL) is a cancer of B-cells, representing the second most common type of non-Hodgkin lymphoma and typically diagnosed at advanced stage in older adults. In contrast to the wide range of available molecular genetic data, limited data relating the metabolomic features of follicular lymphoma are known. Metabolomics is a promising analytical approach employing metabolites (molecules < 1 kDa in size) as potential biomarkers in cancer research. In this pilot study, we performed proton nuclear magnetic resonance spectroscopy (1H-NMR) on 29 cases of FL and 11 control patient specimens. The resulting spectra were assessed by both unsupervised and supervised statistical methods. We report significantly discriminant metabolomic models of common metabolites distinguishing FL from control tissues. Within our FL case series, we also report discriminant metabolomic signatures predictive of progression-free survival.

Metabolomic spectra for phenotypic prediction of malting quality in spring barley

We investigated prediction of malting quality (MQ) phenotypes in different locations using metabolomic spectra, and compared the prediction ability of different models, and training population (TP) sizes. Data of five MQ traits was measured on 2667 individual plots of 564 malting spring barley lines from three years and two locations. A total of 24,018 metabolomic features (MFs) were measured on each wort sample. Two statistical models were used, a metabolomic best linear unbiased prediction (MBLUP) and a partial least squares regression (PLSR). Predictive ability within location and across locations were compared using cross-validation methods. For all traits, more than 90% of the total variance in MQ traits could be explained by MFs. The prediction accuracy increased with increasing TP size and stabilized when the TP size reached 1000. The optimal number of components considered in the PLSR models was 20. The accuracy using leave-one-line-out cross-validation ranged from 0.722 to 0.865 and using leave-one-location-out cross-validation from 0.517 to 0.817. In conclusion, the prediction accuracy of metabolomic prediction of MQ traits using MFs was high and MBLUP is better than PLSR if the training population is larger than 100. The results have significant implications for practical barley breeding for malting quality.

Metagenomic and metabolomic remodeling in nonagenarians and centenarians and its association with genetic and socioeconomic factors.

A better understanding of the biological and environmental variables that contribute to exceptional longevity has the potential to inform the treatment of geriatric diseases and help achieve healthy aging. Here, we compared the gut microbiome and blood metabolome of extremely long-lived individuals (94-105 years old) to that of their children (50-79 years old) in 116 Han Chinese families. We found extensive metagenomic and metabolomic remodeling in advanced age and observed a generational divergence in the correlations with socioeconomic factors. An analysis of quantitative trait loci revealed that genetic associations with metagenomic and metabolomic features were largely generation-specific, but we also found 131 plasma metabolic quantitative trait loci associations that were cross-generational with the genetic variants concentrated in six loci. These included associations between FADS1/2 and arachidonate, PTPA and succinylcarnitine and FLVCR1 and choline. Our characterization of the extensive metagenomic and metabolomic remodeling that occurs in people reaching extreme ages may offer new targets for aging-related interventions.

Single cell mass spectrometry studies reveal metabolomic features and potential mechanisms of drug-resistant cancer cell lines

Irinotecan (Iri) is a key drug to treat metastatic colorectal cancer, but its clinical activity is often limited by de novo and acquired drug resistance. Studying the underlying mechanisms of drug resistance is necessary for developing novel therapeutic strategies. In this study, we used both regular and irinotecan-resistant (Iri-resistant) colorectal cell lines as models, and performed single cell mass spectrometry (SCMS) metabolomics studies combined with analyses from cytotoxicity assay, western blot, flow cytometry, quantitative real-time polymerase chain reaction (qPCR), and reactive oxygen species (ROS). Our SCMS results indicate that Iri-resistant cancer cells possess higher levels of unsaturated lipids compared with the regular cancer cells. In addition, multiple protein biomarkers and their corresponding mRNAs of colon cancer stem cells are overexpressed in Iri-resistance cells. Particularly, stearoyl-CoA desaturase 1 (SCD1) is upregulated with the development of drug resistance in Iri-resistant cells, whereas inhibiting the activity of SCD1 efficiently increase their sensitivity to Iri treatment. In addition, we demonstrated that SCD1 directly regulates the expression of ALDH1A1, which contributes to the cancer stemness and ROS level in Iri-resistant cell lines.

Finding Correspondence between Metabolomic Features in Untargeted Liquid Chromatography-Mass Spectrometry Metabolomics Datasets.

Integration of multiple datasets can greatly enhance bioanalytical studies, for example, by increasing power to discover and validate biomarkers. In liquid chromatography–mass spectrometry (LC–MS) metabolomics, it is especially hard to combine untargeted datasets since the majority of metabolomic features are not annotated and thus cannot be matched by chemical identity. Typically, the information available for each feature is retention time (RT), mass-to-charge ratio (m/z), and feature intensity (FI). Pairs of features from the same metabolite in separate datasets can exhibit small but significant differences, making matching very challenging. Current methods to address this issue are too simple or rely on assumptions that cannot be met in all cases. We present a method to find feature correspondence between two similar LC–MS metabolomics experiments or batches using only the features’ RT, m/z, and FI. We demonstrate the method on both real and synthetic datasets, using six orthogonal validation strategies to gauge the matching quality. In our main example, 4953 features were uniquely matched, of which 585 (96.8%) of 604 manually annotated features were correct. In a second example, 2324 features could be uniquely matched, with 79 (90.8%) out of 87 annotated features correctly matched. Most of the missed annotated matches are between features that behave very differently from modeled inter-dataset shifts of RT, MZ, and FI. In a third example with simulated data with 4755 features per dataset, 99.6% of the matches were correct. Finally, the results of matching three other dataset pairs using our method are compared with a published alternative method, metabCombiner, showing the advantages of our approach. The method can be applied using M2S (Match 2 Sets), a free, open-source MATLAB toolbox, available at https://github.com/rjdossan/M2S.

Network Analysis to Identify Multi-Omic Correlations in the Lower Airways of Children With Cystic Fibrosis.

The leading cause of morbidity and mortality in cystic fibrosis (CF) is progressive lung disease secondary to chronic airway infection and inflammation; however, what drives CF airway infection and inflammation is not well understood. By providing a physiological snapshot of the airway, metabolomics can provide insight into these processes. Linking metabolomic data with microbiome data and phenotypic measures can reveal complex relationships between metabolites, lower airway bacterial communities, and disease outcomes. In this study, we characterize the airway metabolome in bronchoalveolar lavage fluid (BALF) samples from persons with CF (PWCF) and disease control (DC) subjects and use multi-omic network analysis to identify correlations with the airway microbiome. The Biocrates targeted liquid chromatography mass spectrometry (LC-MS) platform was used to measure 409 metabolomic features in BALF obtained during clinically indicated bronchoscopy. Total bacterial load (TBL) was measured using quantitative polymerase chain reaction (qPCR). The Qiagen EZ1 Advanced automated extraction platform was used to extract DNA, and bacterial profiling was performed using 16S sequencing. Differences in metabolomic features across disease groups were assessed univariately using Wilcoxon rank sum tests, and Random forest (RF) was used to identify features that discriminated across the groups. Features were compared to TBL and markers of inflammation, including white blood cell count (WBC) and percent neutrophils. Sparse supervised canonical correlation network analysis (SsCCNet) was used to assess multi-omic correlations. The CF metabolome was characterized by increased amino acids and decreased acylcarnitines. Amino acids and acylcarnitines were also among the features most strongly correlated with inflammation and bacterial burden. RF identified strong metabolomic predictors of CF status, including L-methionine-S-oxide. SsCCNet identified correlations between the metabolome and the microbiome, including correlations between a traditional CF pathogen, Staphylococcus, a group of nontraditional taxa, including Prevotella, and a subnetwork of specific metabolomic markers. In conclusion, our work identified metabolomic characteristics unique to the CF airway and uncovered multi-omic correlations that merit additional study.

Computational Workflow to Study the Diversity of Secondary Metabolites in Fourteen Different Isatis Species

The screening of real features among thousands of ions remains a great challenge in the study of metabolomics. In this research, a workflow designed based on the MetaboFR tool and “feature-rating” rule was developed to screen the real features in large-scale data analyses. Seventy-four reference standards were used to test the feasibility, with 83.21% of real features being obtained after MetaboFR processing. Moreover, the full workflow was applied for systematic characterization of 14 species of the genus Isatis, with the result that 87.72% of real features were retained and 69.19% of the in-source fragments were removed. To gain insights into metabolite diversity within this plant family, 1697 real features were tentatively identified, including lipids, phenylpropanoids, organic acids, indole derivatives, etc. Indole derivatives were demonstrated to be the best chemical markers with which to differentiate different species. The rare existence of indole derivatives in Isatis cappadocica (cap) and Isatis cappadocica subsp. Steveniana (capS) indicates that the biosynthesis of indole derivatives could play a key role in driving the chemical diversity and evolution of genus Isatis. Our workflow provides the foundations for the exploration of real features in metabolomics, and has the potential to reveal the chemical composition and marker metabolites of secondary metabolites in plant fields.

Paramounter: Direct Measurement of Universal Parameters To Process Metabolomics Data in a "White Box".

Choosing appropriate data processing parameters is critical in processing liquid chromatography-mass spectrometry (LC-MS)-based untargeted metabolomics data. The conventional design of experiments (DOE) approach is time-consuming and provides no intuitive explanation why the selected parameters generate the best results. After studying commonly used metabolomics data processing software, this work summarized a set of universal parameters, including mass tolerance, peak height, peak width, and instrumental shift. These universal parameters are shared among different feature extraction programs and are critical to metabolic feature extraction. We then developed Paramounter, an R program that automatically measures these universal parameters from raw LC-MS-based metabolomics data prior to metabolic feature extraction. This is made possible through novel concepts of rank-based intensity sorting, zone of interest, and many others. Paramounter also translates universal parameters to software-specific parameters for data processing in different programs. Applying Paramounter is demonstrated to provide a threefold increase in the extracted metabolites compared to using default parameters in MS-DIAL-based feature extraction. Furthermore, the comparison between Paramounter, AutoTuner, and IPO showed that Paramounter generates 3.7- and 1.6-fold more true positive features than AutoTuner and IPO, respectively. Further validation of Paramounter on 11 datasets covering different sample types, data acquisition modes, and MS vendors proved that Paramounter is a convenient and robust program. Overall, the proposed universal parameters and the development of Paramounter address a critical need in metabolomics data processing, transforming metabolomics feature extraction from a "black box" to a "white box." Paramounter is freely available on GitHub (https://github.com/HuanLab/Paramounter).

Abstract MP45: Metabolomic Profiling Provides Novel Insights Into The Biochemical Mechanism Of Mental Stress-induced Ischemia

Introduction: Mental stress-induced myocardial ischemia (MSI) is a marker of future cardiovascular events. The underlying biochemical mechanisms of this cardiovascular condition are poorly understood. Our objective was to use metabolic profiling to identify biochemical changes associated with mental stress (MS) in patients with stable coronary artery disease (CAD) and investigate differential pathways that are involved in MSI. Methods: A total of 110 Individuals with stable CAD who participated in the Mental Stress Ischemia Prognosis Study (MIPS) underwent a standardized mental stress to provoke of myocardial ischemia, detected with single-photon emission tomography. Plasma samples were obtained before and after MS at 45 and 90 minutes and metabolomic profiling was performed using liquid chromatography/mass spectrometry using a confirmed 500 compounds. Metabolomic features and pathways associated with ischemia using metabolome-wide association studies and Mummichog, respectively. Results: The sample involved a subset case-control for (MSI+ vs MSI-). At 45 minutes and 90 minutes, there were significant stress-induced changes in several compounds. There was a significant upregulation of metabolites associated with lipid signaling et ( p = 0.025, Inosine diphosphate). A comparison between the MSI+ and MS- groups showed differential upregulation of DNA methylation metabolites ( p = .036, 5-Methylcytosine hydrochloride) at 45 minutes and microbiome-related metabolites at 90 minutes (P=0.041, N, N-dimethyl-1,4-phenylenediamine). Pathway analyses were consistent with the aforementioned findings. Conclusion: Upregulation of metabolic pathways involved in DNA methylation and microbial metabolism is differentially associated with MSI. These findings provide novel mechanistic insights into the association between acute mental stress and myocardial ischemia as a marker for cardiovascular vulnerability in patients with CAD

Development of a rational framework for the therapeutic efficacy of fecal microbiota transplantation for calf diarrhea treatment

BackgroundEstablishing fecal microbiota transplantation (FMT) to prevent multifactorial diarrhea in calves is challenging because of the differences in farm management practices, the lack of optimal donors, and recipient selection. In this study, the underlying factors of successful and unsuccessful FMT treatment cases are elucidated, and the potential markers for predicting successful FMT are identified using fecal metagenomics via 16S rRNA gene sequencing, fecal metabolomics via capillary electrophoresis time-of-flight mass spectrometry, and machine learning approaches.ResultsSpecifically, 20 FMT treatment cases, in which feces from healthy donors were intrarectally transferred into recipient diarrheal calves, were conducted with a success rate of 70%. Selenomonas was identified as a microorganism genus that showed significant donor–recipient compatibility in successful FMT treatments. A strong positive correlation between the microbiome and metabolome data, which is a prerequisite factor for FMT success, was confirmed by Procrustes analysis in successful FMT (r = 0.7439, P = 0.0001). Additionally, weighted gene correlation network analysis confirmed the positively or negatively correlated pairs of bacterial taxa (family Veillonellaceae) and metabolomic features (i.e., amino acids and short-chain fatty acids) responsible for FMT success. Further analysis aimed at establishing criteria for donor selection identified the genus Sporobacter as a potential biomarker in successful donor selection. Low levels of metabolites, such as glycerol 3-phosphate, dihydroxyacetone phosphate, and isoamylamine, in the donor or recipients prior to FMT, are predicted to facilitate FMT.ConclusionsOverall, we provide the first substantial evidence of the factors related to FMT success or failure; these findings could improve the design of future microbial therapeutics for treating diarrhea in calves.1SZe_1Amjvg5rTHsSJMvRbVideo abstract

Myco-Synergism Boosts Herbivory-Induced Maize Defense by Triggering Antioxidants and Phytohormone Signaling.

BackgroundBiocontrol strategies are the best possible and eco-friendly solution to develop resistance against O furnacalis and improve the maize yield. However, the knowledge about underlying molecular mechanisms, metabolic shifts, and hormonal signaling is limited.MethodsHere, we used an axenic and a consortium of entomopathogenic Beauveria bassiana OFDH1-5 and a pathogen-antagonistic Trichoderma asperellum GDFS1009 in maize and observed that consortium applications resulted in higher chlorophyll contents and antioxidants activities [superoxide dismutase (SOD), peroxidase (POD), proline, protease, and polyphenol oxidase (PPO)] with a decrease in O. furnacalis survival. We performed a comprehensive transcriptome and an untargeted metabolome profiling for the first time at a vegetative stage in fungal inoculated maize leaves at 0-, 12-, 24-, 48-, and 72-h post insect infestation.ResultsThe consortium of B. bassiana and T. asperellum leads to 80–95% of O. furnacalis mortality. A total of 13,156 differentially expressed genes were used for weighted gene coexpression network analysis. We identified the six significant modules containing thirteen candidate genes [protein kinase (GRMZM2G025459), acyl-CoA dehydrogenase (GRMZM5G864319), thioredoxin gene (GRMZM2G091481), glutathione S-transferase (GRMZM2G116273), patatin-like phospholipase gene (GRMZM2G154523), cytochrome P450 (GRMZM2G139874), protease inhibitor (GRMZM2G004466), (AC233926.1_FG002), chitinase (GRMZM2G453805), defensin (GRMZM2G392863), peroxidase (GRMZM2G144153), GDSL- like lipase (AC212068.4_FG005), and Beta-glucosidase (GRMZM2G031660)], which are not previously reported that are highly correlated with Jasmonic acid - Ethylene (JA-ET) signaling pathway and antioxidants. We detected a total of 130 negative and 491 positive metabolomic features using a ultrahigh-performance liquid chromatography ion trap time-of-flight mass spectrometry (UHPLC-QTOF-MS). Intramodular significance and real time-quantitative polymerase chain reaction (RT-qPCR) expressions showed that these genes are the true candidate genes. Consortium treated maize had higher jasmonic acid (JA), salicylic acid (SA), and ethylene (ET) levels.ConclusionOur results provide insights into the genetics, biochemicals, and metabolic diversity and are useful for future biocontrol strategies against ACB attacks.

Reproducible untargeted metabolomics workflow for exhaustive MS2 data acquisition of MS1 features

Unknown features in untargeted metabolomics and non-targeted analysis (NTA) are identified using fragment ions from MS/MS spectra to predict the structures of the unknown compounds. The precursor ion selected for fragmentation is commonly performed using data dependent acquisition (DDA) strategies or following statistical analysis using targeted MS/MS approaches. However, the selected precursor ions from DDA only cover a biased subset of the peaks or features found in full scan data. In addition, different statistical analysis can select different precursor ions for MS/MS analysis, which make the post-hoc validation of ions selected following a secondary analysis impossible for precursor ions selected by the original statistical method. Here we propose an automated, exhaustive, statistical model-free workflow: paired mass distance-dependent analysis (PMDDA), for reproducible untargeted mass spectrometry MS2 fragment ion collection of unknown compounds found in MS1 full scan. Our workflow first removes redundant peaks from MS1 data and then exports a list of precursor ions for pseudo-targeted MS/MS analysis on independent peaks. This workflow provides comprehensive coverage of MS2 collection on unknown compounds found in full scan analysis using a “one peak for one compound” workflow without a priori redundant peak information. We compared pseudo-spectra formation and the number of MS2 spectra linked to MS1 data using the PMDDA workflow to that obtained using CAMERA and RAMclustR algorithms. More annotated compounds, molecular networks, and unique MS/MS spectra were found using PMDDA compared with CAMERA and RAMClustR. In addition, PMDDA can generate a preferred ion list for iterative DDA to enhance coverage of compounds when instruments support such functions. Finally, compounds with signals in both positive and negative modes can be identified by the PMDDA workflow, to further reduce redundancies. The whole workflow is fully reproducible as a docker image xcmsrocker with both the original data and the data processing template.Graphical

Integrated Genomic and Metabolomic Analysis Illuminates Key Secreted Metabolites Produced by the Novel Endophyte Bacillus halotolerans Cal.l.30 Involved in Diverse Biological Control Activities.

The endophytic strain Cal.l.30, isolated from the medicinal plant Calendula officinalis, was selected among seven Bacillus strains with plant growth promoting activity and strong biological potential against the postharvest fungal pathogen Botrytis cinerea. Treatment by inoculating Cal.l.30 bacterial cell culture or cell free supernatant on harvested grapes and cherry tomato fruits, significantly reduced gray mold disease severity index and disease incidence. Based on 16S rRNA sequence analysis and whole genome phylogeny, Cal.l.30 was identified as Bacillus halotolerans. Genome mining revealed that B. halotolerans Cal.l.30 is endowed with a diverse arsenal of secondary metabolite biosynthetic gene clusters (SM-BGCs) responsible for metabolite production with antimicrobial properties. A sub-set of the identified SM-BGCs (mojavensin A, ‘bacillunoic acid’) appears to be the result of recent horizontal gene transfer events. Its genome was also mined for CAZymes associated with antifungal activity. Further UHPLC-HRMS analysis indicated that Cal.l.30 synthesizes and secretes secondary metabolites with antimicrobial activity, including the lipopeptides, fengycin, surfactin and mojavensin A, bacillaene isoforms, L-dihydroanticapsin and bacillibactin. Other compounds with known antimicrobial activity were also detected, such as azelaic acid, 15- hydroxypentadecanoid acid and 2-hydroxyphenylacetic acid. The genomic and metabolomic features of the B. halotolerans Cal.l.30 provided new perspectives on the exploitation of novel Bacillus sp. as a biocontrol agent.

Metabolomics of Acute vs. Chronic Spinach Intake in an Apc-Mutant Genetic Background: Linoleate and Butanoate Metabolites Targeting HDAC Activity and IFN-γ Signaling.

There is growing interest in the crosstalk between the gut microbiome, host metabolomic features, and disease pathogenesis. The current investigation compared long–term (26 week) and acute (3 day) dietary spinach intake in a genetic model of colorectal cancer. Metabolomic analyses in the polyposis in rat colon (Pirc) model and in wild–type animals corroborated key contributions to anticancer outcomes by spinach–derived linoleate bioactives and a butanoate metabolite linked to increased α–diversity of the gut microbiome. Combining linoleate and butanoate metabolites in human colon cancer cells revealed enhanced apoptosis and reduced cell viability, paralleling the apoptosis induction in colon tumors from rats given long–term spinach treatment. Mechanistic studies in cell–based assays and in vivo implicated the linoleate and butanoate metabolites in targeting histone deacetylase (HDAC) activity and the interferon–γ (IFN–γ) signaling axis. Clinical translation of these findings to at–risk patients might provide valuable quality–of–life benefits by delaying surgical interventions and drug therapies with adverse side effects.

Microbiome and metabolome features of the cardiometabolic disease spectrum

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages—acute coronary syndrome, chronic IHD and IHD with heart failure—and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features.

Length of PM2.5 exposure and alterations in the serum metabolome among women undergoing infertility treatment.

Background:Both acute and chronic exposure to fine particulate matter (PM2.5) have been linked to negative health outcomes. Studies have used metabolomics to describe the biological pathways linking PM2.5 with disease but have focused on a single exposure window. We compared alterations in the serum metabolome following various short- and long-term PM2.5 exposures.Methods:Participants were women undergoing in vitro fertilization at a New England fertility clinic (n = 200). Women provided their residential address and provided a blood sample during controlled ovarian stimulation. PM2.5 exposure was estimated in the 1, 2, and 3 days, 2 weeks, and 3 months prior to blood collection using a validated spatiotemporal model. We utilized liquid chromatography with high-resolution mass spectrometry. We used generalized linear models to test for associations between metabolomic features and PM2.5 exposures after adjusting for potential confounders. Significant features (P < 0.005) were used for pathway analysis and metabolite identification.Results:We identified 17 pathways related to amino acid, lipid, energy, and nutrient metabolism that were solely associated with acute PM2.5 exposure. Fifteen pathways, mostly, pro-inflammatory, anti-inflammatory, amino acid, and energy metabolism, were solely associated with long-term PM2.5 exposure. Seven pathways were associated with the majority of exposure windows and were mostly related to anti-inflammatory and lipid metabolism. Among the significant features, we confirmed seven metabolites with level-1 evidence.Conclusions:We identified serum metabolites and metabolic pathways uniquely associated with acute versus chronic PM2.5 exposure. These different biologic pathways may help explain differences in disease states when investigating different lengths of PM2.5 exposure.