Metabolomic profiling is a powerful approach to characterize human metabolism and help understand common disease risk. Although multiple high-throughput technologies have been developed to assay the human metabolome, no technique is capable of capturing the entire human metabolism. Large-scale metabolomics data are being generated in multiple cohorts, but the datasets are typically profiled using different metabolomics platforms. Here, we compared analyses across two of the most frequently used metabolomic platforms, Biocrates and Metabolon, with the aim of assessing how complimentary metabolite profiles are across platforms. We profiled serum samples from 1,001 twins using both targeted (Biocrates, n = 160 metabolites) and non-targeted (Metabolon, n = 488 metabolites) mass spectrometry platforms. We compared metabolite distributions and performed genome-wide association analyses to identify shared genetic influences on metabolites across platforms. Comparison of 43 metabolites named for the same compound on both platforms indicated strong positive correlations, with few exceptions. Genome-wide association scans with high-throughput metabolic profiles were performed for each dataset and identified genetic variants at 7 loci associated with 16 unique metabolites on both platforms. The 16 metabolites showed consistent genetic associations and appear to be robustly measured across platforms. These included both metabolites named for the same compound across platforms as well as unique metabolites, of which 2 (nonanoylcarnitine (C9) [Biocrates]/Unknown metabolite X-13431 [Metabolon] and PC aa C28:1 [Biocrates]/1-stearoylglycerol [Metabolon]) are likely to represent the same or related biochemical entities. The results demonstrate the complementary nature of both platforms, and can be informative for future studies of comparative and integrative metabolomics analyses in samples profiled on different platforms.

We report an analytical tool to facilitate metabolite identification based on an MS/MS spectral match of an unknown to a library of predicted MS/MS spectra of possible human metabolites. To construct the spectral library, the known endogenous human metabolites in the Human Metabolome Database (HMDB) (8,021 metabolites) and their predicted metabolic products via one metabolic reaction in the Evidence-based Metabolome Library (EML) (375,809 predicted metabolites) were subjected to in silico fragmentation to produce the predicted MS/MS spectra. This spectral library is hosted at the public MCID Web site (www.MyCompoundID.org), and a spectral search program, MCID MS/MS, has been developed to allow a user to search one or a batch of experimental MS/MS spectra against the library spectra for possible match(s). Using MS/MS spectra generated from standard metabolites and a human urine sample, we demonstrate that this tool is very useful for putative metabolite identification. It allows a user to narrow down many possible structures initially found by using an accurate mass search of an unknown metabolite to only one or a few candidates, thereby saving time and effort in selecting or synthesizing metabolite standard(s) for eventual positive metabolite identification.

About

The Human Metabolome Database (HMDB) is a freely available electronic database containing detailed information about small molecule metabolites found in the human body. It is intended to be used for applications in metabolomics, clinical chemistry, biomarker discovery and general education. The database is designed to contain or link three kinds of data: 1) chemical data, 2) clinical data, and 3) molecular biology/biochemistry data. The database currently contains nearly 2500 metabolite entries including both water-soluble and lipid soluble metabolites as well as metabolites that would be regarded as either abundant (> 1 uM) or relatively rare (< 1 nM). Additionally, approximately 5500 protein (and DNA) sequences are linked to these metabolite entries. Each MetaboCard entry contains more than 90 data fields with half of the information being devoted to chemical/clinical data and the other half devoted to enzymatic or biochemical data. Many data fields are hyperlinked to other databases (KEGG, PubChem, MetaCyc, ChEBI, PDB, Swiss-Prot, and GenBank) and a variety of structure and pathway viewing applets. The HMDB database supports extensive text, sequence, chemical structure and relational query searches. Two additional databases, DrugBank and FooDB are also part of the HMDB. DrugBank contains equivalent information on 1500 drugs while FooDB contains equivalent information on 3500 food components and food additives.

Openness

Freely available, but requires permission for commercial re-use or re-distribution:

HMDB is offered to the public as a freely available resource. Use and re-distribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material (HMDB) and the original publication (see below). We ask that users who download significant portions of the database cite the HMDB paper in any resulting publications.

Dataset contains a list of metabolites, their fold change after ozone exposure and a p value for that change. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: This dataset can be accessed by contacting Dr. Robert Devlin (devlin.rober@epa.gov). Format: The dataset was sent to us by the company (Metabolon) that did the metabolite analysis, including the statistical analysis). It is an excel spreadsheet that contains a row for each of the metabolites that were identified, fold changes in each metabolite after air and ozone exposure (and the p value of the ozone-induced change), and the pathway and superpathway to which each metabolite belongs. This dataset is associated with the following publication: Cheng, W., K. Duncan, A. Ghio, C. Ward-Caviness, E. Karoly, D. Diaz-Sanchez, R. Conolly, and R. Devlin. Changes in metabolites present in lung-lining fluid following exposure to humans to ozone. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 163(2): 430-439, (2018).

Database containing detailed information about small molecules produced by human microbiome. Provides metabolite data including structure, names, descriptions, chemical taxonomy, chemical ontology, physico-chemical data, spectra and contains detailed information about microbes that produce these chemicals, enzymatic reactions responsible for their production, bioactivity of chemicals and anatomical location of these chemicals and microbes. Many data fields in the database are hyperlinked to other databases including FooDB, HMDB, KEGG, PubChem, MetaCyc, ChEBI, UniProt, and GenBank. Database is FAIR compliant.The data in MiMeDB are released under the Creative Commons (CC) 4.0 License.

Human metabolism is complex and dynamic and is impacted by genetics, diet, health, and countless inputs from the environment. Beyond the genetics shared by family members, cohabitation leads to shared microbial and environmental exposures. Furthermore, metabolism is affected by factors such as inflammation, antibiotic potential, environmental tobacco smoke (ETS) exposure, metabolic regulation, and environmental exposure to heavy metals within the home. Metabolomics represents a useful analytical method to assay the metabolism of individuals to find potential biomarkers for metabolic conditions that may not be phenotypically obvious or represent unknown physiological processes. As such, we applied untargeted LC-MS metabolomics to archived saliva samples from a racially diverse group of elementary school-aged children and their caregivers collected during the "90-month" assessment of the Family Life Project. We assayed a total of 1,425 saliva samples of which 1,344 were paired into 672 caregiver/child dyads. We compared the metabolomes of children (N = 719) and caregivers (N = 706) within and between homes, performed population-wide "metabotype" analyses, and measured associations between metabolites and salivary biomeasures of inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Dyadic analyses revealed that children and their caregivers have largely similar salivary metabolomes. Although there were differences between the dyads at the individual levels of analysis, dyads explained most (62%) of the metabolome variation. At a population level of analysis, our data clustered into two large groups, indicating that people likely share most of their metabolomes, but that there are distinct "metabotypes" across large sample sets. Lastly, individual differences in several metabolites – which were putative oxidative damage-associated or pathological markers – were significantly correlated with salivary measures indexing inflammation, antioxidant potential, ETS exposure, metabolic regulation, and heavy metals. Implications of the effects of family environment on metabolomic variation at the population, dyadic, and individual levels of analyses for health and human development are discussed.

Metabolites present in human blood document individual physiological states influenced by genetic, epigenetic, and lifestyle factors. Using high-resolution liquid chromatography-mass spectrometry (LC-MS), we performed nontargeted, quantitative metabolomics analysis in blood of 15 young (29 ± 4 y of age) and 15 elderly (81 ± 7 y of age) individuals. Coefficients of variation (CV = SD/mean) were obtained for 126 blood metabolites of all 30 donors. Fifty-five RBC-enriched metabolites, for which metabolomics studies have been scarce, are highlighted here. We found 14 blood compounds that show remarkable age-related increases or decreases; they include 1,5-anhydroglucitol, dimethyl-guanosine, acetyl-carnosine, carnosine, ophthalmic acid, UDP-acetyl-glucosamine, N-acetyl-arginine, N6-acetyl-lysine, pantothenate, citrulline, leucine, isoleucine, NAD+, and NADP+. Six of them are RBC-enriched, suggesting that RBC metabolomics is highly valuable for human aging research. Age differences are partly explained by a decrease in antioxidant production or increasing inefficiency of urea metabolism among the elderly. Pearson’s coefficients demonstrated that some age-related compounds are correlated, suggesting that aging affects them concomitantly. Although our CV values are mostly consistent with those CVs previously published, we here report previously unidentified CVs of 51 blood compounds. Compounds having moderate to high CV values (0.4–2.5) are often modified. Compounds having low CV values, such as ATP and glutathione, may be related to various diseases because their concentrations are strictly controlled, and changes in them would compromise health. Thus, human blood is a rich source of information about individual metabolic differences. In human 24 hr metabolome observation (non-fasting) four volunteers were taken blood after overnight fast without breakfast at 9:00; 10:00, 13:00 and before lunch on the first day. Volunteers had lunches and dinners as usual on that day. On the second day after overnight fast the blood was sampled again at 9:00 at Kyoto university hospital laboratory. The great majority of metabolites hardly fluctuated (117 from 126 metabolites varied less than 2.5-fold on average in four volunteers). Data from three injections of the same sample and three samples prepared from the same donated blood are available under accession number MTBLS263. Whole blood metabolomic data from all 30 subjects are available under accession number MTBLS265. Plasma and RBC data from all 30 subjects can be found under MTBLS266 and MTBLS267, respectively.

Identification of unknown metabolites is a major challenge in metabolomics. Without the identities of the metabolites, the metabolome data generated from a biological sample cannot be readily linked with the proteomic and genomic information for studies in systems biology and medicine. We have developed a web-based metabolite identification tool (http://www.mycompoundid.org) that allows searching and interpreting mass spectrometry (MS) data against a newly constructed metabolome library composed of 8 021 known human endogenous metabolites and their predicted metabolic products (375 809 compounds from one metabolic reaction and 10 583 901 from two reactions). As an example, in the analysis of a simple extract of human urine or plasma and the whole human urine by liquid chromatography-mass spectrometry and MS/MS, we are able to identify at least two times more metabolites in these samples than by using a standard human metabolome library. In addition, it is shown that the evidence-based metabolome library (EML) provides a much superior performance in identifying putative metabolites from a human urine sample, compared to the use of the ChemPub and KEGG libraries.

Saliva is composed of a variety of molecules that modulate the oral cavity. It is mainly composed of water (95%) and several minor components such as electrolytes, proteins, and low molecular weight compounds. It consists of metabolites from drinks, foods, drugs, environmental contaminants, and bacterial by-products. As part of the ongoing human metabolome project, the human cerebrospinal fluid metabolome, the human serum metabolome, and recently the human urine metabolome have been characterized. Unlike urine, serum, and cerebrospinal fluid, saliva metabolomics have not shown much progress. Therefore, the present study aims at characterizing the human saliva metabolome. In an attempt to understand saliva metabolomics, we have undertaken comprehensive multi analytical tools based quantitative metabolomics. The nuclear magnetic resonance (NMR) spectroscopy data is presented in this study.

PURPOSE: Validated biomarkers of food intake (BFIs) have recently been suggested as a useful tool to assess adherence to dietary guidelines or compliance in human dietary interventions. Although many new candidate biomarkers have emerged in the last decades for different foods from metabolic profiling studies, the number of comprehensively validated biomarkers of food intake is limited. Apples are among the most frequently consumed fruits and a rich source of polyphenols and fibers, an important mediator for their health-protective properties.METHODS: Using an untargeted metabolomics approach, we aimed to identify biomarkers of long-term apple intake and explore how apples impact on the human plasma and urine metabolite profiles. Forty mildly hypercholesterolemic volunteers consumed two whole apples or a sugar and energy-matched control beverage, daily for 8 weeks in a randomized, controlled, crossover intervention study. The metabolome in plasma and urine samples was analyzed via untargeted metabolomics.RESULTS: We found 61 urine and 9 plasma metabolites being statistically significant after the whole apple intake compared to the control beverage, including several polyphenol metabolites that could be used as BFIs. Furthermore, we identified several endogenous indole and phenylacetyl-glutamine microbial metabolites significantly increasing in urine after apple consumption. The multiomic dataset allowed exploration of the correlations between metabolites modulated significantly by the dietary intervention and fecal microbiota species at genus level, showing interesting interactions between Granulicatella genus and phenyl-acetic acid metabolites. Phloretin glucuronide and phloretin glucuronide sulfate appeared promising biomarkers of apple intake; however, robustness, reliability and stability data are needed for full BFI validation.CONCLUSION: The identified apple BFIs can be used in future studies to assess compliance and to explore their health effects after apple intake. Moreover, the identification of polyphenol microbial metabolites suggests that apple consumption mediates significant gut microbial metabolic activity which should be further explored.

Metabolomic data from the 'Variability of the Human Serum Metabolome over 3 Months in the EXPOsOMICS Personal Exposure Monitoring Study' paper DOI: 10.1021/acs.est.3c03233 .

The data was originally collected and generated by the multicenter EXPOsOMICS Personal Exposure Monitoring study. Details on data collection and processing are described in the aforementioned paper. The statistical analysis from that paper is available at https://github.com/moosterwegel/variability-metabolites-paper and may contain useful information/code to work with this data.

processed_covariate_data.csv: Rows: 298 Columns: 7 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ age_cat: indicates age category at the time of a PEM session $ sq_sex: indicates the sex of the participant (male, female) as filled in during the screening questionaire $ traf: indicates the exposure to traffic (PM2.5 and UFP) as measured during the PEM sessions. $ bmi_cat: indicates BMI category at the time of a PEM session

processed_lcms_data data.csv contains the processed LCMS data: Rows: 298 Columns: 4297 $ subjectid: hashed identifier subject $ sample_code: indicates if it's the first (A) or second (B) blood sample $ centre: indicates in which centre the data was collected $ compounds: measured features (compounds) are prefixed by the letter X. The name contains information on the measured monoisotopicmass_retentiontime. Non-detects (below limit of detection (LOD) are coded as 1 for the compounds. .... In the datasets each row indicates a measurement on a day (sample_code) and person (subjectid). The datasets can be joined on these variables.

The other data files (annotations.xslx, ancestors_annotations.xlsx, annotations_plus_kegg_pathways.csv) contain the annotations, ancestors of the annotations (to assign a class to a compound based on ChEBI ontology, see our paper for details), annotations plus KEGG pathways respectively.

This data set is downloaded from MetaboLights (http://www.ebi.ac.uk/metabolights/) accession number MTBLS266 Abstract:Metabolites present in human blood document individual physiological states influenced by genetic, epigenetic, and life-style factors. Using high-resolution liquid chromatography-mass spectrometry (LC-MS), we performed non-targeted, quantitative metabolomics analysis in blood of 15 young (29±4 yr) and 15 elderly (81±7 yr) individuals. Coefficients of variation (CV=standard deviation/mean) were obtained for 126 blood metabolites of all 30 donors. Fifty-five RBC-enriched metabolites, for which metabolomics studies have been scarce, are highlighted here. We found fourteen blood compounds that show remarkable age-related increases or decreases; they include 1,5-anhydroglucitol, dimethyl-guanosine, acetyl-carnosine, carnosine, ophthalmic acid, UDP-acetyl-glucosamine, N-acetyl-arginine, N6-acetyl-lysine, pantothenate, citrulline, leucine, isoleucine, NAD+, NADP+. Six of them are RBC-enriched, suggesting that RBC metabolomics is highly valuable for human aging research. Age differences are partly explained by a decrease in anti-oxidant production or increasing inefficiency of urea metabolism among the elderly. Pearson’s coefficients demonstrated that some age-related compounds are correlated, suggesting that aging affects them concomitantly. While our CV values are mostly consistent with those previously published, we here report novel CVs of 51 blood compounds. Compounds having moderate to high CV values (0.4-2.5) are often modified. Compounds having low CV values such as ATP and glutathione may be related to various diseases as their concentrations are strictly controlled, and changes in them would compromise health. Thus human blood is a rich source of information about individual metabolic differences. Data from three injections of the same sample and three samples prepared from the same donated blood are available under accession number MTBLS263. Blood samples drawn from four volunteers four times within 24 h are available under accession number MTBLS264. Whole blood metabolomic data from all 30 subjects are available under accession number MTBLS265. RBC data from all 30 subjects can be found under MTBLS267.

THIS RESOURCE IS NO LONGER IN SERVICE, documented August 23, 2016. Network visualizations in which the expression and predicted flux data are projected over the global human network. These network visualizations are accessible through the supplemental website using the publicly available Cytoscape software (Cline, Smoot et al. 2007). Since many high degree nodes exist in the network, special layouts are required to produce network visualizations that are readily interpretable. To this end we produced network visualizations in which hub nodes are repeated multiple times and hence layouts with a small number of edge crossings can be generated. Contains entries for brain compartments and brain pathways.

Emory University (analyzed the urine samples for pyrethroid metabolites). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Contact Researcher. Format: Pyrethroid metabolite concentration data for 50 adults over six-weeks. This dataset is associated with the following publication: Morgan , M., J. Sobus , D.B. Barr, C. Croghan , F. Chen , R. Walker, L. Alston, E. Andersen, and M. Clifton. Temporal variability of pyrethroid metabolite levels in bedtime, morning, and 24-hr urine samples for 50 adults in North Carolina. ENVIRONMENT INTERNATIONAL. Elsevier Science Ltd, New York, NY, USA, 144: 81-91, (2015).

Metabolites of human or microbial origin have the potential to be important biomarkers of disease state in cystic fibrosis (CF). Clinical sample collection and storage conditions may impact metabolite abundances with clinical relevance. We measured the change in metabolite composition based on untargeted gas chromatography mass spectrometry (GC-MS) when CF sputum samples were stored at either 4°C, -20°C, or -80°C with one or two freeze-thaw cycles. Daily time points were taken for one week and then weekly for 4 weeks (4°C) and 8 weeks (-20°C). The metabolites in samples stored at -20°C maintained similar abundances compared to -80°C over the course of eight weeks (average change in Bray-Curtis distance: 0.06±0.04), and were also stable after one or two freeze-thaw cycles. However, metabolite profiles of samples stored at 4°C shifted after one day and continued to change over the course of four weeks (average change in Bray-Curtis distance: 0.31±0.12). Several amino acids and other metabolite abundances increased with time when stored at 4°C, but remained constant at -20°C. Storage temperature was a significant factor driving the metabolite composition (PERMANOVA R2 = 0.32 to 0.49, p= <0.001). CF sputum samples stored at -20°C at the time of sampling maintain a relatively stable untargeted GC-MS profile. Samples should be frozen on the day of collection, as more than one day at 4°C impacts the global composition of the metabolites in the sample.

Background: The favorable health-promoting adaptations to exercise result from cumulative responses to individual bouts of physical activity. Older adults often exhibit anabolic resistance; a phenomenon whereby the anabolic responses to exercise and nutrition are attenuated in skeletal muscle. The mechanisms contributing to age-related anabolic resistance are emerging, but our understanding of how chronological age influences responsiveness to exercise is incomplete. The objective was to determine the effects of healthy aging on peripheral blood metabolomic response to a single bout of resistance exercise and whether any metabolites in circulation are predictive of anabolic response in skeletal muscle. Methods: Thirty young (20-35 years) and 49 older (65-85 years) men and women were studied in a cross-sectional manner. Participants completed a single bout of resistance exercise consisting of eight sets of 10 repetitions of unilateral knee extension at 70% of one-repetition maximum. Blo..., , , # NMR Data Files

https://doi.org/10.5061/dryad.2547d7x00

Nuclear Magnetic Resonance (NMR) spectra were obtained from plasma samples from young and older individuals at rest and at timepoints (immediate post, 30 minutes, 90 minutes, and 180 minutes) following a single bout of exercise. Proton spectra were acquired using a Bruker 600 MHz Advance III HD spectrometer. Spectra were analyzed using the remote automated Bruker Data Analysis server, which annotates known metabolites based on chemical shifts. The individual PDF files represent de-identified NMR metabolomics reports for each sample that include quantitative data on each analyze as well as the original spectrum for each analyze, signal fitting, and reference ranges. The accompanying excel file provides information on age group, sex, and timepoint for each file.

Description of the data and file structure

The proton NMR spectra for each plasma sample were analyzed using the Bruker ...

Human metabolism is highly variable. At one end of the spectrum, defects of enzymes, transporters, and metabolic regulation result in metabolic diseases such as diabetes mellitus or inborn errors of metabolism. At the other end of the spectrum, favorable genetics and years of training combine to result in physiologically extreme forms of metabolism in athletes. Here, we investigated how the highly glycolytic metabolism of sprinters, highly oxidative metabolism of endurance athletes, and highly anabolic metabolism of natural bodybuilders affect their serum metabolome at rest and after a bout of exercise to exhaustion. We used targeted mass spectrometry-based metabolomics to measure the serum concentrations of 151 metabolites and 43 metabolite ratios or sums in 15 competitive male athletes (6 endurance athletes, 5 sprinters, and 4 natural bodybuilders) and 4 untrained control subjects at fasted rest and 5 minutes after a maximum graded bicycle test to exhaustion. The analysis of all 194 metabolite concentrations, ratios and sums revealed that natural bodybuilders and endurance athletes had overall different metabolite profiles, whereas sprinters and untrained controls were more similar. Specifically, natural bodybuilders had 1.5 to 1.8-fold higher concentrations of specific phosphatidylcholines and lower levels of branched chain amino acids than all other subjects. Endurance athletes had 1.4-fold higher levels of a metabolite ratio showing the activity of carnitine-palmitoyl-transferase I and 1.4-fold lower levels of various alkyl-acyl-phosphatidylcholines. When we compared the effect of exercise between groups, endurance athletes showed 1.3-fold higher increases of hexose and of tetradecenoylcarnitine (C14:1). In summary, physiologically extreme metabolic capacities of endurance athletes and natural bodybuilders are associated with unique blood metabolite concentrations, ratios, and sums at rest and after exercise. Our results suggest that long-term specific training, along with genetics and other athlete-specific factors systematically change metabolite concentrations at rest and after exercise.

An interactive, visual database containing more than 350 small molecule pathways found in humans. More than 2/3 of these pathways (>280) are not found in any other pathway database. SMPDB is designed specifically to support pathway elucidation and pathway discovery in metabolomics, transcriptomics, proteomics and systems biology. It is able to do so, in part, by providing exquisitely detailed, fully searchable, hyperlinked diagrams of human metabolic pathways, metabolic disease pathways, metabolite signaling pathways and drug-action pathways. All SMPDB pathways include information on the relevant organs, subcellular compartments, protein cofactors, protein locations, metabolite locations, chemical structures and protein quaternary structures. Each small molecule is hyperlinked to detailed descriptions contained in the HMDB or DrugBank and each protein or enzyme complex is hyperlinked to UniProt. All SMPDB pathways are accompanied with detailed descriptions and references, providing an overview of the pathway, condition or processes depicted in each diagram. The database is easily browsed and supports full text, sequence and chemical structure searching. Users may query SMPDB with lists of metabolite names, drug names, genes / protein names, SwissProt IDs, GenBank IDs, Affymetrix IDs or Agilent microarray IDs. These queries will produce lists of matching pathways and highlight the matching molecules on each of the pathway diagrams. Gene, metabolite and protein concentration data can also be visualized through SMPDB''s mapping interface. All of SMPDB''s images, image maps, descriptions and tables are downloadable.

Lung cancer is a leading cause of cancer deaths worldwide. Metabolic alterations in tumor cells coupled with systemic indicators of the host response to tumor development have the potential to yield blood profiles with clinical utility for diagnosis and monitoring of treatment. We report results from two separate studies using gas chromatography time-of-flight mass spectrometry (GC-TOF MS) to profile metabolites in human blood samples that significantly differ from non-small cell lung cancer (NSCLC) adenocarcinoma and other lung cancer cases. Metabolomic analysis of blood samples from the two studies yielded a total of 437 metabolites, of which 148 were identified as known compounds and 289 identified as unknown compounds. Differential analysis identified 15 known metabolites in one study and 18 in a second study that were statistically different (p-values <0.05). Levels of maltose, palmitic acid, glycerol, ethanolamine, glutamic acid, and lactic acid were increased in cancer samples while amino acids tryptophan, lysine and histidine decreased. Many of the metabolites were found to be significantly different in both studies, suggesting that metabolomics appears to be robust enough to find systemic changes from lung cancer, thus showing the potential of this type of analysis for lung cancer detection.

Bacteriocins are ribosomally synthesized antimicrobial peptides produced by bacteria, exhibiting diverse structures from post-translationally modified to linear forms. Their diversity and potency make them promising alternatives or adjuncts to antibiotics in combating antimicrobial resistance. Beyond mediating microbe-microbe interactions, bacteriocins may engage with the human host, underscoring the importance of exploring human microbiome-derived bacteriocins and their health implications. Although the traditional activity-based screening approach identifies diverse bacteriocins from the human microbiome, they remain largely untapped in the meta-omics era. Therefore, exploring human microbiome-derived bacteriocins can expand the antimicrobial arsenal and deepen insights into their roles in human health.The datasets provide the support data for "Big Data Omics Analysis Guided Discovery of Bacteriocins from the Human Microbiome". Two separate folders, Dataset (Chapter 2) and Dataset (Chapter 3) support the studies of Chapter 2 and Chapter 3, respectively