Using untargeted metabolomics for detecting exposome compounds

Untargeted metabolomics offers insights into the risks of chronic exposure to mixtures of polycyclic aromatic hydrocarbons at environmentally relevant low concentrations

Polycyclic aromatic hydrocarbons (PAHs) often occur in mixtures, creating complex interactions in humans and other organisms exposed through food. However, the effects of these PAH mixtures at environmentally relevant low concentrations (ERC) on the metabolome have been underexplored. This research investigated the ERC of PAHs in Vembanad estuary biota and examined the impact of chronic exposure to these mixtures using an untargeted metabolomics approach. The study observed that 64% of the aquatic samples analysed from India's Ramsar site (VE) had been detected with one or more PAHs (ΣPAHs5.12-1015.28 ng/g). The non-carcinogenic risk from dietary PAH exposure was low, but cancer risk analysis showed a moderate to high risk for specific areas, particularly Perumbalam. Furthermore, the untargeted metabolomics study revealed that chronic exposure to a PAH mixture at ERC dysregulated metabolites from major classes, including phosphatidylcholines, amino acids, fatty acyls, bile acids, nucleotides, purines, pyrimidines, and vitamins. These metabolites are predominantly associated with key metabolic pathways, including mitochondrial electron transport, pyrimidine metabolism, the citric acid cycle, and butyrate metabolism, all of which play critical roles in cellular energy production, biosynthesis, and regulation. Pathway analysis revealed that long-term exposure to PAH mixtures, even at low doses, significantly affects phenylalanine, tyrosine, and tryptophan metabolism, increasing the likelihood of metabolic and endocrine disorders.

Analysis of thyroid function and structure and serum metabolomics in pregnant rats exposed to airborne contaminants: Combined perchlorate, thiocyanate, and nitrate exposure

OBJECTIVES

Perchlorates, nitrates, and thiocyanates constitute environmental endocrine disruptors; however, health damage caused by absorption through the respiratory tract remains poorly studied. We investigated the effects of inhalation of these pollutants on thyroid function and structure and serum metabolomics in pregnant rats.

METHODS

We established a Sprague-Dawley pregnant rat model exposed to perchlorate, nitrate, and thiocyanate at different gestational stages and compared maternal serum thyroid function levels, foetal development, thyroid morphology, and pathological changes between exposed and non-exposed groups at different concentrations. We used liquid chromatography-tandem mass spectrometry metabolomics methods to investigate corresponding changes in serum metabolites and metabolic biomarkers and identify metabolism-related pathways.

RESULTS

Free triiodothyronine levels in the medium-and high-concentration groups and free thyroxine levels in the low-, medium-, and high-concentration groups were significantly lower than those in the control group. Weight gain was significantly lower in the high-dose group than in the control group. Thyroid weight and organ coefficients were significantly higher in the medium-and high-dose groups at 15 and 19 days of pregnancy than in the control group. Thyroid epithelial cells demonstrated diffuse proliferation and lamination with increased atomisation concentrations, and thyroid cells tended to undergo apoptosis in the group with high thyroid epithelial cell concentration. Eight and 18 metabolic pathways were significantly affected in the GD15 and GD19 groups（GD15: The demise occurring 15 days into gestation,GD19: The demise occurring 15 days into gestation.）, respectively, post-exposure. The involved pathways included lipid, glucose, bile acid, choline, and gamma-aminobutyric acid metabolism and peroxisome proliferation-activated receptors.

CONCLUSIONS

Mixed thiocyanate, nitrate, and perchlorate exposure can result in subchronic toxicity in rats, affecting thyroid function and structure.

Human metabolome variation along the upper intestinal tract

Most processing of the human diet occurs in the small intestine. Metabolites in the small intestine originate from host secretions, plus the ingested exposome1 and microbial transformations. Here we probe the spatiotemporal variation of upper intestinal luminal contents during routine daily digestion in 15 healthy male and female participants. For this, we use a non-invasive, ingestible sampling device to collect and analyse 274 intestinal samples and 60 corresponding stool homogenates by combining five mass spectrometry assays2,3 and 16S rRNA sequencing. We identify 1,909 metabolites, including sulfonolipids and fatty acid esters of hydroxy fatty acids (FAHFA) lipids. We observe that stool and intestinal metabolomes differ dramatically. Food metabolites display trends in dietary biomarkers, unexpected increases in dicarboxylic acids along the intestinal tract and a positive association between luminal keto acids and fruit intake. Diet-derived and microbially linked metabolites account for the largest inter-individual differences. Notably, two individuals who had taken antibiotics within 6 months before sampling show large variation in levels of bioactive FAHFAs and sulfonolipids and other microbially related metabolites. From inter-individual variation, we identify Blautia species as a candidate to be involved in FAHFA metabolism. In conclusion, non-invasive, in vivo sampling of the human small intestine and ascending colon under physiological conditions reveals links between diet, host and microbial metabolism.

Optimization of Mobile Phase Modifiers for Fast LC-MS-Based Untargeted Metabolomics and Lipidomics

Liquid chromatography-mass spectrometry (LC-MS) is the method of choice for the untargeted profiling of biological samples. A multiplatform LC-MS-based approach is needed to screen polar metabolites and lipids comprehensively. Different mobile phase modifiers were tested to improve the electrospray ionization process during metabolomic and lipidomic profiling. For polar metabolites, hydrophilic interaction LC using a mobile phase with 10 mM ammonium formate/0.125% formic acid provided the best performance for amino acids, biogenic amines, sugars, nucleotides, acylcarnitines, and sugar phosphate, while reversed-phase LC (RPLC) with 0.1% formic acid outperformed for organic acids. For lipids, RPLC using a mobile phase with 10 mM ammonium formate or 10 mM ammonium formate with 0.1% formic acid permitted the high signal intensity of various lipid classes ionized in ESI(+) and robust retention times. For ESI(-), the mobile phase with 10 mM ammonium acetate with 0.1% acetic acid represented a reasonable compromise regarding the signal intensity of the detected lipids and the stability of retention times compared to 10 mM ammonium acetate alone or 0.02% acetic acid. Collectively, we show that untargeted methods should be evaluated not only on the total number of features but also based on common metabolites detected by a specific platform along with the long-term stability of retention times.

Integrated Exposomics/Metabolomics for Rapid Exposure and Effect Analyses

The totality of environmental exposures and lifestyle factors, commonly referred to as the exposome, is poorly understood. Measuring the myriad of chemicals that humans are exposed to is immensely challenging, and identifying disrupted metabolic pathways is even more complex. Here, we present a novel technological approach for the comprehensive, rapid, and integrated analysis of the endogenous human metabolome and the chemical exposome. By combining reverse-phase and hydrophilic interaction liquid chromatography (HILIC) and fast polarity-switching, molecules with highly diverse chemical structures can be analyzed in 15 min with a single analytical run as both column's effluents are combined before analysis. Standard reference materials and authentic standards were evaluated to critically benchmark performance. Highly sensitive median limits of detection (LODs) with 0.04 μM for >140 quantitatively assessed endogenous metabolites and 0.08 ng/mL for the >100 model xenobiotics and human estrogens in solvent were obtained. In matrix, the median LOD values were higher with 0.7 ng/mL (urine) and 0.5 ng/mL (plasma) for exogenous chemicals. To prove the dual-column approach's applicability, real-life urine samples from sub-Saharan Africa (high-exposure scenario) and Europe (low-exposure scenario) were assessed in a targeted and nontargeted manner. Our liquid chromatography high-resolution mass spectrometry (LC-HRMS) approach demonstrates the feasibility of quantitatively and simultaneously assessing the endogenous metabolome and the chemical exposome for the high-throughput measurement of environmental drivers of diseases.

Effects of Compound Probiotics on Cecal Microbiome and Metabolome of Shaoxing Duck

This experiment was conducted to investigate the effects of compound probiotics on intestinal microflora and metabolome of Shaoxing ducks. A total of 640 1-day-old Shaoxing ducks were randomly divided into two treatments with eight replicates and forty ducks for each replicate. The ducks were fed basal diet (Ctrl) and basal diet supplemented with 0.15% compound probiotics (MixP). The experiment lasted for 85 days. The results showed that the abundance of Bacteroidetes and Bacteroides in MixP was higher than that in Ctrl (P < 0.05). However, the abundance of Firmicutes and Oscillospira and Desulfovibrio in MixP was lower than that in Ctrl (P < 0.05). Concentrations of 71 metabolites differed significantly (P < 0.05) between the MixP and the Ctrl groups; for example, Pyridoxal (Vitamin B6), L-Arginine, and Betaine aldehyde were up-regulated (P < 0.05), and 7-oxocholesterol, 3-hydroxy-L-kynureni-ne, and N-acetyl-d-glucosamine were down-regulated (P < 0.05). KEGG was enriched in 15 metabolic pathways. The pathways of Vitamin B6 metabolism, Vascular smooth muscle contraction, Vitamin digestion and absorption, and Protein digestion and absorption were influenced by compound probiotics supplementation. Thus, supplementation of compound probiotics improved cecal heath through shifts in the cecal microbiome and metabolome.

A review of environmental metabolism disrupting chemicals and effect biomarkers associating disease risks: Where exposomics meets metabolomics

Humans are exposed to an ever-increasing number of environmental toxicants, some of which have gradually been elucidated to be important risk factors for metabolic diseases, such as diabetes and obesity. These metabolism-sensitive diseases typically occur when key metabolic and signaling pathways were disrupted, which can be influenced by the exposure to contaminants such as endocrine disrupting chemicals (EDCs), along with genetic and lifestyle factors. This promotes the concept and research on environmental metabolism disrupting chemicals (MDCs). In addition, identifying endogenous biochemical markers of effect linked to disease states is becoming an important tool to screen the biological targets following environmental contaminant exposure, as well as to provide an overview of toxicity risk assessment. As such, the current review aims to contribute to the further understanding of exposome and human health and disease by characterizing environmental exposure and effect metabolic biomarkers. We summarized MDC-associated metabolic biomarkers in laboratory animal and human cohort studies using high throughput targeted and nontargeted metabolomics techniques. Contaminants including heavy metals and organohalogen compounds, especially EDCs, have been repetitively associated with metabolic disorders, whereas emerging contaminants such as perfluoroalkyl substances and microplastics have also been found to disrupt metabolism. In addition, we found major limitations in the effective identification of metabolic biomarkers especially in human studies, toxicological research on the mixed effect of environmental exposure has also been insufficient compared to the research on single chemicals. Thus, it is timely to call for research efforts dedicated to the study of combined effect and metabolic alterations for the better assessment of exposomic toxicology and health risks. Moreover, advanced computational and prediction tools, further validation of metabolic biomarkers, as well as systematic and integrative investigations are also needed in order to reliably identify novel biomarkers and elucidate toxicity mechanisms, and to further utilize exposome and metabolome profiling in public health and safety management.

Mass Spectrometry as a Crucial Analytical Basis for Omics Sciences

This review is devoted to the consideration of mass spectrometric platforms as applied to omics sciences. The most significant attention is paid to omics related to life sciences (genomics, proteomics, meta-bolomics, lipidomics, glycomics, plantomics, etc.). Mass spectrometric approaches to solving the problems of petroleomics, polymeromics, foodomics, humeomics, and exosomics, related to inorganic sciences, are also discussed. The review comparatively presents the advantages of various principles of separation and mass spectral techniques, complementary derivatization, used to obtain large arrays of various structural and quantitative information in the mentioned omics sciences.

Using the US EPA CompTox Chemicals Dashboard to interpret targeted and non-targeted GC-MS analyses from human breath and other biological media

The U.S. EPA CompTox Chemicals Dashboard is a freely available web-based application providing access to chemistry, toxicity, and exposure data for ∼900 000 chemicals. Data, search functionality, and prediction models within the Dashboard can help identify chemicals found in environmental analyses and human biomonitoring. It was designed to deliver data generated to support computational toxicology to reduce chemical testing on animals and provide access to new approach methodologies including prediction models. The inclusion of mass and formula-based searches, together with relevant ranking approaches, allows for the identification and prioritization of exogenous (environmental) chemicals from high resolution mass spectrometry in need of further evaluation. The Dashboard includes chemicals that can be detected by liquid chromatography, gas chromatography-mass spectrometry (GC-MS) and direct-MS analyses, and chemical lists have been added that highlight breath-borne volatile and semi-volatile organic compounds. The Dashboard can be searched using various chemical identifiers (e.g. chemical synonyms, CASRN and InChIKeys), chemical formula, MS-ready formulae monoisotopic mass, consumer product categories and assays/genes associated with high-throughput screening data. An integrated search at a chemical level performs searches against PubMed to identify relevant published literature. This article describes specific procedures using the Dashboard as a first-stop tool for exploring both targeted and non-targeted results from GC-MS analyses of chemicals found in breath, exhaled breath condensate, and associated aerosols.

Applying metabolomics to veterinary pharmacology and therapeutics

Metabolomics is the large-scale study of low-molecular-weight substances in a biological system in a given physiological state at a given time point. Metabolomics can be applied to identify predictors of inter-individual variability in drug response, provide clinicians with data useful for decision-making processes in drug selection, and inform about the pharmacokinetics and pharmacodynamics of a drug. It is, therefore, an exceptional approach for gaining new understanding effects in the field of comparative veterinary pharmacology. However, the incorporation of metabolomics into veterinary pharmacology and toxicology is not yet widespread, and this is probably, at least in part, a result of its highly multidisciplinary nature. This article reviews the potential applications of metabolomics in veterinary pharmacology and therapeutics. It integrates key concepts for designing metabolomics studies and analyzing and interpreting metabolomics data, providing solid foundations for applying metabolomics to the study of drugs in all veterinary species.

Characterization of the Human Exposome by a Comprehensive and Quantitative Large-Scale Multianalyte Metabolomics Platform

The exposome, defined as the cumulative measure of external exposures and associated biological responses throughout the lifespan, has emerged in recent years as a cornerstone in biomedical sciences. Metabolomics stands out here as one of the most powerful tools for investigating the interplay between the genetic background, exogenous, and endogenous factors within human health. However, to address the complexity of the exposome, novel methods are needed to characterize the human metabolome. In this work, we have optimized and validated a multianalyte metabolomics platform for large-scale quantitative exposome research in plasma and urine samples, based on the use of simple extraction methods and high-throughput metabolomic fingerprinting. The methodology enables, for the first time, the simultaneous characterization of the endogenous metabolome, food-related metabolites, pharmaceuticals, household chemicals, environmental pollutants, and microbiota derivatives, comprising more than 1000 metabolites in total. This comprehensive and quantitative investigation of the exposome is achieved in short run times, through simple extraction methods requiring small-sample volumes, and using integrated quality control procedures for ensuring data quality. This metabolomics approach was satisfactorily validated in terms of linearity, recovery, matrix effects, specificity, limits of quantification, intraday and interday precision, and carryover. Furthermore, the clinical potential of the methodology was demonstrated in a dietary intervention trial as a case study. In summary, this study describes the optimization, validation, and application of a multimetabolite platform for comprehensive and quantitative metabolomics-based exposome research with great utility in large-scale epidemiological studies.

Recent advances in the application of metabolomics for food safety control and food quality analyses

As one of the omics fields, metabolomics has unique advantages in facilitating the understanding of physiological and pathological activities in biology, physiology, pathology, and food science. In this review, based on developments in analytical chemistry tools, cheminformatics, and bioinformatics methods, we highlight the current applications of metabolomics in food safety, food authenticity and quality, and food traceability. Additionally, the combined use of metabolomics with other omics techniques for "foodomics" is comprehensively described. Finally, the latest developments and advances, practical challenges and limitations, and requirements related to the application of metabolomics are critically discussed, providing new insight into the application of metabolomics in food analysis.

LC-MS-Based Metabolomic Approach Revealed the Significantly Different Metabolic Profiles of Five Commercial Truffle Species

Truffles are ascomycetous ectomycorrhizal fungi that have elevated status in the culinary field due to their unique aroma and taste as well as their nutritional value and potential biological activities. Tuber melanosporum, T. indicum, T. panzhihuanense, T. sinoaestivum, and T. pseudoexcavatum are five commercial truffle species mainly distributed in Europe or China. In this study, an untargeted metabolomics technology based on an ultra-high-performance liquid chromatography-tandem mass spectrometry (UHPLC-MS/MS) method was applied to analyze the metabolic profiles and variations among these five truffle species. In our results, a total of 2376 metabolites were identified under positive ion mode, of which 1282 had significantly differential amounts and covered 110 pathways or metabolisms. Principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) revealed a clear separation from each of these five truffles, indicating a significantly different metabolic profile among them, with the biggest difference between T. melanosporum and the other four truffles. The differential metabolites covered various chemical categories, and a detailed analysis was performed for nine metabolic categories, including amino acids, saccharides and nucleosides, organic acids, alkaloids, flavonoids, carnitines, phenols and alcohols, esters, and sulfur compounds. For each of the nine categories, most of metabolites predominantly accumulated in T. melanosporum compared with the other four truffles. Meanwhile, there were significant differences of the average ion intensity in each category among the five truffles, e.g., higher amounts of amino acids was detected in T. panzhihuanense and T. pseudoexcavatum; T. indicum contained significantly more carnitines, while there were more alkaloids in T. melanosporum. Additionally, some metabolites with biological activities were discussed for each category, such as acetyl-L-carnitine, adenine, neobavaisoflavone, and anandamide. Generally, this study may provide the valuable information regarding the variation of the metabolic composition of these five commercial truffle species, and the biological significance of these metabolites was uncovered to explore the metabolic mechanisms of truffles, which would be helpful for further research on the compounds and potential biological functions in truffles that have not yet been investigated.

Evaluation of In Silico Multifeature Libraries for Providing Evidence for the Presence of Small Molecules in Synthetic Blinded Samples

The current gold standard for unambiguous molecular identification in metabolomics analysis is comparing two or more orthogonal properties from the analysis of authentic reference materials (standards) to experimental data acquired in the same laboratory with the same analytical methods. This represents a significant limitation for comprehensive chemical identification of small molecules in complex samples. The process is time consuming and costly, and the majority of molecules are not yet represented by standards. Thus, there is a need to assemble evidence for the presence of small molecules in complex samples through the use of libraries containing calculated chemical properties. To address this need, we developed a Multi-Attribute Matching Engine (MAME) and a library derived in part from our in silico chemical library engine (ISiCLE). Here, we describe an initial evaluation of these methods in a blinded analysis of synthetic chemical mixtures as part of the U.S. Environmental Protection Agency's (EPA) Non-Targeted Analysis Collaborative Trial (ENTACT, Phase 1). For molecules in all mixtures, the initial blinded false negative rate (FNR), false discovery rate (FDR), and accuracy were 57%, 77%, and 91%, respectively. For high evidence scores, the FDR was 35%. After unblinding of the sample compositions, we optimized the scoring parameters to better exploit the available evidence and increased the accuracy for molecules suspected as present. The final FNR, FDR, and accuracy were 67%, 53%, and 96%, respectively. For high evidence scores, the FDR was 10%. This study demonstrates that multiattribute matching methods in conjunction with in silico libraries may one day enable reduced reliance on experimentally derived libraries for building evidence for the presence of molecules in complex samples.

In vivo solid-phase microextraction sampling combined with metabolomics and toxicological studies for the non-lethal monitoring of the exposome in fish tissue

Various environmental studies have employed the biomonitoring of fish in their aquatic ecosystems in order to identify potential metabolic responses to the exposome. In this study, we applied in vivo solid-phase microextraction (SPME) to perform non-lethal sampling on the muscle tissue of living fish to extract toxicants and various endogenous metabolites. Sixty white suckers (Catastomus commersonii) were sampled from sites upstream, adjacent, and downstream from the oil sands development region of the Athabasca River (Alberta, Canada) in order to track their biochemical responses to potential contaminants. In vivo SPME sampling facilitated the extraction of a wide range of endogenous metabolites, mainly related to lipid metabolism. The obtained results revealed significant changes in the levels of numerous metabolites, including eicosanoids, linoleic acids, and fat-soluble vitamins, in fish sampled in different areas of the river, thus demonstrating SPME's applicability for the direct monitoring of exposure to different environmental toxicants. In addition, several classes of toxins, including petroleum-related compounds, that can cause serious physiological impairment were tentatively identified in the extracts. In vivo SPME, combined with the analysis of contaminants and endogenous metabolites, provided important information about the exposome; as such, this approach represents a potentially powerful and non-lethal tool for identifying the mechanisms that produce altered metabolic pathways in response to the mixtures of different environmental pollutants.

Individualized metabolomics: opportunities and challenges

The goal of advancing science in health care is to provide high quality treatment and therapeutic opportunities to patients in need. This is especially true in precision medicine, wherein the ultimate goal is to link disease phenotypes to targeted treatments and novel therapeutics at the scale of an individual. With the advent of -omics technologies, such as genomics, proteomics, microbiome, among others, the metabolome is of wider and immediate interest for its important role in metabolic regulation. The metabolome, of course, comes with its own questions regarding technological challenges. In this opinion article, I attempt to interrogate some of the main challenges associated with individualized metabolomics, and available opportunities in the context of its clinical application. Some questions this article addresses and attempts to find answers for are: Can a personal metabolome (n = 1) be inexpensive, affordable and informative enough (i.e. provide predictive yet validated biomarkers) to represent the entirety of a population? How can a personal metabolome complement advances in other -omics areas and the use of monitoring devices, which occupy our personal space?

Structure Annotation of All Mass Spectra in Untargeted Metabolomics

Urine metabolites are used in many clinical and biomedical studies but usually only for a few classic compounds. Metabolomics detects vastly more metabolic signals that may be used to precisely define the health status of individuals. However, many compounds remain unidentified, hampering biochemical conclusions. Here, we annotate all metabolites detected by two untargeted metabolomic assays, hydrophilic interaction chromatography (HILIC)-Q Exactive HF mass spectrometry and charged surface hybrid (CSH)-Q Exactive HF mass spectrometry. Over 9,000 unique metabolite signals were detected, of which 42% triggered MS/MS fragmentations in data-dependent mode. On the highest Metabolomics Standards Initiative (MSI) confidence level 1, we identified 175 compounds using authentic standards with precursor mass, retention time, and MS/MS matching. An additional 578 compounds were annotated by precursor accurate mass and MS/MS matching alone, MSI level 2, including a novel library specifically geared at acylcarnitines (CarniBlast). The rest of the metabolome is usually left unannotated. To fill this gap, we used the in silico fragmentation tool CSI:FingerID and the new NIST hybrid search to annotate all further compounds (MSI level 3). Testing the top-ranked metabolites in CSI:Finger ID annotations yielded 40% accuracy when applied to the MSI level 1 identified compounds. We classified all MSI level 3 annotations by the NIST hybrid search using the ClassyFire ontology into 21 superclasses that were further distinguished into 184 chemical classes. ClassyFire annotations showed that the previously unannotated urine metabolome consists of 28% derivatives of organic acids, 16% heterocyclics, and 16% lipids as major classes.

Tools and resources for metabolomics research community: A 2017-2018 update

The scale at which MS- and NMR-based platforms generate metabolomics datasets for both research, core, and clinical facilities to address challenges in the various sciences-ranging from biomedical to agricultural-is underappreciated. Thus, metabolomics efforts spanning microbe, environment, plant, animal, and human systems have led to continual and concomitant growth of in silico resources for analysis and interpretation of these datasets. These software tools, resources, and databases drive the field forward to help keep pace with the amount of data being generated and the sophisticated and diverse analytical platforms that are being used to generate these metabolomics datasets. To address challenges in data preprocessing, metabolite annotation, statistical interrogation, visualization, interpretation, and integration, the metabolomics and informatics research community comes up with hundreds of tools every year. The purpose of the present review is to provide a brief and useful summary of more than 95 metabolomics tools, software, and databases that were either developed or significantly improved during 2017-2018. We hope to see this review help readers, developers, and researchers to obtain informed access to these thorough lists of resources for further improvisation, implementation, and application in due course of time.

Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics

The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.