Ricardo R. da Silva

Illuminating the dark matter in metabolomics

Despite the over 100-y history of mass spectrometry, it remains challenging to link the large volume of known chemical structures to the data obtained with mass spectrometers. Presently, only 1.8% of spectra in an untargeted metabolomics experiment can be annotated. This means that the vast majority of information collected by metabolomics is “dark matter,” chemical signatures that remain uncharacterized (Fig. 1). For a genomic comparison, 80% of predicted genes in the Escherichia coli genome are known. In a bacteriophage metagenome, a well-known frontier of biological dark matter, the amount of known genes is 1–30%, depending on the sample (1). Thus, one could argue that we know more about the genetics of uncultured phage than we do about the chemistry within our own bodies. Much of the chemical dark matter may include known structures, but they remain undiscovered because the reference spectra are not available in mass spectrometry databases. The only way to overcome this challenge is through the development of computational solutions. In PNAS, Duhrkop et al. describe the development of such a computational tool, called CSI (compound structure identification):FingerID (2). The tool is designed to aid in the annotation of chemistries that can be observed by mass spectrometry. CSI:FingerID uses fragmentation trees to connect tandem MS (MS/MS) data to chemical structures found in public chemistry databases. Tools such as this can allow metabolomics with mass spectrometry to become as commonly used and scientifically productive as sequencing technologies have in the field of genomics.

Prioritizing Natural Product Diversity in a Collection of 146 Bacterial Strains Based on Growth and Extraction Protocols

In order to expedite the rapid and efficient discovery and isolation of novel specialized metabolites, while minimizing the waste of resources on rediscovery of known compounds, it is crucial to develop efficient approaches for strain prioritization, rapid dereplication, and the assessment of favored cultivation and extraction conditions. Herein we interrogated bacterial strains by systematically evaluating cultivation and extraction parameters with LC-MS/MS analysis and subsequent dereplication through the Global Natural Product Social Molecular Networking (GNPS) platform. The developed method is fast, requiring minimal time and sample material, and is compatible with high-throughput extract analysis, thereby streamlining strain prioritization and evaluation of culturing parameters. With this approach, we analyzed 146 marine Salinispora and Streptomyces strains that were grown and extracted using multiple different protocols. In total, 603 samples were analyzed, generating approximately 1.8 million mass spectra. We constructed a comprehensive molecular network and identified 15 molecular families of diverse natural products and their analogues. The size and breadth of this network shows statistically supported trends in molecular diversity when comparing growth and extraction conditions. The network provides an extensive survey of the biosynthetic capacity of the strain collection and a method to compare strains based on the variety and novelty of their metabolites. This approach allows us to quickly identify patterns in metabolite production that can be linked to taxonomy, culture conditions, and extraction methods, as well as informing the most valuable growth and extraction conditions.

Three-Dimensional Microbiome and Metabolome Cartography of a Diseased Human Lung

Our understanding of the spatial variation in the chemical and microbial makeup of an entire human organ remains limited, in part due to the size and heterogeneity of human organs and the complexity of the associated metabolome and microbiome. To address this challenge, we developed a workflow to enable the cartography of metabolomic and microbiome data onto a three-dimensional (3D) organ reconstruction built off radiological images. This enabled the direct visualization of the microbial and chemical makeup of a human lung from a cystic fibrosis patient. We detected host-derived molecules, microbial metabolites, medications, and region-specific metabolism of medications and placed it in the context of microbial distributions in the lung. Our tool further created browsable maps of a 3D microbiome/metabolome reconstruction map on a radiological image of a human lung and forms an interactive resource for the scientific community.

Lifestyle chemistries from phones for individual profiling

Significance This paper introduces the concept of skin-associated lifestyle chemistries found on personal belongings as a form of trace evidence. We propose a mass spectrometry-based approach to illuminate chemical traces recovered from personal objects. Using a chemical composite recovered from a swab of a phone, as a representative personal belonging, we can provide insights into personal lifestyle profile by predicting the kind of beauty product the individual uses, the food he/she eats, the medications he/she takes, or the places he/she has been. Therefore, the chemical interpretation of traces recovered from objects found on a crime scene can help a criminal investigator to learn about the lifestyle of the individual who used or touched these objects. Imagine a scenario where personal belongings such as pens, keys, phones, or handbags are found at an investigative site. It is often valuable to the investigative team that is trying to trace back the belongings to an individual to understand their personal habits, even when DNA evidence is also available. Here, we develop an approach to translate chemistries recovered from personal objects such as phones into a lifestyle sketch of the owner, using mass spectrometry and informatics approaches. Our results show that phones’ chemistries reflect a personalized lifestyle profile. The collective repertoire of molecules found on these objects provides a sketch of the lifestyle of an individual by highlighting the type of hygiene/beauty products the person uses, diet, medical status, and even the location where this person may have been. These findings introduce an additional form of trace evidence from skin-associated lifestyle chemicals found on personal belongings. Such information could help a criminal investigator narrowing down the owner of an object found at a crime scene, such as a suspect or missing person.

Coupling Targeted and Untargeted Mass Spectrometry for Metabolome-Microbiome-Wide Association Studies of Human Fecal Samples

Increasing appreciation of the gut microbiomes role in health motivates understanding the molecular composition of human feces. To analyze such complex samples, we developed a platform coupling targeted and untargeted metabolomics. The approach is facilitated through split flow from one UPLC, joint timing triggered by contact closure relays, and a script to retrieve the data. It is designed to detect specific metabolites of interest with high sensitivity, allows for correction of targeted information, enables better quantitation thus providing an advanced analytical tool for exploratory studies. Procrustes analysis revealed that untargeted approach provides a better correlation to microbiome data, associating specific metabolites with microbes that produce or process them. With the subset of over one hundred human fecal samples from the American Gut project, the implementation of the described coupled workflow revealed that targeted analysis using combination of single transition per compound with retention time misidentifies 30% of the targeted data and could lead to incorrect interpretations. At the same time, the targeted analysis extends detection limits and dynamic range, depending on the compounds, by orders of magnitude. A software application has been developed as a part of the workflow to allows for quantitative assessments based on calibration curves. Using this approach, we detect expected microbially modified molecules such as secondary bile acids and unexpected microbial molecules including Pseudomonas-associated quinolones and rhamnolipids in feces, setting the stage for metabolome-microbiome-wide association studies (MMWAS).

Propagating annotations of molecular networks using in silico fragmentation

The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.

Did a plant-herbivore arms race drive chemical diversity in Euphorbia?

The genus Euphorbia is among the most diverse and species-rich plant genera on Earth, exhibiting a near-cosmopolitan distribution and extraordinary chemical diversity, especially across highly toxic macro-and polycyclic diterpenoids. However, very little is known about drivers and evolutionary origins of chemical diversity within Euphorbia. Here, we investigate 43 Euphorbia species to understand how geographic separation over evolutionary time has impacted chemical differentiation. We show that the structurally highly diverse Euphorbia diterpenoids are significantly reduced in species native to the Americas, compared to the Eurasian and African continents, where the genus originated. The localization of these compounds to young stems and roots suggest ecological relevance in herbivory defense and immunomodulatory defense mechanisms match diterpenoid levels, indicating chemoevolutionary adaptation to reduced herbivory pressure. One Sentence Summary Global chemo-evolutionary adaptation of Euphorbia affected immunomodulatory defense mechanisms.

Untargeted Mass Spectrometry-Based Metabolomics Tracks Molecular Changes in Raw and Processed Foods and Beverages

A major aspect of our daily lives is the need to acquire, store and prepare our food. Storage and preparation can have drastic effects on the compositional chemistry of our foods, but we have a limited understanding of the temporal nature of processes such as storage, spoilage, fermentation and brewing on the chemistry of the foods we eat. Here, we performed a temporal analysis of the chemical changes in foods during common household preparations using untargeted mass spectrometry and novel data analysis approaches. Common treatments of foods such as home fermentation of yogurt, brewing of tea, spoilage of meats and ripening of tomatoes altered the chemical makeup through time, through both chemical and biological processes. For example, brewing tea altered its composition by increasing the diversity of molecules, but this change was halted after 4 min of brewing. The results indicate that this is largely due to differential extraction of the material from the tea and not modification of the molecules during the brewing process. This is in contrast to the preparation of yogurt from milk, spoilage of meat and the ripening of tomatoes where biological transformations directly altered the foods molecular composition. Comprehensive assessment of chemical changes using multivariate statistics showed the varied impacts of the different food treatments, while analysis of individual chemical changes show specific alterations of chemical families in the different food types. The methods developed here represent novel approaches to studying the changes in food chemistry that can reveal global alterations in chemical profiles and specific transformations at the chemical level. Highlights We created a reference data set for tomato, milk to yogurt, tea, coffee, turkey and beef. We show that normal preparation and handling affects the molecular make-up. Tea preparation is largely driven by differential extraction. Formation of yogurt involves chemical transformations. The majority of meat molecules are not altered in 5 days at room temperature.

Computational Removal of Undesired Mass Spectral Features Possessing Repeat Units via a Kendrick Mass Filter

AbstractPolymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS1 spectra. The KMF is analogous to mass defect filtering but utilizes homology information via Kendrick mass scaling in combination with chromatographic retention time and the number of observed signals. The KMF is intended to assist in situations in which current data processing methods to remove background, e.g., blank subtraction, are either not possible or effective. The major parameters affecting KMF were investigated using PEG 400 and NIST standard reference material 1950 (metabolites in human plasma). Further exploration of the KMF performance was tested using an extract of a swab known to contain polymers. An illustrative real-world example of skin analysis with polymeric signal is discussed. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers. Graphical Abstractᅟ

CHAPTER 3:Metabolomics

The rise of “omics sciences”, with high-throughput measurements of cellular macromolecules DNA, RNA and proteins, has opened up avenues to the measurement of cellular small organic molecules, which is the foundation of metabolomics. The metabolome is defined as the complete set of small organic molecules produced by a given cell in a given time and space. Metabolomics is therefore defined as the set of analytical techniques used to measure a large subset of the metabolome. In this chapter we focus on the mass spectrometry (MS) platforms applied to metabolomics, under the assumption that no single analytical platform is capable of measuring all of the metabolome. The main MS-based metabolomics approaches are contextualized to molecular classes and metabolic partition targeted in experiment, and a guide for experimental design is explored. Experimental design includes the most recent analytical and computational resources that point towards the possible factors that influence the analysis and, consequently, the results. We seek to enable metabolomics practitioners to correctly design experiments, based on specific biological questions, and to keep in mind which workflow is best suited to the study goal for the metabolites being sampled. In addition, we discuss several issues surrounding the analytical platform and the main MS parameters for acquiring metabolomics data, as well as the application of quality control, and finally the statistical analysis from data. The main goal of metabolomics is the understanding of phenotypical changes through unbiased data analysis interpretation. To achieve this goal, an integrated approach from experimental design to data processing is required.