Mingxun Wang

Molecular cartography of the human skin surface in 3D

Significance The paper describes the implementation of an approach to study the chemical makeup of human skin surface and correlate it to the microbes that live in the skin. We provide the translation of molecular information in high-spatial resolution 3D to understand the body distribution of skin molecules and bacteria. In addition, we use integrative analysis to interpret, at a molecular level, the large scale of data obtained from human skin samples. Correlations between molecules and microbes can be obtained to further gain insights into the chemical milieu in which these different microbial communities live. The human skin is an organ with a surface area of 1.5–2 m2 that provides our interface with the environment. The molecular composition of this organ is derived from host cells, microbiota, and external molecules. The chemical makeup of the skin surface is largely undefined. Here we advance the technologies needed to explore the topographical distribution of skin molecules, using 3D mapping of mass spectrometry data and microbial 16S rRNA amplicon sequences. Our 3D maps reveal that the molecular composition of skin has diverse distributions and that the composition is defined not only by skin cells and microbes but also by our daily routines, including the application of hygiene products. The technological development of these maps lays a foundation for studying the spatial relationships of human skin with hygiene, the microbiota, and environment, with potential for developing predictive models of skin phenotypes tailored to individual health.

The ProteomeXchange consortium in 2017: supporting the cultural change in proteomics public data deposition

The ProteomeXchange (PX) Consortium of proteomics resources (http://www.proteomexchange.org) was formally started in 2011 to standardize data submission and dissemination of mass spectrometry proteomics data worldwide. We give an overview of the current consortium activities and describe the advances of the past few years. Augmenting the PX founding members (PRIDE and PeptideAtlas, including the PASSEL resource), two new members have joined the consortium: MassIVE and jPOST. ProteomeCentral remains as the common data access portal, providing the ability to search for data sets in all participating PX resources, now with enhanced data visualization components. We describe the updated submission guidelines, now expanded to include four members instead of two. As demonstrated by data submission statistics, PX is supporting a change in culture of the proteomics field: public data sharing is now an accepted standard, supported by requirements for journal submissions resulting in public data release becoming the norm. More than 4500 data sets have been submitted to the various PX resources since 2012. Human is the most represented species with approximately half of the data sets, followed by some of the main model organisms and a growing list of more than 900 diverse species. Data reprocessing activities are becoming more prominent, with both MassIVE and PeptideAtlas releasing the results of reprocessed data sets. Finally, we outline the upcoming advances for ProteomeXchange.

Automated Genome Mining of Ribosomal Peptide Natural Products

Ribosomally synthesized and posttranslationally modified peptides (RiPPs), especially from microbial sources, are a large group of bioactive natural products that are a promising source of new (bio)chemistry and bioactivity.1 In light of exponentially increasing microbial genome databases and improved mass spectrometry (MS)-based metabolomic platforms, there is a need for computational tools that connect natural product genotypes predicted from microbial genome sequences with their corresponding chemotypes from metabolomic data sets. Here, we introduce RiPPquest, a tandem mass spectrometry database search tool for identification of microbial RiPPs, and apply it to lanthipeptide discovery. RiPPquest uses genomics to limit search space to the vicinity of RiPP biosynthetic genes and proteomics to analyze extensive peptide modifications and compute p-values of peptide-spectrum matches (PSMs). We highlight RiPPquest by connecting multiple RiPPs from extracts of Streptomyces to their gene clusters and by the discovery of a new class III lanthipeptide, informatipeptin, from Streptomyces viridochromogenes DSM 40736 to reflect that it is a natural product that was discovered by mass spectrometry based genome mining using algorithmic tools rather than manual inspection of mass spectrometry data and genetic information. The presented tool is available at cyclo.ucsd.edu.

Discovering and linking public omics data sets using the Omics Discovery Index.

Yasset Perez-Riverola,†,*, Mingze Baia,b,c,†, Felipe da Veiga Leprevostd, Silvano Squizzatoa, Young Mi Parka, Kenneth Hauga, Adam J. Carrolle, Dylan Spaldinga, Justin Paschalla, Mingxun Wangf, Noemi del-Toroa, Tobias Ternenta, Peng Zhangd,g, Nicola Busoa, Nuno Bandeiraf, Eric W. Deutschh, David S Campbellh, Ronald C. Beavisi, Reza M. Saleka, Ugis Sarkansa, Robert Petryszaka, Maria Keaysa, Eoin Fahyj, Manish Sudj, Shankar Subramaniamj, Ariana Barberak, Rafael C. Jiménezl, Alexey I. Nesvizhskiid, SusannaAssunta Sansonem, Christoph Steinbecka, Rodrigo Lopeza, Juan Antonio Vizcaínoa, Peipei Pingn, and Henning Hermjakoba,c,* aEuropean Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Trust Genome Campus, Hinxton, Cambridge, CB10 1SD, UK

SPLASH, a hashed identifier for mass spectra

Wohlgemuth, G., Mehta, S. S., Mejia, R. F., Neumann, S., Pedrosa, D., Pluskal, T., Schymanski, E. L., Willighagen, E. L., Wilson, M., Wishart, D. S., Arita, M., Dorrestein, P. C., Bandeira, N., Wang, M., Schulze, T., Salek, R. M., Steinbeck, C., Nainala, V. C., Mistrik, R., ... Fiehn, O. (2016). SPLASH, a hashed identifier for mass spectra. Nature Biotechnology, 34(11), 1099-1101. https://doi.org/10.1038/nbt.3689

Significance estimation for large scale metabolomics annotations by spectral matching

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate (FDR) for 70 public metabolomics data sets. We show that the spectral matching settings need to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92 up to +5705%) when compared with a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to assess the scoring criteria for large scale analysis of mass spectrometry based metabolomics data that has been essential in the advancement of proteomics, transcriptomics, and genomics science.Matching fragment spectra to reference library spectra is an important procedure for annotating small molecules in untargeted mass spectrometry based metabolomics studies. Here, the authors develop strategies to estimate false discovery rates (FDR) by empirical Bayes and target-decoy based methods which enable a user to define the scoring criteria for spectral matching.

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.

SweetNET: A Bioinformatics Workflow for Glycopeptide MS/MS Spectral Analysis.

Glycoproteomics has rapidly become an independent analytical platform bridging the fields of glycomics and proteomics to address site-specific protein glycosylation and its impact in biology. Current glycopeptide characterization relies on time-consuming manual interpretations and demands high levels of personal expertise. Efficient data interpretation constitutes one of the major challenges to be overcome before true high-throughput glycopeptide analysis can be achieved. The development of new glyco-related bioinformatics tools is thus of crucial importance to fulfill this goal. Here we present SweetNET: a data-oriented bioinformatics workflow for efficient analysis of hundreds of thousands of glycopeptide MS/MS-spectra. We have analyzed MS data sets from two separate glycopeptide enrichment protocols targeting sialylated glycopeptides and chondroitin sulfate linkage region glycopeptides, respectively. Molecular networking was performed to organize the glycopeptide MS/MS data based on spectral similarities. The combination of spectral clustering, oxonium ion intensity profiles, and precursor ion m/z shift distributions provided typical signatures for the initial assignment of different N-, O- and CS-glycopeptide classes and their respective glycoforms. These signatures were further used to guide database searches leading to the identification and validation of a large number of glycopeptide variants including novel deoxyhexose (fucose) modifications in the linkage region of chondroitin sulfate proteoglycans.

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.

Significance estimation for large scale untargeted metabolomics annotations

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate. Relying on estimations of false discovery rates, we explore the effect of different spectrum-spectrum match criteria on the number and the nature of the molecules annotated. We show that the spectral matching settings needs to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from −92% up to +5705%) when compared to a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to define the scoring criteria for large scale analysis of untargeted small molecule data that has been essential in the advancement of large scale proteomics, transcriptomics, and genomics science.