pymzML v2.0: introducing a highly compressed and seekable gzip format

Python workflow for the selection and identification of marker peptides-proof-of-principle study with heated milk

The analysis of almost holistic food profiles has developed considerably over the last years. This has also led to larger amounts of data and the ability to obtain more information about health-beneficial and adverse constituents in food than ever before. Especially in the field of proteomics, software is used for evaluation, and these do not provide specific approaches for unique monitoring questions. An additional and more comprehensive way of evaluation can be done with the programming language Python. It offers broad possibilities by a large ecosystem for mass spectrometric data analysis, but needs to be tailored for specific sets of features, the research questions behind. It also offers the applicability of various machine-learning approaches. The aim of the present study was to develop an algorithm for selecting and identifying potential marker peptides from mass spectrometric data. The workflow is divided into three steps: (I) feature engineering, (II) chemometric data analysis, and (III) feature identification. The first step is the transformation of the mass spectrometric data into a structure, which enables the application of existing data analysis packages in Python. The second step is the data analysis for selecting single features. These features are further processed in the third step, which is the feature identification. The data used exemplarily in this proof-of-principle approach was from a study on the influence of a heat treatment on the milk proteome/peptidome.

A highly conserved tRNA modification contributes to C. albicans filamentation and virulence

tRNA modifications play important roles in maintaining translation accuracy in all domains of life. Disruptions in the tRNA modification machinery, especially of the anticodon stem loop, can be lethal for many bacteria and lead to a broad range of phenotypes in baker's yeast. Very little is known about the function of tRNA modifications in host-pathogen interactions, where rapidly changing environments and stresses require fast adaptations. We found that two closely related fungal pathogens of humans, the highly pathogenic Candida albicans and its much less pathogenic sister species, Candida dubliniensis, differ in the function of a tRNA-modifying enzyme. This enzyme, Hma1, exhibits species-specific effects on the ability of the two fungi to grow in the hypha morphology, which is central to their virulence potential. We show that Hma1 has tRNA-threonylcarbamoyladenosine dehydratase activity, and its deletion alters ribosome occupancy, especially at 37°C-the body temperature of the human host. A C. albicans HMA1 deletion mutant also shows defects in adhesion to and invasion into human epithelial cells and shows reduced virulence in a fungal infection model. This links tRNA modifications to host-induced filamentation and virulence of one of the most important fungal pathogens of humans.IMPORTANCEFungal infections are on the rise worldwide, and their global burden on human life and health is frequently underestimated. Among them, the human commensal and opportunistic pathogen, Candida albicans, is one of the major causative agents of severe infections. Its virulence is closely linked to its ability to change morphologies from yeasts to hyphae. Here, this ability is linked-to our knowledge for the first time-to modifications of tRNA and translational efficiency. One tRNA-modifying enzyme, Hma1, plays a specific role in C. albicans and its ability to invade the host. This adds a so-far unknown layer of regulation to the fungal virulence program and offers new potential therapeutic targets to fight fungal infections.

Oktoberfest: Open-source spectral library generation and rescoring pipeline based on Prosit

Machine learning (ML) and deep learning (DL) models for peptide property prediction such as Prosit have enabled the creation of high quality in silico reference libraries. These libraries are used in various applications, ranging from data-independent acquisition (DIA) data analysis to data-driven rescoring of search engine results. Here, we present Oktoberfest, an open source Python package of our spectral library generation and rescoring pipeline originally only available online via ProteomicsDB. Oktoberfest is largely search engine agnostic and provides access to online peptide property predictions, promoting the adoption of state-of-the-art ML/DL models in proteomics analysis pipelines. We demonstrate its ability to reproduce and even improve our results from previously published rescoring analyses on two distinct use cases. Oktoberfest is freely available on GitHub (https://github.com/wilhelm-lab/oktoberfest) and can easily be installed locally through the cross-platform PyPI Python package.

Identification of structural and regulatory cell-shape determinants in Haloferax volcanii

Archaea play indispensable roles in global biogeochemical cycles, yet many crucial cellular processes, including cell-shape determination, are poorly understood. Haloferax volcanii, a model haloarchaeon, forms rods and disks, depending on growth conditions. Here, we used a combination of iterative proteomics, genetics, and live-cell imaging to identify mutants that only form rods or disks. We compared the proteomes of the mutants with wild-type cells across growth phases, thereby distinguishing between protein abundance changes specific to cell shape and those related to growth phases. The results identified a diverse set of proteins, including predicted transporters, transducers, signaling components, and transcriptional regulators, as important for cell-shape determination. Through phenotypic characterization of deletion strains, we established that rod-determining factor A (RdfA) and disk-determining factor A (DdfA) are required for the formation of rods and disks, respectively. We also identified structural proteins, including an actin homolog that plays a role in disk-shape morphogenesis, which we named volactin. Using live-cell imaging, we determined volactin's cellular localization and showed its dynamic polymerization and depolymerization. Our results provide insights into archaeal cell-shape determination, with possible implications for understanding the evolution of cell morphology regulation across domains.

Fast insights into chitosan-cleaving enzymes by simultaneous analysis of polymers and oligomers through size exclusion chromatography

The thorough characterization of chitosan-cleaving enzymes is crucial to unveil structure-function relationships of this promising class of biomolecules for both, enzymatic fingerprinting analyses and to use the enzymes as biotechnological tools to produce tailor-made chitosans for diverse applications. Analyzing polymeric substrates as well as oligomeric products has been established as an effective way to understand the actions of enzymes, but it currently requires separate, rather laborious methods to obtain the full picture. Here, we present ultra high performance size exclusion chromatography coupled to refractive index and mass spectrometry detection (UHPSEC-RI-MS) as a straightforward method for the semi-quantitative analysis of chitosan oligomers of up to ten monomers in length. Additionally, the method allows to determine the average molecular weight of the remaining polymers and its distribution. By sampling live from an ongoing enzymatic reaction, UHPSEC-RI-MS offers the unique opportunity to analyze polymers and oligomers simultaneously-i.e., to monitor the molecular weight reduction of the polymeric substrate over the course of the digestion, while at the same time analyzing the emerging oligomeric products in a semi-quantitative manner. In this way, a single simple analysis yields detailed insights into an enzyme's action on a given substrate.

Development and Application of a Comprehensive Nontargeted Screening Strategy for Aristolochic Acid Analogues

Aristolochic acid analogs (AAAs) are naturally occurring carcinogenic and toxic compounds that pose a safety threat to pharmaceuticals and the environment. It is challenging to screen AAAs due to their lack of characteristic mass spectral fragmentation and their presence of structural diversity. A comprehensive nontargeted screening strategy was proposed by taking into account diverse factors and incorporating various self-developed techniques, and a Python3-based toolkit called AAAs_finder was developed for its implementation. The main procedures consist of virtual structure and ultraviolet and visible (UV) spectra database creation, exact mass and UV spectra-based suspect data extraction, tandem mass spectra (MS/MS) anthropomorphic interpretation, and multicondition retention time (RT) prediction-based candidate structures ranking. To initially assess screening feasibility, eight hypothetical unknown samples were subjected to nontargeted screening using the AAAs_finder toolkit and two other advanced tools. The results showed that the former successfully identified all, while the latter two only managed to identify two and three, respectively, indicating that our strategy was more feasible. After that, the strategy was carefully evaluated for false positives and false negatives, instrument dependence, reproducibility, and sensitivity. After the evaluation, the strategy was successfully applied to the screening of AAAs in real samples, such as herbal medicine, spiked soil, and water. Overall, this study proposed a nontargeted screening strategy and toolkit independent of characteristic mass spectral fragmentation and able to overcome challenges posed by structural diversity for the AAAs screening, which is also valuable for other classes of compounds.

Engineering of a chitin deacetylase to generate tailor-made chitosan polymers

Chitin deacetylases (CDAs) emerge as a valuable tool to produce chitosans with a nonrandom distribution of N-acetylglucosamine (GlcNAc) and glucosamine (GlcN) units. We hypothesized before that CDAs tend to bind certain sequences within the substrate matching their subsite preferences for either GlcNAc or GlcN units. Thus, they deacetylate or N-acetylate their substrates at nonrandom positions. To understand the molecular basis of these preferences, we analyzed the binding site of a CDA from Pestalotiopsis sp. (PesCDA) using a detailed activity screening of a site-saturation mutagenesis library. In addition, molecular dynamics simulations were conducted to get an in-depth view of crucial interactions along the binding site. Besides elucidating the function of several amino acids, we were able to show that only 3 residues are responsible for the highly specific binding of PesCDA to oligomeric substrates. The preference to bind a GlcNAc unit at subsite -2 and -1 can mainly be attributed to N75 and H199, respectively. Whereas an exchange of N75 at subsite -2 eliminates enzyme activity, H199 can be substituted with tyrosine to increase the GlcN acceptance at subsite -1. This change in substrate preference not only increases enzyme activity on certain substrates and changes composition of oligomeric products but also significantly changes the pattern of acetylation (PA) when N-acetylating polyglucosamine. Consequently, we could clearly show how subsite preferences influence the PA of chitosans produced with CDAs.

Mobility-Modulated Sequential Dissociation Analysis Enables Structural Lipidomics in Mass Spectrometry Imaging

Spatial lipidomics based on mass spectrometry imaging (MSI) is a powerful tool for fundamental biology studies and biomarker discovery. But the structure-resolving capability of MSI is limited because of the lack of multiplexed tandem mass spectrometry (MS/MS) method, primarily due to the small sample amount available from each pixel and the poor ion usage in MS/MS analysis. Here, we report a mobility-modulated sequential dissociation (MMSD) strategy for multiplex MS/MS imaging of distinct lipids from biological tissues. With ion mobility-enabled data-independent acquisition and automated spectrum deconvolution, MS/MS spectra of a large number of lipid species from each tissue pixel are acquired, at no expense of imaging speed. MMSD imaging is highlighted by MS/MS imaging of 24 structurally distinct lipids in the mouse brain and the revealing of the correlation of a structurally distinct phosphatidylethanolamine isomer (PE 18 : 1_18 : 1) from a human hepatocellular carcinoma (HCC) tissue. Mapping of structurally distinct lipid isomers is now enabled and spatial lipidomics becomes feasible for MSI.

MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments

Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.

mzapy: An Open-Source Python Library Enabling Efficient Extraction and Processing of Ion Mobility Spectrometry-Mass Spectrometry Data in the MZA File Format

Analysis of ion mobility spectrometry (IMS) data has been challenging and limited the full utility of these measurements. Unlike liquid chromatography-mass spectrometry, where a plethora of tools with well-established algorithms exist, the incorporation of the additional IMS dimension requires upgrading existing computational pipelines and developing new algorithms to fully exploit the advantages of the technology. We have recently reported MZA, a new and simple mass spectrometry data structure based on the broadly supported HDF5 format and created to facilitate software development. While this format is inherently supportive of application development, the availability of core libraries in popular programming languages with standard mass spectrometry utilities will facilitate fast software development and broader adoption of the format. To this end, we present a Python package, mzapy, for efficient extraction and processing of mass spectrometry data in the MZA format, especially for complex data containing ion mobility spectrometry dimension. In addition to raw data extraction, mzapy contains supporting utilities enabling tasks including calibration, signal processing, peak finding, and generating plots. Being implemented in pure Python and having minimal and largely standardized dependencies makes mzapy uniquely suited to application development in the multiomics domain. The mzapy package is free and open-source, includes comprehensive documentation, and is structured to support future extension to meet the evolving needs of the MS community. The software source code is freely available at https://github.com/PNNL-m-q/mzapy.

Extreme genome diversity and cryptic speciation in a harmful algal-bloom-forming eukaryote

Harmful algal blooms of the toxic haptophyte Prymnesium parvum are a recurrent problem in many inland and estuarine waters around the world. Strains of P. parvum vary in the toxins they produce and in other physiological traits associated with harmful algal blooms, but the genetic basis for this variation is unknown. To investigate genome diversity in this morphospecies, we generated genome assemblies for 15 phylogenetically and geographically diverse strains of P. parvum, including Hi-C guided, near-chromosome-level assemblies for two strains. Comparative analysis revealed considerable DNA content variation between strains, ranging from 115 to 845 Mbp. Strains included haploids, diploids, and polyploids, but not all differences in DNA content were due to variation in genome copy number. Haploid genome size between strains of different chemotypes differed by as much as 243 Mbp. Syntenic and phylogenetic analyses indicate that UTEX 2797, a common laboratory strain from Texas, is a hybrid that retains two phylogenetically distinct haplotypes. Investigation of gene families variably present across the strains identified several functional categories associated with metabolic and genome size variation in P. parvum, including genes for the biosynthesis of toxic metabolites and proliferation of transposable elements. Together, our results indicate that P. parvum comprises multiple cryptic species. These genomes provide a robust phylogenetic and genomic framework for investigations into the eco-physiological consequences of the intra- and inter-specific genetic variation present in P. parvum and demonstrate the need for similar resources for other harmful algal-bloom-forming morphospecies.

Mass Spectrometry-Based High-Throughput Quantification of Bioproducts in Liquid Culture

To meet the ever-increasing need for high-throughput screening in metabolic engineering, information-rich, fast screening methods are needed. Mass spectrometry (MS) provides an efficient and general approach for metabolite screening and offers the capability of characterizing a broad range of analytes in a label-free manner, but often requires a range of sample clean-up and extraction steps. Liquid extraction surface analysis (LESA) coupled MS is an image-guided MS surface analysis approach that directly samples and introduces metabolites from a surface to MS. Here, we combined the advantages of LESA-MS and an acoustic liquid handler with stable isotope-labeled internal standards. This approach provides absolute quantitation of target chemicals from liquid culture-dried droplets and enables high-throughput quantitative screening for microbial metabolites. In this study, LESA-MS was successfully applied to quantify several different metabolites (itaconic acid, triacetic acid lactone, and palmitic acid) from different yeast strains in different mediums, demonstrating its versatility, accuracy, and efficiency across a range of microbial engineering applications.

3D printer platform and conductance feedback loop for automated imaging of uneven surfaces by liquid microjunction-surface sampling probe mass spectrometry

RATIONALE

Molecular imaging of samples using mass spectrometric techniques, such as matrix-assisted laser desorption ionization or desorption electrospray ionization, requires the sample surface to be even/flat and sliced into thin sections (c. 10 μm). Furthermore, sample preparation steps can alter the analyte composition of the sample. The liquid microjunction-surface sampling probe (LMJ-SSP) is a robust sampling interface that enables surface profiling with minimal sample preparation. In conjunction with a conductance feedback system, the LMJ-SSP can be used to automatically sample uneven specimens.

METHODS

A sampling stage was built with a modified 3D printer where the LMJ-SSP is attached to the printing head. This setup can scan across flat and even surfaces in a predefined pattern ("static sampling mode"). Uneven samples are automatically probed in "conductance sampling mode" where an electric potential is applied and measured at the probe. When the probe contacts the electrically grounded sample, the potential at the probe drops, which is used as a feedback signal to determine the optimal position of the probe for sampling each location.

RESULTS

The applicability of the probe/sensing system was demonstrated by first examining the strawberry tissue using the "static sampling mode." Second, porcine tissue samples were profiled using the "conductance sampling mode." With minimal sample preparation, an area of 11 × 15 mm was profiled in less than 2 h. From the obtained results, adipose areas could be distinguished from non-adipose parts. The versatility of the approach was further demonstrated by directly sampling the bacteria colonies on agar and resected human kidney (intratumoral hemorrhage) specimens with thicknesses ranging from 1 to 4 mm.

CONCLUSION

The LMJ-SSP in conjunction with a conductive feedback system is a powerful tool that allows for fast, reproducible, and automated assessment of uneven surfaces with minimal sample preparation. This setup could be used for perioperative assessment of tissue samples, food screening, and natural product discovery, among others.

Unified and Standardized Mass Spectrometry Data Processing in Python Using spectrum_utils

spectrum_utils is a Python package for mass spectrometry data processing and visualization. Since its introduction, spectrum_utils has grown into a fundamental software solution that powers various applications in proteomics and metabolomics, ranging from spectrum preprocessing prior to spectrum identification and machine learning applications to spectrum plotting from online data repositories and assisting data analysis tasks for dozens of other projects. Here, we present updates to spectrum_utils, which include new functionality to integrate mass spectrometry community data standards, enhanced mass spectral data processing, and unified mass spectral data visualization in Python. spectrum_utils is freely available as open source at https://github.com/bittremieux/spectrum_utils.

MZA: A Data Conversion Tool to Facilitate Software Development and Artificial Intelligence Research in Multidimensional Mass Spectrometry

Modern mass spectrometry-based workflows employing hybrid instrumentation and orthogonal separations collect multidimensional data, potentially allowing deeper understanding in omics studies through adoption of artificial intelligence methods. However, the large volume of these rich spectra challenges existing data storage and access technologies, therefore precluding informatics advancements. We present MZA (pronounced m-za), the mass-to-charge (m/z) generic data storage and access tool designed to facilitate software development and artificial intelligence research in multidimensional mass spectrometry measurements. Composed of a data conversion tool and a simple file structure based on the HDF5 format, MZA provides easy, cross-platform and cross-programming language access to raw MS-data, enabling fast development of new tools in data science programming languages such as Python and R. The software executable, example MS-data and example Python and R scripts are freely available at https://github.com/PNNL-m-q/mza.

Proteogenomics reveals sex-biased aging genes and coordinated splicing in cardiac aging

The risks of heart diseases are significantly modulated by age and sex, but how these factors influence baseline cardiac gene expression remains incompletely understood. Here, we used RNA sequencing and mass spectrometry to compare gene expression in female and male young adult (4 mo) and early aging (20 mo) mouse hearts, identifying thousands of age- and sex-dependent gene expression signatures. Sexually dimorphic cardiac genes are broadly distributed, functioning in mitochondrial metabolism, translation, and other processes. In parallel, we found over 800 genes with differential aging response between male and female, including genes in cAMP and PKA signaling. Analysis of the sex-adjusted aging cardiac transcriptome revealed a widespread remodeling of exon usage patterns that is largely independent from differential gene expression, concomitant with upstream changes in RNA-binding protein and splice factor transcripts. To evaluate the impact of the splicing events on cardiac proteoform composition, we applied an RNA-guided proteomics computational pipeline to analyze the mass spectrometry data and detected hundreds of putative splice variant proteins that have the potential to rewire the cardiac proteome. Taken together, the results here suggest that cardiac aging is associated with 1) widespread sex-biased aging genes and 2) a rewiring of RNA splicing programs, including sex- and age-dependent changes in exon usages and splice patterns that have the potential to influence cardiac protein structure and function. These changes contribute to the emerging evidence for considerable sexual dimorphism in the cardiac aging process that should be considered in the search for disease mechanisms.NEW & NOTEWORTHY Han et al. used proteogenomics to compare male and female mouse hearts at 4 and 20 mo. Sex-biased cardiac genes function in mitochondrial metabolism, translation, autophagy, and other processes. Hundreds of cardiac genes show sex-by-age interactions, that is, sex-biased aging genes. Cardiac aging is accompanied with a remodeling of exon usage in functionally coordinated genes, concomitant with differential expression of RNA-binding proteins and splice factors. These features represent an underinvestigated aspect of cardiac aging that may be relevant to the search for disease mechanisms.

Harmonizing Labeling and Analytical Strategies to Obtain Protein Turnover Rates in Intact Adult Animals

Changes in the abundance of individual proteins in the proteome can be elicited by modulation of protein synthesis (the rate of input of newly synthesized proteins into the protein pool) or degradation (the rate of removal of protein molecules from the pool). A full understanding of proteome changes therefore requires a definition of the roles of these two processes in proteostasis, collectively known as protein turnover. Because protein turnover occurs even in the absence of overt changes in pool abundance, turnover measurements necessitate monitoring the flux of stable isotope–labeled precursors through the protein pool such as labeled amino acids or metabolic precursors such as ammonium chloride or heavy water. In cells in culture, the ability to manipulate precursor pools by rapid medium changes is simple, but for more complex systems such as intact animals, the approach becomes more convoluted. Individual methods bring specific complications, and the suitability of different methods has not been comprehensively explored. In this study, we compare the turnover rates of proteins across four mouse tissues, obtained from the same inbred mouse strain maintained under identical husbandry conditions, measured using either [¹³C₆]lysine or [²H₂]O as the labeling precursor. We show that for long-lived proteins, the two approaches yield essentially identical measures of the first-order rate constant for degradation. For short-lived proteins, there is a need to compensate for the slower equilibration of lysine through the precursor pools. We evaluate different approaches to provide that compensation. We conclude that both labels are suitable, but careful determination of precursor enrichment kinetics in amino acid labeling is critical and has a considerable influence on the numerical values of the derived protein turnover rates.

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Controlled comparison of heavy water or amino acid labeling for protein turnover.

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Delays in amino acid precursor labeling mostly affect high turnover proteins

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Both methods produced similar turnover rates after adjustment of precursor kinetics.

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Recommendations for analytical workflows for protein turnover studies in animals.

A practical guide to interpreting and generating bottom-up proteomics data visualizations

Mass-spectrometry based bottom-up proteomics is the main method to analyze proteomes comprehensively and the rapid evolution of instrumentation and data analysis has made the technology widely available. Data visualization is an integral part of the analysis process and it is crucial for the communication of results. This is a major challenge due to the immense complexity of MS data. In this review, we provide an overview of commonly used visualizations, starting with raw data of traditional and novel MS technologies, then basic peptide and protein level analyses, and finally visualization of highly complex datasets and networks. We specifically provide guidance on how to critically interpret and discuss the multitude of different proteomics data visualizations. Furthermore, we highlight Python-based libraries and other open science tools that can be applied for independent and transparent generation of customized visualizations. To further encourage programmatic data visualization, we provide the Python code used to generate all data Figures in this review on GitHub (https://github.com/MannLabs/ProteomicsVisualization). This article is protected by copyright. All rights reserved.

Performing 2D-1D-2D Mass Spectrometry Imaging Using Strings

The mass spectrometry imaging (MSI) technique is widely used in several fields due to its ability to provide spatial information of samples. However, for existing MSI methods, the sample is typically placed on a two-dimensional (2D) platform and is scanned back and forth. As a result, the platform size limits the imaging size. This paper proposes a new MSI method that involves the initial imprinting of chemicals on a two-dimensional string plane area. The string plane was then unraveled to a one-dimensional (1D) string, and the chemicals imprinted on it were ionized using a lab-made ion source. Finally, a 2D MSI image was reconstructed through data processing (2D-1D-2D mass imaging). Compared with traditional MSI methods, the imaging size is no longer limited by the platform size, making it possible to perform the MSI of large samples. As proof of concept, this method was used to image an intact seedling of Broussonetia papyrifera. As a result, clear and overall MS images were obtained, demonstrating the ability of this method to analyze large samples.

Proteomic Sample Preparation and Data Analysis in Line with the Archaeal Proteome Project

Despite the ecological, evolutionary and economical significance of archaea, key aspects of their cell biology, metabolic pathways, and adaptations to a wide spectrum of environmental conditions, remain to be elucidated. Proteomics allows for the system-wide analysis of proteins, their changes in abundance between different conditions, as well as their post-translational modifications, providing detailed insights into the function of proteins and archaeal cell biology. In this chapter, we describe a sample preparation and mass spectrometric analysis workflow that has been designed for Haloferax volcanii but can be applied to a broad range of archaeal species. Furthermore, proteomics experiments provide a wealth of data that is invaluable to various disciplines. Therefore, we previously initiated the Archaeal Proteome Project (ArcPP), a community project that combines the analysis of multiple datasets with expert knowledge in various fields of archaeal research. The corresponding bioinformatic analysis, allowing for the integration of new proteomics data into the ArcPP, as well as the interactive exploration of ArcPP results is also presented here. In combination, these protocols facilitate an optimized, detailed and collaborative approach to archaeal proteomics.

gQuant, an Automated Tool for Quantitative Glycomic Data Analysis

MALDI-MS-based glycan isotope labeling methods have been effectively and widely used for quantitative glycomics. However, interpretation of the data produced by MALDI-MS is inaccurate and tedious because the bioinformatic tools are inadequate. In this work, we present gQuant, an automated tool for MALDI-MS-based glycan isotope labeling data processing. gQuant was designed with a set of dedicated algorithms to improve the efficiency, accuracy and convenience of quantitation data processing. When tested on the reference data set, gQuant showed a fast processing speed, as it was able to search the glycan data of model glycoproteins in a few minutes and reported more results than the manual analysis did. The reported quantitation ratios matched well with the experimental glycan mixture ratios ranging from 1:10 to 10:1. In addition, gQuant is fully open-source and is coded in Python, which is supported by most operating systems, and it has a user-friendly interface. gQuant can be easily adapted by users for specific experimental designs, such as specific glycan databases, different derivatization types and relative quantitation designs and can thus facilitate fast glycomic quantitation for clinical sample analysis using MALDI-MS-based stable isotope labeling.

A metabolomics pipeline for the mechanistic interrogation of the gut microbiome

Gut microorganisms modulate host phenotypes and are associated with numerous health effects in humans, ranging from host responses to cancer immunotherapy to metabolic disease and obesity. However, difficulty in accurate and high-throughput functional analysis of human gut microorganisms has hindered efforts to define mechanistic connections between individual microbial strains and host phenotypes. One key way in which the gut microbiome influences host physiology is through the production of small molecules^1-3, yet progress in elucidating this chemical interplay has been hindered by limited tools calibrated to detect the products of anaerobic biochemistry in the gut. Here we construct a microbiome-focused, integrated mass-spectrometry pipeline to accelerate the identification of microbiota-dependent metabolites in diverse sample types. We report the metabolic profiles of 178 gut microorganism strains using our library of 833 metabolites. Using this metabolomics resource, we establish deviations in the relationships between phylogeny and metabolism, use machine learning to discover a previously undescribed type of metabolism in Bacteroides, and reveal candidate biochemical pathways using comparative genomics. Microbiota-dependent metabolites can be detected in diverse biological fluids from gnotobiotic and conventionally colonized mice and traced back to the corresponding metabolomic profiles of cultured bacteria. Collectively, our microbiome-focused metabolomics pipeline and interactive metabolomics profile explorer are a powerful tool for characterizing microorganisms and interactions between microorganisms and their host.

Comprehensive glycoproteomics shines new light on the complexity and extent of glycosylation in archaea

Glycosylation is one of the most complex posttranslational protein modifications. Its importance has been established not only for eukaryotes but also for a variety of prokaryotic cellular processes, such as biofilm formation, motility, and mating. However, comprehensive glycoproteomic analyses are largely missing in prokaryotes. Here, we extend the phenotypic characterization of N-glycosylation pathway mutants in Haloferax volcanii and provide a detailed glycoproteome for this model archaeon through the mass spectrometric analysis of intact glycopeptides. Using in-depth glycoproteomic datasets generated for the wild-type (WT) and mutant strains as well as a reanalysis of datasets within the Archaeal Proteome Project (ArcPP), we identify the largest archaeal glycoproteome described so far. We further show that different N-glycosylation pathways can modify the same glycosites under the same culture conditions. The extent and complexity of the Hfx. volcanii N-glycoproteome revealed here provide new insights into the roles of N-glycosylation in archaeal cell biology.

A comprehensive glycoproteomic analysis of Haloferax volcanii reveals the extent and complexity of glycosylation in archaea and provides new insights into the roles of this post-translational modification in various cellular processes, including cell shape determination.

ChemSpectra: a web-based spectra editor for analytical data

ChemSpectra, a web-based software to visualize and analyze spectroscopic data, integrating solutions for infrared spectroscopy (IR), mass spectrometry (MS), and one-dimensional ¹H and ¹³C NMR (proton and carbon nuclear magnetic resonance) spectroscopy, is described. ChemSpectra serves as web-based tool for the analysis of the most often used types of one-dimensional spectroscopic data in synthetic (organic) chemistry research. It was developed to support in particular processes for the use of open file formats which enable the work according to the FAIR data principles. The software can deal with the open file formats JCAMP-DX (IR, MS, NMR) and mzML (MS) proposing these data file types to gain interoperable data. ChemSpectra can be extended to read also other formats as exemplified by selected proprietary mass spectrometry data files of type RAW and NMR spectra files of type FID. The JavaScript-based editor can be integrated with other software, as demonstrated by integration into the Chemotion electronic lab notebook (ELN) and Chemotion repository, demonstrating the implementation into a digital work environment that offers additional functionality and sustainable research data management options. ChemSpectra supports different functions for working with spectroscopic data such as zoom functions, peak picking and automatic peak detection according to a default or manually defined threshold. NMR specific functions include the definition of a reference signal, the integration of signals, coupling constant calculation and multiplicity assignment. Embedded into a web application such as an ELN or a repository, the editor can also be used to generate an association of spectra to a sample and a file management. The file management supports the storage of the original spectra along with the last edited version and an automatically generated image of the spectra in png format. To maximize the benefit of the spectra editor for e.g. ELN users, an automated procedure for the transfer of the detected or manually chosen signals to the ELN was implemented. ChemSpectra is released under the AGPL license to encourage its re-use and further developments by the community.

Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal

The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.

Free Open Source Software for Protein and Peptide Mass Spectrometry- based Science

In the field of biology, and specifically in protein and peptide science, the power of mass spectrometry is that it is applicable to a vast spectrum of applications. Mass spectrometry can be applied to identify proteins and peptides in complex mixtures, to identify and locate post-translational modifications, to characterize the structure of proteins and peptides to the most detailed level or to detect protein-ligand non-covalent interactions. Thanks to the Free and Open Source Software (FOSS) movement, scientists have limitless opportunities to deepen their skills in software development to code software that solves mass spectrometric data analysis problems. After the conversion of raw data files into open standard format files, the entire spectrum of data analysis tasks can now be performed integrally on FOSS platforms, like GNU/Linux, and only with FOSS solutions. This review presents a brief history of mass spectrometry open file formats and goes on with the description of FOSS projects that are commonly used in protein and peptide mass spectrometry fields of endeavor: identification projects that involve mostly automated pipelines, like proteomics and peptidomics, and bio-structural characterization projects that most often involve manual scrutiny of the mass data. Projects of the last kind usually involve software that allows the user to delve into the mass data in an interactive graphics-oriented manner. Software projects are thus categorized on the basis of these criteria: software libraries for software developers vs desktop-based graphical user interface, software for the end-user and automated pipeline-based data processing vs interactive graphics-based mass data scrutiny.

SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides

MOTIVATION

Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes.

RESULTS

Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae.

AVAILABILITY

The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteomic signatures of acute oxidative stress response to paraquat in the mouse heart

The heart is sensitive to oxidative damage but a global view on how the cardiac proteome responds to oxidative stressors has yet to fully emerge. Using quantitative tandem mass spectrometry, we assessed the effects of acute exposure of the oxidative stress inducer paraquat on protein expression in mouse hearts. We observed widespread protein expression changes in the paraquat-exposed heart especially in organelle-containing subcellular fractions. During cardiac response to acute oxidative stress, proteome changes are consistent with a rapid reduction of mitochondrial metabolism, coupled with activation of multiple antioxidant proteins, reduction of protein synthesis and remediation of proteostasis. In addition to differential expression, we saw evidence of spatial reorganizations of the cardiac proteome including the translocation of hexokinase 2 to more soluble fractions. Treatment with the antioxidants Tempol and MitoTEMPO reversed many proteomic signatures of paraquat but this reversal was incomplete. We also identified a number of proteins with unknown function in the heart to be triggered by paraquat, suggesting they may have functions in oxidative stress response. Surprisingly, protein expression changes in the heart correlate poorly with those in the lung, consistent with differential sensitivity or stress response in these two organs. The results and data set here could provide insights into oxidative stress responses in the heart and avail the search for new therapeutic targets.

BMDMS-NP: A comprehensive ESI-MS/MS spectral library of natural compounds

The Bioinformatics & Molecular Design Research Center Mass Spectral Library - Natural Products (BMDMS-NP) is a library containing the mass spectra of natural compounds, especially plant specialized metabolites. At present, the library contains the electrospray ionization tandem mass spectrometry (ESI-MS/MS) spectra of 2739 plant metabolites that are commercially available. The contents of the library were made comprehensive by incorporating data generated under various experimental conditions for compounds with diverse molecular structures. The structural diversity of the BMDMS-NP data was evaluated using molecular fingerprints, and it was sufficiently exhaustive enough to represent the structures of the natural products commercially available. The MS/MS spectra of each metabolite were obtained with different types/brands of ion traps (tandem-in-time) or combinations of mass analyzers (tandem-in-space) at multiple collision energies. All spectra were measured repeatedly in each environment because variations can occur in spectra, even under the same conditions. Moreover, the probability, separability of searching, and transferability of this spectral library were evaluated against those of MS/MS libraries, namely: NIST17 and MoNA.

A mass spectrometry-based high-throughput screening method for engineering fatty acid synthases with improved production of medium-chain fatty acids

Microbial cell factories have been extensively engineered to produce free fatty acids (FFAs) as key components of crucial nutrients, soaps, industrial chemicals, and fuels. However, our ability to control the composition of microbially synthesized FFAs is still limited, particularly, for producing medium-chain fatty acids (MCFAs). This is mainly due to the lack of high-throughput approaches for FFA analysis to engineer enzymes with desirable product specificity. Here we report a mass spectrometry (MS)-based method for rapid profiling of MCFAs in Saccharomyces cerevisiae by using membrane lipids as a proxy. In particular, matrix-assisted laser desorption/ionization time-of-flight (MALDI-ToF) MS was used to detect shorter acyl chain phosphatidylcholines from membrane lipids and a higher m/z peak ratio at 730 and 758 was used as an indication for improved MCFA production. This colony-based method can be performed at a rate of ~2 s per sample, representing a substantial improvement over gas chromatography-MS (typically >30 min per sample) as the gold standard method for FFA detection. To demonstrate the power of this method, we performed site-saturation mutagenesis of the yeast fatty acid synthase and identified nine missense mutations that resulted in improved MCFA production relative to the wild-type strain. Colony-based MALDI-ToF MS screening provides an effective approach for engineering microbial fatty acid compositions in a high-throughput manner.

spectrum_utils: A Python Package for Mass Spectrometry Data Processing and Visualization

Given the wide diversity in applications of biological mass spectrometry, custom data analyses are often needed to fully interpret the results of an experiment. Such bioinformatics scripts necessarily include similar basic functionality to read mass spectral data from standard file formats, process it, and visualize it. Rather than having to reimplement this functionality, to facilitate this task, spectrum_utils is a Python package for mass spectrometry data processing and visualization. Its high-level functionality enables developers to quickly prototype ideas for computational mass spectrometry projects in only a few lines of code. Notably, the data processing functionality is highly optimized for computational efficiency to be able to deal with the large volumes of data that are generated during mass spectrometry experiments. The visualization functionality makes it possible to easily produce publication-quality figures as well as interactive spectrum plots for inclusion on web pages. spectrum_utils is available for Python 3.6+, includes extensive online documentation and examples, and can be easily installed using conda. It is freely available as open source under the Apache 2.0 license at https://github.com/bittremieux/spectrum_utils .

Proof of concept for identifying cystic fibrosis from perspiration samples

The gold standard for cystic fibrosis (CF) diagnosis is the determination of chloride concentration in sweat. Current testing methodology takes up to 3 h to complete and has recognized shortcomings on its diagnostic accuracy. We present an alternative method for the identification of CF by combining desorption electrospray ionization mass spectrometry and a machine-learning algorithm based on gradient boosted decision trees to analyze perspiration samples. This process takes as little as 2 min, and we determined its accuracy to be 98 ± 2% by cross-validation on analyzing 277 perspiration samples. With the introduction of statistical bootstrap, our method can provide a confidence estimate of our prediction, which helps diagnosis decision-making. We also identified important peaks by the feature selection algorithm and assigned the chemical structure of the metabolites by high-resolution and/or tandem mass spectrometry. We inspected the correlation between mild and severe CFTR gene mutation types and lipid profiles, suggesting a possible way to realize personalized medicine with this noninvasive, fast, and accurate method.

In Silico Optimization of Mass Spectrometry Fragmentation Strategies in Metabolomics

Liquid chromatography (LC) coupled to tandem mass spectrometry (MS/MS) is widely used in identifying small molecules in untargeted metabolomics. Various strategies exist to acquire MS/MS fragmentation spectra; however, the development of new acquisition strategies is hampered by the lack of simulators that let researchers prototype, compare, and optimize strategies before validations on real machines. We introduce Virtual Metabolomics Mass Spectrometer (ViMMS), a metabolomics LC-MS/MS simulator framework that allows for scan-level control of the MS2 acquisition process in silico. ViMMS can generate new LC-MS/MS data based on empirical data or virtually re-run a previous LC-MS/MS analysis using pre-existing data to allow the testing of different fragmentation strategies. To demonstrate its utility, we show how ViMMS can be used to optimize N for Top-N data-dependent acquisition (DDA) acquisition, giving results comparable to modifying N on the mass spectrometer. We expect that ViMMS will save method development time by allowing for offline evaluation of novel fragmentation strategies and optimization of the fragmentation strategy for a particular experiment.

glypy: An Open Source Glycoinformatics Library

Glycoinformatics is a critical resource for the study of glycobiology, and glycobiology is a necessary component for understanding the complex interface between intra- and extracellular spaces. Despite this, there is limited software available to scientists studying these topics, requiring each to create fundamental data structures and representations anew for each of their applications. This leads to poor uptake of standardization and loss of focus on the real problems. We present glypy, a library written in Python for reading, writing, manipulating, and transforming glycans at several levels of precision. In addition to understanding several common formats for textual representation of glycans, the library also provides application programming interfaces (APIs) for major community databases, including GlyTouCan and UnicarbKB. The library is freely available under the Apache 2 common license with source code available at https://github.com/mobiusklein/ and documentation at https://glypy.readthedocs.io/ .

psims - A Declarative Writer for mzML and mzIdentML for Python

mzML and mzIdentML are commonly used, powerful tools for representing mass spectrometry data and derived identification information. These formats are complex, requiring non-trivial logic to translate data into the appropriate representation. Most published implementations are tightly coupled to data structures. The most complete implementations are written in compiled languages that cannot expose the complete flexibility of the implementation to external programs or bindings. To our knowledge, there are no complete implementations for mzML or mzIdentML available to scripting languages like Python or R. We present psims, a library written in Python for writing mzML and mzIdentML. The library allows writing either XML format using built-in Python data structures. It includes a controlled vocabulary resolution system to simplify the encoding process and an identity tracking system to manage entity relationships. The source code is available at https://github.com/mobiusklein/psims, and through the Python Package Index as psims, licensed under the Apache 2 common license.

Pyteomics 4.0: Five Years of Development of a Python Proteomics Framework

Many of the novel ideas that drive today's proteomic technologies are focused essentially on experimental or data-processing workflows. The latter are implemented and published in a number of ways, from custom scripts and programs, to projects built using general-purpose or specialized workflow engines; a large part of routine data processing is performed manually or with custom scripts that remain unpublished. Facilitating the development of reproducible data-processing workflows becomes essential for increasing the efficiency of proteomic research. To assist in overcoming the bioinformatics challenges in the daily practice of proteomic laboratories, 5 years ago we developed and announced Pyteomics, a freely available open-source library providing Python interfaces to proteomic data. We summarize the new functionality of Pyteomics developed during the time since its introduction.

Nano LC-MS using capillary columns enables accurate quantification of modified ribonucleosides at low femtomol levels

Post-transcriptional chemical modifications of (t)RNA molecules are crucial in fundamental biological processes, such as translation. Despite their biological importance and accumulating evidence linking them to various human diseases, technical challenges have limited their detection and accurate quantification. Here, we present a sensitive capillary nanoflow liquid chromatography mass spectrometry (nLC-MS) pipeline for quantitative high-resolution analysis of ribonucleoside modifications from complex biological samples. We evaluated two porous graphitic carbon (PGC) materials and one end-capped C18 reference material as stationary phases for reversed-phase separation. We found that these matrices have complementing retention and separation characteristics, including the capability to separate structural isomers. PGC and C18 matrices yielded excellent signal-to-noise ratios in nLC-MS while differing in the separation capability and sensitivity for various nucleosides. This emphasizes the need for tailored LC-MS setups for optimally detecting as many nucleoside modifications as possible. Detection ranges spanning up to six orders of magnitude enable the analysis of individual ribonucleosides down to femtomol concentrations. Furthermore, normalizing the obtained signal intensities to a stable isotope labeled spike-in enabled direct comparison of ribonucleoside levels between different samples. In conclusion, capillary columns coupled to nLC-MS constitute a powerful and sensitive tool for quantitative analysis of modified ribonucleosides in complex biological samples. This setup will be invaluable for further unraveling the intriguing and multifaceted biological roles of RNA modifications.