Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

FREE: Enhanced Feature Representation for Isotopic Envelope Evaluation in Top-Down Mass Spectra Deconvolution

The aim of deconvolution of top-down mass spectra is to recognize monoisotopic peaks from the experimental envelopes in raw mass spectra. So accurate assessment of similarity between theoretical and experimental envelopes is a critical step in mass spectra data deconvolution. Existing evaluation methods primarily rely on intensity differences and m/z similarity, potentially lacking a comprehensive assessment. To overcome this constraint and facilitate a comprehensive and refined assessment of the similarity between theoretical and experimental envelopes, there exists an imperative to systematically explore and identify increasingly efficacious features for assessing this correspondence. We present enhanced feature representation for isotopic envelope evaluation (FREE) that derives diverse feature representations, encapsulating fundamental physical attributes of envelopes, including peak intensity and envelope shape. We trained FREE and evaluated its performance on both the ovarian tumor (OT) (human OT cells) data set and zebrafish (ZF) (brain in mature female ZF) data set. Specifically, comparing the state-of-art method, FREE demonstrates higher performance in multiple evaluation metrics across both the OT and ZF data sets, with a particular emphasis on precision, and it demonstrates accurate predictions of a greater number of positive envelopes among the top-ranked envelopes based on their scores. Moreover, within a cross-species data set of ZF, FREE identified a higher number of proteoform-spectrum matches (PrSMs), increasing the count from 50,795 to 52,927 compared to EnvCNN, the amalgamation of FREE with TopFD also exhibits a commendable capacity to discern 117,883 fragment ions, thus surpassing the 97,554 fragment ions identified through the application of EnvCNN in conjunction with TopFD. To further validate the performance of FREE, we have tested 10 a cross-species top-down proteomes containing 36 subdata set from ProteomeXchange. The results reveal that, after deconvolution with TopFD + FREE, TopPIC identifies more PrSMs across these 10 data sets in both the first and second rounds of experiments. These findings underscore the robustness and generalization capabilities of the FREE approach in diverse proteomes.

Molecular-Level Dysregulation of Insulin Pathways and Inflammatory Processes in Peripheral Blood Mononuclear Cells by Circadian Misalignment

Circadian misalignment due to night work has been associated with an elevated risk for chronic diseases. We investigated the effects of circadian misalignment using shotgun protein profiling of peripheral blood mononuclear cells taken from healthy humans during a constant routine protocol, which was conducted immediately after participants had been subjected to a 3-day simulated night shift schedule or a 3-day simulated day shift schedule. By comparing proteomic profiles between the simulated shift conditions, we identified proteins and pathways that are associated with the effects of circadian misalignment and observed that insulin regulation pathways and inflammation-related proteins displayed markedly different temporal patterns after simulated night shift. Further, by integrating the proteomic profiles with previously assessed metabolomic profiles in a network-based approach, we found key associations between circadian dysregulation of protein-level pathways and metabolites of interest in the context of chronic metabolic diseases. Endogenous circadian rhythms in circulating glucose and insulin differed between the simulated shift conditions. Overall, our results suggest that circadian misalignment is associated with a tug of war between central clock mechanisms controlling insulin secretion and peripheral clock mechanisms regulating insulin sensitivity, which may lead to adverse long-term outcomes such as diabetes and obesity. Our study provides a molecular-level mechanism linking circadian misalignment and adverse long-term health consequences of night work.

Non-targeted isomer-sensitive N-glycome analysis reveals new layers of organ-specific diversity in mice

N-glycosylation is one of the most common protein modifications in eukaryotes, with immense importance at the molecular, cellular, and organismal level. Accurate and reliable N-glycan analysis is essential to obtain a systems-wide understanding of fundamental biological processes. Due to the structural complexity of glycans, their analysis is still highly challenging. Here we make publicly available a consistent N-glycome dataset of 20 different mouse tissues and demonstrate a multimodal data analysis workflow that allows for unprecedented depth and coverage of N-glycome features. This highly scalable, LC-MS/MS data-driven method integrates the automated identification of N-glycan spectra, the application of non-targeted N-glycome profiling strategies and the isomer-sensitive analysis of glycan structures. Our delineation of critical sub-structural determinants and glycan isomers across the mouse N-glycome uncovered tissue-specific glycosylation patterns, the expression of non-canonical N-glycan structures and highlights multiple layers of N-glycome complexity that derive from organ-specific regulations of glycobiological pathways.

The hidden treasures in endophytic fungi: a comprehensive review on the diversity of fungal bioactive metabolites, usual analytical methodologies, and applications

This review provides a comprehensive overview of the key aspects of the natural metabolite production by endophytic fungi, which has attracted significant attention due to its diverse biological activities and wide range of applications. Synthesized by various fungal species, these metabolites encompass compounds with therapeutic, agricultural, and commercial significance. We delved into strategies and advancements aimed at optimizing fungal metabolite production. Fungal cultivation, especially by Aspergillus, Penicillium, and Fusarium, plays a pivotal role in metabolite biosynthesis, and researchers have explored both submerged and solid-state cultivation processes to harness the full potential of fungal species. Nutrient optimization, pH, and temperature control are critical factors in ensuring high yields of the targeted bioactive metabolites especially for scaling up processes. Analytical methods that includes High-Performance Liquid Chromatography (HPLC), Liquid Chromatography-Mass Spectrometry (LC-MS), Gas Chromatography-Mass Spectrometry (GC-MS), Nuclear Magnetic Resonance (NMR), and Mass Spectrometry (MS), are indispensable for the identification and quantification of the compounds. Moreover, genetic engineering and metabolic pathway manipulation have emerged as powerful tools to enhance metabolite production and develop novel fungal strains with increased yields. Regulation and control mechanisms at the genetic, epigenetic, and metabolic levels are explored to fine-tune the biosynthesis of fungal metabolites. Ongoing research aims to overcome the complexity of the steps involved to ensure the efficient production and utilization of fungal metabolites.

PepPre: Promote Peptide Identification Using Accurate and Comprehensive Precursors

Accurate and comprehensive peptide precursor ions are crucial to tandem mass-spectrometry-based peptide identification. An identification engine can derive great advantages from the search space reduction enabled by credible and detailed precursors. Furthermore, by considering multiple precursors per spectrum, both the number of identifications and the spectrum explainability can be substantially improved. Here, we introduce PepPre, which detects precursors by decomposing peaks into multiple isotope clusters using linear programming methods. The detected precursors are scored and ranked, and the high-scoring ones are used for subsequent peptide identification. PepPre is evaluated both on regular and cross-linked peptide data sets and compared with 11 methods. The experimental results show that PepPre achieves a remarkable increase of 203% in PSM and 68% in peptide identifications compared to instrument software for regular peptides and 99% in PSM and 27% in peptide pair identifications for cross-linked peptides, surpassing the performance of all other evaluated methods. In addition to the increased identification numbers, further credibility evaluations evidence the reliability of the identified results. Moreover, by widening the isolation window of data acquisition from 2 to 8 Th, with PepPre, an engine is able to identify at least 64% more PSMs, thereby demonstrating the potential advantages of wide-window data acquisition. PepPre is open-source and available at http://peppre.ctarn.io.

MsPHep: An online application for low molecular weight heparin rapid characterization based on liquid chromatography-tandem mass spectrometry

Low-molecular-weight heparins (LMWHs) are important anticoagulants widely used in clinic. Since they are comprised of complex and heterogenous glycan chains, liquid chromatography-tandem mass spectrometry (LC-MS) is commonly used for structural analysis and quality control of LMWHs to ensure their safety and efficacy. Yet, the structural complexity arising from the parent heparin macromolecules, as well as the different depolymerization methods used for preparing LMWHs, makes processing and assigning the LC-MS data of LWMHs very tedious and challenging. We therefore developed, and here report, an open-source and easy-to-use web application, MsPHep, to facilitate the LMWH analysis based on LC-MS data. MsPHep is compatible with various LMWHs and chromatographic separation methods. With the HepQual function, MsPHep is capable of annotating both the LMWH compound and its isotopic distribution from mass spectra. Moreover, the HepQuant function enables automatic quantification of LMWH compositions without prior knowledge or any database generation. To demonstrate the reliability and system stability of MsPHep, we tested various types of LMWHs that were analyzed with different chromatographic methods coupled to MS. The results show that MsPHep has its own advantages compared to another public tool GlycReSoft for LMWH analysis, and it is available online under an open-source license at https://ngrc-glycan.shinyapps.io/MsPHep.

Molecular investigation of the multi-phase photochemistry of Fe(III)-citrate in aqueous solution

Iron (Fe) is ubiquitous in nature and found as Fe^II or Fe^III in minerals or as dissolved ions Fe²⁺ or Fe³⁺ in aqueous systems. The interactions of soluble Fe have important implications for fresh water and marine biogeochemical cycles, which have impacts on global terrestrial and atmospheric environments. Upon dissolution of Fe^III into natural aquatic systems, organic carboxylic acids efficiently chelate Fe^III to form [Fe^III-carboxylate]²⁺ complexes that undergo a wide range of photochemistry-induced radical reactions. The chemical composition and photochemical transformations of these mixtures are largely unknown, making it challenging to estimate their environmental impact. To investigate the photochemical process of Fe^III-carboxylates at the molecular level, we conduct a comprehensive experimental study employing UV-visible spectroscopy, liquid chromatography coupled to photodiode array and high-resolution mass spectrometry detection, and oil immersion flow microscopy. In this study, aqueous solutions of Fe^III-citrate were photolyzed under 365 nm light in an experimental setup with an apparent quantum yield of (φ) ∼0.02, followed by chemical analyses of reacted mixtures withdrawn at increment time intervals of the experiment. The apparent photochemical reaction kinetics of Fe³⁺-citrates (aq) were expressed as two generalized consecutive reactions of with the experimental rate constants of j₁ ∼ 0.12 min^-1 and j₂ ∼ 0.05 min^-1, respectively. Molecular characterization results indicate that R and I consist of both water-soluble organic and Fe-organic species, while P compounds are a mixture of water-soluble and colloidal materials. The latter were identified as Fe-carbonaceous colloids formed at long photolysis times. The carbonaceous content of these colloids was identified as unsaturated organic species with low oxygen content and carbon with a reduced oxidation state, indicative of their plausible radical recombination mechanism under oxygen-deprived conditions typical for the extensively photolyzed mixtures. Based on the molecular characterization results, we discuss the comprehensive reaction mechanism of Fe^III-citrate photochemistry and report on the formation of previously unexplored colloidal reaction products, which may contribute to atmospheric and terrestrial light-absorbing materials in aquatic environments.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software

BACKGROUND

Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS).

AIM OF REVIEW

This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software.

KEY SCIENTIFIC CONCEPTS OF REVIEW

We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Artificial intelligence in microbial natural product drug discovery: current and emerging role

Covering: up to the end of 2022Microorganisms are exceptional sources of a wide array of unique natural products and play a significant role in drug discovery. During the golden era, several life-saving antibiotics and anticancer agents were isolated from microbes; moreover, they are still widely used. However, difficulties in the isolation methods and repeated discoveries of the same molecules have caused a setback in the past. Artificial intelligence (AI) has had a profound impact on various research fields, and its application allows the effective performance of data analyses and predictions. With the advances in omics, it is possible to obtain a wealth of information for the identification, isolation, and target prediction of secondary metabolites. In this review, we discuss drug discovery based on natural products from microorganisms with the help of AI and machine learning.

Fractionation of sulfated galactan from the red alga Botryocladia occidentalis separates its anticoagulant and anti-SARS-CoV-2 properties

Sulfation pattern and molecular weight (MW) play a key role in the biological actions of sulfated glycans. Besides anticoagulant effects, certain sulfated glycans can also exhibit anti-SARS-CoV-2 properties. To develop a more selective antiviral carbohydrate, an efficient strategy to separate these two actions is required. In this work, low MW fractions derived from the red alga Botryocladia occidentalis sulfated galactan (BoSG) were generated, structurally characterized, and tested for activity against SARS-CoV-2 and blood coagulation. The lowest MW fraction was found to be primarily composed of octasaccharides of monosulfated monosaccharides. Unlike heparin or native BoSG, we found that hydrolyzed BoSG products had weak anticoagulant activities as seen by aPTT and inhibitory assays using purified cofactors. In contrast, lower MW BoSG-derivatives retained anti-SARS-CoV-2 activity using SARS-CoV-2 spike (S)-protein pseudotyped lentivirus vector in HEK-293T-hACE2 cells monitored by GFP. Surface plasmon resonance confirmed that longer chains are necessary for BoSG to interact with coagulation cofactors but is not required for interactions with certain S-protein variants. We observed distinct affinities of BoSG derivatives for the S-proteins of different SARS-CoV-2 strains, including WT, N501Y (Alpha), K417T/E484K/N501Y (Gamma), and L542R (Delta) mutants, and stronger affinity for the N501Y-containing variants. Docking of the four possible monosulfated BoSG disaccharides in interactions with the N501Y mutant S-protein predicted potential binding poses of the BoSG constructs and favorable binding in close proximity to the 501Y residue. Our results demonstrate that depolymerization and fractionation of BoSG are an effective strategy to segregate its anticoagulant property from its anti-SARS-CoV-2 action.

Comparison of Different Labeling Techniques for the LC-MS Profiling of Human Milk Oligosaccharides

Human milk oligosaccharides (HMOs) exhibit various biological activities for infants, such as serving as prebiotics, blocking pathogens, and aiding in brain development. HMOs are a complex mixture of hetero-oligosaccharides that are generally highly branched, containing multiple structural isomers and no intrinsic chromophores, presenting a challenge to both their resolution and quantitative detection. While liquid chromatography-mass spectrometry (LC-MS) has become the primary strategy for analysis of various compounds, the very polar and chromophore-free properties of native glycans hinder their separation in LC and ionization in MS. Various labeling approaches have been developed to achieve separation of glycans with higher resolution and greater sensitivity of detection. Here, we compared five commonly used labeling techniques [by 2-aminobenzamide, 2-aminopyridine, 2-aminobenzoic acid (2-AA), 2,6-diaminopyridine, and 1-phenyl-3-methyl-5-pyrazolone] for analyzing HMOs specifically under hydrophilic-interaction chromatography-mass spectrometry (HILIC-MS) conditions. The 2-AA labeling showed the most consistent deprotonated molecular ions, the enhanced sensitivity with the least structural selectivity, and the sequencing-informative tandem MS fragmentation spectra for the widest range of HMOs; therefore, this labeling technique was selected for further optimization under the porous graphitized carbon chromatography-mass spectrometry (PGC-MS) conditions. The combination strategy of 2-AA labeling and PGC-MS techniques provided online decontamination (removal of excess 2-AA, salts, and lactose) and resolute detection of many HMOs, enabling us to characterize the profiles of complicated HMO mixtures comprehensively in a simple protocol.

(Phospho)Proteomic dataset of ischemia- and ultrasound- stimulated mouse cardiac endothelial cells in vitro

Cardiac endothelial cells respond to both ischemia and therapeutic ultrasound; the proteomic changes underlying these responses are unknown. This data article provides raw and processed data resulting from our global, unbiased phosphoproteomics investigation conducted on primary mouse cardiac endothelial cells exposed to ischemia (2-hour oxygen glucose deprivation) and ultrasound (250 kHz, 1.2 MPa) in vitro [1]. Proteins were extracted from cell lysates and enriched phosphopeptides were analyzed with a high mass accuracy liquid chromatrography (LC) - tandem mass spectrometry (MS/MS) proteomic platform, yielding multiple alterations in both total protein levels and phosphorylation events in response to ischemic injury and ultrasound. This dataset can be used as a reference for future studies on the cardiac endothelial response to ischemia and the mechanistic underpinnings of the cellular response to ultrasound, with the potential to yield clinically relevant therapeutic targets.

Deoxyhypusine synthase promotes a pro-inflammatory macrophage phenotype

The metabolic inflammation (meta-inflammation) of obesity is characterized by proinflammatory macrophage infiltration into adipose tissue. Catalysis by deoxyhypusine synthase (DHPS) modifies the translation factor eIF5A to generate a hypusine (Hyp) residue. Hypusinated eIF5A (eIF5A^Hyp) controls the translation of mRNAs involved in inflammation, but its role in meta-inflammation has not been elucidated. Levels of eIF5A^Hyp were found to be increased in adipose tissue macrophages from obese mice and in murine macrophages activated to a proinflammatory M1-like state. Global proteomics and transcriptomics revealed that DHPS deficiency in macrophages altered the abundance of proteins involved in NF-κB signaling, likely through translational control of their respective mRNAs. DHPS deficiency in myeloid cells of obese mice suppressed M1 macrophage accumulation in adipose tissue and improved glucose tolerance. These findings indicate that DHPS promotes the post-transcriptional regulation of a subset of mRNAs governing inflammation and chemotaxis in macrophages and contributes to a proinflammatory M1-like phenotype.

Phosphoproteomic response of cardiac endothelial cells to ischemia and ultrasound

Myocardial infarction and subsequent therapeutic interventions activate numerous intracellular cascades in every constituent cell type of the heart. Endothelial cells produce several protective compounds in response to therapeutic ultrasound, under both normoxic and ischemic conditions. How endothelial cells sense ultrasound and convert it to a beneficial biological response is not known. We adopted a global, unbiased phosphoproteomics approach aimed at understanding how endothelial cells respond to ultrasound. Here, we use primary cardiac endothelial cells to explore the cellular signaling events underlying the response to ischemia-like cellular injury and ultrasound exposure in vitro. Enriched phosphopeptides were analyzed with a high mass accuracy liquid chromatrography (LC) - tandem mass spectrometry (MS/MS) proteomic platform, yielding multiple alterations in both total protein levels and phosphorylation events in response to ischemic injury and ultrasound. Application of pathway algorithms reveals numerous protein networks recruited in response to ultrasound including those regulating RNA splicing, cell-cell interactions and cytoskeletal organization. Our dataset also permits the informatic prediction of potential kinases responsible for the modifications detected. Taken together, our findings begin to reveal the endothelial proteomic response to ultrasound and suggest potential targets for future studies of the protective effects of ultrasound in the ischemic heart.

Quantitative shotgun proteome analysis by direct infusion

Liquid chromatography mass spectrometry (LC-MS) delivers sensitive peptide analysis for proteomics, but the methodology requires extensive analysis time, hampering throughput. Here, we demonstrate that using gas-phase peptide separation instead of LC enables fast proteome analysis. Using Direct Infusion – Shotgun Proteome Analysis (DI-SPA) by data-independent acquisition mass spectrometry (DIA-MS), we demonstrate the targeted quantification of over 500 proteins within minutes of MS data collection (~3.5 proteins/second). We show the utility of this technology to perform a complex multifactorial proteome study of interactions between nutrients, genotype, and mitochondrial toxins in a collection of cultured human cells. More than 45,000 quantitative protein measurements from 132 samples were achieved in only 4.4 hours of MS data collection. Enabling fast, unbiased proteome quantification without LC, DI-SPA offers an approach to boosting throughput critical to drug and biomarker discovery studies that require analysis of thousands of proteomes.

Proteomic assessment of serum biomarkers of longevity in older men

The biological bases of longevity are not well understood, and there are limited biomarkers for the prediction of long life. We used a high-throughput, discovery-based proteomics approach to identify serum peptides and proteins that were associated with the attainment of longevity in a longitudinal study of community-dwelling men age ≥65 years. Baseline serum in 1196 men were analyzed using liquid chromatography-ion mobility-mass spectrometry, and lifespan was determined during ~12 years of follow-up. Men who achieved longevity (≥90% expected survival) were compared to those who died earlier. Rigorous statistical methods that controlled for false positivity were utilized to identify 25 proteins that were associated with longevity. All these proteins were in lower abundance in long-lived men and included a variety involved in inflammation or complement activation. Lower levels of longevity-associated proteins were also associated with better health status, but as time to death shortened, levels of these proteins increased. Pathway analyses implicated a number of compounds as important upstream regulators of the proteins and implicated shared networks that underlie the observed associations with longevity. Overall, these results suggest that complex pathways, prominently including inflammation, are linked to the likelihood of attaining longevity. This work may serve to identify novel biomarkers for longevity and to understand the biology underlying lifespan.

MASH Explorer: A Universal Software Environment for Top-Down Proteomics

Top-down mass spectrometry (MS)-based proteomics enable a comprehensive analysis of proteoforms with molecular specificity to achieve a proteome-wide understanding of protein functions. However, the lack of a universal software for top-down proteomics is becoming increasingly recognized as a major barrier, especially for newcomers. Here, we have developed MASH Explorer, a universal, comprehensive, and user-friendly software environment for top-down proteomics. MASH Explorer integrates multiple spectral deconvolution and database search algorithms into a single, universal platform which can process top-down proteomics data from various vendor formats, for the first time. It addresses the urgent need in the rapidly growing top-down proteomics community and is freely available to all users worldwide. With the critical need and tremendous support from the community, we envision that this MASH Explorer software package will play an integral role in advancing top-down proteomics to realize its full potential for biomedical research.

EnvCNN: A Convolutional Neural Network Model for Evaluating Isotopic Envelopes in Top-Down Mass-Spectral Deconvolution

Top-down mass spectrometry has become the main method for intact proteoform identification, characterization, and quantitation. Because of the complexity of top-down mass spectrometry data, spectral deconvolution is an indispensable step in spectral data analysis, which groups spectral peaks into isotopic envelopes and extracts monoisotopic masses of precursor or fragment ions. The performance of spectral deconvolution methods relies heavily on their scoring functions, which distinguish correct envelopes from incorrect ones. A good scoring function increases the accuracy of deconvoluted masses reported from mass spectra. In this paper, we present EnvCNN, a convolutional neural network-based model for evaluating isotopic envelopes. We show that the model outperforms other scoring functions in distinguishing correct envelopes from incorrect ones and that it increases the number of identifications and improves the statistical significance of identifications in top-down spectral interpretation.

Short-Term Stable Isotope Probing of Proteins Reveals Taxa Incorporating Inorganic Carbon in a Hot Spring Microbial Mat

The upper green layer of the chlorophototrophic microbial mats associated with the alkaline siliceous hot springs of Yellowstone National Park consists of oxygenic cyanobacteria (Synechococcus spp.), anoxygenic Roseiflexus spp., and several other anoxygenic chlorophototrophs. Synechococcus spp. are believed to be the main fixers of inorganic carbon (Ci), but some evidence suggests that Roseiflexus spp. also contribute to inorganic carbon fixation during low-light, anoxic morning periods. Contributions of other phototrophic taxa have not been investigated. In order to follow the pathway of Ci incorporation into different taxa, mat samples were incubated with [13C]bicarbonate for 3 h during the early-morning, low-light anoxic period. Extracted proteins were treated with trypsin and analyzed by mass spectrometry, leading to peptide identifications and peptide isotopic profile signatures containing evidence of 13C label incorporation. A total of 25,483 peptides, corresponding to 7,221 proteins, were identified from spectral features and associated with mat taxa by comparison to metagenomic assembly sequences. A total of 1,417 peptides, derived from 720 proteins, were detectably labeled with 13C. Most 13C-labeled peptides were derived from proteins of Synechococcus spp. and Roseiflexus spp. Chaperones and proteins of carbohydrate metabolism were most abundantly labeled. Proteins involved in photosynthesis, Ci fixation, and N2 fixation were also labeled in Synechococcus spp. Importantly, most proteins of the 3-hydroxypropionate bi-cycle for Ci fixation in Roseiflexus spp. were labeled, establishing that members of this taxocene contribute to Ci fixation. Other taxa showed much lower [13C]bicarbonate incorporation.IMPORTANCE Yellowstone hot spring mats have been studied as natural models for understanding microbial community ecology and as modern analogs of stromatolites, the earliest community fossils on Earth. Stable-isotope probing of proteins (Pro-SIP) permitted short-term interrogation of the taxa that are involved in the important process of light-driven Ci fixation in this highly active community and will be useful in linking other metabolic processes to mat taxa. Here, evidence is presented that Roseiflexus spp., which use the 3-hydroxypropionate bi-cycle, are active in Ci fixation. Because this pathway imparts a lower degree of selection of isotopically heavy Ci than does the Calvin-Benson-Bassham cycle, the results suggest a mechanism to explain why the natural abundance of 13C in mat biomass is greater than expected if only the latter pathway were involved. Understanding how mat community members influence the 13C/12C ratios of mat biomass will help geochemists interpret the 13C/12C ratios of organic carbon in the fossil record.

Proteoform characterization based on top-down mass spectrometry

Proteins are dominant executors of living processes. Compared to genetic variations, changes in the molecular structure and state of a protein (i.e. proteoforms) are more directly related to pathological changes in diseases. Characterizing proteoforms involves identifying and locating primary structure alterations (PSAs) in proteoforms, which is of practical importance for the advancement of the medical profession. With the development of mass spectrometry (MS) technology, the characterization of proteoforms based on top-down MS technology has become possible. This type of method is relatively new and faces many challenges. Since the proteoform identification is the most important process in characterizing proteoforms, we comprehensively review the existing proteoform identification methods in this study. Before identifying proteoforms, the spectra need to be preprocessed, and protein sequence databases can be filtered to speed up the identification. Therefore, we also summarize some popular deconvolution algorithms, various filtering algorithms for improving the proteoform identification performance and various scoring methods for localizing proteoforms. Moreover, commonly used methods were evaluated and compared in this review. We believe our review could help researchers better understand the current state of the development in this field and design new efficient algorithms for the proteoform characterization.

Mass spectrometric evidence for the mechanism of free-radical depolymerization of various types of glycosaminoglycans

Glycosaminoglycans (GAGs) are large, complex carbohydrate molecules that interact with a wide range of proteins involved in physiological and pathological processes. Several naturally derived GAGs have emerged as potentially useful therapeutics in clinical applications. Natural polysaccharides, however, generally have high molecular weights with a degree of polydispersity, making it difficult to investigate their structural properties. In this study, we establish a free-radical-mediated micro-reaction system and use hydrophilic interaction chromatography (HILIC)-Fourier transform mass spectrometry (FTMS) to profile the degraded products of various types of GAGs, heparin, chondroitin sulfate A, NS-heparosan, and oversulfated chondroitin sulfate (OSCS), to reveal the free-radical degradation mechanism of GAGs. The results show that the bulk fragments of GAGs generated by free-radical degradation can maintain their basic structural units and sulfate substituents. In addition, an abundance of oligomers modified with oxidation at their reducing ends or by dehydration also appeared. We discovered that these modifications were related in terms of the degree of sulfation and the α- or β-linkage of HexNY (Y = SO₃^- or Ac), and especially that the different linkage of the disaccharide unit is the main factor in modification. In addition, the method based on micro-free-radical reaction and HILIC-FTMS is both effective and sensitive, thus suggesting its broad practical value for the structural characterization and in the biological structure-function studies of GAGs.

Open-Source Software Tools, Databases, and Resources for Single-Cell and Single-Cell-Type Metabolomics

In this age of -omics data-guided big data revolution, metabolomics has received significant attention as compared to genomics, transcriptomics, and proteomics for its proximity to the phenotype, the promises it makes and the challenges it throws. Although metabolomes of entire organisms, organs, biofluids, and tissues are of immense interest, a cell-specific resolution is deemed critical for biomedical applications where a granular understanding of cellular metabolism at cell-type and subcellular resolution is desirable. Mass spectrometry (MS) is a versatile technique that is used to analyze a broad range of compounds from different species and cell-types, with high accuracy, resolution, sensitivity, selectivity, and fast data acquisition speeds. With recent advances in MS and spectroscopy-based platforms, the research community is able to generate high-throughput data sets from single cells. However, it is challenging to handle, store, process, analyze, and interpret data in a routine manner. In this treatise, I present a workflow of metabolomics data generation from single cells and single-cell types to their analysis, visualization, and interpretation for obtaining biological insights.

Application of network smoothing to glycan LC-MS profiling

Motivation

Glycosylation is one of the most heterogeneous and complex protein post-translational modifications. Liquid chromatography coupled mass spectrometry (LC-MS) is a common high throughput method for analyzing complex biological samples. Accurate study of glycans require high resolution mass spectrometry. Mass spectrometry data contains intricate sub-structures that encode mass and abundance, requiring several transformations before it can be used to identify biological molecules, requiring automated tools to analyze samples in a high throughput setting. Existing tools for interpreting the resulting data do not take into account related glycans when evaluating individual observations, limiting their sensitivity.

Results

We developed an algorithm for assigning glycan compositions from LC-MS data by exploring biosynthetic network relationships among glycans. Our algorithm optimizes a set of likelihood scoring functions based on glycan chemical properties but uses network Laplacian regularization and optionally prior information about expected glycan families to smooth the likelihood and thus achieve a consistent and more representative solution. Our method was able to identify as many, or more glycan compositions compared to previous approaches, and demonstrated greater sensitivity with regularization. Our network definition was tailored to N-glycans but the method may be applied to glycomics data from other glycan families like O-glycans or heparan sulfate where the relationships between compositions can be expressed as a graph.

Availability and implementation Built Executable

http://www.bumc.bu.edu/msr/glycresoft/ and Source Code: https://github.com/BostonUniversityCBMS/glycresoft.

Supplementary information

Supplementary data are available at Bioinformatics online.

Inferring Protein-Protein Interaction Networks From Mass Spectrometry-Based Proteomic Approaches: A Mini-Review

Studying protein-protein interaction networks provide key evidence for the underlying molecular mechanisms. Mass spectrometry-based proteomic approaches have been playing a pivotal role in deciphering these interaction networks, along with precise quantification for individual interactions. In this mini-review we discuss the available techniques and methods for qualitative and quantitative elucidation of protein-protein interaction networks. We then summarize the down-stream computational strategies for identification and quantification of interactions from those techniques. Finally, we highlight the challenges and limitations of current computational pipelines in eliminating false positive interactors, followed by a summary of the innovative algorithms to address these issues, along with the scope for future improvements.

Quantitative Analysis of Protein Covalent Labeling Mass Spectrometry Data in the Mass Spec Studio

Covalent labeling with mass spectrometry (CL-MS) provides a direct measure of the chemical and structural features of proteins with the potential for resolution at the amino-acid level. Unfortunately, most applications of CL-MS are limited to narrowly defined differential analyses, where small numbers of residues are compared between two or more protein states. Extending the utility of high-resolution CL-MS for structure-based applications requires more robust computational routines and the development of methodology capable of reporting of labeling yield accurately. Here, we provide a substantial improvement in the analysis of CL-MS data with the development of an extended plug-in built within the Mass Spec Studio development framework (MSS-CLEAN). All elements of data analysis-from database search to site-resolved and normalized labeling output-are accommodated, as illustrated through the nonselective labeling of the human kinesin Eg5 with photoconverted 3,3'-azibutan-1-ol. In developing the new features within the CL-MS plug-in, we identified additional complexities associated with the application of CL reagents, arising primarily from digestion-induced bias in yield measurements and ambiguities in site localization. A strategy is presented involving the use of redundant site labeling data from overlapping peptides, the imputation of missing data, and a normalization routine to determine relative protection factors. These elements together provide for a robust structural interpretation of CL-MS/MS data while minimizing the over-reporting of labeling site resolution. Finally, to minimize bias, we recommend that digestion strategies for the generation of useful overlapping peptides involve the application of complementary enzymes that drive digestion to completion.

Metabolite aberrations in early diabetes detected in rat kidney using mass spectrometry imaging

Diabetic kidney disease is a serious complication of diabetes that can ultimately lead to end-stage renal disease. The pathogenesis of diabetic kidney disease is complex, and fundamental research is still required to provide a better understanding of the driving forces behind it. We report regional metabolic aberrations from an untargeted mass spectrometry imaging study of kidney tissue using an insulinopenic rat model of diabetes. Diabetes was induced by intravenous injection of streptozotocin, and kidneys were harvested 2 weeks thereafter. Imaging was performed using nanospray desorption electrospray ionization connected to a high-mass-resolving mass spectrometer. No histopathological changes were observed in the kidney sections; however, mass spectrometry imaging revealed a significant increase in several 18-carbon unsaturated non-esterified fatty acid species and monoacylglycerols. Notably, these 18-carbon acyl chains were also constituents of several increased diacylglycerol species. In addition, a number of short- and long-chain acylcarnitines were found to be accumulated while several amino acids were depleted. This study presents unique regional metabolic data indicating a dysregulated energy metabolism in renal mitochondria as an early response to streptozotocin-induced type I diabetes. Graphical abstract.

A perspective view of top-down proteomics in snake venom research

The venom produced by snakes contains complex mixtures of pharmacologically active proteins and peptides which play a crucial role in the pathophysiology of snakebite diseases. The deep understanding of venom proteomes can help to improve the treatment of this "neglected tropical disease" (as expressed by the World Health Organization [WHO]) and to develop new drugs. The most widely used technique for venom analysis is liquid chromatography/tandem mass spectrometry (LC/MS/MS)-based bottom-up (BU) proteomics. Considering the fact that multiple multi-locus gene families encode snake venom proteins, the major challenge for the BU proteomics is the limited sequence coverage and also the "protein inference problem" which result in a loss of information for the identification and characterization of toxin proteoforms (genetic variation, alternative mRNA splicing, single nucleotide polymorphism [SNP] and post-translational modifications [PTMs]). In contrast, intact protein measurements with top-down (TD) MS strategies cover almost complete protein sequences, and prove the ability to identify venom proteoforms and to localize their modifications and sequence variations.

Comprehensive N-Glycome Profiling of Cells and Tissues for Breast Cancer Diagnosis

Aberrant protein glycosylation is observed in the progression of many types of diseases, including different cancers. In this study, we assess differential N-glycan patterns of human breast cancer cells and tissues by PGC-ESI-MS/MS. Compared with mammary epithelial cells, high-mannose glycans were significantly elevated in breast cancer cells. However, the alteration of N-glycans in tissues was more obvious than that in cells. Sixty-three kinds of different N-glycans were stably identified, and 38 types of them exhibited significant differences between para-carcinoma and breast cancer tissues. High-mannose glycans and core-fucosylated glycans were increased in the breast cancer tissues, while bisected glycans and sialylated glycans were decreased. Moreover, a total of 27 types of N-glycans displayed evident differences between benign breast tumor and breast cancer tissues, and most of them including bisected and sialylated glycans exhibited decreased relative abundances in cancer tissues. Overall, three high-mannose N-glycans (F0H6N2S0, F0H7N2S0, F0H8N2S0) exhibited significant diagnostic accuracy in both breast cancer cells and tissues, suggesting their potential role in biomarkers. Furthermore, a negative correlation between sialylated glycans and age of patients was identified. In conclusion, our results may be beneficial to understand the role that N-glycan plays on the progression of breast cancer and propose potential diagnostic biomarkers.

Non-Anticoagulant Low Molecular Weight Heparins for Pharmaceutical Applications

Heparin is a polypharmacological agent with anticoagulant activity. Periodate oxidation of the nonsulfated glucuronic acid residue results in non-anticoagulant heparin derivative (NACH) of reduced molecular weight. Similar treatment of a low molecular weight heparin, dalteparin, also removes its anticoagulant activity, affording a second heparin derivative (D-NACH). A full structural characterization of these two derivatives reveals their structural differences. SPR studies display their ability to bind to several important heparin-binding proteins, suggesting potential new therapeutic applications.

Joint Precursor Elution Profile Inference via Regression for Peptide Detection in Data-Independent Acquisition Mass Spectra

In data independent acquisition (DIA) mass spectrometry, precursor scans are interleaved with wide-window fragmentation scans, resulting in complex fragmentation spectra containing multiple coeluting peptide species. In this setting, detecting the isotope distribution profiles of intact peptides in the precursor scans can be a critical initial step in accurate peptide detection and quantification. This peak detection step is particularly challenging when the isotope peaks associated with two different peptide species overlap-or interfere-with one another. We propose a regression model, called Siren, to detect isotopic peaks in precursor DIA data that can explicitly account for interference. We validate Siren's peak-calling performance on a variety of data sets by counting how many of the peaks Siren identifies are associated with confidently detected peptides. In particular, we demonstrate that substituting the Siren regression model in place of the existing peak-calling step in DIA-Umpire leads to improved overall rates of peptide detection.

The human brainome: network analysis identifies HSPA2 as a novel Alzheimer’s disease target

Our hypothesis is that changes in gene and protein expression are crucial to the development of late-onset Alzheimer’s disease. Previously we examined how DNA alleles control downstream expression of RNA transcripts and how those relationships are changed in late-onset Alzheimer’s disease. We have now examined how proteins are incorporated into networks in two separate series and evaluated our outputs in two different cell lines. Our pipeline included the following steps: (i) predicting expression quantitative trait loci; (ii) determining differential expression; (iii) analysing networks of transcript and peptide relationships; and (iv) validating effects in two separate cell lines. We performed all our analysis in two separate brain series to validate effects. Our two series included 345 samples in the first set (177 controls, 168 cases; age range 65–105; 58% female; KRONOSII cohort) and 409 samples in the replicate set (153 controls, 141 cases, 115 mild cognitive impairment; age range 66–107; 63% female; RUSH cohort). Our top target is heat shock protein family A member 2 (HSPA2), which was identified as a key driver in our two datasets. HSPA2 was validated in two cell lines, with overexpression driving further elevation of amyloid-β40 and amyloid-β42 levels in APP mutant cells, as well as significant elevation of microtubule associated protein tau and phosphorylated-tau in a modified neuroglioma line. This work further demonstrates that studying changes in gene and protein expression is crucial to understanding late onset disease and further nominates HSPA2 as a specific key regulator of late-onset Alzheimer’s disease processes.10.1093/brain/awy215_video1awy215media15824729224001.

Application of multiplexed ion mobility spectrometry towards the identification of host protein signatures of treatment effect in pulmonary tuberculosis

Rationale:

The monitoring of TB treatments in clinical practice and clinical trials relies on traditional sputum-based culture status indicators at specific time points. Accurate, predictive, blood-based protein markers would provide a simpler and more informative view of patient health and response to treatment.

Objective:

We utilized sensitive, high throughput multiplexed ion mobility-mass spectrometry (IM-MS) to characterize the serum proteome of TB patients at the start of and at 8 weeks of rifamycin-based treatment. We sought to identify treatment specific signatures within patients as well as correlate the proteome signatures to various clinical markers of treatment efficacy.

Methods:

Serum samples were collected from 289 subjects enrolled in CDC TB Trials Consortium Study 29 at time of enrollment and at the end of the intensive phase (after 40 doses of TB treatment). Serum proteins were immunoaffinity-depleted of high abundant components, digested to peptides and analyzed for data acquisition utilizing a unique liquid chromatography IM-MS platform (LC-IM-MS). Linear mixed models were utilized to identify serum protein changes in the host response to antibiotic treatment as well as correlations with culture status end points.

Results:

A total of 10,137 peptides corresponding to 872 proteins were identified, quantified, and used for statistical analysis across the longitudinal patient cohort. In response to TB treatment, 244 proteins were significantly altered. Pathway/network comparisons helped visualize the interconnected proteins, identifying up regulated (lipid transport, coagulation cascade, endopeptidase activity) and down regulated (acute phase) processes and pathways in addition to other cross regulated networks (inflammation, cell adhesion, extracellular matrix). Detection of possible lung injury serum proteins such as HPSE, significantly downregulated upon treatment. Analyses of microbiologic data over time identified a core set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2) which change in response to treatment and also strongly correlate with culture status. A similar set of proteins at baseline were found to be predictive of week 6 and 8 culture status.

Conclusion:

A comprehensive host serum protein dataset reflective of TB treatment effect is defined. A repeating set of serum proteins (TTHY, AFAM, CRP, RET4, SAA1, PGRP2, among others) were found to change significantly in response to treatment, to strongly correlate with culture status, and at baseline to be predictive of future culture conversion. If validated in cohorts with long term follow-up to capture failure and relapse of TB, these protein markers could be developed for monitoring of treatment in clinical trials and in patient care.

UV-POSIT: Web-Based Tools for Rapid and Facile Structural Interpretation of Ultraviolet Photodissociation (UVPD) Mass Spectra

UV-POSIT (Ultraviolet Photodissociation Online Structure Interrogation Tools) is a suite of web-based tools designed to facilitate the rapid interpretation of data from native mass spectrometry experiments making use of 193 nm ultraviolet photodissociation (UVPD). The suite includes four separate utilities which assist in the calculation of fragment ion abundances as a function of backbone cleavage sites and sequence position; the localization of charge sites in intact proteins; the calculation of hydrogen elimination propensity for a-type fragment ions; and mass-offset searching of UVPD spectra to identify unknown modifications and assess false positive fragment identifications. UV-POSIT is implemented as a Python/Flask web application hosted at http://uv-posit.cm.utexas.edu . UV-POSIT is available under the MIT license, and the source code is available at https://github.com/jarosenb/UV_POSIT . Graphical Abstract.

PIXiE: an algorithm for automated ion mobility arrival time extraction and collision cross section calculation using global data association

Motivation

Drift tube ion mobility spectrometry coupled with mass spectrometry (DTIMS-MS) is increasingly implemented in high throughput omics workflows, and new informatics approaches are necessary for processing the associated data. To automatically extract arrival times for molecules measured by DTIMS at multiple electric fields and compute their associated collisional cross sections (CCS), we created the PNNL Ion Mobility Cross Section Extractor (PIXiE). The primary application presented for this algorithm is the extraction of data that can then be used to create a reference library of experimental CCS values for use in high throughput omics analyses.

Results

We demonstrate the utility of this approach by automatically extracting arrival times and calculating the associated CCSs for a set of endogenous metabolites and xenobiotics. The PIXiE-generated CCS values were within error of those calculated using commercially available instrument vendor software.

Availability and implementation

PIXiE is an open-source tool, freely available on Github. The documentation, source code of the software, and a GUI can be found at https://github.com/PNNL-Comp-Mass-Spec/PIXiE and the source code of the backend workflow library used by PIXiE can be found at https://github.com/PNNL-Comp-Mass-Spec/IMS-Informed-Library.

Supplementary information

Supplementary data are available at Bioinformatics online.

Software for Peak Finding and Elemental Composition Assignment for Glycosaminoglycan Tandem Mass Spectra

Glycosaminoglycans (GAGs) covalently linked to proteoglycans (PGs) are characterized by repeating disaccharide units and variable sulfation patterns along the chain. GAG length and sulfation patterns impact disease etiology, cellular signaling, and structural support for cells. We and others have demonstrated the usefulness of tandem mass spectrometry (MS²) for assigning the structures of GAG saccharides; however, manual interpretation of tandem mass spectra is time-consuming, so computational methods must be employed. In the proteomics domain, the identification of monoisotopic peaks and charge states relies on algorithms that use averagine, or the average building block of the compound class being analyzed. Although these methods perform well for protein and peptide spectra, they perform poorly on GAG tandem mass spectra, because a single average building block does not characterize the variable sulfation of GAG disaccharide units. In addition, it is necessary to assign product ion isotope patterns to interpret the tandem mass spectra of GAG saccharides. To address these problems, we developed GAGfinder, the first tandem mass spectrum peak finding algorithm developed specifically for GAGs. We define peak finding as assigning experimental isotopic peaks directly to a given product ion composition, as opposed to deconvolution or peak picking, which are terms more accurately describing the existing methods previously mentioned. GAGfinder is a targeted, brute force approach to spectrum analysis that uses precursor composition information to generate all theoretical fragments. GAGfinder also performs peak isotope composition annotation, which is typically a subsequent step for averagine-based methods. Data are available via ProteomeXchange with identifier PXD009101.

An algorithm to correct saturated mass spectrometry ion abundances for enhanced quantitation and mass accuracy in omic studies

The mass accuracy and peak intensity of ions detected by mass spectrometry (MS) measurements are essential to facilitate compound identification and quantitation. However, high concentration species can yield erroneous results if their ion intensities reach beyond the limits of the detection system, leading to distorted and non-ideal detector response (e.g. saturation), and largely precluding the calculation of accurate m/z and intensity values. Here we present an open source computational method to correct peaks above a defined intensity (saturated) threshold determined by the MS instrumentation such as the analog-to-digital converters or time-to-digital converters used in conjunction with time-of-flight MS. In this method, the isotopic envelope for each observed ion above the saturation threshold is compared to its expected theoretical isotopic distribution. The most intense isotopic peak for which saturation does not occur is then utilized to re-calculate the precursor m/z and correct the intensity, resulting in both higher mass accuracy and greater dynamic range. The benefits of this approach were evaluated with proteomic and lipidomic datasets of varying complexities. After correcting the high concentration species, reduced mass errors and enhanced dynamic range were observed for both simple and complex omic samples. Specifically, the mass error dropped by more than 50% in most cases for highly saturated species and dynamic range increased by 1-2 orders of magnitude for peptides in a blood serum sample.

Characterization of the Ovarian Tumor Peptidome

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of the tumor peptidome has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the tumors peptidome information in cancer research have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. To address this need, we have recently developed an effective and robust analytical platform as well as a novel informatics approach for comprehensive analyses of tissue peptidomes. The ability of this new peptidomics pipeline for high-throughput, comprehensive, and quantitative peptidomics analysis, as well as the suitability of clinical ovarian tumor samples with postexcision delay limited to less than 60min before freezing for peptidomics analysis, has been demonstrated. These initial analyses set a stage for further determination of molecular details and functional significance of the peptidomic activities in ovarian cancer.

NBPMF

Mass spectrometry (MS) is an analytical technique for determining the composition of a sample. In bottom-up techniques, peptide mass fingerprinting (PMF) is widely used to identify proteins from MS dataset. In this article, the authors developed a novel network-based inference software termed NBPMF. By analyzing peptide-protein bipartite network, they designed new peptide protein matching score functions. They present two methods: the static one, ProbS, is based on an independent probability framework; and the dynamic one, HeatS, depicts input data as dependent peptides. The authors also use linear regression to adjust the matching score according to the masses of proteins. In addition, they consider the order of retention time to further correct the score function. In post processing, a peak can only be assigned to one peptide in order to reduce random matches. Finally, the authors try to filter out false positive proteins. The experiments on simulated and real data demonstrate that their NBPMF approaches lead to significantly improved performance compared to several state-of-the-art methods.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Identification of Hip BMD Loss and Fracture Risk Markers Through Population-Based Serum Proteomics

Serum proteomics analysis may lead to the discovery of novel osteoporosis biomarkers. The Osteoporotic Fractures in Men (MrOS) study comprises men ≥65 years old in the US who have had repeated BMD measures and have been followed for incident fracture. High-throughput quantitative proteomic analysis was performed on baseline fasting serum samples from non-Hispanic white men using a multidimensional approach coupling liquid chromatography, ion-mobility separation, and mass spectrometry (LC-IMS-MS). We followed the participants for a mean of 4.6 years for changes in femoral neck bone mineral density (BMD) and for incident hip fracture. Change in BMD was determined from mixed effects regression models taking age and weight into account. Participants were categorized into three groups: BMD maintenance (no decline; estimated change ≥0 g/cm2 , n = 453); expected loss (estimated change 0 to 1 SD below the estimated mean change, -0.034 g/cm2 for femoral neck, n = 1184); and accelerated loss (estimated change ≥1 SD below mean change, n = 237). Differential abundance values of 3946 peptides were summarized by meta-analysis to determine differential abundance of each of 339 corresponding proteins for accelerated BMD loss versus maintenance. Using this meta-analytic standardized fold change at cutoffs of ≥1.1 or ≤0.9 (p < 0.10), 20 proteins were associated with accelerated BMD loss. Associations of those 20 proteins with incident hip fracture were tested using Cox proportional hazards models with age and BMI adjustment in 2473 men. Five proteins were associated with incident hip fracture (HR between 1.29 and 1.41 per SD increase in estimated protein abundance). Some proteins have been previously associated with fracture risk (eg, CD14 and SHBG), whereas others have roles in cellular senescence and aging (B2MG and TIMP1) and complement activation and innate immunity (CO7, CO9, CFAD). These findings may inform development of biomarkers for future research in bone biology and fracture prediction. © 2017 American Society for Bone and Mineral Research.

Construction of a Database of Collision Cross Section Values for Glycopeptides, Glycans, and Peptides Determined by IM-MS

An ion mobility quadrupole time-of-flight mass spectrometer was used to examine the gas-phase structures of a set of glycopeptides resulting from proteolytic digestion of the well-characterized glycoproteins bovine ribonuclease B, human transferrin, bovine fetuin and human α1-acid glycoprotein, the corresponding deglycosylated peptides, and the glycans released by the endoglycosidase PNGase F. When closely related glycoforms did not occur naturally, exoglycosidases were used to achieve stepwise removal of individual saccharide units from the nonreducing termini of the multiantennary structures. Collision cross sections (CCS) were calculated and plotted as a function of mass-to-charge ratio. Linear trendlines were observed for the glycoforms of individual N-linked glycopeptides, the deglycosylated peptides, and the released, deutero-reduced permethylated glycans. For the glycoforms of a given glycopeptide or set of derivatized glycans, the slope of the line connecting CCS values remained similar for the [M+3H]3+ ions observed as the glycan antennae were shortened by stepwise exoglycosidase treatments; this trend was consistent regardless of the peptide length or the saccharide removed. The results form the basis for a database of CCS values and the CCS increments that correspond to changes in glycoform compositions.

Effects of delayed NSAID administration after experimental eccentric contraction injury - A cellular and proteomics study

BACKGROUND

Acute muscle injuries are exceedingly common and non-steroidal anti-inflammatory drugs (NSAIDs) are widely consumed to reduce the associated inflammation, swelling and pain that peak 1-2 days post-injury. While prophylactic use or early administration of NSAIDs has been shown to delay muscle regeneration and contribute to loss of muscle strength after healing, little is known about the effects of delayed NSAID use. Further, NSAID use following non-penetrating injury has been associated with increased risk and severity of infection, including that due to group A streptococcus, though the mechanisms remain to be elucidated. The present study investigated the effects of delayed NSAID administration on muscle repair and sought mechanisms supporting an injury/NSAID/infection axis.

METHODS

A murine model of eccentric contraction (EC)-induced injury of the tibialis anterior muscle was used to profile the cellular and molecular changes induced by ketorolac tromethamine administered 47 hr post injury.

RESULTS

NSAID administration inhibited several important muscle regeneration processes and down-regulated multiple cytoprotective proteins known to inhibit the intrinsic pathway of programmed cell death. These activities were associated with increased caspase activity in injured muscles but were independent of any NSAID effect on macrophage influx or phenotype switching.

CONCLUSIONS

These findings provide new molecular evidence supporting the notion that NSAIDs have a direct negative influence on muscle repair after acute strain injury in mice and thus add to renewed concern about the safety and benefits of NSAIDS in both children and adults, in those with progressive loss of muscle mass such as the elderly or patients with cancer or AIDS, and those at risk of secondary infection after trauma or surgery.

Profiling and quantifying endogenous molecules in single cells using nano-DESI MS

Nano-DESI MS enables sensitive molecular profiling and quantification of endogenous species in single cells in a higher throughput manner.